\
Evaluation of TRIM.FaTE
Volume II: Model Performance Focusing on
Mercury Test Case
Environmental Fate,
Transport, & Ecological
Exposure Module
(TRIM.FaTE)
Risk Characterization
Module
(TRIM.Risk)
Exposure-Event Module
i (TRIM.Expo) 4
( Regulatory Action ) < ~"
f Risk
Management]
\ Decision/
/ Social, \
' Economic,
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\ Factors//
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EPA-453/R-05-002
July 2005
Evaluation of TRIM.FaTE
Volume II: Model Performance Focusing on Mercury Test Case
By:
Mark Lee, David Burch, Margaret E. McVey, Rebecca Murphy, and Baxter Jones
ICF Consulting, Research Triangle Park, North Carolina and Fairfax, Virginia
EPA Contract No. 68-D-01-052 (WA 3-06) and GSA Contract No. GS-10F-0124J (TO 1328)
Randy Maddalena, Deborah Hall Bennett, and Thomas E. McKone
Lawrence Berkeley National Laboratory, Berkeley, California
Interagency Agreement No. DW89786601
Rebecca A. Efroymson, Bradford F. Lyon, and Daniel S. Jones
Oak Ridge National Laboratory, Oak Ridge, Tennessee
Interagency Agreement No. DW89876501
Alison Eyth
University of North Carolina-Center for Environmental Programs, Chapel Hill, North Carolina
EPA Contract No. 68-D-01-052 (WA 3-06) and GSA Contract No. GS-10F-0124J (TO 1328)
Prepared for:
Terri Hollingsworth, EPA Project Officer and Work Assignment Manager
Deirdre Murphy, Technical Lead
Emissions Standards Division
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
This report is being furnished to the U.S. Environmental Protection Agency (EPA) by
ICF Consulting in partial fulfillment of Work Assignment 3-06 under EPA contract 68-D-01-052
and Task Order 1328 under GSA contract GS-10F-0124J. The report has been reviewed and
approved for publication by EPA. It does not constitute Agency policy. The opinions, findings,
and conclusions expressed are those of the authors and are not necessarily those of EPA.
Mention of trade names or commercial products is not intended to constitute endorsement or
recommendation for use.
EPA has employed many models over the past decade for different applications and
purposes associated with emissions of mercury to air. These have included models capable of
long-range, large-scale modeling such as the Regional Lagrangian Model of Air Pollution
(RELMAP), used for the Mercury Study Report to Congress, and more recently the Community
Multi-scale Air Quality (CMAQ) modeling system, which includes simulation of atmospheric
chemistry, as well as models capable of local-scale atmospheric transport such as the Industrial
Source Complex (ISC) model. Mercury deposition estimated via those models has been used as
input for watershed and aquatic ecosystem modeling, e.g., using the indirect exposure
methodology for mercury (IEM-2M) or the Multimedia, Multi-pathway, Multi-receptor
Exposure and Risk Assessment model (3MRA). Unlike many of those model applications,
which informed regulatory decisions by the Agency, the analyses described in this document are
solely for the purpose of model evaluation, as described herein.
JULY 2005 i TRIM.FATE EVALUATION REPORT VOLUME II
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Preface
This document, Evaluation ofTRIM.FaTE, Volume II: Model Performance Focusing on
Mercury Test Case, is part of a series of documentation for the Total Risk Integrated
Methodology (TRIM). Additional evaluation analyses are presented elsewhere (EPA 2002a,
EPA 2004, EPA 2005b) and will be augmented with future applications, while the detailed
documentation of logic, assumptions, algorithms, and equations is provided in comprehensive
Technical Support Documents (TSDs) and/or user's guides for each of the TRIM modules (see
www. epa. gov/ttn/fera).
Primary U.S. EPA technical staff contributing to the planning, analysis, and
interpretation of this TREVI.FaTE test case include Deirdre Murphy (overall technical lead), John
Langstaff (sensitivity analysis and air modeling), Gerry Laniak (model comparison with 3MRA),
and Robert Ambrose (model comparison with 3MRA). Craig Barber also contributed to analysis
and interpretation (bioaccumulation comparison with 3MRA). Other EPA technical staff
contributing to planning and early analyses for the mercury test case were TRIM team members
Ted Palma, Robert Hetes, and Amy Vasu.
Reviewers for this document included: Gerry Laniak, Mike Cyterski, Ellen Cooler, and
Donna Schwede of U.S. EPA's Office of Research and Development; Jeffrey Yurk of U.S. EPA
Region 6; John Irwin of U.S. EPA and National Oceanic and Atmospheric Administration; and
Stephen Kroner of U.S. EPA, Office of Solid Waste.
Inquiries should be directed to Dr. Deirdre Murphy, U.S. EPA, Office of Air Quality
Planning and Standards, C404-01, Research Triangle Park, North Carolina, 27711;
murphy.deirdre@epa.gov.
JULY 2005 ii TRIM.FATE EVALUATION REPORT VOLUME II
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Table of Contents
Page
Disclaimer i
Preface ii
Table of Contents iii
EXECUTIVE SUMMARY ES-1
1. INTRODUCTION AND OBJECTIVES 1-1
2. SPECIFICATIONS OF MODELED SCENARIOS 2-1
3. RESULTS AND DISCUSSION: DYNAMIC MODELING 3-1
3.1 Time Patterns of Mercury Mass Accumulation 3-3
3.2 Mercury Concentration Over Time in Various Compartment Types 3-12
3.2.1 Annual Average Concentrations (and Deposition) 3-12
3.2.2 Selected Instantaneous and Monthly Average Results 3-25
3.3 Speciation: How Do Concentrations of Hg°, Hg2+, and MHg Differ? 3-34
3.3.1 Speciation by Compartment Type 3-34
3.3.2 Spatial Variations in Speciation 3-40
3.3.3 Temporal Variations in Speciation 3-43
3.4 Spatial Variation of Total Mercury Concentration 3-49
3.4.1 Abiotic Compartments 3-49
3.4.2 Biotic Compartments 3-60
3.5 Comparison of Emission Cases 3-73
3.5.1 Emission Case A vs. Emission CaseB 3-73
3.5.2 Emission Case B vs. Emission Case C 3-78
4. RESULTS AND DISCUSSION: STEADY-STATE MODELING 4-1
4.1 Configuring a TRIM.FaTE Scenario for Steady-state Mode 4-1
4.2 Steady-state Results 4-3
4.3 Comparison of Steady-state and Dynamic Results 4-5
5. SENSITIVITY ANALYSIS 5-1
5.1 Analysis Design/Methods 5-1
5.1.1 How Input Values Were Varied 5-2
JULY 2005 iii TRIM.FATE EVALUATION REPORT VOLUME II
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5.1.2 Measures of Sensitivity 5-2
5.1.3 Limitations 5-4
5.1.4 Endpoints Analyzed 5-6
5.2 Influential Input Properties for Individual Compartment Types 5-7
5.2.1 Air 5-7
5.2.2 Surface Soil 5-11
5.2.3 Earthworm 5-14
5.2.4 Leaf 5-17
5.2.5 Surface Water 5-20
5.2.6 Aquatic Food Chain 5-27
5.2.7 Terrestrial Mammals 5-36
5.3 Broadly Influential Properties 5-42
5.3.1 Approach 5-42
5.3.2 Summary of Observations 5-42
5.3.3 Conclusions 5-47
5.4 Summary of Sensitivity Analysis 5-48
6. COMPARISON OF TRIM.FaTE AND 3MRA MODELING RESULTS 6-1
6.1 Approach to Comparison of Modeling Results 6-4
6.1.1 Inherent Differences/Similarities in Spatial Resolution of Model Outputs .... 6-6
6.1.2 Model Set-up and Input Data 6-12
6.2 Air and Leaves 6-19
6.2.1 Divalent Mercury Concentrations in Air 6-19
6.2.2 Divalent Mercury Deposition 6-22
6.2.3 Divalent Mercury Concentrations in Leaves 6-26
6.2.4 Spatial Patterns for Air Concentrations and Deposition 6-28
6.3 Soil and Soil Biota 6-32
6.3.1 Divalent Mercury Concentrations in Surface Soil 6-32
6.3.2 Divalent Mercury Concentrations in Plant Roots 6-34
6.3.3 Divalent Mercury Concentrations in Earthworms 6-39
6.3.4 Spatial Patterns for Surface Soil 6-41
6.4 Surface Water, Sediment, and Fish 6-44
6.4.1 Mercury Concentrations and Speciation in Surface Water 6-44
6.4.2 Mercury Concentrations and Speciation in Sediment 6-48
6.4.3 Methyl Mercury Concentrations in Fish 6-52
6.5 Wildlife 6-58
6.5.1 Total Mercury Concentrations in Small Birds 6-60
6.5.2 Total Mercury Concentrations in Omniverts 6-62
6.5.3 Total Mercury Concentrations in Small Mammals 6-64
JULY 2005 iv TRIM.FATE EVALUATION REPORT VOLUME II
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6.6 Summary of 3MRA-TRIM.FaTE Comparison 6-66
6.6.1 Overview of Results and Model Differences 6-66
6.6.2 Possible Future Areas for Model Comparison 6-68
7. COMPARISONS WITH MEASUREMENT DATA 7-1
7.1 Description of Measurement Data and Relationship to TRIM.FaTE Model Results ..7-1
7.2 Comparison with Air Measurement Data 7-6
7.3 Comparison with Soil and Soil Biota Measurement Data 7-7
7.4 Comparison with Sediment and Aquatic Biota Measurement Data 7-9
7.5 Summary of Measurement Data Comparisons 7-11
8. REFERENCES 8-1
APPENDIX A: DOCUMENTATION OF INPUT PROPERTIES FOR TREVLFaTE
MERCURY TEST CASE A-l
APPENDIX B: DETAILED RESULTS FOR EMISSION CASE B B-l
B. 1 Mass Accumulation Tables B-l
B.2 Concentration Tables and Charts B-5
APPENDIX C: STEADY-STATE: INPUTS AND DETAILED RESULTS C-l
C.I Estimation of Steady-State Inputs for Mercury Test Case C-l
C.I.I Estimating Constant Values for Time-varying Inputs C-l
C. 1.2 Meteorological Inputs C-4
C.1.3 Plant Inputs C-7
C.1.4 Water Body Flow Inputs C-10
C.2 Compartment-specific Steady-state Results C-ll
C.3 Detailed Comparison of Steady-state and Dynamic Simulation Results C-21
APPENDIX D: DETAILED RESULTS FOR SENSITIVITY ANALYSIS D-l
D. 1 Input Properties Assessed in the Mercury Test Case Sensitivity Analysis D-l
D.2 Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties D-23
D.3 Properties with Absolute Elasticities > 0.5 D-71
D.4 Properties with Absolute Sensitivity Scores > 0.5 D-75
APPENDIX E: SUPPLEMENTAL MATERIALS FOR 3MRA-TRIM.FaTE
COMPARISON E-l
E.I Description of 3MRA Processes E-l
E.2 Detailed Results for 3MRA-TRIM.FaTE Comparison E-4
JULY 2005 v TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX F: SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA . . . . F-l
F.I Off-site Air Monitoring Data F-l
F.2 Off-site Soil Monitoring Data F-5
F.3 Off-site Sediment Monitoring Data F-6
F.4 Off-site Biota Monitoring Data F-7
JULY 2005 vi TRIM.FATE EVALUATION REPORT VOLUME II
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EXECUTIVE SUMMARY
The United States Environmental Protection Agency (EPA) recently has developed and
begun applying TRIM.FaTE, a comprehensive multimedia chemical fate and transport model
based on mass transfer and mass balance concepts. This report documents a series of model
evaluation activities for TRIM.FaTE based on its application to mercury air emissions from a
mercury cell chlor-alkali facility (now-closed) in the northeastern United States.1 The mass
balance approach used in TRIM.FaTE, including mercury transformations in various
environmental media and types of biota, ensures that the predicted distribution of mercury in the
environment reflects the total mercury available - mercury is neither created nor destroyed
during the modeling. TRIM.FaTE's mass balance approach incorporates fugacity principles,
deriving from and building on the CALTOX model and the earlier modeling concepts and
formulations of Mackay (Level 1, 2, and 3 partitioning models) and Thibodeaux
(chemodynamics concepts).
As discussed at length in Volume I of this report and highlighted in recent EPA Science
Advisory Board reviews of TRIM.FaTE and 3MRA (another EPA model with a complex
multimedia fate and transport component), model evaluation for a multimedia model such as
TRIM.FaTE is a particularly challenging undertaking. "Validation" of such models, in the
classic sense (e.g., proving the model produces accurate results across a range of input
conditions), is not generally possible, in part because there are no comprehensive data sets of
measured chemical concentrations (and associated contributing pollutant sources) for use in such
comprehensive studies, nor are there other fully validated multimedia models against which
TRIM.FaTE can be benchmarked. Thus, evaluation of TRIM.FaTE is not a yes/no exercise but a
continuing accumulation of evidence leading to model refinement and eventually to increasing
levels of confidence in the model results.
The overall objective for TRIM.FaTE
evaluation, as discussed in Volume I of this
report, is to refine and build confidence in
the model by conducting and publicly
.. • , • .. f , , mass balance model that describes the movement
reporting on a wide-ranging suite of model , ^ ,, r n . .
oo anc[ transformation or pollutants over time,
evaluation activities, of which the mercury
performance evaluation study reported here
is an important example. Other examples
include recent and in-progress evaluation
studies focusing on organic chemicals,
including dioxins/furans and polycyclic
aromatic hydrocarbons. The ongoing
TRIM.FaTE model evaluation has been
designed to be consistent with the Agency's
,. r- 11 i •, human ingestion exposure model.
peer review policy tor models and its & ^
evolving regulatory environmental modeling
guidance.
TRIM.FaTE
TRIM.FaTE is a spatially explicit, compartmental
through a user-defined, bounded system that
includes both biotic and abiotic components
(compartments). TRIM.FaTE predicts pollutant
concentrations in multiple environmental media
and pollutant concentrations and 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
1 This evaluation does not draw conclusions regarding the facility. Rather, it is intended to facilitate
conclusions regarding the performance of TRIM.FaTE.
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TRIM.FaTE is Different
TRIM.FaTE is a transparent computer framework, accompanied by an initial library of
algorithms and values, into which a user loads selected algorithms and input values, along with
the design (e.g., spatial, temporal, and ecosystem details) of the scenario to be modeled. With its
two-way linkages among the various environmental media and biota types being modeled, its
continuous mass-balancing, its scalable complexity, and its transparency to the user, TRIM.FaTE
is significantly different from many other multimedia or single-medium chemical fate and
transport models in common use. As demonstrated through the broad range of analyses
described in this report, TRIM.FaTE allows a user to perform dynamic, mass-balanced studies of
the multimedia fate and transport of mercury in abiotic and biotic media. It also models two-way
transformations of chemicals and keeps track of the reaction products within the mass balance
system - for example, transformations back and forth between methyl mercury and inorganic
divalent mercury, and between the latter and elemental mercury. In another distinguishing
feature, the TRIM.FaTE framework accommodates the simulation of mercury transfers within
terrestrial and aquatic trophic webs using bioenergetic algorithms, which allow uptake of
mercury via food, water, air, and soil and which allow individual species to ingest more than one
type of food. TRIM.FaTE allows modeling scenarios to be set up with as much, or as little,
complexity as desired.
TRIM.FaTE not only estimates chemical concentrations, but allows a full accounting of
chemical mass flows, accumulation, and distribution throughout the modeling system. The
media and biota being modeled are connected to each other, as appropriate, and chemical mass
can flow both ways across the linkages as specified by various transfer processes (e.g.,
deposition, diffusion, volatilization, biouptake, excretion), allowing for physical and biological
feedback mechanisms to be accounted for explicitly (e.g., re-emission from surfaces such as soil,
vegetation, and surface water). Plants and animals exchange chemical mass continuously with
environmental media, which in the case of plants can have a noticeable effect on the overall
distribution of mercury mass in soils, surface water, and air. The distribution of chemical mass
within the modeled system changes over time according to the dynamic transfers and processes
modeled. As an example of the kind of mass balance/distribution problems that can be
addressed, TRIM.FaTE can be used to examine temporal questions related to chemical mass
distribution (e.g., time that might be required for different environmental components to
approach steady-state, changes in chemical distribution after a source stops emitting mercury).
One also can readily examine the impact of including or varying the configuration of a particular
environmental medium or biota type (e.g., terrestrial plants, aquatic macrophytes) on chemical
mass distributions and concentrations in the modeled system.
TRIM.FaTE is Flexible
TRIM.FaTE is designed to be highly flexible in its set-up and adaptable to user-specified
input data and algorithms. Therefore, it can be applied to a variety of problems and questions
related to chemical fate and exposure and risk assessment, such as the multimedia assessment of
risks associated with hazardous and criteria air pollutants. A user can set up a wide range of
study designs in TRIM.FaTE at varying levels of complexity, specifying the time resolution,
spatial scale and resolution, environmental media and biota types to be included, kinds and
format of outputs, and other study characteristics. This report demonstrates this flexibility,
JULY 2005 ES-2 TRIM.FATE EVALUATION REPORT VOLUME II
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illustrating time and spatial resolution of modeling inputs and results, generation of chemical
mass outputs along with predicted concentrations, modeling of mercury speciation and
transformation, modeling in both dynamic and steady-state modes, and adaptability of
TRIM.FaTE to sensitivity analyses and to comparisons with other models and monitoring data.
Sensitivity of TREVLFaTE Results Makes Sense
TRIM.FaTE is working as intended, with the modeling results reflecting the algorithms
and inputs used. The modeling results in most cases also appear to be sensitive to input
parameters known to be important in determining chemical fate and transport. Chapter 5
describes an initial, local sensitivity analysis covering nearly all of the input properties of the
model. As discussed in that chapter, the modeling results are explainable based on the methods
and input values used; this study shows that TRIM.FaTE produces results based on what the user
gives it. Broadly influential properties - those that exert relatively high influence on chemical
concentrations for a range of media types - include mercury emission rates from the source, air
deposition-related properties, mercury transformation rates and Kd (phase partitioning) values,
and water and air temperature. Several parameters also are noted that influence methyl mercury
concentrations in fish through the food chain dynamics simulated in this application of
TRIM.FaTE, including characteristics of the algal and benthic invertebrate communities and
water-column and benthic fish that comprise the aquatic food web. The sensitivity analysis
reported here begins the process of demonstrating that the influence of model inputs on outputs
is consistent with the expectations based on the algorithms employed, which were derived from
what is currently known about mercury fate and transport.
TREVLFaTE Compares Well
In large measure, the TRIM.FaTE test case results are consistent with results of
comparison simulations performed using EPA's 3MRA model, the limited available mercury
measurement data for the test case site, and measurement and modeling data from the literature.
Even given some significant differences in model structure, set-up, and inputs, the long-term
(annual average) mercury concentrations predicted in various environmental media and biota by
TRIM.FaTE and 3MRA (discussed in detail in Chapter 6) are usually within an order of
magnitude, and in most cases closer. Predicted mercury speciations (i.e., fractions in elemental,
divalent, and methyl mercury form) generally agree as well. Simulations using both models
predicted divalent and elemental mercury as the predominant forms of mercury in surface water
and sediment, although due to different transformation factors and processes, 3MRA predicted a
higher percentage of elemental mercury in these media than TRIM.FaTE. For several reasons
related to uncertainty in the literature and in the corresponding modeling methods and inputs,
comparisons of the mercury concentration and speciation results for terrestrial animals are more
uncertain than for the other media. Where results from the two models do not agree closely,
such as for mercury concentrations in the root zone soil and the benthic sediment, the differences
are explainable based on differing model algorithms and/or inputs. The results of this model
comparison have already been used to refine inputs or algorithms for both model applications,
and to raise questions for further examination.
TRIM.FaTE modeling results are placed in the context of the available mercury
measurement data for the test case site in Chapter 7. Measurement data such as these are
JULY 2005 ES-3 TRIM.FATE EVALUATION REPORT VOLUME II
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especially hard to compare with multimedia modeling results, given major uncertainty about
historical releases from the emission source and from other nearby and distant sources (including
non-air sources) that may have contributed to the measured levels. In addition, measurements
are available for very few locations, media, and points in time (especially compared with the
TRIM.FaTE outputs), and most measures are of total mercury, with very limited data on
speciation. Overall, for the limited measurement data that are available, TRIM.FaTE results are
generally consistent with measured values, with most predictions falling within about an order of
magnitude of the measured concentrations. Exceptions are noted for some biota, with modeled
concentrations in a few animal types lower than measured concentrations by more than an order
of magnitude. However, more information about the historical mercury sources and additional
measurement data would be required to make a more conclusive statement regarding model
performance.
Throughout the report, literature data on mercury measurements and modeling results are
cited and compared with the TRIM.FaTE results. The comparison with 3MRA modeling results,
comparison with available measurement data for the test case site, and comparison with literature
reports have continued to increase confidence in TRIM.FaTE and the current set of algorithms.
TRIM.FaTE's Steady-state and Dynamic Modes Are Complementary
As an example of the ability to work at different levels of complexity within the
TRIM.FaTE framework, the model can be applied in either a dynamic or steady-state mode. The
dynamic mode, demonstrated in detail in Chapter 3, allows time resolution in the inputs and
produces time-varying results as appropriate at a user-set level of resolution, but it also requires
substantially more computer resources. The steady-state mode, described in detail in Chapter 4,
provides no time resolution of results and does not accept time-varying inputs (thus requiring
user designation of representative constant values), but yields overall mass distributions and
concentration results in much shorter computer simulation times. Thus, the steady-state mode
has practical advantages for in-depth sensitivity analyses and Monte Carlo analyses of
uncertainty. As shown in Chapter 4, the steady-state mode compares favorably with the dynamic
mode, with generally consistent mercury mass distribution, concentration, and speciation
patterns. As part of the analyses described, the differences in results between the two modes are
disaggregated into differences attributable to input differences (i.e., converting time-varying
inputs for the dynamic mode into constants for the steady-state mode) and differences
attributable to how well the steady-state solution approximates the dynamic results at the end of
a 30-year modeling period.
Dynamic Modeling Results Demonstrate TRIM.FaTE's Capabilities
Both the steady-state and dynamic modes produce results that appear reasonable -
internally consistent, logical in direction and trend, logical in relationships between media, and
logical based on the algorithms and inputs used. Chapter 3 provides a sampling of TRIM.FaTE's
capabilities in the dynamic mode. Two time trends dominate in mercury mass accumulation
from a continuous air source, either: (1) a gradual increase which slows (flattens out) as time
progresses (e.g., in soils, sediment, animals closely linked to soil such as earthworms, soil
arthropods, and the animals that feed on them), or (2) a repeated five-year spiking pattern that
corresponds to variations in the five years of meteorological data inputs used (e.g., in air, leaves,
JULY 2005 ES-4 TRIM.FATE EVALUATION REPORT VOLUME II
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herbivores and most other terrestrial animals, and to a lesser extent surface water and fish). The
latter pattern illustrates the influence of the meteorological inputs, especially the wind and
precipitation data, directly on air, then on leaves, and then moving through the terrestrial
herbivore food chains. At the end of the 30-year dynamic modeling period, most of the mercury
mass remaining in the modeling region is in the soil and benthic sediment compartments.
Mercury concentrations follow time trends similar to those observed for chemical mass
accumulation. The concentration results, which indicate where the intensity of the mercury is
highest and lowest (e.g., which media, locations, times have highest concentrations), are
complementary to the mass results, which indicate where the highest and lowest total amounts of
mercury are (factoring in the overall volume/mass of the various system components). Within
the food chains modeled for both water-column and benthic fish, mercury concentrations follow
expected patterns and are consistent with model inputs (e.g, highest concentrations in carnivores,
then omnivores, then herbivores). For all the animals, both aquatic and terrestrial, it is clearly
evident that the modeled diet affects the temporal pattern and total accumulation of mercury
estimated by TRIM.FaTE. Among atmospheric deposition processes, the wet vapor deposition
of divalent mercury is dominant, followed by dry vapor deposition of divalent mercury. The
time trend for atmospheric deposition differs from that for air concentration because of the
elevated influence of precipitation events on deposition.
The modeled speciation of mercury is generally as expected. Elemental mercury, the
primary emitted form, is dominant in air and, because of its much higher soil mobility than other
forms, in deeper soil layers. Divalent mercury is dominant in most other media except for fish
and piscivorous wildlife, such as the common loon, where methyl mercury dominates.
One interesting finding is the difference between the spatial pattern in the air
concentration and atmospheric deposition results, which is shown to be attributable to the
difference in wind direction patterns when it is raining versus when it is not. As would be
expected, the spatial pattern of surface soil concentrations (and biota closely linked to surface
soil) follows the deposition pattern more closely than the air concentration pattern. Based on
comparative analysis of the different emission cases, the mass and concentration results for
nearly all media and biota other than air and the deeper soils are almost entirely attributable to
the divalent mercury component of the emission. Even when the elemental mercury level is
almost 20 times higher in the emissions, as in this test case, its local multimedia impact is small
relative to the concurrently emitted divalent mercury.
Conclusions
TRIM.FaTE can provide time-series and spatially resolved predictions of mercury mass
and concentration in environmental media and biota that are logical and appear consistent with
expectations based on the algorithms used, which were derived from what is currently known
about mercury fate and transport. Predicted TRIM.FaTE mercury concentrations and speciation
results compare reasonably well with 3MRA modeling results, limited measurement data for the
test case site, and reports from the literature (note that there are not much available data with
which to compare the mass results). TRIM.FaTE simulation in the steady-state mode has some
limitations common to all steady-state modeling formulations, especially related to treatment of
time-varying meteorological input data, but the results are generally reflective of simulation in
JULY 2005 ES-5 TRIM.FATE EVALUATION REPORT VOLUME II
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the dynamic modeling mode, at least under the conditions tested. Thus, sensitivity analysis
based on steady-state simulations, as presented in this report, appears to be informative about
results from simulations in both steady-state and dynamic modes.
Specific observations with regard to results from the application of TRIM.FaTE in this
test case, given the algorithms and inputs used, include the following.
(1) Elemental mercury emitted to air in a model ecosystem yields relatively little local
(within -10 km) deposition and multimedia impact, with emitted divalent mercury
accounting for most of the localized deposition and multimedia impact and most emitted
elemental mercury traveling beyond the local area and potentially depositing over a much
larger area.
(2) Divalent mercury was the dominant mercury species deposited in the test case, and wet
vapor deposition was the dominant process, followed by dry vapor deposition (together,
wet and dry vapor deposition of divalent mercury accounted for approximately 95
percent of total deposition, with all other processes/species less than five percent
combined), and the amount and spatial pattern of atmospheric deposition was highly
dependent on both precipitation and wind direction.
(3) Surface soil, and then benthic sediments and root zone soils, were the largest reservoirs
for locally deposited mercury mass over the 30-year time frame of the dynamic modeling,
and also in the steady-state modeling (where sediment and root zone soil accumulations
were higher than the 30-year results and much closer to surface soil accumulation).
(4) For the modeled surface water bodies, higher trophic level fish and wildlife reached
higher mercury concentrations in 30 years than the lower trophic level animals that were
components of their diets.
(5) In the steady-state modeling, carnivorous fish and piscivorous wildlife reached the
highest mercury concentrations among all animals modeled.
(6) Weather-related temporal (seasonal and annual) patterns are reflected in the dynamic
predictions of mercury mass accumulation and concentration for various environmental
media and biota, such as surface water and terrestrial herbivores.
(7) Specific configuration of and input properties used for aquatic food chains, including diet
components (e.g., proportion benthic invertebrates), ingestion rates, biomass at various
trophic levels (including algae), and predation pressure by piscivorous birds or mammals,
can greatly affect methyl mercury concentrations in fish.
(8) The modeled diets of animals simulated in TRIM.FaTE affect the temporal pattern and
magnitude of their mercury accumulation.
(9) Modeled plant uptake of mercury via roots is low compared with mercury that
accumulates in and on leaves directly from the atmosphere.
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The application and analyses described within this report significantly expand our
knowledge, familiarity, and confidence in the TRIM.FaTE model and the library of algorithms
and inputs used here. That said, as with any model there are areas of relatively greater
confidence and areas of relatively greater uncertainty, the latter of which often reflect scientific
uncertainties about environmental processes. Such areas of greater modeling uncertainty may
provide focus for the attention of future TRIM.FaTE users, and for future evaluation and
potential refinement of inputs and algorithms. In general, the level of confidence in TRIM.FaTE
results is greatest at the scale of annual (or longer-term) concentration and mass results for a
modeling region within 10 to 20 km of a source. Examples of areas of relatively greater
uncertainly and potential focus for future attention include speciation of mercury in wildlife and
sediments, as well as mercury mass accumulation in benthic sediments. The findings of this
study and other test case applications of TRIM.FaTE involving mercury, dioxins/furans, and
polycyclic aromatic hydrocarbons, including previously reported evaluation activities and model
documentation, have all contributed to improved understanding of TRIM.FaTE performance and
confidence in its application as a multimedia modeling approach for local-scale multimedia fate
and transport of air pollutant emissions. The features offered by TRIM.FaTE that distinguish it
from other commonly used multimedia modeling approaches provide incentives for its
application. In future applications, users are encouraged to design appropriate scenarios, paying
close attention to algorithms and inputs, and to critically evaluate results, contributing their
findings to the longer-term knowledge base on TRIM.FaTE and similar models.
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1. INTRODUCTION AND OBJECTIVES
Volume I of this evaluation report (EPA 2002a) describes in detail a wide range of model
evaluation activities for TRIM.FaTE, which were undertaken to implement EPA's overall
evaluation plan for the model as presented in the TRIM Status Report (EPA 1999a) and reviewed
by EPA's Science Advisory Board (EPA 2000a). This document, Volume II of the evaluation
report, describes the mercury test case, a detailed
performance evaluation of TRIM.FaTE based on
mercury emitted to air from a specific industrial
source. Unlike the other types of model
evaluation discussed in Volume I of this
evaluation report, which focus on specific aspects ,or f ^ ., \ ^ mercury es case as
been to contribute to model development,
Mercury Test Case Goal
Since its beginnings in 1999, the primary goal
testing, evaluation, and refinement.
of the model (e.g., inputs, individual process
model s), performance evaluation focuses on the
performance of the model as a whole.
Background on Performance Evaluation
Performance evaluation compares modeling results to some type of benchmark, such as
monitoring data, other modeling results, and expert judgment. Generally, the optimized model,
as modified based on all prior evaluations, is used for performance evaluation. Matching model
output to monitoring data is often considered the most desirable form of performance evaluation.
Although comparing model output to measured values provides useful information on the model,
history "matching" experiments provide only part of the overall picture of a model's quality,
reliability, and relevance (Beck et al. 1997). Several other forms of performance evaluation also
are used. In addition to monitoring data, or in the absence of such data, outputs from other
models and expert opinion about how outputs should look can be used as comparison
benchmarks in performance evaluation. Examples of performance evaluation activities include:
Model-to-model comparison;
• Comparison of model output to measurement data (e.g., measured concentrations in
environmental media and biota);
• Round-robin experiments (where several different users independently set up either the
same model or similar models and generate output using the same data for a particular
case study); and
• Some forms of regional sensitivity analysis (where output is tested against expert
judgment about a plausible bound).
To date, TRIM.FaTE performance evaluation activities have focused on model-to-model
comparisons and the comparison of model outputs to measurement data, along with detailed
review and assessment of the patterns and trends (and underlying reasons for them) observed in
the model outputs, as described in this report.
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The ongoing TRIM.FaTE model evaluation has been designed to be consistent with the
Agency's peer review policy for models (EPA 2005a) and its evolving regulatory environmental
modeling guidance (Habicht 1992, EPA 1994, EPA 1998c, EPA 1999d, and EPA 2003a). Each
successive performance evaluation provides an opportunity to use the model and learn more
about how it works. Beyond the ultimate findings of the performance evaluation itself, the
experience gained through such exercises contributes to an overall understanding of the model,
which ultimately enables both model developers and users to better judge the quality of the
model. In addition to the mercury test case described here, other examples of TRIM.FaTE
model evaluation include recent and in-progress studies focusing on organic chemicals,
including dioxins/furans (EPA 2004, EPA 2005b) and polycyclic aromatic hydrocarbons.
Objectives and Limitations of the TREVLFaTE Mercury Test Case
The primary objectives of the mercury test case are to evaluate the:
• Performance of TRIM.FaTE in dynamic and steady-state simulations of real world
conditions; and
Utility of the steady-state solution for performing sensitivity and uncertainty analyses.
The primary means for evaluating model
performance for the mercury test case is
through consideration of the compatibility of
the TRIM.FaTE results with literature
findings of mercury distribution throughout
the multiple components of ecosystems, as
well as comparison of TRIM.FaTE results to
results generated by alternative models
(including one other multimedia model), and
consideration of the compatibility of the
sensitivity analysis conclusions with the
conceptual models that were the basis for the
TRIM.FaTE library algorithms and
properties.
With regard to the sensitivity analysis,
TRIM.FaTE's steady-state mode has been
employed at a substantial savings in model
run time. Inherent in using this mode rather
than the dynamic mode is the presumption
that sensitivity of steady-state results reflects
or is representative of the sensitivity of
results at time points of interest during a
dynamic simulation.
As noted at the beginning of this
chapter, the main goal of the TRIM.FaTE
Modeling of Mercury Emissions to Air
EPA has employed many models over the past
decade for different applications and purposes
associated with emissions of mercury to air.
These have included models capable of long-
range, large-scale modeling such as the Regional
Lagrangian Model of Air Pollution (RELMAP),
used for the Mercury Study Report to Congress,
and more recently the Community Multi-scale Air
Quality (CMAQ) modeling system, which
includes simulation of atmospheric chemistry, as
well as models capable of local-scale atmospheric
transport such as the Industrial Source Complex
(ISC) model. Mercury deposition estimated via
those models has been used as input for watershed
and aquatic ecosystem modeling, e.g., using the
indirect exposure methodology for mercury (IEM-
2M) or the Multimedia, Multi-pathway, Multi-
receptor Exposure and Risk Assessment model
(3MRA). Unlike many of those model
applications, which informed regulatory decisions
by the Agency, the analyses described in this
document are solely for the purpose of model
evaluation.
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mercury test case has been to support model development, testing, evaluation, and refinement. It
is primarily an evaluation exercise, and the focus of this report is on the simulations performed
for model performance evaluation. Thus, the absolute results of this test case exercise (e.g., the
exact media concentrations estimated by the model) may be of less interest and relevance than
whether the patterns, trends, and general magnitudes observed in the model outputs are
consistent with the expected multimedia behavior of mercury released to air. The values of
specific results can be affected by changes in the values of various input properties (as informed
by the sensitivity analysis), whereas the overall model performance is dependent on the
integration of all algorithms, formulas, and input properties, which have been supplied particular
parameter values for purposes of the test case. Consequently, the primary focus of the material
presented in this document is on the patterns, trends, and general magnitudes of the model
outputs rather than the specific or absolute results.
Background on the Mercury Test Case
Preliminary, limited evaluations of TRIM.FaTE focused primarily on organic chemicals.
An earlier prototype of TRIM.FaTE was compared with two similar models, CalTOX (McKone
1993a, McKone 1993b, McKone 1993c) and SimpleBox (van de Meent 1993, Brandes et al.
1997). The pollutants modeled for that comparison were poly cyclic aromatic hydrocarbons
(PAHs) (EPA 1998a). The mercury test case described in this report addresses the need to
evaluate the performance of the current version of TRIM.FaTE with an inorganic chemical
release scenario, particularly one that includes a persistent and mobile form of inorganic
pollutant that can undergo reversible environmental transformations between different chemical
species in different media. As noted above, separate evaluations of TRIM.FaTE based on
organic chemical release scenarios, including PAHs and dioxins/furans, are recently completed
or in progress (EPA 2004, EPA 2005b, other documentation in preparation).
Mercury is one of the 187 HAPs listed
under section 112(b) of the CAA, is a Great
Waters pollutant of concern (EPA 2000b), is
identified as a pollutant of concern under the
Urban Air Toxics Strategy (EPA 1999b), and
is one of the seven specific pollutants listed
for source identification under section
112(c)(6). In addition, the findings of the
Mercury Study Report to Congress (EPA
1997) indicate that mercury air emissions
may be deposited or transported to water
bodies, resulting in mercury uptake by fish.
Ingestion of mercury-containing fish is the
dominant pathway of concern for health
effects in humans, particularly developmental
effects in children.
TRIM.FaTE
TRIM.FaTE is a spatially explicit, 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). TRIM.FaTE 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 can also be used as inputs to a human
ingestion exposure model.
Mercury can take on multiple forms in the environment and each form has a different set
of physical/chemical property values that influence fate and transport of the pollutant. The three
main forms of mercury include elemental mercury, which is a liquid at room temperature and
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volatilizes into the gas phase; inorganic mercury, which exists as a number of different
compounds in both the particulate phase and gas phase; and organic mercury, which exists as a
number of different compounds and is the most bioavailable form. TRIM.FaTE was specifically
designed to consider reversible transformation and to simultaneously track major chemical
species of a pollutant in multiple environmental media, and it includes appropriate algorithms
and input data for modeling transformation of mercury among its elemental (Hg°), inorganic
(represented as divalent mercury, Hg2+), and organic (represented as methyl mercury, MHg)
forms. Additional background on mercury in the environment is provided in EPA's Mercury
Study Report to Congress (EPA 1997).
Of the four types of stationary sources identified in the 1997 Mercury Study Report to
Congress as having the highest total national emissions of mercury at that time, the chlor-alkali
facility release scenario was selected for the TRIM.FaTE evaluation test case, in part because of
its relatively lower release height for emitted mercury and the potential for local environmental
and human health impacts. One of the primary reasons for selecting the particular facility to
model was that there are relevant monitoring data for mercury in the area. The site is generally
representative of a rural location with a large number of nearby lakes and rivers. The name of
the facility, which is now closed, and its exact location are not identified in this report. This
evaluation does not draw conclusions regarding the facility. Rather, it is intended to facilitate
conclusions regarding the performance of TRIM.FaTE.
In addition to the performance evaluation reported here, the mercury test case site, set-up,
and data have been used by EPA for several years for a variety of TRIM.FaTE model
development and testing purposes. Numerous smaller-scale and reduced complexity analyses
have been performed, including many of the assessments reported in Volume I of this evaluation
report (EPA 2002a), to assist in understanding, troubleshooting, and refining the model during its
developmental phase.
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Role of Environmental Measurements in TRIM.FaTE Performance Evaluation
An extensive review of the literature was undertaken following the Science Advisory Board's initial
comments on the importance of model evaluation for the TRIM project (EPA 1998b). The review focused
on identifying multimedia data sets for use in evaluating the performance of TRIM.FaTE. Several studies
were identified that report chemical measurements in multiple environmental media. The majority of
these studies focus on measuring current chemical concentrations in the environment with little emphasis
on temporal variability or trends. Several of the studies were designed to assess multimedia partitioning
(e.g., atmospheric partitioning among gas, aerosol, and water phases) or to investigate specific
environmental processes such as the transfer rate across an environmental interface. The usefulness of
some of the reported environmental measurements was limited because in many cases the source of the
chemical contamination was not well characterized. Although historical emission patterns can potentially
be reconstructed for certain chemicals using sediment chronology (Cowan et al. 1995), little effort has
gone into matching historical emissions to multimedia environmental concentrations.
None of the studies identified during EPA's literature review provides complete and concurrent
information on chemical concentrations in the five major environmental media (i.e., air, water, sediment,
soil, biota) along with the associated source term(s) and historical environmental characteristics (e.g.,
meteorology, hydrology, landscape properties). Although some of these studies can be used to evaluate
certain aspects of the model, it is important notto overvalue these results when judging the overall quality
of the model (see EPA 1999a for details about the studies identified).
Comparisons of TRIM.FaTE outputs to monitoring data are difficult because complete multimedia data
sets from well-characterized systems (e.g., known source, meteorology, and landscape) to use in a
performance evaluation are not currently available. However, limited data sets are becoming available
through the literature and through unpublished sources (e.g., multimedia monitoring by state or local
agencies). These smaller data sets, including those collected by EPA for the mercury test case site, have
contributed to this performance evaluation of TRIM.FaTE.
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2. SPECIFICATIONS OF MODELED SCENARIOS
This chapter summarizes the specifications of the scenarios modeled for the TRIM.FaTE
mercury test case. It is supplemented by Appendix A, which provides detailed documentation of
the values and references for all of the input properties (e.g., chemical transformation rates and
partition coefficients, soil and surface water parameters) used in the modeling. Three forms of
mercury - elemental (Hg°), divalent (Hg2+), and methyl (MHg) - were included in the modeling,
with transformation among forms modeled where supported by the available data. More details
about specifications used for the steady-state modeling and the sensitivity analysis are provided
in Chapters 4 and 5, respectively.
TRIM.FaTE is a multimedia, mass balancing, compartment model that simulates the
transport and fate of pollutants emitted to air through time and space. It is extremely flexible in
set-up and application, and it can produce a wide variety of results (e.g., mass and concentration
of various chemicals over time and space for dozens of different environmental media and biota).
The modeling concepts, approaches, algorithms, equations, and assumptions used in TRIM.FaTE
are documented in detail in a two-volume TRIM.FaTE Technical Support Document (EPA 2002b
and c, available with other TRIM.FaTE documentation at www.epa. gov/ttn/fera) and are not
discussed at length here. All TRIM.FaTE model runs discussed in this report were performed in
November and December 2003, except for the sensitivity analysis results reported in Chapter 5
and Appendix D. Those model runs were performed in June 2003 (see Chapter 5 for differences
between these sets of model runs).
Overview
The modeling scenarios for this test case are based on a former manufacturing facility in
the northeastern U.S. (now closed) that used a mercury cell chlor-alkali process in the production
of chlorine and consequently was a source of mercury emissions to the atmosphere. Information
available about this facility was used in configuring the source in the TRIM.FaTE mercury test
case scenarios, as described in the next section. Information available for the facility location
was used in the selection of values for environmental setting parameters, as documented in
Appendix A.
To facilitate TRIM.FaTE evaluation using several different types of information, three
different dynamic modeling emission cases (i.e., scenarios) and one steady-state scenario were
modeled. The various TRIM.FaTE scenarios are outlined below.
• Scenario A - source emissions only (no boundary contributions or initial concentrations),
with emission of only divalent mercury. Modeling duration is 30 years, with source
emissions for entire duration. This case is used in the model comparison, which focused
exclusively on divalent mercury emissions (Chapter 6).
• Scenario B - source emissions only (no boundary contributions or initial concentrations),
with emission of both elemental and divalent mercury. Modeling duration is 30 years,
with source emissions for entire duration. This case is used for much of the general
results presentation and analysis (Chapter 3).
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• Scenario C- source emissions plus air boundary contributions and initial concentrations
in environmental media and biota, with emission (and boundary contributions) of both
elemental and divalent mercury. Modeling duration is 40 years, with source emissions
for the first 30 years only. This case incorporates some "background" contamination and
is used in the consideration of available measurement data (Chapter 7).
• Steady-state Scenario - same emissions as Scenario B, but with time-varying input
properties set to constants. This case is used for the sensitivity analysis (Chapter 5), and
the steady-state results are also discussed in the context of the dynamic results for
Scenario B (Chapter 4).
Regardless of which mercury species were emitted from the source in a given scenario,
TRIM.FaTE always modeled the fate of three forms of mercury (including transformations
among the different forms): elemental (Hg°), divalent (Hg2+), and methyl (MHg) mercury.
Source Specifications
The values used for source emissions of mercury to air are based on summarized data
provided by a state agency in 1999. Fugitive emissions make up the bulk of air emissions for the
modeled facility. In the modeling, all source emissions are released directly into one air
compartment (referred to as the source compartment) that is centered (in the x-y plane) on the
location of the source area - no modeling distinction is made between stack and fugitive
emissions. Given that the modeled source height is very low (0.01 m), and that the atmospheric
mixing height is used as the top boundary of the source compartment (and other air
compartments), all modeled air emissions enter the system below the mixing height (i.e., no tall
stacks modeled). Source emission rates are modeled as constant and continuous for an assumed
30-year source operating period. Re-emission of mercury compounds (e.g., from surface soil or
surface water to air) is simulated by TRIM.FaTE throughout the modeling duration according to
the various particle resuspension, volatilization, diffusion, and other process algorithms, as
applicable (see EPA 2002c for algorithm details).
Speciated mercury emissions data were not available for the test case facility. For
Scenarios B and C and the Steady-state Scenario, the total mercury emissions, which were
provided by the state agency, were assumed to be 95 percent elemental mercury and five percent
divalent mercury, which is believed to be within a realistic range of values for a chlor-alkali
facility. For example, Landis et al. (2004) reported that roughly two percent of gaseous mercury
emitted from the cell building roof vent (thought to be the largest source) over a nine-day period
at a chlor-alkali plant in Georgia was inorganic divalent reactive gaseous mercury. Scenario A
included only the divalent mercury emissions. The modeled emission rates are shown in the text
box below. Note that the phase distribution (e.g., particle, gas) of the emitted compounds is not
set as an input in TRIM.FaTE; rather, the model calculates the phase distribution at each time
step based on chemical properties such as Henry's Law constant and other input properties (see
EPA 2002b,c for details).
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Scenario
A
B, C, Steady-state
Modeled Emission Rate (g/day)
Elemental Mercury (Hg°)
0
335.6
Divalent Mercury (Hg2+)
17.663
17.663
Spatial Specifications of the Scenarios
The overall extent of the area for which chemical transport and fate are modeled (i.e., the
modeling region) was set based on the location of the emissions source, expected mobility of the
chemical of primary interest with respect to deposition (i.e., divalent mercury, which deposits at
much higher rates than elemental mercury), locations of receptors of interest (e.g., water bodies
supporting robust fish populations), and watershed boundaries for the water bodies of interest
(see EPA 2003b for discussion of considerations in setting TRIM.FaTE spatial boundaries and
spatial layouts). This test case was intended to be a local analysis focused on nearby water
bodies; therefore, the modeling region boundaries were set to encompass the water bodies and
watersheds of main interest, and not necessarily to capture the deposition of a large fraction of
the emitted mercury mass.
The modeling layouts used for the
mercury test case (i.e., the number, size,
shape, and location of all volume elements,
which are the spatial entities in which
compartments are located) are shown on the
same scale in Exhibits 2-1 and 2-2 for the air
and surface parcels, respectively (see text
box for basic spatial terminology; for further
discussion of TRIM.FaTE spatial concepts
and definitions, see EPA 2002b). As
evident from these layouts, the internal and
outer boundaries of the air parcels (Exhibit
2-1) do not line up exactly with those for the
surface parcels (Exhibit 2-2), which is
typical for TRIM.FaTE applications to date.
These differences result from the differing
considerations for modeling the movement
of chemicals in different types of media (air
versus soil and water). The air layout
extends beyond the surface layout in all
directions because of the desire to account
for at least some of the "blow-back" of
airborne chemical into the area of primary
Basic TRIM.FaTE Spatial Terminology
Parcel - 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 (by adding a vertical dimension) and do not
change for a given scenario.
Volume element - A bounded three-dimensional
space that defines the location of one or more
compartments.
Compartment - The TRIM.FaTE modeling unitthat
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. Within a compartment it is assumed
that all chemical mass is homogeneously distributed
and is in phase equilibrium.
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Exhibit 2-1
Layout of Air Parcels
Air Parcels
• . ..' Watershed Regions I I Parcel Boundary
Water
N
A 1-
1
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Exhibit 2-2
Layout of Surface Parcels (same scale as Exhibit 2-1)
Surface Parcels
, • Watershed Regions I I Parcel Boundary
Water
A 1 ° 1
A\ Kilometers
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interest (represented, in this case, by the surface layout) that results from changes in wind
direction.1
The surface parcel layout consists of 20 soil parcels (including a small source parcel
centered on the emission source), four lake parcels, one large river parcel, and two small stream
parcels (total of 27 surface parcels). The air configuration includes 30 parcels arranged in an
approximate radial grid originating from the source parcel, which is centered on the emission
source (for the mercury test case scenarios, the source air parcel lines up exactly with the source
surface parcel) (see EPA 2002a for evaluation of different air parcel layouts, including the
approximate radial grid design used here). Similar to the surface parcel layout, the air grid
extends farther to the east and southeast so that it covers the water bodies of interest, and their
watersheds, in that direction. The air grid was scaled so that the inner air parcels line up at least
in part with the surface parcels, and with at least one air parcel extending beyond the boundary
of the surface parcel layout on all sides (i.e., the air parcel grid is larger than the surface parcel
grid). The total size of the air grid is approximately 227 km2, and the total area encompassed by
the surface parcel layout is approximately 126 km2.
Soil parcel boundaries were located to minimize overland flow between adjacent
terrestrial parcels (including the external boundary of region), with some attention also given to
maintaining homogeneity of land use and plant type patterns. Some of the initial soil parcels
were then subdivided to provide additional spatial resolution. Availability of monitoring data,
intended to be used in comparisons with model outputs, was also considered in developing the
soil parcel layout. Water bodies were selected for inclusion as parcels in the modeling scenarios
primarily based on their size and proximity to the emission source and the availability of
monitoring data. Six of the seven water bodies are part of the same system of lakes and streams
feeding into the river.2
Compartments Modeled in the Scenarios
Consistent with the concepts employed in TRIM.FaTE (see TRIM.FaTE TSD Volume 1,
Chapters 3 and 5 for more detail), volume elements were created with the same horizontal
dimensions of the parcels. For example, a volume element was configured for each air parcel
with the same x and y dimensions. The vertical dimension (i.e., the height) of all the air volume
elements was set to vary with the mixing height, which varied over time according to the
meteorological input data. A single layer of air volume elements was used for all scenarios.
1 In TRIM.FaTE, chemical mass in air that crosses the external air boundary enters an air sink and cannot
re-enter the modeling region. Therefore, if the external boundaries of the air and surface layouts line up exactly,
transport of chemical mass to air beyond the boundaries of the surface parcels makes that mass unavailable to the
surface parcels for the entire modeling period (i.e., there can be no modeled "blow-back" of this mass).
Although the river near the facility on which the scenarios are based is tidal, it was modeled for test case
purposes as a non-tidal river because the TRIM.FaTE library did not include algorithms to accommodate tidal
influence on pollutant movement. Additionally, because the river was not a primary focus for the evaluation, the
entire extent of the river within the modeling region was designated as a single parcel. For these reasons, results for
the river compartment are not emphasized in this report and are not used in consideration of the measurement data.
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For each surface parcel, one of the following was created:
• Set of soil volume elements (surface soil, root zone soil, and vadose zone soil) and a
ground water volume element; or
Pair of surface water and sediment volume elements.
As illustrated in Exhibit 2-3, the soil and ground water volume elements are aligned exactly with
the x-y dimensions of the land parcels, and situated vertically in series just below the layer of air
volume elements (i.e., the land surface serves as the bottom of the air volume elements and the
top of the surface soil volume elements). Similarly, the surface water and sediment volume
elements are aligned exactly with the x-y dimensions of the surface water parcels, and situated
vertically with the surface water volume element just below the layer of air volume elements and
the sediment volume element just below its corresponding surface water volume element. The
vertical dimension (i.e., depth) of all abiotic volume elements other than air was set as a constant
(non-time-varying) value.
Exhibit 2-3
Schematic of Volume Element Layering: Two Hypothetical Surface Parcels
Surface Water
Sediment
Land Parcel
Surface Soil
Root Zone Soil
Vadose Zone Soil
Ground Water
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Abiotic compartments are associated with each of the volume elements as follows:
• 30 air compartments (corresponding to the 30 air volume elements);
20 each of surface soil, root zone soil, vadose zone soil, and ground water compartments
(corresponding to the 20 each surface soil, root zone soil, vadose zone soil, and ground
water volume elements, respectively); and
• Seven each of surface water and sediment compartments (corresponding to the seven
surface water and seven sediment volume elements, respectively).
Biotic compartments (representing biological populations within the ecosystem) are
associated with the various abiotic volume elements. The biota included in the test case were
selected based on the availability of monitoring data, the need for model comparisons, the need
to adequately account for mass distribution of mercury, and/or the need to represent particular
trophic levels. The different biotic compartment types, representing plants and animals in both
aquatic and terrestrial ecosystems, that are included in the test case scenarios are shown in
Exhibit 2-4. Exhibits 2-5 and 2-6 provide further detail on the relationship between volume
elements and biotic compartment types, and on the modeled spatial distribution of the various
biota. Additional details about the biotic aspects of the model set-up, including the modeled
population densities for each compartment, are provided in Appendix A.
Input property values were set with a consideration of site or region-specific information,
where appropriate. Additional details on the model setup and documentation for input property
values are reported in Appendix A.
Temporal Aspects of the Dynamic Scenarios
Given the history of the facility being modeled and the timing of the available monitoring
data, the modeling period was set at 30 years for emission cases A and B, and 40 years for
emission case C (the source emission duration remained at 30 years for case C). Thus, the
emission source started emitting at the beginning of the modeling period (roughly considered to
be late 1960s) and continued for 30 years (most of the monitoring data are from the late 1990s).
The output time step for the three dynamic scenarios was set to two hours; that is, the model
provided outputs in terms of moles, mass, and concentration of elemental, divalent, and methyl
mercury for each of the 417 compartments at two-hour intervals throughout the 30-year
modeling period. Two hours was selected as the output time step to strike a balance between
volume of model outputs and adequate time resolution to capture anticipated time-varying
results. These outputs are instantaneous values ("snapshots") every two hours, not some type of
time-averaged values. Thus, the raw output was voluminous for each emission case, with more
than 100 million calculated values each for moles, mass, and concentration. Most of the
subsequent data review and analysis was performed on annual (and in some cases monthly)
averages of the two-hour instantaneous output data.
The vast majority of numeric property values set for the test case scenarios are modeled
as constant over time (see Appendix A for input values), but a few inputs - notably
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Exhibit 2-4
Biotic Compartment Types Modeled for the Mercury Test Case
Biotic Compartment Type
Representative Subgroup or Species
Terrestrial Plants
Leaf
Particle on leaf
Stem
Root
Of three vegetation types - deciduous forest, coniferous forest,
grasses/herbs (stems and roots not currently modeled in
TRIM.FaTE for deciduous and coniferous forest vegetation
types)
Terrestrial Animals
Soil detritivore
Ground-invertebrate feeder
Herbivore
Insectivore
Omnivore
Carnivore
Soil arthropod, Earthworm
Short-tailed shrew
Meadow vole, White-tailed deer
Black-capped chickadee
Mouse
Long-tailed weasel, Red-tailed hawk
Semi-aquatic Animals B
Insectivore
Omnivore
Piscivore
Carnivore
Tree swallow
Mallard, Raccoon
Common loon
Mink, Bald eagle
Aquatic Plants
Macrophyte
Submerged aquatic vegetation generalized from Elodea sp.
Aquatic Animals
Benthic invertebrate
Benthic omnivore
Benthic carnivore
Water-column herbivore
Water-column omnivore
Water-column carnivore
These compartment types represent trophic niches arising either
from a benthic or water-column source. b
3 The term "semi-aquatic" is used in TRIM.FaTE documentation to refer to birds and mammals that reside and/or
nest on land but that include at least some aquatic biota in their diets.
b These compartment types were not parameterized using the concept of a single representative species that might
feed on organisms from more than one trophic level or from both 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.
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Exhibit 2-5
Volume Element Relationships and Spatial Distribution of Biotic Compartments
Volume Element
Air
Surface soil
Root zone soil
Vadose zone soil
Ground water
Surface water
Sediment
Associated Biotic Compartment Types
None
Terrestrial plant: leaf, particle on leaf, stem, root
Terrestrial animal: soil detritivore (soil arthropod), ground-
invertebrate feeder (short-tailed shrew), herbivore (meadow
vole, white-tailed deer), insectivore (black-capped
chickadee), omnivore (mouse), carnivore (long-tailed weasel,
red-tailed hawk)
Semi-aquatic animal: insectivore (tree swallow), omnivore
(raccoon), carnivore (mink, bald eagle)
Terrestrial animal: soil detritivore (earthworm)
None
None
Aquatic plant: macrophyte
Semi-aquatic animal: omnivore (mallard), piscivore
(common loon)
Aquatic animal: water-column herbivore, omnivore,
carnivore
Aquatic animal: benthic invertebrate, omnivore, carnivore
Notes on Spatial Distribution of Biotic Compartments a
-
All surface soil volume elements (VEs) except source;
vegetation types vary across VE (see Exhibit 2-6); stem
and root only included for grasses/herbs vegetation type
All surface soil VEs except source, Nl, and Wl, except
that meadow vole only included for grasses/herbs
vegetation type (SW2, NE2)
All surface soil VEs except source, Nl, and Wl, except
that raccoon and mink also not included for three VEs that
do not border modeled lakes or river (ESE2, W2, NE3)
All root zone soil VEs except source
-
-
All surface water VEs except the two small streams
All surface water VEs except the two small streams
All surface water VEs except the two small streams
All sediment VEs except the two small streams
a No biotic compartment types modeled for source volume elements because they are considered too industrial/contaminated. No animal compartment types
modeled for surface soil volume elements Nl and Wl because they are considered too developed/urban (note that earthworm is modeled for root zone soil
volume elements Nl and Wl).
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Exhibit 2-6
Spatial Variation of Vegetation Types and Terrestrial/Semi-aquatic Animal Compartment
Types3
Surface Parcels
I I Source | 1 Deciduous
[~~| Coniferous
~~l Grasse&IHeibs
a As modeled, the source parcel has no associated animals, and soil parcels Nl and Wl have earthworm only. Except
where footnoted, all other soil parcels have the following associated animal compartment types: bald eagle, black-
capped chickadee, earthworm, long-tailed weasel, mink, mouse, raccoon, red-tailed hawk, short-tailed shrew, soil
arthropod, tree swallow, and white-tailed deer.
b These parcels have all the species listed in footnote a, plus the meadow vole.
0 These parcels have all the species listed in footnote a, except for the raccoon and mink.
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meteorological and seasonal parameters - are time-varying (see list in Section 4.1). The
seasonally varying properties included in these scenarios are those affecting plants (i.e., hours of
daylight, length of growing season) and meteorological properties such as air temperature.
These scenarios did not include simulation of certain winter weather-related conditions (e.g.,
frozen precipitation, snow or ice covered surfaces).
In the case of the meteorological properties, the best and most complete data available for
the vicinity of the facility were used to construct a five-year hourly data set for 1987 to 1991 of
all TRIM.FaTE meteorological data inputs. The data set is a composite from three
meteorological data measurement stations - wind speed and direction and air temperature from a
nearby station (within 10 km), precipitation rate from a different nearby station with more
complete records (within 20 km), and the upper air data needed to estimate mixing height from a
station roughly 150 km to the southwest. The five-year meteorological input data set is repeated
as needed to provide values for the full analysis (e.g., six times for a 30-year modeling period).
Pre-processing of the raw meteorological data (e.g., units conversions, setting minimum values)
to facilitate use in TREVI.FaTE followed the approach outlined in the TRIM.FaTE User's Guide
(EPA 2003b). A wind rose representing the entire five-year composite wind data set (roughly
43,800 data points for both wind direction and speed) is provided in Exhibit 2-7. Winds are
predominantly from the northwest and south, and very rarely from the east (similar to on-site
meteorological data for 1998-99; see wind rose in Appendix F). Wind speeds reported in the
original data source as zero (roughly 20 percent of all hourly values) were set to a minimum
value of 0.75 m/sec for TRIM.FaTE modeling, which explains the absence of calm winds in the
wind rose. Note that the lowest reported non-zero value in the original data source is 1.03
m/sec.3
As noted earlier, Scenario C included initial concentrations in each compartment in the
system (intended to correspond to the point in time when the facility being modeled began
operation). These initial concentrations were developed using a separate preliminary
TRIM.FaTE simulation. The purpose of this preliminary simulation was to represent historical
mercury contamination unrelated to the industrial facility source included in scenarios. In
setting the duration of this preliminary model run, the time needed for the slowest responding
compartments (e.g., sediment, vadose zone soil) to reach steady-state was considered. A 30-year
dynamic simulation was performed in which boundary contributions of mercury in air set at
"background" levels were the only source of mercury introduced to the modeling region (i.e., no
emissions source within the region's boundaries was modeled, nor were any boundary
contributions other than via air). Background concentrations of mercury in the air that flows
across the boundary and into the test case modeling region - 1.6E-09 g/m3 for elemental mercury
and 1.6E-11 g/m3 for divalent mercury - were typical concentrations for the eastern U.S.
atmosphere as reported in the Mercury Study Report to Congress (EPA 1997). The resulting
environmental media and biota concentrations for each compartment at the end of 30 years (i.e.,
the final two-hour snapshots) were used as initial concentrations. Although mercury
concentrations did not reach steady-state for most compartment types by 30 years (and some
3 Early testing with TRIM.FaTE's air component showed that inputting wind speeds of zero causes mass
buildups in the source compartment that can produce artifactual results. A minimum value of 0.75 m/sec (an
approximation for a lower reporting limit) was adopted for initial test cases and is suggested in the TRIM.FaTE
User's Guide (EPA 2003b).
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Exhibit 2-7
Wind Rose Representing TRIM.FaTE Five-year Input Data Set
WIND ROSE PLOT
Mercury Test Case - 5-year Data Set
Wind Speed (mis)
Q SPLAY
Wind Speed
AVG. WIND SPEED
3.64 mis
ORIENTATION
Direction
(blowing from)
UNIT
m/s
CALM WINDS
0.00%
WRPLOT \Xew3.5 by Leftes Environmental Softo/are -w
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likely would take hundreds of years to get there), in most cases the rate of increase was tapering
off by year 30. Thus, given model run time considerations along with the significant
uncertainties about the timing and magnitude of air background in the site vicinity, a 30-year
dynamic run was considered sufficient for the purposes of establishing initial concentrations.4
To place the modeled Scenario C initial concentrations in the context of measurement
data, they were compared with measured values reported in the Mercury Study Report to
Congress (EPA 1997). In general, the ranges (across compartment locations) of initial
concentrations of total mercury calculated by TRTM.FaTE in the 30-year model run described
above are roughly comparable to the measurement data presented for most media, with
TRIM.FaTE concentrations generally lower than measured data (see text box below). For
surface soil, Scenario C initial concentrations fall within the lower end of the range of typical
measured concentrations for U.S. soils reported. Initial concentrations for surface water are also
within the relatively broad range of measurement data reported for freshwater lakes, though at
the low end of that range. For sediment, Scenario C initial concentrations are lower than the
range of measured data but still within an order of magnitude of most reported concentrations.
Differences are greater for fish. Scenario C initial concentrations for higher trophic level fish
(e.g., water-column carnivore) are lower than reported measured values by one to two orders of
Medium
Case C Initial
Cone (total Hg)
Measurement Data Reported in Mercury Study Report to Congress
Surface
soil
21-47ng/gDW,
~100%Hg2+
Reported values range from 8 to 406 ng/g dry wt
"Typical" US soils reported by NJDEPE to range from 8 to 117 ng/g dry wt
Most Hg reported to be Hg2+, with some MHg as well (0.3% to >10%)
Surface
water
0.34 -0.63 ng/L,
89% Hg2^ 10%
Hg°, l%MHg
Reported values for freshwater lakes range widely; from <0.1 to 74 ng/L
total mercury; most values between 1 to 10 ng/L
Concentrations vary widely; seasonality may be one factor invariability
Sediment
12-18 ng/g DW,
>99%Hg2+
Means for U.S. lake sediment samples range from 70 to 310 ng/g dry wt
Other lakes (WI, MM) range from 34 to 753 ng/g
RTC reports "concentrations exceeding 200 ng/g are not unusual"
Fish-
WCC '
9.1-13 ng/g WW,
>99% MHg
Fish-
WCO
2-2.9 ng/g WW,
88% MHg
Fish-BC
1.4-2 ng/g WW,
95% MHg
Fish-BO
0.45-0.66 ng/g
WW, 57% MHg
Mean Hg concentrations reported for two nationwide studies were 110 and
260 ng/g fresh wt across all species
Means from these two studies were higher for bass and trout, lower for
catfish
Measured Hg concentrations in sportfish species from various other studies
were generally similar to data from the above two nationwide studies
Most values were between 100 and 1,000 ng/g, with some outliers on both
low and high ends
• bass: <100 to 600 or higher, some > 1,000 ng/g
• panfish: <50 to 700, some around 1,000 ng/g
• "bottom feeders": 50 to -500 ng/g, but lower than most other fish types
a WCC = water-column carnivore, WCO = water-column omnivore, BC = benthic carnivore, BO = benthic
omnivore.
4 Steady-state model runs were not used to develop initial concentrations because boundary contributions of
a chemical cannot currently be modeled by TRDVLFaTE's steady-state mode.
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magnitude. Differences are even greater for water-column omnivores, with Scenario C initial
concentrations lower by two to three orders of magnitude and for benthic fish, with initial
concentrations lower than reported measured values by up to three orders of magnitude.
It is expected that some of the reported measurement locations were selected to include
sites of interest, including sites subject to contamination from nearby sources of mercury (i.e.,
the measured values being compared may not be fully comparable "background" values).
Moreover, as noted above, initial concentrations based on a 30-year TRIM.FaTE model run have
not reached steady-state for some compartment types. In fact, of the compartment types
compared, the ones farthest from reaching steady-state after 30 years - surface water, sediment,
and fish, all at least an order of magnitude below expected steady-state levels - are lowest in
relation to the reported ranges of measurement data (see Chapter 4 for comparisons of steady-
state to dynamic modeling results for TRIM.FaTE). Therefore, it seems reasonable that the
Scenario C initial concentrations estimated by TRIM.FaTE are toward the lower end of, or in
some cases below, the ranges of reported measured values.
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3. RESULTS AND DISCUSSION: DYNAMIC MODELING
This chapter presents the TRIM.FaTE dynamic modeling results for the mercury test
case. The main purpose of this chapter is to provide a broad cross-section of the extensive
modeling results and give a sense of the overall patterns and trends in the data, rather than to
focus in-depth on any particular parts of the data. Steady-state modeling results, sensitivity
analysis results, comparisons with another multimedia model, and evaluations against
monitoring data are presented in subsequent chapters of this report. A few steady-state modeling
results related to mercury speciation are included in Section 3.3 for comparison with the
dynamic modeling results.
As described in Chapter 2, TRIM.FaTE produced voluminous results for this test case -
detailed time-series data for three species of mercury for various abiotic media and numerous
biota at varying locations - and only selected results are highlighted here. Appendix B contains
additional summary tables and charts to supplement the results presented in this chapter.
TRIM.FaTE modeling processes and algorithms are noted in some of the results discussions, but
detailed descriptions are not provided here (see EPA 2002b,c for more information; also, the
series of process tables in Chapter 6 provides a summary of the processes modeled for certain
compartment types).
The presentation of results starts with a summary of the distribution of total mercury
mass over time among the different compartment types (Section 3.1). Then, Section 3.2 presents
the total mercury concentration results over time for various compartment groupings. Section
3.3 addresses differences in the results for the three different species of mercury modeled.
Spatial variations in the total mercury concentration results are presented in Section 3.4, and
comparisons among the three dynamic emission cases are presented in Section 3.5.
Except for Section 3.5, all of the results discussed in this chapter are for emission case B
(both elemental mercury and divalent mercury emitted from the source, no boundary
contributions or initial concentrations included). In general, the mass and concentration results
presented are annual averages, which are calculated by averaging the bi-hourly instantaneous
output data over each year of the modeling period.1 With the exception of one section devoted to
analysis of the mercury speciation results, most of the data are presented as total mercury (i.e.,
sum of elemental mercury, divalent mercury, and mercury portion of methyl mercury).
Because interpretation of all the results for animals is so strongly dependent on what they
eat, a summary of the modeled animal diets and soil ingestion rates is provided for reference here
at the beginning of the results chapter (Exhibit 3-1; see Appendix A for full referencing of the
values). Also, referral back to the site layout maps in Exhibits 2-1 and 2-2 is helpful for
interpreting the compartment-specific results.
1 For leaf and particle-on-leaf compartments of the deciduous forest and grasses/herbs vegetation types,
annual averages usually are estimated from the bi-hourly output data only for the days when leaves were present
(i.e., they represent a growing season average for the given year).
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Exhibit 3-1
Diets for Animal Compartment Types Modeled for Mercury Test Case
Animal Species
(Trophic Level/Niche)
White-tailed deer
(terrestrial herbivore)
Meadow vole (terrestrial
herbivore)
Mouse (terrestrial
omnivore)
Black-capped chickadee
(terrestrial insectivore)
Short-tailed shrew
(terrestrial ground-
invertebrate feeder)
Weasel (terrestrial
carnivore)
Red-tailed hawk
(terrestrial carnivore)
Mink (semi-aquatic
carnivore)b
Bald eagle (semi-aquatic
carnivore)
Land- or
Water-
based3
L
L
L
L
L
L
L
L
L
Terrestrial,
Aquatic, or
Mixed Diet
T
T
T
T
T
T
T
M
M
Modeled Diet Fractions
100% terrestrial plant
100% terrestrial plant
50% terrestrial plant
50% soil arthropod
70% soil arthropod
30% terrestrial plant
58.5% earthworm
41.5% soil arthropod
50% mouse
25% short-tailed shrew
25% meadow vole
30. 3% mouse
25.7% black-capped
chickadee
20% short-tailed shrew
20% meadow vole
4% soil arthropod
23% mouse
23% meadow vole
17% benthic invertebrate
15% benthic omnivore
10.3% water-column
herbivore
8% black-capped chickadee
3.7% water-column omnivore
23% mouse
17% benthic carnivore
17% benthic omnivore
11% water-column carnivore
11% water-column herbivore
11% water-column omnivore
10% black-capped chickadee
Modeled Soil
Ingestion Rate
(kg/kg-day)
0.00013
0.0006
0.001
0
0.0611
0
0
0
0
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Animal Species
(Trophic Level/Niche)
Raccoon (semi-aquatic
omnivore)
Tree swallow (semi-
aquatic insectivore)
Mallard (semi-aquatic
omnivore)
Common loon (semi-
aquatic piscivore)
Water-column
herbivore0
Water-column
omnivore0
Water-column
carnivore0
Benthic omnivore0
Benthic carnivore0
Land- or
Water-
based3
L
L
W
W
W
W
W
W
W
Terrestrial,
Aquatic, or
Mixed Diet
M
A
M
A
A
A
A
A
A
Modeled Diet Fractions
69% benthic invertebrate
21% earthworm
4.6% benthic omnivore
4% water-column herbivore
1 .4% water-column omnivore
100% benthic invertebrate
(represents aquatic insects)
66.5% terrestrial plant
33.5% benthic invertebrate
50% benthic omnivore
50% water-column omnivore
100% algae
100% water-column
herbivore
100% water-column
omnivore
100% benthic invertebrate
100% benthic omnivore
Modeled Soil
Ingestion Rate
(kg/kg-day)
0.0029
0
0.00085
0
n/a
n/a
n/a
n/a
n/a
a Refers to the volume element with which the animal compartment is associated in TRIM.FaTE. "W" means the
compartment is associated with a surface water or sediment volume element, and "L" means the compartment is
associated with a surface soil volume element.
b In TRIMFaTE documentation, "semi-aquatic" refers to animals that reside and/or nest on land but that are
modeled as having aquatic biota in their diet.
c Not modeled as individual species; see footnote to Exhibit 2-4.
3.1 Time Patterns of Mercury Mass Accumulation
This section presents a series of charts showing the accumulation of total mercury mass
over time within various parts of the modeling system. As noted above, the data presented are
for emission case B. Corresponding data are presented in tabular form in Appendix B.I.
In emission case B, mercury mass was input to the modeling system only via emissions
to air from a single industrial source. Source emissions were modeled at the following rates,
which were assumed to be continuous and constant over the entire 30-year modeling period.
• Elemental mercury (Hg°) - 335.6 g/day (~ 123,000 g/yr, or 3.68 million grams over 30 years)
• Divalent mercury (Hg2+) - 17.663 g/day (~ 6,450 g/yr, or 0.19 million grams over 30 years)
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Therefore, approximately 353 g/day of total mercury were input to the modeling system
(~ 129,000 g/yr, or 3.87 million grams over 30 years), and the modeling results confirm that all
of the input mass was accounted for by TRIM.FaTE throughout the modeling period. The
speciation of air emissions for the test case was based on an assumption that 95 percent of the
total mercury emitted was in the form of elemental mercury.
Overall Mass Distribution
Exhibit 3-2 shows a very broad picture of where the emitted mass accumulates for the
mercury test case scenario. By far, most of the total mercury mass (>99 percent) ends up in the
air sinks (i.e., transported via wind advection beyond the modeling region boundaries), not a
surprising result given that all emissions are to air and nearly all are in a gaseous/vapor form
(based on the speciation assumption and the phase distribution algorithms/input data used), and
that the size of the air modeling region is relatively small (maximum source-to-boundary
distance is 16 km, minimum is 5.2 km). Thus, the wind quickly blows most of the emitted
mercury mass beyond the modeling region boundaries, where it is tracked by TRIM.FaTE for
mass balance accounting purposes but its transport and fate are no longer modeled. For divalent
mercury, which deposits at a more rapid rate than elemental mercury, a lesser amount (92
percent) of the emitted mass is in the air sinks at the end of 30 years.2
Exhibit 3-2 - Log Scale
Total Mercury Mass: Overall Distribution in Compartments and Sinks
1.0E+00
1234567
9 1011 12131415161718192021222324252627282930
Year
a Includes soil advection and surface water advection sinks (i.e., transported outside the modeling region via soil runoff/erosion and
surface water outflow).
The other eight percent of the emitted divalent mercury, roughly 15 kg, remains in the compartments of
the modeling region or is in the other (non-air) sinks at the end of 30 years. The modeled total amount of divalent
mercury deposited from air to the surface (soil, surface water, and plants) over 30 years is roughly 40 kg (21 percent
of the 193 kg emitted). The difference, about 25 kg of divalent mercury, is believed to be primarily a result of re-
emission to air from soil, surface water, and plant leaves, in most cases following transformation to elemental
mercury. A small amount of the deposited divalent mercury is transformed to methyl mercury (net of less than 1 kg).
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Compared with the air sinks, a much smaller (roughly 1,000-fold) amount of the total mercury
mass accumulates in the surface soil advection and surface water advection sinks. This mass
leaves the modeling region via soil runoff and erosion across the boundaries or surface water
outflows. These results show that the relatively small size of the modeling region would not
allow tracking of the overall impact of mercury air emissions from the facility on ecological or
human health. The initial design of the test case (including sizing of the modeling region),
however, was focused on the area in the immediate facility vicinity, especially the local ponds
and lakes and their watersheds.
As shown in Exhibit 3-2 (and Appendix Table B-l), most (>99 percent) of the total
mercury mass remaining within the modeling region (i.e., not in the sinks) at any time is in the
abiotic compartments. The abiotic mass increases steadily over time, following a similar time
pattern as air sink accumulation (i.e., accumulation appears to be roughly proportional to
emissions). The abiotic mass as a percentage of total mercury mass in the system is fairly
constant over the 30-year period, in the range of 0.3 to 0.4 percent and declining slightly over
time. The amount of mercury mass in biota is much lower than in the abiotic media, which is in
part a result of the lower relative volume (and mass) of the biotic compartments. After the first
two years the mercury mass in biota does not appear to be increasing over the modeling period,
but it actually is very slowly increasing over time. At 30 years, approximately 0.001 percent of
the total mass in the system is in biotic compartments.
Mass Distribution in Abiotic Compartments
The patterns of total mercury mass accumulation in abiotic compartment types are shown
in Exhibit 3-3. All soil compartment types and sediment accumulate mass steadily over time,
and at 30 years all appear to be increasing at roughly similar rates (mercury in vadose zone soil,
which has by far the lowest mass of any soil compartment type throughout the modeling period,
Exhibit 3-3 - Log Scale
Total Mercury Mass: Abiotic Compartment Types"
-All Air
Compartments
-All Surface Soil
Com partm ents
• All Root Zone
Soil
Compartments
-All Vadose Zone
Soil
Compartments
-All Surface
Water
Com partm ents
-All Sediment
Compartments
1.0E-04
1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
Year
3 G round water not included (2.4E-06 g at year 30).
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increases fastest during the 30-year period and is still increasing at a somewhat higher rate at 30
years). Among abiotic media, surface soil has by far the most mercury at 30 years, roughly
12,000 grams, sediment has approximately 550 grams, and root zone soil has roughly 210
grams.3 Mass in air follows a five-year repeating pattern and does not increase over time. The
five-year repeating pattern of results, which shows up for air and several other compartment
types, corresponds to the five-year repeating meteorological input data used in the modeling and
indicates a strong relationship between the results for a given compartment type and the
meteorological data inputs. Mass accumulation in surface water increases slowly over time,
probably as a result of continuing inputs from air (and as an indirect result of mass build-up in
sediment and surface soils), and follows a less pronounced (i.e., smaller peaks and valleys) five-
year repeating pattern. Total mercury mass accumulation in ground water is very low, more than
five orders of magnitude lower than in the vadose zone soil at 30 years.
Mass Distribution in Biotic Compartments
Mercury mass accumulation in four broad groupings of biota is presented in Exhibit 3-4.
During the 30-year period, mass accumulation ranks as follows:
terrestrial plants » aquatic plants > terrestrial/semi-aquatic animals > aquatic animals
Exhibit 3-4 - Log Scale
Total Mercury Mass: Terrestrial Plants, Terrestrial/Semi-aquatic Animals,
Aquatic Plants, and Aquatic Animals
All Terrestrial
Plant
Co m pa rtm e nts
o—All Terrestrial/
Sem i-aquatic
Animal
Co m pa rtm e nts
All A qu atic
Plant s
Com partm ents
• All Aquatic
Animal
Com partm ents
1234567
1011 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
a Macrophyte com partm ents only; algae not included in this grouping because they are modeled as a phase of surface water(notas a distinct
com partm ent type).
Note that in the mercury test case model runs, the sediment compartment was allowed to continually
accumulate chemical mass, with no mass removal to a sediment burial sink. A TRIM.FaTE user could modify this
approach to modeling chemical mass accumulation in sediment by changing the algorithms used in the library.
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After the first couple of years, the annual average mass of mercury in terrestrial plant
compartments follows a non-increasing, five-year repeating pattern, reflecting a relationship with
air and with the meteorological input data. Mass in terrestrial/semi-aquatic animal
compartments follows a similar non-increasing, five-year repeating pattern, albeit at a much
lower level of mass (>100-fold lower). As shown in Appendix B.I tables, the mercury mass in
terrestrial/semi-aquatic animal compartments is dominated by mercury mass in the white-tailed
deer, which has the highest total biomass density of the animal species included in the modeled
scenario. This dominance of the white-tailed deer, combined with the fact that the species is 100
percent herbivorous, explains the high degree of similarity in time patterns of mass accumulation
for terrestrial plants and animals. Mass in aquatic plant (macrophyte) compartments is
increasing slowly and follows a slight five-year repeating pattern, similar to surface water, which
seems consistent with the partitioning approach used to model transfers between surface water
and macrophytes. Mass in aquatic animal compartments increases steadily during the modeling
period but is still relatively low at 30 years. Other than terrestrial plant compartments, which
have accumulated roughly 45 grams of total mercury at 30 years, the biotic compartments have a
very small amount of mercury mass (<1 gram for terrestrial/semi-aquatic animals, macrophytes,
and aquatic animals combined at 30 years). As noted previously, this is in part a result of the
lower relative volume (and mass) of the biotic compartments. As shown in Section 3.2, total
mercury concentrations for some of the biotic compartment types span similar ranges as
concentrations for some of the abiotic compartment types.4
Exhibit 3-5 shows total mercury mass accumulation patterns in the four terrestrial plant
compartment types, along with macrophytes for comparison. By far, the largest mass
accumulation is in the leaves, followed by the stem (due to method limitations, stems and roots
were only modeled in the four grasses/herbs volume elements, but this relative ranking would
likely hold true regardless). The time trend of mass accumulation for leaves, particles on leaves,
and stems is very similar - a non-increasing, five-year repeating pattern. All three compartment
types are strongly affected, directly and/or indirectly, by the meteorological data inputs. In
contrast, the root compartment type starts at very low mercury mass and accumulates over time
in a smooth pattern - it surpasses the mass in particles on leaves around year 25 and is
continuing upward at year 30. The root is less directly affected by weather patterns and is more
affected by exchanges of mass with root zone soil, which has a similarly shaped smooth upward
time trend.
4 In the context of an exposure or risk assessment, the accumulated total mercury mass results for biota
discussed in this section would be more relevant to chemical body burden estimates than to chemical intake
estimates (for which the mercury concentration results discussed in Section 3.2 would be more relevant).
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Exhibit 3-5 - Log Scale
Total Mercury Mass: Plant Compartment Types
1.0E + 02
1.0E-01
E 1.0E-02
S
1.0E-03
1.0E-04
1.0E-06
7
All Leaf
Com partments
—e—All Particle-on-
leaf
Com partments
All Stem a
Com partments
—•—All Root a
Com partments
- - » - -All Macrophyte
Com partments
1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 20 21 22 23 24 25 26 27 28 29 30
Year
' Stem and root only modeled in the four grasses/herbs volume elements (vs. 19 volume elements for leaf and par tides on leaf).
Patterns of total mercury mass accumulation in fish and benthic invertebrates, which
were modeled for four ponds and one river, are shown in Exhibit 3-6. The three benthic animal
compartment types follow an accumulation pattern that increases smoothly and is very similar to
the pattern for sediment, as would be expected because partitioning of mercury between
sediment and biota is what drives the benthic invertebrate mass accumulation. Benthic
omnivores, in turn, eat benthic invertebrates in this test case scenario, and benthic carnivores eat
benthic omnivores. Although their mercury mass accumulation is small relative to most abiotic
and plant compartment types, benthic invertebrates dominate mass accumulation among aquatic
animals (as noted in footnote 3, chemical mass in sediment was not transferred to a sediment
burial sink in this test case). Mercury mass accumulation in the three benthic animal
compartment types spans two orders of magnitude at year 30, primarily because of the much
higher value for benthic invertebrates.
The mercury mass in the three water-column fish compartment types at year 30 is close
(roughly within a factor of two, with herbivores highest), with mass increasing over time and
following a slight five-year repeating pattern. The pattern, which is most apparent for herbivores
and least apparent for carnivores, is similar to the pattern for surface water, which is directly
affected by meteorological data inputs. Surface water partitions mercury mass to algae, which is
the food source for water-column herbivores in this test case scenario, which are eaten by water-
column omnivores, which are eaten by water-column carnivores.
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Exhibit 3-6 - Log Scale
Total Mercury Mass: Fish and Benthic Invertebrate Compartment Types
-All Water-
column
Herbivore
Compartments
-All Water-
column
Omnivore
Compartments
• All Water-
column
Carnivore
Compartments
-All Benthic
Invertebrate
Compartments
-All Benthic
Omnivore
Compartments
-All Benthic
Carnivore
Compartments
1 234567
1011 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
Mass accumulation in terrestrial and semi-aquatic animal compartment types is presented
in Appendix Table B-6. It is not shown in a chart here because of complexities in making
comparisons as a result of variations in the number of volume elements in which each species is
present (e.g., some species, such as the meadow vole, are only present in a subset of land-based
volume elements, and others, such as the common loon, are only present in surface water volume
elements). However, it is clear that terrestrial and semi-aquatic animal compartments
accumulate only a very small proportion of the total mercury mass emitted over 30 years.
Within the modeled ecosystem for this test case, the white-tailed deer accumulates by far the
most total mercury of any terrestrial/semi-aquatic animal (approximately 95 percent of total
mercury mass present in terrestrial/semi-aquatic animals), largely because of its relatively high
biomass in the ecosystem; the mouse is next highest, accumulating roughly 2.5 percent of total
mercury mass. The temporal mass accumulation patterns for terrestrial/semi-aquatic animals, as
with aquatic animals, are typically similar to those of their sources of food (or to other sources of
mercury uptake, as in the cases of soil arthropods, earthworms, and benthic invertebrates).
Summary of Mass Accumulation Over Time
For the test case modeling scenario, greater than 99 percent of the total mercury emitted
(92 percent of divalent mercury) to air ends up in the air sinks (i.e., passes out of the modeling
region via wind advection). As noted earlier, this modeling result is not inconsistent with the
test case focus on local impacts. Within the modeling region, the compartment types can be
grouped according to mass accumulation at the end of the 30-year modeling period, as shown in
Exhibit 3-7. By far most of the remaining mercury mass ends up in the abiotic compartments
and terrestrial plant leaves, with surface soil accumulating the highest amount (22 times
sediment and 57 times root zone soil, the next highest compartment types at 30 years). The
mercury mass distribution results reported here are dependent not only on the physical/chemical
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Exhibit 3-7
Summary of Accumulation of Total Mercury Mass within Modeling Regiona
Accumulated Mass of Total
Mercury at Year 30 (g)
>10,000
>1,000 - 10,000
>100- 1,000
>10- 100
>1 -10
>0.01 - 1
>io-4- io-2
>io-6 - io-4
-------
properties of mercury but on the model set-up and other inputs for the test case. Items such as
the overall size, shape, and orientation of the modeling region (with respect to wind direction),
depth/height of the various abiotic compartments, number and kind of biotic compartments
included, and biomass density used for each biotic compartment could have an impact on the
mercury mass distribution.
With respect to the pattern of mass accumulation over time (as represented by the annual
average mass), the compartment types can be classified as either displaying a smooth trend or a
"spiking" that repeats in a five-year pattern, corresponding to the five-year repeating
meteorological input data. The following text box summarizes how the compartment types
break out, along with whether they are increasing in mass at 30 years or flat.
Mass Accumulation Patterns of Compartment Types
Smooth Time Trend
Repeating Five-year Spiking
Flat3
no compartment types
Mass Increasing
Slowly (< 5% over
years 25 to 30)
no compartment types
Mass Increasing
More Rapidly
(> 5% over years
25 to 30)
All three soils, ground water
Root
Soil arthropod, earthworm
Short-tailed shrew
Raccoon
Sediment
Benthic invertebrate, both benthic fish
Tree swallow
Air
Leaf, particle on leaf, stem
White-tailed deer
Black-capped chickadee
Mallard
Mouse
Meadow vole
Red-tailed hawk
Long-tailed weasel
Mink
Surface waterb
Macrophyteb
All three water-column fishb
Bald eagleb
Common loonb
Not perceptibly increasing over 30 years, but very small increases may be occurring for some compartment types.
' Spiking is generally less pronounced than other noted compartment types.
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3.2 Mercury Concentration Over Time in Various Compartment Types
This section presents a series of charts comparing the concentration of total mercury over
time among selected compartment types for emission case B. Atmospheric deposition data are
also presented, immediately after the air concentration modeling results. Only a subset of the
compartment types modeled are covered in this section; additional tables and charts of mercury
concentration over time are presented in Appendix B.2. In both this section and Appendix B.2,
the focus is on the following locations. (Appendix B.2 contains a complete set of full-page
charts for all compartment types, both soil locations, and both surface water locations.)
• Compartments SW2 and SSE4 for all soil, ground water, and associated biotic
compartment types (a few results also are presented for compartment W2); and
Swetts Pond and Brewer Lake compartments for all surface water, sediment, and
associated biotic compartment types
Swetts Pond, a relatively small water body near the emission source, was selected for
presentation of surface water and related results, in part because it has been a focus of
monitoring data collection. Brewer Lake was selected because it is the largest lake modeled and
is farther from the source than Swetts Pond. Compartments SW2 and SSE4 were selected
because they provide locations at different distances and directions from the source; moreover,
SSE4 almost entirely surrounds Swetts Pond, and SW2 is the location of some relevant
monitoring data collection. The site layout maps in Exhibits 2-1 and 2-2 show the location of
these and other specific compartments discussed.
3.2.1 Annual Average Concentrations (and Deposition)
Air Compartments
TRIM.FaTE is a multimedia model focused primarily on impacts to media other than air.
It is not intended to be used as an air dispersion model for human inhalation exposure and risk
assessments, largely because it does not provide the spatially detailed results (especially near the
emission source) that are preferable for such assessments. Deposition and partitioning from air,
however, provide the initial inputs of chemical mass to the other media being modeled.5 Thus,
evaluation of the air results is important. Exhibit 3-8 presents examples of the pattern of
concentration of total mercury in air over time and distance for compartments oriented in two
directions - south-southeast and west - from the source (see Exhibit 2-1 for location and relative
distance from source of specific air compartments). For all directions and distances, the air
concentrations of mercury immediately begin a five-year repeating pattern that remains steady
over time (i.e., does not increase or decrease, simply spikes up and down in a repeating pattern).
This reflects the strong influence of the meteorological input data, which follows a five-year
5 The current version of TRIM.FaTE produces essentially a vertical average concentration over the full
volume element height. Most deposition processes are driven by the vertical distribution of a chemical in air, not
just the concentration at ground-level, so this approach seems reasonable for most types of deposition. However,
vapor dry deposition is driven by ground-level concentrations, and therefore may be underestimated by TRIM.FaTE.
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Exhibit 3-8
Total Mercury Concentration in Air vs. Time at Increasing Distance from the Source
(a) Increasing Distance in South-Southeast Direction
1.0E-09
« 3.0E-10
c
c
<
2.0E-10
1.0E-10
O.OE+00
°^
f~«^-. f~^- f~^-^ f~^ r^
••--•. ^.- •»-».~v;.^
«• ••
••--•.>•'..
-Air in
SSE1
-Air in
SSE2
Air in
SSE3
-Air in
SSE4
•Air in
SSE5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
(b) Increasing Distance in West Direction
6.0E-10
-Air in
WNW1
-Air in
WSW1
Air in W2
-Air in W3
O.OE+00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
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repeating pattern, on mercury concentration in air. The time pattern is similar at the various
distances in a given direction, and the peak-to-valley ratio is similar at various distances also
(i.e., similar level of fluctuation). However, the time pattern differs for the two different
directions shown, presumably due to the effects of wind direction and speed. As expected, air
concentration decreases with distance, with larger rates of decrease close to the source. The
TRIM.FaTE-modeled annual average concentrations across all air compartments (except
source), roughly 0.1 to 1 ng/m3 elemental mercury, are similar to the short-term median modeled
air concentration within 10 km of a chlor-alkali plant emitting elemental and divalent mercury at
rates within a factor of two of the rates used in the simulation discussed here, 0.58 ng/m3
elemental mercury (Landis et al. 2004).6
Atmospheric Deposition Flux
Deposition is the major process by which mercury mass is transferred from the air to the
surface in TRIM.FaTE. Four types of deposition are modeled by TRIM.FaTE - wet particle, dry
particle, wet vapor, and dry vapor. As an example of the time pattern of deposition, the total
mercury deposition fluxes to surface soil in parcel SW2 are displayed in Exhibit 3-9. The air
concentrations for the corresponding air parcel (SSW2) are also plotted. Of the four types of
deposition, wet vapor and dry vapor deposition are the predominant forms for total mercury (30-
year average of 78% and 22% of the total mercury deposition flux, respectively, for SW2). Wet
particle and dry particle deposition are much smaller (on average 0.02% of the total deposition
flux each, for SW2), consistent with information reported in the Mercury Study Report to
Congress (EPA 1997).
Similar to the air concentrations, all four deposition fluxes follow five-year repeating
patterns that remain steady over time. The repeating patterns of the dry particle and dry vapor
deposition fluxes are very similar to the SSW2 air concentration pattern. However, the wet
particle and wet vapor deposition patterns are slightly different (the amplitude is larger and the
time-series peak on different years, reflecting the impact of rain). Wet deposition only occurs
when there is precipitation, and the wet deposition flux patterns are highly influenced by
precipitation frequency and amount and other meteorological conditions (such as wind direction)
when it is raining. The differences in these precipitation-related input data from year to year
affect the wet deposition more than the dry deposition or air concentration.
The levels of dry vapor deposition for divalent mercury modeled by TRIM.FaTE in this
test case are similar to the levels modeled by Landis et al. (2004) for the area within a 10 km
radius of a chlor-alkali plant in Georgia emitting elemental and divalent mercury at rates within a
factor of two of the rates used in the simulation discussed here; levels of elemental mercury dry
vapor deposition modeled by TRIM.FaTE are somewhat lower. Those authors report an
annualized average dry vapor deposition flux of 2.8 ug/m2-yr (maximum of 320 ug/m2-yr near
the emission source) for reactive gaseous mercury within the 10 km radius (and 4.6 ug/m2-yr
The estimated mercury emission rates used in this test case are within a factor of two of the values
reported for elemental mercury (measured at 518 g/day, Kinsey et al. 2004, versus 336 g/day here) and divalent
mercury (estimated at 10.4 g/day, Landis et al. 2004, versus 17.7 g/day here) emissions over a nine-day period at a
chlor-alkali plant in Georgia.
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Exhibit 3-9 - Log Scale
Total Mercury Deposition Flux to Soil Surface vs. Time: SW2
•£ 1.0E-07
3
E 1.0E-09
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
aBecause of the differences in the air and surface parcel layouts, the boundaries of the SSW2 air parcel do not match
those of the SW2 surface parcel (see Exhibits 2-1 and 2-2), but this air parcel does have substantial overlap with
the surface parcel (among air parcels, SSW2 has the most overlap with surface parcel SW2).
(maximum of 500 ug/m2-yr) for total mercury, including elemental). Comparable TRIM.FaTE
values for average divalent mercury dry vapor deposition flux to soil range from approximately
1.3 to 15 ug/m2-yr (approximately 1.5 to 17 ug/m2-yr for total mercury dry vapor deposition)
across the modeling region (i.e., within 4.7 km to 14 km of the source depending on direction,
given the asymmetric soil parcel layout), with soil parcel SW2 roughly 3.0 ug/m2-yr (roughly 3.3
ug/m2-yr for total mercury) (see Section 3.4 for more details about deposition flux of mercury to
surface soil across the modeling region). The TRIM.FaTE source parcel, which is most
comparable to the maximum deposition locations reported in Landis et al. (2004), has an average
divalent mercury dry vapor deposition flux of 330 ug/m2-yr (360 ug/m2-yr for total mercury).
Elemental mercury comprises almost 40 percent of the total mercury dry vapor deposition
modeled by Landis et al. (2004), compared with roughly 10 percent as modeled by TRIM.FaTE
in this test case.
The following text box shows, for deposition to all soil parcels over the entire modeling
period in the case B scenario, the percent that each mercury species contributes to the total
mercury deposition flux and to each of the four types of deposition. The predominant species
that deposits is divalent, consistent with information summarized in the Mercury Study Report to
Congress (EPA 1997). There is some elemental mercury deposition, and only trace amounts of
methyl mercury deposition. Both elemental and divalent mercury are emitted from the source in
this scenario, explaining in part why they make up almost all of the total deposition flux.
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Elemental mercury emissions are 19 times higher than divalent for case B, as noted in Chapter 2.
However, divalent mercury is the predominant species that deposits, due to its relatively high
vapor washout ratio (i.e., ratio of concentration in rain to concentration in vapor) and vapor dry
deposition velocity. The methyl mercury concentration in air is much smaller than either of the
other two species because it only exists in air due to emissions from the surface following
transformation of mercury deposited in other forms.
Mercury
Species
Hg2+
Hg°
MHg
Total Hg
Percent of Deposition Flux - All Soil Parcels/Entire Modeling Period a
Total Deposition
95% (100%)
5.0% (100%)
~ 0% (100%)
100% (100%)
Wet Particle
~ 100% (~ 0%)
~ 0% (~ 0%)
~ 0% (64%)
100% (~ 0%)
Dry Particle
~ 100%(~0%)
~ 0% (~ 0%)
~ 0% (36%)
100% (~ 0%)
Wet Vapor
97.6% (68.7%)
2.4% (32.6%)
0% (0%)
100% (66.9%)
Dry Vapor
89. 8% (3 1.3%)
10.2% (67.4%)
~ 0% (~ 0%)
100% (3 3.1%)
a Percent of total Hg deposition flux by Hg species for each deposition type and for total deposition (columns sum to
100%). Values in parentheses are percent of an individual Hg species deposition flux by deposition type (rows sum
to 100%).
Divalent mercury accounts for almost 100 percent of the modeled wet particle and dry
particle deposition fluxes, nearly 98 percent of the wet vapor deposition flux, and roughly 90
percent of the dry vapor deposition flux. Elemental mercury accounts for a small amount of the
wet vapor deposition flux and approximately 10 percent of the dry vapor deposition flux, which
is the dominant process for elemental mercury deposition in this test case.
The modeled fraction of total mercury deposition flux that is wet deposition, roughly 67
percent, is consistent with limited data summarized in the Mercury Study Report to Congress
(EPA 1997), which indicates wet deposition fractions (for rain) of 45 percent and 63 percent for
two different locations in Wisconsin. It also is consistent with data reported in Landis et al.
(2004) for locations near a chlor-alkali plant in Georgia, which yields a wet deposition fraction
for total mercury of roughly 70 percent (based on measured total wet deposition and modeled dry
vapor deposition for locations roughly 30 km apart).
In addition to deposition of mercury from air to the surface, TRIM.FaTE also estimates
mercury re-emission (after being first deposited from air) from surface soil, surface water, and
leaves to air. A comparison was made between TRIM.FaTE modeled emission fluxes of
elemental mercury from surface soil to air and the measured soil-to-air fluxes in the vicinity of a
chlor-alkali plant reported in Southworth et al. (2004). TRIM.FaTE fluxes at three different
points in time over the 30-year modeling duration vary from 4.2 to 17 ng/m2-hr for the source
compartment (increasing over time as soil concentration increases), which has the highest soil
concentrations of mercury, to 0.07 to 0.18 ng/m2-hr for soil compartment ESE2 and 0.019 to 0.08
ng/m2-hr for soil compartment W2 (and are lower at the edge of the modeling region where soil
concentrations are lower). Note that TRIM.FaTE modeled soil emission fluxes of divalent and
methyl mercury are negligible. Measured levels reported in Southworth et al. for five locations
very near the emission source (e.g., 50 m away; data most comparable to TRIM.FaTE source
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compartment) range from 2 to 13 ng/m2-hr for elemental mercury, with a background level of 1
ng/m2-hr 5 km away from the source; the authors report these fluxes to be lower bounds.
Thus, the TRIM.FaTE source compartment flux is similar to the measured flux range in
Southworth et al. (and TRIM.FaTE surface soil concentration of total mercury is also
comparable, 1 to 2 ug/g versus an average of roughly 5 ug/g for Southworth et al.). The other
TRIM.FaTE compartment fluxes are considerably lower than the measured background level in
Southworth et al. (TRIM.FaTE surface soil concentrations of total mercury also are considerably
lower, 0.005 to 0.01 ug/g versus 0.3 ug/g for Southworth et al.). Moreover, the TRIM.FaTE
modeling results are consistent with the finding reported in Southworth et al. that soil emission
flux of elemental mercury is linearly correlated with total mercury concentration in surface soil
(see Figure 10 in that paper). Analysis of 19 surface soil compartments (source compartment
omitted) in year 14 shows a strong linear correlation (R2 = 0.88, y= 17x + 0.01) between
instantaneous elemental mercury emission flux and annual average total mercury surface soil
concentration for that year. On an equal soil concentration basis, the TRIM.FaTE emission
fluxes are higher, which is consistent with the measured values being reported as lower bounds.
Plant Compartments
Exhibit 3-10 illustrates the dynamics of total mercury concentration for the plant
compartment types in SW2 (modeled as grasses/herbs), along with the corresponding surface soil
and air compartments for reference. The leaf, particle-on-leaf, and stem concentrations quickly
Exhibit 3-10 - Log Scale
Total Mercury Concentration in Air, Soil, and Plants vs. Time: SW2 (grasses/herbs)
12345
"Each annual average data point shown for leaf and particle on leaf is the average of values during the days (May 13 - September 29 each year)
for which leaves were modeled as present during the entire day (i.e., represents a growing season average).
b Because of the differences in the air and surface parcel layouts, the boundaries of the SSW2 air parcel do not match those of the SW2 surface
parcel (see Exhibits 2-1 and 2-2), but this air parcel does have substantial overlap with the surface parcel (among air parcels, SSW2 has the
most overlap with surface parcel SW2).
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reach five-year repeating patterns that remain steady over time, as does the overlying air sorbed
to particles that can blow off or be washed off the leaf.) The stem pattern is smoother (only
minor spiking) than those for leaf and particle on leaf (which, as expected given the modeling
methods, track very closely with each other). In contrast, the root concentration follows a
smooth upward track and is still increasing relatively rapidly at year 30, similar to the surface
(and root zone) soil concentration, although the rate of increase for both root and soil has
decreased by year 30 compared with earlier years (note that, as modeled in the test case
scenarios, the root receives mercury inputs only from the root zone soil). The magnitude of total
mercury concentration in the plant compartments at year 30 ranks as follows (note that root is the
only one in which mercury concentration is perceptibly increasing):
particle on leaf > leaf > stem » root
However, even though the particle-on-leaf concentrations of total mercury are consistently
higher than the leaf and stem compartments, much less mass of total mercury accumulates in the
parti cle-on-leaf compartments, in part because the volume of those compartments is small
relative to the leaf and stem compartments (see Section 3.1). For the three compartment
locations chosen for analysis, the ratio of parti cle-on-leaf concentration to leaf concentration for
total mercury is 5.7 (SW2, grasses/herbs), 22 (SSE4, coniferous), and 22 (W2, coniferous) (see
Appendix B.2 for data on compartments SSE4 and W2). The leaf-to-stem concentration ratio for
total mercury is 15 for SW2 at year 30, and the stem-to-root concentration ratio is 63. These
latter two ratios cannot be derived for SSE4 or W2 because stems and roots were not modeled
for coniferous plants.7
Examining the results for all three compartment locations selected for analysis (see
Appendix B.2) indicates that regardless of direction from the emission source, leaf and particle-
on-leaf compartments quickly reach a non-increasing, five-year repeating pattern of total
mercury concentration. As observed for the air results, the time patterns (i.e., patterns of peaks
and valleys) vary for different directions from the source, presumably due to directional
differences in air concentration and deposition that result from variations in the meteorological
data.
We are unaware of reported patterns of plant accumulation of mercury in the literature
for comparison with these results. In general, the results in Exhibit 3-10 show that mercury
concentrations in soil increase over time as mercury deposition from the source (through air)
7 In TRDVLFaTE, individual birth, growth, and death of plants (or animals) are not modeled explicitly. For
plants, however, the seasonal events that are modeled address some issues associated with individual growth. The
leaf litter that falls to the ground in the fall contains all of the chemical accumulated in and on the leaves during the
growing season. With litter fall at the end of each growing season, however, accumulation of contaminant in the
leaves (see EPA 2002c for description of process algorithms) begins anew at the beginning of the next growing
season. Therefore, the concentration of mercury in the plant leaves, particles on leaves, and stems (which receive
mass input from leaves) does not show an increase between years 1 and 30. For trees, the current TRIM.FaTE
library does not model woody stems or roots (lack of appropriate data to develop algorithms and parameter values
for mercury). Thus, the trunk and roots cannot accumulate the chemical. For deciduous trees, the leaf fall at the end
of the growing season is the same as for the herbs and grasses; in this application, TRIM.FaTE resets the leaf
compartment concentrations to zero for the beginning of the next growing season. For coniferous trees, there is a
continuous loss of a portion of the needles throughout the year. In the mercury test case modeled scenarios,
achievement of equilibrium concentrations of mercury in the leaves appears to occur within a year.
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continues, but more slowly in later years as the entire terrestrial system approaches an
equilibrium. The plant roots reflect the soil concentration, but also indicate accumulation of
mercury in the roots over time compared with soil concentrations, which is consistent with the
limited data available on mercury accumulation by plant roots (see additional discussion of root
uptake in Section 6.3.2). The mercury concentrations in plant stems, leaves, and particles on
leaves for the grasses/herbs does not continue to increase after the first year. This pattern
reflects litter fall at the end of the growing season, when the mercury in the leaves and particles
on leaves is deposited to soil.
Surface Water, Sediment, and Associated Biotic Compartments
Exhibit 3-11 shows total mercury concentrations over time for surface water and related
compartments for Swetts Pond. TRIM.FaTE models quite a few mass transfer and
transformation processes for mercury in surface water and sediment (see process tables in
Section 6.4 for a summary, or EPA 2002c for details), making it sometimes challenging to
interpret results.
The top chart in Exhibit 3-11 shows surface water and water-column biota (plus common
loon, which has a diet of 50 percent water-column omnivores and 50 percent benthic omnivores),
and the bottom chart shows sediment and sediment biota (plus tree swallow and raccoon, which
have diets containing 100 percent and 69 percent benthic invertebrates, respectively). Surface
water and macrophytes have a similar slowly increasing, five-year repeating pattern of total
mercury concentration, reflecting the continuing input of mercury to surface water from air, the
impact of meteorological input data on surface water, and the partitioning of mercury between
surface water and macrophytes. Total mercury concentrations in the macrophytes, however, are
much higher than surface water concentrations (close to 1,000 times higher throughout the 30
years; note that macrophyte density was modeled as 1 kg/L).
Mercury concentrations in the three water-column fish compartments and the common
loon follow gradually increasing trend lines, with concentrations in the herbivore and omnivore
showing a slightly more pronounced five-year repeating pattern, probably reflecting the algal
diet of the herbivore.8 The time patterns appear generally smoother (i.e., showing less of the
variable pattern of the surface water compartment) at successively higher levels in the food chain
- the water-column carnivore and common loon have smoother total mercury concentration time
lines than the water-column herbivore and omnivore. (The spiking in the time pattern is more
apparent for Brewer Lake, shown in Appendix Chart B-4b.)
The five-year repeating pattern originates with the pattern of atmospheric deposition in
this case study that resulted from use of five years of meteorologic data for the site, then
repeating the five years of data six times to provide 30 years of data for the total simulation.
That repeating pattern is reflected in mercury deposition to soil and surface water and is reflected
in everything that depends on mercury deposition. The algae in surface water are in
"instantaneous" equilibrium with surface water mercury concentrations, and so directly reflect
Although total mercury concentration data were not examined in detail for algae, concentrations in algae
would be expected to follow a pattern similar to surface water, based on the modeling methods.
JULY 2005 3-19 TRIM.FATE EVALUATION REPORT VOLUME II
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the five-year repeating pattern of the meteorologic data. The herbivorous fish, not surprisingly,
closely track the concentrations of mercury in their food, the algae.
In terms of relative magnitude of total mercury concentration, the water-column fish
compartments follow the expected order:
carnivore > omnivore (4.5:1 at year 30)
omnivore > herbivore (1.2:1 at year 30)
Although herbivores have the lowest concentration, their relatively high total biomass causes
them to accumulate the most total mercury mass among the water-column fish (see Section 3.1).
The total mercury concentration in the common loon tracks very closely with (and slightly lower
than) the water-column omnivore, which comprises half of its modeled diet.
Total mercury concentrations in sediment, all benthic biota, and tree swallows and
raccoons feeding out of Swetts Pond follow a smooth upward time trend, reflecting the steady
accumulation of mercury in sediment, the partitioning between sediment and benthic
invertebrates, and the diets of the upper trophic level benthic fish and the two semi-aquatic
animals. Total mercury concentrations in benthic fish and invertebrates are somewhat lower but
generally within an order of magnitude of water-column fish. The total mercury concentrations
for the benthic invertebrate and two benthic fish compartments are fairly close (all within
roughly a factor of three at year 30 for both water bodies). After a few years, SSE4 tree swallow
concentrations of total mercury mirror closely the benthic omnivore time pattern, reflecting that
both have a diet of 100 percent benthic invertebrates. Total mercury concentrations in SSE4
raccoons are increasing a little more slowly than concentrations in the other benthic and related
compartments shown, possibly because their diet has a sizable (21 percent) non-aquatic
component consisting of animals (earthworms) having much lower mercury concentrations than
benthic invertebrates for this location.
Exhibit 3-12 shows modeled concentrations of methyl mercury over time for fish and
selected other animals in Swetts Pond. Given the significance of organic mercury compounds,
including methyl mercury, in fish, these data are presented here in addition to the total mercury
data in Exhibit 3-11 (see also Exhibit 3-24 for comparative data in table form). The top chart in
Exhibit 3-12 shows surface water and water-column biota (plus common loon), and the bottom
chart shows sediment and sediment biota (plus tree swallow and raccoon). As expected, the
concentrations of methyl mercury in fish and fish-eating wildlife increase over time and are
higher in the higher trophic levels, as are the percentages of total mercury that is methyl
mercury. Also, as expected, methyl mercury concentrations in fish consistently increase up
through the food chain (carnivore > omnivore > herbivore). The ratios of methyl mercury
concentrations for the fish and benthic invertebrate compartments are:
water-column carnivore > water-column omnivore (5.1:1 at year 30)
water-column omnivore > water-column herbivore (2.2:1 at year 30)
benthic carnivore > benthic omnivore (4.9:1 at year 30)
benthic omnivore > benthic invertebrate (3.2:1 at year 30)
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Exhibit3-11-Log Scale
Total Mercury Concentration in Surface Water and Related Biota vs. Time: Swetts Pond
(a) Water-column and Related Blotlc Compartments
1234567
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
-Surface
Water
—e— Com mon
Loon
Macrophyte
-Water-
column
Herbivore
-Water-
column
O mn ivore
-Water-
column
Carnivore
(b) Benthic and Related Biotic Compartments
1.0E-06
1.0E-11
12345678 9101112131415161718192021222324252627282930
Year
-Benthic
Invertebrate
-Benthic
Omnivore
Benthic
Carnivore
-Sediment
•Raccoon
-Tree Swaltow
"Results shown for compartment SSE4, where semi-aquatic animals feed from Swetts Pond.
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Exhibit 3-12 - Log Scale
Methyl Mercury (as Hg) Concentration in Surface Water and Related Biota vs. Time:
Swetts Pond
(a) Water-column and Related Biotic Compartments
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
(b) Benthic and Related Biotic Compartments
aResults shown for compartment SSE4, where semi-aquatic animals feed from Swetts Pond.
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Methyl mercury concentrations in the raccoon and tree swallow track closely with their aquatic
diet component, benthic invertebrates, in which (like the macrophyte) the mercury is primarily in
the divalent form (see Section 3.3 for more discussion of mercury speciation).
Terrestrial Animal Compartments
Nine terrestrial animal compartment types (including soil arthropod and earthworm) were
included in the TRIM.FaTE mercury test case. Exhibit 3-13 shows examples of total mercury
concentrations over time for several of these animals in two trophic groupings: (a) terrestrial
herbivores and omnivores, whose diets include vegetation, and (b) terrestrial carnivores, whose
diets include various herbivores and omnivores.
In the top chart of Exhibit 3-13, total mercury concentrations in the four terrestrial animal
compartment types for which terrestrial plants (only leaf and particles on leaf are modeled as
ingested) comprise a major portion of their diet are shown, along with leaf compartment
concentrations for reference: white-tailed deer (diet =100 percent terrestrial plants), meadow
vole (100 percent), mouse (50 percent, with remainder soil arthropod), and black-capped
chickadee (30 percent, with remainder soil arthropod).9 In this chart, the five-year repeating time
patterns of concentration for leaf and all four animals are strikingly similar. Because the mercury
concentrations in soil arthropods are so low, the arthropod portion of the mouse and chickadee
diets likely has negligible impact on mercury mass accumulation in these animals. This is
illustrated by the temporal concentration pattern for mercury in the chickadee, which was
modeled as having zero soil ingestion. If soil arthropods were having a substantial impact, the
chickadee pattern - similar to the leaf, not to soil or soil arthropods - would be smoother and
increasing and would look less like the leaf pattern. The total mercury concentrations among the
four animals are all similar in magnitude - roughly a four-fold range - with white-tailed deer
having the lowest concentration, although their greater biomass in the test case scenario leads
deer to accumulate by far the most mercury mass among the animals (see Appendix Table B-6).
The bottom chart in Exhibit 3-13 shows results for two terrestrial carnivores (along with
their major diet components in this application): long-tailed weasel (diet = 25 percent shrew,
balance is mouse and vole) and red-tailed hawk (20 percent shrew, balance is mouse, vole,
chickadee, and very small amount of soil arthropod). Total mercury concentrations in the shrew
follow a smooth upward time trend line, similar to the patterns for surface soil (which it ingests at
a relatively high rate) and for its biotic diet components, soil arthropods and earthworms. All the
other animals on the chart have strikingly similar five-year repeating time patterns. Total
mercury concentrations in the mouse, vole, and chickadee, as shown on the top chart, follow the
pattern of the leaf. Total mercury concentrations in the weasel and hawk, which have similar
diets, have very similar increasing, five-year repeating patterns. The basic five-year repeating
pattern for the two carnivores reflects the herbivores in their diet, while the damping of amplitude
9 Seeds and berries, which are a component of the diet of chickadees, were not modeled explicitly (i.e., as
separate compartments) in this TRIM.FaTE application. Rather, leaves and particles on leaves were used to
represent plant material in the chickadee diet. It is recognized, however, that mercury accumulation and the
adherence of dust particles may differ among various types of plant material.
JULY 2005 3-23 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-13 - Log Scale
Total Mercury Concentration in Terrestrial Animals vs. Time: SW2 (grasses/herbs)
(a) Terrestrial Herbivore and Omnivore Compartments
1.0E-06
o 1.0E-07
1.0E-08
1234567
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
a Each annual average data point shown for leaf and particle on leaf is the average of values during the days (May 13 to September 29 each year) for which leaves
were modeled as present (i.e., represents a growing season average).
(b) Terrestrial Carnivore (Weasel and Hawk) Compartments
12345
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
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(compared to the herbivores) and the increase over time probably reflect the shrew (increasing
smoothly over time) portion of their diet.10
3.2.2 Selected Instantaneous and Monthly Average Results
This section presents instantaneous and monthly average concentration results for a subset
of compartments to show patterns not apparent from the annual average results discussed
elsewhere and to illustrate the types of temporal results that are available from TRIM.FaTE.
The presentation here includes bi-hourly instantaneous estimates and monthly averages for
examples of air, surface soil, surface water, and water-column herbivore and carnivore
compartments. The instantaneous results are "snapshots" of model predictions, not averages of
some smaller time step (see "Temporal Aspects of the Dynamic Scenarios" subsection in Chapter
2), while the monthly averages (as well as the annual averages presented elsewhere in the report)
are simply arithmetic averages of the instantaneous results, which were output at a two-hour
frequency during the simulation. The bi-hourly results are presented for the last year of the
simulation, and the monthly results are presented for the last five years.11
The overall trends in total mercury concentration estimates and the magnitude of their
variation within the time period presented are summarized in Exhibit 3-14. More detailed
analyses are provided in the subsequent subsections.
10 The lifetime of wildlife is not explicitly considered in TRIM.FaTE. The bioenergetic model of mercury
accumulation (i.e., based on mercury intake, transformation, and excretion rates by individual animals) used in this
application of TRIM.FaTE does not account for loss of mercury from the wildlife compartments by death of
individuals due to disease, starvation, or senescence, when the mercury either would be returned to the soil or
ingested by scavengers (not modeled here). Moreover, it does not account for loss of mercury from wildlife
compartments via emigration (e.g., dispersal of juveniles), with the population size being maintained by immigration
of individuals from other, possibly less contaminated areas. Thus, it is possible that modeled mercury accumulation
in the wildlife compartments will be somewhat higher than would be the case if the loss of mercury via death and
emigration of individuals were included. Because inclusion of the non-predator-associated wildlife death would
result in mercury transfers to scavenger species, soil, water, and air, some fraction of this transferred mercury would
be re-entrained into the terrestrial food web. Consequently, the extent to which the mercury accumulation in wildlife
compartments might be reduced with explicit modeling of organism death (e.g., involving use of species-specific
mortality rates for disease, starvation, and senescence) and associated mercury transfers to soil, water, and air is not
easily characterized without additional analysis.
11 Monthly and instantaneous results in this section are presented for different lengths of time because the
main purpose is to show the different patterns of variation. One year is sufficient for presenting the variation in the
bi-hourly instantaneous results, but five years shows the variation more completely for the monthly results.
Additionally, a five-year set of bi-hourly data points would be cumbersome to present.
JULY 2005 3-25 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-14
Descriptions of Instantaneous and Averaged Outputs for Selected Compartments
Compartment
Type"
Air
Total
deposition flux
to surface soilb
Leaf
Surface soil
Surface water
Water-column
herbivore
Water-column
carnivore
Compartment
SSW2 and SSE4
SW2
SW2 (grasses/herbs)
and SSE4
(coniferous)
SW2, SSE4, El, SE1
Swetts, Brewer
Swetts
Swetts
Long-term
Temporal Trend
steady
steady
increasing
increasing
increasing
increasing
increasing
Range of Variation
Instantaneous
Estimates (year 30)
6+ orders of
magnitude
10+ orders of
magnitude
SW2:+1 order of
magnitude;
SSE4: factor of 1.6
slight variations
factor of 1.6
factor of 1.1
none visible
Monthly Averages
(years 26-30)
~1 order of magnitude
~1 order of magnitude
SW2:~1 order of
magnitude;
SSE4: factor of 1.6
slight variations
factor of 1.5
factor of 1.2
none visible
All trends and ranges based on total mercury concentration, except deposition row based on total mercury flux.
b All values in this column are compartment types except for total deposition flux to surface soil.
Air
Instantaneous total mercury concentrations in the air compartments vary greatly over the
year (six or more orders of magnitude in the compartments analyzed). Exhibit 3-15 shows six
months of instantaneous output for air compartment SSE4.12 This large variability in total
mercury air concentration is due to the hour-to-hour variability in meteorological input data and
the effect these data have on air concentrations (wind speed and direction and precipitation rate
largely determine how much chemical is blown into and removed from a compartment, and
mixing height directly affects air compartment volumes and thus chemical concentrations). The
monthly average concentrations of total mercury in the air compartments vary by one or more
orders of magnitude (SSE4 and SSW2 shown in Exhibit 3-16). Note that the annual average air
concentrations discussed in Section 3.2.1 vary less than an order of magnitude (approximately 50
to 60 percent).
12
Due to the high variability in the instantaneous air concentrations, a graph showing a full year of
instantaneous concentration is too cluttered to distinguish the patterns.
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Exhibit 3-15 - Log Scale
Instantaneous Total Mercury Concentration in Air: SSW2 (July to December, Year 30)
1.0E-14
7/1 7/11 7/21 7/31 8/10 8/20 8/30 9/9 9/19 9/29 10/9 10/19 10/29 11/8 11/18 11/28 12/8 12/18 12/28
Date (data plotted at 2-hour intervals)
Instantaneous total mercury deposition flux to surface soil varies even more than air
concentration, up to 10 orders of magnitude for surface parcel SW2 (not shown on a chart),
primarily because wet vapor deposition flux is so highly variable over time (depending for
example on whether and how hard it is raining, and which direction the wind is blowing). Dry
vapor deposition flux for total mercury is also highly variable, however, with roughly a nine
order-of-magnitude range. The reason that dry vapor deposition flux variability is higher than air
concentration variability is due to mercury speciation differences - deposition is dominated by
divalent mercury, which is more variable in both air concentration and deposition than elemental
mercury, which dominates air concentration (i.e., the greater variability in divalent mercury air
concentration is swamped by the dominant elemental mercury air concentration).
Surface Soil and Leaves
The instantaneous total mercury surface soil concentrations in compartments SE1 and El
follow an increasing pattern (Exhibit 3-17; not on log scale), which is expected in all surface soil
compartments. The instantaneous concentration shows only slight fluctuations hour-to-hour,
which are not significant relative to the amount that the concentrations increase over the year.
Most of the larger increases in surface soil concentration (which are still quite small; note scale
on Exhibit 3-17) occur around large deposition events (defined here as the top one percent of total
deposition fluxes to surface soil occurring during the 30-year simulation), which are indicated on
Exhibit 3-17 for the two compartments shown. This is because wet deposition accounts for most
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Exhibit 3-16 - Log Scale
Monthly Average Total Mercury Concentration in Surface Soil, Leaf, and Air
(Years 26 to 30)
(a) SW2 - Grasses/Herbs
1.0E-06
1.0E-11
Jan- May-
YR26 YR26
Jan- May- Sep- Jan-
YR27 YR27 YR27 YR28
May- Sep-
YR28 YR28
Month
Jan-
YR29
May- Sep-
YR29 YR29
Jan- May- Sep-
YR30 YR30 YR30
3 Leaf concentrations included are only for months during the growing season, when leaves are present. Futhermore, the average
monthly concentration in leaves for the month of May each year is an average of daily concentrations from May 14-31 because no leaves
are modeled as present before May 14.
'Because of the differences in the air and surface parcel layouts, the boundaries of the SSW2 air parcel do not match those of SW2
surface parcel, but there is substantial overlap (see footnote on Exhibit 3-10).
(b) SSE4 - Coniferous Forest
Jan- May-
YR26 YR26
Jan-
YR27
Jan- May-
YR28 YR28
Sep- Jan-
YR28 YR29
May- Sep-
YR29 YR29
Jan- May- Sep-
YR30 YR30 YR30
a Because of the differences in the air and surface parcel layouts, the boundaries of the SSE4 air parcel do not match those of SSE4
surface parcel, but there is substantial overlap (see footnote on Exhibit 3-10).
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Exhibit 3-17
Instantaneous Total Mercury Concentration in Surface Soil: El and SE1 (Year 30)
4.16E-08
4.14E-08
_ 4.12E-08
4.10E-08
4.08E-08
Q 4.06E-08
s
4.04E-08
4.02E-08
4.00E-08
3.98E-08
1/1 1/21 2/10 3/1 3/21 4/10 4/30 5/20 6/9 6/29 7/19 8/8 8/28 9/17 10/7 10/27 11/16 12/6 12/26
Date (data plotted at 2-hour intervals)
Top 1% of hourly deposition fluxes onto surface soil indicated: SE1 *, E1*
of the mercury deposition to the soil surface (see Exhibit 3-9). Therefore, when it rains, a larger
amount of chemical mass is transferred from the air to the soil. (Note that not all precipitation
events are among the top deposition events for a given compartment, depending on the
compartment location relative to the source and the wind direction during the precipitation event.
Thus, the top deposition times differ for different compartment locations, as clearly shown in
Exhibit 3-17. This is why the top deposition events are indicated on the exhibit rather than the
top precipitation events, which do not correlate nearly as well with concentration increases.) The
monthly average soil concentrations increase slowly over five years and have very slight yearly
fluctuations when examined closely (although they are not apparent on the log scale in Exhibit 3-
16). The steadily increasing monthly average concentration of total mercury in surface soil
compartments is similar to the pattern seen for annual average mercury concentration (Exhibit 3-
10) and is due to the higher rate of mercury input than mercury removal for the surface soil
compartment during the 30-year simulation period.
Monthly average total mercury concentrations in grasses/herbs and coniferous forest leaf
compartments are shown along with the overlying air and associated surface soil in Exhibit 3-16.
The grasses/herbs leaf compartment (Exhibit 3-16a) only has non-zero monthly average
concentrations for five months of the year (although the May average only represents half of the
month) because those are the only months when leaves are modeled for this vegetation type in
this simulation. Each year, the monthly average concentrations increase during these months as
the mass transferred into the leaves (e.g., from the air) builds up during the growing season. At
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the end of each growing season, any mercury that has accumulated in deciduous plant leaf
compartments (and particle-on-leaf compartments) transfers to the surface soil via litterfall.
The monthly average mercury concentrations in the coniferous forest leaf compartment
(Exhibit 3-16b) are not increasing and fluctuate slightly over the five-year period. The monthly
coniferous leaf time series is much smoother than the monthly air concentrations, but appears to
be influenced by peaks in the air concentrations (e.g., around January, Year 28 and January, Year
29). The steady, slightly fluctuating pattern of monthly average concentrations in leaves is
similar to the annual average mercury concentration in leaves presented in Section 3.2.1 and
Appendix B.
Aquatic Compartment Types
Exhibit 3-18 (not on log scale) shows the instantaneous estimates of total mercury
concentrations in Swetts Pond and Brewer Lake for year 30. The instantaneous surface water
concentrations in Swetts Pond and Brewer Lake fluctuate by a factor of approximately 1.6 during
this year. The fluctuations appear to be associated with large deposition events, which are
indicated in the exhibit, although not every increase is accompanied or preceded by one of the top
one percent of deposition times. Such increases may result from longer-term deposition events
Exhibit 3-18
Instantaneous Total Mercury Concentration in Surface Water:
Swetts Pond and Brewer Lake (Year 30)
1.40E-10
1.20E-10
O.OOE+00
1/21 2/10 3/1 3/21 4/10 4/30 5/20 6/9 6/29 7/19 8/8 8/28 9/17 10/7 10/27 11/16 12/6 12/26
Date (data plotted at 2-hour intervals)
Top 1% of hourly deposition fluxes into waterbodies indicated: Swetts *, Brewer*
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(e.g., lower intensity rain that continues over a long period of time) or deposition events slightly
below the one percent threshold used for displaying the top events in the exhibit. (Water
temperature does not play a role in the observed fluctuations, given that it was modeled as a
constant 293° K.) The larger fluctuations of the surface water concentrations than the surface soil
concentrations (compare Exhibits 3-17 and 3-18) may be partially due to the difference in
baseline mercury concentration between the two compartment types. Because the total mercury
concentration is much higher in the surface soil, the deposition inputs would not be expected to
have as much relative impact. Therefore, large deposition events would be expected to cause
higher fluctuations in concentration in the surface water than the surface soil. Exhibit 3-19 (not
on log scale) displays the monthly average surface water concentrations of total mercury in
Swetts Pond over the last five years of the simulation. The Swetts Pond monthly average
concentrations vary by a factor of approximately 1.5 over the five years of data. The pattern of
monthly averages is very similar to the monthly air concentration pattern for the overlying air
parcel (not shown).
Exhibit 3-19
Monthly Average Total Mercury Concentration in Surface Water and Water-column
Fish: Swetts Pond (Years 26 to 30)
-Water-
column
Carnivore
-Water-
column
Om nivore
-Water-
column
Herbivore
-Surface
Water
YR26 YR26 YR26 YR27 YR27 YR27 YR28 YR28 YR28 YR29 YR29 YR29 YR30 YR30 YR30
Month
Exhibit 3-20 (not on a log scale) shows the instantaneous total mercury concentrations in
water-column herbivores and carnivores in Swetts Pond. The water-column herbivore
concentrations fluctuate during the year and appear to track the surface water concentrations, with
a slight delay and smoother peaks (e.g., peaks at the end of January, late March, end of June, and
early September). This is because the water-column herbivore eats 100 percent algae which is
modeled as a phase of the surface water. The water-column carnivore instantaneous
concentrations of total mercury increase steadily throughout the year, with no fluctuations. Once
again, this is due to the diet of the water-column carnivore (100 percent water-column omnivore)
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and its position on the modeled aquatic food chain. Unlike in the case of the water-column
herbivore, temporal changes in surface water concentration do not have any direct effect on the
time pattern of mercury accumulation in the water-column carnivore. Moreover, the higher
mercury concentration in water-column carnivores probably dampens any potential fluctuations
from varying mercury inputs.
The apparent stair-step pattern of the time-series results from the small output time step
and the fact that TRIM.FaTE instantaneous output concentrations were reported to four
significant figures. Each "step" consists of approximately 100 instantaneous concentrations with
the same value (at a larger output time step, or if outputs were reported with more significant
figures, the line would appear to be a smooth, upward-sloping line). Monthly average total
mercury concentrations in the aquatic biota show similar patterns (Exhibit 3-19). The water-
column herbivore monthly average concentrations fluctuate with a seemingly seasonal pattern of
higher concentrations around January, matching the peaks in the surface water compartment. The
water-column omnivore monthly concentrations are smoother than the herbivore with very slight
fluctuations. The water-column carnivore concentrations increase steadily throughout the five
years with little apparent fluctuation. Note that total mercury concentration in all three fish
compartment types increase about the same percentage over the five years, roughly 15 percent.
Exhibit 3-20
Instantaneous Concentration of Total Mercury in Water-column Carnivore and Herbivore:
Swetts Pond (Year 30)
3.6E-07
18.5E-07
3.1E-07
4/30 5/20 6/9 6/29 7/19 8/8 8/28
Date (data plotted at 2-hour intervals)
9/17 10/7 10/27 11/16 12/6 12/26
18.0E-07
"Note that the two fish types are plotted on separate y-axes, but the scale increments of the two axes are the same.
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Summary of Instantaneous and Monthly Results
Short-term concentration patterns in a compartment are highly influenced by the
relationship between the compartment and the air, the turnover rate (i.e., mass transfers in versus
mass transfers out) of chemical mass in the compartment, and the pattern of the chemical
concentration in the compartment(s) providing mass input to the compartment. As expected,
instantaneous concentration results show resolution that is damped in monthly or annual averages,
as illustrated in Exhibit 3-21, which shows annual averages, monthly averages, and instantaneous
outputs plotted together for surface water of Swetts Pond (not on log scale). Instantaneous
fluctuations in concentrations are due primarily to fluctuations in time-varying input data (e.g.,
meteorological data that directly affect deposition or air concentration). TRIM.FaTE has the
flexibility to allow for input data treated as constants in this test case (e.g., surface water
properties) to vary over time as well, and additional patterns in short-term concentrations would
be expected if more input properties (e.g., surface water parameters such as temperature or depth)
varied hourly or seasonally. As expected, monthly averages do not fluctuate as much as
instantaneous estimates (but more than annual averages), but do appear to show some seasonal
patterns based on seasonal meteorological data patterns.
Exhibit 3-21
Instantaneous and Average Concentration of Total Mercury in Surface
Water: Swetts Pond (Years 26 to 30)
3 1.1E-10 --
Jan- May- Sep- Jan- May-
YR26 YR26 YR26 YR27 YR27
Sep- Jan- May- Sep- Jan- May- Sep- Jan- May- Sep-
YR27 YR28 YR28 YR28 YR29 YR29 YR29 YR30 YR30 YR30
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3.3 Speciation: How Do Concentrations of Hg°, Hg2+, and MHg Differ?
This section examines mercury speciation profiles of predicted concentrations in abiotic
and biotic compartments for the mercury test case (emission case B). The analyses here focus on
the relative concentrations of the different mercury species; therefore, for all of the bar charts in
this section, a 100 percent stacked column is used. The data used to generate these charts are
concentrations of elemental mercury, divalent mercury, and methyl mercury (as mercury). Note
that if this analysis focused on mass fractionation of mercury species in specific compartments,
the results would be identical to the concentration-based fractions generated here.
3.3.1 Speciation by Compartment Type
The relative speciation profiles for various compartment types are compared below.
These charts are grouped by ecosystem type. Compartments for each ecosystem were selected to
compare the overall speciation for compartments that may be related to one another due to food
chain and other relationships. Both abiotic and biotic compartment types are included in these
analyses. For analyses and charts in Sections 3.3.1 and 3.3.2, the annual average concentrations
for each compartment for the 30th modeling year are used.
Aquatic Ecosystem
Mercury speciation is presented for selected compartment types that are included in a
typical aquatic ecosystem in the mercury test case. Compartment types included in these analyses
are:
Surface water;
Water-column herbivore, omnivore, and carnivore;
• Common loon;
Sediment;
• Benthic invertebrate; and
• Benthic omnivore and carnivore.
Speciation results for the aquatic ecosystems in Swetts Pond and Brewer Lake are presented in
Exhibit 3-22.
In general, the basic speciation profile is similar for each set of aquatic compartment types
across the two water bodies. This trend is evident regardless of the size of the water body; the
modeled volume of Brewer Lake is almost 25 times the volume of Swetts Pond, and the modeled
depth is eight meters (Brewer) versus three meters (Swetts). However, small differences in
speciation profiles between surface water bodies were observed (these variations are discussed in
more detail in Section 3.3.2).
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Exhibit 3-22
Mercury Speciation Profile of Various Compartment Types Present in Water Bodies:
Year 30 (Annual Average)
(a) Swetts Pond
Benthic
n vertebrate
Benthic Benthic
O m n ivore C arn ivore
Compartment Type
(b) Brewer Lake
Surface Water- Water- Water- Common Sediment Benthic Benthic Benthic
Water column column column Loon Invertebrate Omn ivore Carnivore
Herbivore Om n ivore Carnivore
Compartment Type
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Abiotic Compartments. Most (more than 80 percent) of the mercury in the surface water
compartments for these two lakes is estimated to be divalent, with most of the balance being
elemental. Nearly all (more than 98 percent) of the mercury in the sediment for both water bodies
is divalent. Thus, divalent mercury dominates the speciation in the abiotic compartments for the
modeled lakes.
Biotic Compartments. For water-column herbivores in the two lakes shown in Exhibit 3-
22, roughly half of the mercury is methyl (48 and 55 percent), with the remainder present in
divalent form. A larger fraction of the mercury in water-column omnivores (roughly 90 percent)
and carnivores (roughly 99 percent) is methyl mercury. As evident from the bar charts, divalent
mercury plays a bigger role in the benthic food chain. For the benthic fish, most of the mercury
in the carnivore (roughly 95 percent) is methyl mercury, and mercury in the omnivore is a little
more than half methyl (50 to 60 percent). Most (about 90 percent) of the mercury in benthic
invertebrates is divalent. The common loon, which feeds on both the water-column and benthic
fish, has roughly 80 percent methyl mercury.
Thus, methyl mercury dominates the modeled speciation in the fish compartments -
especially at higher trophic levels - and the semi-aquatic animal compartments with a 100 percent
fish diet (i.e., common loon), but the benthic invertebrate compartment has mostly divalent
mercury. Based on the literature, methyl mercury is expected to be the dominant species of
mercury in predatory fish (Bloom 1992). In the modeling results, methyl mercury is not as
dominant in lower trophic levels, consistent with the findings of Mason et al. (2000), who
concluded that the overall trophic status of the tested organism was indicated by the percentage of
mercury in its tissues that was methyl mercury (i.e., percent methyl mercury increased with
increasing trophic status). For example, non-predatory benthic invertebrates had more divalent
mercury than methyl mercury in their tissues. Bloom (1992) also suggests that lower percent
methyl mercury levels might be found in aquatic biota from non-natural (i.e., contaminated)
systems. For example, high percentages of inorganic (i.e., divalent) mercury have been observed
in stonerollers, a small fish that feeds exclusively on periphyton (algae often have a relatively
high ratio of inorganic to organic mercury), in a mercury-contaminated stream at the Y-12 facility
in Oak Ridge (Hill et al. 1996). This finding is consistent with the relatively high divalent
mercury fraction in the modeled water-column herbivore, which has a diet of 100 percent algae.
With respect to reported mercury speciation in benthic invertebrates, in Onondaga Lake,
NY, only about 25 percent of the mercury in benthic macroinvertebrates was observed to be
methyl mercury (Becker and Bigham 1995). A variety of results were reported in studies of
Duncan Lake in Northern Quebec (Tremblay et al. 1996), in which seven different aquatic insect
taxa were classified by feeding type (i.e., detritivores, grazers, predators, and combinations of
these). The percent methyl mercury of total mercury ranged from 10.5 percent for the mayfly (a
detritivore) to 75.1 percent for the dragonfly (an obligate predator). The average percent methyl
mercury was 31.7 percent for non-predators (4 of 7 taxa), 39.5 percent for non-obligate predators
(6 of 7 taxa), and 44.5 percent for all taxa. Biota-sediment accumulation factors for mercury used
as inputs for this TRIM.FaTE test case were derived from values for mayfly, and therefore the
speciation presented in Exhibit 3-22 (i.e., 11 percent methyl mercury after 30 years; see also
Exhibits 3-27 and 3-32) tends to represent that of non-predatory benthic invertebrates.
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Terrestrial Ecosystem
Soil Layers and Ground Water. Speciation results are presented for surface soil, root
zone soil, vadose zone soil, and ground water compartments associated with parcel SW2 (Exhibit
3-23). Divalent mercury dominates the speciation profile in the surface soil compartment. For
subsurface soil and ground water compartments, reduced (elemental) mercury predominates.
Mercury in vadose zone soil and ground water compartments is essentially 100 percent elemental
mercury, the most mobile form of the three species in soil. Note, however, that there are
considerably lower total mercury concentrations in the vadose zone and ground water than in the
overlying soil compartments (see, for example, Appendix Table B-8a).
Exhibit 3-23
Mercury Speciation Profile of Soil Layer Compartments in SW2:
Year 30 (Annual Average)
:=T
SurfaceSoil
RootZoneSoil VadoseZoneSoil
Compartment Type
Ground W ater
Animals Mercury speciation for several of the compartment types in a terrestrial
ecosystem is presented here, with a focus on the animal compartments.13 Compartment types
discussed are air, root zone soil, earthworm, surface soil, mouse, raccoon, and white-tailed deer.
Exhibit 3-24 presents the speciation profile for these compartment types associated with parcel
SW2. Note that air parcels SSW2 and SSW4 both overlay parts of surface parcel SW2.
In general, the bulk (at least 95 percent) of the mercury in surface soil and animals that eat
terrestrial diets (e.g., leaves, other land animals) or partial terrestrial diets (e.g., raccoon, which
also eats benthic invertebrates and fish) is estimated to be divalent mercury. The majority
13
1 Note that there is more uncertainty associated with the test case results for terrestrial biota compartments
relative to the results for aquatic biota compartments. The parameters used in the mercury mass transport and
transformation algorithms related to terrestrial biota have been studied less, resulting in greater uncertainty.
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(around 60 percent) of the mercury in root zone soil is present as elemental mercury, with the
remainder present as divalent mercury. This reflects the lower mobility of divalent mercury
relative to elemental mercury in soil (EPA 1997). It is noted that concentrations of total mercury
in root zone soil are much lower than those for surface soil (see, for example, Appendix Table B-
8a). Speciation for the earthworm compartment mirrors that of the root zone soil, which is
consistent with the fact that the earthworm partitions mercury from the root zone soil, not the
surface soil. Most of the mercury in air is elemental, similar to the modeled emissions profile.
Exhibit 3-24
Mercury Speciation Profile of Selected Terrestrial Compartments in SW2 and
Corresponding Air Parcels: Year 30 (Annual Average)
Raccoon in
SW2
White-tailed
Deer in SW2
Compartment Type
Terrestrial Plants. Mercury speciation for each of the plant compartment types is
presented here for a terrestrial ecosystem associated with parcel SW2, where grasses/herbs is the
vegetation type (see Exhibit 3-25). Included in this chart are air, root zone soil, and four plant
compartment types (root, stem, leaf, particle on leaf). As in the rest of this section, the results
presented for air and root zone soil reflect the annual average concentrations for year 30 of the
simulation; speciation results for the plant compartments reflect the average concentrations during
the growing season only for year 30 (i.e., averages for the three mercury species were calculated
from the results for May 13 through September 29 of that year). Air parcels SSW2 and SSW4
both overlay parts of surface parcel SW2.
Most (essentially 100 percent for stem, leaf, and particle on leaf, roughly 90 percent for
root) of the mercury in the grasses/herbs plant compartments for parcel SW2 is present as divalent
mercury, with some methyl mercury evident in the root compartment. This noticeable presence
of methyl mercury in the root compartment, coincident with a lack of noticeable methyl mercury
in the root zone soil compartment, likely reflects the difference in partitioning coefficients among
the mercury species. The partition coefficient (root zone soil to root) for methyl mercury is
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approximately 10 times greater than that for divalent mercury, while the value for elemental
mercury is negligible.
The speciation in plant compartments for coniferous and deciduous forests was also
examined as a part of this analysis. Only leaf and particle-on-leaf compartments are included in
the coniferous and deciduous plant composite compartments; root and stem compartments for
woody plants are not included in the test case due to significant uncertainties in modeling
transfers to and from those compartments. The speciation profiles for coniferous and deciduous
leaf and particle-on-leaf compartments across the modeling region are very similar to the trends
observed in the corresponding grasses/herbs compartments (nearly 100 percent divalent mercury).
Thus, most of the mercury in each of the plant compartments across the modeling region is
present as divalent mercury, with some methyl mercury evident in the root compartments.
Exhibit 3-25
Mercury Speciation Profile of Terrestrial Grasses/Herbs Plant Compartments in SW2 and
Corresponding Air Parcels: Year 30 (Annual Average)
Air in SSW 2
Air in SSW4 RootZone Soil
in SW 2
Root in SW2 Stem in SW 2
Leaf in SW 2
P a rticle on Leaf
in SW 2
Compartment Type
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3.3.2 Spatial Variations in Speciation
In general, no major spatial variations in speciation were observed for most abiotic and
biotic compartment types included in the mercury test case. The speciation profile for a
compartment type is, for the most part, relatively constant across the modeling region. This is to
be expected because most of the spatially varying aspects of a scenario, such as air concentration
and deposition rate, are not expected to affect mercury speciation for a given compartment type.
One divergence from this observation is the variation in speciation profiles for the different water
body compartments that were included in the test case.
The surface water compartment type is the only compartment type for which noticeable
variations in speciation were observed for compartments located in different parts of the modeling
region. It is important to note that the water body compartments in the mercury test case were
characterized individually; the property values for water body compartments reflect the site-
specific variation between the various water bodies. For example, site-specific surface areas,
depths, temperatures, and flow/flush rates were assigned to each surface water compartment. By
contrast, the basic properties for other compartment types (e.g., surface soil) were identical or
nearly the same for all compartments included in the scenario (e.g., all surface soil compartments
were assigned the same depth, soil density, and temperature). Therefore, many of the differences
in speciation observed in the various surface water compartments are likely a result of different
water body characteristics rather than different compartment locations.
Speciation profiles for the surface water compartments included in the mercury test case
scenario are presented in Exhibit 3-26, based on total water column concentrations (i.e., phase-
specific concentrations, such as dissolved mercury, were not considered).
Exhibit 3-26
Mercury Speciation Profile of Various Surface Water Compartments:
Year 30 (Annual Average)
B rew er Lake
Fields Pond
S w etts Pond
Com partm ent
T h u rston Pond
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In general, the majority of the mercury in surface water is divalent, with elemental
mercury comprising the bulk of the remaining mercury. Among the lakes, speciation differences
are fairly small, with the larger (and deeper) ones having higher fractions of elemental mercury.
There is a large difference in speciation between mercury in the river and the four lakes, with a
considerably higher fraction of elemental mercury in the river. Variations in surface water
speciation could be related to the surface area-to-volume ratio, water body depth, residence time,
or other characteristics of the water bodies, which can affect the relative amount of elemental
mercury that transfers into and out of, and transforms within, a compartment.
Although surface water speciations of mercury do vary for the different water bodies,
speciations for sediment and most fish compartment types do not appear to vary much across
water bodies (see, for example, Exhibit 3-22 for a presentation of the mercury speciation in
sediment and fish compartments for Swetts Pond and Brewer Lake). The steady-state results (see
Chapter 4 for detailed discussion of the steady-state modeling) for aquatic animal compartments
for all four lakes included in the mercury test case are presented in Exhibit 3-27. Methyl and
divalent mercury concentration results (elemental mercury is negligible) and the corresponding
percent methyl mercury for each of the five fish compartments and the benthic invertebrate
compartment are presented in this table. For most of these compartments, the overall variation in
percent methyl mercury between water bodies is small (i.e., generally within a few percent); for
benthic animals, the speciation fractions are nearly identical. The most notable difference in
mercury speciation in fish across water bodies is for the water-column herbivore (100 percent
algae diet), which varies more across the lakes, with the percent methyl mercury ranging from 36
to 51 percent (and which also has a considerably higher percent methyl mercury in the river than
in the lakes).
Food chain multiplier ratios are presented along the bottom of Exhibit 3-27 for reference;
these values are very stable across the four lakes.
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Exhibit 3-27
Speciated Mercury Results for Fish and Benthic Invertebrates in Four Lakes/Ponds: Steady-state
SwettsPond
MHg
Hg*
Total
%MHg
Thurston Pond
MHg
Hg*
Total
%MHg
Brewer Lake
MHg
Hg*
Total
%MHg
Fields Pond
MHg
Hg2+
Total
%MHg
Methyl and Divalent Mercury Concentrations and Percent Methyl Mercury: Steady-state a
wc-cb
WC-O
WC-H
B-C
B-O
B-l
1.41E-04
2.70E-05
1.21E-05
1.07E-04
2.07E-05
6.35E-06
2.26E-06
6.28E-06
2.12E-05
5.83E-06
1.63E-05
5.18E-05
1.44E-04
3.33E-05
3.33E-05
1.13E-04
3.71 E-05
5.90E-05
98.4%
81.1%
35.4%
94.8%
56.0%
10.8%
6.19E-05
1.17E-05
5.22E-06
5.08E-05
9.75E-06
2.94E-06
1.06E-06
2.94E-06
9.87E-06
2.73E-06
7.62E-06
2.40E-05
6.29E-05
1.47E-05
1.51 E-05
5.36E-05
1.74E-05
2.74E-05
98.3%
80.0%
34.6%
94.9%
56.1%
10.8%
1.00E-04
1.84E-05
8.01E-06
4.26E-05
7.86E-06
2.30E-06
8.34E-07
2.29E-06
7.58E-06
2.18E-06
6.01 E-06
1.86E-05
1.01E-04
2.07E-05
1.56E-05
4.48E-05
1.39E-05
2.13E-05
99.2%
88.9%
51.4%
95.1%
56.7%
10.8%
8.26E-05
1.58E-05
7.37E-06
5.19E-05
1.01 E-05
3.26E-06
1.11E-06
3.09E-06
1.07E-05
2.91 E-06
8.16E-06
2.66E-05
8.37E-05
1.89E-05
1.81 E-05
5.48E-05
1.82E-05
3.03E-05
98.7%
83.7%
40.8%
94.7%
55.3%
10.8%
Food Chain Multipliers: Steady-state
WC-C/WC-O
WC-O/WC-H
B-C/B-O
B-CVB-I
5.2
2.2
5.2
3.3
0.4
0.3
0.4
0.3
4.3
1.0
3.0
0.6
5.3
2.2
5.2
3.3
0.4
0.3
0.4
0.3
4.3
1.0
3.1
0.6
5.4
2.3
5.4
3.4
0.4
0.3
0.4
0.3
4.9
1.3
3.2
0.7
5.2
2.1
5.1
3.1
0.4
0.3
0.4
0.3
4.4
1.0
3.0
0.6
' Hg2+ concentration results in mg/kg wet weight, MHg and total concentration results in mg/kg wet weight as Hg.
' WC = water-column, B = benthic, C = carnivore, O = omnivore, H = herbivore, I = invertebrate.
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3.3.3 Temporal Variations in Speciation
Abiotic Compartments
Overall, speciation within most of the abiotic compartments included in the mercury test
case does not appear to vary much over the time frame of the scenario (i.e., 30 years). The
speciation profile for some of these compartments stabilizes relatively quickly at a profile similar
(or nearly identical) to the speciation profile calculated from the steady-state simulation results.
For example, there are not perceptible variations in mercury speciation in surface soil, surface
water, and sediment compartments for years 1, 10, and 30 or the steady-state simulation results.
See Exhibits 3-28a, 3-28b, and 3-28c for speciation profiles of the surface soil compartment in
SW2 and surface water and sediment compartments in Swetts Pond corresponding to these time
points. Speciation profiles in each of these exhibits were calculated from annual average
concentrations for years 1,10, and 30, and from the simulation end results for the steady-state
run.
Exhibit 3-28a
Mercury Speciation Profile of Surface Soil Compartment in SW2:
Years 1,10, 30, and Steady-state
100%
90% -
80% -
70% -
60%
50%
40% -
30%
20% -
10% -
0% 4-
10
30
Steady-state
Year
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Exhibit 3-28b
Mercury Speciation Profile of Surface Water Compartment in Swetts Pond:
Years 1,10, 30, and Steady-state
40%
30%
Steady-state
Exhibit 3-28c
Mercury Speciation Profile of Sediment Compartment in Swetts Pond:
Years 1,10, 30, and Steady-state
Steady-state
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Biotic Compartments
Temporal aspects of speciation profiles in some of the biotic compartments are slightly
more variable. These changes may be a result of one or more factors, possibly including:
Relationships between biotic compartments and their surrounding abiotic environments
(e.g., uptake of mercury via inhalation and ingestion, excretion of mercury to the
environment);
• Various mercury transformation reactions that occur at different rates in different biotic
compartment types (e.g., methylation, which is an important process affecting mercury
speciation in fish); and
Food web relationships that connect a biotic compartment with multiple other biotic
compartments via consumption.
For the fish and common loon compartment types, the fraction of mercury as methyl
mercury increases over time. See, for example, the speciation profiles for the water-column
herbivore, omnivore, and carnivore compartments and the benthic carnivore compartment in
Swetts Pond presented in Exhibit 3-29.u The speciation profile for the common loon
compartment in Swetts Pond also changes over time (Exhibit 3-30). Possible factors in this
temporal increase in methyl mercury fraction include both the slightly higher uptake rate of
methyl mercury into algae (compared to divalent mercury) and the slower rate of methyl mercury
excretion from fish than that for divalent mercury. For common loons in eastern Canada,
Scheuhammer et al. (1998) have observed that the proportion of methyl mercury is 80 to 100
percent of total mercury in the breast muscle but only five to seven percent of total mercury in
livers and kidneys. Whole body concentrations generally have not been measured; blood and
feather measurements are much more common. The test case modeling results presented in
Exhibit 3-30 appear to be consistent with the data reported in Scheuhammer et al.
This trend of increasing percent methyl mercury is not observed for all aquatic biota
compartment types. The speciation profiles for the benthic invertebrate and benthic omnivore
compartments in Swetts Pond are nearly constant over time (Exhibit 3-31).
Exhibit 3-32 presents detailed speciated mercury results for the aquatic animal
compartments for Swetts Pond that are summarized in Exhibits 3-29 and 3-31. This table shows
concentration of methyl and divalent mercury (elemental mercury is negligible) over time (and
steady-state) for Swetts Pond. The right side of the table provides methyl mercury percentages,
and food chain multiplier ratios are provided along the bottom. As shown in the bar charts, the
percentage of methyl mercury increases over time (except for benthic invertebrates) and is higher
in the higher trophic levels. Also, as expected, methyl mercury concentrations in fish consistently
increase up through the food chain (carnivore > omnivore > herbivore), as shown by the food
chain multipliers for methyl mercury.
14 All biotic speciation profiles discussed in this section were calculated from annual average
concentrations for years 1 through 10 and year 30, and from the simulation end results for the steady-state model
run.
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Exhibit 3-29
Mercury Speciation Profile of Aquatic Biota Compartments in Swetts Pond:
Years 1-10, 30, and Steady-state
(a) Water-column Carnivore
DMHg (as Hg)
Hg2
• HgO
6 7 8 9 10 30 Steady-
Water-column Omnivore
(c) Water-column Herbivore
(d) Benthic Carnivore
III
DMHg (as Hg)
Hg2
DHgO
DMHg (as Hg)
Hg2
DHgO
345
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Exhibit 3-30
Mercury Speciation Profile of Common Loon Compartment in Swetts Pond:
Years 1 -10, 30, and Steady-state
Exhibit 3-31
Mercury Speciation Profile of Benthic Invertebrate and Benthic Omnivore Compartments
in Swetts Pond: Years 1-10, 30, and Steady-state
(a) Benthic
Invertebrate
(b) Benthic
Omnivore
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Exhibit 3-32
Speciated Mercury Results for Fish and Benthic Invertebrates in Swetts Pond: Years 1,10, 30, and Steady-state
Year
MHg (g/kg wet weight as Hg)
1
10
30
SS
Hg^g/kg wet weight)
1
10
30
SS
Total Hg (g/kg wet weight)
1
10
30
SS
Concentrations
WC-C3
WC-0
WC-H
B-C
B-O
B-l
2.01E-08
1.51E-08
1.11E-08
3.30E-10
2.76E-10
1.82E-10
6.32E-07
1.32E-07
6.13E-08
5.35E-08
1.20E-08
3.92E-09
1.81E-06
3.55E-07
1.61E-07
2.87E-07
5.85E-08
1.83E-08
1.41E-04
2.70E-05
1.21E-05
1.07E-04
2.07E-05
6.35E-06
1.45E-09
7.03E-09
3.23E-08
4.12E-11
2.34E-10
1.14E-09
7.66E-09
2.20E-08
7.75E-08
3.11E-09
9.13E-09
3.03E-08
1.79E-08
5.07E-08
1.74E-07
1.58E-08
4.49E-08
1.46E-07
2.26E-06
6.28E-06
2.12E-05
5.83E-06
1.63E-05
5.18E-05
2.15E-08
2.22E-08
4.34E-08
3.71E-10
5.10E-10
1.32E-09
6.40E-07
1.54E-07
1.39E-07
5.67E-08
2.11E-08
3.43E-08
1.82E-06
4.05E-07
3.35E-07
3.03E-07
1.03E-07
1.65E-07
1.44E-04
3.33E-05
3.33E-05
1.13E-04
3.71E-05
5.90E-05
food Chain Multipliers
WC-C/WC-0
WC-O/WC-H
B-aB-0
B-Q/B-I
1.3
1.4
1.2
1.5
4.8
2.1
4.5
3.1
5.1
2.2
4.9
3.2
5.2
2.2
5.2
3.3
0.2
0.2
0.2
0.2
0.3
0.3
0.3
0.3
0.4
0.3
0.4
0.3
0.4
0.3
0.4
0.3
1.0
0.5
0.7
0.4
4.2
1.1
2.7
0.6
4.5
1.2
2.9
0.6
4.3
1.0
3.0
0.6
Percent Methyl Mercury
1
93.3%
68.3%
25.6%
88.9%
54.1%
13.8%
10
98.8%
85.7%
44.2%
94.5%
56.8%
11.4%
30
99.0%
87.5%
48.1%
94.8%
56.6%
11.1%
SS
98.4%
81.1%
36.4%
94.8%
56.0%
10.8%
' WC = water-column, B = benthic, C = carnivore, O = omnivore, H = herbivore, I = invertebrate.
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3.4 Spatial Variation of Total Mercury Concentration
This section examines spatial variations in total mercury concentrations for emission case
B of the test case. The data used to create the tables and charts included in this section are
concentrations of total mercury, which are calculated by summing the concentrations of
elemental mercury, divalent mercury, and methyl mercury (as mercury) output by TRIM.FaTE.
With the exception of the air results, these tables and charts present the average total mercury
concentrations for the final (i.e., 30th) year of the emission case B simulation. The tables and
charts for the air compartments present the average total mercury concentrations over the final
five years of the simulation.15 This section does not address temporal patterns of mercury mass
and concentration or variations in mercury speciation over the modeling region; these topics
were addressed in Sections 3.1 through 3.3, respectively, of this report.
3.4.1 Abiotic Compartments
Seven abiotic compartment types were included in the mercury test case simulations: air,
surface soil, root zone soil, vadose zone soil, surface water, sediment, and ground water. This
section describes the spatial variations in total mercury concentrations for each of these
compartment types, with the exception of vadose zone soil and ground water. These two
compartment types accumulated very little mercury mass over the 30-year modeling period (see
Section 3.1) and have little impact on the endpoints of main interest in this scenario, and thus
they were not included in this analysis. In addition to total mercury concentrations in the abiotic
compartments, this section also presents the spatial pattern of deposition of total mercury from
air to surface soil to help illustrate how chemical mass moves from the air into the rest of the
system across the modeled region.
Air
Exhibit 3-33 displays the variation across the air parcel layout of the total mercury
concentrations (averaged over the final five years of the simulation) in the air compartments. In
general, concentrations decrease as distance from the source increases, with the highest
concentration occurring in the source compartment (compartment where mercury mass emitted
from source is initially input). This pattern was expected based on the spread of chemical mass
over a larger area with increasing distance from the source. Air concentrations east and north of
the source tend to be slightly higher than concentrations west and south of the source. This
pattern is consistent with the predominant combination of wind speed and wind direction.
15 This approach to averaging the air concentration data better represents the "ending" concentration for the
30-year modeling period because the air results are highly dependent on the meteorological data used and five years
of meteorological input data was repeated throughout the period.
JULY 2005 3 -49 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-33
Spatial Variations in Total Mercury Concentrations: Air Compartments
Average Concentrations for Final Five Years
Air
Total Mercury (g/m3)
^B Greater than 5.5 t-19
^B 2.89E-10-5.51E-10
I | 1.52E-10-2.89E-10
NXS1 Less than 1.52E-10
. Watershed Regions I I Parcel Boundary
N 101
A
Kilometers
JULY 2005
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To illustrate the size of the differences across air compartments, Exhibit 3-34 presents the
total mercury concentrations ordered from highest to lowest. These concentrations are inclusive
of both the particulate and gaseous fractions of mercury mass (with gaseous fraction
predominant). Generally, the highest concentrations in compartments other than the source
occur in compartments adjacent to the source (i.e., compartment names ending in "1"), although
these concentrations are substantially lower than the concentration in the source compartment.
The difference between the source compartment concentration and concentrations in adjacent
compartments is approximately an order of magnitude. With additional distance from the
source, concentration is further reduced more gradually, dropping by a little over an order of
magnitude from the compartments adjacent to the source to the compartment with the lowest
concentration (W3, roughly 3 km west of the source).
Exhibit 3-34
Total Mercury Concentrations: Air Compartments
Compartment
Source
NNE1
ENE1
ESE1
NNW1
SSE1
SSW1
NNE2
WSW1
ESE2
WNW1
ENE2
NNW2
SSE3
NNE3
Average
Concentration,
Years 26-30
(g/ni3)
2.1E-08
l.OE-09
l.OE-09
l.OE-09
8.7E-10
8.0E-10
7.3E-10
4.3E-10
4.1E-10
3.9E-10
3.8E-10
3.7E-10
3.0E-10
3.0E-10
2.7E-10
Compartment
SSW2
ESE3
ENE3
NNW3
SSE3
ENE4
ESE4
SSW3
W2
SSW4
ENE5
SSE4
ESE5
SSE5
W3
Average
Concentration,
Years 26-30 (g/m3)
2.5E-10
2.4E-10
2.2E-10
1.8E-10
1.8E-10
1.7E-10
1.6E-10
1.5E-10
1.3E-10
1.3E-10
1.3E-10
1.2E-10
1.1E-10
9.2E-11
8.0E-11
JULY 2005
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Deposition from Air to Surface Soil
The spatial distribution of total mercury deposition flux16 to the surface soil (averaged
over the final five years of the simulation) and the relative contributions from wet and dry
deposition to the total deposition flux are displayed in Exhibit 3-35. Like the air concentrations,
the deposition fluxes generally decrease with distance from the source. The highest deposition
fluxes are found north and south of the source, whereas the highest air concentrations are north
and east of the source. Close to the source, the total deposition is higher to the west than the
east.
The relative contributions from wet and dry deposition follow a spatial pattern which is
related to the meteorological data patterns. The wind speed and direction - which control the
direction of mercury advection in air - during precipitation events can highly influence the
locations receiving the most wet deposition. Exhibit 3-36 is a wind rose showing the wind speed
and direction only during precipitation events. This exhibit shows that during rain events the
wind blows predominantly toward the north and southwest and infrequently toward the east.
Conversely, the overall predominant wind directions are to the north and southeast, and the wind
blows infrequently to the west (see Exhibit 2-7). The difference between the overall wind
patterns and the wind patterns when it is raining help to explain how the wet deposition percent
of the total is highest to the west and the percent of deposition that is dry deposition is highest to
the east. As described in Section 3.2, nearly 70 percent of the deposition of mercury to soil
within the modeling region occurs during precipitation events. Therefore, the different spatial
pattern in wet and dry deposition helps to explain why the total deposition spatial pattern is
slightly different from the air concentration pattern (especially close to the source).
To illustrate the size of the deposition flux differences across surface soil compartments,
Exhibit 3-37 shows the total mercury deposition fluxes ordered from highest to lowest. The
deposition flux to the source compartment is more than an order of magnitude greater than the
deposition flux to the adjacent compartments. The highest deposition fluxes to compartments
other than the source occur in compartments adjacent to the source. With additional distance
from the source, the deposition flux (like the air concentration) decreases more gradually,
dropping a little over an order of magnitude from the compartments adjacent to the source to the
compartment with the lowest deposition flux (ESE5, roughly 7 km southeast of the source).
16 The deposition flux of total mercury was estimated by summing the wet vapor, wet particle, dry vapor,
and dry particle deposition fluxes to each surface soil compartment.
JULY 2005 3 -52 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-35
Spatial Variations in Total Mercury Deposition Flux: Surface Soil Compartments
Average Deposition Flux
from Air to Surface Soil for Final Five Years
Total Mercury Deposition
Flux (gfin2-day)
^H >7.2E-08
^H 3.6E-08 - 7.2E-08
I I 1.8E-08 - 3-6E-08
r~l < 1-8E-08
l~_' Watershed Regions
N
A
Fraction of Total Mercury
Deposition as Wet or Dry
Wet Deposition -^^
Dry Deposition *""*-*
I I Parcel Boundary
0 1
Kilometers
- -V.'
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-36
Wind Rose Representing TREVLFaTE Five-year Input Data Set
During Precipitation Events
WIND ROSE PLOT
Mercury Test Case - 5-year Data Set (Precipitation Events Only)
Wind Speed (m/s)
DISPLAY
Wind Speed
AVG. WIND SPEED
4.42 m/s
ORIENTATION
Direction
(blowing from)
UNIT
m/s
CALM WINDS
0.00%
WRPLOT View3.5by Lakes Envitvnmental Software -vwm.lakes-enMitoimertal.com
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-37
Total Mercury Deposition Flux: Surface Soil Compartments
Compartment
Source
Nl
SE1
Wl
El
N2
SW2
SSE2
NE2
ESE2
Annual Average
Deposition Flux,
Years 26-30
(g/m2-day)
3.9E-06
1.5E-07
1.2E-07
1.2E-07
1.1E-07
4.8E-08
4.1E-08
3.8E-08
3.6E-08
3.5E-08
Compartment
W2
SSE3
NE3
ESE3
SSE4
ESE4
SSE5
E4
SE6
ESE5
Annual Average
Deposition Flux,
Years 26-30
(g/m2-day)
3.4E-08
2.0E-08
1.8E-08
1.6E-08
1.5E-08
1.2E-08
LIE-OS
LIE-OS
8.7E-09
8.6E-09
Surface Soil
The spatial variation of annual average concentrations of total mercury across the surface
soil compartments for the 30th year of the simulation is shown in Exhibit 3-38. This pattern is
slightly different from the pattern of concentrations for the air compartments. Like the air
compartments, the highest estimated concentration in surface soil is found in the source
compartment and concentrations decrease with distance from the source. However, higher
surface soil concentrations are generally found to the north and west of the source, whereas the
highest air concentrations generally occur to the north and east. Overall, the spatial pattern of
surface soil concentrations is consistent with the pattern of deposition described above and its
role in transporting chemical mass to the soil from the air compartments.
To illustrate the size of the differences across compartments, Exhibit 3-39 lists the total
mercury concentrations ordered from highest to lowest. Because the boundaries of the air and
surface soil compartments do not match exactly, it is difficult to compare the order of the
compartments in Exhibits 3-34 and 3-39 on a one-to-one basis. However, the general patterns of
concentrations can be compared. The overall range of surface soil concentrations across the
modeling region (approximately three orders of magnitude) is similar to the range of air
concentration results, although the range for soil is a little bigger (and the size of the soil
modeling region is smaller than the air modeling region). In addition, like the air compartments,
the highest concentrations in surface soil compartments other than the source generally occur
with parcels adjacent to the source (i.e., compartment names ending in "1"), although the
decrease in surface soil compartment concentration between the source and adjacent soil parcels
JULY 2005
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Exhibit 3-38
Spatial Variations in Total Mercury Concentrations: Surface Soil Compartments
Annual Average Concentrations for 30th Year
Soil - Surface
Total Mercury (gig dry weight)
^| Greater than 2.82 E-08
I I 1.26E-08 • 2.82E-08
I I S.59E-09 - 1.26E-08
Less than 5.59E-09
• Watershed Regions I I Parcel Boundary
N
A
1
Klometers
I
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Exhibit 3-39
Total Mercury Concentrations: Surface Soil Compartments
Compartment
Source
Nl
Wl
SE1
El
N2
ESE2
NE2
SSE2
SW2
Annual Average
Concentration,
Year 30
(g/g dry weight)
2.2E-06
6.3E-08
5.0E-08
4.1E-08
4.1E-08
1.4E-08
1.2E-08
1.2E-08
1.2E-08
LIE-OS
Compartment
W2
SSE3
NE3
ESE3
ESE4
SSE5
ESE5
SSE4
SE6
E4
Annual Average
Concentration,
Year 30
(g/g dry weight)
9.2E-09
6.5E-09
5.9E-09
4.5E-09
4.0E-09
3.6E-09
3.1E-09
3.1E-09
3.1E-09
2.5E-09
is greater (by approximately 1.7 fold) than for air. Additionally, the further decrease in
concentration over the remaining distance to the outermost parcels is somewhat greater for
surface soil compared to air (e.g., highest-to-lowest compartment concentration ratio, excluding
the source compartment, is 25 for surface soil and 13 for air), even though the surface soil parcel
layout is smaller than the air layout. Thus, for the test case scenario, the spatial pattern of total
mercury concentration in surface soil is generally consistent with the pattern for air and for
atmospheric deposition, but surface soil concentrations decrease somewhat more rapidly with
distance from the source.
Root Zone Soil
Exhibit 3-40 presents the spatial variation across the surface parcel layout of annual
average concentrations of total mercury in the root zone soil compartments for the 30th year of
the simulation. The patterns of root zone soil concentrations closely resemble the concentration
patterns for surface soil. Given that the great majority of the chemical mass transported into the
root zone soil compartments comes directly from the surface soil compartments, the general
patterns in the two compartment types are expected to be similar.
To illustrate the size of the differences across root zone soil compartments, Exhibit 3-41
presents the total mercury concentrations ordered from highest to lowest. The ranking of
compartments based on concentration for root zone soil compartments is similar to the ranking
for surface soil compartments (Exhibit 3-39). The primary difference is that the surface soil
concentration in SE1 is higher than in El, and the root zone soil concentration in El is higher
than in SE1. For both compartment types, however, the difference between El and SE1 is small.
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Exhibit 3-40
Spatial Variations in Total Mercury Concentrations: Root Zone Soil Compartments
Annual Average Concentrations for 30th Year
Soil - Root Zone
Total Mercury (g/g dry weight)
•• Greater than 7.21E-12
^| 3-62E-12-7.21E-12
I I 1.82E-12 - 3-62E-12
Lass than 1.82E-12
Parcel Boundary
Watershed Regions
N
A
» v-..
'.
\
" '
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-41
Total Mercury Concentrations: Root Zone Soil Compartments
Compartment
Source
Nl
Wl
El
SE1
N2
ESE2
NE2
SSE2
SW2
Annual Average
Concentration,
Year 30
(g/g dry weight)
4.7E-10
1.4E-11
1.1E-11
1.1E-11
l.OE-11
3.9E-12
3.5E-12
3.4E-12
3.1E-12
3.1E-12
Compartment
W2
SSE3
NE3
ESE3
ESE4
SSE4
SSE5
ESE5
E4
SE6
Annual Average
Concentration,
Year 30
(g/g dry weight)
2.4E-12
1.8E-12
1.8E-12
1.5E-12
1.2E-12
1.1E-12
1.1E-12
9.4E-13
9.1E-13
9.1E-13
The only other difference between the ranking of surface soil and root zone soil concentrations is
that the SSE4 and E4 parcels have higher rank orders and the SSE5, ESE5, and SE6 parcels have
lower rank orders in root zone soil than in surface soil. These slight differences may be
explained in part by the spatial differences in the deposition flux of elemental and divalent
mercury from air to soil (refer to Section 3.3 for a more detailed description of speciation in soil)
or by differences in the assigned plant types at these locations (SSE4 and E4 have coniferous
plants and SSE5, ESE5, and SE6 have deciduous plants).
The overall range of root zone soil concentration results is also similar to the ranges of
surface soil and air concentration results (approximately three orders of magnitude, with the
range for root zone soil smaller than for surface soil and larger than for air). The difference in
root zone soil compartment concentrations between the source parcel and adjacent parcels is
slightly less than that seen for surface soil, as is the difference in compartment concentrations
between adjacent parcels and the outermost parcels. This may be due in part to the fact that
there are fewer loss processes for root zone soil than for surface soil; specifically, erosion and
runoff are modeled for surface soil but not root zone soil. Overall, for the test case scenario, the
spatial pattern of total mercury concentration in root zone soil is very similar to the pattern for
surface soil, but concentration in root zone soil decreases more slowly with distance from the
source.
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Surface Water
The spatial variation of annual average concentrations of total mercury across the surface
water compartments for the 30th year of the simulation is shown in Exhibit 3-42.17 There are a
number of different factors that may contribute to the concentration differences between the
water bodies, including:
• Air and surface soil concentrations in the parcels adjacent to each water body;
Surface area and depth of each water body;
Size of the watershed associated with each water body;
Incoming and outgoing flow characteristics of the water bodies; and
Proximity of each water body to the emission source.
The highest surface water concentration is found in the Swetts Pond compartment, which was
expected given that it is the closest water body to the source, and, along with Thurston Pond, has
the smallest depth (both water bodies are three meters deep).
Sediment
The spatial variation of annual average concentrations of total mercury across the
sediment compartments for the 30th year of the simulation is also shown in Exhibit 3-42. The
spatial patterns of sediment concentrations are nearly identical to the patterns for the surface
water compartment, with the highest concentration in the Swetts Pond compartment. Given that
the sediment compartments receive chemical mass solely from the surface water compartments,
the similarities between the patterns in surface water and sediment are not surprising.
3.4.2 Biotic Compartments
To simplify the presentation of results for the biotic compartment types, they are grouped
into the following five categories:
Terrestrial plants;
Terrestrial animals;
• Semi-aquatic animals;
• Water-column fish; and
• Benthic animals.
For the terrestrial plant, terrestrial animal, and semi-aquatic animal categories, the discussion
focuses on one or two compartment types as examples of the overall spatial variations for the
category. To facilitate interpretation of patterns for the terrestrial and semi-aquatic animals,
compartment types with fewer numbers of different dietary compartment types are presented.
For the water-column fish and benthic animal categories, each compartment type is presented.
17 Results not shown for the river compartment. This water body, which is an estuary, was modeled as a
river in the mercury test case to assist in general model evaluation, but results are not considered representative of
the actual conditions.
JULY 2005 3 -60 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-42
Spatial Variations in Total Mercury Concentrations: Surface Water and Sediment
Compartments
Fields
Surface water: 6.0E-11 g/L
Sediment: 1JE-09 g/g dry weight
Brewer
Surface water: 4.3E-11 g/L
Sediment: 1.2E-09 g/g dry weight
Swetts
Surface water: 9.8E-11 g/L
Sediment: 2.8E-09 g/g dry weight
Thu
Surface water: 5.0E-11 g/L
Sediment: 1.5E-09 g/g dry weight
Annual Average Total Mercury Concentrations for 30th Year
Surface Water and Sediment
"„". Watershed Regions ] Parcel Boundary
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Terrestrial Plants
Spatial variation in annual average total mercury concentrations for the leaf
compartments in the 30th year of the simulation is presented in Exhibit 3-43.18 Note that no
plants were included in the source volume element, and thus the source compartment is not
shaded. In interpreting these results, it is important to consider differences in how litter fall and
dormancy were simulated for the different vegetation types in this scenario. For deciduous
forest and grasses/herbs vegetation types, litter fall is simulated through the essentially complete
transfer of chemical mass from the leaf to the surface soil over one month during the fall. As the
chemical mass is transferred from the leaves to the soil, chemical concentrations in the leaf
compartments for these vegetation types decrease to zero. In simulation of dormancy, these leaf
compartments do not begin accumulating chemical mass again until the spring. For coniferous
forest vegetation types, however, litter fall is simulated via a low, constant rate of transfer of
chemical mass from the leaf to the surface soil, and no period of dormancy is simulated.
The patterns of spatial variation for the leaf compartments appear to be driven both by
proximity to the source and vegetation type. In general, the highest concentrations occur with
leaf compartments in parcels close to the source assigned with coniferous vegetation, while
parcels assigned deciduous forest and grasses/herbs vegetation, regardless of location, generally
have the lowest concentrations. Given the lack of complete litter fall each year and the lack of a
period of dormancy for the coniferous forest compartments, it is not surprising that they
accumulate more chemical mass than other vegetation types. Additionally, the generally lower
concentrations in the deciduous forest leaf compartments are at least partially attributable to the
fact that deciduous forest was the vegetation type assigned to the parcels farthest from the
source.
Terrestrial Animals
Spatial variations in total mercury concentration across the surface parcel layout are
presented for the white-tailed deer and leaf compartments in Exhibit 3-44. The leaf compartment
concentrations are included in this exhibit to help illustrate the relationship between the white-
tailed deer and its diet in this scenario (i.e., 100 percent leaves and particles on leaves19). In
general, concentrations of total mercury in the white-tailed deer are closely related to the leaf
compartment concentrations. The highest concentrations in white-tailed deer compartments are
found with parcels that are assigned coniferous forest vegetation, and the lowest concentrations
are found with parcels that are assigned deciduous forest and grasses/herbs vegetation. This
pattern is likely due in part to the way the diets of herbivorous animals are modeled in this
application of TRIM.FaTE. For deciduous and grasses/herbs vegetation types, there is no
1 8
For leaf compartments representing deciduous forest and grasses/herbs vegetation types, annual average
concentrations presented in this section were calculated based on modeling results for the entire year (i.e., including
zeros for time periods when leaves are absent).
19 In this scenario, the diets of white-tailed deer were comprised entirely of leaves and particles on leaves,
regardless of the type of vegetation present in each surface parcel. As a result, white-tailed deer in parcels assigned
coniferous forest vegetation were assumed to consume coniferous leaves, which may not be entirely representative
of their actual diets.
JULY 2005 3 -62 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-43
Spatial Variations in Total Mercury Concentrations: Leaf Compartments3
Annual Average Concentrations for 30th Year
Leaf
Total Mercury (g/lcg wet weight)
CH
Greater than 9.06E-Q7
3.63E-07 - 9.06E-07
1.456-07 - 3-$3E-07
5.83E-08-1.45E-07
Less than 5.B3E-08
Watershed Regions I I Parcel Boundary
Con - Coniferous Dec - Deciduous GH - Grasses/Herbs
N
A
1
aFor all leaf compartments, annual average concentrations were calculated based on modeling results for the entire
year (i.e., including, for deciduous and grasses/herbs vegetation types, zeros for time periods when leaves are
absent).
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Exhibit 3-44
Spatial Variations in Total Mercury Concentrations: White-tailed Deer and Leaf
Compartments" b
Annual Average Concentrations for 30th Year
Total Mercury
White-tailed Deer
(g^g wet wight)
Leaf (g/kg wet weight)
I Greater than 1.75E-06 I • Greater than 9.06E-07
| j 3.63E-07 - g.0^-07
5.67E-07 - 1.756-06 CZl '-«E-07 - 3.63E-07
£53 5.836-08 -1.45E -07
V 183E-°7-567E-°7 "
0 5.92E-08-1,836-07
Lass than 5.92E-OB
Watershed Regions | | Parcel Boundary
Con - Coniferous Dec • Deciduous GH • Grasses/Herbs
Kilometers
aFor all leaf compartments, annual average concentrations were calculated based on modeling results for the entire
year (i.e., including, for deciduous and grasses/herbs vegetation types, zeros for time periods when leaves are
absent).
b White-tailed deer not modeled in volume elements Nl or Wl because these areas are considered too developed.
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chemical mass in the leaf and particle-on-leaf compartments during the non-growing season
because the leaves are assumed to have fallen from the plants during the once-a-year litter fall
event. Conversely, the leaf and particle-on-leaf compartments for parcels assigned coniferous
vegetation remain on the plants throughout the simulation and therefore contain chemical mass
at all times. Because the composition of the diets of herbivorous animals is assumed to be
constant for the entire simulation, the white-tailed deer in parcels assigned deciduous and
grasses/herbs vegetation do not consume chemical mass during the non-growing season,
resulting in lower concentrations than white-tailed deer in a comparable locations assigned
coniferous vegetation. In future applications, the diets of herbivorous animals assigned to
parcels with vegetation that undergo a single litter fall event per year (e.g., deciduous and
grasses/herbs vegetation) may be refined to better reflect the change in the diets of these animals
during the non-growing season.
Semi-aquatic Animals
Exhibit 3-45 presents the variation across the surface parcel layout in total mercury
concentration for the raccoon, surface soil, and benthic invertebrate compartments. Benthic
invertebrate concentrations are included because benthic invertebrates comprise nearly 70
percent of the raccoon's diet in this scenario. Surface soil concentrations are included because
the concentrations in the earthworm, the second largest component of the raccoon's diet (21
percent), show correlation with soil concentrations (see Appendix Chart B-2). The spatial
pattern of total mercury concentrations in raccoons shows a correlation with the patterns for
surface soil and benthic invertebrates. In parcels closer to the source, there appears to be a
stronger correlation with soil concentrations, consistent with the modeling result that raccoons in
these parcels obtain the majority of their chemical mass from soil or biota associated with the
soil. For example, the highest raccoon concentrations occur in the parcels El and SE1, which
are both adjacent to the source parcel and have the highest soil concentrations among the parcels
containing raccoons. Raccoons in these parcels ingesf benthic invertebrates from the river,
which has the lowest benthic invertebrate concentrations among the water bodies. Conversely,
there appears to be a stronger correlation with benthic invertebrate concentrations in parcels
further from the source, consistent with the modeling result that raccoons in these parcels obtain
the majority of their chemical mass from benthic invertebrates. For example, the third highest
raccoon concentration occurs in parcel SSE4, which has some of the lowest soil concentrations
in the modeling region, and is adjacent to Swetts Pond (where SSE4 raccoons obtain the aquatic
portion of their diet), which has the highest benthic invertebrate concentrations.
Exhibit 3-46 presents the variation across the surface parcel layout in total mercury
concentration for the common loon and surface water compartments. The surface water
compartment concentrations are included in this figure to help illustrate their relationship with
the common loon compartments. In general, the common loon concentrations appear to be
closely related to the surface water concentrations. When ordered from highest total mercury
concentrations to lowest, the ranking of compartments for common loon compartments is similar
to the ranking for surface water compartments. The only difference in the rankings is that the
common loon concentrations are slightly higher in Brewer Lake than Thurston Pond, whereas
the surface water concentrations are slightly higher in Thurston Pond than Brewer Lake. These
results are reasonable considering that the loon's diet consists entirely of water-column and
benthic omnivores (each is 50 percent), water-column omnivores typically have higher total
JULY 2005 3 -65 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-45
Spatial Variations in Total Mercury Concentrations: Raccoon, Surface Soil, and
Benthic Invertebrate Compartments"
Annual Average Concentrations for 30th Year
Total Mercury
Raccoon Benthc Invertebrate Surface Soil
Average Concentration Average Concentration Average Concentration
Year 30 (gAg wet weight) Year 30 (gfltg wet weight) Year 30 (g/g dry weight)
Greater than 7.72E-08
4.WE-08 - 7.72E-C8
2.92E-08 - 4.04E-08
IT
1
Less than 2.92E-Q8
!."..". Watershed Regions I I Parcel Boundary
1 0 1
Greater than 2.S2E-08
1.26E-08-2.S2E-08
5.59E-09-1.26E-08
Less than 5.59E-09
A
Kilometers
a Raccoons not included in volume elements Nl or Wl because these areas are considered too developed, or in
volume elements W2, ESE2, or NE3 because these areas do not border any surface water bodies containing fish.
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Exhibit 3-46
Spatial Variations in Total Mercury Concentrations: Common Loon and Surface Water
Compartments
Reids
Surface water: 6.0E-11 g/L
Common Loon: 2.0E-07 g/kg wet weight
Brewer
Surface water: 4.3E-11 g/L
Common Loon: 1.8E-07 g/kg wet weight
Swetts
Surface water 9.8E-11 g/L
Common Loon: 3.4E-07 g/kg wet weight
Thurstgn
Surface water: 5.0E-11 g/L
Common Loon: 1.7E-07 g/kg wet weight
Annual Average Concentrations for 30th Year
Total Mercury
[."..". Watershed Regions I I Parcel Boundary
N
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mercury concentrations than benthic omnivores in this scenario, and water-column omnivore
concentrations are higher in Brewer Lake than Thurston Pond.
Water-column Fish
The spatial variation in methyl mercury concentrations across all three water-column fish
compartment types, as well as the variation of total mercury in surface water compartments, is
presented in Exhibit 3-47. Because methyl mercury is preferentially accumulated up the water-
column fish food chain, the concentrations in the fish compartments are presented as methyl
mercury instead of total mercury. This figure uses a slightly different format from the previous
figures in that it presents the results for all three fish compartments for each surface water
volume element as bar charts (see Appendix Table B-10 for actual values for Swetts Pond and
Brewer Lake). These bars show the concentrations in each fish compartment relative to the other
compartments of the same type in different volume elements, as well as to the other fish
compartment types in the same volume element. The surface water compartment concentrations
are included in this exhibit to illustrate the relationships between surface water and water-
column fish concentrations.
Overall, the fish concentrations are closely related to the surface water concentrations.
When each water body is ranked relative to water-column fish and surface water concentrations,
the resulting order is similar. The highest and second highest surface water concentrations of
total mercury occur in the same water bodies (Swetts Pond and Fields Pond, respectively) as the
highest fish concentrations of methyl mercury. The only difference between the rank orders is
that the water-column fish concentrations of methyl mercury are higher in Brewer Lake than
Thurston Pond, whereas the surface water concentrations of total mercury are higher in Thurston
Pond than Brewer Lake. This difference appears to be related to differences in mercury
speciation between the water bodies; specifically, total mercury in Brewer Lake in comprised of
a slightly higher percentage of methyl mercury than total mercury in Thurston Pond. Because
water-column fish accumulate methyl mercury more rapidly than divalent mercury, this
difference in speciation is magnified up the food chain.
The accumulation of methyl mercury with increasing trophic level can be illustrated by
the use of food chain multipliers. These ratios show the increase in methyl mercury
concentrations across each step of the food chain and are calculated by dividing the
concentration in a higher trophic-level organism by the concentration in the next lower trophic-
level organism. Exhibit 3-48 presents methyl mercury food chain multiplier values for the
water-column fish in the four water bodies. These values were calculated based on annual
average concentration for year 30 for the water-column carnivore to water-column omnivore
relationship (WCC:WCO) and for the water-column omnivore to water-column herbivore
relationship (WCO:WCH). This exhibit illustrates a similar pattern of methyl mercury
bioaccumulation across fish trophic levels for the different water bodies. The spatial consistency
in these ratios is related to similarities in the characterization of the fish compartments among
the various water bodies, as well as to similarities in the predation on these compartments by
semi-aquatic animals (e.g., the amount of water-column omnivores consumed by raccoons from
a particular water body is relative to the total biomass of water-column omnivores in that water
body).
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Exhibit 3-47
Spatial Variations in Mercury Concentrations: Water-column Fish and Surface Water
Compartments
Water Column Fish
Methyl Mercury
(g/kg wet weight)
Carnivore
Annual Average Concentrations for 30th Year
Surface Water
Total Mercury
(g/L dry weight)
^B 9.8E-11 (Swells)
I | 6.0E-11 (Fields)
| | 5-OE-11 (Thurston)
34.3E-11 (Brewer)
I | Parcel Boundary
A
Kilometers
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Exhibit 3-48
Water-column Food Chain Multipliers for Methyl Mercury
(Based on Average Concentrations for Year 30)
Water Body
Brewer Lake
Fields Pond
Swetts Pond
Thurston Pond
Average
Std. Deviation
WCCrWCO
5.4
5.1
5.1
5.1
5.2
0.1
WCOrWCH
2.3
2.1
2.2
2.2
2.2
0.1
Benthic Animals
The spatial variation in methyl mercury concentrations across all three benthic animal
compartment types, as well as the spatial variation in total mercury concentration across
sediment compartments, is presented in Exhibit 3-49. Because methyl mercury is preferentially
accumulated up the benthic animal food chain, the concentrations in the benthic animal
compartments are presented as methyl mercury instead of total mercury. As with the
presentation for water-column fish in the previous section, a combination of bar charts and
shading are employed to present the concentration results for the benthic animal compartments
and the sediment compartment.
Consistent with the modeling approach used in which the sediment compartment is the
source of chemical mass to the benthic food chain compartments, the benthic animal
concentrations are closely related to the sediment concentrations. When each water body is
ranked relative to benthic animal and sediment concentrations, the resulting order is identical.
That is, the water body (Swetts Pond) with the highest sediment total mercury concentration also
has the highest benthic carnivore, omnivore, and invertebrate concentrations of methyl mercury,
and so forth.
Food chain multiplier values were calculated for the benthic food chain, as they were for
the water-column food chain. Exhibit 3-50 presents food chain multipliers for the benthic
carnivore to benthic omnivore relationship (BC:BO) and for the benthic omnivore to benthic
invertebrate relationship (BO:BI). As with the water-column food chain multipliers, this exhibit
illustrates a similar pattern of methyl mercury bioaccumulation across fish trophic levels for the
different water bodies. As stated in the previous section, the spatial consistency in these ratios is
related to similarities in the characterization of the fish compartments among the various water
bodies, as well as to similarities in the predation on these compartments by semi-aquatic animals
(e.g., the amount of benthic omnivores consumed by raccoons from a particular water body is
relative to the total biomass of benthic omnivores in that water body).
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Exhibit 3-49
Spatial Variations in Mercury Concentrations: Benthic Animal and Sediment
Concentrations
Annual Average Concentrations for 30th Year
Sediment
Total Mercury
(gig dry weight)
^H 2.8E-09 (Swells)
| [ 1.7E-09 (Fields)
j 1.5E-09 (Thurston)
il 1.2E-09 (Brewer)
| | Parcel Boundary
Benthic Animals
Methyl Mercury
(g/kg wet weight)
A
Kilometers
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Exhibit 3-50
Benthic Food Chain Multipliers for Methyl Mercury
(Based on Average Concentrations for Year 30)
Water Body
Brewer Lake
Fields Pond
Swetts Pond
Thurston Pond
Average
Std. Deviation
BC:BO
5.2
4.9
4.9
5.0
5.0
0.1
BO:BI
3.3
3.0
3.2
3.2
3.2
0.1
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3.5 Comparison of Emission Cases
This section compares the dynamic modeling results for the different emission cases:
Case A - only Hg2+ emitted (17.663 g/day), with no initial media or biota concentrations
of mercury and no boundary contributions;
• Case B - both Hg2+ and Hg° emitted (17.663 and 335.6 g/day, respectively), with no
initial media or biota concentrations of mercury and no boundary contributions (results
for this case are the focus of the other sections of Chapter 3 and of Chapters 4 and 5); and
• Case C - both Hg2+ and Hg° emitted (17.663 and 335.6 g/day, respectively), with initial
media and biota concentrations of mercury and boundary contributions of mercury in air.
First, case A is compared with case B to examine the incremental effect that including elemental
mercury air emissions has on multimedia concentrations of various mercury species. Then, case
B is compared with case C to examine the impact of including contributions of "background"
(i.e., not from the test case plant emissions) mercury on the modeled multimedia concentrations.
Most comparisons in this section are based on annual average concentrations for individual
compartments for year 30, although some time series charts are presented as well.
3.5.1 Emission Case A vs. Emission Case B
Case A was included in the test case mainly to serve as the basis for comparisons of
TRIM.FaTE results with 3MRA results (see Chapter 6). However, case A also was compared
with case B - which is the primary case analyzed in this report - to assess whether elemental
mercury emitted to air along with divalent mercury produces substantially different multimedia
modeling results local to the source (i.e., within 10 miles) than divalent mercury emitted alone.
Because divalent mercury deposits from air to soil and surface water at a much faster rate than
elemental mercury (i.e., over a given distance, a larger fraction of divalent mercury in air will
deposit than elemental mercury in air), and because both cases have the exact same emissions of
divalent mercury, major differences between the cases were not expected for most non-air
compartments. (See Section 3.2 for comparison of modeled deposition fluxes for divalent and
elemental mercury.)
Air Compartments
Of all the TRIM.FaTE compartment types modeled, air has by far the largest differences
in total mercury concentrations between case A and case B. Divalent mercury concentrations in
air for case A and case B are very similar, which makes sense given that the same emission rate
was used for divalent mercury and no rapid transformations in air were modeled (oxidation of
elemental mercury to divalent mercury was modeled at a relatively slow rate, which has limited
effect over the distances modeled). Thus, the differences in case B total mercury concentrations
are a direct result of the additional elemental mercury emitted from the source. Note that the
ratio of total mercury emissions mass for case B to case A is 20.
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Case B air concentrations of total mercury are higher than case A concentrations by a
range of 21 times (for the source compartment) to 35 times (for air compartments ESE4 and
ESE5). The ratio is lowest at the source compartment and increases with distance from the
source, with a small dropoff at the edges of the modeling region. It is suspected that this spatial
pattern results because divalent mercury deposits from air to land and surface water much more
rapidly than elemental mercury, which tends to stay in the air over the modeling distances used
in this test case. Thus, divalent mercury concentrations in air tend to decrease faster with
distance from the source than elemental mercury, which results in case B:case A air
concentration ratios for total mercury getting higher as the distance from the source increases
(i.e., total mercury concentration in air drops off more slowly with distance in case B (mostly
elemental) than in case A (mostly divalent)). The reason that the ratio decreases in the edge air
compartments is that these compartments are not fully underlain by surface compartments, which
are the main source of elemental mercury in air for case A - thus, this result is an artifact of the
compartment layout.
Case B:case A air concentration ratios for divalent mercury are very close to 1.0 (always
greater) throughout the modeling region, though they do increase very minimally with distance
from the source (highest ratio is 1.01 for air compartment ESE5), possibly as a result of the slow
oxidation of the elemental mercury emitted to air in case B to divalent mercury.
Compartments Other than Air
Key differences between case A and case B for other compartment types are summarized
in Exhibit 3-51. Out of 33 compartment types modeled in this test case, only seven (other than
air) have any appreciable differences for total mercury, and only three for divalent mercury. For
all compartment types other than air and those shown in Exhibit 3-51, there is less than 10
percent difference between case A and case B in year 30 average concentrations for total
mercury and for divalent mercury in any compartment modeled (i.e., at any location). Thus, for
those compartment types, the emission of elemental mercury to air has minimal impact (relative
to concurrent emission of divalent mercury at five percent of the total) on long-term modeled
concentrations of either divalent or total mercury at locations near the source.
Exhibit 3-52 shows the total mercury concentrations for one of the differing compartment
types, root zone soil, at one location over the full 30-year modeling period, illustrating the time
series results for the two cases. The differences in both magnitude and time pattern of the results
are attributable to the effects of the emitted elemental mercury in case B, which is considerably
more mobile in soils than divalent mercury. (Other total mercury time series charts are not
presented here for case A, given the similarity of most of them to the charts presented for case B
(see Section 3.2 and Appendix B.2). Some additional time series charts for case A are shown in
Chapter 6.)
The explanation of the case A versus case B results appears to be reasonably
straightforward for the various soil layers, which are the compartment types (other than air)
where case B has the greatest relative impact. The spatial patterns of the case B to case A
relationship for all soil layers follow the pattern for air (i.e., higher ratios farther from the
source), and for a related reason to that explained above for air. Similar to the air concentrations,
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Exhibit 3-51
Case BrCase A Ratios for Compartment Types Other than Aira
Compartment
Type
Root zone soil
Vadose zone
soil
Ground water
Surface water
Macrophyte
Earthworm
Tree swallow
Total Hg
Range of Case B:
Case A Ratios'5
1.3-2.2
2.1-5.9
2.5-8.0
1.0-2.1
1.0-3.6
1.4-2.2
1.0- 1.9
Hg2+
Range of Case B:
Case A Ratios'5
All -1.0
All -1.0
2.8-9.5
All -1.0
1.0-3.8
All- 1.0
1.0-1.8
Comments
Source always lowest, ratio increases
with distance (follows air pattern)
Source always lowest, ratio increases
with distance (follows air pattern)
Source always lowest, ratio increases
with distance (follows air pattern)
Only river >1 .0 for total Hg
Only river >1 .0 for total Hg and Hg2+;
total Hg ratio tracks Hg2+ closely (total
Hg -90% Hg2+)
Earthworm matches root zone soil
exactly (except source compartment,
with 1.3 ratio for root zone soil, was not
modeled for earthworm)
Total Hg ratio tracks Hg2+ closely (total
Hg~75%Hg2+)
a Only compartment types with at least a 10 percent difference in at least one compartment are shown.
b All ratios based on annual average concentrations for individual compartments for year 30.
the air deposition of elemental mercury drops off more slowly with distance than the air
deposition of divalent mercury. Thus, the contribution of airborne elemental mercury to soil in
case B drops off more slowly with distance than the contribution of airborne divalent mercury to
soil in both cases, and for the deeper soil layers elemental mercury is an important contributor to
total mercury. As a result, the case B:case A total (and elemental) mercury ratios are higher for
compartments more distant from the source.
As shown in Exhibit 3-51, the impact of the elemental mercury emitted in case B on total
mercury concentrations gets larger as the soil layers get deeper, with negligible percent
difference for surface soil (not shown in Exhibit 3-51, see Exhibit 3-53 for an example) and
highest percent difference for ground water. A small amount of the elemental mercury in air in
case B deposits to the surface soil, where it quickly either re-volatilizes to air or, because it is
mobile in soils, infiltrates to deeper soil levels. The small additional amount of elemental
mercury in surface soil is swamped by the much greater level of divalent mercury, and thus there
is negligible effect on total mercury in surface soil. Conversely, as a portion of the elemental
mercury moves downward through the soil it has much greater relative impact because divalent
mercury levels drop off so quickly with depth in soil (divalent mercury is relatively immobile in
soils) (see also the speciation results for soil layers in Section 3.3). Exhibit 3-53 illustrates these
spatial and vertical patterns with a detailed example of the soil concentration results for two
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o
o
O
c
c
specific compartment locations, one at the edge of the modeling region (E4) and one adjacent to
the source (El). For both locations, the ratios for total mercury match divalent mercury for
surface soil, they match elemental mercury for vadose zone soil and ground water, and they are
intermediate for root zone soil (i.e., both divalent and elemental mercury are important in root
zone soil).
Exhibit 3-52
Case A vs. Case B: Time Series of Total Mercury Concentrations, Root Zone Soil in SW2
3.5E-12
3E-12
f
I" 2.5E-12
•51
o
^ 2E-12 -
1.5E-12 -
1E-12 -
5E-13 -
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
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Exhibit 3-53
Detailed Example of Soil Concentration Results for Case A vs. Case Ba
Compartment
Type/Location
Surface soil/E4
Root zone soil/
E4
Vadose zone
soil/E4
Ground water/
E4
Surface soil/El
Root zone soil/
El
Vadose zone
soil/ El
Ground water/
El
Elemental Mercury
Cone B/
Cone A
8.8E-13/
2.3E-13
6.9E-13/
1.9E-13
4.5E-15/
7.6E-16
3.0E-18/
3.7E-19
8.4E-12/
3.8E-12
7.0E-12/
3.0E-12
4.1E-14/
1.2E-14
2.6E-17/
6.2E-18
Ratio
3.8
3.6
5.9
8.1
2.2
2.3
3.4
4.2
Divalent Mercury
Cone B/
Cone A
2.4E-9/
2.4E-9
2.2E-13/
2.2E-13
5.6E-20/
5.5E-20
8.6E-23/
9.1E-24
4.0E-8/
4.0E-8
3.6E-12/
3.6E-12
9.2E-18/
9.2E-18
7.4E-22/
1.5E-22
Ratio
1.0
1.0
1.0
9.5
1.0
1.0
1.0
4.9
Total Mercury
Cone B/
Cone A
2.5E-9/
2.5E-9
9.1E-13/
4.1E-13
4.5E-15/
7.6E-16
3.0E-18/
3.7E-19
4.0E-8/
4.0E-8
1.1E-11/
6.7E-12
4.1E-14/
1.2E-14
2.6E-17/
6.2E-18
Ratio
1.0
2.2
5.9
8.1
1.0
1.6
3.4
4.2
a All concentrations are annual average values for year 30, in g/g dry wt.
The apparently anomalous result that case B has considerably higher divalent mercury in
ground water than case A, while there is negligible difference in divalent mercury concentrations
in the other soil layers, possibly results from a simplification in the way ground water is modeled
in TRIM.FaTE. (Note: Because of its focus on air pollutants of priority concern for multimedia
exposures, which in general are highly bioaccumulative and relatively immobile in soil systems,
the TRIM.FaTE library includes relatively simple ground water transfer algorithms.) Certain
ground water compartments are not linked to any other compartment and behave essentially as a
partial sink - any chemical mass that reaches those compartments stays there (subject to
transformation or degradation, as applicable) throughout the modeling period. Because there is a
very slow transformation rate of elemental to divalent mercury in ground water, some of the
additional elemental mercury that reaches ground water in case B gets transformed to divalent
mercury over the 30-year modeling period. Even though this is an extremely small amount, the
amount of divalent mercury that reaches ground water via downward advection from surface soil
(i.e., the amount in case A) is so low that even a very small amount of transformed elemental
mercury may possibly make a difference.
With respect to other compartment types, earthworm tracks root zone soil almost exactly
(i.e., the case B:case A ratios for every compartment location are virtually identical), which
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reflects the rapid partitioning approach used to model earthworm accumulation of mercury. The
additional elemental mercury in root zone soil in case B appears to be directly responsible for the
additional elemental mercury observed in earthworms in case B.
For surface water and macrophytes (which accumulate mercury via partitioning with
surface water), the additional elemental mercury emitted in case B has a sizable impact on total
mercury concentration for the river compartment, but only a small impact (<5 percent increase)
on the pond compartments. Primarily because it is closer to the source, the contribution of
elemental mercury via air deposition is more important (relative to other inputs of mercury mass)
for total mercury in the river compartment than in the other surface water compartments; thus,
the difference between case B and case A is larger for the river. For surface water the total
mercury increase in case B appears to result directly from higher elemental mercury levels
deposited from air. In the case of macrophytes, the higher total mercury in case B appears to be
because elemental mercury is transferred to macrophytes via partitioning from surface water and
rapidly transformed to divalent mercury, which is then accumulated.
The results for the tree swallow compartment type are more complicated. The only case
B:case A total mercury ratios greater than 1.1 for tree swallows are for compartments where the
food source (tree swallow diet modeled as 100 percent benthic invertebrates, which represent
emerging benthic insects) is the river. However, the benthic invertebrate results do not vary
much at all in case B versus case A, so the food source does not appear to fully explain the
variation in tree swallow results. The compartments for which the tree swallow food source is
the river also are closer to the emission source than those compartments where the food source is
one of the ponds. Detailed examination of the mass flux results for individual compartments in
case B indicates that inhalation of elemental mercury can be an important source of total mercury
for tree swallows for certain compartments, depending on their proximity to the source and the
contribution of mercury from their food source. For compartments more distant from the source,
inhalation is strongly dominated by ingestion of benthic invertebrates. Thus, the much higher
elemental mercury levels in air in case B have a notable impact on total mercury in tree swallows
near the source (where airborne elemental mercury is highest), with much less impact as
elemental mercury disperses with distance. Further complicating these relationships is the
simulation of a fairly rapid transformation of elemental mercury in tree swallows to divalent
mercury, which explains the similar spatial relationship seen for divalent mercury as for total
mercury.
3.5.2 Emission Case B vs. Emission Case C
Case C was included in the test case modeling runs mainly to serve as the basis for
comparisons of TRIM.FaTE results with available measurement data for mercury for the
modeling region (see Chapter 7). However, case C also was compared with case B - which is
the primary case analyzed in this report - to assess the impact of the modeled "background" (i.e.,
not from the test case facility emissions) mercury in air relative to mercury emitted from the test
case facility.
In viewing the mercury concentration results for case C relative to case B, it is helpful to
know the differences in the mercury inputs used for the two cases. Exhibit 3-54 summarizes the
mercury mass inputs over 30 years for case B and case C. The two cases have identical inputs
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from the industrial facility being modeled (same amounts of mercury, same forms, same time
pattern of emissions). However, case C also has a relatively small amount of mercury present in
environmental media and biota at the start of the modeling period (i.e., historical "background"
contamination of the modeling region that originates from contaminated air flowing into the
region prior to modeling) and a relatively large input of mercury over the 30-year modeling
duration from air boundary contributions (i.e., concurrent "background" contamination from
contaminated air flowing into the region during modeling). In fact, roughly 24 times more total
mercury (4.7 times more divalent mercury) enters the modeling system from air boundary
contributions than from the test case facility air emissions. Just like the mass emitted from the
test case facility, the vast majority of the input mass originating in boundary contributions is
elemental mercury that ends up in air sinks (i.e., leaves the modeling region before deposition
and uptake into the ecosystem). Note that, based on the available modeling results, it generally
is not possible to differentiate the relative impacts of the initial concentrations portion of
"background" from the boundary contributions portion (e.g., how much of the mercury
concentration in a given compartment results from which portion).
Exhibit 3-54
Summary of Mercury Mass Inputs in Cases B and C (over 30 years)
Source of Mass
Test case facility air emissions
Initial mass present in media/biota
Air boundary contributions
Total mass inputs
Case B (kg)
Total
3,871a
0
0
3,871a
Divalent
194
0
0
194
Case C (kg)
Total
3,871a
41b
91,674C
95,586d
Divalent
194
39
907
1,140
Assumed to be 95 percent Kg and 5 percent Kg .
b Roughly 96 percent Hg2+, 3 percent Hg°, and 1 percent MHg.
c Assumed to be 99 percent Hg° and 1 percent Hg2+.
d Roughly 99 percent Hg° and 1 percent Hg2+.
In addition to the differences in mercury mass inputs for the two cases, the boundary
contributions in case C are a different kind of source than the test case facility. The test case
facility is modeled as a point/area source at a fixed location within the modeling compartment
grid. The air boundary contributions, in contrast, are modeled as a volume source that moves
when the wind shifts direction and that over the course of time entirely surrounds the modeling
region boundary (i.e., wind-blown mercury in air enters the region from all directions over time,
changing with the wind direction). Thus, most of the mercury mass in case C enters into the
eight air compartments at the edges of the modeling region, while all of the mercury mass in case
B enters into the air source compartment near the center.
Many of the patterns seen in comparing case C with case B result from these two key
differences in the mercury mass inputs. As expected, the 30-year annual average concentrations
are always higher in case C than in case B (case C can be viewed simply as additional mercury
mass inputs overlaid on case B). The amount of differences and percent differences vary by
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compartment type, compartment location, and mercury species. Among all 417 compartments,
the median case C:case B concentration ratio for total mercury at year 30 is 8.1 (i.e., for more
than half the compartments modeled, the total mercury concentration difference is less than a
factor of 10), the 75th percentile is 18, and the 90th percentile is 29. For roughly 79 percent of the
compartments modeled, the case C total mercury concentration is within a factor of 20.
Exhibit 3-55 shows the range of case C:case B ratios (i.e., relative differences) for all
compartment types modeled except meadow vole, which was only modeled as present in two
compartments (and therefore the range is not comparable). In terms of percent increase, the
highest impacts of the case C "background" contributions are seen in two groups:
• The root zone and vadose zone soil and ground water compartment types, plus the soil
invertebrates (which partition mercury from root zone soil); and
Sediment, benthic invertebrates/fish, and tree swallow (diet is 100 percent benthic
invertebrates).
Case C produces the smallest increases in the terrestrial plant, terrestrial mammal and
bird, and mallard compartments (terrestrial plants are a key part of the food chain for mallards
and the terrestrial mammals and birds), with air, surface water, macrophytes, water-column fish,
and most semi-aquatic animals in the middle. Many of the same compartment type relationships
are seen here as evident in other analyses in this chapter (e.g., very close tracking of results for
surface water/macrophyte, root zone soil/earthworm, tree swallow/benthic omnivore,
sediment/benthic invertebrate, weasel/hawk).
There is much less spatial variability in mercury concentrations in land-based
compartment types in the case C results than in the case B results. Spatial variability is damped
in case C because most of the mass inputs come fairly evenly from all directions around the
modeling region boundary, rather than 100 percent from one central source as in case B. For
example, the maximum:minimum concentration ratios for total mercury for selected
compartment types are as follows:
• Air - 13 for C, 236 for B (1.8 for C, 12 for B excluding source compartment);
• Root zone soil - 21 for C, 510 for B (2.1 for C, 16 for B excluding source compartment);
Mouse - 33 for C, 219 for B;
Short-tailed shrew - 2.1 for C, 16 for B; and
White-tailed deer - 3 6 for C, 23 3 for B.
A similar result - less overall spatial variability in total mercury concentrations within a
compartment type for case C than case B - is seen for compartment types in the four ponds that
were modeled. In relative terms, case C has much lower effect on the river compartments than
on those in any of the ponds, probably at least in part due to its location relative to the edge of
the modeling region.
JULY 2005 3 -80 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 3-55
Case B vs. Case C: Range of Differences in Total Mercury Concentration
by Compartment Type a
Land-based
Compartment Type b
Air (30)
Surface soil (20)
Root zone soil (20)
Vadose zone soil (20)
Ground water (20)
Leaf (19)
Particle-on-leaf (19)
Root (4)
Stem (4)
Arthropod (17)
Earthworm (19)
Black-capped
chickadee (17)
Mouse (17)
White-tailed deer (17)
Short-tailed shrew
(17)
Red-tailed hawk (17)
Long-tailed weasel
(17)
Raccoon (15)
Bald eagle (17)
Mink (15)
Tree swallow (17)
Lowest
C:B ratio
1.1
1.0
1.0
1.1
1.3
1.6 conif
1.6 conif
3.2
1.4
2.8
3.1
1.6
1.6
1.6
1.9
1.6
1.7
2.0
1.7
1.6
3.4
Highest
C:B ratio
24
22
34
57
120
8.0 conif
8.0 conif
10
3.9
43
34
13
14
13.
22
13
13
27
21
16
31
Water-based
Compartment Type b
Surface water (7)
Sediment (7)
Macrophyte (5)
Water-column
herbivore (5)
Water-column
omnivore (5)
Water-column
carnivore (5)
Benthic invertebrate
(5)
Benthic omnivore (5)
Benthic carnivore (5)
Mallard (5)
Common loon (5)
Lowest
C:B ratio
5.0
3.4
5.7
3.3
3.4
3.5
3.4
3.4
3.4
1.6
3.5
Highest
C:B ratio
24
37
21
20
19
19
31
31
32
4.1
21
a All ratios based on annual average concentrations for individual compartments for year 30.
b Number of compartments modeled for each type shown in parentheses.
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When viewed on a percent difference basis, all of the air, land, and land-based biota
compartment types have the greatest percent increase at the edges of the modeling grid -
compartment locations SE6, SSE5, and ESE5 have the highest case C:case B ratios. Likewise,
the lowest percent increase is always seen at the center of the grid, either the source
compartment or (for compartment types not modeled as present in the source compartment)
compartment locations SE1, El, or Nl. This pattern results from a combination of two factors:
(1) case B concentrations for these compartment types are usually higher near the source and
decline with distance (toward the edges); and (2) the mass contributions across the boundaries in
case C produce relatively low spatial variation because they enter the system from all directions
over time (as the wind direction changes) and not from a single fixed location. Thus, the roughly
similar increases in mass from case C produce higher percent increases in concentration at the
edges (where case B is lower) than at the center (where case B is higher).
For the water-based compartment types, Thurston Pond always has the highest case
C:case B ratio and the river always has the lowest. The spatial pattern for water-based
compartments is suspected to be at least partly due to the same factors playing a role in the land-
based compartments, as described above - proximity to the edge of the modeling region is
associated with higher ratios.
As noted in Chapter 2, the case C modeling duration is 40 years, including 10 years
following the shutoff of source emissions at the end of year 30. Exhibits 3-56 through 3-58
illustrate for case C the time series patterns of total mercury concentration in selected
compartment types over the 40 years (compare with Exhibits 3-10, 3-11, and 3-13 for case B).
The basic time patterns - spiking versus smooth, increasing versus flat - for the various
compartment types in case C are similar to case B, with case C always higher in concentration.
The order of the compartment types on a chart, from high to low concentration, also is the same
for the two emission cases. There are, however, some differences apparent from examination of
the charts.
• In case C, some compartment types (e.g., air, leaf, particle on leaf, stem, terrestrial and
some semi-aquatic animals) show a concentration drop after the halt of emissions from
the source at year 30. These compartment types are the ones that have a non-increasing
pattern over the first 30 years. The dropoff is not dramatic, indicating that the boundary
contributions (which continue after year 30) are more important quantitatively than the
source emissions for the compartment locations shown. The size of the dropoff would be
expected to vary depending on a compartment's position relative to the source and the
modeling region boundary. Note that soil, surface water, sediment, and related biotic
compartments, which have increasing concentrations before year 30, continue to rise
after year 30, although in some cases less rapidly than before.
Compartment types with smoothly increasing plots (e.g., soil, sediment) tend to increase
more slowly in case C (i.e., look flatter on the charts), reflecting the fact that case C starts
at an initial concentration based on 30 years of air boundary ("background")
contributions rather than at zero. Thus, the relatively rapid increases in early years in
case B are not seen in case C. For example, the total mercury concentration in root
increases about three and a half orders of magnitude in 30 years in case B, but less than
one order of magnitude in 40 years in case C.
JULY 2005 3 -82 TRIM.FATE EVALUATION REPORT VOLUME II
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Compartment types with spiking in the time pattern of annual average concentrations
show less spiking in case C, for a similar reason to that explaining the lower spatial
variability. Temporal variability is damped in case C because most of the mass inputs
come more evenly over time from the boundary contributions, rather than from a single
central source. No matter what the wind direction, there is always some boundary
contribution to a given compartment in case C. This is not true for the source
contribution, which is dependent on wind direction and thus more time variable.
Note that in the case B charts, the initial concentration, which is always zero, is not plotted. If it
were, there would be a steep increase in year 1 for all compartment types, which differs from
most of the case C plots, where there is not much change in year 1 from the initial concentration.
However, because deciduous leaves in TRIM.FaTE return to zero mercury concentration each
year after litter fall, animal compartment types that have deciduous leaves in their diet show a
large increase in year 1 in case C (because initial mercury concentrations of such animal
compartments are an instantaneous concentration on December 31, when deciduous leaf
concentrations equal zero, and are considerably lower than the annual average concentrations
that follow, which reflect both growing (leaf concentration greater than zero) and non-growing
(leaf concentration equals zero) seasons).
Exhibit 3-56 - Log Scale
Case C Total Mercury Concentration in Air, Soil, and Plants vs. Time:
SW2 (grasses/herbs)
1.0E-05
£
1
fr
1.0E-06 -
1.0E-07
o 1.0E-08
I
o
o
1.0E-09
1.0E-10
-e-e-e-e
1.0E-08
o
1
•Surface
Soil
-Leaf a
Particle on
Leaf a
——Root
-»- • Stem
1.0E-10
Initial 2 4
Cone
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Year
Each annual average data point shown for leaf and particle on leaf is the average of values during the days (May 13 to September 29 each year) for which leaves were modeled as
present (i.e., represents a growing season average). Initial concentration is set to zero (not plotted).
b Because of the differences in the air and surface parcel layouts, the boundaries of the SSW2 air parcel do not match those of the SW2 surface parcel (see Exhibits 2-1 and 2-2), but this
air parcel does have substantial overlap with the surface parcel (among air parcels, SSW2 has the most overlap with surface parcel SW2).
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Exhibit 3-57 - Log Scale
Case C Total Mercury Concentration in Surface Water and Related Biota vs. Time:
Swetts Pond
(a) Water-column and Related Biotic Compartments
1.0E-04
1.0E-05
£ I 1.0E-07
ro j_
£ a
o
v 1.0E-08
1.0E-09
1.0E-10
-Surface water
Initial 246
Cone
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Year
-Common Loon
Macrophyte
-Water-column
Herbivore
-Water-column
O m n ivo re
-Water-column
Carnivore
(b) Benthic and Related Biotic Compartments
1.0E-05
£1
1.0E-06
c e
a) S
1.0E-07
g
<
1.0E-08
Initial 2
Cone
10
12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Year
aResults shown for compartment SSE4, where semi-aquatic animals feed from Swetts Pond.
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Exhibit 3-58 - Log Scale
Case C Total Mercury Concentration in Terrestrial Animals vs. Time: SW2 (grasses/herbs)
1.0E-05
(a) Terrestrial Herbivore and Omnivore Compartments
3 Each annual average data point shown for leaf and particle on leaf is the average of values during the days (May 13 to September 29 each year) for
which leaves were modeled as present (i.e., represents a growing season average). Initial concentration is set to zero (not plotted).
(b) Terrestrial Carnivore (Weasel and Hawk) Compartments
1.0E-05
I1 1.0E-
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
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4. RESULTS AND DISCUSSION: STEADY-STATE MODELING
The TRIM.FaTE model can be run in two different modes: dynamic mode, which
estimates pollutant concentrations in each compartment over time at user-defined intervals; and
steady-state mode, which estimates the pollutant concentrations in each compartment at steady-
state (i.e., when the distribution of mass within the modeling system is no longer changing given
a constant source term or emission rate). This chapter presents the TRIM.FaTE steady-state
modeling results for the mercury test case. The main purpose of this chapter is to provide an
overview of the steady-state results and give a sense of how these steady-state results compare to
the results from the dynamic simulations. The steady-state mode is used as the basis for the
sensitivity analysis of TRIM.FaTE described in Chapter 5. For additional description of the
steady-state mode, refer to the TRIM.FaTE Technical Support Document (EPA 2002b,c) and
TRIM.FaTE User's Guide (EPA 2003b).
Section 4.1 describes how the TRIM.FaTE scenario used for the mercury test case was
configured to run in steady-state mode and highlights how this configuration differed from that
of the dynamic simulations. Section 4.2 provides an overview of the results from the steady-
state simulation, and Section 4.3 discusses these results in the context of the results from the
dynamic simulations.
4.1 Configuring a TRIM.FaTE Scenario for Steady-state Mode
To generate steady-state results using TRIM.FaTE, no model inputs can be assigned
time-varying values. Therefore, all time-varying inputs in a dynamic scenario must be replaced
with representative constant values to generate steady-state results for that scenario. In the
dynamic scenarios for the mercury test case, the following inputs were assigned time-varying
values:
Air temperature;
Wind speed;
• Wind direction;
• Mixing height;
• Precipitation rate;
• isDay (0 at night, 1 during the day);
• AllowExchange (0 during non-growing season, 1 during growing season);
Litter fall rate (for deciduous forest and grasses/herbs, user-specified rate during litter fall
and zero at all other times); and
River flush rate (i.e., flow) and current velocity.
In order to provide a sound basis of comparison with the results from the dynamic
simulations, constant values for these inputs were calculated with the objective that the resulting
steady-state conditions closely approximate the system modeled in the dynamic simulations.
Several of the constant values are simply arithmetic averages of the time-varying values. The
resulting constant values used in the steady-state analysis are provided in Exhibit 4-1. The
methodology used to calculate these values is provided in Appendix C.I.
JULY 2005 4-1 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 4-1
Constant Input Values for Time-varying Properties
Used in Steady-state Modeling
Input
Air temperature
Allow Exchange (for air-to-plant algs.)
AllowExchange (for other plant algs.)
River current velocity
River flush rate
Wind speed
is Day (for air-to-plant algs.)
is Day (for other plant algs.)
Litter fall rate
Precipitation rate
Mixing height
Steady-state Value
280 K
0.426
0.386
0.166m/s
531.24/yr
3.64m/s
0.552
0.609
0.013 /day
0.0041 m/day
887m
All of the algorithms used in the steady-state simulation were identical to those used in
the dynamic mode, with the exception of the algorithms that estimate air-to-air advective
transfers. Using a constant wind speed and direction with the air-to-air transfer algorithms that
were developed for the dynamic mode can result in a much different spatial distribution of
chemical mass than is estimated when wind speed and direction are allowed to vary. Therefore,
a new air-to-air advective transfer algorithm that does not require wind speed and direction was
developed for the steady-state mode. The new algorithm uses a constant transfer factor (first-
order rate constant, in units of "per day') for each direction across each air-to-air interface in the
modeling area. These steady-state transfers were estimated by averaging the hourly air-to-air
advective transfers (for each interface) calculated using the dynamic mode of TRTM.FaTE over
the five-year meteorological input data period.1 Because wind direction is only used in the air-
to-air advective transfer algorithm that was replaced, this input is not required to run
TRIM.FaTE in steady-state mode and thus is not included in Exhibit 4-1. (The wind speed,
however, is still used in algorithms for volatilization from surface water.) The resulting steady-
state transfer factors for the air-to-air advection algorithm are provided in Appendix C. 1 along
with additional details on the development of the steady-state algorithms.
1 Simple averaging (arithmetic mean) of transfers across each interface was used in the mercury test case.
Other approaches to developing the "most representative" steady-state transfers may be investigated in the future.
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The next step in configuring TRIM.FaTE to run the mercury test case in steady-state
mode was to set up the scenario. This process was identical to that used to set up a dynamic
scenario, with the following exceptions.
The steady-state scenario used constant values for time-varying inputs (as described
above).
• The steady-state scenario used the air-to-air advection algorithm developed for steady-
state simulations (as described above).
• All outgoing links from ground water compartments were disabled. Because the ground
water compartments in the mercury test case have extremely slow chemical loss
processes, TRIM.FaTE is not able to calculate a steady-state solution for the modeled
system unless the ground water compartments are treated as virtual sinks (i.e.,
compartment that can gain pollutant mass, but not lose it). This is accomplished by
disabling all of the outgoing links from the ground water compartments, which
essentially eliminates all of the processes by which ground water compartments lose
mass. Because TRIM.FaTE cannot estimate steady-state solutions for sinks, it does not
estimate a steady-state solution for the ground water compartments after these links are
disabled.
4.2 Steady-state Results
After configuring TRIM.FaTE as described in Section 4.1, a steady-state simulation was
performed for emission case B (i.e., source emissions of divalent and elemental mercury, no
boundary contributions, and no initial concentrations) using the same model configuration as the
dynamic simulation described in detail in Chapter 3 (e.g., same spatial layout, same chemicals),
with the exception of the inputs and air-to-air transfer algorithm described in Section 4.1. This
section summarizes the results from the steady-state simulation. The data described in this
section are masses and concentrations of total mercury, which are calculated by summing the
mass and concentrations of elemental mercury, divalent mercury, and methyl mercury (as
mercury) output by TRIM.FaTE.
Exhibit 4-2 summarizes the mass and concentration results from the steady-state
simulation. The total mercury mass in each compartment type was estimated by summing the
mass of all three mercury species across all compartments of each compartment type. Likewise,
the average total mercury concentration in each compartment type was estimated by averaging
the total mercury concentrations across all compartments of each compartment type (note:
averages not weighted by size of compartment). Appendix C.2 presents the compartment-
specific total mercury mass and concentration results. The remainder of this section summarizes
the results of the steady-state simulation with respect to the mass results. The concentration
results are described within the context of the dynamic results in Section 4.3.
JULY 2005 4-3 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 4-2
Steady-state Simulation Mass and Concentration Results by Compartment Type
Compartment Type
Total Mercury Mass
Mass (g)
% of Total in
Modeling System9
Total Mercury Concentration
Ave. Cone.
Units
Abiotic Media
Air
Soil - surface
Soil - root zone
Soil - vadose zone
Surface water
Sediment
2.7E+01
1.7E+05
5.6E+04
2.9E+03
1.8E+02
1.4E+05
7.4E-03%
46.1%
15.5%
0.81%
0.05%
37.5%
3.8E-10
3.7E-07
1 .9E-09
5.6E-11
6.7E-09
5.8E-07
g/m3
g/g dry weight
g/g dry weight
g/g dry weight
g/L
g/g dry weight
Terrestrial Plants
Leaf- deciduous forest
Leaf- coniferous forest
Leaf -grasses/herbs
Particle-on-leaf-decid. forest
Particle-on-leaf - conif. forest
Particle-on-leaf - grasses/herbs
Root - grasses/herbs
Stem - grasses/herbs
1.2E+01
1.0E+02
4.7E+00
6.0E-04
7.5E-03
1 .4E-03
5.8E+00
1.0E-01
3.2E-03%
0.03%
1 .3E-03%
1 .6E-07%
2.1E-06%
3.9E-07%
1 .6E-03%
2.9E-05%
4.7E-07
1 .5E-06
7.1E-07
3.0E-06
3.2E-05
4.1E-05
5.2E-07
4.0E-08
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
Terrestrial Animals
Earthworm
Arthropod
Short-tailed shrew
Meadow vole
White-tailed deer
Black-capped chickadee
Mouse
Long-tailed weasel
Red-tailed hawk
1 .2E-01
4.4E-03
1.0E-02
9.6E-04
4.3E-01
1 .9E-04
1.2E-02
4.9E-05
7.1E-05
3.3E-05%
1 .2E-06%
2.8E-06%
2.7E-07%
1 .2E-04%
5.2E-08%
3.3E-06%
1 .3E-08%
2.0E-08%
3.5E-08
1 .7E-07
8.5E-06
3.7E-07
1 .5E-06
6.0E-06
3.2E-06
5.9E-07
1.1E-06
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
Semi-aquatic Animals
Tree swallow
Mallard
Mink
Raccoon
Common loon
Bald eagle
1.3E-02
2.5E-04
6.0E-05
2.3E-03
2.6E-05
7.4E-05
3.6E-06%
6.8E-08%
1 .7E-08%
6.2E-07%
7.2E-09%
2.0E-08%
6.6E-06
3.1E-06
2.4E-06
3.4E-06
2.1E-05
8.9E-06
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
Aquatic Plants*
Macrophyte
3.4E+01
9.5E-03%
4.3E-06
g/kg wet weight
Aquatic Animals
Water-column carnivore
Water-column herbivore
Water-column omnivore
Benthic carnivore
Benthic omnivore
Benthic invertebrate
9.5E-02
1.5E-01
6.6E-02
6.1E-02
1.7E-01
5.3E+00
2.6E-05%
4.3E-05%
1 .8E-05%
1 .7E-05%
4.7E-05%
1 .5E-03%
7.9E-05
1 .7E-05
1 .8E-05
5.3E-05
1 .7E-05
2.8E-05
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
g/kg wet weight
a Calculated relative to mass within the modeling system not including the mass in sinks because
TRIM.FaTE does not generate steady-state results for sinks. Thus, the sum of the values in the column
labeled "% of Total in Modeling System" equals 100 percent.
b Algae are not represented as aquatic plants in this simulation; rather they are represented in the surface
water estimates as a phase of surface water compartment instead of as a separate compartment.
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As shown in Exhibit 4-2, most of the mercury mass at steady-state is in the abiotic
compartments, particularly the soil and sediment compartments.2 Overall, the abiotic
compartments comprise over 99.9 percent of the total mercury mass in the modeling region.
Among abiotic media, surface soil has the most mercury at steady-state (approximately 46
percent of the mercury in the modeling region), followed by sediment (approximately 38
percent), root zone soil (16 percent), and vadose zone soil (0.81 percent).3 The remaining abiotic
compartment types, surface water and air, contain 0.05 percent and less than 0.01 percent,
respectively, of the mercury in the modeling region.
The amount of mercury mass in biota is much lower than in the abiotic media, which is in
part a result of the lower relative volume of the biotic compartments. Of the biotic
compartments, the leaf, root, macrophyte, and benthic invertebrate compartments contain the
most mercury mass. The coniferous leaf compartments contain the most mass among the biotic
compartments, likely due in part to the fact that coniferous plants are not assumed to lose all of
their foliage each year like deciduous and grasses/herbs plants. The benthic invertebrate
compartments have substantially higher amounts of mercury than the rest of the animals
associated with benthos and surface water, likely due in part to the higher amounts of mercury
mass in the sediment compartments and higher biomass of the benthic invertebrates.
4.3 Comparison of Steady-state and Dynamic Results
In this section, the results from the steady-state simulation described in Section 4.2 are
compared to the corresponding dynamic simulation results described in Chapter 3. As in Section
4.2, this comparison is based on the masses and concentrations of total mercury from each
simulation. The first part of this section compares the overall distribution of mass for the steady-
state and dynamic simulations, and the second section compares the concentrations for selected
compartment types estimated by the steady-state and dynamic simulations.
Comparison of the Overall Distributions of Mass
Although the results from steady-state and dynamic simulations cannot be directly
compared for sinks, the relative distribution of mass among the compartments provides some
insight into how the steady-state mode compares to the dynamic mode. Generally, the
distribution of mass in the steady-state simulation is similar to the distribution in the dynamic
simulation. In both simulations, the abiotic compartments contain nearly all of the total mercury
mass in the non-sink compartments. The primary difference between the steady-state and
dynamic results with regard to the distribution of mass among the abiotic compartments is that
the estimated total mercury mass in the root zone soil, vadose zone soil, sediment, and surface
water compartments is considerably higher relative to the other abiotic compartments in the
2 Note that TRIMFaTE does not generate steady-state estimates for sinks; therefore, the information on
distribution of pollutant mass in the steady-state simulation results is limited to the compartments in the modeling
region.
3 For comparison, roughly 94 percent of the total mercury mass in the compartments is in surface soil at
year 30 in the dynamic modeling results, with 4 percent in sediment, 2 percent in root zone soil, and 0.01 percent in
vadose zone soil. See Section 4.3 for discussion.
JULY 2005 4-5 TRIM.FATE EVALUATION REPORT VOLUME II
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steady-state simulations. This result is not surprising as the mass in these compartment types
appears to be increasing more rapidly than the other abiotic compartment types at the 30th year of
the dynamic simulation (i.e., these compartment types are "farther" from steady-state at year 30).
For biotic compartments, the pattern of mercury mass accumulation in the steady-state
simulation is slightly different from the pattern in the dynamic simulation. For the steady-state
simulation, the pattern is:
terrestrial plants » aquatic plants > aquatic animals > terrestrial/semi-aquatic animals
whereas the pattern for the dynamic simulation is:
terrestrial plants » aquatic plants ~ terrestrial/semi-aquatic animals > aquatic animals
The difference between these mass accumulation patterns is reasonable because, based on the
results from the dynamic simulation, the total mercury mass in aquatic plants and animals
appears to be increasing more rapidly at the 30th year of the dynamic simulation than the mass in
the terrestrial and semi-aquatic animals.
The distribution of mass among the plant compartments in the steady-state simulation
was also slightly different from the distribution in the dynamic simulation. At the end of the
dynamic simulation, the leaf compartments contain the majority of the mass, followed (in order)
by the stem, root, and particle-on-leaf compartments. In the steady-state simulation, the leaf
compartments also contain the majority of the mass, but the root compartments contain
substantially more mass than the stem and particle-on-leaf compartments. This result is likely
due to the strong relationship between the root concentration and the concentration in the root
zone soil, which is still increasing at year 30 of the dynamic simulation.
Comparison of Compartment Concentrations
Exhibit 4-3 compares the arithmetic average steady-state concentrations (in the column
labeled "Steady-state") for each compartment type to the arithmetic average concentrations for
each compartment type for the 30th year of the dynamic simulation (in the column labeled
"Dynamic"). Additionally, the "SS : Dynamic" column of Exhibit 4-3 presents the average of
the compartment-specific ratios of steady-state to dynamic results for each compartment type. A
similar pattern of results is seen when comparing the mass results for the two modes (not
shown).
With a few notable exceptions, the steady-state concentrations are higher than the
dynamic concentrations. For some abiotic compartment types, such as root zone soil, vadose
zone soil, and sediment, the steady-state:dynamic ratio is high because the dynamic
concentrations for these compartment types are still increasing at the end of the dynamic
simulation, indicating that the compartments have not reached steady-state. Likewise, some of
the biotic compartment types with higher steady-state:dynamic ratios (e.g., earthworm, root,
benthic invertebrate) are closely tied to these abiotic compartment types and would be expected
to have similar concentration patterns.
JULY 2005 4-6 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 4-3
Comparison of Steady-state (SS) Concentrations to 30th Year
Dynamic Concentrations, by Compartment Type
Compartments
Air0
Soil - surface
Soil - root zone
Soil - vadose zone
Surface water
Sediment
Leaf - decid. forest0
Leaf - conif. forest0
Leaf - grasses/herbs0
Particle-on-leaf - decid. forest0
Particle-on-leaf - conif. forest0
Particle-on-leaf -grasses/herbs0
Root - grasses/herbs
Stem - grasses/herbs0
Macrophyte
Earthworm
Arthropod
Short-tailed shrew
Meadow vole0
White-tailed deer0
Black-capped chickadee0
Mouse0
Long-tailed weasel
Red-tailed hawk
Tree swallow
Mallard
Mink0
Raccoon
Common loon
Bald eagle0
Water-column herbivore
Water-column omnivore
Water-column carnivore
Benthic invertebrate
Benthic omnivore
Benthic carnivore
Units
g/m3
g/g dry
g/gdry
g/g dry
B/L
g/gdry
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
Total Hg Concentrations
SS
3.8E-10
3.7E-07
1.9E-09
5.6E-11
6.7E-09
5.8E-07
4.7E-07
1.5E-06
7.1E-07
3.0E-06
3.1E-05
4.1E-05
5.2E-07
4.0E-08
4.3E-06
3.4E-08
1.7E-07
8.5E-06
3.7E-07
1.5E-06
6.0E-06
3.2E-06
5.9E-07
1.1E-06
6.6E-06
3.1E-06
2.3E-06
3.4E-06
2.1E-05
8.9E-06
1.6E-05
1.8E-05
7.8E-05
2.8E-05
1.7E-05
5.3E-05
Dynamic3
1.1E-09
1.3E-07
2.7E-11
8.5E-14
l.OE-10
2.9E-09
8.8E-08
6.4E-07
4.1E-07
5.8E-07
1.4E-05
9.1E-05
8.9E-10
6.9E-08
4.2E-08
1.3E-10
2.0E-10
5.7E-07
1.8E-07
6.8E-07
2.6E-06
1.4E-06
1.3E-07
3.1E-07
3.1E-08
2.0E-06
1.9E-07
4.2E-08
1.8E-07
2.2E-07
1.7E-07
2.2E-07
9.9E-07
9.0E-08
5.6E-08
1.7E-07
Dynamic
w/SS
Inputs3
3.8E-10
1.2E-07
2.3E-11
5.8E-14
7.0E-10
2.0E-08
4.7E-07
1.5E-06
7.0E-07
3.0E-06
3.1E-05
4.0E-05
2.1E-09
3.9E-08
2.7E-07
3.6E-10
9.8E-10
2.7E-06
3.2E-07
1.5E-06
5.8E-06
3.0E-06
3.4E-07
7.5E-07
1.6E-07
7.0E-07
4.7E-07
2.1E-07
1.3E-06
7.9E-07
1.3E-06
1.6E-06
7.3E-06
6.0E-07
3.8E-07
1.1E-06
Ratios
SS:
Dynamicb
0.7
16
290
2,000
77
230
5.8
2.5
3.3
5.6
2.4
2.6
640
0.8
87
300
1,000
18
2.0
2.3
2.5
2.8
7.0
6.1
130
15
32
110
97
45
78
69
67
260
260
260
SS:
Dynamic
w/SS Inputs"
1.0
3.3
90
1,000
12
37
1.0
1.0
1.0
1.0
1.0
1.0
220
1.0
14
91
170
3.2
1.2
1.1
1.2
1.3
2.2
2.1
23
7.2
9.6
17
15
8.9
12
10
10
41
41
42
Dynamic
w/SS Inputs
: Dynamic"
0.6
5.1
3.3
2.1
5.9
6.1
5.8
2.5
3.2
5.6
2.4
2.6
3.3
0.8
5.6
3.5
5.8
5.7
1.7
2.1
2.1
2.2
3.1
2.9
3.8
1.7
2.7
5.4
6.3
3.6
6.2
6.4
6.3
5.9
5.9
5.9
a Average concentrations for dynamic simulations are based on average concentration for the 30th year of the simulation,
unless otherwise noted.
b These values represent the averages of the various ratios calculated for all compartments for each compartment type, not the
ratios of the corresponding average concentrations. Therefore, these values are not exactly equal to the ratios of the
concentrations in the previous two columns (i.e., average of ratios does not equal ratio of averages).
0 Indicates dynamic concentration was average of years 26-30, rather than year 30 (all leaf and particle on leaf averaged for
entire year, zeros included, to facilitate comparison to steady-state).
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However, some of the differences between the steady-state and dynamic simulations
cannot be easily attributed to compartments that had yet to reach their steady-state values in the
dynamic simulation. For example, many of the individual compartment results showed steady-
state values less than dynamic values (e.g., air compartments), which was not an expected result.
In light of this, an additional dynamic simulation (referred to as "dynamic with steady-state
inputs") was performed to help determine which differences are attributable to compartments
that had not reached steady-state after 30 years and which are due to the constant input values
used in place of dynamic values for the steady-state simulation (i.e., which are "modeling"
differences vs. which are "input" differences). This new simulation used the exact same constant
inputs and algorithms as the steady-state simulation (including the steady-state air-to-air
advection algorithm), but was run for 30 years using the dynamic mode instead of using the
model's steady-state solution. The results of this simulation, as well as comparisons of these
results to the steady-state (in the column "SS : Dynamic w/SS Inputs") and dynamic results (in
the column "Dynamic w/SS Inputs : Dynamic"), are presented in Exhibit 4-3. A more detailed
comparison of these results is presented in Appendix C.3.
Assuming the model is performing as expected, the differences between the steady-state
results and dynamic with steady-state inputs results should be strictly due to compartments not
reaching steady-state within 30 years of the dynamic simulation. There are no ratios of steady-
state to dynamic with steady-state inputs results less than one (which would have indicated that
the model was not performing as expected), and many of the compartment types that appear to
reach steady-state within 30 years (e.g., air, leaves) have ratios for all compartments of exactly
one (meaning the results for the two runs are identical). Furthermore, the largest ratios are found
in compartments that are expected, based on the results of the dynamic simulation, to take much
longer than 30 years to reach steady-state (e.g., root, vadose zone soil). Therefore, when
TRIM.FaTE is supplied the exact same constant inputs for both steady-state and dynamic modes,
the steady-state concentration is always equal to or greater than the average dynamic
concentration, as would be expected, and the magnitudes of the differences appear to be logical.
Likewise, the differences between the dynamic results and dynamic with steady-state
inputs results should be strictly due to the approximation of time-varying inputs with constants in
the latter simulation. Both simulations used TRIM.FaTE's dynamic mode and ran for 30 years
with the same configuration, except the dynamic with steady-state inputs simulation used
constants instead of time-varying values for the properties listed in Exhibit 4-1. With a few
exceptions (e.g., air compartments), the dynamic with steady-state inputs results are generally
higher than the dynamic results and there appears to be a spatial pattern in the ratios of these
results for the individual compartments (see Appendix C.3). The ratios of dynamic with steady-
state inputs results to dynamic results are consistently highest to the south and east of the facility
and lowest to the north and west.
The differences between the dynamic and dynamic with steady-state inputs simulations
appear to be driven, at least in part, by the combination of the constant advective transfers
between air compartments and the constant precipitation rate used in the dynamic with steady-
state inputs simulation. For both simulations, the primary route for transport of mercury from air
to surface soil and surface water is wet deposition. Several of the primary inputs used in
calculating when, where, and how much wet deposition will occur (i.e., precipitation rate, wind
speed, and wind direction) are time-varying inputs for which constant approximations were used
JULY 2005 4-8 TRIM.FATE EVALUATION REPORT VOLUME II
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in the dynamic with steady-state inputs simulation. The methodology used to approximate these
constant values did not account for the possibility that there may be a correlation between rain
events and wind direction.
Further analysis of the dynamic meteorological data indicates that in fact the overall
predominant wind direction is not the same as the predominant wind direction when it is raining
(i.e., the predominant wind directions are from the south and northwest, and the predominant
wind directions when it is raining are from the south and east). As expected based on these
findings, the highest total mercury concentrations in air occur to the east of the source and the
highest deposition occurs to the north and west of the source. The predominant wind direction in
the dynamic with steady-state inputs simulation is roughly from the northwest (based on the
amount of total mercury in the air advection sinks), which is consistent with the predominant
wind direction in the dynamic simulation and reasonable considering the methodology used to
estimate the constant advective transfers in this simulation (see Appendix C.I for an explanation
of this methodology).
Based on these results, there appears to be a correlation between wind direction and
precipitation in the dynamic meteorological data that may not have been captured in the
estimation of the precipitation rate and constant advective transfers between air compartments
for the steady-state inputs. Because the dynamic with steady-state inputs simulation uses a
constant precipitation rate, it is likely that more deposition occurred in the direction of the
predominant winds (i.e., towards the southeast of the source) in this simulation. The increased
deposition in the dynamic with steady-state simulation may explain the higher concentrations in
soil and surface water and lower concentrations in air in this simulation because more chemical
mass is being removed from the air and deposited onto the soil and surface water than in the
dynamic simulation. Furthermore, because the spatial layout used in this scenario is not
symmetrical and includes more parcels (and covers more distance) to the southeast of the source,
more mercury accumulation occurred within the modeling domain in the dynamic with steady-
state inputs simulation. This is consistent with the fact that the concentrations in surface soil,
surface water, and biotic compartments that are closely tied to surface soil and surface water are
generally higher in the dynamic with steady-state simulation than in the dynamic simulation.
Overall, the steady-state mode appears to be operating as expected based on these
comparisons. When identical inputs and algorithms are used to run TREVI.FaTE in steady-state
and dynamic modes, the ratios of the steady-state results to the comparable dynamic results seem
reasonable. However, it appears that the methodology for estimating constant values for time-
varying inputs, particularly estimation of the constant advective transfer factors for air and the
precipitation rate, might not be fully capturing the variations in and correlations between the
time-varying properties. Replacing time-varying values with constant values within a complex
model is quite complicated and additional research may be needed to determine if there are
methods that could be used to more accurately capture these temporal variations with constant
values. Nevertheless, these comparisons show that the steady-state results for the mercury test
case, as a whole, approximate the dynamic results well enough that sensitivity results generated
using the steady-state mode can be generalized to dynamic scenarios, although these results will
not capture any changes in model sensitivity over time. However, because some compartments,
such as sediment and vadose zone soil, may take thousands of years to reach steady-state, the
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steady-state results may not be appropriate for evaluating impacts on compartments that are not
expected to reach steady-state within the expected duration of the emission source.
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5. SENSITIVITY ANALYSIS
This chapter presents an evaluation of the sensitivity analysis performed for the mercury
test case using TRIM.FaTE. The main purpose of this evaluation is to answer the following
questions.
Which properties have the largest influence on model results?
• Are the findings consistent with expectations based on the algorithms used in the
simulation and the natural processes being modeled?
To evaluate their relative importance, the properties included in the analysis were ranked on the
basis of their influence on or contribution to the variation in model outputs. This analysis and
the resulting rankings will help in prioritizing future data collection efforts for similar
TRIM.FaTE applications. Further, the results of this sensitivity analysis were evaluated to
determine if they are consistent with expectations based on the scientific principles underlying
the model. Unusual results were investigated to determine if they point to deficiencies in the
model algorithms or selected input values.
This chapter begins with a description of the analysis design and methodology (Section
5.1). Then, Section 5.2 describes the most influential properties with regard to mercury
concentration in selected compartment types. The input properties that are influential with
regard to mercury concentration in a number of different compartment types are described in
Section 5.3. Section 5.4 provides a brief summary and a discussion of possible follow-up
sensitivity analyses. For additional description of the input properties used in TRIM.FaTE,
along with a key between common names used for properties and their TRIM.FaTE code names,
see Module 16 of the TRIM.FaTE User's Guide (EPA 2003b) and the technical support
documents (EPA 2002b,c).
5.1 Analysis Design/Methods
The sensitivity of model outputs to changes in approximately 800 properties relevant to
the mercury test case simulation was assessed in this analysis. The properties included in the
analysis all use numeric values (versus equations) in the mercury test case, and so are sometimes
referred to as "input" properties. A complete list of the properties assessed is provided in
Appendix D.I.1 The impact of changes to the values of these properties on model predictions is
estimated by performing a TRIM.FaTE simulation for each model input property in which the
value of the property is varied and comparing the results of that simulation with results from the
base case simulation (i.e., the simulation using all original/unchanged property values). The
1 All TRIM.FaTE numerical input properties relevant to the mercury test case scenario were included in the
sensitivity analysis, with a few exceptions: (1) spatial layout inputs, such as volume element depth, (2) inputs that
are fractions that sum to 1.0, such as diet fractions for animals, and (3) convergence properties for the differential
equation solver. Time-varying inputs, such as rainfall rate, were included as constant values, as explained in
Chapter 4 and Appendix C.I. Of the more than 1,000 input properties varied as part of the sensitivity analysis model
runs, approximately 800 are applicable to the compartment types selected for assessment and are listed in Appendix
D.I.
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theoretical approach for this sensitivity analysis is described in more detail in Chapter 6 of
Volume I of the TRIM.FaTE Technical Support Document (EPA 2002b).
This sensitivity analysis was performed for emission case B (source emissions of both
divalent and elemental mercury, no boundary contributions or initial concentrations) using
TRIM.FaTE's steady-state mode and the scenario described in Chapter 4 and Appendix C.I.2
The steady-state mode was selected for this analysis because of its much faster execution time
(several minutes compared to several days for the dynamic mode) and the large number of
simulations needed for this analysis (over 1,000, one for each property varied). As described in
Chapter 4, the steady-state configuration of the mercury test case site approximates the results of
the dynamic simulations well enough that the results from this analysis can be generalized to
dynamic scenarios and provide a reasonable basis for evaluation of the most influential input
properties for the mercury test case. Limitations in using the steady-state mode are discussed in
Section 5.1.4.
5.1.1 How Input Values Were Varied
As described above, one TRIM.FaTE simulation was performed for each mercury test
case input property varied in the sensitivity analysis. In each simulation, the base value of one of
these properties was reduced by one percent and the resulting changes in output values were
recorded. This amount of variation was chosen because it keeps most properties within their
range of reasonable values and introduces enough variation to reveal an effect if there is one.3
The values for each property were varied simultaneously in all compartments (i.e., all locations)
where they are used. For example, values for the water temperature property were varied in all
water bodies in a single simulation instead of performing separate simulations for each water
body.
5.1.2 Measures of Sensitivity
After the TRIM.FaTE simulations (one for each input property varied) were completed,
the outputs were compared to the outputs from the base case to produce measures of the change
in TRIM.FaTE results associated with changes in each of the property values. Two measures of
sensitivity, the elasticity and the sensitivity score, were calculated by TRIM.FaTE from the
results of these simulations. Elasticity indicates "structural" sensitivity, while sensitivity score
The sensitivity analysis model runs were completed a few months earlier than all the other model runs
described in this report, and there are differences in one algorithm (mercury uptake by algae from surface water) and
a few input values (mercury uptake rate by algae, soil ingestion rate for five animal species). Moreover, a different
set of wind data was used. However, the steady-state modeling scenario used for the sensitivity analysis is judged to
be appropriate for evaluating the relative influence of different properties on the model results, though care should be
taken when interpreting results related to algae uptake and soil ingestion (e.g., properties related to algae uptake may
be more influential than indicated here because of subsequent changes to the mercury uptake by algae algorithm).
3 Although varying property values by a larger percentage or in the opposite direction (i.e., positive relative
to the base value) could possibly generate additional useful results, such investigations were beyond the scope of the
current analysis, and one percent was determined to be adequate for the current analysis. In addition, it was
anticipated that an input variation of one percent would result in a model response that was approximately linear (the
elasticity, as calculated for this analysis, is based on the assumption that the input-output relationship is linear).
JULY 2005 5-2 TRIM.FATE EVALUATION REPORT VOLUME II
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indicates "actual" sensitivity after accounting for the estimated variability in an input property.
The elasticity provides information useful for understanding how the model operates and is used
to compare with expected results, given knowledge of the model and the processes being
simulated. The sensitivity score is useful in the context of assessing the influence of input
properties, or how the variability of the input property affects the variability of the results.
For this report, calculations of elasticity and sensitivity score were based on the mercury
concentration results. TRIM.FaTE also has the capability to produce these calculations based
on either mercury mass or moles results (TRIM.FaTE mass transfer and transformation
calculations are performed on the basis of moles). Sensitivity analysis results are the same for
moles and mass (except for the molecular weight property, which is used to convert moles to
mass), but results calculated based on concentration differ for any properties used in the
conversion from mass to concentration, which varies for different compartment types. For
example, for surface soil the concentration-based results differ from the mass-based results for
properties included in the conversion, including solids density, soil water content, and soil air
content. Therefore, in interpreting the results presented in this chapter, it is important to keep in
mind that they are based on mercury concentrations.
Elasticity is the percent change in a model output value resulting from a one percent
change in the value of a particular property, with all other properties unchanged. A positive
value of elasticity results from an increase in an input value giving an increased output value, or
a decrease in an input value giving a decreased output value. A negative value of elasticity
means that an input increase resulted in an output decrease, or vice-versa. The equation for
elasticity is provided below.
Elasticity =
p°
where:
y° = model output value, base case
Ay = change in model output value
p° = model input property value, base case
Ap = change in model input property value
For example, if a decrease of 1.0 percent in the input property "algae growth rate" results in a 1.1
percent increase in methyl mercury concentrations in fish, then the elasticity is -1.1.
The sensitivity score is the elasticity weighted by a normalized measure of the variability
and/or uncertainty of the model input property, which takes the form of a normalized range or
normalized standard deviation of the input property. It provides a measure of the variation in the
output value resulting from the natural variability and uncertainty of the input property by
weighting the elasticity by the coefficient of variation (CV) of the input property. The CVs
quantify the degree of natural variability of the input property and the uncertainty of the estimate
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of the input property. It is equal to the standard deviation divided by the mean of the property,
where the standard deviation reflects both variability and uncertainty. The equation for
sensitivity score is provided below.
P
where:
Ay/Ap =
P°/y° =
cv =
Sensitivity Score = — x — jr- x CV
A;? y
change in output y per change in input p
ratio of base case values of the input (p) and output (y)
coefficient of variation of input p (standard deviation/mean)
The CVs were estimated for each of the model input properties analyzed. Where
available, CVs from the literature were assigned to the model input properties. The remaining
properties were assigned to classes (i.e., A, B, C, or D) according to their estimated degree of
combined variability and uncertainty. Quantitative values for these estimated CVs were
assigned according to Exhibit 5-1. Note that these are preliminary estimates of CVs, which can
be refined as additional information becomes available.
Exhibit 5-1
CVs Assigned for Each Class of Properties
Variability and
Uncertainty
Low
Moderate
High
very high
CV Class
A
B
C
D
CV Value
0.05
0.3
1.0
3.0
The discussion of the sensitivity results in this report focuses on the elasticity estimates
because they provide an assessment of the impact each input property has on the model outputs
without being affected by the CV estimates, many of which are based on professional judgment.
Sensitivity scores are provided in Appendix D.2 for all properties assessed.
5.1.3 Limitations
As described above, the design of this analysis involved varying input property values by
a set percentage of their nominal values and comparing the resulting outputs to the base case
outputs. This approach is not amenable to assessing the sensitivity of model outputs to model
structure (e.g., the overall mass balance design of TRIM.FaTE), formulas or algorithms, or
spatial layout of the scenario. In particular, although the TRIM.FaTE sensitivity analysis feature
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has capabilities that allow examination of some of these kinds of user inputs,4 the sensitivity
analysis performed for the mercury test case and described here does not address:
Model algorithms (e.g., alternative formulations for a given process);
Spatial layout of the scenario (i.e., size, shape, orientation, and number of parcels;
dimensions and numbers of volume elements; links between compartments);
• Ecosystem and food webs defined by the user;
• Time-varying inputs;
Inputs that are fractions that sum to 1.0, such as diet fractions for animals;
Step function inputs (e.g., chloride and pH in the formula for partitioning of mercury in
surface water with algae); and
Convergence properties for the differential equation solver.
This sensitivity analysis also does not explicitly address correlations among model input
properties, although a number of likely correlations are recognized in the analytic design (e.g., in
the development of certain steady-state input values for time-varying properties; see Appendix
C. 1) and some are discussed in the results section. Also, because the sensitivity analysis is
conducted around a single point (i.e., using a constant nominal value for each parameter, which
may exist within the parameter space for a short time), the interpretation of the results is
technically limited to the specific conditions of the simulation. Furthermore, inputs applicable
only to compartment types not included as endpoints for this assessment (e.g., macrophytes,
roots and stems, various animal species) are not addressed.
In addition, there are some limitations imposed by the use of the steady-state mode. A
steady-state approach by definition cannot evaluate the sensitivity of results for different years or
seasons. Further, inherent in our use of the steady-state mode here is the presumption that
sensitivity results for the steady-state mode are informative to the dynamic mode, and that the
steady-state scenario developed truly represents the steady-state form of our dynamic scenario
(see Chapter 4 and Appendix C). The sensitivity to changes in time-varying input values (e.g.,
changes in precipitation rate) and to the resolution of input data time steps (e.g., meteorological
data) cannot be evaluated using the steady-state mode.
For example, AllowExchange properties for the terrestrial plant compartment types are
specified as 0 or 1 (and can switch back and forth) for the duration of a dynamic model run.
These properties are assigned a constant, intermediate value for the purpose of steady-state
model runs. Specifying a percentage change in the constant value does not make mechanistic
4 For example, the TRIM.FaTE sensitivity analysis can be run in dynamic as well as steady-state mode, and
in dynamic mode the sensitivity of outputs to time-varying inputs can be assessed. Also, different kinds of
sensitivity analyses using TRIM.FaTE could be designed to assess changes in step function inputs or spatial layout.
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sense in the dynamic context. However, the sensitivity analysis results for Allow Exchange
properties give clues about how the model would respond to changes in times or dates on which
processes are turned on or off (e.g., litter fall).
5.1.4 Endpoints Analyzed
The mercury test case includes 417 abiotic and biotic compartments, 33 of which were
selected for analysis of sensitivity results (Exhibit 5-2).5 Compartments were selected to provide
Exhibit 5-2
Output Compartments Selected for Analysis of Sensitivity Results
Compartment Type
Compartments a
Abiotic
Air
Soil
Surface water
Sediment
ESE1, SSE1, SSE3
SSE4, SW2
River, Swetts Pond
River, Swetts Pond
Terrestrial Plants
Leaf- coniferous forest
Leaf - grasses/herbs
SSE4
SW2
Terrestrial Biota
Herbivore (white-tailed deer)
Omnivore (mouse)
Soil detritivore (earthworm)
SSE4, SW2
SSE4, SW2
SSE4, SW2
Semi-aquatic Biota
Piscivore (common loon)
Omnivore (raccoon)
River, Swetts Pond
SSE4, SW2
Aquatic Biota
Water-column carnivore
Water-column omnivore
Water-column herbivore
Benthic carnivore
Benthic omnivore
Benthic invertebrate
River, Swetts Pond
River, Swetts Pond
River, Swetts Pond
River, Swetts Pond
River, Swetts Pond
River, Swetts Pond
See Exhibits 2-1 and 2-2 for maps of compartment locations.
Note that results were generated by TRDVLFaTE for all 417 compartments in these sensitivity simulations;
however, measures of sensitivity were only estimated and analyzed for the 33 selected compartments.
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breadth of coverage across the different media, with consideration of how the compartments
interact, and with a focus on the aquatic food chain because it is of particular interest for this
analysis. In total, 17 different compartment types are included (note that the two different
vegetation types included for leaf, coniferous forest and grasses/herbs, are counted here as
separate compartment types). Two different locations were selected for each compartment type,
except three for air and one for each of the two leaf compartment types. Results were analyzed
for elemental mercury (Hg°), divalent mercury (Hg2+), and methyl mercury (MHg), except that
elemental mercury was not analyzed for the three water-column fish compartment types because
of its extremely low modeled concentrations.
5.2 Influential Input Properties for Individual Compartment Types
A review of the elasticity values for the relationship between individual properties and
the model results for specific compartment types of interest can provide confidence in how the
model is performing. This section discusses the most influential properties, as defined by
absolute elasticity values, with regard to model results for the compartment types selected for
evaluation (see Exhibit 5-2). Each subsection focuses on a particular compartment type or group
of compartment types and includes one or more exhibits presenting those properties for which
elasticity values are above 0.1 (absolute value). In this chapter, the focus is primarily on the
compartments associated with surface parcel SW2 or Swetts Pond, although other locations are
discussed as appropriate. For a more detailed record of elasticities and sensitivity scores for all
the compartments and mercury species examined, refer to Appendix D.2. Following the
exhibit(s) in each section there is a discussion of the individual properties and what is known
about their relationship to the compartment results (e.g., explaining why the elasticity values
make sense, or in a few cases where further investigation may be needed to fully explain the
results obtained).
5.2.1 Air
This section describes findings regarding the elasticity of the relationship between model
properties and divalent and elemental mercury concentration results for three air compartments,
SSE3 (overlies Swetts Pond), ESE1, and SSE1.6 An overview of the elasticity values for these
air compartments is provided first, followed by a more detailed discussion of the chemical-
specific elasticities for elemental and divalent mercury for these compartments.
Overall Trends
For both divalent and elemental mercury concentrations in air, most of the properties
with the highest elasticities are steady-state air advective transfers (referred to as air advective
transfers on charts and remaining text) between various pairs of compartments. In Exhibit 5-3,
this is illustrated for divalent mercury in the SSE3 air compartment. For divalent mercury, there
are only three properties other than air advective transfers across all three locations (i.e., SSE3,
6 Sensitivity analysis results for methyl mercury in air are presented in Appendix D.2. In this and all
following sections, the focus is on the dominant mercury species, in most cases divalent mercury. Results for the
other mercury species are included in Appendix D.2.
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ESE1, and SSE1) that rank among the top 30 properties with the highest elasticities (emission
rate of divalent mercury, rainfall rate, and vapor washout ratio of divalent mercury). In these air
compartments, emission rate of divalent mercury has the highest elasticity for divalent mercury
concentrations. Similarly, emission rate of elemental mercury has the highest elasticity for
elemental mercury concentrations in all three air compartments.7
Exhibit 5-3
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Air Compartment SSE3
Emission Rate, Hg2-i
Air Advective Transfer [
Rainfall Rate d
Vapor Washout Ratio, Hg2+ C
Air Advective Transfer [
Air Advective Transfer I
Air Advective Transfer I
Air Advective Transfer C
Air Advective Transfer C
Air Advective Transfer I
Air Advective Transfer C
Air Advective Transfer C
Air Advective Transfer [
Air Advective Transfer [
Air Advective Transfer [
I] Air Advective Transfer
II Air Advective Transfer
H Air Advective Transfer
H Air Advective Transfer
Zl Air Advective Transfer
H Air Advective Transfer
] Air Advective Transfer
] Air Advective Transfer
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
Elasticity
0.2
0.4
0.6
0.8
1.0
The sensitivity of the TREVI.FaTE air algorithms (and associated model outputs) to
changes in the air advective transfer properties is not surprising because these properties - which
are constants used in the TRIM.FaTE steady-state mode to represent time-varying wind speed
and direction data (see Appendix C.I) - are the primary drivers of transport of chemical mass
between air compartments.8 The range of elasticity values for these properties, which includes
7 Mixing height (i.e., height of the air compartment layer) would also be expected to have a high elasticity
for divalent and elemental mercury concentrations because it directly influences the compartment volume (which, in
turn, directly affects the predicted air concentration). As described above, however, none of the spatial properties
were varied in the sensitivity analysis.
8 For the mercury test case, 124 air advective transfer properties were used: two for each internal boundary
between air parcels (one in each direction), and one for each external boundary (outward direction only).
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both positive and negative elasticities, is also not surprising. For example, a positive change in a
particular air advective transfer property value would be expected to increase transport of mass
from the sending to receiving compartment and thus result in an overall reduction in mass in the
sending compartment (negative elasticity) and an overall increase in mass in the receiving
compartment (positive elasticity). It is important to note, however, that the air advective transfer
property values were varied one at a time. This approach does not fully represent the impact of
changes in wind speed and/or direction because it only measures the impact of the change across
one interface of a compartment, whereas a change in wind speed and/or direction would impact
advection across all interfaces of that compartment. It is possible that this approach results in
overestimating the maximum impact of these properties because it only captures, for example, an
increase in chemical mass moving into a compartment across a particular interface (which would
increase the compartment concentration and thus result in a higher elasticity) and not the
associated increase in mass moving out of the compartment across another interface (which
would decrease the compartment concentration and thus result in a lower elasticity). A more in-
depth examination of the sensitivity of air concentrations to changes in wind speed and direction
was beyond the scope of this initial, broadly scoped sensitivity analysis.
In the remainder of the exhibits in this chapter, the elasticities associated with all air
advective transfer factor properties are combined into a single bar (instead of multiple bars, as
presented in Exhibit 5-3) that extends from the most negative elasticity value associated with an
air advective transfer to the most positive elasticity value, with a tick mark on the bar for each
absolute elasticity value greater than 0.1. This provides a simple summary of the range of
impacts associated with changes in these properties without obscuring the impacts of other
properties. Although not as dominant as for the air concentration results, these properties are
relatively influential for concentrations of most of the mercury species and compartment types
examined, reflecting the importance of wind speed and direction for "downstream" media
concentrations of mercury that originate in deposition from air. In most cases, the air advective
transfer property results are not discussed further in the following sections about compartment
types other than air, but the elasticity values are shown on the exhibits and in Appendix D.2.
Divalent Mercury Concentration
As mentioned in the previous section, there are only three properties other than air
advective transfers that rank among the 30 properties with the highest elasticities for divalent
mercury concentrations in air compartments (emission rate of divalent mercury, rainfall rate, and
vapor washout ratio of divalent mercury). The elasticity values for these three properties are
much higher than those for any other properties (see Exhibit 5-4 for the SSE3 air compartment).
Of these, emission rate has by far the highest elasticity value. The elasticities for rainfall rate
and vapor washout ratio are identical, reflecting their multiplicative relationship in the equations
where they both occur. These findings are consistent with the modeling of wet deposition as the
dominant removal process (other than air advection, which is reflected in the air advective
transfer properties) of divalent mercury in air and source emissions as the dominant addition
process.
The elasticity for the relationship between the emission rate of divalent mercury and
divalent mercury concentration in air is +1.0 for all three locations. As expected the elasticity is
positive, which means as the emissions of divalent mercury increase, so do the divalent mercury
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concentrations in air. The magnitude of the elasticity indicates that air concentrations of divalent
mercury are directly proportional to emissions of divalent mercury (e.g., for every one percent
increase in emission rate of divalent mercury, the concentration of divalent mercury in each of
these three air compartments increases one percent).
Exhibit 5-4
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Air Compartment SSE3 (Air Advective Transfers
Collapsed into One Bar)
Emission Rate, Hg2+
Rainfall Rate
Vapor Washout Ratio, Hg2+
Air Advective Transfer
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
Elasticity
0.2
0.4
0.6
0.8
1.0
The elasticities for rainfall rate and vapor washout ratio of divalent mercury are identical
to each other at each location (-0.1 at SSE1, -0.12 atESEl, and -0.29 at SSE3). The negative
values are expected given that as the amount of precipitation increases, so does the wet
deposition, which removes divalent mercury from the air. Likewise, as the vapor washout ratio
increases, more divalent mercury is removed from the air by a given amount of precipitation and
deposited. It also makes sense that the elasticities of these two properties are identical because
they are both multipliers in the numerator of the wet deposition of vapor algorithm. The
difference in elasticity values across the different locations may be related to the distance from
the source (SSE3 is farther from the source than ESE1 and SSE1). As concentrations of divalent
mercury decrease with distance from the source, the relative importance of properties related to
deposition increases.
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Elemental Mercury Concentration
The only property other than air advective transfers that ranks among the 30 properties
with the highest elasticities for elemental mercury concentrations in air compartments is
emission rate of elemental mercury. The absolute elasticity values for all other properties are
less than 0.1. This suggests a simple modeling relationship for elemental mercury in air, with
emission rate the single dominant factor (beyond the wind-related air advective transfer
properties).
The elasticity for emission rate of elemental mercury is +1.0 for SSE1 and ESE1 and
+0.99 for SSE3. The elasticity is positive, which means as the emissions of elemental mercury
increase, so do the elemental mercury concentrations in air. Elemental mercury deposits from air
at a much lower rate than divalent mercury (which explains why more elemental mercury,
relative to the amount emitted, ends up in the air advection sinks than divalent mercury - see
Sections 3.2 and 6.2.2 for additional discussion of relative deposition of different forms of
mercury), and therefore deposition processes (and their associated properties) do not have a big
impact on elemental mercury concentrations in air. Likewise, transformation processes do not
have a substantial impact on elemental mercury concentrations in air, which is consistent with
the low transformation rate used for elemental mercury in air. Therefore, it appears reasonable
that the elemental mercury concentrations in air are not very sensitive to any properties other
than emission rate and air advective transfers (i.e., removal of elemental mercury by processes
other than air advection is minimal relative to the addition of elemental mercury).
5.2.2 Surface Soil
This section describes findings regarding the elasticity of the relationship between input
properties and divalent mercury concentration results in two surface soil compartments, SW2
and SSE4. Emission rate of divalent mercury has the highest elasticity value among the included
properties for divalent mercury concentrations in the analyzed surface soil compartments. The
next highest elasticity values are for two properties used to estimate wet deposition of vapor
(rainfall and vapor washout ratio of divalent mercury) and two other properties used to estimate
erosion of surface soil (fraction of area available for erosion and total erosion rate). The
properties with the highest elasticity values are generally very similar between the two locations;
however, the elasticity values are generally higher for results associated with the SSE4 surface
soil compartment. All of the properties with absolute elasticities greater than 0.1 for divalent
mercury concentrations in the SSE4 surface soil compartment are presented in Exhibit 5-5.
The properties exhibiting high elasticity values are logical, and those with the three
highest values are also the three with highest elasticity for divalent mercury concentrations in the
air compartments (see Section 5.2.1). The high, positive elasticity of divalent mercury in surface
soil to the emission rate of divalent mercury (elasticities of+1.0 in SW2 and +0.99 in SSE4)
appears to reflect the fact that the air is the primary source of chemical mass in soil. The second
and third highest elasticity values are for properties that impact wet deposition, rainfall rate and
the divalent mercury vapor washout ratio (both with elasticities of+0.69 for SW2 and +0.61 for
SSE4). These properties have positive elasticities for divalent mercury concentrations in surface
soil, whereas they have negative elasticities for air concentrations. This is consistent with the
algorithms used because increasing these property values results in more removal from air
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(therefore lower air concentrations) and more deposition to surface soil (therefore higher surface
soil concentrations). The elasticities for these two properties are identical because they are both
used as multipliers in the numerator of the wet deposition of vapor from air to surface soil
algorithms.
Exhibit 5-5
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Surface Soil Compartment SSE4
Emission Rate, Hg2+
Fraction of Area Available for r-
Erosion (Soil) L
Total Erosion Rate (Soil) [j
Reduction Rate, Hg2+ (Soil) [^
Solids Density (Soil)
Allow Exchange Steady-state r
for Air I
Wet Dep Interception r
Fraction (Leaf - Coniferous) L
J Rainfall Rate
i Vapor Washout Ratio,
I Hg2+
Tir
I Air Advective Transfer
i Water Content
1 (Leaf - Coniferous)
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
Elasticity
0.2
0.4
0.6
0.8
1.0
The properties with the next two highest elasticities, fraction of area available for erosion
and total erosion rate (both with elasticities for divalent mercury of-0.56 for the SW2 surface
soil compartment and -0.57 for the SSE4 surface soil compartment), play a role in the algorithms
simulating pollutant transfers associated with erosion. These elasticities are negative, which
means that increasing the amount of area from which erosion can occur or increasing the erosion
rate results in a net reduction in the concentration of divalent mercury in surface soil
compartments at these locations (although there is some gain and some loss). This is consistent
with erosion as a loss process for surface soil compartments, although it is possible that for some
compartments, depending on the spatial layout used and a particular compartment's location in
the layout, elasticity values for these properties could be positive (i.e., erosion could be a net
gain process for a given surface soil compartment). It is also reasonable that the elasticities for
these properties are the same because they are both used as multipliers in the numerator of the
same algorithms (i.e., erosion from surface soil to surface soil, surface soil sink, and surface
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water). Their relatively high magnitude (especially compared to runoff-related parameters)
appears reasonable given that divalent mercury in soil is mostly in the solid phase.
The properties with the sixth and seventh highest elasticity values for divalent mercury in
surface soil are divalent mercury reduction rate (with elasticities of-0.38 in SW2 and -0.37 in
SSE4) and solids density in surface soil (with elasticities of -0.38 in SW2 and -0.37 in SSE4).
The elasticities to these properties are negative and approximately the same in both locations.
Their magnitudes are identical, although this appears to be a coincidence as they are not are used
in the same algorithms. The negative elasticity for divalent mercury reduction rate is consistent
with the algorithms used, in which a higher reduction rate equates to more divalent mercury
transforming to elemental mercury, resulting in lower divalent mercury concentrations. The
negative elasticity for solids density of surface soil is due to its use in the equation used by
TRIM.FaTE to convert the surface soil outputs from moles (the units used internally by
TRIM.FaTE) to concentration (in g/g dry weight). When elasticity for the solids density
property is calculated based on divalent mercury moles in surface soil, the elasticity values are
positive in both analyzed locations (+0.69 in SW2 and +0.63 in SSE4). The positive elasticities
for solids density are consistent with how this property is used in algorithms associated with
erosion (i.e., higher solids density results in a slower erosion velocity, which results in less
chemical loss via erosion).
Starting with the ninth highest elasticity, the rank order of the properties becomes
increasingly different for the two locations. Because many of these properties are involved in
calculating mercury transfers involving plants, this could be related, at least in part, to the fact
that the two parcels are assigned different types of vegetation (coniferous plants for SSE4 and
grasses/herbs for SW2). The leaf properties AllowExchange for air (elasticity of-0.16 in SSE4
and -0.01 in SW2), wet deposition interception fraction (elasticity of-0.11 in SSE4 and -0.0085
in SW2), and water content (elasticity of+0.11 in SSE4 and +0.0054E-03 in SW2) are ranked
between 8 and 10 for SSE4 and between 15 and 18 for SW2.
AllowExchange is a key property in the algorithms describing pollutant transfers
involving plants. This property indicates the presence of viable vegetation and in a dynamic
simulation is a time-varying value alternating between 0 (indicating dormancy) and 1 (indicating
the growing season). For steady-state simulations, however, AllowExchange is set to a constant
value between 0 and 1 reflective of the fraction of the year that plants exchange mass with other
compartments (see Section 4.1). Therefore, vegetation with higher AllowExchange values in
steady-state simulations receives more chemical mass via air deposition than vegetation with
lower AllowExchange values. Specifically, an increase in AllowExchange for air for both
locations/types of vegetation (coniferous and grasses/herbs) results in more interception of
deposition by the plants, which increases the amount of accumulated mass in plants and
decreases the amount deposited to surface soil. Thus, it is reasonable that elasticities for this
property are negative in both locations. It also appears reasonable that the absolute value of the
elasticity for SSE4 is greater than for SW2 because the value of AllowExchange for air is greater
for SSE4. Several other properties (e.g., litter fall rate, wet mass per area) also have different
values for the two vegetation types, which may also be contributing to the observed differences
in elasticity.
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Likewise, an increase in wet deposition interception fraction for both locations/types of
vegetation (coniferous and grasses/herbs) results in more of the wet deposited divalent mercury
being deposited to plants instead of soil, resulting in less divalent mercury depositing to soil and
therefore lower soil concentrations. It is therefore reasonable that elasticities for this property
are negative in both locations. It also appears reasonable that the absolute value of the elasticity
for SSE4 is greater than for SW2. The SSE4 surface soil compartment (in which coniferous
trees are located) has higher Allow Exchange values than SW2 (in which grasses/herbs are
located). This results in more interception by plants, relatively speaking, in SSE4 than in SW2.
Thus, the same relative change (e.g., one percent) in wet deposition interception fraction in both
locations would have a more substantial relative impact in SSE4 than in SW2, which is
consistent with the results.
An increase in leaf water content for both locations/types of plants (coniferous and
grasses/herbs) results in a decrease in the dry deposition interception fraction, resulting in more
divalent mercury depositing to soil and therefore higher soil concentrations. Therefore, as
expected, the elasticities for this property are positive at both locations. It also appears
reasonable that the elasticity for SSE4 is greater than for SW2. Due to the differences in
AllowExchange values for the two locations (described above), the same relative increase (e.g.,
one percent) in water content in both locations would have a more substantial relative impact in
SSE4 than in SW2, which is consistent with the results.
5.2.3 Earthworm
This section describes findings regarding the elasticities of the relationships between
model properties and divalent mercury concentration results in the earthworm. The properties
demonstrating absolute elasticity values greater than 0.1 for divalent mercury in the SW2
earthworm compartment are presented in Exhibit 5-6 and discussed below. Except for the first
two, most of these are soil properties, reflecting the modeling approach based on partitioning of
divalent mercury from soil to earthworm.
The most influential property affecting the concentration of divalent mercury in the
earthworm, expressed on a wet-weight basis, is water content. The high negative elasticity for
this property (-5.25) reflects the very strong influence of water content (percent water) on the
wet-weight concentration of divalent mercury in worms. This property is used to convert the
worm/soil dry-weight partition coefficient (a separate input property) to a wet-weight partition
coefficient (higher water content yields lower wet-weight partition coefficient, hence the
negative elasticity), which is then used to calculate the soil-to-worm transfer factor. The water
content of earthworms in nature can vary substantially and is generally high (e.g., 80 to 85
percent; 84 percent is the base value for this scenario). The reason for the high magnitude of the
elasticity relates to the form of the equation using water content - a (1 - water content) term is
used as a multiplier - and the base value of 0.84. At this base value, a 1 percent reduction in
water content results in a 5.25 percent increase in (1 - water content).9
9 Note that if TRIMFaTE was designed to use as an input a partition coefficient based on lab-measured
wet-weight soil and worm concentrations, the worm water content would have no influence on the wet-weight
concentration of divalent mercury in the worm (assuming algorithms had not been added to correct for differences in
water content for TRIM.FaTE soil and earthworm compartments compared with the original data).
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Exhibit 5-6
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Earthworm Compartment SW2
E = -525
Partition Coefficient,
Hg2+ (Worm)
Emission Rate, Hg2+
1
1
Average Vertical Downward
Velocity (Soil)
l/y Hg''4-
Total Erosion Rate (Soil) |
-
II 1 II 1
Reduction Rate, Hg2+ (Soil) I
-
Solids Density (Soil) |
Kd, MHg (Soil) |
Demethylation Rate, MHg (Soil) I
Water Content (Worm)
Reduction Rate, Hg2+
(Soil- Root Zone)
Solids Density
(Soil - Root Zone)
I
I Rainfall Rate
| | Air Advective Transfer
| Methylation Rate, Hg2+ (Soil)
.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.
Elasticity
Following water content, the next two properties, the worm/soil dry-weight partition
coefficient and the facility emission rate for divalent mercury, show equally high positive
elasticities (+1.0). The worm/soil dry-weight partition coefficient is the property in TREVI.FaTE
that defines the net extent of divalent mercury uptake by worms from the soils. Similar findings
for facility emission rate (close to directly proportional effect of divalent mercury emissions on
divalent mercury concentrations) are seen in other compartment types as well. This reflects the
fact that facility emissions (no boundary contributions and no initial concentrations) are the only
source of mercury to this simulation.
The root zone soil properties reduction rate and solids density both have high negative
elasticities (-0.98) for divalent mercury concentrations in the earthworm. Higher values for the
divalent mercury reduction rate in root zone soil result in lower amounts and concentrations of
divalent mercury in the root zone soil, and less divalent mercury to partition into earthworms.
The relatively high negative elasticity for solids density here is consistent with the relationship
seen for this property in surface soil. At higher values for solids density in soil, concentrations
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of divalent mercury in the soil on mass/mass-basis are lower (see Section 5.2.2).10 Hence,
because the partitioning is modeled as a concentration-driven process, less divalent mercury
partitions into earthworms.
The next four most influential properties on divalent mercury concentration in the
earthworm (i.e., average vertical downward velocity, +0.96; divalent mercury Kd, -0.82; vapor
washout ratio, +0.69; and rainfall rate, +0.69) reflect the propensity of divalent mercury to reach
the root zone soil (three positive elasticities) and the propensity of divalent mercury to remain
sorbed to surface soil particles (negative elasticity), and hence to remain in the surface soil layer
rather than to move to subsurface soil layers via diffusion or percolation. At higher values for
the average vertical downward velocity of water percolating through surface soil, the amount of
divalent mercury reaching the subsurface soil layers (including the root zone soil layer where the
earthworms are located in TRIM.FaTE) is larger; hence, the large positive elasticity. It is
reasonable that Kd would have a relatively large negative elasticity because at higher values for
divalent mercury Kd in surface soils, more divalent mercury is sorbed to surface soil particles,
and less is available dissolved in water to percolate downward to the root zone soil.
Additionally, it makes sense that both rainfall rate and vapor washout ratio have relatively high
positive elasticities, as they do for surface soil. At higher values for both of these properties, the
higher the amount of divalent mercury that is deposited to surface soils per unit time; hence,
more divalent mercury is available for percolation to the subsurface soil layers, including the
root zone layer, where some of it is available for uptake by earthworms.
Following these properties, the next four most influential properties (excluding air
advective transfers, discussed in Section 5.2.1) on divalent mercury concentrations in
earthworms are the fraction of area available for erosion, total erosion rate, solids density in
surface soil, and divalent mercury reduction rate in surface soils. As the fraction of area
available for erosion and the total erosion rate increase, the concentration of divalent mercury in
surface soil decreases as more divalent mercury sorbed to surface soil particles is removed from
a given surface soil compartment (for this particular compartment location, losses via erosion are
greater than gains via erosion). Hence, less divalent mercury can percolate into the root zone
soil layer, and a moderate negative elasticity (-0.56) for both properties results. Likewise, as the
solids density and divalent mercury reduction rate in surface soil decrease, the concentration of
divalent mercury in the surface soil increases. Hence, more divalent mercury can diffuse and
percolate from the surface soil into the root zone soil, where it is taken up by earthworms, with a
resulting moderate negative elasticity (-0.38) for both properties. Note that the reason surface
soil solids density is influential on divalent mercury concentration in earthworms differs from
the reason discussed in Section 5.2.2 for surface soil. Rather than being related to the units
conversion from moles to dry-weight concentration (which uses solids density in the
denominator), in this case the reason is that solids density is used in the denominator of the
equation for Zsolid, which is then used to calculate Ztotal, which is then used to calculate the
effective advection rate from surface to root zone soil (see TRIM.FaTE TSD Volume II for more
10 The negative elasticity of solids density for divalent mercury concentration in soil is due to its use in the
TRIM.FaTE equation that converts the surface soil outputs from moles (the units used internally by TRIM.FaTE) to
concentration (in g/g dry weight).
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details). Thus, higher solids density in surface soil yields lower advection (percolation) to root
zone soil, which leads to lower divalent mercury concentrations in the earthworm.
The remaining properties with absolute elasticity values that exceed 0.1 are discussed
below.
The relatively small negative elasticity associated with the relationship between methyl
mercury Kd in surface soil and the concentration of divalent mercury in earthworm (-
0.12) is reasonable. At higher values of the methyl mercury Kd in surface soils, a higher
proportion of the methyl mercury would be sorbed to soil particles. Although most of the
mercury in surface soils is in the divalent form, a higher proportion of the methyl
mercury in surface soil being sorbed to soil particles means less methyl mercury reaching
the root zone soil layer where demethylation would convert it to divalent mercury.
The small negative elasticity for demethylation rate of methyl mercury in surface soils (-
0.11) seems puzzling initially. At higher rates of demethylation in surface soils, the
amount of divalent mercury, the product of methyl mercury demethylation, also should
be higher. Therefore, the amount of divalent mercury that reaches the subsurface layers
would be expected to increase also. However, the Kd for divalent mercury (50,000) is
much higher than the Kd for methyl mercury (3,000). Higher demethylation rates in
surface soil means higher ratios of divalent to methyl mercury in surface soil, which
means that less mercury overall is in the aqueous phase and available for diffusion or
percolation to subsurface layers. Thus, less methyl mercury reaches the root zone, where
it is available for transformation to divalent mercury and uptake into earthworms.
• At higher rates of divalent mercury methylation in surface soils, the proportion of
mercury in surface soil that is in the divalent form compared with the methylated form is
lower. Given the much higher Kd for divalent mercury than methyl mercury, as
discussed above, at higher methylation rates the ratio of methyl to divalent mercury is
higher, meaning that more mercury overall is in the aqueous phase and available for
diffusion and percolation to the root zone soil layer. Thus, more methyl mercury reaches
the root zone, where it is available for transformation to divalent mercury and uptake by
the earthworm, resulting in an positive elasticity (+0.11). This is an inverse relationship
to the one described above for demethylation rate.
5.2.4 Leaf
This section describes findings regarding the elasticity of the relationships between input
properties and divalent mercury concentration results for the leaf compartments. Properties with
absolute elasticity values greater than 0.1 for divalent mercury in the SSE4 leaf compartment
(coniferous forest) and SW2 leaf compartment (grasses/herbs) are shown in Exhibits 5-7 and 5-8
and discussed below. For the most part, the same properties have absolute elasticity values
greater than 0.1 for both vegetation types. Differences in elasticity between the two
compartments are largely due to the different vegetation types, not the different locations. The
two vegetation types have different base values for many of the compartment properties, such as
litter fall rate, wet mass per area, leaf dimensions, and Allow Exchange, and thus comparing leaf
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Exhibit 5-7
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Coniferous Forest Leaf Compartment SSE4
Allow Exchange Steady State for Air
(Leaf - Coniferous)
Emission Rate, Hg2+
Wet Dep Interception Fraction
(Leaf - Coniferous)
Water Content
(Leaf- Coniferous)
Litter Fall Rate r-
(Leaf - Coniferous) I—
Transfer Factor to Leaf Particle,,
Hg2+ (Leaf -Coniferous) I
Wet Mass per Area
(Leaf - Coniferous)
]J Air Advective Transfer
Allow Exchange Steady State for
Other (Leaf - Coniferous)
J Vapor Washout Ratio, Hg2+
J Rainfall Rate
Vapor Dry Deposition Velocity,
Hg2+ (Soil)
Attenuation Factor
(Leaf - Coniferous)
0.0
Elasticity
Exhibit 5-8
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Grasses/Herbs Leaf Compartment SW2
Emission Rate, Hg2+
Allow Exchange Steady State for Air
(Leaf-Grasses/Herbs)
Wet Dep Interception Fraction
(Leaf- Grasses/Herbs)
Water Content
(Leaf- Grasses/Herbs)
Air Advective Transfer
Litter Fall Rate (Leaf -
Grasses/Herbs)
Wet Mass per Area (Leaf -
Grasses/Herbs)
-i Vapor Dry Deposition Velocity,
J Hg2+ (Soil)
n Attenuation Factor
J (Leaf - Grasses/Herbs)
J Rainfall Rate
-i Vapor Washout Ratio,
J Hg2+
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
Elasticity
0.2
0.4
0.6
0.8
1.0
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results for these two locations is more like comparing the two surface water bodies (which have
significantly different property base values) in Section 5.2.5 than like comparing the different
locations for the other compartment types (e.g., air, surface soil, mammals).
The rank order of properties influencing the concentration of divalent mercury in leaves
of grasses/herbs and coniferous forest compartments is consistent with expectations. As with
other compartment types, the concentration of divalent mercury in the leaf is directly
proportional to the facility emission rate of divalent mercury (i.e., +1.0 for both coniferous
forests and grasses/herbs). The concentration of divalent mercury in leaves is also highly or
moderately sensitive to wet mass per area (elasticity of-0.82 for coniferous forests and -0.87 for
grasses/herbs); transfer factor to leaf particle (coniferous forest -0.48, grasses/herbs -0.05); litter
fall rate constant (coniferous forest -0.51, grasses/herbs -0.95); and several other properties that
control exchange of mercury between leaves and air under rain and non-rain conditions.
The wet mass per area property has a large negative influence on the concentration of
divalent mercury in leaves. The larger the wet biomass of leaves per unit surface area, the lower
the concentration of divalent mercury in leaves for a given deposition (and uptake) rate owing to
dilution of mercury in the leaves by their increased biomass. Note that deposition of mercury
from air to leaf occurs on a per-surface-area basis, not per-mass.
The elasticity for the transfer factor to leaf particle property is much larger for conifers
than for grasses/herbs. This could possibly be due to the fact that the AllowExchcmge for other
property value is higher for conifers (base value of 1.0 versus 0.386 for grasses/herbs), reflecting
that coniferous leaves (needles) are present 12 months of the year whereas the leaves of
grasses/herbs are present a fraction of the year. The AllowExchange for other property is a factor
in all exchanges between leaf and non-air compartments, such as the leaf particle, herbivores,
and stems. The elasticity for the litter fall rate property is higher for grasses/herbs than conifers,
reflecting the six times higher base value used for grasses/herbs. For both vegetation types, as
litter fall rate increases, the divalent mercury mass in the leaf compartment decreases (hence, the
negative elasticity).
Several other properties that control exchange of mercury between leaves and air under
rain and non-rain conditions also are influential on divalent mercury concentrations in leaves,
including AllowExchange for air (coniferous forest and grasses/herbs, +1.0); wet deposition
interception fraction (coniferous forest +0.70, grasses/herbs +0.83); vapor washout ratio and
rainfall rate (coniferous forest +0.35, grasses/herbs +0.64); attenuation factor (coniferous forest
+0.17, grasses/herbs +0.13); water content (coniferous forest -0.65, grasses/herbs -0.53); and
vapor dry deposition velocity (coniferous forest +0.27, grasses/herbs +0.14).
JULY 2005 5-19 TRIM.FATE EVALUATION REPORT VOLUME II
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5.2.5 Surface Water and Sediment
This section describes findings regarding the elasticity of the relationships between input
properties and divalent mercury concentrations in surface water and sediment compartments.
Properties with elasticity values greater than 0.1 for divalent mercury in Swetts Pond surface
water and sediment compartments are shown in Exhibits 5-9 and 5-10, respectively. Elasticities
relevant to methyl and elemental mercury in these compartments and to all three mercury species
in the river surface water and sediment compartments are presented in Appendix D.2.
In the subsections that follow, properties are discussed in the following categories:
(1) Properties with similar influence on divalent mercury concentrations in both surface
water and sediment compartments; and
(2) Properties with influence on divalent mercury concentrations in either surface water or
sediment compartments (but not both).
Influential Properties in Both Surface Water and Sediment Compartments
All 18 properties that are influential on divalent mercury concentrations in surface water
(i.e., elasticity greater than 0.1) are also influential on divalent mercury concentrations in
sediment. These properties affect the overall input or removal of divalent mercury mass to the
surface water/sediment system.11 For all but the suspended solids particle density in surface
water (represented by rho in the TREVI.FaTE library), these properties affect input to and
removal of divalent mercury mass from the surface water, and the mass to/from sediment
"follows" (chemical mass can only reach the sediment by traveling "through" surface water).
Suspended solids particle density influences removal of chemical mass from sediment (see
discussion further below) which in turn affects total chemical mass in the surface water/sediment
system.
Eight properties that are influential on divalent mercury concentrations in both surface
water and sediment compartments at Swetts Pond have elasticities that are similar to those
observed for surface water and sediment at the river.
The positive elasticity of nearly 1.0 associated with emission rate of divalent mercury is
reasonable because the emission rate dictates the total mass of divalent mercury in the
system. Divalent mercury is the species that dominates deposition, and therefore this
species also drives the transfer of mercury mass from air to surface water/sediment
systems. The positive elasticity near 1.0 indicates a directly proportional relationship.
11 All references to surface water and sediment concentrations in this section refer to total (dissolved +
sorbed to suspended or benthic sediment) chemical concentrations, unless otherwise specified.
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Exhibit 5-9
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Surface Water Compartment Swetts Pond
Solids Density (Surface Water;
Emission Rate, Hg2^
Water Temperature i
(Surface Water) L
Reduction Rate, Hg2+ (Soil) Q
Solids Density (Soil) [_
Reduction Rate, Hg2+
(Surface Water)
Time to Reach Alpha of p
Equilibrium, HgO (Macrophyte) *-
Horizontal Wind Speed [
E = 6.06
Flushes per Year
(Surface Water)
^ Rainfall Rate
^ Vapor Washout Ratio, Hg2+
I || || 1 Air Advective Transfer
1 Fraction of Area Available for
' Erosion (Soil)
| Total Erosion Rate (Soil)
Allow Exchange Steady-state for
Air (Leaf- Coniferous)
] Biomass Per Area (Macrophyte)
n Partition Coefficient, HgO
J (Macrophyte)
Dimensionless Viscous Sublayer
Thickness (Surface Water)
0.0
Elasticity
Exhibit 5-10
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Sediment Compartment Swetts Pond
Solids Density (Surface Water)
Emission Rate, Hg2+
Water Temperature r
(Surface Water) L
Reduction Rate, Hg2+ (Soil)|
Solids Density (Soil) Q
Reduction Rate, Hg2+|—
(Surface Water)
Time to Reach Alpha of ,
Equilibrium, HgO (Macrophyte) L
Suspended Sediment r
Concentration (Surface Water) *-
Horizontal Wind Speed [
Flushes per Year
(Surface Water)
-i Kd, Hg2+
J(Surface Water)
_] Rainfall Rate
-• Vapor Washout Ratio,
-J Hg2+
| Air Advective Transfer
Fraction of Area Available for
Erosion (Soil)
-llotal Erosion Rate (Soil)
Allow Exchange Steady-state for
Air (Leaf- Coniferous)
| Biomass Per Area (Macrophyte)
—I Partition Coefficient, HgO
—' (Macrophyte)
-i Dimensionless Viscous Sublayer
J Thickness (Surface Water)
0.0
Elasticity
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• Flushes per year of the surface water compartment has a large negative elasticity for both
surface water and sediment compartment types. This is logical because this property
directly affects the amount of mass removed from the surface water/sediment system.
Higher elasticities were evident for this property in the river compartments with regard to
divalent mercury (see Appendix D.2). This is consistent with the larger role of flushing
in mercury removal from the river. Advection to a flush rate sink accounts for nearly 90
percent of the mass removal from river surface water but only 7 percent of the mass
removal from Swetts surface water (not counting "removal" of divalent mercury via
reduction reactions).
• Elasticities for rainfall rate and divalent mercury vapor washout ratio are positive and
nearly identical for concentrations in surface water and sediment at Swetts Pond (and
similar for Swetts Pond and river compartments). Two other properties - fraction of soil
area available for erosion and total erosion rate - also have very similar positive
elasticities for concentrations in both surface water and sediment. These results are
consistent with expectations because input of divalent mercury to a given system is
positively proportional to the transfers from air to surface water/surface soil driven by
these properties (i.e., wet deposition of divalent mercury for rain and vapor washout
ratio, as described in Section 5.2.1, and input to surface water from eroding soil for
fraction of area available and total erosion rate, as described in Section 5.2.2).
The negative elasticities for reduction rate in surface soil and soil solids density seem
reasonable. The similarity of the elasticities for these two properties appears to be a
coincidence as they are not used in the same algorithms. A decrease in the value for
reduction rate would be expected to result in an increase in the amount of divalent
mercury in surface soil available for transfer to the aquatic system. A decrease in solids
density results in a higher erosion velocity, which results in more chemical transferred to
the surface water via erosion. The elasticities for both of these properties - as well as
fraction of soil area available for erosion and total erosion rate, which also influence
concentrations in soil - are discussed in more detail in Section 5.2.2 (surface soil
compartments). For solids density, note that although a negative elasticity is observed
for this property for chemical concentrations in surface soil (due to the conversion from
moles to concentration), a positive elasticity is calculated for chemical moles in surface
soil. This results in the negative elasticities for these two properties for divalent mercury
concentrations in surface water and sediment.
For these eight properties, the differences between the elasticities observed for divalent
mercury concentrations in Swetts Pond and the river are small despite the differences in the
configuration of each system. Differences between Swetts pond and the river include different
input values for some water body characteristics that affect mass transfer rates (e.g., flush rate,
suspended sediment concentration, and depth, which affects the surface area to volume ratio) and
a different parcel layout (which in turn affects the surface area of soil comprising the effective
watershed for the receiving surface water compartment). A plausible explanation for the similar
elasticities may be that these eight properties all primarily affect processes that occur outside of
the water body system (i.e., transfers from air to surface soil/surface water; transfers from
surface soil to surface water, primarily through erosion). The exception to this is flush rate,
JULY 2005 5-22 TRIM.FATE EVALUATION REPORT VOLUME II
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which directly affects mass removal from the system, and for which the largest differences in
elasticities were observed.
Six properties are relatively influential (i.e., absolute value of elasticity 0.1 to 0.5) for
divalent mercury concentrations in Swetts Pond surface water and sediment but much less
influential for divalent mercury concentrations in river surface water and sediment (i.e., elasticity
absolute values less than 0.01). These include:
• Water temperature;
• Reduction rate of divalent mercury in surface water;
• Steady-state AllowExchange property for conifer leaf compartments (AllowExchange
dictates active plant growth and presence of leaves);
• Time to reach alpha of equilibrium of elemental mercury in macrophyte;
• Biomass of macrophytes per area; and
• Partitioning coefficient for dissolved elemental mercury in the water column and
macrophyte.
For all of these properties, elasticities for divalent mercury concentrations in surface water and
sediment compartments in Swetts were very similar. In contrast to the eight properties discussed
previously, these six properties all appear to influence processes that occur within the surface
water/sediment system. Therefore, it seems reasonable that different elasticities for these
properties are observed for Swetts Pond and the river due to the differences in the two systems
(especially flush rate). Possible reasons for specific differences are discussed below.
The three properties related to macrophytes (partitioning time, biomass per area, and
partitioning coefficient) are all involved in the uptake of mercury by macrophytes in the surface
water, and the difference in values between locations suggest that this process is relatively more
important for Swetts Pond than for the river. Indeed, the transfer rate of divalent mercury from
surface water to macrophytes is about four times larger for Swetts Pond than the river and also
comprises a much larger fraction of the total divalent mercury mass transfer out of surface water
(35 percent of total transfer factor for mass transfer from Swetts Pond, versus less than 1 percent
of transfer factor from the river, based on the detailed mass transfer output for the test case
steady-state scenario). Reasons for the difference in surface water to macrophyte transfer rate
(and presumably the reverse transfer as well, i.e., back into macrophytes) are not entirely clear
but are quite possibly due to differences in residence time driven by the input value for flush rate
(see next paragraph). Additionally, the total macrophyte mass is actually greater for the river
(because the area of the surface water volume element is larger), but the dissolved fraction of
divalent mercury mass in the surface water is greater and the total surface water volume is
smaller at Swetts Pond (which would both result in increased transfer to macrophytes based on
the use of these properties in this algorithm).
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As noted previously, the input value for flushes per year is very different for these two
locations (4.3 per year for Swetts Pond surface water, versus 531 per year for the river). This
difference seems like a possible explanation for the difference in elasticities of these six
properties with regard to divalent mercury concentrations in Swetts Pond and the river. For
example, advective flushing to a sink is the dominant mass transfer process for removal of
mercury from the river surface water compartments, and this contributes to lower residence
times for mercury in the river (thus reducing potential for transfers into macrophyte or
sediment). This explanation may also be relevant to the difference between the two systems with
regard to elasticities for reduction rate (i.e., the residence time of mercury is much shorter in the
river, and therefore reduction of divalent mercury is comparatively less in the river than in
Swetts Pond). In short, the much larger water flow rate through the river system "swamps" other
processes that are more important in the more static Swetts Pond.
Allow Exchange of the conifer leaf compartments influences the amount of mercury
transferred from air to the leaf compartment. As shown in Exhibit 5-5, this property inversely
affects the amount of mass deposited to surface soil and that is then available for transfer to
Swetts Pond surface water via erosion and runoff. It is hypothesized that differences in
vegetation types (and the associated AllowExchange values) for the watersheds around Swetts
Pond and the river may be a factor in the different sensitivities of model outputs to these
properties for the two locations.
The higher elasticity for water temperature in Swetts Pond compartments (elasticity of
-0.54 for divalent mercury concentrations in both surface water and sediment) than in river
compartments (elasticity of-0.01 for both compartments) may be related to the classification of
Swetts Pond as a lake rather than a flowing water body. The algorithms used for lakes to
describe mercury transfers between air and surface water include a role for water temperature
(i.e., Henry's law constant and the water Schmidt number vary with temperature). An increase
in water temperature results in a decrease in transfer from water to air (which matches the
negative elasticity observed in this analysis). For flowing water bodies, a different algorithm is
used that does not depend on temperature. However, it should be noted that diffusion to air
comprises an extremely small fraction of the total divalent mercury mass transfer out of the
surface water compartment at steady-state (less than 10"4 percent for both sites). Therefore, it is
expected that these concentrations would be sensitive to water temperature via these processes
only if the small change in temperature leads to an extremely large (relative) change in the
transfer factor. Water temperature is also used in the algorithms for ingestion of food and
excretion by aquatic biota (see Section 5.2.6), but it is not clear how these algorithms might
affect Swetts Pond and the river differently. Overall, the reasons for the differences in elasticity
for water temperature between the two systems may need further examination.
Solids density of suspended particles in surface water is a highly influential property
(elasticities up to +6 and higher) for concentrations of divalent mercury in surface water and
sediment compartments at Swetts Pond. Mercury concentrations in fish compartments and some
biotic compartments that obtain their food from the aquatic environment (e.g., raccoons) are also
highly sensitive to this property due to their relationship through the food chain to surface water
and sediment concentrations. An investigation of TRTM.FaTE algorithms involving solids
density in surface water suggest that the sensitivity of this property is a special situation related
to the configuration of the TRIM.FaTE test case library (see accompanying text box). Although
JULY 2005 5-24 TRIM.FATE EVALUATION REPORT VOLUME II
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not incorrect, these very high elasticities could be misleading if not interpreted in the context of
this situation.
Solids density in surface water is much more influential for concentrations in Swetts
Pond compartments than for river compartments. This suggests that sediment burial, which is
affected by solids density in surface water, plays a larger role proportionally in removing mass
from the surface water at Swetts Pond than at the river. This is consistent with the observation
that advection via flushing plays a larger role in the river than in Swetts Pond and appears to
swamp out some other processes.
Influential Properties in Sediment Compartment Type Only
Two properties were identified as particularly influential on divalent mercury
concentrations in sediment but much less so on concentrations in surface water. It appears that
these two properties directly influence processes that dictate chemical concentrations in
sediment, whereas properties discussed previously in this section directly influence chemical
concentrations in surface water (which then affect chemical concentrations in sediment via
deposition/resuspension and other processes). Mercury concentrations in sediment seem to be
closely correlated with concentrations in surface water but the reverse does not appear to be true.
In other words, processes that directly affect chemical concentrations in sediment have much less
impact on concentrations in surface water, probably because there are other, more dominant
processes occurring in surface water.
Kd (partitioning coefficient) of divalent mercury in surface water is particularly
influential for divalent mercury concentrations in Swetts Pond sediment but much less so and in
a negative direction for surface water (elasticities of+0.8 for sediment, -0.04 for surface water).
The directions of these results make sense because increasing Kd results in increased chemical
sorption to the solid phase in surface water and therefore more divalent mercury mass available
for transfer to the sediment via deposition from surface water. The much larger absolute
elasticity for Kd for concentrations in sediment compared to concentrations in surface water also
seems logical. The dominant mass transfer process that influences chemical concentrations in
sediment is deposition (for which Kd directly affects amount of mass transferred), while the
sediment resuspension that occurs in reverse may be less significant for surface water chemical
concentrations relative to other processes that affect divalent mercury transfers into and out of
the surface water. The elasticity for Kd is about twice as large for chemical concentrations in
Swetts Pond sediment than those in river sediment. This may be due to the greater dominance of
flush rate for the river with regard to mercury removal from the surface water, making it
unavailable for transfer to the sediment. Conversely, transfer of chemical mass from surface
water to sediment via deposition is more dominant (relative to total mass transferred out of
surface water) in Swetts Pond than in the river. As a result, in Swetts Pond, a larger fraction of
the total mercury mass in the system resides in the sediment (i.e., the sediment to surface water
mass ratio is about four times larger in Swetts Pond than in the river), which is consistent with
the higher sensitivity for Kd for Swetts Pond. More investigation is needed to fully explain these
observations.
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Solids Density of Suspended Particles in Surface Water
High elasticities for surface water solids density are related to the use of this property in the algorithm representing
movement of mass from sediment to a sediment burial sink (i.e., "permanent" burial of chemical below the sediment
layer). The TRIM.FaTE library for the mercury test case is set up to maintain a constant volume of unconsolidated
benthic sediment in order to satisfy the condition that the depth of the volume element sediment layer remains
constant. Any net increase in the volume of sediment particles added to the benthic sediment via deposition of
suspended sediment in the surface water is offset by a corresponding transfer of sediment volume to a sediment
burial sink representing the consolidated sediment. The chemical mass burial rate to this sink is in turn calculated
based on the amount of chemical sorbed to benthic sediment particles.
In calculating mass transfers via sediment deposition, resuspension, and burial, several user-specified properties
(i.e., sediment deposition velocity, suspended sediment concentration, benthic sediment concentration, solids density
of suspended particles, and solids density of benthic sediment particles) are used to calculate resuspension velocity
and volumetric deposition and resuspension rates. For the mercury test case, including the sensitivity analysis base
case, sediment burial does not occur because (based on the user-specified property values used in the test case) the
suspended sediment volumetric deposition rate is exactly equal to the benthic sediment resuspension rate. There
is no increase inbenthic sediment volume; consequently, sediment transfer to the burial sink (and the corresponding
transfer of chemical mass) is zero.
For the sensitivity analysis, solids density in surface water is decreased by one percent while the solids density of
benthic sediment particles remains the same. This difference in particle density (in combination with the formula
properties used in the TRIM.FaTE library) results in a deposition rate greater than the resuspension rate. In order
to maintain constant benthic sediment volume, the net difference is offset by a positive transfer of sediment to the
sediment burial sink. As a result, mercury mass is also transferred from the benthic sediment to the sink, resulting
in a net removal of chemical mass from the surface water/sediment system. The presence of this mass removal
process results in a decrease in sediment and surface water concentrations that drives the high positive elasticity
for surface water and sediment compartments (i.e., a decrease in solids density in surface water results in lower
mercury concentrations in surface water and sediment compartments).
Separate test runs indicate that if the solids densities for both surface water and sediment are reduced in parallel,
the elasticities of concentrations in Swetts Pond surface water and sediment are significantly less than the values
obtained in the sensitivity analysis (elasticities of about+1.0 for sediment concentrations and+0.1 or less for surface
water concentrations). This occurs because the volumetric deposition and resuspension rates are affected in the
same proportions, and no sediment burial occurs (i.e., the transfer factor for the mass transferto the sediment burial
sink is zero). These results reflect the correlation between surface water and sediment solids density in the context
of model sensitivity and suggest that the values for these properties should be selected carefully in any model
applications.
Further testing indicates that the mercury test case scenario is not sensitive to increases in surface water solids
density. In other words, the sensitivities of mercury concentrations in surface water, sediment, and other
compartments to solids density in surface water are nonlinear around zero. This is the result of a conditional
statement in the TRIM.FaTE algorithm for transfer to a sediment burial sink that prevents the calculation of a
negative burial rate. If the volumetric resuspension rate is greater than the suspended sediment deposition rate -
the condition that results from increasing surface water solids density - the model assumes a value of zero for
sediment burial to prevent the occurrence of a negative transfer factor. Testing also confirms that surface water and
sediment concentrations are sensitive to benthic sediment particle density by the same amount but in the opposite
direction - in other words, increasing the sediment solids density by one percent results in larger elasticities for
concentrations in surface water and sediment similar to those observed for a one percent decrease in surface water
solids density.
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Suspended sediment concentration in surface water is a relatively influential property on
divalent mercury concentrations in the river sediment compartment but less influential on
concentrations in Swetts Pond sediment (elasticities of-0.6 for river sediment, -0.14 for Swetts
sediment). It is not intuitive why this elasticity is negative (i.e., a lower value for this property
results in a higher divalent mercury concentration in benthic sediment). Possibly, the decreased
suspended sediment in surface water might result in a shift in "loss" transfers from surface water
from particle deposition to advection out of surface water to the flush rate sink. Alternatively,
decreased suspended sediment in the surface water might result in less of the chemical remaining
in the suspended sediment due to a decrease in surface area in suspended sediment, thereby
resulting in more chemical mass transferred to the benthic sediment via other processes (i.e.,
diffusion and deposition of algae). This property is not influential for chemical concentrations in
surface water in either system, possibly because sediment deposition/resuspension processes
drive chemical concentrations in sediment, while other processes dictate chemical concentrations
in surface water. Alternatively, the total chemical loss rate from surface water does not change
but simply shifts some mass transfer from deposition (to sediment) to advection (to the sink).
The difference between the river and Swetts Pond may be related to the input values assigned for
suspended sediment concentration - the suspended sediment concentration in surface water is
much larger for the river than for Swetts Pond (river > Swetts by 10 times). Consequently, the
fraction of total divalent mercury mass in the surface water column that is sorbed to suspended
sediment is greater at the river than at Swetts Pond. The different flush rates for these two
systems may also play a role in the different elasticities that are observed. However, more
investigation of the roles of specific processes and algorithms is needed to fully explain the
elasticity values for this property.
5.2.6 Aquatic Food Chain
This section describes findings regarding the elasticities of the relationships between
model input properties and methyl mercury concentrations in benthic invertebrates, benthic
carnivores, benthic omnivores, water-column carnivores, water-column herbivores, and water-
column omnivores in Swetts Pond. The properties demonstrating absolute elasticity values
greater than 0.1 for methyl mercury in the Swetts Pond benthic invertebrate and water-column
carnivore compartments are presented in Exhibits 5-11 and 5-12, respectively, and are discussed
below. In comparison with the other TRIM.FaTE compartment types and mercury species
examined, more properties are influential on methyl mercury concentration (i.e., absolute
elasticity > 0.1) in the aquatic food chain compartment types.
The scenario analyzed included two independent food chains, a benthic food chain and a
water-column food chain (see Appendix I-B in EPA 2002a). The benthic invertebrate
compartment (Exhibit 5-11) represents the base of the benthic food chain. The water-column
carnivore (Exhibit 5-12) is the top aquatic predator in the aquatic food chain.
This section is organized in four main parts by the type of property that influences the
results: (1) abiotic properties, (2) properties related to fish compartment biomass, (3) algal and
benthic invertebrate compartment properties, and (4) other properties of biotic compartments.
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Abiotic Properties
In general, the elasticities of the relationships between abiotic properties and methyl
mercury concentrations in the benthic invertebrate and water-column carnivore compartments of
Swetts Pond seem appropriate. The discussion in this section is organized as follows. First,
properties that most influence (i.e., absolute elasticity values > 0.1) results for both compartment
types are discussed. Then properties that most influence methyl mercury concentrations in the
benthic invertebrate compartment, but not the water-column carnivore compartment, are
discussed. Finally, properties that most affect results in the water-column carnivore
compartment, but not the benthic invertebrate compartment are discussed.
Exhibit 5-11
Input Properties with Absolute Elasticity Value > 0.1 -
Methyl Mercury Concentration in Benthic Invertebrate Compartment Swetts Pond
Solids Density (Surface Water)
b = -1.U1
Sediment Partition Coefficient;
MHg (Benthic Invertebrate) .
Emission Rate, Hg2-i
Methylation Rate, Hg2+
pnrn=jtv (Sediment)
1
AirAdvective Iranster | || || |llll
Reduction Rate, HCK+ (Soil) I
Solids Density (Soil) |
Time to Reach Alpha of . '.
Equilibrium, HgO (Macrophyte) ' :
Suspended Sediment . :
Concentration (Surface Water) ' :
Horizontal Wind Speed I
Demethylation Rate, MHg (Sediment)
1
1
1 Kd. Hd2+
Flushes per Year (Surface Water)
(Surface Water)
1 Rainfall Rate
1 vapor washout Ratio, HCK+
1 II II 1
1 lotal brosion Rate (Soil)
| solids Density (Sediment)
Allow Exchange Steady State for
Air (Leaf - Coniferous)
1 Biomass Per Area
' (Macrophyte)
1 Partition Coefficient, HgO
(Macrophyte)
1 Dimensionless Viscous Sublayer
' Thickness (Surface Water)
.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1
Elasticity
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Exhibit 5-12
Input Properties with Absolute Elasticity Value > 0.1 -
Methyl Mercury Concentration in Water-column Carnivore Compartment Swetts Pond
Solids Density (Surface Water)
E = -1.39
E = -1
Assimilation btticiency from l-ood, MHg
(Water-column Herbivore)
Algae Uptake Rate, MHg (Surface Water)
Emission Rate, Hg2+
Sediment Deposition L
elocity (Surface Water)
Assimilation Efficiency from Food,
MHg (Water-column Carnivore) _
Vapor Washout
Ratio, Hg2+
Assimilation Efficiency from Food,
MHg (Water-column Omnivore)
Relative Excretion Rate, MHg
(Water-column Herbivore)
Relative Excretion Rate, MHg
(Water-column Omnivore)
Body Weight
(Water-column Carnivore)
L
Solids Density (Soil)
Demethylation Rate, MHg (Soil) [_
Reduction Rate, Hg2+ (Surface Water) ]_
Demethylation Rate, MHg (Sediment)
Horizontal Wind Speed |
Time to Reach Alpha of Equilibrium,
HgO (Macrophyte)
Wet Dep Interception Fraction
(Leaf - Coniferous) |
E = 4.93
Water Temperature (Surface
Algae Density (Surface Water)
Algae Growth Rate (Surface Water)
Algae Radius (Surface Water)
Kelative bxcretion Kate, MHg
(Water-column Carnivore)
Kd, MHg (Surface Water)
Flushes per Year (Surface Water)
Suspended Sediment
•Concentration (Surface Water)
H Methylation Rate,
Hg2+ (Surface Water
I Rainfall Rate
Number of Fish per Square
_l
Meter (Water-column Herbivori)
Body Weight (Water-
column Herbivore)
Number of Fish per Square Meter
(Water-column Carnivore)
Air Advective Transfer
Reduction Rate, Hg2+ (Soil)
Fraction of Area Available
J for Erosion (Soil)
] Total Erosion Rate (Soil)
Methylation Rate, Hg2+ (Soil)
Allow Exchange Steady State
for Air (Leaf - Coniferous)
Methylation Rate, Hg2+ (Sediment)
| Dimensionless Viscous Sublayer
Thickness (Surface Water)
I Biomass Per Area (Macrophyte)
| Partition Coefficient, HgO
(Macrophyte)
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
Elasticity
0.2
0.4
0.6
0.8
1.0
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Properties Influencing Methyl Mercury Concentrations in Both Benthic and Water-
column Compartments. For methyl mercury concentrations in both the benthic invertebrate and
water-column carnivore compartment types, the most influential property is solids density
(represented by rho in the TRIM.FaTE library) for the particles suspended in the surface water
(elasticity of+7.0 for benthic invertebrates and +4.9 for water-column carnivores). An
investigation of TRIM.FaTE algorithms involving solids density in surface water suggests that
the sensitivity of results to this property is a special situation related to the configuration of the
TRIM.FaTE test case library (see text box in Section 5.2.5).
As shown in Exhibits 5-11 and 5-12, five other abiotic properties - emission rate, water
temperature, flushes per year, rainfall rate, and divalent mercury vapor washout ratio - exhibit a
relatively high degree of influence on methyl mercury concentrations in both compartments. As
with many of the compartment types, the mercury concentrations, including methyl mercury
concentrations, in all aquatic organism compartments are directly proportional to the facility
emission rate for divalent mercury (elasticity of+1.0). The moderate negative influence of water
temperature on methyl mercury concentrations in the benthic invertebrate compartment may be
the result of higher rates of volatilization of elemental and divalent mercury from the surface
water into the air with higher water temperatures, resulting in lower mercury concentrations in
the aquatic system. The large negative influence of the water temperature property on the
methyl mercury concentrations in the water-column carnivore (elasticity value of-1.4) and the
other fish compartments (see Appendix D.2) may reflect both the previous relationship and the
fact that water temperature is in the exponential position in the equation that estimates food
ingestion rates for fish. The property flushes per year determines the surface water dilution rate;
thus, the higher the number of flushes per year and dilution rate, the lower the concentrations of
methyl mercury in the surface water, sediments, and aquatic biota. The high positive elasticity
for the relationships between both rainfall rate and divalent mercury vapor washout ratio and
concentrations of methyl mercury in the aquatic animal compartments is expected. Similar to the
findings for surface soil, higher values for both of those properties result in more divalent
mercury being deposited from the air to surface water (and then to sediments).
Four soil properties related to the movement of mercury in soils to surface water via
erosion - divalent mercury reduction rate, surface soil solids density, fraction of area available
for erosion, and total erosion rate - exhibited moderate degrees of influence on methyl mercury
concentrations in the benthic invertebrate and fish compartments. The negative elasticity values
for the influence of divalent mercury reduction rate in soils on methyl mercury concentrations in
aquatic organisms indicate that at lower rates of transformation of divalent to elemental mercury
in soil, more divalent mercury remains in the soil for transport to surface water via erosion.
(Elemental mercury tends to volatilize back into the air from surface soil.) The negative
elasticity for soil solids density on methyl mercury concentrations in aquatic biota is consistent
with the findings for the surface soil compartment on the relationship between soil solids density
and mercury concentrations in soil. Lower values for soil solids density results in higher soil
mercury concentrations (see Section 5.2.2), hence more mercury is available for erosion into the
surface water. The positive elasticities for fraction of area available for erosion and total erosion
rate on methyl mercury concentrations in the aquatic biota is expected because as more soil
erodes, more of the total mercury mass sorbed to soil particles will be transferred to surface
water (and sediments), where it can enter the aquatic food chains.
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Properties Influencing Methyl Mercury Concentrations in Benthic Food-chain
Compartments. Methyl mercury concentrations in the benthic invertebrate compartment (and
the fish compartments in the benthic food chain) are sensitive to several abiotic properties that
affect methyl mercury concentrations in sediments - mercury methylation and demethylation
rates in sediment, Kd for divalent mercury (surface water), and divalent mercury reduction rate
in surface water. Methyl mercury concentrations in benthic invertebrates are directly
proportional to the sediment methylation rate and inversely proportional to the sediment
demethylation rate, as expected. At higher values for the divalent mercury Kd in surface water,
methyl mercury concentrations in benthic invertebrates are higher. This result is expected
because at higher values of divalent mercury Kd, more of the divalent mercury entering the
surface water is sorbed to suspended sediment particles, and more divalent mercury reaches the
sediments (where it is methylated) through deposition of suspended sediment particles. The
moderate negative relationship between the reduction rate of divalent mercury in surface water
and methyl mercury concentrations in benthic invertebrates reflects the fact that higher reduction
rates in surface water transform more of the divalent mercury into elemental mercury, which
leaves the surface water via volatilization into the air.
Similarly, sediment porosity and sediment solids density influence methyl mercury
concentrations in the benthic invertebrate compartment but not the water-column carnivore
compartment. A high negative elasticity exists between sediment porosity and methyl mercury
concentrations in benthic invertebrates. The less porous the sediment, the higher the fraction of
the sediment volume that is comprised of solid particles to which methyl mercury tends to sorb.
Because the partitioning coefficient that defines the extent of methyl mercury uptake from
sediments by benthic invertebrates is based on bulk sediments (not the interstitial water), lower
values of sediment porosity result in higher concentrations of methyl mercury in bulk sediments
and benthic invertebrates. A moderate positive elasticity holds for the relationship between the
sediment solids density property and concentrations of methyl mercury in benthic invertebrates
because of the positive influence of sediment solids density on the concentration of mercury in
bulk sediment (see Section 5.2.5).
Properties Influencing Methyl Mercury Concentrations in Water-column Food-chain
Compartments. Methyl mercury concentrations in the water-column fish compartments are
sensitive to variation in several properties that influence the dissolved concentration of mercury
in surface water. Three properties that influence the rate at which mercury is removed from the
water column by sedimentation exhibit strong negative relationships to methyl mercury
concentrations in the water-column fish: methyl mercury Kd in surface water, suspended
sediment concentration, and sediment deposition velocity. The negative elasticity for methyl
mercury Kd in surface water and methyl mercury concentrations in the water-column carnivore
is appropriate. The lower that Kd value, the less mercury is "scavenged" from the water column
by sorption to sediments and deposition to the sediment bed, and the more mercury remains in
the water-column for transport into and through the water-column food chain. The negative
elasticity values associated with both the suspended sediment concentration and sediment
deposition velocity properties reflect the same process.
The positive elasticity associated with the relationship between divalent mercury
methylation rate in surface water and methyl mercury concentrations in the water-column
carnivore is expected for all of the water-column fish compartments. Similarly, the elasticity
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values for the other properties that affect methyl mercury concentrations in the water-column
carnivore compartment (i.e., demethylation and methylation rates in soil, reduction rate of
divalent mercury in surface water, demethylation and methylation rates in sediment, and the
dimensionless viscous sublayer thickness for surface water) are similar to those for the other
water-column fish compartments.
Fish Compartment Biomass Properties
For the aquatic ecosystems in this application, a food chain was constructed with a
trophic pyramid of biomass in fish intended to reflect that of northern lakes in the US (see
Appendix I-B in EPA 2002a). Because this application used a bioenergetics model to simulate
mercury transfers through the food chains, the distribution of biomass among trophic levels
affects the distribution of contaminant amount and concentration in each trophic level. In an
ecosystem with a different pyramid of biomass, a different distribution of contaminants across
trophic levels would be expected.
The discussion in this section explores the influence of biomass at the different trophic
levels on mercury concentration in all fish compartments. In this scenario, the biomass (per unit
area) for the fish compartments was set as the product of two properties: body weight (BW) and
number offish per square meter (# offish). Exhibit 5-13 illustrates the influence of biomass in
each Swetts Pond fish compartment on methyl mercury concentrations in each Swetts Pond fish
compartment. The scenario analyzed included two independent food chains, a benthic food
chain and a water-column food chain.
The elasticity values in Exhibit 5-13 reveal three general patterns of influence offish
compartment biomass properties on methyl mercury concentrations in the fish compartments:
influence of biomass changes at the bottom of the food chain, influence of biomass changes at
the top of the food chain, and influence of biomass changes in the middle of the food chain.
Biomass in the fish compartments at the bottom of the fish food chain (i.e., benthic
omnivore in the benthic food chain and water-column herbivore in the water-column food chain,
respectively) demonstrate a positive influence on methyl mercury concentrations in all fish
compartments (positive elasticity values). A greater biomass in the bottom food chain
compartments results in higher methyl mercury concentrations in those fish compartments. The
reason for this is because the greater the biomass in a bottom-trophic-level-fish compartment, the
more methyl mercury is transferred from its food source into that fish compartment. However,
given the same biomass of higher-trophic level fish, a smaller proportion of the total methyl
mercury in the bottom trophic level fish can be removed each day the higher-trophic level fish.
That results in higher methyl mercury concentrations in the bottom trophic level compartments.
Those higher concentrations are transferred up the food chain, so that concentrations in the upper
trophic levels are also positively affected by biomass of the bottom trophic level compartments.
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Exhibit 5-13
Analysis of Influence of Biomass in One Trophic Level on Methyl Mercury
Concentrations in Other Trophic Levels of the Aquatic Food Chain
Biomass Properties for:
Influence on Methyl
Mercury Concentrations in:
(Sign) and Elasticity Value
forBW/#ofFish a
Benthic Food Chain
Omnivore
Omnivore
Carnivore
Carnivore
Carnivore
Omnivore
Carnivore
Omnivore
(+)0.32 /0.34
(+) 0.32 70.34
(-)< 0.1 70.22
(-) 0.23 7 0.27
Water-column Food Chain
Herbivore
Herbivore
Herbivore
Omnivore
Omnivore
Omnivore
Carnivore
Carnivore
Carnivore
Carnivore
Omnivore
Herbivore
Carnivore
Omnivore
Herbivore
Carnivore
Omnivore
Herbivore
(+) 0.48 7 0.54
(+) 0.48 7 0.54
(+) 0.48 7 0.54
< | 0.1
< | 0.1
(-) 0.44 7 0.51
(-) 0.46 7 0.34
(-) 0.43 7 0.50
< | 0.1
3 All data for Swetts Pond compartments.
Biomass in the fish compartments at the top of a fish food chain (i.e., carnivore
compartments in both food chains, respectively) demonstrates a negative influence on methyl
mercury concentrations in all fish compartments in that food chain. A greater biomass in a top
carnivore compartment results in lower methyl mercury concentrations in that fish compartment.
That result occurs because the greater the biomass of the top carnivore, the more methyl mercury
they remove each day from their prey compartment. That results in lower methyl mercury
concentrations in the prey, and therefore lower concentrations of methyl mercury in the top
carnivore. The negative elasticity values are larger for the relationships observed in the water-
column food chain (i.e., maximum of-0.50) than for the relationships observed in the benthic
food chain (i.e., maximum of-0.27). That is because the starting biomass of the water-column
carnivore is approximately 30 percent of the biomass of the water-column omnivore, while the
starting biomass of the benthic carnivore is approximately 10 percent of the biomass of the
benthic omnivore.
The effect of changes in the properties related to biomass for a middle-trophic-level
compartment (i.e., the water-column omnivore) is only pronounced (i.e., exhibits an absolute
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elasticity value greater than 0.1) for methyl mercury concentrations its prey compartment (i.e.,
the water-column herbivore). The negative elasticity of that relationship is similar to that for the
relationship between the biomass of the top carnivore and methyl mercury concentrations in its
prey compartment. The starting biomass of the water-column omnivore compartment is 36
percent of the biomass of the herbivore compartment. The lack of a pronounced effect of water-
column omnivore biomass on methyl mercury concentration in that compartment (and therefore
the higher trophic level compartment) probably reflects opposing influences related to the higher
and lower trophic level compartments on methyl mercury in the middle-trophic-level-fish
compartment.
Algal and Benthic Invertebrate Properties
Algae forms the base of the water-column food chain, while benthic invertebrates form
the base of the benthic food chain. Several algal and benthic invertebrate-related properties
influence methyl mercury concentrations in the compartments in the water-column and benthic
food chains, respectively.
The concentration of methyl mercury in all water-column fish is inversely proportional to
the value of several algal properties (i.e., algal density, algal growth rate, and algae radius,
elasticity -1.0) and directly proportional to the algae uptake rate for methyl mercury from surface
water (elasticity +1.0). The former three properties together affect algae biomass, with higher
values for the algal density, algal growth rate, and algae radius properties resulting in a higher
algae biomass. For a fixed algae methyl mercury uptake rate not normalized to algae biomass,
the higher the algae biomass, the lower the concentration of methyl mercury in algae, and hence
the lower the concentration of methyl mercury in the water-column fish. The opposite is true for
the algae uptake rate property. At higher values of that property, the mass and concentration of
methyl mercury in algae are higher, and hence the methyl mercury concentrations in all fish in
the water-column food chain are higher.
The concentration of methyl mercury in benthic invertebrates (and in the benthic fish
compartments, see Appendix D.2) is directly proportional to the benthic invertebrate sediment
partition coefficient (elasticity +1.0), which reflects the extent to which methyl mercury in bulk
sediment concentrates in the benthic invertebrate compartment. Unlike the role of the bottom-
trophic-level fish compartment biomass, benthic invertebrate biomass exerts little influence on
methyl mercury concentrations in higher trophic level compartments (elasticity values <|0.1|).
That is because the biomass of the benthic omnivore compartment is only five percent of the
biomass of the benthic invertebrate compartment.
Other Biotic Properties
Several other properties of the aquatic biotic compartments influence methyl mercury
concentrations in those and other biotic compartments. Methyl mercury concentrations in all
aquatic biota compartments are somewhat sensitive (i.e., absolute elasticity values -0.1) to three
macrophyte properties that influence the extent to which elemental mercury in the water column
partitions into the macrophytes rather than remains available for oxidation to divalent mercury
(and methylation to methyl mercury) and transfer to the sediments via sedimentation and
diffusion. Those three properties are the time to reach alpha (i.e., 95 percent) of equilibrium for
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elemental mercury in macrophytes, the macrophyte/water partition coefficient for elemental
mercury, and the biomass of macrophytes per unit area. For shorter times to equilibrium and
higher macrophyte/water partition coefficients, higher concentrations of mercury in macrophytes
result. The reason that the macrophyte biomass and partition coefficient properties have a
positive influence on concentrations of methyl mercury concentrations in water-column
carnivores is probably related to speciation and transformation of mercury in the surface water
and in macrophytes, although additional analysis would be needed to determine the reasons for
this positive elasticity with confidence. Increasing the amount of elemental mercury uptake into
macrophytes changes the mercury speciation profile in surface water (less elemental, more
divalent and methyl), which affects the uptake of methyl mercury into fish.
Methyl mercury concentration in the water-column carnivore fish compartment is
sensitive to two biotic properties associated with fish compartments in addition to the biomass-
related properties described above. These are the chemical assimilation efficiency from food and
relative excretion rate for methyl mercury. As expected, the concentration of methyl mercury in
water-column carnivores increases with increasing assimilation efficiency of methyl mercury by
all three water-column fish compartments, with that for the water-column herbivore being the
strongest relationship (positive elasticity of 1.0 for the herbivore property vs -0.50 for the
assimilation rate of the other two water-column fish compartments). An opposing influence is
exhibited by the excretion rate for methyl mercury property for all three water-column fish
compartments.12 In this case, the strongest relationship is for the water-column carnivore
excretion rate (negative elasticity of-1.0 vs -0.5 for the excretion rate associated with the water-
column herbivore and omnivore compartments). Similar trends are seen in the other fish
compartments (see Appendix D.2).
One terrestrial plant property for the leaf compartment type - Allow Exchange for air -
has a negative influence on methyl mercury concentrations in aquatic biota. As described in
Section 5.2.2, at lower values of $\Q Allow Exchange property, higher concentrations of mercury
occur in surface soil. AllowExchange indicates the presence of viable vegetation, and in a
dynamic simulation its value varies by season. For the steady-state mode, its value was set to a
constant reflecting the proportion of the year that is the growing season. The negative elasticity
values for the relationship of that property to methyl mercury concentrations in the benthic
invertebrate and water-column carnivore compartments indicates that at higher values of
AllowExchange, mercury concentrations in surface water and sediments are lower. Higher
values of AllowExchange mean that mercury is tied up in the vegetation for longer periods of
time (i.e., in the steady-state mode, more mercury is associated with the vegetation), and less
mercury is reaching the soil surface. The lower concentrations of mercury in surface soil results
in a lower input of surface soil mercury to surface water via erosion.
12 See Appendix A. 1.3 of TRIM.FaTE TSD Volume II for details of how excretion is estimated. In addition
to evaluating the relative excretion rate for divalent mercury and/or elemental mercury properties (base value set to
3.0 for both), the relative excretion rate for methyl mercury property (base value set to 1.0) was varied to examine
the influence of the algorithm used to calculate methyl mercury excretion rate. As expected, this excretion rate has a
large influence on methyl mercury concentration for the applicable compartment type, and lesser influence for
compartment types representing higher trophic levels (e.g., excretion rate for carnivores has high negative elasticity
for carnivores, excretion rate for omnivores and herbivores has smaller negative elasticity for carnivores).
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5.2.7 Terrestrial Mammals
This section describes findings regarding the elasticities of the relationships between
model properties and divalent mercury concentration results in the mouse, raccoon, and deer
compartments in SW2. The properties demonstrating absolute elasticity values greater than 0.1
for divalent mercury in those compartments are presented in Exhibits 5-14, 5-15, and 5-16,
respectively, and discussed below.
Exhibit 5-14
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Mouse Compartment SW2
E = -1.01
Emission Rate, Hg2+
Assimilation Efficiency from
Soils, Hg2+ (Mouse)
Soil Ingestion Rate (Mouse)
Total Erosion Rate (Soil) |
-
Air Advective Transfer | 1 1 |
-
Solids Density (Soil) |
-
Reduction Rate, Hg2+ (Soil) |
Total Excretion Rate, Hg2+
(Mouse)
I
Ratio Hg2+
| Rainfall Rate
I
.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1
Elasticity
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Exhibit 5-15
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in White-tailed Deer Compartment SW2
F = -1 m
Emission Rate, Hg2+
Rainfall Rate
Soil Ingestion Rate "
(White-tailed Deer)
l_itt°r Fall Pat° (L°af -
Air Advective I ranster II III
Bodv Weiaht (White-tailed Ueer) I
I
I
Water Content (I eaf -
I
Reduction Rate. HCK+ (boil) I
Vapor Dry Deposition Velocity,
Hg2+ (Soil)
(White-tailed Deer)
I
Hg2+
|
I
Allow Exchange -^tpady -^tate for
II I I
Transfer Factor to Leaf Particle.
| Wei Dep Inleiceplion Fiaclion
(Leaf - Grasses/Herbs)
Number of Individuals per
Square Meter (White-tailed Deer)
Total Erosion Rate (Soil)
Solids Density (Soil)
I 1 Food Ingestion Rate
• ' (White-tailed Deer)
.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1
Elasticity
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Exhibit 5-16
Input Properties with Absolute Elasticity Value > 0.1 -
Divalent Mercury Concentration in Raccoon Compartment SW2
E = -1.01
Soil Ingestion Rate
(Raccoon)
Emission Rate, Hg2+
Assimilation Efficiency from
Soils, Hg2+ (Raccoon)
Total Erosion Rate (Soil) |
Air Advective Transfer | | || |
Reduction Rate, Hg2+ (Soil) |
Solids Density (Soil) |
Total Excretion Rate,
Hg2+ (Raccoon)
| Rainfall Rate
I
.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1
Elasticity
The top two most influential properties for divalent mercury concentrations in all three
terrestrial mammals are facility emission rate of divalent mercury and total excretion rate of
divalent mercury. As with many of the other compartment types, the facility emission rate for
divalent mercury has a directly proportional positive influence on divalent mercury
concentrations in terrestrial animal compartments (elasticity value of+1.0). The effect of total
divalent mercury excretion rate on the concentrations of divalent mercury in the three mammal
compartment types (elasticity values of-1.0) is logical because excretion is the only modeled
process by which the mammals can eliminate divalent mercury from their bodies.
The relative sensitivity of divalent mercury concentration in the three mammalian species
to the remaining input properties varies according to the relative soil ingestion rate, body weight,
diet, and food ingestion rate for the three species.
For all three mammal compartment types, the next four most influential properties are
ones that strongly influence the incidental intake of divalent mercury in the soil, either directly
(assimilation efficiency from soils, soil ingestion rate) or indirectly by influencing the amount of
divalent mercury in surface soil (vapor washout ratio, rainfall rate). The fact that the
concentration of divalent mercury in all three mammals is much more sensitive to the
assimilation efficiency from soils and soil ingestion rate properties than to the food ingestion rate
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property indicates that, in the scenario modeled, incidental ingestion of divalent mercury sorbed
to soil particles is a more important exposure route for divalent mercury for these species than
ingestion of divalent mercury in food. That reflects the overall lower concentration of divalent
mercury in biota than in soils at steady-state.13 Each species is discussed in turn below.
The positive elasticity values for assimilation efficiency from soils (+0.97) and soil
ingestion rate (+0.91) are reasonable for the mouse given its relatively high direct soil ingestion
rate (0.02 kg/kg-day used in the sensitivity analysis). The higher sensitivity of mouse divalent
mercury concentration to soil ingestion properties than to food ingestion properties indicates that
at steady-state the majority of divalent mercury intake by the mouse is the incidental ingestion of
divalent mercury in soil. The positive elasticity values for vapor washout ratio for divalent
mercury and rainfall rate (both +0.68) reflect the influence of those properties on the amount of
divalent mercury deposited to the soil (and also to leaves) per unit time. The higher the values
for those two properties, the higher the soil (and leaf) concentration of divalent mercury, which
then is taken up by the mouse through incidental ingestion of soil (and in some cases ingestion of
leaves).
The concentration of divalent mercury in the raccoon is as sensitive to soil ingestion rate
and assimilation efficiency from soils as it is to facility emission rate, whereas concentration in
the mouse was slightly less sensitive to those two properties than to the facility emission rate.
This reflects the slightly higher body-weight-normalized soil ingestion rate for the raccoon
(0.094 kg/kg-day used in the sensitivity analysis) than for the mouse. The positive elasticity
values for the vapor washout ratio for divalent mercury and rainfall rate are nearly identical to
the values for the mouse.
The elasticity values for the same four properties (i.e., assimilation efficiency from soils,
soil ingestion rate, vapor washout ratio, and rainfall rate) for the white-tailed deer differ slightly
from values for the mouse and raccoon. The higher importance of vapor washout ratio and
rainfall rate over the soil intake properties for the deer probably reflects the importance of vapor
washout ratio and rainfall rate not only for deposition of divalent mercury to soils, but also for
deposition of divalent mercury to leaves, which comprise 100 percent of the diet of deer (only 50
percent of the diet of the mouse). Also, deer have a much lower incidental soil ingestion rate
(0.001 kg/kg-day used in the sensitivity analysis) than the raccoon or mouse.
The similarities between the raccoon and mouse results compared with the white-tailed
deer results end after the first six most influential properties. The next four most influential
properties (aside from the air advective transfer property, not discussed in this section) are the
same for both the raccoon and mouse - both compartment types have the same relative negative
elasticity values for the fraction of area available for erosion, total erosion rate, surface soil
solids density, and divalent mercury reduction rate in surface soil properties. These four
properties all relate to the incidental ingestion of soil. The elasticity values for the raccoon are
13 As described in Section 5.1, a previous version of the TRIM.FaTE library was used for the sensitivity
analysis. That version inadvertently had soil ingestion rates for some animals that were higher than appropriate for
the modeled scenario. The modeled rate for raccoon was 32 times too high, for mouse was 20 times too high, and
for deer was 7.7 times too high.
JULY 2005 5-39 TRIM.FATE EVALUATION REPORT VOLUME II
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slightly more negative than for the mouse, as expected given the raccoon's somewhat higher soil
ingestion rate. Higher values for the fraction of area available for erosion and total erosion rate
properties result in lower concentrations of divalent mercury in surface soil, because more
divalent mercury sorbed to surface soil particles is removed (by erosion) from a given surface
soil compartment. Lower values for both the solids density and divalent mercury reduction rate
properties in surface soil result in higher divalent mercury concentrations in soil, as described in
Section 5.2.2.
In contrast to the raccoon and mouse, the next five most influential properties (after the
top six) for the white-tailed deer are ones affecting the concentration of divalent mercury in plant
leaves: litter fall rate, AllowExchange (both properties), transfer factor to leaf particle, and wet
deposition interception fraction. Because the deer has a relatively low soil ingestion rate
compared with the raccoon and mouse, and because its diet is 100 percent plant leaves, it makes
sense that the concentration of divalent mercury in deer would be relatively more sensitive to the
concentration of divalent mercury in leaves.
• The moderately negative elasticity (-0.44) for litter fall rate indicates that the lower the
value used for steady-state litter fall rate in the sensitivity analysis (i.e., the lower the
amount of divalent mercury transferred from the leaf compartment to the soil
compartment), the higher the amount of divalent mercury remaining in leaves, and the
higher the rate of divalent mercury ingestion with leaves by the deer.14
• The moderate positive elasticities for the three properties AllowExchange for air,
AllowExchange for other, and wet deposition interception fraction are reasonable. Lower
values for those properties result in lower concentrations of divalent mercury in leaves, as
already discussed for the sensitivity analysis for plant leaf compartments (Section 5.2.4).
The moderate positive elasticity for the transfer factor [from the leaf] to leaf particle
property (+0.37) means that lower values for that transfer factor results in lower
concentrations of divalent mercury in the deer. Thus, as more divalent mercury moves
from the leaf to the leaf particle, the deer is ingesting more divalent mercury. Given that
the deer eats both the leaves and leaf particles, the sensitivity to the transfer factor to leaf
particle is not a result of a redistribution of divalent mercury between the leaf and
particle-on-leaf compartments. Further investigation would be needed to pinpoint the
exact reason for this result. One possibility is that after divalent mercury is transferred
from the leaf to leaf particles, it can sorb to the particles and is less available for diffusion
back into the air or for exchange with the stem compartment than is the divalent mercury
that remains in the plant leaves. Note that the lack of sensitivity of divalent mercury in
the deer to other properties related to divalent mercury in particles on leaves indicates the
relatively low absolute mass of divalent mercury in particles on leaves compared to in
leaves.
14 The vegetation in SW2 is grasses/herbs. Litter fall for this type of vegetation reflects the dieback of
grass stems and leaves at the end of the growing season rather than leaves actually falling, as is the case for
deciduous trees. In the steady-state mode used for the sensitivity analysis, a single value is used for litter fall rate,
which accounts for the fraction of the year that leaves are present. See Appendix C. 1 for details.
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For both the raccoon and the mouse, none of the remaining properties exhibit an absolute
elasticity value greater than 0.1. For the deer, several additional properties show moderate
elasticity values, with some of these properties reflecting the fact that the biomass of the deer is
sufficient to influence the distribution of divalent mercury between the deer and the plants.
Both the deer body weight (BW) and the number of individuals per square meter (N)
properties show moderate negative elasticity values (elasticity for both is -0.32). Thus, at lower
values for deer biomass (i.e., lower values for either BW or N), the divalent mercury in the
grasses/herbs reflects the lower total mass of divalent mercury transferred from the leaf
compartment to the deer compartment via grazing, and concentrations of divalent mercury in the
grasses/herbs leaf compartment are higher. Fewer deer ingesting grasses with a higher
concentration of divalent mercury achieve higher body divalent mercury concentrations than
more deer grazing on grass with lower concentrations of divalent mercury; hence, the negative
elasticity.15 This result is consistent with the guidance to users of TREVI.FaTE that it is
important to include the major herbivores in the system being modeled because they can
influence the distribution of mercury mass in the system.
Moderate negative elasticity values resulted for the fraction of area available for erosion
(-0.30), total erosion rate (-0.30), surface soil solids density (-0.21), and divalent mercury
reduction rate (-0.21) properties, as was the case for the mouse and raccoon; the values are just
lower in magnitude than the values for those properties for the mouse and raccoon and lower in
relative influence on deer divalent mercury concentrations than other properties. This reflects
the relatively lower importance of soil ingestion as a route of divalent mercury intake for deer
compared with the mouse and raccoon and compared with ingestion of divalent mercury in
leaves by the deer.
At higher values for water content in grasses/herbs, the dry deposition interception
fraction by plant leaves is lower according to the equation of Baes et al. (1984) (Equation 7-2 in
TRIM.FaTE TSD Volume II), thus decreasing the concentration of divalent mercury in leaves
and in deer (elasticity -0.25). There is a moderate positive elasticity for food ingestion rate
(+0.14), which appears reasonable given the importance of ingestion of divalent mercury in the
leaves of grasses/herbs for the deer. A similar positive elasticity for vapor dry deposition
velocity for divalent mercury to soil (+0.11) also makes sense. The same positive elasticity for
that property is evident for both the mouse and the raccoon, however it ranks as the 12th most
influential property for those species, while it ranks as the 21st most influential property for the
deer.
15 Note that an alternative hypothesis that the concentration of divalent mercury in deer decreases because
the deer compartment biomass increases, thereby diluting the divalent mercury mass in the deer compartment, cannot
be true because the deer food ingestion rate is normalized to deer body weight (i.e., kg grass ingested per kg deer
biomass per day). In other words, the total mass of grass (and divalent mercury) ingested by the deer increases
linearly in direct proportion to increasing deer biomass, leaving the ratio of mercury ingested/deer biomass the same.
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5.3 Broadly Influential Properties
Previous discussion in this chapter has focused on influential properties for selected
compartment types. In this section, test case sensitivity analysis results are analyzed to highlight
broadly influential properties. More specifically, the focus of this section is on properties to
which mercury concentrations in numerous compartments and compartment types are
particularly sensitive, using elasticity as a measure of sensitivity. These results are expected to
contribute to the overall performance evaluation of the model. In addition, because of their
broad influence, identification of these properties could be useful for setting data collection
priorities for similar TRIM.FaTE applications or focusing future TRIM.FaTE research.
5.3.1 Approach
The following criterion was used to identify a subset of properties with notable influence
on mercury concentrations in multiple types of compartments.
Properties with absolute elasticities greater than 0.5 for concentration results for
at least one of the three mercury species in at least five different compartment
types were considered to be broadly influential.
These properties are the primary focus of the discussion in this section. As with the analysis of
individual compartment types, the results of this analysis reflect the specific output
compartments selected for inclusion in the sensitivity analysis (see Exhibit 5-2). For example, if
concentrations in aquatic biota are sensitive to changes in a particular property, that property
could be interpreted as more broadly influential than properties that affect semi-aquatic biota
because more aquatic biota compartment types were included in the detailed evaluation of the
sensitivity analysis. However, as described at the beginning of this chapter, output
compartments were selected for this sensitivity analysis both to provide a breadth of coverage
and to focus on results of interest, such as the aquatic food chain. In general, it is expected that
the results presented here are useful for providing overall conclusions regarding broadly
influential parameters in the context of the current application. It should not be concluded that
properties not identified here as broadly influential are unimportant for individual compartment
types (see previous sections) or for TRIM.FaTE applications using a different scenario (e.g.,
different input values, different algorithms, different compartment types).
5.3.2 Summary of Observations
A summary of the broadly influential properties for the mercury test case based on the
two criteria defined above is presented in Exhibit 5-17. For each property listed in this table,
three values are reported:
Number of different compartment types (out of the 17 total that were assessed) for which
the concentration results for at least one of the three mercury species demonstrated an
absolute elasticity greater than 0.5;
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• Total number of simulation outputs (out of the 93 total that were assessed) with an
absolute elasticity value greater than 0.5 (where an output is the concentration of one of
the three mercury species in one of the 33 different compartments); and
Mean absolute elasticity of the values greater than 0.5 (i.e., those identified in the
previous bullet).
In addition, the mercury species included in the outputs with absolute elasticity greater than 0.5
are listed in the column next to the total number of outputs. A complete list of properties for
which at least one absolute elasticity value greater than 0.5 was obtained (regardless of how
many compartment types were affected) is presented in Appendix D.3.
Exhibit 5-17
Summary of Broadly Influential Properties Based on Elasticity
Property
Emission rate, Hg2+
Vapor washout ratio, Hg2+
Rainfall rate
Solids density (rho) of surface water solids
Water temperature
Emission rate, Hg°
Suspended sediment deposition velocity
Flush rate (per year) of surface water body
Suspended sediment concentration
Henry's Law constant, Hg°
Kd in surface water, Hg2+
Demethylation rate in surface soil, MHg
Methylation rate in surface soil, Hg2+
Air temperature
Porosity of sediment
Fraction of surface soil area available for
erosion
Total erosion rate of surface soil
Demethylation rate in sediment, MHg
Number of
Compartment
Types > 0.5
17
17
17
14
12
11
11
10
10
10
9
8
8
7
6
6
6
6
Outputs > 0.5
Number of
Outputs
78
71
71
47
38
15
13
48
29
12
19
11
11
13
25
20
20
11
Hg Species
Affected
all 3
all 3
all 3
all 3
all 3
Hg°
Hg°, MHg
all 3
all 3
Hg°
all 3
MHg
MHg
Hg°, MHg
all 3
all 3
all 3
MHg
Mean
Absolute
Elasticity a
0.98
0.65
0.65
4.69
3.63
0.84
0.73
0.88
0.67
0.80
0.74
0.96
0.95
1.07
1.13
0.55
0.55
1.00
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Property
Methylation rate in sediment, Hg2+
Kd in surface water, MHg
Methylation rate in surface water, Hg2+
Steady-state air advective transfer factor
Sediment partition coefficient for benthic
invertebrates, Hg2+
Sediment partition coefficient for benthic
invertebrates, MHg
Solids density (rho) of sediment particles
Reduction rate in sediment, Hg2+
Number of
Compartment
Types > 0.5
6
6
6
5
5
5
5
5
Outputs > 0.5
Number of
Outputs
11
11
8
10
9
9
8
5
Hg Species
Affected
MHg
MHg
MHg
all3
Hg2+
Hg°
Hg°
Hg°
Mean
Absolute
Elasticity a
0.95
0.72
0.66
0.53
0.85
0.99
0.98
0.87
a Mean absolute elasticities reported here are calculated using only elasticities with an absolute value > 0.5.
Specific reasons for why mercury concentrations in a given compartment type are
sensitive to particular properties are not discussed in this section; that information is presented in
Section 5.2. However, some general observations can be made regarding the properties included
in Exhibit 5-17. Many of the most broadly influential properties influence concentrations of two
or three mercury species. In addition, most of the properties presented in this table are broadly
influential because they influence concentrations in "upstream" compartments that, in turn,
affect concentrations in other, "downstream" compartment types. Although the relationships
between compartment types in the mercury test case can be complex (e.g., multiple mass transfer
processes can exist on a single link between compartments; mass can flow both ways on a link
via different processes and at different rates due to competing or feedback mechanisms
represented by the algorithms), the overall flow of mass through the whole collection of
compartments in the test case can be generalized. A simplified conceptual model of mass flow
through the main compartment types/categories included in the mercury test case is presented in
Exhibit 5-18.
As a result of these relationships, properties that are influential on mercury
concentrations in upstream compartments (generally, compartments closer to the left-hand side
of Exhibit 5-18) can also be influential on downstream compartment concentrations due to the
predominant movement of mercury mass through the compartments included in the test case
scenario. For example, if high elasticity is observed between a property and concentration in air,
this property may also be influential on concentrations in soil and surface water because the
mercury mass in these downstream compartments is transferred from air compartments via
deposition and other processes. If the elasticity for this property is also high for soil
concentrations, elevated elasticities may in turn be observed for concentrations in compartments
further downstream, such as plants and terrestrial biota. In general, properties to which upstream
compartments are highly sensitive are more broadly influential (based on the criteria used for
this analysis) than properties to which only downstream compartments are highly sensitive.
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Exhibit 5-18
Simplified Mass Flow Diagram - Mercury Test Case Scenario
Terrestrial/
semi-aquatic
biota
Properties used in the test case also can be broadly influential if they are used in more
than one model algorithm (i.e., for modeling multiple fate and transport processes). For
example, Henry's Law constant - which influences partitioning of mercury from the air to rain,
thereby driving the deposition rate to soil/water - is also used in the calculation of other fugacity
capacities (i.e., Z-values) that drive partitioning in compartment types other than air. In addition,
water temperature influences fish ingestion and excretion rates (elasticities are very large due to
the use of temperature in the exponent) and may also affect partitioning between surface water
and air or sediment.
A generalized summary of the relationships between the broadly influential properties
and the various types of compartments is presented in Exhibit 5-19. Note that this table presents
a very general overview and includes numerous simplifications (e.g., some properties may be
influential on concentrations on just one mercury species in downstream compartments; some
properties are not influential on all downstream compartments). For more detailed information
on specific elasticities for these properties and reasons and discussion regarding the quantitative
sensitivities, refer to Section 5.2.
The total number of outputs (i.e., compartment-chemical combinations included in the
sensitivity analysis) for which the elasticity associated with each identified property is greater
than 0.5 is also presented in Exhibit 5-17. Although this value was not used to rank or group the
properties discussed here, it does provide an additional indication of influence. For a given
property, a large total number of outputs for which the elasticity is high (in this case, > 0.5) can
reflect that property's influence on multiple mercury species and/or at multiple locations. For
example, the divalent mercury emission rate is influential to 78 outputs across 17 compartment
types. By contrast, the emission rate for elemental mercury is influential to a smaller number of
compartment types (11) and a much smaller number of outputs (15). Divalent mercury is more
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Exhibit 5-19
Downstream "Mass Flow" Effects - Influential Properties and Compartment
Types Potentially Affected
Property
Compartment Type
Directly Affected
"Downstream"
Compartment Types
Potentially Affected
Emission rate, Hg2+ and Hg°
Vapor washout ratio, Hg2+
Rainfall rate
Henry's Law constant, Hg°a
Air temperature
Steady-state air advective transfer factor
Air
All other compartment types
Demethylation rate in surface soil, MHg
Methylation rate in surface soil, Hg2+
Total erosion rate of surface soil
Fraction of surface soil available for erosion
Surface soil
Terrestrial animals and plants,
surface water, sediment
Solids density (rho) of surface water solids
Suspended sediment deposition velocity
Flush rate (per year) of surface water body
Suspended sediment concentration
Kd in surface water, Hg2+ and MHg
Methylation rate in surface water, Hg2+
Water temperaturea
Surface water
Water-column and benthic
food-chain biota, sediment
Porosity of sediment
Demethylation rate in sediment, MHg
Methylation rate in sediment, Hg2+
Solids density (rho) of sediment particles
Reduction rate in sediment, Hg2+
Sediment
Benthic food-chain biota,
surface water
Sediment partition coefficient for benthic
invertebrates, Hg2+ and MHg
Benthic invertebrate
Other benthic food-chain biota
a These are examples of properties that directly affect a variety of compartment types beyond those listed here.
reactive; it follows that the emission rate for this species is ultimately more broadly influential
on multimedia mercury concentrations in this sensitivity analysis than the emission rate for
elemental mercury. By the same reasoning, it is logical that non-chemical-specific properties
that influence concentrations of all three mercury species can also be more broadly influential
than chemical-specific properties. For example, sediment porosity and demethylation rate of
methyl mercury in sediment are both sediment properties that have substantial influence (at least
one elasticity value > 0.5) on six compartment types. However, porosity affects concentrations
of all three mercury species at multiple locations and is highly influential on 25 outputs, while
demethylation rate affects only methyl mercury concentrations and, as a result, is highly
influential on only 11 outputs.
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5.3.3 Conclusions
Although other methods for sorting, grouping, and analyzing the sensitivity results may
also be appropriate, the elasticity-based analysis described here was considered to be a logical
approach that provides useful observations for the purposes of model evaluation. In general, the
results of this analysis seem appropriate - the most broadly influential properties are generally
those that influence concentrations in "upstream" compartments (e.g., air), where the
concentration/mass influences concentrations in other compartments. These results are also
consistent with the sensitivity observations for specific compartment types discussed in Section
5.2 (i.e., mercury concentrations in "downstream" compartments such as fish are generally
sensitive to a larger number of properties than "upstream" compartments like air). Additionally,
there is a small subset of properties (e.g., Henry's Law constant, air temperature) that directly
influence concentrations in a variety of compartments.
One use of this analysis, in conjunction with the compartment-specific sensitivity
analysis presented in Section 5.2, is to be able to give priority to the more influential properties
in data collection efforts for similar TRTM.FaTE applications. It is noted, however, that the use
of elasticity values (rather than sensitivity scores) in these analyses precluded consideration of
the uncertainty and variability associated with the property values, as represented by their
coefficients of variation. As described at the beginning of this chapter, sensitivity scores (which
take into account the estimated uncertainty and variability associated with a parameter by
multiplying elasticity by the CV) were also calculated for each property/output combination
included in the sensitivity analysis. These sensitivity scores can be grouped in the same way that
absolute elasticity values were used to define another set of broadly influential properties (i.e.,
absolute sensitivity scores greater than 0.5 could considered as "influential"). A complete list of
the broadly influential properties for the mercury test case based on sensitivity scores is
presented in Appendix D.4. In general, many of the same properties are influential when
sensitivity scores rather than elasticities are used to define "influence." However, note that some
properties in Exhibit 5-16 that are broadly influential based on elasticity are less influential when
sensitivity scores are used to quantify influence. For example, rainfall rate is a less influential
property when influence is based on sensitivity scores rather than elasticity because the CV
assigned to rain is low (0.1) relative to the CVs assigned to other broadly influential properties
(e.g., emission rate, with a CV of 1 for both mercury species; vapor washout ratio for Hg2+, with
a CV of 3). These sensitivity results can be useful for focusing data collection efforts, assuming
there is a relatively high degree of confidence in the CVs assigned. Note that some properties
were assigned preliminary CVs if literature values were not identified; these CV estimates may
need to be refined prior to drawing strong conclusions based on sensitivity score.
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5.4 Summary of Sensitivity Analysis
The sensitivity analysis described in this chapter, which follows a relatively simple
analytic design befitting an initial, broadly scoped investigation of TRIM.FaTE's sensitivity to
changes in model inputs, provides much useful information that contributes to model evaluation.
The results indicate that the model appears to be working as expected, given the physical,
chemical, and biological processes being modeled and the algorithms and input values in place
for the mercury test case, thus increasing the overall confidence that the model is performing as
designed. As explained throughout the chapter, most results appear to be logical, reasonable,
and in line with expectations given the algorithms and prior knowledge of the processes being
modeled. Thus, this analysis fortifies the evaluation results and strengthens confidence in
TRIM.FaTE's performance. Evaluation of the sensitivity analysis results also has led to
enhanced understanding of how various aspects of the model are working in real applications
(e.g., the surface water solids density results discussed in Section 5.2.5).
These results affirm that many different input properties can have a strong influence on
the modeling results, depending on the compartment type and chemical under consideration.
Compartment types farther removed from the point of pollutant entry into the modeling scenario
(i.e., "downstream" compartment types) typically have a large number of influential properties,
sometimes much larger than the more "upstream" compartment types. As an example, contrast
the results for air and surface soil to those for water-column carnivore. Water-column carnivore
has 41 properties with elasticity value > 0.1 compared with four for air and 11 for surface soil.
The large number of potentially influential properties underscores the number and complexity of
processes and compartment interactions being modeled by TRIM.FaTE.
Some properties have a strong influence, characterized by a high elasticity for an
individual compartment type, and a subset of these properties has a broad influence as well,
characterized by a high elasticity for multiple compartment types. Among the properties that are
broadly influential are mercury emission rates from the source, air deposition-related properties,
mercury transformation rates, Kd values, and water and air temperature. An important caveat to
these results is that other properties not identified here also can be influential, depending on the
modeling scenario (e.g., compartment types, spatial layout, algorithms, input values selected by
the user) being evaluated. Moreover, as described in the limitations discussion in Section 5.1,
not all properties were considered in this sensitivity analysis.
These sensitivity analysis results can inform data collection for future TRIM.FaTE
applications, as well as help focus areas for further model development. For TRIM.FaTE
applications similar to the mercury test case, properties near the top of the tornado charts in
Section 5.2 (and tables in Appendix D.2), especially those appearing on Exhibit 5-17, should be
given careful consideration in data collection and the selection of input values. Information on
variability and uncertainty associated with properties, such as sensitivity score information,
should also be considered. If one or two compartment types are the major focus of an
application, then properties influential for those should be given primary attention.
Additional sensitivity and related analyses could be done to build on these findings,
including:
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Analyses assessing sensitivity to properties not considered here;
More detailed studies focused on individual compartments, properties, and chemicals of
interest (some such analyses were done in the course of investigating the results reported
here);
More complex studies designed to account for known correlations among properties;
Studies of sensitivity to changes in spatial layout;
Studies of sensitivity to different representations of the biota in an ecosystem;
Studies of sensitivity to time-varying inputs, using TRIM.FaTE's dynamic mode;
Studies of how sensitivity may change over time, using TRIM.FaTE's dynamic mode;
and
Monte Carlo analyses of variability and uncertainty based on sampling property values
from specified distributions.
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6. INITIAL COMPARISONS OF TRIM.FaTE AND 3MRA MODELING
RESULTS
This chapter presents an initial set of comparisons between TRIM.FaTE modeling results
for the mercury test case and corresponding results from a somewhat customized application of
3MRA, an EPA multimedia fate and transport, exposure, and risk model developed originally for
analysis of hazardous waste management policies. This set of modeling analyses and
comparisons is an important part of EPA's evaluation plan for both of these models.1 The
ultimate objective of this work is to enhance the level of confidence in both models. The
immediate objective of the initial comparative work reported here was to identify similarities and
differences between the models through comparisons of results, and then identify areas for
further investigation and areas where refinement of inputs or algorithms may be appropriate.
This chapter describes the first set of comparisons between these two multimedia models.
Further analysis and comparison of TRIM.FaTE and 3MRA is envisioned, leading to increased
confidence in both models. Note that the current analysis focuses on comparisons of modeling
results for a specific application and is not intended to be a comprehensive comparative review
of all modeling concepts, structures, algorithms, and data inputs for these two complex models.
Extensive documentation is available for both models via EPA's website (see Chapters 1 and 2
for TRIM.FaTE references; see next subsection for 3MRA references).
Following an introductory overview of 3MRA to provide context for the comparison of
model results, Section 6.1 describes the approach taken for this analysis. Sections 6.2 through
6.5 present and compare the modeling results for related groups of media - air and leaf (Section
6.2), surface soil, roots, and earthworm (Section 6.3), surface water, benthic sediment, and fish
(Section 6.4), and wildlife (Section 6.5).
Overview of 3MRA
3MRA is an environmental modeling system designed to facilitate site-based human and
ecological risk assessments at local, regional, and national scales. 3MRA combines data bases
containing chemical, climatological, and site data with a series of 17 science-based simulation
models within a fully integrated software architecture to provide a user the ability to execute
Monte Carlo-based assessment methodologies. See Appendix E for a discussion of the overall
systems design of 3MRA and a list of the system processors that collectively manage the
execution of the 3MRA modeling system.
Within 3MRA, the Multi-media Simulation Processor (MMSP) manages the invocation,
execution, and error handling associated with the 17 individual science models that simulate
source release, multimedia fate and transport, foodweb dynamics, and human/ecological
exposure and risk. Exhibit 6-1 illustrates the 3MRA multimedia model design contained within
1 In addition to the work reported in this document and TRIM.FaTE Evaluation Report: Volume I (EPA
2002a), other evaluation activities for TRIM.FaTE include comprehensive test cases with PAHs and dioxins/furans,
which include comparisons with other EPA multimedia modeling methods (EPA 2004, EPA 2005b, other
documentation in preparation).
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the MMSP, highlighting the modules that were used as part of this model comparison study. The
science modules used in this assessment are: the atmospheric, watershed, and surface water
modules (which simulate the fate and transport of contaminants through the multimedia
environment) and the terrestrial food web and aquatic food web modules (which simulate the
contaminant uptake through the food web). The modules not used are the vadose zone, aquifer,
farm food chain, human and ecological exposure, and human and ecological risk modules. The
modules included in 3MRA represent a "linked media" model, meaning that individual
simulation modules, representing each element of a risk assessment, are executed in a logical
sequence from source to fate and transport to food web to exposure and risk (when they are all
implemented in the simulation). Although certain components of SMRA's chemical fate and
transport modules maintain a mass balance, it is not a mass balance model in the same sense as
TRIM.FaTE.
To download the 3MRA model and access a series of documents describing the 3MRA
modeling system in detail, the reader is referred to the following web sites:
• http://www.epa.gov/ceampubl/mmedia/index.htm (modeling system); and
• http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk.htm (documentation).
In addition to employing only a subset of the 3MRA modules, there were several aspects of this
application of 3MRA which differ from their description in the documents cited above. These
differences are noted within this chapter.
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Exhibit 6-1. Linkages Among the Source, Fate, Transport, Exposure, and Risk Modules for the 3MRA Modeling System
Surface
Impoundment
Aerated
Tank
Landfill
Waste Pile
Land
Application
Unit
Sources
13MRA modules used in this model comparison are shaded.
Transport
Foodchain
Exposure/Risk
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6.1 Approach to Comparison of Modeling Results
Through a series of discussions beginning in 2002, the TRIM.FaTE team and the 3MRA
team designed the overall approach for the initial comparisons of modeling results described
here. The TRIM.FaTE mercury test case site was chosen as the modeling location. One
important decision was that the two teams would, after initial consultations on comparison goals
and endpoints and the setting of a few ground rules (e.g., emission rate, time period), work
independently in setting up and running the models. The two teams independently developed
conceptual site layouts and model input data, with no attempt made to match the layouts or to
reconcile the input data (with a few exceptions, noted in Section 6.1.2). The reasoning behind
this decision was that the comparison would be most informative if both teams set up and
applied their models in the way they judged best for this application, rather than trying to match
the model set up and input data as closely as possible.2 Thus, rather than a fairly narrow
comparison focused on the algorithms, the approach provides a broad comparison of model set
up, model algorithms, and model input data. Of course, the selected approach results in a more
complex comparison of results - there are more possible explanations for any differences
observed - but also allows for a more robust analysis. To date, the teams have only begun the
process of reconciling the extent to which comparisons of model outputs that are presented here
are driven by differences in conceptual site layout, modeling assumptions and algorithms, and
the model input data used.
One initial determination was the selection of the chemicals and endpoints to be
compared - which chemicals in which media and at which locations would be the focus of the
comparison. Because of the significance of mercury, and given that both models have the
capability to handle mercury and that much prior work had been done with TRIM.FaTE related
to mercury, it was selected as the chemical on which to focus (U.S. EPA 2002a). Exhibit 6-2
lists the comparison endpoints. Endpoints were selected based on several criteria, including:
• To provide a broad set of media, including both abiotic and biotic, for comparison;
• To provide multiple locations so that spatial trends/differences could be examined; and
• To cover media of particular interest, such as upper trophic-level fish, and locations of
particular interest, such as Swetts Pond.
For this application, 3MRA modeled methyl mercury (MHg) in fish; total mercury in
wildlife; elemental (Hg°), divalent (Hg2+), and methyl mercury in surface water and sediment;
and divalent mercury in all other media. TRIM.FaTE modeled all three forms of mercury in all
media, based on reversible first-order transformation processes. In some cases, the
transformation between mercury forms was presumed negligible and transformation rates for the
TRIM.FaTE application were set to zero. For many of the media compared here, the focus is on
divalent mercury because it is considered to be the most significant environmental form for those
media. For fish, the comparison focuses on methyl mercury for the same reason. For surface
water and benthic sediment, all three forms of mercury are compared, given that all can be
Note that for some processes, 3MRA was set up in a simplified form to facilitate the comparison.
JULY 2005 6^4 TRIM.FATE EVALUATION REPORT VOLUME II
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important and both models were set up to model all three forms. For wildlife, the comparison
focuses on total mercury, largely because of substantial uncertainties in modeling speciation (see
Section 6.5). Because both models are designed primarily for long-term (years rather than
months or days) applications, it was decided that all comparisons would be based on annual
average mercury concentrations.
Exhibit 6-2
Endpoints Selected for Comparison of Modeling Results
Medium
Air
Surface soil
Surface water (whole
water)
Benthic sediment
Fish
Terrestrial plant-leaf
Terrestrial plant-root
Earthworm
Birds
Mammals
Mercury
Species
Hg2+
Hg2+
Hg°,Hg2+,
MHg, Total Hg
Hg°,Hg2+,
MHg, Total Hg
MHg
Hg2+
Hg2+, Total Hg
Hg2+, Total Hg
Total Hg
Total Hg
Comparison Locations a
3MRA
Watershed 4 -
Watershed 10 -
Watershed 11 -
Watershed 14 -
Watershed 4 -
Watershed 10 -
Watershed 1 1 -
Watershed 14 -
Watershed 9 -
Swetts Pond (1,7) -
Brewer Lake (1, 11) -
Swetts Pond (1,7) -
Brewer Lake (1, 11) -
Swetts Pond (1,7) -
Brewer Lake (1, 11) -
Watershed 11 (Habitat 3) -
Watershed 11 (Habitat 3) -
Watershed 4 (Habitat 1 1) -
Watershed 9 (Habitat 9) -
Watershed 11 (Habitat 3) -
Watershed 14 (Habitat 8) -
Watershed 4 (Habitat 1 1) -
Watershed 11 (Habitat 3) -
Watershed 4 (Habitat 1 1) -
Watershed 9 (Habitat 9) -
Watershed 11 (Habitat 3) -
Watershed 14 (Habitat 8) -
TRIM.FaTE
SSE3, SSE4
SSE1, ESE1
W2, SSW2
W2,NNW2
SSE4
SE1,E1
SW2
N2,W2
NE2, El
Swetts Pond
Brewer Lake
Swetts Pond
Brewer Lake
Swetts Pond
Brewer Lake
SW2
SW2
SSE4
NE2, El
SW2
N2,W2
SSE4
SW2
SSE4
NE2, El
SW2
N2,W2
* See maps in Exhibits 6-4 (air), 6-5 (soil and water), and 6-6 (habitat) for locations. Two TRIM.FaTE parcels were
used in cases where the 3MRA location was on or near a parcel boundary.
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6.1.1 Inherent Differences/Similarities in Spatial Resolution of Model Outputs
For this comparison, 3MRA and TRIM.FaTE take different approaches to estimating
chemical concentrations over space. As a result of the underlying model design, TRIM.FaTE
outputs inherently are values associated with a volume of space, or the associated biotic
population. In contrast, most outputs from this application of 3MRA are values associated with
a point in space, or the biotic population at that point. Note that in other applications of 3MRA,
some of these point-based outputs are more typically based on spatially averaged areas.
For all abiotic media, TRIM.FaTE (in its deterministic mode, as run here) estimates a
single value for chemical concentration that applies over the volume associated with a given
compartment at a given point in time.3 For surface water and benthic sediment, 3MRA follows a
comparable approach. For air, however, 3MRA estimates a concentration at a point in space,
which in this application is ground level at the centroid of selected delineated watersheds.
Therefore, the air comparisons are between essentially a volume-average value from
TRIM.FaTE and a value at a point in space from 3MRA. For soil, 3MRA estimates an average
concentration within a soil core of particular depth at a specific location (which is the same as
the air location). Thus, the soil comparisons are between essentially a volume-average
concentration from TRIM.FaTE and a depth-averaged concentration at a specific location from
3MRA.
The spatial resolution for biota, conceptually, is more similar between the two models.
Both TRIM.FaTE and 3MRA estimate a single chemical concentration that applies to a
particular population4 that may have associations with one or more spatial locations (e.g.,
pertaining to residence, grazing, predation). For fish, the similar spatial resolution of the two
models for surface water and sediment leads to spatial comparability in the results. For
land-based biota, a simplified approach was taken in the application of 3MRA such that the
spatial associations and their role in the conceptual approach to pollutant transfers into wildlife
differ between the two models. This difference is described in Section 6.5. It is notable here,
however, that for this model comparison application the 3MRA wildlife population results were
directly derived from the 3MRA soil results, which as described above are for a specific point
location5 (versus being derived for a particular area by TRIM.FaTE). Similarly, the 3MRA
vegetation results are also for a specific point location (based on soil and air predictions there)
3 TRIM.FaTE estimates the chemical mass in a compartment associated with an environmental medium
volume element (e.g., air, surface water, soil layer) or a biological population (e.g., raccoon, earthworm). Because in
TRIM.FaTE the chemical mass is assumed to be homogeneously distributed within a compartment's volume or
population, compartment concentrations generally may be considered to be average concentrations for the volume or
population represented.
4 Neither TRIM.FaTE nor 3MRA attempt to model population dynamics. The populations modeled
represent the same species living in a defined area.
5 The 3MRA module that was used to model the wildlife accumulation, the Terrestrial Food Web,
calculates chemical concentrations in biota based on media concentrations and empirical BAFs. The Ecological
Exposure module, which was not used in this 3MRA model simulation, calculates biota intake rates based on diet
and food chain for predator species.
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versus the TRIM.FaTE compartment approach for a particular area. The differing approaches to
spatial resolution in modeling mercury concentrations are summarized in Exhibit 6-3.
Given the above differences in basic approach, particularly for abiotic media, combined
with the fact that the spatial layouts for the two models were designed independently, it is not
possible to get an exact spatial match for comparing the modeling results. The comparison
locations were selected to provide the best possible spatial matching, but would not be expected
to yield perfectly matched results even if the models worked in exactly the same way. In other
words, there is some built-in incompatibility (i.e., expectation of different results) in the
approaches because of the inherent differences in spatial resolution of the results. In many cases,
two TRIM.FaTE locations are compared to a single 3MRA location in an attempt to bound the
3MRA location using the closest spatial matches.
Given the differences described above, the general approach for selecting matched
locations for comparison across the two models' results (see Exhibit 6-2 for the location
matches) differed by medium, as described below.
• For surface water, benthic sediment, and fish, the same water body was selected. In this
case, there is both a good location match (same water body) and good comparability
between the spatial aspects of the measure (in effect, both models provide spatial-average
concentrations for the same location).
For air and soil, the TRIM.FaTE parcel that the 3MRA estimation point (watershed
centroid) falls within was selected. If a 3MRA point falls near a boundary, multiple
TRIM.FaTE parcels were selected to bound the 3MRA location. An important difference
between the air concentration estimates from the two models is that the TRIM.FaTE
concentrations are essentially volume averages (based on dividing the mass in a
compartment by the compartment volume) while the 3MRA concentrations are point
concentrations at ground level.
For land-based biota, the animal and plant compartments associated with the TRIM.FaTE
parcel in which the 3MRA estimation point (watershed centroid matched to relevant biota
habitat) is located were selected. Consistent with the matching approach for soil, if a
3MRA point falls near a TRIM.FaTE parcel boundary, multiple TRIM.FaTE parcels
were selected.
Exhibit 6-4 is a map showing the TRIM.FaTE air parcel layout along with the 3MRA
estimation points, which are at the centroids of the watersheds delineated in the 3MRA set-up
process. Exhibit 6-5 is a similar map showing the TRIM.FaTE surface parcel layout along with
the 3MRA estimation points for soil, which also are at the centroids of the 3MRA-delineated
watersheds. Note that the TRIM.FaTE parcel layouts differ for air and surface, but the 3MRA
estimation points are the same on both maps (meaning that the deposition flux to a surface point
in 3MRA is directly correlated with the air point above, while the deposition flux to a
TRIM.FaTE surface parcel could be affected by multiple air compartments if more than one air
compartment overlaps it). Exhibit 6-6 is a map overlaying the 3MRA-defined habitats on the
TRIM.FaTE surface parcel layout, with the 3MRA watershed centroid locations shown as well.
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Exhibit 6-3
General Approach to Spatial Resolution in Modeling of Mercury Concentrations
Medium/Biota
TRIM.FaTE
3MRA (in this application)
Air
Estimate for volume of air associated
with an air compartmenta
Point estimate at ground level at
watershed centroid
Soil
Estimate for volume of soil associated
with a soil (surface or root zone)
compartment
Depth-averaged point estimate (e.g., for
top 1 cm, or for top 5 cm) at watershed
centroid
Surface water
Estimate for volume of surface water
associated with a surface water
compartment
Estimate for full volume of water bodv
Sediment
Estimate for volume of sediment
associated with a sediment compartment
Estimate for full volume of water body
sediment
Fish
Estimate for fish compartment
representing a population of given type
(e.g., benthic carnivore) and size in a
water body
Estimate for all fish of given type (e.g.,
T4) in a water body
Leaf
Estimate for leaf compartment
associated with a surface soil parcel
Point estimate associated with a pair of
soil and air point concentrations at
watershed centroidb
Root
Estimate for root compartment
associated with a surface soil parcel
Point estimate associated with a pair of
soil and air point concentrations at
watershed centroidb
Earthworm
Estimate for earthworm compartment
associated with a surface soil parcel
Point estimate associated with a soil
point concentration at watershed
centroid b
Mammal/bird
Estimate for mammal or bird
compartment representing a population
of a given species and size associated
with a given parcel (and perhaps linked
to food, water, and predators in other
parcels)
Point estimate for given species
associated with a soil point
concentration at watershed centroidb
a In TRIMFaTE it is assumed that chemical mass within a compartment is homogeneously distributed.
Consequently, the compartment concentration generally may be considered to be an average for the associated
volume of media or biological population.
b This approach is different from SMRA's usual application, in which it derives spatially averaged values for
vegetation, earthworms, mammals, and birds based on extent of overlap of a delineated habitat with soils having
different modeled concentrations.
For this application, in order to simplify the layout, the 3MRA team matched each habitat with a
single watershed centroid, which was then used to estimate mercury concentrations for the land-
based plants and animals associated with that habitat (see Exhibit 6-2 for the habitat-watershed
matches relevant to this model comparison and Section 6.1.2 for a description of the habitats).
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Exhibit 6-4
Spatial Layout for Air - Both Models
• 3MRA Watershed Centroids
I I TRIM.FaTE Air Parcels
~ Watershed Regions
Not all watershed boundaries are shown. See Exhibit 6-9 for additional delineation of the watersheds.
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Exhibit 6-5
Spatial Layout for Surface Soil and Surface Water - Both Models
• 3MRA Watershed Centroids
I I TRIM FaTE Surface Parcels
l~^_l Watershed Regions"
N
A
1 0 1
—,™
Kilometers
Not all watershed boundaries are shown. See Exhibit 6-9 for additional delineation of the watersheds.
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Exhibit 6-6
Overlay of 3MRA Habitats on TREVLFaTE Surface Layout
I I TRIM.FaTE Parcel Boundaries
• 3MRA Watershed Centroids
Watershed Boundaries
Not all watershed boundaries are shown See Exhibit 8-9 for additional delineation of the watersheds.
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6.1.2 Model Set-up and Input Data
As noted in the introduction, the site conceptualization and model set-up were done
independently for the two models, as was the selection of most input data. The TREVI.FaTE set-
up is described in Chapters 1 and 2, and the input data used are documented in Appendix A. The
3MRA set-up process is described in a separate subsection below. TREVI.FaTE emission case A
(constant and continuous emission of 17.663 grams per day of divalent mercury from ground-
level fugitive sources for 30 years, with no initial chemical concentrations or boundary
contributions) is used as the basis for all comparisons to the 3MRA simulation, which matched
the TREVI.FaTE source characteristics. The emissions simulation time period that was selected
for comparison was 30 years, even though 3MRA was run for an additional 170 years after the
source was shut off to provide additional information to the 3MRA team (total of 200 years).
The focus of the results comparison reported here is on the 30-year source operating period that
was modeled by both 3MRA and TREVI.FaTE.6
Although it is most frequently applied in a Monte Carlo analysis mode, 3MRA was used
in a deterministic mode for this initial comparative analysis - that is, a single set of parameter
inputs was used to calculate one set of model outputs. Most of the parameter input values used
for 3MRA were selected randomly from the parameter frequency distributions contained in
3MRA for the region corresponding to the test case site location. These distributions are
described in the previously referenced 3MRA documentation. Consistent with the original
design of this comparison, no attempt was made to match (or even compare) most data inputs,
other than the location of the emission source. One prominent exception, as noted above, is the
emission pattern and rate, which was set equal to that used for TRIM.FaTE. For both models, a
guiding principle for developing all aspects of the model set-up and selecting all input data was
to follow the approach that would most likely be used to apply each model to the given site,
using the data bases and methods that have been developed for each model.
Although the same location (i.e., source coordinates) was used as a basis for developing
meteorological data, the two teams identified and processed their meteorological data inputs
separately. Thus, this important set of inputs differs between the models. For TREVI.FaTE, a
five-year (1987 to 1991) data set was repeated through the 30-year modeling period. The data
set is a composite from three meteorological data measurement stations - wind speed and
direction and air temperature from a nearby station, precipitation rate from a different nearby
station with more complete records, and the upper air data needed to estimate mixing height
from a station roughly 100 miles to the southwest. For 3MRA, 14 years (1961 to 1964, 1979 to
1982, 1984 to 1989) of data were compiled from one station - the station used for TREVI.FaTE's
upper air data (100 miles southwest of the source) - and run in the air model (ISCST3). The
resulting air concentrations and deposition fluxes were averaged at each location to generate
single representative air concentration and deposition values. Wind roses derived from both full
data sets are provided to give a sense of the comparability of the input wind data used for the two
models (Exhibit 2-7 for TREVI.FaTE, Exhibit 6-7 for 3MRA). Overall wind direction patterns
The 3MRA simulation involved first running the air model (ISCST3) for a 14-year period using the
selected hourly meteorological data. The resulting hourly mercury air concentrations and deposition rates were
averaged over the entire 14-year period to obtain constant values at each location. These estimates were then used as
constant air concentration and deposition inputs to the other 3MRA modules for the 200-year period.
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Exhibit 6-7
Wind Rose Representing 3MRA 14-year Input Data Set
WIND ROSE PLOT
14-year Data Set for 3MRA (non-SCIM)a
Wind Speed (m/s)
DISPLAY
Wind Speed
AVG. WIND SPEED
415m/s
ORIENTATION
Direction
(blowing from)
UNIT
mis
CALM WINDS
5.58%
WRFLOT View3.5by Lakes Enurcnmental Software-wvw.bkes-enurvnmertal*
a This wind rose represents the entire set of meteorological data used for the 3MRA application. Thus it is labeled "non-SCIM" - it was not
produced using the Sampled Chronological Input Model (SCIM) option which pulls a sample of the meteorological data.
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look generally similar (peaks from the south and northwest, very low frequency from the east),
but there are noticeable differences. The average wind speed for the 3MRA input data is a little
higher than for the TRJM.FaTE input data, 4.15 m/sec versus 3.64 m/sec. As shown below (in
centimeters), the annual rainfall totals input for the two models are similar with respect to
cumulative total, average, and variability, although there are notable year-to-year differences
(and likely greater differences at smaller time scales):
TRIM.FaTE: 93, 78, 112, 123, 113 (cumulative 30-year rainfall = 3,114 cm; mean
annual rainfall =104 cm; standard deviation of mean =18 cm); and
3MRA: 99, 118,98,88, 156,86, 116, 101, 123,87, 113, 104, 111, 106 (cumulative 30-
year rainfall = 3,227 cm; mean annual rainfall = 108 cm; standard deviation of mean =18
cm).
In addition to source location and emissions, the other input parameters that were
matched between the two modeling simulations were the solids:water partition coefficients (Kd)
used for the three mercury species in surface water and in benthic sediment. For both models
this parameter was set to values used in EPA's Mercury Study Report to Congress (EPA 1997).
Surface water: Hg° = 1,000, Hg2+ = 100,000, MHg = 100,000 L/kg; and
Benthic sediment: Hg° = 3,000, Hg2+ = 50,000, MHg = 3,000 L/kg.
Approach to 3MRA Set-up for this Application
Set-up of the 3MRA model for conducting site assessments involves delineation of
spatial features and specification of the modeling-based connectivity among them. Exhibit 6-8
displays the key spatial features that can be included in a 3MRA simulation. Also listed are GIS-
based sources of information for describing the features. 3MRA allows delineation of
physiographic features to conform to site-specific natural boundaries. Spatial features
characterized for this model comparison application of 3MRA include specification of the area
of interest (AOI), watersheds, a surface water network, and ecological habitats (including home
ranges for resident species of interest). In addition, point locations where the atmospheric model
reports mercury air concentrations and deposition fluxes were specified. The deposition fluxes
are used to estimate location-specific soil concentrations.
The extent of the AOI to be modeled by 3MRA is generally constrained only by the
availability of data and the spatial domain of the science modules. 3MRA applications to date
have simulated AOIs extending to 15 kilometers from the source, with typical simulations
extending a few kilometers. The AOI for this study (described in Chapter 2), however, was
bounded, in terms of extent, primarily on the basis of specific water bodies that may be most
affected by the mercury releases to the air from the source being modeled.
The approach taken for site set-up for this 3MRA application is characterized as a site-
specific screening assessment. The physiographic layout is site-specific, with environmental
data reflecting a combination of site-specific data, when readily available, and data
representative of the region within which the site exists. The 3MRA modeling system includes
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Exhibit 6-8
GIS View of Site-based Spatial Overlays for 3MRA Modeling System
Human Receptors
(census and land use data)
Ecological Habitats and Receptors
(land use, wetlands, T&E species,
etc., data)
Watersheds
(DEMS)
Waterbodies
Streams, Lakes, and Wetlands
(OEMs Reach Files,GIRAS, NWI)
Base Grid,
Area of Interest
(AOI)
x = facility centroid
CH = waste management unit
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regional data bases of model parameters. When site-specific information is not available for a
model input, a random sample is taken from the regional data base and assigned to the site.
For this application, the following spatial features were delineated using GIS information
sources:
• Watershed sub-basins;
• Surface water network (and associated reach definitions); and
• Ecological habitats (and associated species home ranges).
Watershed sub-basins for the AOI, along with the associated surface water network, are
shown in Exhibit 6-9. Watersheds are modeled as a homogeneous land unit, each independent of
others (i.e., there is no runoff or erosion of soils between watersheds). For this study 18
watershed sub-basins were delineated and modeled. With 3MRA, mercury concentrations in soil
within the watershed can be estimated as a function of the atmospheric deposition reported at
various locations within the watershed.7 For this application, however, a single point was
assigned to the centroid of each watershed for estimating air deposition and soil concentration.
Watersheds deliver runoff, erosive fluxes of soil-based particles, and associated
contaminant to surface waters. In 3MRA, surface water networks are constructed based on the
connectivity among surface waters in the AOI. One or more surface water networks can be
simulated within 3MRA. Each surface water network is segmented into "reaches," reflecting
individual ponds/lakes, wetlands, and segments of streams/rivers between tributaries. As shown
in Exhibit 6-9, there is a single surface water network configured for this model application. The
network consists of 15 reaches, three within the main river, which is the receiving water body for
flow from all other reaches within the AOI. Other surface water reaches include four stream
reaches, four wetland reaches, and four lake reaches. Surface water reaches receive contaminant
loadings from the atmosphere and from watersheds. In this application of 3MRA, each water
body receives runoff/erosion-based loadings from a single watershed and atmospheric loadings
(i.e., deposition) based on a single air point (the centroid of the watershed). This connectivity
can be inferred from Exhibit 6-9. However, 3MRA does have the flexibility to allow for surface
water reaches to receive atmospheric contaminant loadings from multiple air points located
within the area of specific reaches, and watershed loadings may be weighted (similar to the air
points) to allow runoff and erosion to affect multiple water body reaches (these capabilities were
not implemented in this assessment).
Ecological habitats are delineated for a 3MRA application based on simultaneous
consideration of land use, surface water locations, watershed boundaries, and regional ecosystem
classifications. For this application, as shown in Exhibit 6-6, 12 specific habitat areas were
delineated: one residential, one lake, one stream, two forest wetlands, two crop, two forests, and
three ponds. Each habitat area can be assigned a list of animal species whose home ranges are
7 If multiple deposition points in a watershed are used, the individual deposition fluxes are assigned
weighting factors that determine their relative impact on the entire watershed. For example, 10 air points may be
located within a single watershed, each with a weighting factor of 0.1. The single soil concentration estimated for
this watershed, for purposes of estimating erosive fluxes, would be a function of the weighted average deposition
flux.
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Exhibit 6-9
Watershed and Surface Water Reach Network Delineated for this Application
Streams
Water Body Network
_ j Watershed Boundaries
X Source
Water Body Network Key
1. Stream/River 6. Wetland
2. Stream/River 7. Lake/Pond
3. Stream/River 8. Stream
4. Stream 9. Lake/Pond
5. Stream 10. Wetland
11 Lake/Pond
12. Lake/Pond
13. Wetland
14. Wetland
15. Stream
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contained within the habitat. Species can be assigned with the goal of representing a complete
food web, thus enabling the estimation of chemical doses as a function of diet. For this
application the species list for individual habitats was assigned based on regional ecosystem
considerations, as opposed to a site-specific investigation (see Section 6.5 for a list of wildlife
species included). Home ranges are typically assigned randomly within the habitat area with the
condition that predator-prey relationships must be preserved (i.e., home ranges must overlap).
For this application, however, home ranges were assigned such that overlap occurs for all species
(thus all potential prey for a predator is available for dietary consumption).
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6.2 Air and Leaves
This section provides a comparison of the modeling approaches affecting mercury
concentrations in the air, deposition fluxes, and concentrations in the leaves, as well as a
comparison of these modeling outputs. The comparison of outputs focuses primarily on
TRIM.FaTE surface parcel SW2, TRIM.FaTE air parcels SSW2 and W2, 3MRA watershed 11,
and 3MRA habitat 3 (Exhibit 6-6). These locations were selected because of the relatively good
spatial match between 3MRA and TRIM.FaTE, and because habitat 3 and TRIM.FaTE surface
parcel SW2 have similar vegetation types that can be compared. The spatial distribution of air
concentrations and deposition fluxes is also discussed for the two models for the entire modeling
region. In the two media being compared in this section, divalent mercury was the only mercury
species modeled with 3MRA. Three mercury species were modeled with TRIM.FaTE (divalent,
elemental, and methyl), but only divalent concentrations are presented because concentrations of
the other two species are negligible in air and plant leaves.
6.2.1 Divalent Mercury Concentrations in Air
In this analysis, the initial input of chemical mass to all other media modeled with 3MRA
and TRIM.FaTE comes from air. However, TRIM.FaTE and 3MRA use different methods for
simulating chemical fate in the air (see Exhibit 6-10 for a comparison of air-related mass transfer
and transformation processes used in the two models). 3MRA's air modeling is performed with
EPA's Industrial Source Complex Short-Term Model, Version 3 (ISCST3). The fate and
transport algorithms in ISCST3 are based on Gaussian dispersion equations that are solved for a
given set of temporal and spatial circumstances. For this 3MRA application, the long-term
average air concentration at ground level at each watershed centroid location was calculated
using ISCST3 and 14 years of meteorological data, and each resulting value was applied to the
entire corresponding 3MRA watershed as a constant throughout the duration of the simulation.
ISCST3 formulates a steady-state representation of the contaminant plume each hour. The mass
of contaminant in the ISCST3 plume is consistent with the mass emitted during that hour, but it
is not a mass balance model because mass is not tracked hour-to-hour. TRIM.FaTE, which is a
mass balance model, includes a grid-based air model in which chemical mass moves between air
compartments via advection algorithms. Also, chemical mass is transferred by diffusion from
multiple compartment types to the air in the TRIM.FaTE simulation. The chemical mass in a
TRIM.FaTE air compartment is assumed to be distributed evenly throughout the compartment so
that the concentration is constant (over space) for that compartment at a time-step. In this
analysis, air concentrations calculated by TRIM.FaTE vary with time, while the air
concentrations applied in 3MRA are constant values (i.e., each watershed centroid's long-term
average derived by ISCST3). An additional study was performed by the 3MRA team to learn
more about how the different methods used to model air concentrations (and deposition) affect
the results. See the text box at the end of Section 6.2.2 for a summary of that study.
Exhibit 6-11 shows a comparison between the divalent mercury concentrations in
TRIM.FaTE air compartments W2 and SSW2 and in 3MRA air over watershed 11 (based on the
air concentration calculated at centroid 11). Two TRIM.FaTE locations are presented because
3MRA centroid 11 is near the border of TRIM.FaTE air parcels W2 and SSW2. Air
concentrations modeled with 3MRA are higher than the comparable TRIM.FaTE air
concentrations by five-fold (SSW2) to 11-fold (W2). In comparisons of air concentrations at
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other locations, 3MRA results are higher than TRIM.FaTE by four- to nine-fold (Appendix E).
The TRIM.FaTE results spike up and down through the five-year meteorological data period
(and then repeat), and the 3MRA results are calculated as a long-term average and are thus
constant over time.
Exhibit 6-10
Summary of Mass Transfer and Transformation Processes Modeled: Air a
TRIM.Fate (Hg°, Hg2+, MHg)
3MRA (Hg2+ only)
Advection, air-to-air (horizontal), via compartment
model [G or L]b
Dry deposition of particles from air to surface soil,
surface water, and particles-on-leaf [L]
Resuspension of particles from surface soil to air [G]
Blowoff of particles-on-leaf to air [G]
Diffusion (dry deposition) of vapors from air to surface
soil, surface water, and leaf [L]
Diffusion from surface soil, surface water, and leaf to
air [Hg° and MHg only for surface soil and leaf] [G]
Wet deposition of particles from air to surface soil,
surface water, and particles-on-leaf [L]
Wet deposition of vapors from air to surface soil,
surface water, and leaf [Hg° and Hg2+ only] [L]
Inhalation of air by wildlife [L]
Advection, air-to-air, via Gaussian plume model [G or
L]
Dry deposition of particles from air to surface soil,
surface water, and leaf [L] °
Dry deposition of vapors from air to surface soil,
surface water, and leaf [L] °
Wet deposition of particles from air to surface soil,
surface water, and leaf [L] °
Wet deposition of vapors from air to surface soil,
surface water, and leaf [L]
MethylationofHg°(0)d
Demethylation of MHg (0)
Reduction of Hg2+ to Hg° (0)
Oxidation of Hg° to Hg2+ (0.00385/day)
Hg transformation in air not modeled
a This and similar charts in this chapter include primarily descriptions of 3MRA as it is applied in this model
comparison for the TRIM.FaTE mercury test case. 3MRA includes additional process-based fate and transport
algorithms for other chemicals (organics and metals), plus several additional modules. For a description of the
complete 3MRA multi-media modeling system, see the referenced 3MRA documentation.
b G = gain process, L = loss process, G or L indicates can be either.
0 Process not modeled in this application, although 3MRA/ISCST3 has this capability for some chemicals.
d First-order rate constant shown in parentheses for all transformation reactions.
The differing long-term average air concentrations - generally within a half to full order
of magnitude, depending on location - are probably a result of the different air modeling
approaches and perhaps the different meteorological data used. A key modeling difference is
that the TRIM.FaTE air concentration is an average in the full volume of the air compartment
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 6-11
Divalent Mercury Concentration in Air vs. Time: Near Source, Southwest"
1.0E-10
E
2
c
o
1
1.0E-11
o
o
1.0E-12
*v
*^
1234567
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Hg2+:3MRA Location 11
-Hg2+: TRIM.FaTE W2 —H— Hg2+: TRIM.FaTE SSW2
a Annual average for TRIM.FaTE is based on instantaneous estimates every two hours throughout the year and represents an average
concentration over a volume that extends from the ground to the mixing height. 14-year average for 3MRA (based on instantaneous estimates
every hour throughout the period) is applied to entire period and is a point concentration at ground level.
(e.g., from ground level up to the mixing height), while the 3MRA value is the point
concentration at ground level at the centroid of the watershed.8 In the comparison of the
Gaussian plume model to the compartment model performed by the 3MRA team (see text box in
Section 6.2.2), it was shown that the vertical average concentrations calculated from ISCST3
were lower than the ground-level concentrations at the same point, and closer to the
compartment model results. Therefore, it is likely that vertical average results from
3MRA/ISCST3 for the full model application would be closer to the TRIM.FaTE results. An
additional input identified as a possible reason for the different air concentrations simulated by
the two models is the TRIM.FaTE air compartment size. The height of the compartments vary
each hour based on the mixing height (ranges from 20 to 3,257 meters for this run; mean = 887
meters). In the Gaussian/compartment model comparison, it was demonstrated that on average,
these heights may be large compared to the height of the Gaussian plume at the same distances
from the source. If that is the case, air concentrations modeled with TRIM.FaTE would
consistently be smaller than even the vertically averaged ISCST3 results because they are being
averaged over a larger height. Different modeled deposition fluxes may also be a factor
contributing to the lower TRIM.FaTE air concentrations (see Section 6.2.2).
Note that the ground-level concentrations are expected to be higher than the volume-averaged
concentrations because the source is emitting mercury at ground level.
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6.2.2 Divalent Mercury Deposition
Exhibit 6-10 shows that mercury is transferred from the air to the surface soil, surface
water, and leaves via deposition in both TRIM.FaTE9 and 3MRA. In this model comparison,
four types of deposition were modeled with TRIM.FaTE (dry particle, dry vapor, wet particle,
and wet vapor), and one type of deposition (wet vapor, which is expected to be the dominant
process for divalent mercury) was modeled with 3MRA.10 Wet vapor deposition of divalent
mercury is modeled with 3MRA (via ISCST3) by a scavenging ratio approach, so that the
amount of chemical removed from the plume by wet deposition is a function of the scavenging
rate coefficient and plume height (see Exhibit 6-12). TRIM.FaTE models wet vapor deposition
of all forms of mercury using a washout ratio, which is based on Henry's Law. Both methods
are a function of mercury concentration in air and rainfall rate.
In this model application, an attempt was made to ensure the consistency of values for the
ISCST3 scavenging coefficient and the TRIM.FaTE washout ratio consistent. To do this, a
plume height of 1,000 meters was assumed (similar to the average TRIM.FaTE mixing height),
and a scavenging coefficient was calculated for use in the ISCST3 simulations based on this
assumption (see Exhibit 6-12). However, because the deposition calculations done by ISCST3
are still dependent on plume height (which varies), and the TRIM.FaTE mixing height varies
hourly, setting the scavenging coefficient and washout ratio equal at one plume depth will not
result in equal deposition results at all times and locations.
Exhibit 6-12
Comparison of Parameters and Inputs Used to Calculate Wet Vapor Deposition
Scavenging Ratio Approach (3MRA/ISCST3)
Scavenging
coefficient
Plume height
Air concentration
ofHg2+
Rainfall rate
0. 00044 hr/mm-seca
Varies over space and time
Varies over space and time
Varies over time
Henry's Law Approach (TRIM.FaTE)
Washout Ratio
Plume Height
Air Concentration
ofHg2+
Rainfall Rate
1.6E06m3[air]/m3[rain]a
Not applicable13
Varies over space and time
Varies over time
a Constant values were used in both model application for the scavenging coefficient and the washout ratio.
b TRIM.FaTE does not model a plume height, but the concentration of mercury in the air is a function of mixing
height. Therefore, deposition is influenced somewhat by this height which varies by hour.
Exhibits 6-13 and 6-14 present the deposition flux comparison at the same location used
for the air concentration comparisons. These are downward flux values which do not take into
account resuspension or re-emission (i.e., they are not "net" deposition fluxes). Note that for
In TRIMFaTE chemicals transferred to the leaf compartment via air deposition of particles go first to the
particle-on-leaf compartment, which then exchanges chemical mass with the leaf compartment.
10
3MRA/ISCST3 has the ability to model other types of deposition, but these processes were not
implemented in this comparison model run.
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deposition, TRIM.FaTE outputs are for the surface parcels, not for the air parcels (note also that
only one TRIM.FaTE surface parcel is needed for a reasonable spatial match to the 3MRA
location, compared to two TRIM.FaTE air parcels). On average, TRIM.FaTE total deposition
flux of divalent mercury is three-fold higher than 3MRA divalent mercury wet vapor deposition
flux at this location. TRIM.FaTE wet vapor deposition flux is 2.4-fold greater than the 3MRA
wet vapor deposition flux and accounts for the majority (approximately 80 percent) of the
TRIM.FaTE divalent mercury deposition. Other comparison locations are presented in
Appendix E for which the TRIM.FaTE divalent mercury deposition fluxes are higher than the
3MRA wet vapor deposition fluxes by a greater amount (10-fold at 3MRA Swetts Pond location
and 34-fold at 3MRA watershed 1). It appears from these three comparison locations that the
difference in deposition fluxes between the two models increases with distance from the source
(see Section 6.2.4). Exhibit 6-14 shows that the TRIM.FaTE deposition fluxes follow a five-year
repeating pattern, which is related to the air concentrations and the five-year repeating set of
meteorological data. 3MRA deposition fluxes are modeled as constant over time at a given
location (long-term average based on the 14-year meteorological data set), just like the air
concentrations.
Exhibit 6-13
Average Deposition Flux (g/m2-day) for Divalent Mercury: Near Source, Southwesta
Process
Dry particle deposition
Dry vapor deposition
Wet particle deposition
Wet vapor deposition
Total
TRIM.FaTE Surface Soil
Compartment SW2
7.5E-12
8.1E-09
6.3E-12
3.1E-08
3.9E-08
3MRA Watershed 11
~
~
~
1.3E-08
1.3E-08
a For TRIM.FaTE, the average is derived as the arithmetic average of instantaneous estimates every two hours during
the 30-year simulation period, while for 3MRA, it is an average from the results of the 14-year simulation of the air
dispersion model ISCST3 (note that this average value was applied in 3MRA as a constant deposition flux).
The difference in deposition fluxes of divalent mercury between the models is probably a
result of the different methods used to calculate deposition, with some contribution from
different meteorology data. The additional types of deposition modeled with TRIM.FaTE also
add to the difference, but not as much because wet vapor deposition is the predominant form of
divalent mercury deposition in TRIM.FaTE for the test case scenario. As presented in Section
6.2.1, the TRIM.FaTE divalent mercury air concentrations are lower than the 3MRA air
concentrations (which is the opposite pattern from the deposition results). Therefore, in an
attempt to factor out differences in modeled air concentrations, a comparison was also made of
the ratio of average deposition flux to average air concentration (i.e., to see how deposition
fluxes would compare between the models if air concentrations were identical). For TRIM.FaTE
surface parcel SW2 this ratio is between 4,100 and 6,980, and the 3MRA ratio for watershed 11
is 240. This higher ratio of deposition flux to air concentration for TRIM.FaTE indicates that for
a given amount of divalent mercury in the air, more mercury is transferred to the surface than for
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3MRA. This may help explain the lower TRIM.FaTE air concentrations described in Section
6.2.1. However, the 3MRA air concentration is a ground-level value, while the TRIM.FaTE
concentration is a volume-averaged value, so the ratios are not directly comparable (i.e., if the
TRIM.FaTE air concentration was a ground-level value as well, the corresponding ratio probably
would be smaller and thus closer to the 3MRA ratio).
Exhibit 6-14 - Log Scale
Divalent Mercury Deposition Flux from Air to Soil Surface vs. Time: Near Source,
Southwesta
1.0E-07
1.0E-08
•5,
1.0E-09
I
§" 1.0E-10 -
1.0E-11
1.0E-12
6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
-Hg2+: TRIM.FaTE Dry Particle Deposition Flux from Air to Soil SW2
-Hg2+: TRIM.FaTE Wet Particle Deposition Flux from Air to Soil SW2
•Hg2+: 3MRA Wet Vapor Deposition Flux from Airto Soil Location 11
-Hg2+: TRIM.FaTE Dry Vapor Deposition Flux from Air to Soil SW2
-Hg2+: TRIM.FaTE Wet Vapor Deposition Flux from Airto Soil SW2
"Annual average for TRIM.FaTE based on instantaneous estimates every two hours throughout the year. 14-year average for 3MRA (based on instantaneous
estimates every hour throughout the period) applied to entire period.
In addition to these observations, further research comparing the ISCST3 air and
deposition modeling to the TRIM.FaTE compartment modeling approach for divalent mercury
(see text box below) provides more insight into the differences between the deposition values. In
the supplemental study, it was observed that because the ISCST3 scavenging coefficient input
(which is treated as a constant) was set to the value for a plume height of 1,000 meters, not until
the plume reaches that size would the deposition flux modeled with ISCST3 be expected to equal
the TRIM.FaTE deposition flux. However, it was also observed in the supplemental study that
by the time (i.e., distance from the source) the ISCST3 plume height reaches 1,000 meters, much
of the divalent mercury mass in the TRIM.FaTE air compartments has already deposited,
meaning that the deposition fluxes are still very different because they are proportional to the
concentration (i.e., TRIM.FaTE is removing a greater portion of divalent mercury mass from the
air near the source while 3MRA is removing a greater portion of the mercury mass from the air
farther from the source).
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Detailed Air Concentration and Deposition Study
A detailed follow-up study was performed by the 3MRA team to further compare the different approaches of
TRDVLFaTE and 3MRA to atmospheric transport and wet deposition of vapor phase contaminant (Laniak and
Schwede, in preparation). The objective of this study was to isolate the processes that are used to calculate air
concentrations and deposition fluxes in the two models, and compare these approaches and resulting predictions
independent of the many other processes that affect and complicate the full model comparison. A secondary goal
was to see under what conditions a compartment model could be parameterized to produce air concentration and
deposition predictions similar to a Gaussian plume model.
The two models use different conceptual approaches to air modeling, as discussed in Section 6.2.1. TRIMFaTE
is a compartment model which primarily uses advective transport for air modeling, while 3MRA uses ISCST3,
which is a Gaussian plume model. Wet vapor deposition is also calculated differently by the two models (see
Section 6.2.2). 3MRA uses an approach in which a scavenging coefficient is applied over the depth of a plume
to remove contaminant via wet deposition. TRIM.FaTE uses a washout ratio which is independent of the actual
plume depth and is a function only of contaminant concentration in air.
Differences in air concentrations and deposition results were compared for multiple simulations performed with
a simplified compartment layout (similar to TRIM.FaTE compartment volumes in the east-southeast direction, but
rotated so the downwind axis is directly west to east) and a single hour of meteorology. Consistent with results
from the entire 3MRA/TRIM.FaTE comparison described in this document, the compartment model air
concentrations were lower than the ground-level Gaussian plume air concentrations modeled with the simplified
layout. After confirming this result, the 3MRA team performed several other simulations to test different
hypotheses about the differences between the models. Some key topics analyzed are described below.
Averaging: The Gaussian results presented in the full comparison represent plume centerline, ground-level
concentrations. In order to obtain concentrations more comparable to the volume-averaged results from the
compartment model, the Gaussian results were vertically and laterally averaged across the plume. This reduced
the differences, bringing the Gaussian results to within a factor of two of the compartment model results for some
simulations, although in all cases, the compartment results were still lower (ranging from 0.05 to 0.7 times the
overall average concentrations predicted by the Gaussian plume model).
Atmospheric Stability: When the Gaussian plume model was simulated with an unstable atmosphere, the
Gaussian concentrations were closer to the compartment model predictions than when a stable atmosphere was
modeled. The Gaussian plume spread is greater in an unstable atmosphere, so the concentrations were lower,
leading to a closer match to the compartment model.
Matching Volumes: Since averaged Gaussian plume concentrations were still less than the concentrations
simulated with the compartment model, and a less stable atmosphere (causing more plume spread) resulted in
Gaussian concentrations slightly closer to the compartment model concentrations, an attempt was made to reduce
the size of the compartment volumes so that they matched the volume of the Gaussian plume at the relevant
distances from the source (using vertical and lateral spread parameters from the Gaussian plume model). This
exercise showed that the compartment model could be parameratized to match the Gaussian plume model and that
a major difference between the air concentration predictions stemmed from the compartment volumes being larger
than the Gaussian plume volume.
When deposition was added to the simulation, more differences were identified due to the different methods used
to calculate deposition. The method used by ISCST3 (scavenging coefficient) removes a greater portion of the
contaminant mass farther from the source, and the method used by the compartment model (washout ratio) removes
more contaminant near the source. In one set of analyses, the compartment model deposition was modified so that
the washout ratio varied with distance, and the results were much more similar to ISCST3. This detailed study of
the air and deposition methods provides valuable insight into the different methods used to model transport in the
air and deposition for the two models. However, it is necessary to remember that in full 3MRA and TRIM.FaTE
simulations, the layouts are more complex, there is variable meteorological data used, and there are many other
processes affecting air concentrations.
JULY 2005 6-25 TRIM.FATE EVALUATION REPORT VOLUME II
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6.2.3 Divalent Mercury Concentrations in Leaves
Exhibit 6-15 shows the mercury gain and loss processes associated with leaves for both
TRIM.FaTE and 3MRA. In addition to receiving mercury by deposition processes, leaves in
3MRA also obtain mercury from the soil, and leaves in TRIM.FaTE obtain mercury by transfer
from the stems (which get mercury from the soil). Mercury is lost from leaves in TRIM.FaTE by
multiple processes. Some of these loss processes are also accounted for by the empirical loss
rate constant used by 3MRA (but are not tracked in a mass-balance sense).
Exhibit 6-15
Summary of Mass Transfer and Transformation Processes Modeled: Leaf
TRIM.Fate (Hg°, Hg2+, MHg)
3MRA (Hg2+ only)
Dry deposition of particles from air to particles-on-leaf
(and subsequent exchange to leaf) [G]a
Diffusion (dry deposition) of vapors from air to leaf
[G]
Diffusion from leaf to air [Hg° and MHg only] [L]
Wet deposition of particles from air to particles-on-leaf
(and subsequent exchange to leaf) [G]
Wet deposition of vapors from air to leaf [Hg° and
Hg2+ only] [G]
Exchange from leaf to particles-on-leaf [L]
Exchange from leaf to stem [L]
Exchange from stem to leaf (preceded by root zone
soil-to-stem uptake) [G]
Deposition from leaf to surface soil during litter fall
[L]
Ingestion of leaf by certain wildlife [L]
Dry deposition of particles from air to leaf [G]b
Dry deposition of vapors from air to leaf [G]b
Net loss of chemical from leaf surface [L]
Wet deposition of particles from air to leaf [G]b
Wet deposition of vapors from air to leaf [G]
Soil-to-leaf uptake [G]
Litter fall deposition assumed to be included in
empirical studies used to develop model [L]
Methylation of Hg2+ (0)c
Demethylation of MHg (0.03/day)
Reduction of Hg2+ to Hg° (0)
Oxidation of Hg° to Hg2+ (1,000,000/day)
Hg transformation in leaf not modeled
a G = gain process, L = loss process.
b Process not modeled in this application, although 3MRA/ISCST3 has this capability for some chemicals.
0 First-order rate constant shown in parentheses for all transformation reactions.
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In Exhibit 6-16, divalent mercury concentrations in leaves from TRIM.FaTE surface
parcel SW2 (grasses/herb) are compared with 3MRA leaf concentrations in habitat 3 (which is
"matched to" air and soil from watershed 11). The divalent mercury concentrations in
TRIM.FaTE leaf compartments at this location are on average three-fold greater than divalent
mercury concentrations in the 3MRA leaves. This seems to follow directly from the similarly
higher deposition fluxes for the TRIM.FaTE model at this location, illustrating the significant
role of deposition on modeled leaf concentrations of divalent mercury. As shown in Exhibit 6-
15, in both models deposition plays a part in the transfer of mercury from the air to the leaves.
The TRIM.FaTE leaf concentrations follow a five-year repeating (and non-increasing) pattern
based on the repeating meteorological data, and the 3MRA concentrations follow a fairly
constant, but increasing, pattern over the 30-year modeling period. The concentration in
TRIM.FaTE leaves in the first year of the five-year pattern is noticeably lower than the other
values, presumably because of meteorology differences. This is not the same pattern seen in the
annual average deposition fluxes to the parcel (Exhibit 6-14), but the TRIM.FaTE leaves for
grasses/herbs vegetation type are modeled only for the growing season (annual average is
calculated only based on the growing season period). Given that the meteorology (such as wind
speed and direction and rainfall) is different in those growing season months from that in the rest
of the year, it seems reasonable that the leaf concentrations follow a different pattern from the
deposition flux.
Exhibit 6-16 - Log Scale
Divalent Mercury Concentration in Leaves (grasses/herbs) vs. Time:
Near Source, Southwest
1.0E-06
1.0E-07
o
O
Hg2+:3MRA Habitat 3 -B-Hg2+: TRIM.FaTE SW2
1.0E-I
" Each TRIM.FaTE annual average data point shown is the average of values during the days (May 13 - September 29 each year) for which leaves
were modeled as present during the entire day (i.e., represents a growing season average).
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6.2.4 Spatial Patterns for Air Concentration and Deposition
The spatial variation in long-term average divalent mercury air concentrations and
deposition fluxes for 3MRA and TRIM.FaTE is shown in Exhibits 6-17 and 6-18. Both maps
are scaled to the same size, with the TREVI.FaTE concentrations and rates shown via background
shading and the 3MRA results shown via dots of various sizes. On the air concentration map
(Exhibit 6-17), the concentration ranges are the same for the two models, and the increment (in
logarithmic units) for each concentration range is equal (i.e., a change in pattern or dot size
reflects the same proportional increase for both models). On the deposition map (Exhibit 6-18),
the concentration ranges differ for the two models because the model results span different
numerical ranges, but the increment for each concentration range is equal (in logarithmic units)
both within a model and across the two models.
TRIM.FaTE air concentrations shown in Exhibit 6-17 are highest east and north of the
source and lowest to the west. 3MRA concentrations are highest to the east, similar to
TRIM.FaTE, and lowest to the northwest, then southwest, again similar to TRIM.FaTE. The
directional differences in TRIM.FaTE annual average concentrations are fairly small, roughly
three-fold between the highest and lowest air compartments at the same distance from the source
(i.e., in the same "ring"). There are not enough data points to judge the relative magnitude of the
directional differences for 3MRA. The ratio of the maximum to the minimum air concentration
(excluding the source compartment) for the TRIM.FaTE layout is approximately 15. The
maximum-to-minimum ratio for the 3MRA layout (excluding location 10, which is just adjacent
to the source compartment, and all locations falling outside of the TRIM.FaTE layout) is
approximately 24. Thus, there appears to be somewhat greater spatial variation in 3MRA air
concentration results compared to TRIM.FaTE air results. A likely contributor to the observed
difference in the spatial variation of the concentrations is the different types of spatial data being
compared (i.e., point estimates at ground level for 3MRA versus volumetric averages for
TRIM.FaTE) as well as the different size areas that these concentrations represent.
Because of the asymmetry of the TRIM.FaTE surface layout (for which deposition
outputs are provided) and the small number of data points for both models, it is difficult to
evaluate TRIM.FaTE and 3MRA deposition results by direction. Exhibit 6-18 indicates that the
TRIM.FaTE deposition fluxes are very similar in the four parcels comprising the first "ring"
around the source, and also in the five parcels comprising the second. (Note though that the
surface parcel rings in TRIM.FaTE, unlike the inner air parcel rings, are not symmetrical.) The
numerical results indicate that TRIM.FaTE deposition fluxes appear to be highest north and west
of the source (different from air concentration patterns). 3MRA deposition fluxes appear highest
to the west and lowest to the east, which is different from the 3MRA air concentration pattern.
The directional differences between the deposition and air concentration patterns in both models
probably can be explained by the weather patterns. For instance, the predominant wind direction
when it is raining (which affects directional pattern of wet deposition) is not necessarily the same
as the overall predominant wind direction (which is more closely linked to air concentration and
dry deposition). A comparison of the wind roses in Exhibits 2-7 and 3-37 illustrates this
difference for the TRIM.FaTE input data. The wind-rain relationship is important because the
predominant type of deposition in TRIM.FaTE and the only type of mercury deposition in
3MRA occurs only when it is raining (i.e., wet vapor). When the TRIM.FaTE dry deposition
JULY 2005 6-28 TRIM.FATE EVALUATION REPORT VOLUME II
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spatial pattern is examined separately, the spatial pattern is a closer match to the air
concentration pattern, as expected, because precipitation does not affect modeled dry deposition.
The ratio of the maximum-to-minimum total deposition flux (excluding the source
compartment) for the TRIM.FaTE layout is approximately 18. The maximum-to-minimum ratio
for the 3MRA layout (excluding location 10, which is just adjacent to the source compartment,
and all locations falling outside of the TRIM.FaTE layout) is approximately 61. This illustrates
the greater spatial variation in 3MRA deposition fluxes compared to TRIM.FaTE. As with the
air concentrations, likely contributors to this difference are the comparison between 3MRA point
estimates and TRIM.FaTE volumetric averages and the different size areas represented by the
points or averages. Also, for both models there appears to be more spatial variation in deposition
flux than in air concentration. These observations are related to the discussion in Section 6.2.2
that showed that the difference in deposition fluxes between the two models increases with
distance (and that these differences are probably caused by the different methods used to
calculate deposition fluxes).
JULY 2005 6-29 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 6-17
Spatial Variation in Divalent Mercury Concentrations in Air
\
Average Concentrations for Final Five Years
Air
Divalent Mercury (g/m3)
O
o
o
N
A
3MRA
Greater lhan 5.1 E-11
2.6E-11-5.1E-11
1.4E-11-2.SE-11
6.9E-12-1.4E-11
Le55than6.9E-12
TRIM.FaTE
HI Greaterthan5.1E-11
I I 2.6E-11 -5.1E-11
P | 1.4E-11-2.6E-11
[~n S.9E-12-1.4E-11
| | Le55lhan6.9E-12
i _' Watershed Regions
1
Not all watershed boundaries are shown. See Exhibit 6-9 for additional delineation of the watersheds.
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Exhibit 6-18
Spatial Variation in Divalent Mercury Deposition Fluxes
Average Deposition Flux from Air to Surface Soil
DivalGnt Mercury (g/m2-day)
3MRA - wet dep/constant TRIM.FaTE - total dep/30-yr avg
Greater than 6.4E-09
3.4E-09 - 6.4E-09
^B Beater than 9.8E-08
| | 4.8E-OB - 9.8E-OS
|~~~1 2.3E-08 - 4.8E-08
| 1 1.1E-Oe-2.3E-08
I | Less than 1.1E-08
1.6E-09-3.4E-09
8.0E-10-1.6E-09
© 3.9E-10-8.0E-10
O Less than 3.9E-10
Watershed Regiccis
Not all watershed boundaries are shown. See Exhibit 6-9 for additional delineation of the watersheds.
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6.3 Soil and Soil Biota
This section provides a comparison between modeled mercury concentrations in 3MRA
and TRIM.FaTE surface soil, plant roots, and earthworms and a discussion of the differences
between the approaches to modeling mercury in soil and associated biota. The root zone, or
"deeper" soil, is handled differently by the two models, contributing to some of the differences
observed for associated biota (i.e., plant roots and earthworms). One major difference regarding
the modeling of deeper soil is highlighted in a text box in Section 6.3.2. Surface soil and
earthworm results are presented for TRIM.FaTE surface parcel SSE4 and 3MRA watershed 4
(habitat 11). The plant root comparison is made at TRIM.FaTE parcel SW2 and 3MRA habitat 3
(which corresponds to watershed 11), because the vegetation is similar at this location in the two
models (same comparison location as for leaf).
Divalent mercury is the only species modeled with 3MRA in these media. Three species
of mercury (divalent, elemental, and methyl) are modeled with TRIM.FaTE in these media, but
results are presented only for divalent mercury when it is the predominant form. When other
forms of mercury contribute significant percentages of the total mercury concentrations (>10%),
then total and divalent mercury results are both shown for TRIM.FaTE.
6.3.1 Divalent Mercury Concentrations in Surface Soil
Exhibit 6-19 summarizes the TRIM.FaTE and 3MRA fate processes for mercury in the
surface soil (i.e., top 1 cm of soil in both models). In both TRIM.FaTE and 3MRA, mercury is
transferred to the surface soil from the air via deposition. TRIM.FaTE also simulates diffusion
of mercury vapor from the air to the surface. Both models transfer mercury via erosion and
runoff from the soil surface to water bodies; however, only TRIM.FaTE is set up to allow for
runoff and erosion to transfer mercury from one surface soil location to another.
The divalent mercury concentrations in surface soil are compared in Exhibit 6-20 at
3MRA location 4 and TRIM.FaTE compartment SSE4. The divalent mercury concentrations in
3MRA soil at this and other locations (see Appendix E) are lower than the divalent mercury
concentrations in the corresponding TRIM.FaTE surface soil compartments. The difference is
less than a factor of six at most locations. This pattern is the opposite of the relative
concentrations estimated for air by the two models, but follows from the higher TRIM.FaTE
deposition fluxes (TRIM.FaTE deposition flux to parcel SSE4 is 11-fold higher than the 3MRA
deposition flux at location 4). The shapes of the TRIM.FaTE and 3MRA surface soil
concentration time series are similar with both curves showing smooth increases in
concentration.
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Exhibit 6-19
Summary of Mass Transfer and Transformation Processes Modeled: Surface Soil
TRIM.Fate (Hg°, Hg2+, MHg)
3MRA (Hg2+ only)
Dry deposition of particles from air to surface soil [G]a
Resuspension of particles from surface soil to air [L]
Diffusion (dry deposition) of vapors from air to surface
soil [G]
Diffusion (volatilization) from surface soil to air [Hg°
and MHg only] [L]
Wet deposition of particles from air to surface soil [G]
Wet deposition of vapors from air to surface soil [Hg°
and Hg2+ only] [G]
Runoff (dissolved phase) from surface soil to surface
soil and surface water [G or L]
Erosion (solid phase) from surface soil to surface soil
and surface water [G or L]
Percolation from surface soil to root zone soil [L]
Diffusion from surface soil to root zone soil [L]
Diffusion from root zone soil to surface soil [G]
Deposition from leaf and particles-on-leaf to surface
soil during litterfall [G]
Washoff of particles-on-leaf to surface soil [G]
Ingestion of surface soil by wildlife [L]
Elimination to surface soil by wildlife [G]
Dry deposition of particles from air to surface soil [G]
Dry deposition of vapor from air to surface soil [G]'
Wet deposition of particles from air to surface soil [G]b
Wet deposition of vapors from air to surface soil [G]
Runoff (dissolved phase) from surface soil to surface
water based on delineated watershed [L]
Erosion (solid phase) from surface soil to surface water
based on delineated watershed [L]
Percolation from surface soil to deeper soil [L]
Diffusion from surface soil to deeper soil [L]
Methylation of Hg2+ (0.00 I/day)c
Demethylation of MHg (0.06/day)
Reduction of Hg2+ to Hg° (0.0000125/day)
Oxidation of Hg° to Hg2+ (0)
Hg transformation in soil not modeled
a G = gain process, L = loss process, G or L indicates can be either.
b Process not modeled in this application, although 3MRA/ISCST3 has this capability for some chemicals.
0 First-order rate constant shown in parentheses for all transformation reactions.
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Exhibit 6-20 - Log Scale
Divalent Mercury Concentration in Surface Soil vs. Time: Swetts Pond Watershed
1.0E-08
1.0E-09 -
HI
< 1.0E-10
1.0E-11
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Hg2+: 3MRA Location 4 -»-Hg2+: TRIM.FaTE SSE4
6.3.2 Divalent Mercury Concentrations in Plant Roots
Plant roots in both models transfer mercury to and from the deeper soil as presented in
Exhibit 6-21. Both models use empirical bioconcentration factors to model root mercury
accumulation, but TRIM.FaTE uses a time-dependent approach11 while 3MRA assumes
equilibrium conditions. Transfer and transformation processes for the deeper (root zone) soil
also are presented in Exhibit 6-21, given the prominent role of the deeper soil in accumulation of
mercury mass by plant roots. The deeper soil layer is described in more detail in the
accompanying text box.
Exhibit 6-22 compares the divalent mercury concentrations in TRIM.FaTE and 3MRA
plant roots and deeper (root zone) soil at TRIM.FaTE parcel SW2 and 3MRA watershed 11
(which is matched to habitat 3). The divalent mercury concentrations in 3MRA roots are higher
than the divalent and total mercury concentrations in the TRIM.FaTE roots (grasses/herbs); the
difference is about an order of magnitude by the end of the 30-year modeling period. The
greater difference earlier in the simulation is likely related to the more dynamic nature of the
TRIM.FaTE approach to pollutant accumulation compared to the 3MRA equilibrium approach.
As noted elsewhere, TRIM.FaTE can be ran in steady-state or dynamic mode. Results for the latter are
presented in this chapter.
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Exhibit 6-21
Summary of Mass Transfer and Transformation Processes Modeled:
Root and "Deeper" Soil
TRIM.Fate (Hg°, Hg2+, MHg)
3MRA (Hg2+ only)
Root
Partitioning from root zone (RZ) soil (1-56 cm) to root,
based on a time-to-equilibrium model and empirical BCF
[G]a
Partitioning from root to RZ soil, based on a time-to-
equilibrium model and empirical BCF [L]
MethylationofHg2+(0)b
Demethylation of MHg (0)
Reduction of Hg2+ to Hg° (0)
Oxidation of Hg° to Hg2+ (0)
Partitioning from deeper soil (top 5 cm) to root at
equilibrium, based on empirical BCF [G]
Hg transformation in root not modeled (equivalent
to TRIM.FaTE)
Deeper Soil °
Percolation from surface soil to RZ soil [G]
Diffusion from surface soil to RZ soil [G]
Diffusion from RZ soil to surface soil [L]
Percolation from RZ soil to vadose zone soil [L]
Diffusion from RZ soil to vadose zone soil [L]
Diffusion from vadose zone soil to RZ soil [G]
Partitioning from RZ soil to root, based on a time-to-
equilibrium model and empirical BCF [L]
Partitioning from root to RZ soil, based on a time-to-
equilibrium model and empirical BCF [G]
Partitioning from RZ soil to earthworm/arthropod, based
on time-to-equilibrium model and empirical BCFs [L]
Partitioning from earthworm/arthropod to RZ soil, based
on time-to-equilibrium model and empirical BCFs [G]
Methylation of Hg2+ (0.00 I/day) b
Demethylation of MHg (0.06/day)
Reduction of Hg2+ to Hg° (0.0000 125/day)
Oxidation of Hg° to Hg2+ (0)
All surface soil processes (see Exhibit 6-19)
Percolation from surface soil to deeper soil [G]
Diffusion from surface soil to deeper soil [G]
Percolation from deeper soil to vadose zone soil [L]
Hg transformation in deeper soil not modeled
a G = gain process, L = loss process.
b First-order rate constant shown in parentheses for all transformation reactions.
0 In this model comparison, the TRIM.FaTE root zone soil compartment (55 cm deep, directly under the 1 cm of
surface soil) is defined differently from 3MRA deeper soil (top 5 cm, including 1 cm of surface soil).
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Exhibit 6-22 - Log Scale
Divalent and Total Mercury Concentration in Roots (grasses/herbs) and Associated Soil vs.
Time: Near Source, Southwest
1.0E-05
1.0E-13
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
-Hg2+: 3MRA Habitat 3 - Plant Root
-Total Hg: TRIM.FaTE SW2 - Plant Root (Grasses/Herbs)
-Hg2+: TRIM.FaTE SW2 - Root Zone Soil
-Hg2+: TRIM.FaTE SW2 - Plant Root (Grasses/Herbs)
-Hg2+: 3MRA Location 11 - Top 5 cm Soil
-Total Hg: TRIM.FaTE SW2 - Root Zone Soil
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As described in the accompanying text box, the lesser depth for the deeper soil layer in
the 3MRA simulation than the TRIM.FaTE simulation contributes to the higher mercury
concentrations predicted by 3MRA for this soil layer. Because the plant roots (and earthworms)
in both models obtain all of their mercury from this soil layer (see Exhibits 6-21 and 6-23), it
follows that the mercury concentrations predicted by 3MRA for these biota are also higher. Note
that in both models, the depth of this soil layer can be specified by the user.
A way to compare the impact of the different approaches to modeling mercury
accumulation in roots, which is independent from differing soil concentrations, is to compare the
factor by which divalent mercury is concentrated in roots from the associated soil. Given the
equilibrium aspect of the 3MRA approach, this value is 0.005 throughout the simulation for
every habitat. The dynamic nature of TRIM.FaTE, however, means that this value varies until
the system reaches equilibrium. The TRIM.FaTE value for this factor is 0.31 for the near source,
southwest parcel (SW2) at the end of the simulation, while it is 0.24 for year 1. All four
TRIM.FaTE parcels with the grasses/herbs vegetation type (i.e., Nl, NE2, Wl and SW2) have a
ratio of approximately 0.31 at the end of the simulation. The overall higher factor associated
with the TRIM.FaTE approach contributes to the finding that the two models' root
concentrations are less different than their associated deeper soil concentrations.
Data on uptake of divalent mercury by plant roots in the open literature are sparse; we
identified a single experimental study that was not used to derive the uptake ratio used in
TRIM.FaTE. Chunilall et al. (2004) examined divalent mercury uptake by roots and
stems/leaves of spinach plants grown in soil to which mercuric sulfate was added at levels
ranging from 10 to 50 ppm mercury. Assuming that the roots in this study were 75 percent water
and assuming that the soil moisture content was low, after 10 weeks of growth, the wet-weight
divalent mercury accumulation factors for roots in this study ranged from 0.2 to 1.6. This range
is similar to the wet-weight divalent mercury accumulation factors of 0.24 to 0.31 that resulted in
the TRIM.FaTE simulation and higher than the accumulation factor of 0.005 used in the 3MRA
simulation.
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Deeper (Root Zone) Soil
Both 3MRA and TRIM.FaTE allow for modeling of soil layers deeper than the surface layer. The deeper soil
modeled with 3MRA in this simulation is the soil from the surface to 5 cm deep (see diagram below). Therefore,
the 3MRA deeper soil is inclusive of the surface soil layer (0 to 1 cm). In this model simulation, the TRIM.FaTE
deeper soil (i.e., root zone soil compartment) is the soil from a depth of 1 cm to 56 cm. Unlike the 3MRA deeper
soil, the TRIM.FaTE deeper soil does not include the top 1 cm surface layer. The mercury concentrations calculated
forthe deeper soil inboth models are representative of the average overthe entire depth. Therefore, this difference
in deeper soil definition means that deeper, lower concentration soil is essentially "averaged in" to calculate the
TRIM.FaTE deeper soil concentration value. Moreover, the higher concentration of the top 1 cm of soil is averaged
into the 3MRA deeper soil concentration. The result of these differences in definition is that the 3MRA deeper soil
has a much higher divalent mercury concentration than the TRIM.FaTE deeper soil (see Exhibit 6-22).
TRIM.FaTE/
Surface Soil
(0 - 1 cm)
TRIM.FaTE Deeper
(Root Zone) Soil
(1 - 56 cm)
\ 3MRA
Surface Soil
(0-1 cm)
The table below provides an example of divalent mercury concentration averages for the surface and deeper soil
layers from both models. Additionally, ranges were estimated for TRIM.FaTE soil concentration between 0-5 cm
and 3MRA soil concentrations forthe depth of 1-56 cm. The concentration modeled in the surface soil with 3MRA
is lower than the concentration modeled with TRIM.FaTE. Additionally, the concentration directly modeled with
3MRA in the deeper soil (0-5 cm) is lower than the range estimated for TRIM.FaTE at that same depth. The upper
end of the concentration range estimated for 3MRA in the 1-56 cm deeper soil is higher than the TRIM.FaTE
modeled value for that depth.
Divalent Mercury Concentration in Soil Layers, Year 30 Average - Swetts Pond Watershed
Soil Layer
Surface soil
Deeper soil (per 3MRA)
Deeper soil (TRIM.FaTE root zone)
Soil Depth
0- 1cm
0-5cm
1-56 cm
Soil Concentration (g/g dry wt)
TRIMFaTE- SSE4
3.0E-9
[6.0E-10
-------
6.3.3 Divalent Mercury Concentrations in Earthworms
As shown in Exhibit 6-23, earthworms in both models accumulate mercury from contact
with the deeper soil. Similar to the plant roots, both models use empirical bioconcentration
factors to estimate earthworm mercury accumulation, but TRIM.FaTE uses a time-dependent
approach while 3MRA assumes equilibrium conditions.
Exhibit 6-23
Summary of Mass Transfer and Transformation Processes Modeled: Earthworm
TRIM.Fate (Hg°, Hg2+, MHg)
3MRA (Hg2+ only)
Partitioning from root zone soil (1-56 cm) to
earthworm, based on a time-to-equilibrium model and
empirical BCF [G]a
Partitioning from earthworm to root zone soil, based on
a time-to-equilibrium model and empirical BCF [L]a
Ingestion of earthworm by wildlife [L]
Partitioning from deeper soil (top 5 cm) to earthworm
at equilibrium, based on empirical BCF [G]
MethylationofHg2+(0)b
Demethylation of MHg (0)
Reduction of Hg2+ to Hg° (0)
Oxidation of Hg° to Hg2+ (0)
Hg transformation in earthworm not modeled
(equivalent to TRIM.FaTE)
a G = gain process, L = loss process.
b First-order rate constant shown in parentheses for all transformation reactions.
Given the much higher concentrations in deeper soil for 3MRA, it would be expected that
the 3MRA earthworm mercury concentrations would be higher than the TRIM.FaTE
concentrations (see deeper soil discussion in Section 6.3.2). Exhibit 6-24 shows that divalent
mercury concentrations in 3MRA earthworms in habitat 11 are about three orders of magnitude
higher than total and divalent mercury concentrations in the TRIM.FaTE SSE4 earthworm
compartment. More locations are compared in Appendix E, and 3MRA concentrations are
consistently three to four orders of magnitude higher than TRIM.FaTE concentrations.
As was done in Section 6.3.2 for the plant root results, the ratios of earthworm to deeper
soil divalent mercury concentrations (wet-weight) can be compared between the two models.
The value is 0.31 for 3MRA and 0.037 for TRIM.FaTE in the near source parcel (SW2) at the
end of the simulation (it varies very little across all TRIM.FaTE parcels). The higher value for
3MRA is opposite the situation for roots, where the ratios indicate that 3MRA accumulates less
mercury mass in the roots per deeper soil mercury mass than TRIM.FaTE. These differences
explain why even though both earthworms and roots obtain mercury from the deeper soil in both
models, the root concentrations from 3MRA and TRIM.FaTE are only one order of magnitude
different while the earthworm concentrations are three to four orders of magnitude different.
Ultimately, the model-to-model differences in these ratios result from the different
bioaccumulation factors used by the two models for this application.
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Exhibit 6-24 - Log Scale
Divalent and Total Mercury Concentration in Earthworms and Associated Soil vs. Time:
Swetts Pond Watershed
1.0E-06
1.0E-07
1.0E-14 -
1.0E-15
1234567
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
-Hg2+: 3MRA Habitat 11 - Earthworm
-Hg2+: 3MRA Location 4 - Top 5 cm Soil
-Hg2+: TRIM.FaTE SSE4 - Earthworm
-Hg2+: TRIM.FaTE SSE4 - Root Zone Soil
-Total Hg: TRIM.FaTE SSE4 - Earthworm
-Total Hg: TRIM.FaTE SSE4 - Root Zone Soil
With respect to typical literature values for earthworm accumulation of mercury (see
Sample et al. 1998), the calculated ratios (i.e., bioaccumulation factors, or BAFs) for earthworms
for both models appear to be low. Data in the literature indicate that earthworm BAFs change
with soil concentration, and they tend to be higher with lower soil mercury concentrations.
Using the 30 observations in Appendix A of Sample et al. (1998) from five separate studies, it
appears that the concentration of total mercury in earthworms is higher than the soil
concentration only for soil concentrations less than approximately 1 mg/kg dry weight. At
higher soil mercury concentrations, the earthworm tissue concentrations tend to be lower than
the soil concentrations (see text box).
The soil mercury concentrations for both 3MRA and TRIM.FaTE are well below 1
mg/kg dry or wet weight, hence one would expect bioaccumulation at such low soil
concentrations. Even though earthworms are approximately 80 percent water, and a wet-weight
BAF would be somewhat lower than a dry-weight BAF depending on the soil water content, one
would still expect bioaccumulation of mercury in earthworms at the low soil concentrations
predicted in both 3MRA and TRIM.FaTE. Although the 3MRA and TRIM.FaTE deeper soil
concentrations predicted in this test case are much lower than any found in the literature studies
reviewed, it appears that higher BAF values may be more appropriate than the ones calculated
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from both the TRIM.FaTE and 3MRA results. These results indicate a possible area for further
research and refinement of model input values affecting earthworm bioaccumulation.
The shapes of the TRIM.FaTE and 3MRA earthworm time series are similar at all
locations. Total and divalent mercury concentrations in earthworms in the same TRIM.FaTE
parcel are not identical, reflecting the same proportional representation of elemental mercury in
the TRIM.FaTE earthworm compartments as predicted for the associated root zone soil
compartment. For simplicity of presentation, only total and divalent concentrations are shown in
Exhibit 6-24.
Earthworm Dry-weight Bioaccumulation Factors (BAFs) for Total Mercury
(mg[Hg]/kg[earthworm dry wt]/mg[Hg]/kg[soil dry wt])a
Soil Hg Concentration Interval
(mg[Hg]]/kg[soil dry wt])
0.010- 0.050
0.051 -0.100
0.101 -0.250
0.251 - 1.00
1.01 -5.00
9.9, 269 (actual values)
Number of
Observations
3
5
9
5
6
2
Average Soil [Hg]
(mg[Hg]]/kg[soil dry wt])
0.020
0.80
0.19
0.52
2.8
139
Average Earth-
worm BAF
29
8
2.5
0.94
0.15
0.044
a Data from Sample et al. (1998).
6.3.4 Spatial Pattern for Surface Soil
Exhibit 6-25 shows the spatial variation in divalent mercury concentration results for
surface soil from 3MRA and TRIM.FaTE. The map is scaled to the same size as Exhibits 6-17
and 6-18. As with the previous maps, the TRIM.FaTE concentrations are shown via background
shading and the 3MRA concentrations are shown via dots of various sizes. The increment for
each range category is equal (in logarithmic units) both within a model and across the two
models (i.e., a change in pattern or dot size reflects the same proportional increase for both
models). However, the concentration range categories differ for the models because of the
limited overlap of the modeled concentration ranges.
Because of the asymmetry in the locations of results and the few data points for both
models, it is difficult to evaluate TRIM.FaTE and 3MRA surface soil results by direction. Like
the deposition patterns, TRIM.FaTE soil concentrations appear to be highest north of the source,
but concentrations to the west and east are fairly close (and much closer than for air
concentrations). Overall, the soil directional pattern generally corresponds to the air
concentration pattern, with some differences (such as higher concentrations to the west close to
the source) that appear to reflect the deposition patterns. 3MRA surface soil concentrations
appear highest to the southwest and lowest to the north and east, which is somewhat different
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from the 3MRA air concentration pattern (highest to the northeast) but appears to be similar to
the deposition pattern (highest to the southwest).
The TRIM.FaTE maximum-to-minimum ratio for divalent mercury soil concentration for
the entire layout (excluding the source compartment) is approximately 26. The 3MRA
maximum-to-minimum ratio for the entire layout (excluding location 10, which is just adjacent
to the source compartment, and all locations falling outside of the TRIM.FaTE layout) is
approximately 48. The slightly higher ratio for 3MRA soil results (less than a factor of two),
which is consistent with the findings for air concentrations and deposition fluxes, may be
attributable to the point estimate outputs of 3MRA versus the homogenous compartments of
TRIM.FaTE. Both TRIM.FaTE and 3MRA results show a greater maximum-to-minimum ratio
for the soil concentrations than the air concentrations, indicating a similarity in the importance of
deposition in divalent mercury transport near the source.
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Exhibit 6-25
Spatial Variation in Divalent Mercury Concentrations in Surface Soil
Average Concentrations for 30th Year
Surface Soil
Divalent Mercury (g/g dry wt)
3MRA
Greaterlhan2.2E-09
9.9E-10-2.2E-09
Q 4.4E-10-9.9E-10
O 2.0E-10-4.4E-10
O Less than 2.0E-10
I _ I Watershed Regions
Not all watershed boundaries are shown. See Exhibit 6-9 for additional delineation of the watersheds.
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6.4 Surface Water, Sediment, and Fish
This section presents a comparison of the modeling approaches affecting mercury
concentration and speciation in surface water, sediment, and fish, as well as a comparison of the
outputs for these media from TRIM.FaTE and 3MRA simulations. For all output comparisons in
this section, mercury concentration results for Swetts Pond are used because it is the primary
surface water body discussed in other chapters of this report. This location corresponds to the
TRIM.FaTE parcel labeled as Swetts Pond and the 3MRA location (1,7) for surface water,
sediment, and fish. For mercury speciation comparisons, results are also presented for Brewer
Lake to show the variation across water bodies. Additional comparisons of results for these
media are presented in Appendix E.
6.4.1 Mercury Concentrations and Speciation in Surface Water
As shown in Exhibit 6-26, TRIM.FaTE and 3MRA use different methods for simulating
chemical transport and transformation processes in surface water. Key differences in the two
simulations being compared are listed below.
In TRIM.FaTE, chemical exchanges between macrophytes and surface water are
modeled explicitly, which may contribute to observed differences in concentration and
speciation of mercury in surface water as compared to 3MRA surface water.
• In TRIM.FaTE, algae are modeled explicitly in surface water and participate in
partitioning of the various mercury species from surface water, which may contribute to
different concentrations and speciation profiles than 3MRA surface water.
As shown in Exhibit 6-27 for Swetts Pond, the TRIM.FaTE total and divalent mercury
concentrations in surface water are similar, with TRIM.FaTE total mercury being less than two-
fold different from 3MRA total mercury (all comparisons in this section based on total water-
column concentrations, not dissolved concentrations). In Brewer Lake (see Appendix E, Chart
E7-b), the difference is greater, with TRIM.FaTE total mercury less than five-fold higher than
3MRA total mercury. The higher TRIM.FaTE surface water concentrations for the two water
bodies are consistent with the higher atmospheric deposition fluxes modeled by TRIM.FaTE.
The larger difference between total mercury concentrations in Brewer Lake compared to Swetts
Pond may partially result from the larger difference in deposition fluxes between the two models
with increasing distance from the source (see Section 6.2.2). In general, both the TRIM.FaTE
and 3MRA mercury concentrations (total, divalent, and methyl) are higher in the surface water
of Swetts Pond than of Brewer Lake because Swetts Pond is a smaller (shallower) water body
and is closer to the emission source.
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Exhibit 6-26
Summary of Mass Transfer and Transformation Processes Modeled:
Surface Water and Macrophyte
TRIM.FaTE (Hg°, Hg2+, MHg) a
3MRA (Hg°, Hg2+, MHg)
Surface Water
Advective (bulk) flow from surface water to surface
water (downstream only) [G or L] b
Dispersive flow from surface water to surface water
(both directions) [G or L]
Advective flow from ground water to surface water [G]
Runoff (dissolved phase) from surface soil to surface
water (downgradient only) [G]
Erosion (solid phase) from surface soil to surface water
(downgradient only) [G]
Dry deposition of particles from air to surface water
[G]
Diffusion (dry deposition) of vapors from air to surface
water [G]
Diffusion (volatilization) from surface water to air [L]
Wet deposition of particles from air to surface water
[G]
Wet deposition of vapors from air to surface water
[Hg° and Hg2+ only] [G]
Particle deposition (including algae phase) from
surface water to sediment [L]
Particle resuspension from sediment to surface water
[G]
Diffusion from surface water to sediment [L]
Diffusion from sediment to surface water [G]
Partitioning from surface water to macrophyte, based
on a time-to-equilibrium model and empirical BCF [L]
Partitioning from macrophyte to surface water, based
on a time-to-equilibrium model and empirical BCF [G]
Elimination to surface water by fish [G]
Advective (bulk) flow along reaches within water body
network (downstream only) [G or L]
Dispersive flow along reaches within water body
network (both directions) [G or L]
Advective flow between ground water and surface
water (both directions) [G or L]
Runoff (dissolved phase) from surface soil to surface
water (downgradient only) [G]
Erosion (solid phase) from surface soil to surface water
(downgradient only) [G]
Dry deposition of particles from air to surface water
[G]c
Dry deposition of vapors from air to surface water [G] °
Diffusion (volatilization) from surface water to air [L]
Wet deposition of particles from air to surface water
[G]c
Wet deposition of vapors from air to surface water [G]
Effects of particle settling and resuspension and
diffusive exchanges on contaminant fate are modeled
using a bulk sediment-water exchange term [G or L]
~
~
~
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TRIM.FaTE (Hg°, Hg2+, MHg) a
3MRA(Hg°,Hg2+,MHg)
Ingestion of surface water algae phase by fish [L]
Ingestion of surface water by wildlife [L]
Elimination to surface water by wildlife [G]
Methylation of Hg2+ (0.00I/day)d
Demethylation of MHg (0.013/day)
Reduction of Hg2+ to Hg° (0.0075/day)
Oxidation of Hg° to Hg2+ (0)
Reduction of MHg to Hg° (0)
Methylation of Hg2+ (9.9E-6/day)
Demethylation of MHg (0.024/day)
Reduction of Hg2+ to Hg° (0.04/day)
Oxidation of Hg° to Hg2+ (0.0024/day)
Reduction of MHg to Hg° (0.0026/day)
Macrophyte
Partitioning from surface water to macrophyte, based
on a time-to-equilibrium model and empirical BCF [G]
Partitioning from macrophyte to surface water, based
on a time-to-equilibrium model and empirical BCF [L]
Macrophytes not included in this model application
Methylation of Hg2+(0)d
Demethylation of MHg (0)
Reduction of Hg2+ to Hg° (0)
Oxidation of Hg° to Hg2+ (l.OE+9/day)
Macrophytes not included in this model application
a Algae are modeled explicitly as a phase of surface water in TRDVLFaTE, which affects the phase distribution of
mercury species in surface water, and thereby affects the concentration and speciation of mercury in surface water
and related compartment types.
b G = gain process, L = loss process, G or L indicates can be either.
0 Process not modeled in this application, although 3MRA/ISCST3 has this capability for some chemicals.
d First-order rate constant shown in parentheses for all transformation reactions.
In both models, the mercury concentrations increase over time. In both Swetts Pond and
Brewer Lake, the TRIM.FaTE total and divalent mercury concentrations increase with a slight
five-year repeating pattern corresponding to the five years of meteorological data that were used
as inputs to the TRIM.FaTE model. The 3MRA total and divalent mercury concentrations in
both surface water bodies spike every few years (with less pronounced spikes in Brewer Lake),
corresponding to the 14 years of meteorological data that were used as inputs to the 3MRA
model. Even though the mercury deposition and air concentration are input as constants in this
3MRA simulation, these spikes are related to the repeating meteorological data set because the
3MRA watershed module inputs and uses the hourly meteorological data for processes such as
runoff and erosion.
While the total mercury concentration predicted by the two models for each water body is
not that different, representation of the three mercury species and their temporal pattern varies.
Exhibit 6-28 displays the mercury speciation profile in surface water at year 30 of the modeling
period. The exhibit shows the majority of mercury predicted in Swetts Pond and Brewer Lake
by TRIM.FaTE is in the divalent form at year 30 (i.e., 93 and 85 percent of total mercury as
divalent mercury in Swetts Pond and Brewer Lake, respectively), with lesser amounts of
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elemental (six and 14 percent) and methyl (one and one percent) mercury. 3MRA predicts
noticeably greater representation by elemental and methyl mercury (i.e., 40 and eight percent in
Swetts Pond, 63 and six percent in Brewer Lake). The methyl mercury percentages for both
models are within the ranges reported in EPA'sMercwry Study Report to Congress (EPA 1997),
which cites percent methyl mercury ranges in fresh surface waters of 1 to 12 percent in Swedish
lakes, 2 to 14 percent in Swedish mires, 1 to 6 percent in Swedish runoff, and less than 2.5
percent in Lake Crescent, WA. In a study of 92 lakes in New Hampshire and Vermont, Kamman
et al. (2004) measured percent methyl mercury in the hypolimnion (1 meter above sediment-
water interface) to be 9.19 percent, with a median value of 6.68 percent, and in the epilimnion
(subsurface at approximately 0.2 meters) the percent methyl mercury to be 2.17 with median
value 18.28 percent.
Exhibit 6-27 - Log Scale
Mercury Concentration in Surface Water vs. Time: Swetts Pond
1.0E-09 -1
1.0E-10
1.0E-11
1.0E-12 --•
1.0E-13
1.0E-14
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
Total Hg: TRIM.FaTE Swetts Pond —«— Hg2+: TRIM.FaTE Swetts Pond
-Total Hg: 3MRA Location (1,7) —•— Hg2+: 3MRA Location (1,7)
HgO: TRIM.FaTE Swetts Pond
•HgO: 3MRA Location (1,7)
-MHg: TRIM.FaTE Swetts Pond
•MHg: 3MRA Location (1,7)
Throughout the simulation time period, the relationship between the three mercury
species predicted by TRIM.FaTE does not vary substantially, but the relationship predicted by
3MRA does. This is shown in Exhibit 6-27, particularly with regard to the relationship between
divalent and elemental mercury. Initially in 3MRA, the divalent mercury concentration is almost
an order of magnitude greater than that for elemental mercury, but they are quite similar by 30
years. In Brewer Lake, the 3MRA concentrations of elemental mercury surpass those of divalent
mercury by year 9 of the simulation (see Appendix Chart E-7b).
The mercury speciation differences in surface water between the two models' outputs are
likely due to differences in input values and in the simulation of some mass transfer processes,
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including (1) different rate constants for the conversion of mercury from one species to another
(see Exhibit 6-26), (2) differences in the magnitude of atmospheric deposition at these distances
from the source (see Section 6.2.2), (3) partitioning of the various forms of mercury to algae in
TRIM.FaTE surface water but not in 3MRA surface water, (4) uptake of the various forms of
mercury from surface water by macrophytes (and subsequent rapid conversion of elemental to
divalent mercury within macrophytes) that are included in TRIM.FaTE but not in 3MRA, and (5)
substantially different input values for suspended solids concentration (4.3 mg/L in the
TRIM.FaTE scenario, total of suspended sediment and algae, versus roughly 150 mg/L for
3MRA), which can affect phase distribution and thus the fate of the various mercury species.
Exhibit 6-28
Mercury Speciation Profile in Surface Water at Year 30
100%
90%
TRIM 3MRA
Brewer Lake
TRIM
Swells Pond
3MRA
6.4.2 Mercury Concentrations and Speciation in Sediment
TRIM.FaTE and 3MRA use different methods for simulating chemical transport and
transformation processes in sediment (see Exhibit 6-29). One key process difference between
the two simulations is that in TRIM.FaTE, particle deposition from surface water to sediment
includes deposition from the algae phase, which is not modeled explicitly in 3MRA.
As shown in Exhibit 6-30 for Swetts Pond, the TRIM.FaTE predictions for total and
divalent mercury concentrations in sediment are higher than the corresponding 3MRA
predictions by one to two orders of magnitude. The same relationship is observed in the
sediment of Brewer Lake (see Appendix Chart E-8b), with slightly larger differences. Methyl
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mercury concentrations are higher in TRIM.FaTE sediments than 3MRA sediments to varying
degrees, and 3MRA elemental mercury concentrations are consistently greater than TRIM.FaTE
elemental mercury concentrations in sediments. This pattern is generally similar (except for
methyl mercury) to that observed in surface water, as would be expected; however, the
magnitude of the concentration difference is greater in sediment than in surface water. This is at
least partly a result of the larger percentage of total mercury as divalent mercury in surface water
in TRIM.FaTE, which deposits more rapidly to the sediment than elemental mercury (which is at
a much higher percentage in surface water in 3MRA). In general, TRIM.FaTE and 3MRA
mercury concentrations (total, divalent, and methyl) are higher in the sediment of Swetts Pond
than of Brewer Lake, which also follows the pattern seen for concentrations in surface water.
Exhibit 6-29
Summary of Mass Transfer and Transformation Processes Modeled: Sediment
TREVLFaTE (Hg°, Hg2*, MHg)
3MRA(Hg°,Hg2+,MHg)
Particle deposition (including algae phase) from
surface water to sediment [G]a
Particle resuspension from sediment to surface water
[L]
Diffusion from surface water to sediment [G]
Diffusion from sediment to surface water [L]
Partitioning from sediment to benthic invertebrate,
based on time-to-equilibrium model and empirical BCF
[L]
Partitioning from benthic invertebrate to sediment,
based on time-to-equilibrium model and empirical BCF
[G]
Effects of particle settling and resuspension and
diffusive exchanges on contaminant fate are modeled
using a bulk sediment-water exchange term [G or L]
Partitioning between pore water and benthos, based on
empirical B AFs for surface water [G or L]
Methylation of Hg2+ (0.000 I/day)b
Demethylation of MHg (0.0501/day)
Reduction of Hg2+ to Hg° (l.OE-6/day)
Oxidation of Hg° to Hg2+ (0)
Methylation of Hg2+ (0.00037/day, upper sed)
Demethylation of MHg (0.0015/day, upper sed)
Reduction of Hg2+ to Hg° (0)
Oxidation of Hg° to Hg2+ (0)
a G = gain process, L = loss process.
b First-order rate constant shown in parentheses for all transformation reactions.
For both models, the mercury concentrations increase over time with relatively smooth
patterns. In contrast to the surface water concentrations, the TRIM.FaTE concentrations in
sediment do not show the five-year repeating pattern corresponding to the meteorological data;
however, the 3MRA concentrations of divalent and methyl mercury in sediment do show slight
fluctuations, similar to the spikes observed in the surface water concentrations (see Exhibit 6-
27). This difference is possibly due to the spikes in surface water concentrations being greater
for 3MRA than TRIM.FaTE, and therefore not being damped out completely in the sediment by
the mass transfer processes as they are for TRIM.FaTE.
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Exhibit 6-30 - Log Scale
Mercury Concentration in Sediment vs. Time: Swetts Pond
1.0E-08
1.0E-14
1234567
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
Total Hg: TRIM.FaTE Swetts Pond •
-Total Hg: 3MRA Location (1,7)
-Hg2+: TRIM.FaTE Swetts Pond
-Hg2+:3MRA Location (1,7)
-HgO: TRIM.FaTE Swetts Pond
-HgO:3MRA Location (1,7)
-MHg: TRIM.FaTE Swetts Pond
-MHg:3MRA Location (1,7)
Exhibit 6-31 displays the mercury speciation profile in sediment at year 30 of the
modeling period. The percent of mercury in each form in sediment differs between the two
models. Nearly all of the mercury modeled by TRIM.FaTE in the sediment is divalent mercury
at year 30 (i.e., 99 percent of total mercury as divalent mercury in Swetts Pond and Brewer
Lake). In contrast, at year 30 the majority of mercury modeled by 3MRA in the sediment is
elemental mercury (i.e., 59 percent and 78 percent of total mercury as elemental mercury in
Swetts Pond and Brewer Lake, respectively). The percent of mercury that is methyl mercury in
the sediment is small for both models (i.e., 0.2 percent in both water bodies for TRIM.FaTE; 1.0
percent in Brewer Lake for 3MRA; and 2.4 percent for Swetts Pond for 3MRA). The observed
speciation differences are likely a result of many contributing factors, including to at least some
degree all the possible factors mentioned for surface water and also including the different
mercury transformation rate constants used by the two models for sediment.
A few sources were identified in the literature in which authors reported the percent of
mercury measured as methyl mercury in sediment. In 92 New Hampshire and Vermont lakes,
percent measured methyl mercury in surficial sediment (0 to 5 cm) ranged from 0.24 to 7.84
percent (mean 1.84, median 1.46 percent; Kamman et al. 2004). The authors note that these
results are similar to measurements made at six locations in the Quabbin Reservoir in
Massachusetts (Gilmour et al. 1992) that ranged from 0.1 to 3.1 percent (at a seventh location,
the methyl mercury was measured at 16.3 percent). In the Mercury Study Report to Congress
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(EPA 1997), the percent methyl mercury measured in four lakes in Finland was reported to be
between 0.03 and 6 percent (Verta and Matilainen 1995). No sources were identified that
reported the percent of elemental mercury in lake sediment. Thus, it is difficult to determine
whether the speciation calculated for TRIM.FaTE or for 3MRA is more representative of
sediment; however, methyl mercury speciation from both models falls within the range of values
reported in the literature.
Exhibit 6-31
Mercury Speciation Profile in Sediment at Year 30
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
TRIM 3MRA
Brewer Lake
TRIM 3MRA
Swells Pond
From a mass balance perspective, one would expect the TRIM.FaTE results to show
more mercury mass in the Swetts Pond system than the 3MRA results, given that the
TRIM.FaTE deposition flux of mercury from air is approximately an order of magnitude higher
(air deposition is a primary source of modeled mercury inputs to Swetts Pond, along with soil
erosion and runoff; see Exhibits 6-26 and 6-29 for all sources of mercury to the surface water
and sediment). Unlike TRIM.FaTE, 3MRA cannot provide mass results for individual
compartments/media, but the concentration results for the two models imply that there is more
mercury mass in the Swetts Pond system in the TRIM.FaTE simulation. TRIM.FaTE predicts
higher mercury concentrations (and thus mass, given similar media volumes modeled) in the key
mass-accumulating media in Swetts Pond, including sediment (one and a half orders of
magnitude), surface water (slightly higher), and macrophytes (not modeled in 3MRA). 3MRA
predicts slightly higher mercury concentrations in fish, but the fish biomass is relatively very
small (compared to the volume of the water bodies), and thus the amount of mercury in fish is
negligible in a mass balance context.
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6.4.3 Methyl Mercury Concentrations in Fish
As shown in Exhibit 6-32, TRIM.FaTE and 3MRA use different methods for simulating
chemical transport and transformation processes in fish. Key differences in the two simulations
are presented below.
• TRIM.FaTE uses a bioenergetics12 approach for accumulation of methyl mercury in fish,
whereas 3MRA uses a bioaccumulation factor (BAF) approach based on the dissolved
water concentration.
For purposes of this model comparison, both 3MRA and TRIM.FaTE expressed chemical
concentrations in aquatic organisms at integer trophic levels. 3MRA evaluated two
trophic levels: T4 fish (i.e., secondary carnivores that are the apex predator species in the
system) and T3 fish (i.e., primary carnivores that may be both predator and prey).13
TRIM.FaTE also evaluated T2 (i.e., benthic invertebrates that consume detritus (largely
decaying plant material) and herbivorous fish).14
• TRIM.FaTE modeled two distinct aquatic food chains, a benthic and a water-column
food chain, whereas 3MRA did not in this application. 3MRA assumed that methyl
mercury concentrations in fish are directly related to methyl mercury concentrations in
surface water regardless of the amount of benthic prey consumed by the fish.
Bioaccumulation factors based on mercury concentrations in sediment were not used in
3MRA for this model comparison.
• Because TRIM.FaTE is a chemical mass-balanced model, estimates of total fish biomass
at T2, T3, and T4 are needed to run the model, whereas they are not needed for 3MRA.
The estimates offish biomass at each integer trophic level in TRIM.FaTE represented all
populations and portions of populations offish at that trophic level in the modeled
surface water body.
• In both 3MRA and TRIM.FaTE, the modeled chemical concentration at an integer
trophic level was intended to be close to what would be found in a fish of average size
and age at that trophic level, rather than in fish of a particular species (which may feed on
prey from multiple trophic levels). With TRIM.FaTE, chemical concentrations in fish of
a particular species (e.g., largemouth bass, which feed on smaller fish and invertebrates at
smaller adult sizes and feed more exclusively on fish at larger adult sizes, consuming
prey from both the water-column and the benthic environment) were estimated from the
12 The bioenergetics approach used by TRIM.FaTE allows the user to explicitly incorporate multiple
exposure pathways for fish. The user can assign more than one diet item to each type offish, creating a web-like set
of trophic relationships. However in this application, each TRIM.FaTE fish compartment was set to have a diet that
is only the fish in the trophic level below it.
13 Tl and T2 type aquatic organisms can be modeled with 3MRA, but were not in this application.
14 Odum (1971) defines four basic trophic levels: Tl = primary producers (plants); T2 = primary consumers
(herbivores); T3 = secondary consumers (primary carnivores that consume herbivores); and T4 = tertiary consumers
(secondary carnivores that consume primary carnivores).
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modeled concentrations for appropriate TRIM.FaTE trophic levels and estimates of the
relative biomass for the species in the different trophic levels.15
Exhibit 6-32
Summary of Mass Transfer and Transformation Processes Modeled: Fish
TREVLFaTE (Hg2+, Hg2*, MHg)
3MRA (MHg only)
Ingestion of surface water algae phase by fish (water-
column herbivores only) [G]a
Ingestion of benthic invertebrates by fish (benthic
omnivores only) [G]
Ingestion of fish by fish (benthic carnivore and water-
column omnivore and carnivore) [G or L]
Elimination to surface water by fish [L]
Ingestion of fish by semi-aquatic wildlife [L]
Empirical bioaccumulation factor (BAF) relates
dissolved water-column concentration and whole body
and filet concentration of MHg in T3 and T4 fish (this
factor is intended to represent all relevant gains and
losses under equilibrium conditions) [G or L]
MethylationofHg2+(0)b
Demethylation of MHg (0)
Reduction of Hg2+ to Hg° (0)
Oxidation of Hg° to Hg2+ (l.OE+6/day)
Hg transformation in fish not modeled explicitly;
however, MHg concentrations predicted for fish use
MHg-specific BAFs from empirical studies and should
reflect methylation in vivo
a G = gain process, L = loss process, G or L indicates can be either.
b First-order rate constant shown in parentheses for all transformation reactions.
For purposes of this model comparison, the methyl mercury concentration time series for
the 3MRA T4 fish, the two TRIM.FaTE top trophic level fish compartments modeled (i.e.,
benthic carnivore, water-column carnivore), and three top predator fish species developed from
TRIM.FaTE fish compartment data were evaluated (Exhibit 6-33). The top predator fish species
were developed as an additional comparison with 3MRA fish (which are modeled using
empirical BAFs) and as a demonstration of how the concentration calculated in a fish species
based on diet would differ from the TRIM.FaTE T3 and T4 fish. See the accompanying text box
for discussion of how TRIM.FaTE fish compartment outputs were used to develop mercury
concentration predictions for the top predator fish species.
As shown in Exhibit 6-33 for Swetts Pond, the shapes of the time series for the
TRIM.FaTE and 3MRA fish are similar, showing increased concentrations with time. In
general, the methyl mercury concentrations for top predator fish in 3MRA and TRIM.FaTE are
similar despite the different methods used to model methyl mercury uptake and accumulation in
fish. Specifically, the methyl mercury concentrations are all within approximately one order of
magnitude by year 30, with the TRIM.FaTE water-column carnivore compartment within a
factor of three of the 3MRA T4 fish. The Brewer Lake results are even closer, with the 3MRA
15 ,
1 The fish bioenergetic model in TRIM.FaTE also can be used to directly simulate individual fish species
that feed at multiple trophic levels in one or more environments. In that case, estimates of total fish biomass are
calculated for species instead of for integer trophic levels.
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T4 fish falling within the span of the various TRIM.FaTE results (see Appendix Chart E-9b).
The 3MRA T4 fish concentrations are likely greater than the TRIM.FaTE largemouth
bass/smallmouth bass/northern pike concentrations because the consumption behavior of these
fish species is a hybrid of 70 percent mid-trophic level fish (i.e., omnivores) and 30 percent top
trophic level fish (i.e., carnivores). Of the two top predator fish compartments modeled in
TRIM.FaTE, the methyl mercury concentrations are greater for the one associated with the
water-column food chain than the one associated with the benthic food chain. The same pattern
is observed in Brewer Lake.
Using TRIM.FaTE Outputs to Calculate Concentrations in Specific Fish Types
TRIM.FaTE pollutant concentration predictions in fish compartments can be used - along with
information on the diets of specific fish species (as used in setting up the TRIM.FaTE scenario) - to
calculate pollutant concentration estimates for specific fish types. Such a calculation was done in this
model comparison to develop methyl mercury concentration estimates for some top predator species (i.e.,
largemouth bass/smallmouth bass/northern pike). These estimates are derived using the relevant model
outputs (i.e., the compartments exhibiting feeding behavior of the species of interest) and fractional
values representing the relative prevalence of those feeding behaviors by the species of interest. For
example, data consulted in setting up the simulation indicated that 3 5 percent of the largemouth bass diet
in lakes in the region consists of benthic invertebrates (which is the diet of the TRIM.FaTE benthic
omnivore compartment in this simulation), 35 percent consists of water-column herbivores (same diet
as water-column omnivore compartment), 1 5 percent consists of benthic omnivores (same diet as benthic
carnivore compartment), and 15 percent consists of water-column omnivores (same diet as water-column
carnivore compartment) .a Therefore, the estimated methyl mercury (MHg) concentration for largemouth
bass would be:
(0.35 * [MHgbenthlc ommvore]) + (0.35 * [MHgwater.column ommvore]) + (0.15 * [MHgbenthlc c_]) + (0.15 *
^er_co|umn camivoreJ/'
aDietary fractions fromKelso and Johnson (1991).
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Exhibit 6-33 - Log Scale
Methyl Mercury Concentration in Fish vs. Time: Swetts Pond
1.0E-05
— 1.0E-06
g 1.0E-07 -
o
o
2, 1.0E-08
"* 1.0E-09 -
1.0E-10
7
1 234567
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
-MHg:3MRAT4Fish
-MHg: TRIM.FaTE Water-column Carnivore
-MHg: TRIM.FaTE Benthic Carnivore
-MHg: TRIM.FaTE Largemouth Bass/Smallmouth Bass/Northern Pike
In both modeling approaches, the fish methyl mercury concentrations in the highest
accumulating fish are dependent on the concentrations of dissolved methyl mercury in the
surface water. This relationship is explicit and direct in 3MRA, where mercury accumulation in
fish is derived from the dissolved water concentration of methyl mercury and an empirical BAF.
In TRIM.FaTE, the same relationship is true, but a bioenergetics approach is used in which the
methyl mercury is transferred among media and biota based on food chain relationships (i.e.,
ingestion of food contaminated with mercury), beginning with methyl mercury in the surface
water. For example, the methyl mercury concentration in surface water affects the concentration
of methyl mercury in the algae phase, which is consumed by the water-column herbivorous fish,
which are consumed by the next trophic level water-column fish and then by the T4 water-
column fish according to the defined ingestion rates and modeled contamination levels. Lower
methyl mercury concentrations are predicted in the TRIM.FaTE benthic carnivore compartment
than in the water-column carnivore compartment, indicating lower mercury accumulation in the
TRIM.FaTE benthic food chain.
Because the two models use different methods to predict mercury accumulation in fish
and because the two models predict somewhat different surface water concentrations of the
various forms of mercury, it is informative to evaluate the ratios between dissolved surface water
concentrations and fish tissue concentrations of methyl mercury (i.e., BAFs calculated from the
model estimates of surface water-dissolved methyl mercury concentrations and fish tissue methyl
mercury concentrations) in addition to the fish tissue methyl mercury concentration time series.
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The dissolved methyl mercury concentrations in surface water, the methyl mercury
concentrations in fish, and the ratios between the surface water and fish concentrations (i.e.,
calculated BAFs) for TRIM.FaTE and 3MRA for Swetts Pond at year 30 are presented in Exhibit
6-34. These BAFs are within the range of methyl mercury BAFs identified in the literature for
upper trophic level fish that are typically consumed by humans. The range of BAF values
presented in EPA's Water Quality Criterion for the Protection of Human Health: Methylmercury
is 500,000 to 10,000,000 (Glass et al. 1999, Lores et al. 1998, Miles and Fink 1998, Watras et al.
1998, Mason and Sullivan 1997, as cited in EPA 2001). EPA's Mercury Study Report to
Congress provides a range of BAFs for methyl mercury in T4 fish from 4,000,000 to 11,400,000
(EPA 1997). The methyl mercury water quality criterion document also notes that within any
single trophic level, empirically derived BAFs for methyl mercury from studies nationwide vary
by up to two orders of magnitude. Therefore, the BAFs derived from this TRIM.FaTE
simulation and used in the 3MRA simulation fall within the ranges reported in the current
scientific literature.
Exhibit 6-34
TRIM.FaTE and 3MRA Methyl Mercury Concentrations in Surface Water and Fish
and Calculated BAFs in Swetts Pond at Year 30
3MRA
T4 fish
TRIM.FaTE
Water-column carnivore
Benthic carnivore
Largemouth bass/smallmouth
bass/northern pike
Surface Water
dissolved MHg
concentration (g/L)
3.2E-13
~
7.4E-13
~
~
~
Fish
MHg concentration
(g/kg, ww)
~
2.2E-6
~
1.8E-6
2.8E-7
4.5E-7
Calculated BAF
fish:dissolved water
(L/kg)
~
6.8E6
~
2.4E6
3.8E5
6.2E5
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As might be expected based on the water-column mercury concentrations for Swetts
Pond that are presented in Exhibit 6-27, the dissolved water concentrations of methyl mercury in
TRIM.FaTE and 3MRA are similar, with the 3MRA concentration being less than three-fold
lower than the TRIM.FaTE concentration.16 The slightly higher 3MRA fish results may be
explained by the slightly higher BAFs observed for 3MRA (calculated value shown in Exhibit 6-
34 matches the 3MRA input BAF used, as expected) versus TRIM.FaTE at year 30 in the
simulations. Note that the fish tissue methyl mercury concentrations appear to be continuing on
an upward trend (see Exhibit 6-33) at year 30 and also that the BAF for the TRIM.FaTE water-
column carnivore compartment is still increasing somewhat with time (see accompanying text
box). Thus, the 3MRA and TRIM.FaTE calculated BAFs will be slightly closer as TRIM.FaTE
approaches steady-state.
TRIM.FaTE BAFs Increase Over Time
In this 3MRA simulation, a constant BAF value for T4 fish is used (i.e., 6.8E6), assuming equilibrium
conditions. Because TRIM.FaTE uses a different approach to predict methyl mercury accumulation in
fish, its calculated BAF values for top predator fish increase over time until an equilibrium condition is
achieved. Using the TRIM.FaTE water-column carnivore in Swetts Pond for example, the calculated
BAF increases with time as shown below:
YearlOBAF = 2.19E6
Year20BAF = 2.37E6
Year 30 BAF = 2.43E6
The calculated BAF increases rather dramatically at the very early stages of the modeling period (first
couple years, not shown) and then the rate of increase tapers off, as shown above. The steady-state BAF
value for the TRIM.FaTE water-column carnivore in Swetts Pond, calculated from the steady-state
modeling results (see Chapter 4), is 2.6E6, which is consistent with the dynamic modeling results shown
above.
16 The relationship for dissolved water concentration of methyl mercury (TRIM.FaTE higher than 3MRA)
is opposite that for whole water concentration of methyl mercury (3MRA higher) because the two models predicted
different phase distributions of methyl mercury. Based on the inputs used, TRIM.FaTE estimated 69 percent of
methyl mercury in surface water in the dissolved phase, and 3MRA estimated 5 to 7 percent (varies by year in
3MRA). This difference is thought to be largely due to the very different values used for total suspended solids, 4.3
mg/L for TRIM.FaTE versus an average of roughly 150 mg/L for 3MRA (varies by year in 3MRA). The methyl
mercury water quality criterion document (EPA 2001) cites 61 percent as a default value for dissolved fraction of
methyl mercury in lakes (geometric mean of literature values), corresponding to a total suspended solids value in the
range of 1 to 2 mg/L (higher suspended solids = lower dissolved fraction). Pankow and McKenzie (1991) found
typical total suspended solids values for lakes in eastern Washington state range from 0.5 to 5 mg/L and for rivers
range from 5 to 50 mg/L.
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6.5 Wildlife
This section compares the modeling approaches used by 3MRA and TRIM.FaTE for
simulation of mercury accumulation in selected terrestrial wildlife and the results obtained by
those approaches. It is important to note that, as mentioned in Section 6.1.1 and discussed
below, some aspects of the 3MRA approach were specific to this application and do not
necessarily reflect the way that 3MRA is usually employed.
For all of the comparisons presented in this section, the mercury concentration results are
for TRIM.FaTE parcel SSE4 comprising the Swetts Pond watershed and 3MRA habitat 11 and
watershed location 4, representing the Swetts Pond watershed. This location was selected
because the Swetts Pond area is a focus of the overall mercury test case (see Section 3.2) and
because adequate comparison data are available. Comparison results for additional locations are
presented in Appendix E. The 3MRA body burden results for wildlife are for total mercury
(unspeciated), and even though TRIM.FaTE provides speciated mercury results, the TRIM.FaTE
total mercury results (sum of elemental, divalent, and methyl) are presented here to facilitate
comparisons. Furthermore, as noted previously in this report, there is a relatively high level of
uncertainty about the rate of transformation of mercury in terrestrial animals. Since the mercury
speciation in wildlife is based on these transformation rate constants, there is also a high level of
uncertainty about the TRIM.FaTE results for individual mercury species in wildlife.
As shown in Exhibit 6-35, TRIM.FaTE simulates different chemical uptake, elimination,
and transformation processes in wildlife from those represented in 3MRA. The 3MRA wildlife
species that are the focus of this comparison, listed in Exhibit 6-36, are limited to those for
which the 3MRA framework derives contaminant concentrations (EPA 1999c).17 The
TRIM.FaTE wildlife species are compared to each 3MRA wildlife category in Exhibit 6-36.
For the wildlife compared, the largest difference in estimating chemical concentrations
(i.e., body burden) between the two models is that TRIM.FaTE uses bioenergetics to simulate
food web transfers of mercury, whereas for the prey species modeled in this application, 3MRA
uses soil-based empirical BAFs. Specifically, chemical uptake by wildlife in TRIM.FaTE is
predicted through the simulation of wildlife exposures via dietary and inhalation pathways, as
well as elimination losses for each animal modeled. Therefore, the wildlife in TRIM.FaTE are
obtaining mercury from the air, surface soil, surface water, plants, terrestrial and semi-aquatic
animals, fish, and benthic invertebrates that are contaminated with mercury, as appropriate for
each species modeled. It is important to note that several TRIM.FaTE wildlife compartment
types - including those represented by mink, raccoon, and tree swallow - obtain a significant
portion of their diets from surface water sources (e.g., fish and benthic invertebrates). As noted
previously (see Section 6.4.3), fish and benthic invertebrate accumulation patterns in
TRIM.FaTE differ from those for biota that are more directly influenced by air and soil
concentrations, such as terrestrial plants and earthworms. For the 3MRA prey species modeled
17 3MRA categorizes wildlife as either prey or predator. For prey, it predicts body burdens (i.e., biota
concentrations) and for the predators it predicts pollutant intake (e.g., mg/kg/day) based on the simulated diet for
each predator. The prey species (for which biota concentrations are predicted) are the focus of these comparisons.
TRIM.FaTE can also predict pollutant intake for wildlife, but that option was not employed for this application.
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in this application, chemical uptake was simulated by SMRA's Terrestrial Food Web Module
using mercury concentrations in deeper soil (i.e., top 5 cm of soil), rather than surface soil (i.e.,
top 1 cm of soil), and empirical BAFs for each wildlife category (e.g., small birds, small
mammals). These different approaches are important because the TREVI.FaTE results are highly
influenced by diet, as discussed further in each section below.
Exhibit 6-35
Summary of Mass Transfer and Transformation Processes Modeled: Wildlife
TREVLFaTE (Hg°, Hg2*, MHg)
3MRA (Total Hg)
Inhalation of air by wildlife [G]a
Ingestion of surface soil by wildlife [G]
Elimination to surface soil by wildlife [L]
Ingestion of surface water by wildlife [G]
Elimination to surface water by wildlife [L]
Ingestion of leaf and particles-on-leaf by certain
wildlife [G]
Ingestion of terrestrial/semi-aquatic animals (including
earthworm) by certain wildlife [G or L]
Ingestion of fish/benthic invertebrates by certain semi-
aquatic wildlife [G]
Chemical body burden (mg/kg) for prey species (i.e.,
excluding top predators) included in this application
estimated using empirical BAFs that express the
relationship between chemical concentrations in upper
soil horizons (top 5 cm of soil used here) and chemical
residue concentrations in animals; the empirical BAFs
are intended to represent all relevant pathways of
exposure such as the ingestion of contaminated biota
and mediab
Methylation of Hg2+ (0)c
Demethylation of MHg (0.09/day)
Reduction of Hg2+ to Hg° (0)
Oxidation of Hg° to Hg2+ (1.0/day)
Transformation of Hg in wildlife not modeled
a G = gain process, L = loss process, G or L indicates can be either.
b 3MRA also has a separate module, not used in this model comparison, that calculates applied doses (in mg/kg-day)
for all species based on: ingestion of surface soil, ingestion of surface water, ingestion of leaf and particles-on-leaf,
ingestion of terrestrial/semi-aquatic animals (including earthworm) at lower trophic levels, and ingestion of
fish/benthic invertebrates by semi-aquatic animals.
0 First-order rate constant shown in parentheses for all transformation reactions.
Another important difference is that TRIM.FaTE results are developed for wildlife
populations representing trophic/functional groups (see Exhibit 2-4). The results for each
wildlife compartment, therefore, represent an average concentration for that population, which is
assigned to a particular volume element but may have associations (e.g., via predators and/or diet
components) with other volume elements. For this model comparison, the 3MRA results are the
maximum pollutant concentration among all species modeled for a wildlife category (see Exhibit
6-36) in a habitat associated with a particular 3MRA watershed.18
18
3MRA has been developed to report the minimum and/or maximum values among all the species in each
wildlife category. For this application, however, only the maximum values were reported.
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Exhibit 6-36
Wildlife Species Modeled by TRIM.FaTE and 3MRA in Comparison Categories
3MRA
Wildlife for Which Concentrations Are
Derived a
Category
Small birds
Omniverts b
Small mammals
Representative
Species
Marsh wren
Spotted sandpiper
Tree swallow
American kestrel
American robin
American
woodcock
Belted kingfisher
Great blue heron
Green heron
Herring gull
Long-tailed weasel
Mallard duck
Mink
Raccoon
River otter
Short-tailed weasel
Deer mouse
Least weasel
Little brown rat
Meadow vole
TRIM.FaTE
Representative Species/Subgroups Identified for Comparison
Subgroup
Terrestrial insectivore
Semi-aquatic aerial insectivore
Terrestrial carnivore
Semi-aquatic omnivore
Semi-aquatic carnivore
Terrestrial ground-invertebrate feeder
Terrestrial herbivore
Terrestrial omnivore
Representative
Species
Black-capped chickadee
Tree swallow
Long-tailed weasel
Mallard duck
Raccoon
Mink
Short-tailed shrew
Meadow vole
Mouse
a 3MRA has been developed to report the maximum and/or minimum modeled concentration values among all the
species in each wildlife group. For this application, however, only the maximum concentrations were reported.
b Omnivert is a term used in 3MRA for omnivorous vertebrates.
6.5.1 Total Mercury Concentrations in Small Birds
The 3MRA total mercury concentration in the small bird wildlife group was compared to
two small bird compartments (insectivores) in TRIM.FaTE: the tree swallow and the black-
capped chickadee. As noted previously, the 3MRA value is derived directly from the
concentration of mercury in the deeper soil (top 5 cm) at a specific point, while the two
TRIM.FaTE small bird compartments receive mercury from their diet and through the inhalation
pathway. In the case of the tree swallow, its diet is comprised wholly of insects who spend a
stage of their life as benthic invertebrates in the neighboring water body. The chickadee was
assigned a diet of 70 percent soil arthropods and 30 percent leaf material.
In general, the total mercury concentration in 3MRA small birds and the total mercury
concentration in TRIM.FaTE tree swallow are within one to two orders of magnitude, depending
on the comparison location, even though the modeling approach is quite different. As shown in
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Exhibit 6-37 for the Swetts Pond watershed, the total mercury concentrations in the 3MRA small
birds are higher than, but similar to, the total mercury concentrations in the TRIM.FaTE tree
swallow compartment. Although the direction of this relationship remains the same in the
TRIM.FaTE near source, southwest location, which is compared to 3MRA habitat 3 (see
Appendix Chart E-lOb), the total mercury concentrations in 3MRA small birds are much higher
than in the TRIM.FaTE tree swallow compartment at this location. These differences can be
attributed to differences in the TRIM.FaTE food sources at these two locations. Specifically, the
TRIM.FaTE tree swallow consumes flying aquatic insects that spend a life stage as benthic
invertebrates in the adjacent surface water body. In the TRIM.FaTE results, Swetts Pond is more
contaminated with mercury than the modeled river compartment, resulting in higher total
mercury concentrations in the tree swallow in the Swetts Pond watershed location than in the
near source, southwest location. Furthermore, in 3MRA, the concentration of mercury in the
small birds is calculated directly from the deeper soil (0-5 cm depth) concentration, which yields
much higher values in habitat 3 than habitat 11 because the former is closer to the emission
source.
Exhibit 6-37 also shows that the 3MRA small bird and TRIM.FaTE tree swallow time
series curves for total mercury are similar in shape. This is expected because the 3MRA small
bird time series curve is similar to the 3MRA deeper soil (0-5 cm depth) curve (from which the
small bird concentrations are calculated directly), and the TRIM.FaTE tree swallow curve is
similar to the TRIM.FaTE sediment curve (from which the mercury in the diet of the tree
swallow compartment is derived).
The TRIM.FaTE chickadee is not as good of a match to the 3MRA small bird because, as
mentioned earlier, the chickadees' diet includes both soil arthropods and leaf material, the latter
of which contains higher concentrations of mercury.19 In addition, the inclusion of plants in the
chickadees' diet results in a time series pattern that is highly influenced by the varying
concentration of mercury in terrestrial plants (related to the varying concentration in air) instead
of the mercury accumulation in the soil which influences the 3MRA small bird. Regardless, the
results for the two models are within an order of magnitude at both locations.
19 Seeds and berries, which are a component of the diet of chickadees, were not modeled explicitly (i.e., as
separate compartments) in this TRIM.FaTE application. Rather, leaves and particles on leaves were used to
represent plant material in the chickadee diet. It is recognized, however, that mercury accumulation and the
adherence of dust particles may differ among various types of plant material.
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Exhibit 6-37 - Log Scale
Total Mercury Concentration in Small Birds vs. Time: Swetts Pond Watershed
1.0E-05
1.0E-10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
-Total Hg:3MRA Habitat 11 -Small Bird —A—Total Hg: TRIM.FaTE SSE4-Tree Swallow —*-Total Hg: TRIM.FaTE SSE4 - Chickadee
6.5.2 Total Mercury Concentrations in Omniverts
The 3MRA maximum total mercury concentration in the omnivert (omniverous
vertebrate) category was compared to four wildlife compartments in three representative
subgroups in TRIM.FaTE: a terrestrial carnivore (long-tailed weasel), two semi-aquatic
omnivores (mallard duck and raccoon), and a semi-aquatic carnivore (mink). As noted
previously, the 3MRA total mercury concentration is derived directly from the concentration of
mercury in the deeper soil (0-5 cm depth) at a specific point, while the four TRIM.FaTE wildlife
compartments receive mercury from their diets and through the inhalation pathway. In the case
of the long-tailed weasel, its diet is comprised of terrestrial animals. The mallard consumes
terrestrial leaf material and benthic invertebrates, and the raccoon consumes benthic
invertebrates and fish plus earthworms. The mink consumes fish and benthic invertebrates along
with terrestrial animals.
In general, the maximum total mercury concentrations for the 3MRA omnivert category
are within approximately one order of magnitude of the TRIM.FaTE compartments identified for
comparison. At most of the locations, the TRIM.FaTE compartments have higher total mercury
concentrations. Exhibit 6-38 displays the total mercury concentration time series for the Swetts
Pond watershed, where the maximum total mercury concentrations for the 3MRA omniverts are
lower than the total mercury concentrations in all the TRIM.FaTE compartments. The
differences in total mercury concentrations between the models are partially due to the inclusion
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of food items from surface water sources in the diets of some TRIM.FaTE wildlife
compartments, as well as to the differences in modeled soil and leaf concentrations. The
differences among the various TRIM.FaTE compartment types are due to dietary differences, as
listed above.
The total mercury concentration in 3MRA omniverts is derived directly from the mercury
concentration in deeper soil (0-5 cm depth); therefore, that time series curve is similar in shape
to the 3MRA soil time series curve. Interestingly, the shape of the TRIM.FaTE raccoon time
series curve is similar to that of the 3MRA omnivert. However, this is largely a reflection of the
similar shapes of the TRIM.FaTE sediment curve and the 3MRA deeper soil curve, because the
TRIM.FaTE raccoon obtains mercury primarily from benthic invertebrates (69 percent of diet),
which derive their mercury directly from sediments.
Exhibit 6-38 - Log Scale
Total Mercury Concentration in Omniverts vs. Time: Swetts Pond Watershed
1.0E-06
1.0E-10
1 234567
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
-Total Hg: 3MRA Habitat 11 - Omnivert
-Total Hg: TRIM.FaTE SSE4 - Mink
-Total Hg: TRIM.FaTE SSE4 - Long-tailed Weasel -A-Total Hg: TRIM.FaTE Swetts - Mallard
-Total Hg: TRIM.FaTE SSE4 - Raccoon
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6.5.3 Total Mercury Concentrations in Small Mammals
The 3MRA maximum total mercury concentration in the small mammal category was
compared to concentrations in three wildlife compartments in three representative subgroups in
TRIM.FaTE: a terrestrial ground-invertebrate feeder (short-tailed shrew), a terrestrial herbivore
(meadow vole), and a terrestrial omnivore (mouse). As noted previously, the 3MRA total
mercury concentration is derived directly from the concentration of mercury in the deeper soil
(0-5cm depth) at a specific point, while the three TRIM.FaTE wildlife compartments receive
mercury from their diet and through the inhalation pathway. In the case of the TRIM.FaTE
short-tailed shrew compartment, the diet consists of earthworms and soil arthropods, which are
exposed to mercury in root zone soil. The short-tailed shrew also consumes relatively large
amounts of contaminated surface soil in TRIM.FaTE. The meadow vole's diet consists solely of
leaf material, and the mouse's diet is divided evenly between leaf material and soil arthropods.
In general, as shown in Exhibit 6-39 for the Swetts Pond watershed, the maximum total
mercury concentrations for 3MRA small mammals are lower than the total mercury
concentrations in the TRIM.FaTE mouse, short-tailed shrew, and meadow vole, with the
concentrations falling within approximately two orders of magnitude by year 30. These
concentration differences result because the two models use different approaches to predict
mercury concentrations in wildlife. Specifically, 3MRA uses the deeper soil (0-5 cm depth)
concentration and an empirical BAF to estimate total mercury concentrations in small mammals.
TRIM.FaTE uses a bioenergetics/food chain approach based on the animal's diet and mercury
contamination levels. The use of a bioenergetics approach in TRIM.FaTE also results in the
differences in total mercury concentrations observed among the TRIM.FaTE small mammal
compartment types at different locations. As shown in Exhibit 6-39, the shapes of the
TRIM.FaTE short-tailed shrew and 3MRA small mammal time series curves are similar,
reflecting the consumption of contaminated surface soil by the shrew as well as the source of the
short-tailed shrews' diet (i.e., earthworms and soil arthropods, which accumulate mercury from
the root zone soil compartment in TRIM.FaTE). Because the surface soil mercury
concentrations in TRIM.FaTE are higher than the deeper soil (0-5 cm depth) mercury
concentrations in 3MRA and because the short-tailed shrew consumes relatively large amounts
of contaminated surface soil, it appears reasonable that the total mercury concentrations in the
TRIM.FaTE short-tailed shrew compartment would be higher than in the analogous 3MRA small
mammal. The TRIM.FaTE mouse consumes terrestrial plants and soil arthropods. Its time
series curve reflects the cyclical pattern of mercury accumulation in terrestrial plants, as well as
the higher mercury concentrations in leaf material than in soil arthropods. The shape of the time
series curve for the TRIM.FaTE meadow vole compartment (see Appendix Chart E-12c) is
similar to the mouse compartment curve, reflecting the meadow voles' terrestrial plant diet.
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Exhibit 6-39 - Log Scale
Total Mercury Concentration in Small Mammals vs. Time: Swetts Pond Watershed
1.0E-06
1.0E-07
o
1
§ 1.0E-08
c
o
o
HI
1.0E-09
1.0E-10
1234567
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Total Hg:3MRA Habitat 11 -Small Mammal -•- Total Hg: TRIM.FaTE SSE4- Mouse —*-Total Hg: TRIM.FaTE SSE4 - Short-tailed Shrew
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6.6 Summary of 3MRA-TRIM.FaTE Comparison
In a comparison between two complex multimedia models such as this, it is not expected
that results will match exactly, given the different methods, algorithms, and inputs used by the
two models. However, for most media, the results of this comparison of 3MRA and TRIM.FaTE
applications are fairly close (within an order of magnitude), especially considering the
differences in inputs and model processes. The ultimate objective of this work is to enhance the
level of confidence in both models, and this comparison met that objective since differences in
results were in most cases explainable and additional information was gained to help both teams
in future applications (e.g., which input properties are highly sensitive to change, what properties
or algorithms may need to be adjusted or investigated further).
6.6.1 Overview of Results and Model Differences
Some of the specific differences between the applications of the two models that clearly
influence the results are the different:
Methods of estimating chemical concentrations over space (volume average versus point
concentrations);
Methods for simulating chemical fate in the air;
Meteorological input data;
• Soil depths and soil layers modeled;
• Mercury transformation rates (e.g., in water and sediment);
• Values used for suspended solids fraction in surface water;
• Processes modeled in surface water (e.g., algae, macrophytes); and
Methods used to simulate chemical uptake by wildlife.
Many of the variations between results can be explained by these modeling differences. For
instance, the differences between the mercury concentrations in air (where the 3MRA mercury
concentrations are higher than TRIM.FaTE concentrations) can be explained by the different air
modeling methods (Gaussian plume versus advective transport between compartments), the
comparison of point versus volume average, and the meteorological data. However, the air
concentrations have similar general directional patterns for the two models (highest in the same
direction).
Unlike the air concentrations, the TRIM.FaTE deposition fluxes are higher than 3MRA
deposition fluxes, and the difference between the fluxes increases with distance from the source.
The methods used to calculate deposition are not the same for the two models, which helps to
explain why the relative difference between the models is not the same as in the air. The spatial
deposition patterns for the two models vary from the patterns observed for air concentrations
because of weather patterns (the predominant wind direction when it is raining is not the same as
the overall predominant wind direction). As expected based on the deposition fluxes, the
mercury concentrations in TRIM.FaTE leaves and surface soil compartments are greater than the
corresponding concentrations in these 3MRA media.
In the deeper soil (i.e., root zone soil), plant roots, and earthworms, the 3MRA mercury
concentrations are greater than the TRIM.FaTE mercury concentrations. This is due primarily to
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the different depths of the deeper soil used in the two model applications. The TRIM.FaTE
application deeper soil (root zone compartment) is much deeper, and does not include the top 1
cm of soil, while the 3MRA deeper soil is shallower and includes the top 1 cm of soil.
Therefore, it is not surprising that the 3MRA deeper soil mercury concentrations are greater than
the corresponding TRIM.FaTE concentrations. Also, because in both models roots and
earthworms both obtain their mercury directly from the deeper soil, it is reasonable that the
concentrations modeled with 3MRA would be higher. However, the ratio of mercury in the roots
to the soil is greater for TRIM.FaTE than 3MRA, and the ratio of mercury in earthworms to the
soil is greater for 3MRA than TRIM.FaTE, identifying an area where further investigation would
be useful.
The total mercury concentrations in the surface water bodies are very similar for the two
model applications with TRIM.FaTE values slightly higher, which is once again consistent with
the deposition fluxes. However, the speciation of mercury in the water bodies is very different,
due to different processes, mercury transformation rates, and suspended sediment concentrations
modeled in the water bodies, with the 3MRA surface water bodies having much larger
percentages of elemental and methyl mercury than the TRIM.FaTE water bodies have. As for
the surface water, the mercury concentrations in the fish are very similar for the two models. It
appears that the slight differences in mercury concentrations in fish are a result of the different
uptake factors used by the models and that the concentrations in the fish for TRIM.FaTE have
not yet reached an equilibrium level. In the sediment, TRIM.FaTE total mercury concentrations
are also higher than 3MRA concentrations (like the surface water), but by a greater magnitude
than in the surface water. The mercury speciation is very different for the two models in the
sediment - divalent mercury is predominant for TRIM.FaTE, but elemental mercury is
predominant for 3MRA.
In the applications presented here, the two models estimate chemical concentrations in
wildlife differently - TRIM.FaTE uses bioenergetics to simulate food web transfers of mercury,
whereas for the prey species modeled in this application, 3MRA uses soil-based empirical BAFs.
The comparisons between the mercury concentrations in animals vary depending on (among
other factors) the diets of the animals and the comparison locations. In some cases, TRIM.FaTE
concentrations are higher (most omniverts and small mammals), and in some cases 3MRA
concentrations are higher (some small birds).
Overall, the largest differences between model results were seen in earthworms, deeper
soil, and sediment. The difference in the deeper soil results is largely due to the large difference
in soil depth (a user-specified parameter). This depth difference also explains the mercury
concentrations in earthworms (along with different factors used for earthworm mercury
accumulation). The sediment differences can be explained mostly by the different processes that
were modeled in the surface water and sediment by the two models.
The comparison helped to identify some of the strengths and limitations of the
TRIM.FaTE and 3MRA modeling approaches and individual process models, thus informing
scientists with regard to future applications pertinent to these models. Both models are flexible
in terms of adjusting inputs, layouts, and outputs, which may - depending on the level of
complexity desired - have corresponding cost in terms of effort required for their use. The
overall difference in their design is 3MRA's use of multiple models (including several EPA
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legacy models, such as ISCST3 and EXAMS) to simulate the transport of pollutant from source
to receptor of interest, and TRIM.FaTE's use of a fully coupled, compartmentalized environment
in which bi-directional pollutant transfers between compartments are tracked, with complete
accounting for mass. Within those differing designs, a better understanding of the similarities
and differences in individual process modeling has been achieved, with some implications for
future uses.
6.6.2 Possible Future Areas for Model Comparison
As mentioned at the beginning of this chapter, the focus of this analysis was primarily on
conducting an initial comparison for TRIM.FaTE and 3MRA focusing on outputs from various
model compartments and was not intended to be a comprehensive comparison of modeling
concepts, structures, algorithms, and data inputs of the two models. Where possible,
explanations for the differences between TRIM.FaTE and 3MRA results are suggested in this
chapter, and in a few cases model processes and parameters are examined in detail to explain
different results. Findings were specifically informative with regard to several aspects of
TRIM.FaTE model set-up (e.g., layering of soil depending on the specific application site;
earthworm partition coefficient values; methods and inputs used for sediment modeling; and the
calculations used for deposition).
Additional investigation in several areas may be useful to further the understanding of
and confidence in both models. A few areas where model results differ, and more in-depth
evaluations might be informative, are listed below.
• Deposition is the major means by which mercury mass is transferred from the air to the
surface in both model applications. The detailed examination of air and deposition
modeling processes that was performed by the 3MRA team helps to explain some of the
reasons for the differences observed in results of the two model applications. However,
further investigation could provide a deeper user understanding of the two modeling
approaches in similar multimedia applications. Such investigation might consider dry
deposition, chemical reactions, building effects, and complex terrain.
• The difference in the deeper soil definition employed in the applications of the two
models helps to explain much of the discrepancy between the 3MRA and TRIM.FaTE
root and earthworm concentrations, as shown in Exhibits 6-20 and 6-22. However,
further investigation relating the TRIM.FaTE processes and input parameters and the
3MRA BAF values might also be informative to future applications.
• Multiple hypotheses for differences observed in mercury speciation in surface water and
sediment, as well as in total mercury concentrations in sediment are presented in Sections
6.4.1 and 6.4.2, including different mercury conversion rate constants, different
atmospheric deposition fluxes, partitioning to algae with TRIM.FaTE but not 3MRA,
uptake and conversion by macrophytes with TRIM.FaTE but not 3MRA, and different
suspended solids values. A more in-depth and comprehensive consideration of the
current literature and scientific knowledge regarding the salient processes would further
inform this assessment.
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While comparisons of the wildlife results highlighted differences in modeling approaches
employed by the two models, investigation and review of the literature would improve
understanding of strengths and limitations of the two approaches and of the input values
employed.
Valuable insight might be gained by re-running one or both of the models with various
inputs and options matched more closely including possibly the site-layouts and
comparison points, meteorological data, inclusion of dry deposition, and certain salient
input parameters.
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7. COMPARISONS WITH MEASUREMENT DATA
Comparing multimedia model outputs with chemical monitoring data is challenging
because a comprehensive and accurate history of a site is rarely available, making it extremely
difficult - if not impossible - to accurately match modeling conditions to actual site conditions.
This is particularly true when attempting to characterize the sources of chemicals for a modeling
exercise. In addition, the available measurement data may not be geographically and temporally
representative of the natural system being modeled, and accurate multimedia modeling is a
challenging exercise due to the complexity of natural systems. Nevertheless, even a limited
measurement data set can provide an opportunity for informative model-to-data comparison. In
the early stages of model evaluation, these analyses can serve as a helpful diagnostic tool and
lead to discoveries of potentially important processes or inter- and intramedia relationships.
The purpose of the comparisons discussed in this chapter is to contribute to the overall
model evaluation of TRIM.FaTE through an analysis of model results in the context of relatively
recent measurement data for the modeled site. This exercise provides another frame of reference
for evaluation and interpretation of TRIM.FaTE modeling results. It is important to note that
this comparative analysis is not meant to be a validation of the performance of the model, and
model results should not be interpreted as more or less "correct" based on their value relative to
the available measurement data presented here. The modeling for this application was not
designed to account for all sources that may have contributed to mercury levels in near-site
environmental media. The TRIM.FaTE results compared to measurement data in this section
represent predicted media and biota concentrations of mercury resulting from facility emissions
case C (i.e., source emissions plus air boundary contributions and initial concentrations in media
and biota). However, there may have been significant and direct mercury releases to soil or
water from the modeled facility and other sources in the modeling region that were not included
in the TRIM.FaTE modeling scenario. Sources outside the modeling region that contributed to
mercury contamination within the modeling region via transport across the boundary in media
other than air also were not considered (e.g., contamination entering the region via surface water
inflows). It is important to keep these considerations in mind when interpreting the comparisons
presented in this chapter.
7.1 Description of Measurement Data and Relationship to TRIM.FaTE
Model Results
Appendix F provides details on the off-site monitoring data sets identified for the area in
the vicinity of the test case site. For each data set, the following information is provided:
• Environmental medium;
• Number of data points (including, where relevant, the number of duplicates and
measurements below the detection limit);
• Measurement endpoint(s) and units;
Sampling date(s) and location(s);
• Purpose of monitoring;
Range, mean, and standard deviation of the data set;
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• Raw data (i.e., actual measurement data values), or a summary of the data set where
presentation of all data values is not feasible; and
• Other relevant information.
Off-site measurement data for comparison were identified for air, soil, lake/pond surface water
and sediment, and various biota. Some measurement data were also identified for the river,
including concentrations in surface water, sediment, and biota (eel and minnow). However, the
river is tidal at this site but was modeled for test case and general model evaluation purposes as a
simple river, and the modeling does not account for tidal influence on river flow. Therefore, the
river data are excluded from the measurement to model comparison and are not presented in
Appendix F.
Measurement data discussed in this evaluation were collected between 1995 and 2000
through different sampling events and over a range of time scales. As a result, the monitoring
data do not exactly match the temporal representation of the long-term (30-year) source used in
the model run. However, because the test case facility began operating in the late 1960s, the
modeled concentrations at the end of the 30-year model run are expected to represent a temporal
scale similar to the actual source contributions. For this analysis, point measurements of the
monitoring data (or, where many data points were available, statistical summaries of the
measurement data) are presented for comparison with temporally averaged modeling results (i.e.,
concentrations in environmental media and biota) from TRIM.FaTE. In spatial terms, the
TRIM.FaTE results in general represent a defined volume (i.e., a volume element) for abiotic
media, and a population associated with a defined area (i.e., a parcel) for biota.
At the end of the 30-year emissions simulation, most of the modeled concentrations for
the non-air compartments included in this comparison are still increasing. Therefore, with the
exception of concentrations in air, the TRIM.FaTE results compared to measurement data in this
chapter are the average concentration of the 30th year of the model run. An annual average was
selected for comparison in order to account for fluctuations that occur over the course of a year
due to variations in meteorological and other data inputs. For air compartments, the long-term
average concentration does not appear to be increasing over the course of the 30-year modeling
time scale (i.e., there appears to be no significant accumulation of mass in the air compartments
after the very beginning of the modeling period), but concentrations do vary significantly from
hour to hour on a five-year basis due to the five-year meteorological data set used. Therefore,
statistics calculated from the last five years of modeled air results (i.e., years 26 through 30) are
used for the comparison with measurement data. More details regarding temporal variation in
TRIM.FaTE results are presented in Chapter 3 of this report.
The parcel layout for the test site was constructed, in part, based on the available
monitoring data so that data comparisons would be relevant and meaningful (e.g., a surface
parcel was defined in the vicinity of a park where several samples were taken; some
measurement data were available for each of the four lakes included in the test case). Sampling
locations for which measurements are available are presented in Exhibits 7-1 and 7-2 for air and
non-air media. TRIM.FaTE air and surface parcels for the test case are overlaid on these maps
for reference. Descriptions of the sampling locations corresponding to the codes on the maps are
presented in Exhibit 7-3.
JULY 2005 7-2 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 7-1
Locations of Air Monitoring Stations (with TRIM.FaTE Air Parcel Layout)
NNVY3
NNWC
• • •
NNE3
NNE2
SSW2
SSWS
ESE2
SSE2
SSE3
ENE4
ESE4
ESE5
Air Parcels
I I Parcel Boundary V\feter
• Off-site Air Monitoring Station
1 0 1
A Kilometers
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Exhibit 7-2
Sampling Locations for Non-Air Measurement Data
(with TRIM.FaTE Surface Parcel Layout)
Surface Parcels
I I Water I I Parcel Boundary
* Off-Site Monitoring Locations
AN 1 o i
Kilometers
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Exhibit 7-3
Key for Off-site Monitoring Location Maps"
Map
Code
AAl
AA2
AA3
SSI
SD1
SD2
SD3
SD4
Bl
B2
B3
B4
B5
Location
Near facility (450 m to SE)
Near facility (1300 m to NNW)
Near facility (1950 m to NNW)
Park (2200m to SSW)
Swetts Pond
Fields Pond
Brewer Lake
Thurston Pond
Park (2200m to SSW)
Swetts Pond
Fields Pond
Brewer Lake
Thurston Pond
Monitoring Data
Ambient air: 1 sample location (Sept 1998 - Nov 1999)
Ambient air: 1 sample location (Sept 1998 - Sept 1999)
Ambient air: 1 sample location (Sept 1998 - Sept 1999)
Surface soil: 1 sample location (1995)
Surface soil: 3 sample locations (1997)
Sediment (deepest part of water body) (1996)
Sediment (deepest part of water body) (1996)
Sediment (deepest part of water body) (1996)
Sediment (deepest part of water body) (1996)
Deer mouse: 1 sample location (1995)
Earthworm: 1 sample location (1995)
Short-tailed shrew: 1 sample location (1995)
White perch: 1 sample location (1996)
White perch: 1 sample location (1996)
White perch: 1 sample location (1996)
White perch: 1 sample location (1996)
a Measurement data were also identified for abiotic and biotic media in the river; however, these data were excluded
from the measurement to model comparison.
The measurement data presented in this section are compared with model results obtained
for emission case C, as noted earlier. This scenario, which includes at least some "background"
mercury contamination, is assumed to be the closest approximation of the actual conditions at
the site of the three dynamic modeling emission cases that were modeled. However, as noted
previously, it is likely that this modeling scenario does not account for all sources of mercury
affecting environmental media at the site (and in particular does not cover releases directly to
soil or water). In addition, it is important to note that the background air concentration used to
define the boundary contributions for this scenario is based on previous analysis by EPA for the
Mercury Study Report to Congress (EPA 1997) and is assumed to be generally representative of
mercury concentrations in air across the U.S. for the industrial (present-day) time period.
Specific local or regional sources in the vicinity of the test case facility were not accounted for in
this analysis. Thus, mercury contamination from "background" (i.e., other than the modeled
source) is likely to be underestimated. Moreover, there is substantial uncertainty about the
emission rates and mercury speciation assumed for the modeled source, and the emitted amounts
of various forms of mercury may be over- or underestimated.
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The model setup and emission scenario for the TRIM.FaTE runs used to generate the
modeling data are described in more detail in Chapter 2.
7.2 Comparison with Air Measurement Data
A large number (thousands) of measurements of total gaseous mercury (TGM) were
collected from three stationary monitoring locations near the facility between August 1998 and
September 1999. The facility was in operation during this period and was emitting mercury.
According to information in the report summarizing air monitoring results, the instrumentation
used to measure TGM at these sites poorly detects non-elemental mercury (Earth Tech 1999);
therefore, the measurements may under-report the actual amount of divalent mercury. However,
given that TRIM.FaTE predicts airborne mercury in the test case emission scenario C to be
predominantly (>98 percent) elemental mercury in the vapor phase, it seems reasonable to
compare the measurement data for TGM with TRIM.FaTE results for total concentrations of
mercury in air. In Exhibit 7-4, air measurements are compared with ambient concentrations of
total mercury estimated by TRIM.FaTE for several corresponding air compartments.
Measurement location AA1 falls on the boundary between two TRIM.FaTE air parcels;
therefore, TGM measurements for this location are compared with estimated total mercury
concentrations for the two corresponding TRIM.FaTE air compartments (ESE1 and SSE1). The
other two measurement locations, AA2 and AA3, are situated within TRIM.FaTE air parcels
NNW1 andNNW2, respectively.
Exhibit 7-4
Comparison of Monitoring Data for Total Gaseous Mercury with TRIM.FaTE
Modeled Concentrations of Total Mercury in Air
1.0E+03
1.0E+02 --
c
c 1.0E+01
o
're
"E
HI
u
c
o
o
1.0E+00 ---
1.0E-01
Dash mark indicates median concentration; box indicates
25th to 75th percentile range; whiskers indicate full range
of all measurement data for that location.
Monitoring
Location AA1
TRIM.FaTE:
AirSSE! &ESE1
Monitoring
Location AA2
TRIM.FaTE:
Air NNW1
Monitoring
Location AA3
TRIM.FaTE:
Air NNW2
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Given the large number of measurements, the air monitoring data for each site are
presented in box and whisker format (representing variability over time) in Exhibit 7-4.
TRIM.FaTE two-hour results for the last five years of the model run for the corresponding air
compartments are also plotted in box and whisker format (also primarily representing variability
over time) to show the range of measured and modeled data.
Overall, the model output is in relatively close agreement with the measured values.
Median measured and modeled concentrations are nearly identical for these comparisons. The
distributions of the TRIM.FaTE air results for these compartments are tighter than the spread of
measurement data between the 25th and 75th percentiles. For both sets of air comparisons, the
total spread between the minimum and maximum values spans a similar range (i.e., two to three
orders of magnitude).
It is important to note that the meteorological data used as inputs for TRIM.FaTE
modeling correspond to measurement stations near the modeling region but not actually at the
site, and are not for the exact time period represented by the monitoring data. As noted earlier,
lack of knowledge about the historical source emissions may also contribute to the observed
differences. Therefore, the actual conditions at the site would not be expected to exactly match
the data used as TRIM.FaTE inputs. Because air concentrations depend heavily on
meteorological conditions, some of the difference between measured and modeled air
concentrations may be a result of the difference between site-specific conditions and the
meteorological data used for TRIM.FaTE. It is noted, however, that the input meteorology data
for wind speed and direction used for TRIM.FaTE are generally similar to the limited on-site
data that are available. Hourly measurements of wind speed and direction were recorded at the
facility between November 1998 through October 1999 as a component of the TGM monitoring
program carried out during that time and were reported in the air monitoring report. Data
recovery for wind direction and wind speed measurements for this time period was 96.7 percent
and 93.5 percent, respectively. Overall, the wind direction was reported to be predominantly
from the south and the northwest, and very rarely from the east (see Appendix F for the wind
rose based on measurement data that was included with the monitoring report). This is very
similar to the prevailing wind directions for the meteorological data used as TRIM.FaTE inputs
(see Figure 2-7 for a wind rose based on TRIM.FaTE modeling data). Average wind speed of
the measurement data was reported to be 6.99 miles per hour, which is converted to 3.21 m/sec;
this value is similar to the mean of the TRIM.FaTE modeling data of 3.64 m/sec.
7.3 Comparison with Soil and Soil Biota Measurement Data
Measurements of mercury in soil and terrestrial organisms were collected as part of a site
investigation conducted in 1995 and 1997 at a park located southwest of the facility, which
roughly corresponds to surface parcel SW2. Measurement data are plotted along with predicted
TRIM.FaTE concentrations for corresponding surface soil and biotic compartments located at
SW2 in Exhibit 7-5. Measured mercury concentrations are plotted as x's, and TRIM.FaTE
annual average concentrations for the 30th year of the model run plotted as dots.
Four soil measurements were collected at this location, with only one measurement
reported above the level of detection (LOD). The three soil values below the LOD are
JULY 2005 7-7 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 7-5
Comparison of Measurement Data for Soil and Terrestrial Organisms with
TRIM.FaTE Modeled Concentrations
11
a, s
= §. I.Uh-UJ -
0 a-
£"5"
3 °
ll
I- -o
1 np.n? -
X
•
X
•
•
•
X Measurement
Data: Park
— Measured Soil
Samples at LOD
(n=3) *
• TRIM.FaTE:
Concentration for
SW2 (Average,
Year 30)
Soil
Earthworm
Shrew
Mouse
3 Note that the LOD reported for these three samples is higher than the actual measurement reported for the fourth sample. These
three samples were taken during a different year from the fourth (presumably under different analytical conditions).
represented in Exhibit 7-5 with a single dash mark at the LOD (see exhibit footnote). Soil
samples were described as surface soil but the sampling depth was not specified; for this
comparison, it is assumed that the measurement data correspond to the surface soil compartment
in TRIM.FaTE (i.e., depth of 1 cm). TRIM.FaTE model results indicate that soil concentrations
are still increasing at 30 years, so the last year of modeling data were used to estimate average
concentrations in the surface soil. The TRIM.FaTE average concentration of total mercury for
surface soil in parcel SW2 is lower than the measurement data but within a factor of three of the
detected value (within a factor of five of the LOD for the other three samples).
For terrestrial organisms, one measurement each for earthworm and short-tailed shrew
and two measurements for deer mouse were collected in 1995 or 1997 at the park. Measured
data represent whole-body, wet weight total mercury concentrations. TRIM.FaTE total mercury
concentrations for biotic compartments (and therefore representative of whole-body, wet weight)
are presented for the corresponding compartments located at SW2 and are presented in Exhibit
7-5 with the measurement data. TRIM.FaTE concentrations represent the average for the last
year of the model run. For all three organisms, the model predictions for mercury are less than
the measured values, with differences of nearly five orders of magnitude for the earthworm and
one to two orders of magnitude for the shrew and mouse. Due to the limited number of
measurement data points, it is not possible to judge the representativeness of the measurement
data. For the available data, however, it is apparent that measured and modeled results are more
similar for the shrew and mouse than for the earthworm. One possible explanation for the
greater difference observed for the earthworm could be that the earthworm compartment in this
TRIM.FaTE application is associated with relatively deep root zone soil, not surface soil, and for
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simplicity no direct contact with surface soil (e.g., during rain events, when earthworms might be
expected to move to the surface) is modeled for the earthworm in the test case. Modeled
concentrations in root zone soil are approximately three to four orders of magnitude lower than
concentrations in surface soil. Similarly, off-line calculations indicate that if total mercury
concentration in the earthworm compartment is estimated using TRIM.FaTE surface soil
concentration for SW2, the result is also about three to four orders of magnitude higher (i.e., on
the order of 10"3 ppm) and consequently within two orders of magnitude of the measurement
data. This relationship would also affect other terrestrial organisms that ingest earthworms.
7.4 Comparison with Sediment and Aquatic Biota Measurement Data
Lake sediment samples were collected and analyzed for total mercury in 1996 as part of a
study to evaluate the impact of local air emission sources. One sample was obtained for each of
the four lakes/ponds included in the test case, with samples taken from sediment at the deepest
part of the water body and reported as total mercury concentration (dry weight). These data are
compared to TRIM.FaTE sediment compartment concentrations in Exhibit 7-6 (note that the
samples may not be representative of the entire sediment in each lake). TRIM.FaTE
concentrations presented here are the average for year 30 and represent a volume-average for the
entire sediment compartment (with one sediment compartment representative of a single lake).
Model results for all four sediment compartments are about an order of magnitude lower than the
corresponding measured value.
Exhibit 7-6
Comparison of Measurement Data for Lake Sediment with TRIM.FaTE
Modeled Concentrations
1.0E+00
1.0E-01 --
I?
C T3
8 O)
C £
O O)
O E
>, —•
1.0E-02 --
1 .OE-03
x Measured Values
(n=1 at each
location)
• TRIM.FaTE
Sediment
Concentration
(Average, Year 30)
Swetts Pond
Fields Pond
Brewer Lake
Thurston Pond
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White perch also were collected from each of these four lakes during the same 1996
study and analyzed for total mercury, with measured concentrations reported for skinless fillets.
These results are presented in Exhibit 7-7 along with TRIM.FaTE modeled values for total
mercury representing whole-body concentrations (year 30 annual average).1 For this
comparison, a representative concentration for white perch was estimated by combining results
for two TRIM.FaTE omnivore compartments based on white perch dietary habits (see Section
6.4.3 for more discussion of estimating fish species concentrations based on TRIM.FaTE fish
compartment results). The unadjusted concentrations for four TRIM.FaTE fish compartments
(i.e., water-column and benthic omnivores and carnivores) in each water body are also presented
in Exhibit 7-7 for comparison. The representative modeled values are approximately two orders
of magnitude below the measured white perch fillet values. In general, the TRIM.FaTE
concentrations for water-column fish compartments are generally higher than the representative
modeled values for white perch (and therefore closer to the measured data for white perch).
Unfortunately, no information is available on the measured concentration of total mercury in the
water column from this study; therefore, it is not possible to calculate water-to-fish concentration
ratios for measurement data that might be useful in interpreting differences between the
predicted and measured values.
Exhibit 7-7
Comparison of Measurement Data for Fish with TRIM.FaTE Modeled Concentrations
1.0E+01
1.0E+00 -
1.0E-01 -
•|5 1.0E-02 -I
o
1.0E-03
X
-I-
6
•
A
o
Swells Pond Fields Pond
3 All TRIM.FaTE values are annual average for year 30.
Brewer Lake
Thurslon Pond
x Measured
Concentrations:
White Perch Filet
• TRIM.FaTE:
Representative of
White Perch
o TRIM.FaTE: Water-
column Carnivore
TRIM.FaTE: Benthic
Carnivore
o TRIM.FaTE: Water-
column Omnivore
n TRIM.FaTE: Benthic
Omnivore
1 Whole fish concentrations of methyl mercury are usually lower than fillet concentrations, but quantitative
data on the relationship between methyl mercury concentrations in whole fish versus fillets were not identified for
the current analysis.
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7.5 Summary of Measurement Data Comparisons
As noted in the introduction to this chapter, the relatively limited measurement data set
identified for the test case site can be useful as an additional analytical tool for model
evaluation.2 Based on the results of this comparison, additional consideration of the
configuration of TRIM.FaTE earthworm and fish compartments may be appropriate. For
earthworms, it may be reasonable to establish a link between the surface soil and earthworm
compartments in order to account for exposure of earthworms as a result of bioturbation (i.e.,
mixing of soil by organisms). For fish compartments, it would be helpful to derive appropriate
conversion factors to facilitate a better comparison of whole body fish concentrations to skinless
fillet concentrations. In addition, the availability of additional measurement data would help to
clarify the relationship between lake water-column concentrations and mercury levels offish
residing in those lakes.
As stated previously, emission case C results are expected to provide the most
appropriate modeling results for comparison with measurement data because this case accounts
for at least some of the background contributions of mercury. However, to provide additional
context for the analysis presented in this chapter, it may be useful to consider the contribution of
the modeled background to the TRIM.FaTE results compared here with measurement data. The
incremental effects of background can be assessed by considering the corresponding
compartment concentrations for emission case B (i.e., same emissions but no boundary
contributions/initial concentrations). A more complete discussion of case B and case C results is
presented in Section 3.5.2. In addition, the relative impact of modeled background is
summarized in Exhibit 7-8 for compartments included in the comparison with measurement data.
It is unlikely that the major sources of mercury within the modeling region over the 30-
year operation of the facility are fully accounted for by the case C boundary contributions and
initial concentrations used in this analysis. As a result, it seems reasonable that the modeled
TRIM.FaTE values are generally lower than the concentrations included in the limited
monitoring data that were identified. In addition, the analysis of time patterns of TRIM.FaTE
test case concentrations suggests that concentrations in most non-air compartments are still
increasing at the end of the 30-year emission period (see Chapter 3 for details). It is possible that
the predicted rates of some fate and transport processes in TRIM.FaTE are defined such that
mercury mass is modeled to accumulate in abiotic and biotic media slower than it actually does.
Alternatively, there may be processes that are not accounted for by TRIM.FaTE algorithms that
would contribute to higher media concentrations, thereby contributing to the difference between
modeled and measured data. It also is possible, as noted earlier, that historical mercury
emissions from the modeled source are underestimated or that the limited observational data are
not representative of the overall natural system being modeled.
2 In addition to the measurements described here, measured values were also identified for mercury
concentrations in sediment and organisms in the river, and blood collected from loons that were associated with
water bodies in the area and the state in general. However, as described above, the river compartment was not
modeled at the level of detail necessary to capture the tidal influence on river flows present at the site. In addition,
blood levels in loons were not estimated from the whole body concentrations modeled for loon compartments. As a
result, these additional measurements are not included in the current comparison.
JULY 2005 7-11 TRIM.FATE EVALUATION REPORT VOLUME II
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Exhibit 7-8
Effect of Modeled Background Concentrations on TRIM.FaTE Results Included
in Comparison with Measurement Data
TRIM.FaTE Compartment
Air - SSE1, ESE1, NNW1, and NNW2
Surface soil - SW2
Earthworm - SW2
Short-tailed shrew - SW2
Mouse - SW2
Sediment in four ponds/lakes
Water-column fish and benthic fish in four
ponds/lakes
Case C Results vs. Case B Results
3 to 4 times higher for
(SSE1,ESE1,NNW1)
NNW2
closer air parcels
; 9 times higher for
6 times higher
9 times higher
6 times higher
4 times higher
13 to 20 times higher
13 to 30 times higher
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http://epa.gov/osa/spc/htm/modelpr.htm
U.S. EPA (Environmental Protection Agency). 2005b. Evaluation of TRIM.FaTE, Volume IV:
Model Performance Focusing on Dioxin Test Case. Office of Air Quality and Planning
Standards: Research Triangle Park, NC. In preparation.
U.S. EPA (Environmental Protection Agency). 2004. Evaluation of TREVI.FaTE, Volume III:
Model Comparison Focusing on Dioxin Test Case. EPA-453/R-04-002. Office of Air Quality
and Planning Standards: Research Triangle Park, NC. December.
U.S. EPA (Environmental Protection Agency). 2003a. Draft Guidance on the Development,
Evaluation, and Application of Regulatory Environmental Models. Council for Regulatory
Environmental Modeling (CREM), Office of Research and Development: Washington, DC.
November.
U.S. EPA (Environmental Protection Agency). 2003b. Total Risk Integrated Methodology:
TRIM.FaTE User's Guide. Office of Air Quality Planning and Standards: Research Triangle
Park, NC. March.
U.S. EPA (Environmental Protection Agency). 2002a. Evaluation of TRIM.FaTE, Volume I:
Approach and Initial Findings. EPA-453/R-02-0012. Office of Air Quality and Planning
Standards: Research Triangle Park, NC. September.
JULY 2005 8-3 TRIM.FATE EVALUATION REPORT VOLUME II
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U.S. EPA (Environmental Protection Agency). 2002b. Total Risk Integrated Methodology:
TRIM.FaTE Technical Support Document. Volume I: Description of Module. EPA-453/R-02-
01 la. Office of Air Quality Planning and Standards: Research Triangle Park, NC. September.
U.S. EPA (Environmental Protection Agency). 2002c. Total Risk Integrated Methodology:
TRIM.FaTE Technical Support Document. Volume II: Description of Chemical Transport and
Transformation Algorithms. EPA-453/R-02-01 Ib. Office of Air Quality Planning and
Standards: Research Triangle Park, NC. September.
U.S. EPA (Environmental Protection Agency). 2001. Water Quality Criterion for the Protection
of Human Health: Methylmercury, Final. EPA-823-R-01-001. Office of Water, Office of
Science and Technology: Washington, DC. January.
U.S. EPA (Environmental Protection Agency). 2000a. An SAB Advisory on the Agency's
Total Risk Integrated Methodology (TRIM). EPA-SAB-EC-ADV-00-004. Science Advisory
Board: Washington, DC. May.
U.S. EPA (Environmental Protection Agency). 2000b. Deposition of Air Pollutants to the Great
Waters: Third Report to Congress. EPA-453/R-00-005. Office of Air Quality Planning and
Standards: Research Triangle Park, NC. June.
U.S. EPA (Environmental Protection Agency). 1999a. Total Risk Integrated Methodology:
Status Report. EPA-453/R-99-010. Office of Air Quality Planning and Standards: Research
Triangle Park, NC. November.
U.S. EPA (Environmental Protection Agency). 1999b. National Air Toxics Program: The
Integrated Urban Strategy. Federal Register 64: 38705-38740. July 19.
U.S. EPA (Environmental Protection Agency). 1999c. Terrestrial Food Web Module:
Background and Implementation for the Multimedia, Multipathway, and Multireceptor Risk
Assessment (3MRA) for HWIR99. Office of Solid Waste: Washington, DC. October.
U.S. EPA (Environmental Protection Agency). 1999d. Advisory on the White Paper on the
Nature and Scope of Issues on Adoption of Model Use and Acceptability Guidance. EPA-SAB-
EC-ADV-99-011. Science Advisory Board: Washington, DC. July.
U.S. EPA (Environmental Protection Agency). 1998a. 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: Research Triangle Park, NC. March.
U.S. EPA (Environmental Protection Agency). 1998b. Advisory on the Total Risk Integrated
Methodology (TRIM). EPA-SAB-EC-ADV-99-003. Science Advisory Board: Washington,
DC. December.
U.S. EPA (Environmental Protection Agency). 1998c. White Paper on the Nature and Scope of
Issues on Adoption of Model Use and Acceptability Guidance, External Review Draft. Science
Policy Council: Washington, DC.
JULY 2005 8-4 TRIM.FATE EVALUATION REPORT VOLUME II
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U.S. EPA (Environmental Protection Agency). 1997. Mercury Study Report to Congress.
Volume III: Fate and Transport of Mercury in the Environment. EPA-452/R-97-005. Office of
Air Quality Planning and Standards: Research Triangle Park, NC and Office of Research and
Development: Cincinnati, OH.
U.S. EPA (Environmental Protection Agency). 1994. Report of the Agency Task Force on
Environmental Regulatory Modeling: Guidance, Support Needs, Draft Criteria, and Charter.
EPA 500-R-94-001. Office of Solid Waste and Emergency Response: Washington, DC.
van de Meent, D. 1993. SIMPLEBOX: A generic multimedia fate evaluation model. Report
No. 672720 001. National Institute of Public Health and Environmental Protection (RIVM).
Bilthoven, Netherlands.
Verta, M. and T. Matilainen. 1995. Methylmercury distribution and partitioning in stratified
Finnish forest lakes. Water, Air, and Soil Pollution 80:585-588.
Watras, C.J., Back, R.C., Halvorsen, S., Hudson, R.J.M., Morrison, K.A., and S.P. Wente. 1998.
Bioaccumulation of mercury in pelagic freshwater food webs. Science of the Total Environment
219:183-208.
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APPENDIX A
DOCUMENTATION OF INPUT PROPERTIES FOR
TRIM. FaTE MERCURY TEST CASE
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Appendix A
DOCUMENTATION OF INPUT PROPERTIES FOR TRIM.FaTE
MERCURY TEST CASE
This appendix contains the following sets of tables, including supplemental tables where
appropriate, listing and describing the model input properties used in the TRIM.FaTE mercury
test case:
Chemical-independent parameters for abiotic compartment types;
• Chemical-independent parameters for biotic compartment types;
• Chemical-dependent (i.e., value varies by chemical) parameters independent of
compartment type;
Chemical-dependent parameters for abiotic compartment types;
• Chemical-dependent parameters for biotic compartment types; and
• Source, meteorological, and other input parameters.
For each property listed, the parameter name, input units, value used, and a reference are given.
Full citations for each reference are provided at the end. Several attachments, referred to in the
tables, provide additional detailed documentation. [For a small number of properties modeled
as time-varying, different values were used in steady-state model runs. See Chapter 4 and
Appendix C for details.]
Within the framework of the TRIM.FaTE computer model, several different kinds
of "properties" are defined and used. The input properties listed in this appendix fall into the
following categories of TRIM.FaTE properties:
• Compartment properties (includes by far the largest number of input parameters);
• Volume element (VE) properties;
Link properties;
Chemical properties;
• Source properties; and
• Scenario properties.
In the following tables, the property category is identified for all input properties that are not
compartment properties.
Note that the units listed in these tables are the units in which model input values
need to be expressed for the algorithms in the TRIM.FaTE library used for the test case. In a
few cases, these computer model input units do not match the units used for the same parameter
in equations and derivations in TRIM.FaTE Technical Support Document Volume II:
Description of Chemical Transport and Transformation Algorithms (EPA 453/R-02-01 Ib,
September 2002). In such cases, there are internal units conversions in the computer model that
account for the differences.
Note on Chemical Abbreviations
Throughout the attached tables, Hg(0) = elemental mercury,
Hg(2) = divalent mercury, and MHg = methyl mercury.
July 2005 A-l TRIM.FaTE Evaluation Report Volume II
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Chemical-Independent / Abiotic
(same values used for all air compartments)
Air Compartment Type
Parameter Name
Atmospheric dust particle load
Density of air
Density of dust particles
Fraction organic matter on particulates
Height [VE property]3
Particulate washout ratio
Units
kg[dust particles]/m3[air compartment]
g/cm3
kg[dust particles]/m3[dust particles]
unitless (wet wt)
m
m3[air]/m3[rain]
Value Used
6.15E-08
0.0012
1,400
0.2
mixing height (varies
hourly)b
200,000
Reference
Bidleman 1988
EPA 1997
Bidleman 1988
Harnerand Bidleman 1998
local met data, 1987-1991
Mackay et al. 1986
a Height of air volume elements is set in TRIM.FaTE using two properties, the bottom of the volume element (set at 0 meters for the mercury test case)
and the top of the volume element (set to the mixing height, which varies hourly, for the mercury test case).
bA minimum value of 20 meters was used for the mercury test case.
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Chemical-Independent / Abiotic
(same values used for all soil compartments of each type, except where noted)
Soil Compartment Types
Parameter Name
Units
Value Used
Reference
Surface Soil Compartment Type
Air content
Average vertical velocity of water
(percolation)
Boundary layer thickness above surface soil
Density of soil solids (dry weight)
Depth [VE property]3
Erosion fraction [Link property]
Fraction of area available for erosion
Fraction of area available for runoff
Fraction of area available for vertical diffusion
Organic carbon fraction
Runoff fraction [Link property]
Total erosion rate
Total runoff rate
Water content
volume[air]/volume[compartment]
m3[water]/m2[surface area]-day (or
m/day)
m
kg[soil]/m3[soil]
m
unitless
m2[area available]/m2[total]
m2[area available]/m2[total]
m2[area available]/m2[total]
kg [organic carbon]/kg[soil wet wt]
unitless
kg[soil solids]/m2[surface soil]-day
m3[water]/m2[surface soil]-day
volume[water]/volume[compartment]
0.438
6.00E-04
0.005
2600
0.01
link-specific13
1
1
1
0.0166
link-specific13
2.89E-04
0.00101
0.16
average for region in McKone et al. 2001
assumed as 0.2 times average precipitation for
region in McKone et al. 2001
Thibodeaux1996
Caltox value cited in McKone et al. 2001
Caltox value cited in McKone et al. 2001
estimated from site watershed and topo mapsc
professional judgment; area assumed rural
professional judgment; area assumed rural
professional judgment; area assumed rural
average for region in McKone et al. 2001
estimated from site watershed and topo mapsc
average for region in McKone et al. 2001
average for region in McKone et al. 2001
average for region in McKone et al. 2001
Root Zone Soil Compartment Type
Air content
Average vertical velocity of water
(percolation)
volume[air]/volume[compartment]
m3[water]/m2[surface area]-day (or
m/day)
0.36
6.00E-04
average for region in McKone et al. 2001
assumed as 0.2 times average precipitation for
region in McKone et al. 2001
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Chemical-Independent / Abiotic
(same values used for all soil compartments of each type, except where noted)
Soil Compartment Types
Parameter Name
Density of soil solids (dry weight)
Depth [VE property]3
Organic carbon fraction
Water content
Units
kg[soil]/m3[soil]
m
kg[organic carbon]/kg[soil wet wt]
volume[water]/volume[compartment]
Value Used
2,600
0.55
0.0166
0.16
Reference
Caltox value cited in McKone et al. 2001
average for region in McKone et al. 2001
average for region in McKone et al. 2001
average for region in McKone et al. 2001
Vadose Zone Soil Compartment Type
Air content
Average vertical velocity of water
(percolation)
Density of soil solids (dry weight)
Depth [VE property]3
Organic carbon fraction
Water content
volume[air]/volume[compartment]
m3[water]/m2[surface area]-day (or
m/day)
kg[soil]/m3[soil]
m
kg[organic carbon]/kg[soil wet wt]
volume[water]/volume[compartment]
0.216
6.00E-04
2,600
0.75
0.00128
0.16
McKone etal. 1998
assumed as 0.2 times average precipitation for
region in McKone et al. 2001
Caltox value cited in McKone et al. 2001
professional judgment
McKone etal. 1998
McKone etal. 1998
Ground Water Compartment Type
Depth [VE property]3
Organic carbon fraction
Porosity
Recharge rate to surface water [Link
property]
Solid material density in aquifer
m
kg[organic carbon]/kg[soil wet wt]
volume [total pore
space]/volume[compartment]
m3[water]/m2[area]-day
kg[soil]/m3[soil]
3
0.01
0.2
1 .42E-04
2,600
Caltox value cited in McKone et al. 2001
Schwarzenbach et al. 1993
Caltox value cited in McKone et al. 2001
McKone etal. 1998
Caltox value cited in McKone et al. 2001
Set using the volume element properties named "top" and "bottom."
b See attached erosion/runoff fraction table.
c See Attachment A-1 for method summary.
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Values Used for Erosion and Runoff Fractions: Surface Soil Compartment Type
Sending Compartment
Source
E1
SE1
W1
N1
NE2
ESE2
SSE2
SW2
W2
N2
NE3
ESE3
Receiving Compartment3
River
River
River
N1
River
River
E1
River
E1
NE2
SSE2
ESE3
SSE3
River
River
Surface Soil Advection Sink
W1
SW2
N1
W2
River
Surface Soil Advection Sink
NE2
E4
Stream N
Surface Soil Advection Sink
ESE2
Fields
Brewer
Stream N
Runoff/Erosion Fraction13
1.00
1.00
1.00
0.40
0.60
1.00
0.10
0.90
0.05
0.10
0.80
0.05
0.70
0.30
0.80
0.20
0.90
0.10
0.10
0.50
0.35
0.05
0.05
0.05
0.70
0.20
0.15
0.15
0.10
0.60
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Values Used for Erosion and Runoff Fractions: Surface Soil Compartment Type
Sending Compartment
SSE4
SSE3
ESE4
E4
ESE5
SE6
SSE5
Receiving Compartment3
ESE3
SSE3
Swetts
Surface Soil Advection Sink
SSE2
SSE4
River
Surface Soil Advection Sink
SSE4
Brewer
Stream S
NE3
ESE5
Fields
Surface Soil Advection Sink
Brewer
Stream S
Surface Soil Advection Sink
Stream S
Thurston
Surface Soil Advection Sink
SSE4
ESE4
Stream S
Thurston
Surface Soil Advection Sink
Runoff/Erosion Fraction13
0.10
0.05
0.75
0.10
0.10
0.05
0.05
0.80
0.10
0.80
0.10
0.10
0.05
0.70
0.15
0.80
0.10
0.10
0.70
0.10
0.20
0.15
0.20
0.05
0.55
0.05
aAdjacent compartments not receiving any runoff/erosion (i.e., runoff/erosion fraction = 0) not shown in
table.
bl_ink properties. Same values used for both runoff fraction and erosion fraction for a given link. All
values estimated using site watershed and topographic maps (see Attachment A-1 for method
summary).
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(same values
Surface Water Compartment Type
Chemical-Independent / Abiotic
used for all surface water compartments, except where noted)
Parameter Name
Algae carbon content (fraction)
Algae density in water column
Algae growth rate constant
Algae radius
Algae water content (fraction)
Average algae cell density (pervol cell,
not water)
Boundary layer thickness above sediment
Bulk water flow [Link property]3
Chloride concentration
Chlorophyll concentration
Current velocity0
Depth [VE propertyf
Dispersion coefficient for exchange
between surface water compartments
[Link propertyf
Dimensionless viscous sublayer thickness
Units
g[carbon]/g[algae dry wt]
g [algae wet wt]/L[water]
1/day
urn
unitless
g[algae]/m3[algae]
m
m3[water]/day
mg [ch lo rid e]/L [wate r]
mg[chlorophyll]/L[water]
m/s
m
m2/day
unitless
Value Used
0.465
0.0025
0.7
2.5
0.9
1,000,000
0.02
varies by water
body - see attached
table
varies by water
body - see attached
table
0.0053
varies by water
body - see attached
table
varies by water
body - see attached
table
2.25E-04
4
Reference
APHA1995
derived from Millard et al. 1996
Hudson et al. 1994 as cited in Mason et al. 1995b
Mason et al. 1995b
APHA1995
Mason et al. 1995b, Mason et al. 1996
Cal EPA 1993
for River, average of annual flow data for nearest
USGS gauging station; for other water bodies,
calculated using runoff coefficient (1 .5 cfs/mi2, mean
for relevant watershed) and watershed dimensions
raw lake data supplied by state agency, 1999k
average forSwetts Pond, 1990 and 1997 data; raw
data supplied by state agency, 1999
calculated from flow and cross-sectional area
estimated based on state lake average (provided by
state agency, 1999), USGS map data, and/or
professional judgment
median of values cited in Ambrose et al. 1995 from
a study of Lake Erie by Di Toro and Connolly 1 980
Ambrose et al. 1995
July 2005
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Chemical-Independent / Abiotic
(same values used for all surface water compartments, except where noted)
Surface Water Compartment Type
Parameter Name
Distance between midpoints [Link
property]3
Drag coefficient for water body
Flush rate6
Organic carbon fraction in suspended
sediments
PH
Suspended sediment density
Suspended sediment deposition velocity
Total suspended sediment concentration
Water temperature [VE property]
Units
m
unitless
1/year
unitless
unitless
kg[suspended sediment
particles]/m3[suspended sediment
particles]
m/day
kg[suspended sediment
particles]/m3[water]
degrees K
Value Used
varies by water
body - see attached
table
0.0011
varies by water
body - see attached
table
0.02
6.8
2,650
2
varies by water
body - see attached
table
293
Reference
calculated from CIS maps
Ambrose et al. 1995
Swetts Pond - supplied by state agency, 1999; River
- calculated based on flow data for closest USGS
gauging station (upstream) and volume of River
compartment
McKone et al. 2001
raw data supplied by state agency, 1999f
EPA1998b
EPA 1997
raw lake data supplied by state agency, 19999
professional judgment
Applies to all surface water compartments connected to other surface water compartments.
bAverage of available lake data, except for River; average of available data for closest USGS gauging station for River.
cApplies to flowing water bodies only (i.e., rivers, streams).
dSet using the volume element properties named "top" and "bottom."
eApplies to all surface water compartments connected to a flush rate sink (i.e., all or part of discharge modeled to a sink).
fAverage of five values from two ponds in the same state used for all water bodies.
9State average for lakes, except for River; Schwalen and Kiefer 1996 for River.
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Surface Water Compartment Properties that Vary by Water Body
Parameter
Bulk water flow [Link property]3
Chloride concentration
Current velocity
Depth [VE property]0
Distance between midpoints [Link property]
Flush rate
Surface areaj
Total suspended sediment concentration
Units
m3/day
mg/L
m/sec
m
m
1/year
m2
kg/m3
Swetts
Pond
N/A
2.8.
N/A
3
N/A
4.31
396,000
0.0018
Thurston
Pond
8,060
2.8.
N/A
3
2,075d
N/A
635,000
0.0018
Stream
South
16,000
2.8.
0.0247
0.1
3,015e
N/A
165,000
0.0018
Brewer
Lake
43,000
2.8.
N/A
8
2,230f
N/A
3,670,000
0.0018
Fields
Pond
53,000
2.8.
N/A
4
4,1959
N/A
701,000
0.0018
Stream
North
74,100
2.8.
0.736
0.2
3,715h
N/A
34,500
0.0018
River
N/A
3.4
b
6
N/A
i
3,990,000
0.015
Flow at discharge point to connecting surface water compartment.
bVaries monthly, from 0.088 in August to 0.425 in April. Different value used for steady-state modeling (see Chapter 4).
cSet using the volume element properties named "top" and "bottom."
dThurston Pond - Stream South midpoint.
eStream South - Brewer Lake midpoint.
fBrewer Lake - Fields Pond midpoint.
9Fields Pond - Stream North midpoint.
hStream North - River midpoint.
Varies monthly, from 283 in August to 1,360 in April. Different value used for steady-state modeling (see Chapter 4).
jNot actually a required input property, but calculated from parcel coordinates, which are model inputs.
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Sediment Compartment Type
Chemical-Independent / Abiotic
(same values used for all sediment compartments)
Parameter Name
Depth [VE property]3
Organic carbon fraction
Porosity of the sediment zone
Solid material density in sediment
Units
m
kg[organic carbon]/kg[soil wet wt]
m3[pore water]/m3[sediment
compartment]
kg[sediment particles]/mj[sediment
particles]
Value Used
0.05
0.02
0.6
2,650
Reference
Caltox value cited in McKone et al. 2001
Caltox value cited in McKone et al. 2001
EPA1998a
EPA1998a
Set using the volume element properties named "top" and "bottom."
July 2005
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Chemical-Independent / Biotic
(same values used for all terrestrial plant compartments of a given type)
Terrestrial Plant Compartment Types
Parameter Name
Units
Deciduous3
Value Used
Reference
Coniferous3
Value Used
Reference
Grass/Herb3
Value Used
Reference
Leaf Compartment Type
Allow exchange
Average leaf area index
Calculate wet dep interception
fraction (boolean)
Correction exponent, octanol to lipid
Degree stomatal opening
Density of wet leaf
Leaf wetting factor
Length of leaf
Lipid content of leaf
Litter fall rate
Stomatal area, normalized for
effective diffusion path length
Vegetation attenuation factor
Water content
Wet dep interception fraction (user
supplied)
1=yes, 0=no
m2[total leaf
area]/m2[underlying
soil area]
1=yes, 0=no
unitless
unitless
kg[leaf wet
wt]/m3[leaf]
m
m
kg[lipid]/kg[leaf wet
wt]
1/day
1/m
m2/kg
kg[water]/kg[leaf wet
wt]
unitless
seasonal13
3.4
0
0.76
1
820
3.00E-04
0.1
0.00224
seasonal0
200
2.9
0.8
0.2
see note b
Harvard Forest,
dominant red oak and
red maple, http://
cdiac.esd.ornl.gov
professional judgment
from roots, Trapp 1995
set to 1 for daytime
based on professional
judgment (stomatal
diffusion is turned off at
night using a different
property, IsDay)
Paterson etal. 1991
1 E-04 to 6E-04 for
different crops and
elements, Muller and
Prohl 1993
professional judgment
European beech,
Riederer 1995
see note c
Wlmer and Fricker
1996
grass/hay, Baes et al.
1984
Paterson etal. 1991
calculated based on 5
years of local met data,
1987-1991
1
5
0
0.76
1
820
3.00E-04
0.01
0.00224
0.0021d
200
2.9
0.8
0.2
professional
judgment
representative value
for conifers, personal
comm., N. Nikolov,
ORNL1999
professional
judgment
from roots, Trapp
1995
set to 1 for daytime
based on
professional
judgment (stomatal
diffusion is turned off
at night using a
different property,
IsDay)
Paterson etal. 1991
1 E-04 to 6 E-04 for
different crops and
elements, Muller and
Prohl 1993
professional
judgment
European beech,
Riederer 1995
see note d
Wilmer and Fricker
1996
grass/hay, Baes et al.
1984
Paterson etal. 1991
calculated based on
5 years of local met
data, 1987-1991
seasonal13
5
0
0.76
1
820
3.00E-04
0.05
0.00224
seasonal0
200
2.9
0.8
0.2
see note b
mid-range of 4-6 for
old fields, personal
comm., R.J.
Luxmoore, ORNL
1999
professional judgment
from roots, Trapp
1995
set to 1 for daytime
based on professional
judgment (stomatal
diffusion is turned off
at night using a
different property,
IsDay)
Paterson etal. 1991
1 E-04 to 6 E-04 for
different crops and
elements, Muller and
Prohl 1993
professional judgment
European beech,
Riederer 1995
see note c
Wilmer and Fricker
1996
grass/hay, Baes et al.
1984
Paterson etal. 1991
calculated based on 5
years of local met
data, 1987-1991
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Chemical-Independent / Biotic
(same values used for all terrestrial plant compartments of a given type)
Terrestrial Plant Compartment Types
Parameter Name
Wet mass of leaf per unit area
Units
kg[leaf wet
wt]/m2[surface soil]
Deciduous3
Value Used
0.6
Reference
calculated from leaf
area index, leaf
thickness (Simonich &
Hites 1994), density of
wet foliage
Coniferous3
Value Used
2
Reference
calculated from leaf
area index, leaf
thickness (Simonich
& Hites 1994),
density of wet foliage
Grass/Herb3
Value Used
0.6
Reference
calculated from leaf
area index and Leith
1975
Particle-on-leaf Compartment Type
Allow exchange
Volume particle per area leaf
1=yes, 0=no
m3[leaf
particles]/m2[leaf]
seasonal13
1.00E-09
see note b
based on particle
density and size
distribution for
atmospheric particles
measured on an
adhesive surface, Coe
and Lindberg 1987
1
1.00E-09
professional
judgment
based on particle
density and size
distribution for
atmospheric particles
measured on an
adhesive surface,
Coe and Lindberg
1987
seasonal13
1.00E-09
see note b
based on particle
density and size
distribution for
atmospheric particles
measured on an
adhesive surface,
Coe and Lindberg
1987
Root Compartment Type - Nonwoody Plants Only6
Allow exchange
Correction exponent, octanol to lipid
Lipid content of root
Water content of root
Wet density of root
Wet mass per area
1=yes, 0=no
unitless
kg[lipid]/kg [root wet
wt]
kg[water]/kg[root wet
wt])
kg[root wet
wt]/m3[root]
kg[root wet
wt]/m2[surface soil]
seasonal13
0.76
0.011
0.8
820
1.4
see note b
Trapp 1995
from bean root, Trapp
1995
professional judgment
soybean, Paterson et
al. 1991
temperate grassland,
Jackson et al. 1996
Stem Compartment Type - Nonwoody Plants Only6
Allow exchange
Correction exponent, octanol to lipid
Density of phloem fluid
Density of xylem fluid
1=yes, 0=no
unitless
kg[phloem]/m3[phloe
m]
kg[xyl e m]/m3[xyl e m]
seasonal13
0.76
1,000
900
see note b
from roots, Trapp
1995
professional judgment
professional judgment
July 2005
A-13
TRIM.FaTE Evaluation Report Volume II
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Chemical-Independent / Biotic
(same values used for all terrestrial plant compartments of a given type)
Terrestrial Plant Compartment Types
Parameter Name
Flow rate of transpired water per
leaf area
Fraction of transpiration flow rate
that is phloem rate
Lipid content of stem
Water content of stem
Wet density of stem
Wet mass per area
Units
m3[water]/m2 [leaf]-
day
unitless
kg[lipid]/kg [stem wet
wt]
kg[water]/kg[stem
wet wt]
kg [stem wet
wt]/m3[stem]
kg [stem wet
wt]/m2[surface soil]
Deciduous3
Value Used
Reference
Coniferous3
Value Used
Reference
Grass/Herb3
Value Used
0.0048
0.05
0.00224
0.8
830
0.24
Reference
Crank et al. 1981
Paterson etal. 1991
leaves of European
beech, Riederer 1995
Paterson etal. 1991
professional judgment
calculated from leaf
and root biomass
density based on
professional judgment
See attached table for assignment of vegetation types to surface soil volume elements.
bBegins May 12 (set to 1), ends September 30 (set to 0). Set to average days of last and first frost for modeling location. Different value used for steady-state modeling (see
Chapter 4).
°Begins September 30, ends October 29; rate = 0.15/day during this time (value assumes first-order relationship and that 99 percent of leaves fall in 30 days). Rate is zero at all
other times. Different value used for steady-state modeling (see Chapter 4).
dValue assumes first-order relationship and that 99 percent of leaves fall in six years.
eRoots and stems are not modeled for deciduous or coniferous forest in the current version of TRIM.FaTE.
July 2005
A-14
TRIM.FaTE Evaluation Report Volume II
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Terrestrial Vegetation Types3
Surface Soil
Volume
Element
source
E1
SE1
W1
N1
NE2
ESE2
SSE2
SW2
W2
N2
NE3
ESE3
SSE3
E4
ESE4
SSE4
ESE5
SSE5
SE6
Deciduous
Forest
X
X
X
X
X
X
X
X
Coniferous
Forest
X
X
X
X
X
X
X
Grasses/
Herbs
X
X
X
X
None
X
Assignments made based on review of land use maps.
July 2005
A-15
TRIM.FaTE Evaluation Report Volume II
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Chemical-Independent/ Biotic
(same values used for all macrophyte compartments, except where noted)
Aquatic Plant Compartment Type
Parameter Name
Units
Value Used
Reference
Macrophyte Compartment Type
Biomass per water area3
Density of macrophytes
kg/m2
kg/L
1.5
1
Bonaretal. 1993
professional judgment
aThe macrophyte compartment type was included in the Swetts Pond, Thurston Pond, Brewer Lake, Fields Pond, and River surface water
compartments. The macrophyte compartment type was not included in the Stream S or Stream N compartments.
July 2005
A-16
TRIM.FaTE Evaluation Report Volume II
-------
Chemical-Independent/ Biotic
(same values used for all terrestrial animal compartments of a given type, except where noted)
Terrestrial Animal Compartment Types
Parameter Name
Units
Value Used
Reference
Soil Detritivore Compartment Type - Earthworm
Density
Density per soil area3
Water content of worm
kg [worm wet wt]/L[worm]
kg[worm wet wt]/m2[soil]
unitless
1
0.045
0.84
professional judgment
avg of oak and beech values in Satchell 1983
EPA 1993
Soil Detritivore Compartment Type - Soil Arthropod
Biomass per soil area3
Body weight (BW)
kg[arthropod wet wt]/m2[soil]
kg
3.01 E-04
1.31E-04
grasshopper, Porter etal. 1996
grasshopper, Porter etal. 1996
Presence/absence varies by surface soil compartment; see attached table.
July 2005
A-17
TRIM.FaTE Evaluation Report Volume II
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Chemical-Independent / Biotic
(same values used for all terrestrial animal compartments of a given type, except where noted)
Terrestrial Animal Compartment Types
Parameter Name
Units
Terrestr al Ground-
Invertebrate Feeder - Short
tailed Shrew
Value Used
Reference
Terrestrial Herbivore -
Meadow Vole
Value Used
Reference
Terrestrial Herbivore -
White-tailed Deer
Value Used
Reference
Terrestrial Insectivore -
Black-capped Chickadee
Value Used
Reference
Terrestrial Omnivore -
Mouse
Value Used
Reference
Terrestrial Carnivore -
Long-tailed Weasel
Value Used
Reference
Terrestrial Carnivore -
Red-tailed hawk
Value Used
Reference
All Other Terrestrial Animal Compartment Types3
Bodyweight(BW)
Food ingestion rateb
Fraction diet- black-capped chickadee
Fraction diet- mouse
Fraction diet - terrestrial plants
Fraction diet - short-tailed shrew
Fraction diet - soil arthropod
Fraction diet- vole
Fraction diet- worm
Fraction excretion to soil
Fraction excretion to water
Population per soil areac
Scaling constant A- inhalation rate
Scaling constant B- inhalation rate
Scaling constant A - water ingestion rate
kg
kg[diet wet
wt]/kg[body wet
wt]-day
unitless
unitless
unitless
unitless
unitless
unitless
unitless
unitless
unitless
#/m2
unitless
unitless
unitless
0.022
0.47
0.415
0.585
1
0
6.10E-04
0.546
0.8
0.099
0.01 5-0.029 kg
reported for
Manitoba, Silva
and Downing
1995
Barrett and
Stueck 1976
Whitakerand
Ferraro 1963
Whitakerand
Ferraro 1963,
slugs
represented by
earthworms,
Ithaca, NY
professional
judgment
professional
judgment
average value
for state,
contact at state
university
Stahl 1 967
Stahl 1 967
Calderand
Braun 1983
0.0441
0.097
1
1
0
0.006
0.546
0.8
0.099
Reich 1981
mean Microtus
spp., Dark et al.
1983, Burtand
Grossenheider
1976, Dice
1922
professional
judgment
professional
judgment
professional
judgment
average of
0.01 1/m2, Klaas
etal. 1998, and
0.0015/m2,
Getz1961
Stahl 1 967
Stahl 1 967
Calderand
Braun 1983
74.8
0.05
1
1
0
4.60E-05
0.546
0.8
0.099
Silva and
Downing
1995
Mautz et al.
1976
professional
judgment
professional
judgment
professional
judgment
12-80/km2,
forest avg
from Smith
1987,
Torgerson
and Porath
1984, Wishart
1984, Cook
1984
Stahl 1 967
Stahl 1 967
Calderand
Braun 1983
0.0108
0.74
0.3
0.7
1
0
3.50E-05
0.409
0.8
0.059
Dunning 1993
calculated
from Bell
1990,
Dunning 1993
Martin et al.
1951
Smith 1993,
Martin et al.
1951
professional
judgment
professional
judgment
avg of 0.2
and 0.3 /ha in
British
Columbia,
Smith 1993
Lasiewski and
Calder 1971
Lasiewski and
Calder 1971
Calder and
Braun 1983
0.02
0.2
0.5
0.5
1
0
0.0023
0.546
0.8
0.099
North
America,
Silva and
Downing
1995
Green and
Millar 1987
professional
judgment
professional
judgment
professional
judgment
professional
judgment
average of
range 6-
57/ha, Wolff
1985
Stahl 1 967
Stahl 1 967
Calderand
Braun 1983
0.147
0.0735
0.5
0.25
0.25
1
0
6.50E-06
0.546
0.8
0.099
Mumford and
Whitaker
1982
calc from
Brown and
Lasiewski
1972, Golley
1961, EPA
1993
professional
judgment
professional
judgment
professional
judgment
professional
judgment
professional
judgment
average of 6-
7/ha,
Svendsen
1982
Stahl 1967
Stahl 1967
Calderand
Braun 1983
1.13
0.12
0.257
0.303
0.2
0.04
0.2
1
0
6.7E-07
0.409
0.8
0.059
North
America,
Dunning 1993
Preston and
Beane 1993
approximate
from Sherrod
1978
approximate
from Sherrod
1978
approximate
from Sherrod
1978
approximate
from Sherrod
1978
approximate
from Sherrod
1978
professional
judgment
professional
judgment
average of
range 0.0034
and 0.01 for
Colorado,
EPA 1993
Lasiewski and
Calder 1971
Lasiewski and
Calder 1971
Calderand
Braun 1983
A-18
TRIM.FaTE Evaluation Report Volume II
-------
Chemical-Independent / Biotic
(same values used for all terrestrial animal compartments of a given type, except where noted)
Terrestrial Animal Compartment Types
Parameter Name
Scaling constant B - water ingestion rate
Soil ingestion rate
Units
unitless
kg[soil dry
wt]/kg[body wet
wt]-day
Terrestr
Invertebrate
taile
Value Used
0.9
0.0611
al Ground-
Feeder - Short
d Shrew
Reference
Calderand
Braun 1983
Talmage and
Walton 1 993
Terrestrial Herbivore -
Meadow Vole
Value Used
0.9
0.0006
Reference
Calderand
Braun 1983
calculated using
data from Beyer
atal. 1994
Terrestrial Herbivore -
White-tailed Deer
Value Used
0.9
0.00013
Reference
Calderand
Braun 1983
calculated
using data
from Beyer at
al. 1994
Terrestrial Insectivore -
Black-capped Chickadee
Value Used
0.67
0
Reference
Calder and
Braun 1983
assumed,
rarely
observed on
ground, Smith
1993
Terrestrial Omnivore -
Mouse
Value Used
0.9
0.001
Reference
Calderand
Braun 1983
calculated
using data
from Beyer at
al. 1994
Terrestrial Carnivore -
Long-tailed Weasel
Value Used
0.9
0
Reference
Calderand
Braun 1983
professional
judgment
Terrestrial Carnivore -
Red-tailed hawk
Value Used
0.67
0
Reference
Calderand
Braun 1983
professional
judgment
See attached table for documentation of compartment links for ingestion, inhalation, and excretion. For test case, all terrestrial animals were assumed to get 100% of their diet from a single volume element (i.e., FractionSpecificCompartmentDiet link property
always set to 1.0).
bSee Attachment A-2 for documentation of food ingestion rate calculations.
cPresence/absence varies by surface soil compartment; see attached table.
A-19
TRIM.FaTE Evaluation Report Volume II
-------
Population (or Biomass) per Soil Area: Terrestrial Animal Compartment Types
(number of animals per m2, except where noted)
Compartment Type
Terrestrial Ground-
Invertebrate Feeder -
Short-tailed Shrew
Terrestrial Herbivore -
Meadow Vole
Terrestrial Herbivore -
White-tailed Deer
Terrestrial Insectivore -
Black-capped Chickadee
Terrestrial Omnivore -
Mouse
Terrestrial Carnivore -
Long-tailed Weasel
Terrestrial Carnivore -
Red-tailed hawk
Soil Detritivore
Compartment Type -
Earthworm (kg/m2)a
Soil Detritivore
Compartment Type - Soil
Arthropod (kg/m2)
Surface Soil Volume Element
Source
0
0
0
0
0
0
0
0
0
E1
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
SE1
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
W1
0
0
0
0
0
0
0
4.50E-02
0
N1
0
0
0
0
0
0
0
4.50E-02
0
NE2
6.10E-04
6.00E-03
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
ESE2
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
SSE2
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
SW2
6.10E-04
6.00E-03
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
W2
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
N2
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
NE3
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
ESE3
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
SSE3
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
E4
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
ESE4
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
SSE4
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
ESE5
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
SSE5
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
SE6
6.10E-04
0
4.60E-05
3.50E-05
2.30E-03
6.50E-06
6.70E-07
4.50E-02
3.01 E-04
'Associated with root zone soil volume elements (rather than surface soil).
A-20
TRIM.FaTE Evaluation Report Volume II
-------
Semi-aquatic Animal Compartment Types
Chemical-Independent / Biotic
(same values used for all semi-aquatic animal compartment types, except where noted)
Parameter Name
Units
Semi-aquatic Insectlvore -
Tree Swallow
Value Used
Reference
Semi-aquatic Omnlvore -
Mallard
Value Used
Reference
Semi-aquatic Omnlvore -
Raccoon
Value Used
Reference
Semi-aquatic Plsclvore -
Common Loon
Value Used
Reference
Semi-aquatic Carnivore -
Mink
Value Used
Reference
Semi-aquatic Carnivore -
Bald Eagle
Value Used
Reference
All Compartment Types?
Body weight (BW)
Food ingestion rate"
Fraction diet - benthic carnivores'
Fraction diet - benthic
invertebrates
Fraction diet - benthic omnivoresc
Fraction diet - black-capped
chickadee
Fraction diet - emerging benthic
insect (benthic invertebrate)
Fraction diet - mouse
Fraction diet - terrestrial plants
kg
(kg[dietwet
wt]/kg[body wet wt]
day)
unitless
unitless
unitless
unitless
unitless
unitless
unitless
0.0201
0.198
1
Dunning 1993
calculated
from Williams
1988, Quinney
and Ankney
1985, and Bell
1990
professional
judgment
1.13
0.1
0.335
0.665
Nelson and
Martin 1953
Heinz et al.
1987
EPA 1993 and
professional
judgment
Martin etal.
1951
6.35
0.11
0.69
0.046
Anderson
1979
based on
allometric
equation
(Nagy et
al. 1999) and
professional
representing
molluscs,
Crustacea,
Tyson 1950
Tyson 1950
4.13
0.23
0.5
Dunning 1993
Barr 1996
professional
judgment
0.831
0.14
0.17
0.15
0.08
0.23
Mumford and
Whitaker1982
mink in
captivity,
Bleavins and
Aulerich 1981
Hamilton
1940,
Sealander
1943,
Korschgen
1958, Burgess
and Bider
1980
Hamilton
1940,
Sealander
1943,
Korschgen
1958, Burgess
and Bider
1980
Hamilton
1940,
Sealander
1943,
Korschgen
1958, Burgess
and Bider
1980
Hamilton
1940,
Sealander
1943,
Korschgen
1958, Burgess
and Bider
1980
4.74
0.12
0.17
0.17
0.1
0.23
Dunning 1993
Stalmaster
and
Gessaman
1984
EPA 2002
EPA 2002
professional
judgment
professional
judgment
A-21
TRIM.FaTE Evaluation Report Volume II
-------
Semi-aquatic Animal Compartment Types
Chemical-Independent / Biotic
(same values used for all semi-aquatic animal compartment types, except where noted)
Parameter Name
Fraction diet - vole
Fraction diet - water-column
carnivores'
Fraction diet - water-column
herbivores'
Fraction diet - water-column
omnivoresc
Fraction diet - worm
Fraction excretion to soil
Fraction excretion to water
Population per soil area"
Scaling constant A - inhalation rate
Scaling constant B - inhalation rate
Units
unitless
unitless
unitless
unitless
unitless
unitless
unitless
#/m2
unitless
unitless
Semi-aquatic Insectivore -
Tree Swallow
Value Used
1
0
7.00E-04
0.409
0.8
Reference
professional
judgment
professional
judgment
De Graaf et al.
1981
Lasiewski and
Calder1971
Lasiewski and
Calder1971
Semi-aquatic Omnivore -
Mallard
Value Used
0.5
0.5
9.30E-06
0.409
0.8
Reference
professional
judgment
professional
judgment
average of
0.012 and
0.0174/ha,
North Dakota,
EPA 1993
Lasiewski and
Calder1971
Lasiewski and
Calder1971
Semi-aquatic Omnivore -
Raccoon
Value Used
0.04
0.014
0.21
0.5
0.5
varies by
compartment,
depending on
available
shoreline;
based on 2
raccoons per
km shoreline
0.546
0.8
Reference
Tyson 1950
Tyson 1950
coastal
mudflats of
SW
Washington,
Tyson 1950
professional
judgment
professional
judgment
Kaufman 1982
Stahl 1967
Stahl 1967
Semi-aquatic Piscivore -
Common Loon
Value Used
0.5
0.5
0.5
4.90E-08
0.409
0.8
Reference
professional
judgment
professional
judgment
professional
judgment
State Dept
Inland
Fisheries &
Wildlife
Lasiewski and
Calder1971
Lasiewski and
Calder1971
Semi-aquatic Carnivore -
Mink
Value Used
0.23
0.103
0.037
0.5
0.5
varies by
compartment,
depending on
available
shoreline;
based on 0.6
mink per km
shoreline
0.546
0.8
Reference
Hamilton
1940,
Sealander
1943,
Korschgen
1958, Burgess
and Bider
1980
Hamilton
1940,
Sealander
1943,
Korschgen
1958, Burgess
and Bider
1980
Hamilton
1940,
Sealander
1943,
Korschgen
1958, Burgess
and Bider
1980
professional
judgment
professional
judgment
Marshall 1936
Stahl 1967
Stahl 1967
Semi-aquatic Carnivore -
Bald Eagle
Value Used
0.11
0.11
0.11
0.5
0.5
1.30E-08
0.409
0.8
Reference
assumed
based on
approximate
trophic levels
of several
consumed fish
species
assumed
based on
approximate
trophic levels
of several
consumed fish
species
assumed
based on
approximate
trophic levels
of several
consumed fish
species
professional
judgment
professional
judgment
State Dept
Inland
Fisheries &
Wldlife
Lasiewski and
Calder1971
Lasiewski and
Calder1971
TRIM.FaTE Evaluation Report Volume II
-------
Semi-aquatic Animal Compartment Types
Chemical-Independent / Biotic
(same values used for all semi-aquatic animal compartment types, except where noted)
Parameter Name
Scaling constant A - water
ingestion rate
Scaling constant B - water
ingestion rate
Soil ingestion rate
Units
unitless
unitless
kg[soil dry
wt]/kg[body wet wt]
day
Semi-aquatic Insectivore -
Tree Swallow
Value Used
0.059
0.67
0
Reference
Calderand
Braun 1983
Calderand
Braun 1983
professional
judgment
Semi-aquatic Omnivore -
Mallard
Value Used
0.059
0.67
0.00085
Reference
Calder and
Braun 1983
Calder and
Braun 1983
calculated using
data from Beyer
atal. 1994
Semi-aquatic Omnivore -
Raccoon
Value Used
0.099
0.9
0.0029
Reference
Calder and
Braun 1983
Calder and
Braun 1983
calculated using
data from Beyer
atal. 1994
Semi-aquatic Piscivore -
Common Loon
Value Used
0.059
0.67
0
Reference
Calder and
Braun 1983
Calder and
Braun 1983
professional
judgment
Semi-aquatic Carnivore -
Mink
Value Used
0.099
0.9
0
Reference
Calder and
Braun 1983
Calder and
Braun 1983
professional
judgment
Semi-aquatic Carnivore -
Bald Eagle
Value Used
0.059
0.67
0
Reference
Calder and
Braun 1983
Calder and
Braun 1983
professional
judgment
"See attached table for documentation of compartment links for ingestion, inhalation, and excretion. For test case, all semi-aquatic animals were assumed to get 100% of their water-based diet from a single volume element and
100% of their land-based diet from a single volume element (i.e., FractionSpecificCompartmentDiet link property always set to 1.0).
"See Attachment A-2 for documentation of food ingestion rate calculations.
'Proportion of fish in wildlife species diet based on EPA 1993 and professional judgment; distribution of fish in diet among the different fish compartment types based on general size classes of fish consumed (EPA 2002) and relative
abundance offish in the different compartment types (trophic levels).
"Presence/absence of mallard and common loon varies by surface water compartment, and presence/absence of other species varies by surface soil compartment; density value for mink and raccoon varies by surface soil
compartment; see attached table.
A-23
TRIM.FaTE Evaluation Report Volume II
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Population per Soil or Water Area: Semi-aquatic Animal Compartment Types
(number of animals per m2 of soil or water)
Compartment Type
Semi-aquatic Insectivore - Tree
Swallow
Semi-aquatic Omnivore - Raccoon
Semi-aquatic Carnivore - Mink
Semi-aquatic Carnivore - Bald
Eagle
Surface Soil Volume Element
Source
0
0
0
0
E1
7.00E-04
9.06E-07
2.72E-07
1 .30E-08
SE1
7.00E-04
9.06E-07
2.72E-07
1 .30E-08
W1
0
0
0
0
N1
0
0
0
0
NE2
7.00E-04
9.06E-07
2.72E-07
1 .30E-08
ESE2
7.00E-04
0
0
1 .30E-08
SSE2
7.00E-04
9.06E-07
2.72E-07
1 .30E-08
SW2
7.00E-04
1 .23E-06
3.70E-07
1 .30E-08
W2
7.00E-04
0
0
1 .30E-08
N2
7.00E-04
1 .23E-06
3.70E-07
1 .30E-08
NE3
7.00E-04
0
0
1 .30E-08
ESE3
7.00E-04
7.65E-07
2.29E-07
1 .30E-08
SSE3
7.00E-04
9.06E-07
2.72E-07
1 .30E-08
E4
7.00E-04
2.01E-06
6.02E-07
1 .30E-08
ESE4
7.00E-04
7.00E-07
2.10E-07
1.30E-08
SSE4
7.00E-04
7.00E-07
2.10E-07
1.30E-08
ESE5
7.00E-04
2.01 E-06
6.02E-07
1.30E-08
SSE5
7.00E-04
6.52E-07
1.96E-07
1.30E-08
SE6
7.00E-04
6.52E-07
1.96E-07
1 .30E-08
Compartment Type
Semi-aquatic Omnivore - Mallard
Semi-aquatic Piscivore - Common
Loon
Surface Water Volume Element
Swetts
Pond
9.30E-06
4.90E-08
Thurston
Pond
9.30E-06
4.90E-08
Stream S
0
0
Brewer
Lake
9.30E-06
4.90E-08
Fields
Pond
9.30E-06
4.90E-08
Stream N
0
0
River
9.30E-06
4.90E-08
TRIM.FaTE Evaluation Report Volume II
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Compartment Links for Terrestrial and Land-based Semi-aquatic Animals
Type of Link
Compartment Link for Inhalation
Compartment Link for Drinking Water
Compartment Link for Terrestrial Diet Components3
Compartment Link for Aquatic Diet Components11
Surface Soil Volume Element that Animal Is Based in
Source
n/a
n/a
n/a
n/a
E1
ESE1
River
E1b
River
SE1
SSE1
River
SE1C
River
W1
n/a
n/a
n/a
n/a
N1
n/a
n/a
n/a
n/a
NE2
ENE2
River
NE2
River
ESE2
ESE2
River
Stream N
ESE2b
River
SSE2
SSE2
River
SSE2C
River
SW2
SSW2
River
SW2
River
W2
W2
River
W2C
River
Type of Link
Compartment Link for Inhalation
Compartment Link for Drinking Water
Compartment Link for Terrestrial Diet Components3
Compartment Link for Aquatic Diet Components11
Surface Soil Volume Element that Animal Is Based in
N2
NNW2
River
N2b
River
NE3
ENE3
River
Stream N
NE3b
River
ESE3
ESE3
Fields
ESE3b
Fields
SSE3
SSE3
River
SSE3C
River
E4
ENE4
Fields
E4b
Fields
ESE4
ESE4
Brewer
ESE4C
Brewer
SSE4
SSE4
Swetts
SSE4C
Swetts
ESE5
ESE5
Brewer
ESE5b
Brewer
SSE5
SSE4
Thurston
SSE5C
Thurston
SE6
ESE5
Thurston
SE6C
Thurston
3Same links for soil ingestion and for excretion to surface soil.
bNE2 for voles in diet.
CSW2 for voles in diet.
dSame links for excretion to surface water.
My 2005
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TRIM.FaTE Evaluation Report Volume II
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Compartment Links for Water-based Semi-aquatic Animals
Type of Link
Compartment Link for Inhalation
Compartment Link for Drinking Water
Compartment Link for Terrestrial Diet Components
Compartment Link for Soil Ingestion3
Compartment Link for Aquatic Diet Components'3
Surface Water Volume Element that Animal Is Based In
Swetts Pond
SSE3
Swetts
NE2
SSE4
Swetts
Thurston Pond
SSE5
Thurston
NE2
SSE5
Thurston
Brewer Lake
ESE4
Brewer
NE2
ESE4
Brewer
Fields Pond
ESE4
Fields
NE2
ESE3
Fields
River
SSW2
River
W1
SSE2
River
Same links for excretion to surface soil.
bSame links for excretion to surface water.
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Aquatic Animal Compartment Types
Chemical-Independent / Biotic
(same values used for all aquatic animal compartments of a given type, except where noted)
Parameter Name
Units
Water-column Carnivore
Value Used
Reference
Water-column Herbivore
Value Used
Reference
All Fish Compartment Types'
Body weight (BW)
Fraction diet - algae
Fraction diet - benthic invertebrates
Fraction diet - benthic omnivores
Fraction diet - water-column herbivores
Fraction diet - water-column omnivores
Fraction lipid weight
Population per water areab
kg[fish wet wt]
unitless
unitless
unitless
unitless
unitless
kg[lipid]/kg[fish
wet wt]
#/m2
2
1
0.057
8.95E-05
professional
judgment
value set based on
definition of trophic
levels
Thomann 1989
biomass per area
divided by body
weight of
individual; biomass
(1.79E-04kg/m2)
taken as mean of
selected lake data
in Kelso and
Johnson 1991
0.025
1
0.034
0.0658
professional
judgment
value set based on
definition of trophic
levels
Thomann 1989
biomass per area
divided by body
weight of
individual; biomass
(0.00165 kg/m2)
taken as mean of
selected lake data
in Kelso and
Johnson 1991
Water-column Omnlvore
Value Used
Reference
Benthic Carnivore
Value Used
Reference
Benthic Omnlvore
Value Used
Reference
0.25
1
0.07
0.00234
professional
judgment
value set based on
definition of trophic
levels
Thomann 1989
biomass per area
divided by body
weight of
individual; biomass
(5.85E-04 kg/m2)
taken as mean of
selected lake data
in Kelso and
Johnson 1991
2
1
0.057
1.07E-04
professional
judgment
value set based on
definition of trophic
levels
Thomann 1989
biomass per area
divided by body
weight of
individual; biomass
(2.14E-04kg/m2)
taken as mean of
selected lake data
in Kelso and
Johnson 1991
0.25
1
0.07
0.00755
professional
judgment
value set based on
definition of trophic
levels
Thomann 1989
biomass per area
divided by body
weight of
individual; biomass
(0.00189 kg/m2)
taken as mean of
selected lake data
in Kelso and
Johnson 1991
Parameter Name
Units
Value Used
Reference
Benthic Invertebrate Compartment Type
Biomass per water areab
Body weight (BW)
kg/m2
kg[inv wet wt]
0.0373
2.55E-04
value for Bewer
Lake, state agency
professional
judgment
For test case, all aquatic animals were assumed to get 100% of their diet from a single volume element (i.e., FractionSpecificCompartmentDiet link property always set to 1.0).
bAII six aquatic animal compartment types were included in the Swetts Pond, Thurston Pond, Brewer Lake, Fields Pond, and River surface water compartments. No aquatic animal types were included in the Stream S or Stream N
compartments.
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Independent of Compartment Type3
Parameter Name
Diffusion coefficient in pure air
Diffusion coefficient in pure water
Henry's Law constant
Melting point
Molecular weight
Octanol-water partition coefficient (Kow)
Vapor washout ratio
Units
m2[air]/day
m2[water]/day
Pa-m3/mol
degrees K
g/mol
L[water]/kg[octanol]
m3[air]/m3[rain]
Value
Hg(0)b
0.478
5.54E-05
719
234
201
4.15
1,200
Hg(2)b
0.478
5.54E-05
7.19E-05
550
201
3.33
1.6E+06
MHgb
0.456
5.28E-05
0.0477
443
216
1.7
0
Reference
EPA 1997
EPA 1997
EPA 1997
GARB 1994
EPA 1997
Mason et al. 1996
EPA 1997, based on Petersen et al.
1995
All parameters in this table are TRIM.FaTE chemical properties.
bOn this and all following tables, Hg(0) = elemental mercury, Hg(2) = divalent mercury, and MHg = methyl mercury.
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Abiotic
(same values used for all air compartments, except where noted)
Air Compartment Type
Parameter Name
Initial concentration
Boundary concentration [VE propertyf
Particle dry deposition velocity
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
Units
g/m3
g/m3
m/day
1/day
1/day
1/day
1/day
Value
Hg(0)
a
1 .60E-09
500
N/A
0
0.00385
0
Hg(2)
a
1.60E-11
500
N/A
0
0
0
MHg
a
0
500
0
0
0
0
Reference
Case C based on "boundary contributions
only" run
EPA 1997
Caltox value cited in McKone et al. 2001
professional judgment
professional judgment
low end of half-life range (6 months to 2
years) in EPA 1 997
professional judgment
aSet to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each
compartment.
bOnly used in model runs specified as including non-zero boundary contributions (Case C, and also the "boundary contributions only" run). Only
applicable for air volume elements with at least one boundary on the outer edge of the modeling region (zero boundary contribution for all
internal air compartments).
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Abiotic
(same values used for all soil compartments of each type)
Soil Compartment Types
Parameter Name
Units
Value
Hg(0)
Hg(2)
MHg
Reference
Surface Soil Compartment Type
Initial concentration
Input characteristic depth (user supplied)
Soil-water partition coefficient
Use input characteristic depth (boolean)
Vapor dry deposition velocity
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
g/m3
m
L[water]/kg[soil wet
wt]
0 = no, Else = yes
m/day
1/day
1/day
1/day
1/day
a
0.08
1,000
0
8.64
N/A
0
0
0
a
0.08
58,000
0
864
N/A
0.001
0
1 .25E-05
a
0.08
7,000
0
0
0.06
0
0
0
Case C based on "boundary contributions only" run
not used (model set to calculate value)
EPA 1997
professional judgment
EPA1997b
range reported in Porvari and Verta 1995 is 3E-2 to
6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions
range reported in Porvari and Verta 1995 is 2E-4 to
1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions
value assumed in EPA 1997
value used for unfilled surface soil (2cm), 10%
moisture content, in EPA 1997; general range is
(0.0013/day)*moisture content to
(0.0001/day)*moisture content for forested region
(Lindberg 1996; Carpi and Lindberg 1997)
Root Zone Soil Compartment Type
Initial concentration
Input characteristic depth (user supplied)
Soil-water partition coefficient
Use input characteristic depth (boolean)
g/m3
m
L[water]/kg[soil wet
wt]
0 = no, Else = yes
a
0.08
1,000
0
a
0.08
58,000
0
a
0.08
7,000
0
Case C based on "boundary contributions only" run
not used (model set to calculate value)
EPA 1997
professional judgment
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Abiotic
(same values used for all soil compartments of each type)
Parameter Name
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
Units
1/day
1/day
1/day
1/day
Value
Hg(0)
N/A
0
0
0
Hg(2)
N/A
0.001
0
3.25E-06
MHg
0.06
0
0
0
Reference
range reported in Porvari and Verta 1995 is 3E-2 to
6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions
range reported in Porvari and Verta 1995 is 2E-4 to
1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions
value assumed in EPA 1997
value used for tilled surface soil (20cm), 10%
moisture content, in EPA 1997 (Lindberg 1996;
Carpi and Lindberg, 1997)
Vadose Zone Soil Compartment Type
Initial concentration
Input characteristic depth (user supplied)
Soil-water partition coefficient
Use input characteristic depth (boolean)
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
g/m3
m
L[water]/kg[soil wet
wt]
0 = no, Else = yes
1/day
1/day
1/day
1/day
a
0.08
1,000
0
N/A
0
0
0
a
0.08
58,000
0
N/A
0.001
0
3.25E-06
a
0.08
7,000
0
0.06
0
0
0
Case C based on "boundary contributions only" run
not used (model set to calculate value)
EPA 1997
professional judgment
range reported in Porvari and Verta 1995 is 3E-2 to
6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions
range reported in Porvari and Verta 1995 is 2E-4 to
1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions
value assumed in EPA 1997
value used for tilled surface soil (20cm), 10%
moisture content, in EPA 1997 (Lindberg 1996;
Carpi and Lindberg, 1997)
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Abiotic
(same values used for all soil compartments of each type)
Parameter Name
Units
Value
Hg(0)
Hg(2)
MHg
Reference
Ground Water Compartment Type
Initial concentration
Soil-water partition coefficient
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
g/L
L[water]/kg[soil wet
wt]
1/day
1/day
1/day
1/day
a
1,000
N/A
0
1.00E-08
0
a
58,000
N/A
0.001
0
3.25E-06
a
7,000
0.06
0
0
0
Case C based on "boundary contributions only" run
EPA 1997
range reported in Porvari and Verta 1995 is 3E-2 to
6E-2 /day; value is average maximum potential
demethylation rate constant under anaerobic
conditions
range reported in Porvari and Verta 1995 is 2E-4 to
1E-3 /day; value is average maximum potential
methylation rate constant under anaerobic
conditions
small default nonzero value (0 assumed in EPA
1997)
value used for tilled surface soil (20cm), 10%
moisture content, in EPA 1997 (Lindberg 1996;
Carpi and Lindberg, 1997)
aSet to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each compartment.
"Values in EPA 1997 actually 50 (HgO) and 2,500 (Hg2). Listed values used inadvertantly.
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Abiotic
(same values used for all surface water compartments)
Surface Water Compartment Type
Parameter Name
Initial concentration
Algal surface area-specific uptake
rate constant
Dow ("overall Kow")
Solids-water partition coefficient
Vapor dry deposition velocity
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
Units
g/L
nmol/[um2-day-nM]
L[water]/kg[octanol]
L[water]/kg[solids wet
wt]
m/day
1/day
1/day
1/day
1/day
Value
Hg(0)
a
0
0
1,000
N/A
N/A
0
0
0
Hg(2)
a
2.04E-10
b
100,000
864
N/A
0.001
0
0.0075
MHg
a
3.60E-10
c
100,000
N/A
0.013
0
0
0
Reference
Case C based on "boundary contributions only" run
Mason et al. 1996; zero assumed for Hg(0)
derived from Figure 2 in Mason et al. 1996
EPA 1 997
EPA 1 997d
average of range of 1 E-3 to 2.5E-2/day from Gilmour and
Henry 1991
value used in EPA 1997; range is from 1E-4 to 3E-3/day
(Gilmour and Henry 1991)
professional judgment
value used in EPA 1997; reported values range from less
than 5E-3/day for depths greater than 17m, up to 3.5/day
(Xiao et al. 1 995; Vandal et al. 1 995; Mason et al. 1 995a;
Amyotetal. 1997)
Set to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each compartment.
bTRIM.FaTE Formula Property (i.e., calculated, not an input), which varies from 0.025 to 1.625 depending on input pH and chloride concentration.
cTRIM.FaTE Formula Property (i.e., calculated, not an input), which varies from 0.075 to 1.7 depending on input pH and chloride concentration.
dValue in EPA 1997 actually 2,500 (Hg2). Listed value used inadvertantly.
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Sediment Compartment Type
Chemical-Dependent / Abiotic
(same values used for all sediment compartments)
Parameter Name
Initial concentration
Solids-water partition coefficient
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
Units
g/m3
L[water]/kg[solids
wet wt]
1/day
1/day
1/day
1/day
Value
Hg(0)
a
3,000
N/A
0
0
0
Hg(2)
a
50,000
N/A
1 .OOE-04
0
1 .OOE-06
MHg
a
3,000
0.0501
0
0
0
Reference
Case C based on "boundary contributions only" run
EPA 1997
average of range of 2E-4to 1E-1 /day from Gilmourand
Henry 1991
value used in EPA 1997; range is from 1E-5 to 1E-3/day
(Gilmourand Henry 1991)
professional judgment
inferred value based on presence of Hg(0) in sediment
porewater (EPA 1997; Vandal et al. 1995)
Set to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each compartment.
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Biotic
(same values used for all terrestrial plant compartments of each type, except for initial concentration)
Terrestrial Plant Compartment Types3
Parameter Name
Units
Value
Hg(0)
Hg(2)
MHg
Reference
Leaf Compartment Type
Initial concentration
Transfer factor to leaf particle
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
Particle-on-leaf Compartment T
Initial concentration
Transfer factor to leaf
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
g/kg
1/day
1/day
1/day
1/day
1/day
_b
0.002
N/A
0
1.0E+06
0
_b
0.002
N/A
0
0
0
_b
0.002
0.03
0
0
0
Case C based on "boundary contributions
only" run
professional judgment (assumed 1% of
transfer factor from leaf particle to leaf)
calculated from Bache et al. 1973
assumed from Gay 1975, Bache et al. 1973
professional judgment; assumed close to
instantaneous
professional judgment
ype
g/kg
1/day
1/day
1/day
1/day
1/day
b
0.2
N/A
0
0
0
b
0.2
N/A
0
0
0
b
0.2
0
0
0
0
Case C based on "boundary contributions
only" run
professional judgment
professional judgment
professional judgment
professional judgment
professional judgment
Root Compartment Type - Nonwoody Plants Onlyc
Initial concentration
Alpha for root-root zone bulk soil
Root/root-zone-soil-water partition
coefficient
g/kg
unitless
m3[bulk root
soil]/m3[root]
b
0.95
0
b
0.95
0.18
b
0.95
1.2
Case C based on "boundary contributions
only" run
selected value
Hg2- geometric mean Leonard et al. 1998,
John 1972, Hogg et al. 1978; MHg-
assumed, based on Hogg et al. 1978
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Biotic
(same values used for all terrestrial plant compartments of each type, except for initial concentration)
Terrestrial Plant Compartment Types3
Parameter Name
t-alpha for root-root zone bulk soil
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
Units
day
1/day
1/day
1/day
1/day
Value
Hg(0)
21
N/A
0
0
0
Hg(2)
21
N/A
0
0
0
MHg
21
0
0
0
0
Reference
professional judgment
professional judgment
professional judgment
professional judgment
professional judgment
Stem Compartment Type - Nonwoody Plants Onlyc
Initial concentration
Transpiration stream concentration
factor (TSCF)
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
g/kg
m3[soil pore
water]/m3[xylem fluid]
1/day
1/day
1/day
1/day
_b
0
N/A
0
0
0
_b
0.5
N/A
0
0
0
_b
0.2
0.03
0
0
0
Case C based on "boundary contributions
only" run
calculation from Norway spruce, Scots
pine, Bishop et al. 1998
calculated from Bache et al. 1973
professional judgment
professional judgment
professional judgment
TRIM.FaTE currently includes four kinds of terrestrial plants (i.e., vegetation types): deciduous forest, coniferous forest, grasses/herbs, and agricultural
(agricultural not used in mercury test case). Same chemical-dependent values used for each, except for initial concentration in Case C.
bSet to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each compartment.
cRoots and stems are not modeled for deciduous or coniferous forest vegetation type in the current version of TRIM.FaTE.
July 2005
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Biotic
(same values used for all macrophyte compartments)
Aquatic Plant Compartment Type
Parameter Name
Units
Value
Hg(0)
Hg(2)
MHg
Reference
Macrophyte Compartment Type
Initial concentration
Alpha for macrophyte
Macrophyte/water partition coefficient
Oxidation rate
t-alpha
g/kg
unitless
L[water]/kg[mac
rophyte wet wt]
1/day
day
a
0.95
0.883
1.00E+09
18
a
0.95
0.883
0
18
a
0.95
4.4
0
18
Case C based on "boundary contributions only"
run
selected value
Elodea densa, Ribeyre and Boudou 1994
professional judgment
experiment duration from Ribeyre and Boudou
1994
Set to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each compartment.
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Chemical-Dependent / Biotic
(same values used for all terrestrial animal compartments of each type)
Terrestrial Animal Compartment Types
Parameter Name
Units
Soil Detritivore - Earthworm
Initial concentration
Alpha for worm-bulk soil
Earthworm/dry-soil partition coefficient
t-alpha for worm-bulk soil
g/kg
unitless
kg [soil dry
wt]/kg[worm dry wt]
day
Value
Hg(0)
a
0.95
0.36
21
Hg(2)
MHg
Reference
a
0.95
0.36
21
a
0.95
0.36
21
Case C based on "boundary contributions
only" run
selected value
Bulletal. 1977
assumed same as metals in earthworms,
Janssen et al. 1997
Soil Detritivore - Soil Arthropod
Initial concentration
Alpha for arthropod-soil
Arthropod/bulk-soil partition coefficient
t-alpha for arthropod-soil
g/kg
unitless
kg[soil wet
wt]/kg[arthropod wet
wt])
day
a
0.95
0
21
a
0.95
0.46
21
a
0.95
2.9
21
Case C based on "boundary contributions
only" run
selected value
Hg(2) - median from Talmage and Walton
1993; MHg - median from Nuorteva and
Nuorteva 1982
assumed same as metals in earthworms,
Janssen et al. 1997
All Other Terrestrial Animal Compartment Types0
Initial concentration
Assimilation efficiency for inhalation
Assimilation efficiency from arthropods
Assimilation efficiency from food
Assimilation efficiency from terrestrial
plants
g/kg
unitless
unitless
unitless
unitless
a
0.75
1
1
1
a
0.4
1
1
1
a
0.75
1
1
1
Case C based on "boundary contributions
only" run
Hg(0) - based on human values, ATSDR
1997, Teisingerand Fiserova-Bergerova
1965; Hg(2) - based on dog value, EPA
1997; MHg - assumed same as Hg(0)
set to 1 c
set to 1 c
set to 1 c
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Chemical-Dependent / Biotic
(same values used for all terrestrial animal compartments of each type)
Terrestrial Animal Compartment Types
Parameter Name
Assimilation efficiency from soils
Assimilation efficiency from water
Assimilation efficiency from worms
Total elimination rate
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
Units
unitless
unitless
unitless
1/day
1/day
1/day
1/day
1/day
Value
Hg(0)
1
1
1
0.05
N/A
0
1
0
Hg(2)
1
1
1
0.48
N/A
0
0
0
MHg
1
1
1
0.26d/0.086e
0.09
0
0
0
Reference
setto1c
setto1c
setto1c
see Attachment A-3 for documentation
for rats, Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
aSet to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each compartment.
bTest case includes Terrestrial Ground-Invertebrate Feeder- Short-tailed Shrew, Terrestrial Herbivore - Meadow Vole, Terrestrial Herbivore - White-
tailed Deer, Terrestrial Insectivore - Black-capped Chickadee, Terrestrial Omnivore - Mouse, Terrestrial Carnivore - Long-tailed Weasel, and Terrestrial
Carnivore - Red-tailed Hawk.
CAII ingestion assimilation efficiencies set to 1 to be consistent with excretion rate calculations. Excretion rates are all based on ingested (not absorbed)
dose, hence assimilation efficiency must equal 1.
dValue for all mammals.
eValue for all birds.
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TRIM.FaTE Evaluation Report Volume II
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Chemical-Dependent / Biotic
(same values used for all semi-aquatic animal compartments of each type)
Semi-aquatic Animal Compartment Types3
Parameter Name
Initial concentration
Assimilation efficiency for inhalation
Assimilation efficiency from arthropods
Assimilation efficiency from food
Assimilation efficiency from terrestrial
plants
Assimilation efficiency from soils
Assimilation efficiency from water
Assimilation efficiency from worms
Total elimination rate
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
Units
g/kg
unitless
unitless
unitless
unitless
unitless
unitless
unitless
1/day
1/day
1/day
1/day
1/day
Value
Hg(0)
b
0.75
1
1
1
1
1
1
0.05
N/A
0
1
0
Hg(2)
b
0.4
1
1
1
1
1
1
0.48
N/A
0
0
0
MHg
b
0.75
1
1
1
1
1
1
0.26d/0.086e
0.09
0
0
0
Reference
Case C based on "boundary contributions only"
run
Hg(0) - based on human values, ATSDR 1997,
Teisingerand Fiserova-Bergerova 1965; Hg(2) •
based on dog value, EPA 1997; MHg -
assumed same as Hg(0)
set to 1C
set to 1C
set to 1C
set to 1C
set to 1C
set to 1C
see Attachment A-3 for documentation
for rats, Takeda and Ukita 1970
professional judgment
professional judgment
professional judgment
Test case includes Semi-aquatic Insectivore - Tree Swallow, Semi-aquatic Omnivore - Mallard, Semi-aquatic Omnivore - Raccoon, Semi-aquatic
Piscivore - Common Loon, Semi-aquatic Carnivore - Mink, and Semi-aquatic Carnivore - Bald Eagle.
bSet to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each compartment.
CAII ingestion assimilation efficiencies set to 1 to be consistent with excretion rate calculations. Excretion rates are all based on ingested (not absorbed)
dose, hence assimilation efficiency must equal 1.
dValue for all mammals.
eValue for all birds.
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Chemical-Dependent / Biotic
(same values used for all aquatic animal compartments of each type)
Aquatic Animal Compartment Types
Parameter Name
Units
Value
Hg(0)
Hg(2)
MHg
Reference
Benthic Invertebrate Compartment Type
Initial concentration
Alpha of equilibrium for sediment partitioning
Benthic invertebrate-bulk sediment partition
coefficient
t-alpha for equilibrium for sediment
partitioning
g/kg
unitless
kg[bulk
sediment]/kg[invert
ebrate wet wt]
day
a
0.95
0.0824
14
a
0.95
0.0824
14
a
0.95
5.04
14
Case C based on "boundary contributions only" run
selected value
Hg(0) - assumed based on Hg(2) value; Hg(2) and
MHg -Saouteretal. 1991
experiment duration from Saouter et al. 1991
All Fish Compartment Types0
Initial concentration
Assimilation efficiency from food
Elimination adjustment factor
Demethylation rate
Methylation rate
Oxidation rate
Reduction rate
g/kg
unitless
unitless
1/day
1/day
1/day
1/day
a
0.04
3
N/A
0
1.0E+06
0
a
0.04
3
N/A
0
0
0
a
0.2
1
0
0
0
0
Case C based on "boundary contributions only" run
Phillips and Gregory 1979
Trudel and Rasmussen 1997
professional judgment
professional judgment
professional judgment
professional judgment
Set to zero for Cases A and B. Values for Case C set to final concentration from 30-year "boundary contributions only" run for each compartment.
bTest case includes Benthic Carnivore, Benthic Omnivore, Water-column Carnivore, Water-column Herbivore, and Water-column Omnivore.
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Source, Meteorological, and Other Input Data and Settings
Parameter Name
Units
Value Used
Reference
Source Inputs (all TRIM.FaTE source properties) (only one source modeled for mercury test case)
Emission rate, HgO
Emission rate, Hg2
Source height
g/day
g/day
m
335.6a
17.663
0.01
Total mercury based on estimates supplied by state agency, 1999,
speciation assumed to be 95% HgO
Total mercury based on estimates supplied by state agency, 1999,
speciation assumed to be 5% Hg2
Assumed value for ground-level fugitive emissions
Meteorological Inputs (all TRIM.FaTE scenario properties, except mixing height)
Air temperature
Horizontal wind speed
Wind direction
Rainfall rate
Mixing height (used to set air VE
property named "top")
Day/night
degrees K
m/sec
degrees clockwise
from N (blowing
from)
m3[rain]/m2[surface
area]-day
m
1=day, 0=night
Other Settings (all TRIM.FaTE scenario properties
Start of simulation
End of simulation
Simulation time step
Output time stepd
date/time
date/time
hr
hr
varies hourlyb
varies hourlyb
varies hourlyb
varies hourlyb
varies hourlyb
varies hourlyd
Local composite met data, 1987-1991 (ave = 280 K)
Local composite met data, 1987-1991 (ave = 3.64 m/sec); used minimum
value of 0.75 m/sec for mercury test case
Local composite met data, 1987-1991
Local composite met data, 1987-1991 (annual totals = 93, 78, 112, 123,
and 113 cm)c
Calculated from hourly local composite met data, 1987-1991 (used
calculated values for rural setting) (ave = 887 m); used minimum value of
20 m for mercury test case
Based on sunrise/sunset data for source latitude and longitude
1/1/1987, midnight
1/1/2017, midnight or
1/1/2027, midnight
1
2
Selected to match start of meteorological data set
Selected to provide 30-year (Case A and B) or 40-year (Case C) modeling
period
Selected value
Selected value
Value used for Dynamic Cases B and C and Steady-state Case. Set to 0 for Dynamic Case A.
blnput data used repeats in five-year cycle throughout modeling period based on 1987-1991 meteorological data. Different value (and approach for
wind data) used for steady-state modeling (see Chapter 4).
cRainfall data missing for April 1988 in data source; zero precipitation assumed for that month.
dDifferent value used for steady-state modeling (see Chapter 4).
eOutput time step is set in TRIM.FaTE using the scenario properties "simulationStepsPerOutputStep" and "simulationTimeStep."
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Satchell, I.E. 1983. Earthworm ecology in forest soils. In: I.E. Satchell. Earthworm ecology:
From Darwin to Vermiculture. London, England: Chapman and Hall. pp. 161-177.
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Attachment A-1
Summary of Method Used to Estimate Runoff/Erosion Fractions for the
Mercury Test Case
Starting point. Final surface parcel layout map (GIS) for the modeling region, along with
watershed delineation map (GIS) and USGS topographic map (hard copy) for the modeling
region.
Step 1. Identified the main watersheds within the modeling region and define the watershed
boundaries.
Step 2. For each soil parcel, identified the watersheds located within (or partly within) the
parcel, and the percentage of the parcel surface area covered by each watershed. The
percentages of parcel area covered by each watershed were calculated using GIS software.
Step 3. For each soil parcel (i.e., sending parcel for runoff/erosion), determined all neighboring
parcels (i.e., potential receiving parcels, which can be either soil or surface water parcels or soil
advection sinks) based on the existence of a common border.
Step 4. Created a transparent map overlay of watershed boundaries and surface parcel
boundaries, scaled to the topographic map. Lined up the overlay on the topographic map.
Step 5. For each watershed portion located within each soil parcel (sending parcel), estimated
the percentage of the watershed portion surface area that drains into each neighboring parcel
(receiving parcel). Made these estimates based on the elevation data and water body locations
shown on the topographic map. Drainage was assumed to be downhill and perpendicular to lines
of elevation. It was assumed that water will not cross watershed boundaries. Estimates were
made in 5% increments.
See Table 1 for illustrative sample data. For example, assume Parcel A contains two different
watersheds. Watershed 1 covers 60% of the parcel. 50% of the Watershed 1 portion in Parcel A
is estimated to drain to Parcel B, with 25% estimated to drain to Parcel C and 25% to Parcel D.
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Table 1
Sample Data and Calculations
Sending
Parcel Watershed
A 1
A 2
B 1
% Parcel
60%
40%
100%
Receiving
Parcel
B
C
D
B
C
D
A
C
E
F
% Runoff
50%
25%
0%
50%
50%
0%
0%
0%
100%
% Total Flow
(% Parcel x % Runoff)
30%
15%
15%
0%
20%
20%
0%
0%
0%
100%
The % Runoff estimates were done independently by two people to minimize error in map
interpretation and as a check on subjective judgments in assigning percentages.
Step 6. Compared % Runoff estimates made by different people to identify and reconcile
obvious discrepancies. For the mercury test case, estimates differing by 20% or more were re-
evaluated. After discrepancies were addressed, we averaged the estimates and rounded to the
nearest 5%.
Step 7. For each watershed within each sending parcel, estimated the % Total Flow to each
receiving parcel by multiplying the % Parcel by the % Runoff (see Table 1 for sample
calculations).
Step 8. For each combination of sending parcel-receiving parcel (e.g., A->B, B->A, A->C),
summed the % Total Flow for all contributing watersheds. These sums were then converted to
the runoff/erosion fractions used as inputs to TRIM FaTE for the mercury test case. As a QA
check, we made sure that the values for each sending parcel sum to 100%. For the Table 1
illustrative example, the runoff/erosion percentages are shown for each parcel combination in
Table 2.
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Table 2
Sample Runoff/Erosion Percentages and Fractions
Parcel Combination
A^B
A^C
A^D
B^A
B^C
B^E
B^F
Runoff/Erosion %
30%
35%
35%
0%
0%
0%
100%
Runoff/Erosion
Fraction
0.3
0.35
0.35
0
0
0
1.0
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Attachment A-2
Documentation of Food Ingestion Rate Values for Terrestrial and Semi-
aquatic Animals for Mercury Test Case
(1) Chickadee
Food ingestion rate: 0.74 kg[food wet wt]/kg[body weight (BW) wet]-day
Smith (1993) reports that while no data on nutrition and food ingestion by black-capped
chickadees are available, parids of comparable size require 10 kcal/day (41.8 kJ/day). Assuming
that the chickadee diet consists 100 percent of insects, the chickadee wet body weight (BW) is
0.0108 kg (Dunning 1993), and energy and water content of insects are 22.1 kJ/g dry weight
(5.28 kcal/g dry wt) and 76.3 percent, respectively (Bell 1990), daily food ingestion by
chickadees would be 0.74 kg[food wet wt]/kg[BW wet]-day.
(2) Short-tailed shrew
Food ingestion rate: 0.47 kg[food wet wt]/kg[BW wet]-day
The mean daily ingestion rate of shrews in Barrett and Stueck (1976) was 0.49 kg[food wet
wt]/kg[BW wet]-day. The value of 0.47 that was included in the mercury test simulations is
close to the value from Barrett and Stueck (1976). Caged shrews were fed mealworms, which
have essentially the same water content as the natural prey of shrews.
(3) Meadow vole
Food ingestion rate: 0.097 kg[food wet wt]/kg[BW wet]-day
Food intake by meadow voles when exposed to 14-h days was 0.095 ± 0.002 (mean ± SE)
kg[food wet wt]/kg[BW wet]-day; intake by individuals exposed to 10-h days was 0.085 ± 0.005
kg/kg-day (wet wt) (Dark et al. 1983). Mean food consumption by prairie voles (assumed to
weigh 35 g; Burt and Grossenheider 1976) was 0.088 kg/kg-day (wet wt) and 0.12 kg/kg-day
(wet wt) when ambient temperatures were 21 degrees and 28 degrees Celsius, respectively (Dice
1922).
(4) White-tailed deer
Food ingestion rate: 0.05 kg[food wet wt]/kg[BW wet]-day
Mautz et al. (1976) reported a 1.74 kg/day diet for a 35 kg deer, which represents maintenance of
the deer through the winter. There is no value adjustment for summer, because the energy
required by females to thermoregulate and gestate in the winter might be roughly equivalent to
the energy for late gestation and lactation.
(5) Tree swallow
Food ingestion rate: 0.198 kg[food wet wt]/kg[BW wet]-day
Female tree swallows in New Brunswick, Canada in the summer were observed to require 5.73 ±
1.40 kJ/g-day (mean ± SD; n=10; Williams 1988). Using body weights reported in Williams
(1988, 22.6 g), assuming that the diet consists exclusively of insects (Quinney and Ankney
JULY 2005 A-55 TRIM.FATE EVALUATION REPORT VOLUME II
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1985), and that the energy and water content of insects are 22.09 kJ/g dry weight (5.28 kcal/g dry
wt) and 76.3 percent, respectively (Bell 1990), daily food consumption by tree swallows is
estimated to be 0.198 ± 0.048 kg[food wet wt]/kg[BW wet]-day. [Note: the calculation for food
ingestion rate is incorrect; it should have been 0.34 kg/kg-day (wet wt).]
(6) White-footed mouse
Food ingestion rate: 0.20 kg[food wet wt]/kg[BW wet]-day
Green and Millar (1987) observed an ingestion rate of 3.4 g/day, for laboratory mice fed Purina
rat chow of an average weight of 21 g eating standard food, or a food ingestion rate of 0.16
kg[food dry wt]/kg[BW wet]-day. Body weight and gut dimensions of male and female mice did
not differ, so data from both sexes were pooled (reported as g food consumed/individual-day).
Food ingestion rate was normalized to body weight using body weights reported in study. The
water content of Purina rat chow is approximately 0.10 (or 10 percent). The water content (WC)
of the natural diet of white-footed mice is higher. Their diet includes seeds (WC of 0.09),
vegetation (WC of 0.10 for mature dry grass, 0.7 to 0.88 for growing grasses), and soil
arthropods (WC of 0.60 to 0.70) (US EPA 1993). We assumed that the diet consists primarily of
seeds and dry grasses (80 percent), but includes also soil arthropods (20 percent), for an overall
moisture content of approximately 0.20. The wet food ingestion rate = the dry food ingestion
rate divided by (1-WC), or in this case, the wet food ingestion rate = 0.20 kg[food wet
wt]/kg[BW]-day (i.e., 0.16 kg[food dry wt]/kg[BW]-day/(l-0.20)).
(7) Long-tailed weasel
Food ingestion rate: 0.0735 kg[food wet wt]/kg[BW wet]-day
Brown and Lasiewski (1972) reported the mean metabolism of male and female long-tailed
weasels to be 1.36 ± 0.2 and 0.84 ± 0.12 (SE) kcal/hr, respectively. Assuming that male and
female weasels weigh 0.297 kg and 0.153 kg (Brown and Lasiewski 1972), respectively, that the
diet consists exclusively of small mammals with an energy content of 5163 kcal/kg (or 5.163
kcal/g) dry weight (Golley 1961), and that the water content of small mammals is 68 percent (US
EPA 1993), male and female weasels consume 0.067 and 0.080 kg[food wet wt]/kg[BW wet]-
day, respectively.
(8) Red-tailed hawk
Food ingestion rate: 0.12 kg[food wet wt]/kg[BW wet]-day
Preston and Beane (1993) cite a study (Craighead and Craighead 1956) in which males ate an
average of 147 g/day (13 percent of body weight) and females an average of 136 g/day (11
percent of body weight) during fall-winter. Males ingested 82 g/day (7 percent of body weight),
and females only 85 g/day (7 percent), during spring-summer. To be conservative, the fall-
winter food ingestion rate (average of 12 percent) was used.
(9) Mallard
Food ingestion rate: 0.1 kg[food wet wt]/kg[BW wet]-day
Heinz et al. (1987) report that mallards maintained in the laboratory consumed 0.1 kg[food dry
wt]/kg[BW wet]-day. The water content of this diet (mostly seeds) ranged from 7-10 percent.
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Because the plant material consumed by mallards consists largely of seeds, and the mean water
content of seeds is 9.3 percent (US EPA 1993), the food ingestion rate used by Heinz may be
used to represent the wet weight food ingestion rate without adjusting for water content.
(10) Mink
Food ingestion rate: 0.14 kg[food wet wt]/kg[BW wet]-day
Bleavins and Aulerich (1981) reported a food ingestion rate of 0.14 kg[food wet wt]/kg[BW
wet]-day for male and female mink in captivity. The diet consisted of chicken (20 percent),
commercial mink cereal (17 percent), fish scraps (13 percent), beef parts, cooked eggs, powdered
milk, and added water. The water content of that diet as fed to the mink was 66.2 percent, which
is roughly equivalent to the water content of a natural mink diet.
(11) Raccoon
Food ingestion rate: 0.11 kg[food wet wt]/kg[BW wet]-day
Using a body weight of 6.35 kg for an adult raccoon from EPA (1993) and the allometric
equation for omnivorous mammals from Nagy et al. (1999), it is estimated that a raccoon would
need 548 kcal daily or 86 kcal/kg-day. Assuming 0.95 kcal/g as an average gross energy content
of the diet (wet wt), and an assimilation efficiency of 0.85, a raccoon would need 678 g of the
diet daily, or 0.11 kg[food wet wt]/kg[BW wet]-day.
(12) Common loon
Food ingestion rate: 0.23 kg[food wet wt]/kg[BW wet]-day
Assuming a diet of 100 percent fish, a gross energy content of 1.2 kcal/g[fish wet wt], and an
energy assimilation efficiency of 79 percent for seabirds eating fish and using Nagy et al.'s
(1999) allometric equation for seabirds consuming fish, we calculated a food ingestion rate for
loons of 0.23 kg[food wet wt]/kg[BW wet]-day.
(13) Bald eagle
Food ingestion rate: 0.12 kg[food wet wt]/kg[BW wet]-day
The value of 0.12 kg[food wet wt]/kg[BW wet] is for adults with an assumed body weight of 4.5
kg eating 100 percent fish. It is based on a field study conducted in the winter by Stalmaster and
Gessaman (1984) where the eagles were provisioned with fish of known weights.
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References for Attachment A-2
Barrett, G.W. and K.L. Stueck. 1976. Caloric ingestion rate and assimilation efficiency of the
short-tailed shrew. Blarina brevicauda. OhioJ. Sci. 76:25-26.
Bell, G.P. 1990. Birds and mammals on an insect diet: a primer on diet composition analysis in
relation to ecological energetics. Stud. Avian Biol. 13: 416-422.
Bleavins, M.R. and RJ. Aulerich. 1981. Feed consumption and food passage time in mink
(Mustela vison) and European ferrets (Mustela putorius furo). Lab. Anim. Sci. 31:268-269.
Brown, J.H. and R.C. Lasiewski. 1972. Metabolism of weasels: the cost of being long and thin.
Ecology. 53:939-943.
Burt, W.H. and R.P. Grossenheider. 1980. A Field Guide to the Mammals of North America
North of Mexico. Boston, MA: Houghton Mifflin Co.
Craighead, JJ. and F.C. Craighead. 1956. Hawks, Owls and Wildlife. Harrisburg, PA: The
Stackpole Co. and Washington, DC: Wildl. Manage. Inst.
Dark, J., I. Zucker, and G.N. Wade. 1983. Photoperiodic regulation of body mass, food intake,
and reproduction in meadow voles. Am. J. Physiol. 245:R334.
Dice, L.R. 1922. Some factors affecting the distribution of the prairie vole, forest deer mouse,
and prairie deer mouse. Ecology 3: 29-47.
Dunning, J.B. 1993. CRC Handbook of Avian Body Masses. Boca Raton, FL: CRC Press, pp.
371.
Golley, F.B. 1961. Energy values of ecological materials. Ecology. 42:581-584.
Green, D.A. and J.S. Millar. 1987. Changes in gut dimensions and capacity of Peromyscus
maniculatus relative to diet quality and energy needs. Can. J. Zool. 65:2159-2162.
Heinz, G.H., D.J. Hoffman, AJ. Krynitsky, and D.M.G. Weller. 1987. Reproduction in
mallards fed selenium. Environ. Toxicol. Chem. 6:423-433.
Mautz, W.W., H. Silver, J.B. Hayes, and W.E. Urban. 1976. Digestibility and related
nutritional data for seven northern deer browse species. J. Wildl. Mgmt. 40:630-638.
Nagy, K.A., LA. Girard, and T.K. Brown. 1999. Energetics of free-ranging mammals, reptiles,
and birds. Ann. Rev. Nutr. 19:247-277.
Preston, C.R. and R.D. Beane. 1993. Red-tailed Hawk. (Buteo jamaicensis). In: Poole, A. and
F. Gill, eds. The Birds of North America, No. 52. Philadelphia, PA: The Academy of Natural
Sciences, and Washington, D.C.: The American Ornithologists' Union.
JULY 2005 A-58 TRIM.FATE EVALUATION REPORT VOLUME II
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Quinney, T.E. and C.D. Ankney. Prey size selection by tree swallows. Auk 102: 245-250.
Smith, S.M. 1993. Back-capped Chickadee. (Parus atricapillus). In: Poole, A. andF. Gill, eds.
The Birds of North America, No. 39. Philadelphia, PA: The Academy of Natural Sciences, and
Washington, D.C.: The American Ornithologists' Union.
Stalmaster, M.V., and J.A. Gessaman. 1984. Ecological energetics and foraging behavior of
overwintering bald eagles. Ecol. Monogr. 54: 407-428.
U.S. EPA (U.S. Environmental Protection Agency). 1993. Wildlife Exposure Factors
Handbook, Volume I. Washington, D.C.: Office of Research and Development. EPA/600/R-
93/187a.
Williams, J.B. 1988. Field Metabolism of tree swallows during the breeding season. Auk 105:
706-714.
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Attachment A-3
Documentation of Total Elimination Rate Values for Terrestrial and Semi-
aquatic Animals for the Mercury Test Case
First-order rate constants used to derive the mercury elimination rate constants for
wildlife in the current TRIM.FaTE library are summarized in Table 1. Supporting information is
presented in the subsections that follow.
Table 1
Mean First-order Rate Constants (day *) for Elimination of Mercury
from Birds and Mammals
mammals
birds
Chemical
Species
Hg2+
Hg°
organic Hg
Hg2+
Hg°
organic Hg
Urine and
Feces (Euf)
0.483
0.0502b
0.26a
0.48e
0.0502b
0.02823
Lactation
(E^
0.00001
ob
0.00001C
NA
NA
NA
Eggs
(Eegg)
NA
NA
NA
Of
Ob
0.0244
Fur, Feathers, or
Hair (Eff)
0.00001
ob
0.00014d
0.00011s
ob
0.0559
a Averages of elimination rate constants for oral and dietary doses.
bRate constant based on inhalation study for mammals; same value assumed for birds.
'Assume same as lactation rate constant for Hg2+.
d Averages of elimination rate constants for oral dose and injection.
e Assume same as elimination rate constant to mammalian urine and feces.
f No information available.
8 Assume same as elimination rate constant to mammal fur.
For each mercury species, the total elimination rate constant for birds or mammals is
equal to the sum of the excretion rate constants in Table 1 for urine and feces; lactation
(mammals), and fur, hair (mammals), or feathers (birds). In the current TRIM.FaTE library,
chemical excretion to eggs is assumed to remain within the bird population compartment, hence
it is not included in the total bird elimination rate constant for organic mercury.
Elemental Mercury
Elemental mercury vapor is rapidly absorbed in the lungs (75 to 85 percent in humans),
and to a much lesser extent (three percent), it can be absorbed dermally (ATSDR 1997, U.S.
EPA 1997). Five human subjects inhaled from 107 to 202 |_ig[Hg]/m3[air] and retained an
average of 74 percent of the dose (Teisinger and Fiserova-Bergerova 1965). The inhaled vapor
readily distributes throughout the body and can cross the blood-brain and placental barriers.
Rats exposed for 5 hours to 1.4 mg/m3 radio-labeled mercury vapor retained an average
body burden of 0.256 mg/kg BW (37 |ag[Hg]/rat) and had excreted (urine and feces) 8.5 percent
of the initial body burden in 1 day, 24.8 percent in 5 days, and 42.9 percent in 15 days (Hayes
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and Rothstein 1962). Cherian et al. (1978) exposed 5 human volunteers to approximately 1 |J,Ci
of radio-labeled Hg vapor for approximately 19 minutes. Mean cumulative excretion over the
first 7 days after exposure was 2.4 percent of the retained dose in urine and 9.2 percent in feces
for a total excretion of 11.6 percent of the retained dose (Cherian et al. 1978).
Rates of excretion of elemental mercury by mammals (rats and humans) are summarized
in Table 2 (mean value presented in Table 1). No information on excretion by avian species is
available.
Table 2
Excretion of Inhaled Elemental Mercury (Hg°) in Mammals
Test
Species
Rat
Rat
Rat
Human
Dose
0.256
mg/kg
0.256
mg/kg
0.256
mg/kg
1 nCi
Elimination
Route
urine + feces
urine + feces
urine + feces
urine + feces
Percent
of Dose
8.5
24.8
42.9
11.6
Days
1
5
15
7
x + SE
Rate
Constant
(Day1)
0.08883
0.05700
0.03736
0.01761
Source
Hayes & Rothstein 1962
Hayes & Rothstein 1962
Hayes & Rothstein 1962
Cherian etal. 1978
0.05020 + 0.01518
Divalent Mercury
Divalent mercury can be absorbed through oral, dermal, and inhalation routes; however,
absorption is lower than for elemental mercury by all routes. In mice, only 20 percent of the
administered dose is absorbed from the GI tract, 2-3 percent of the dose was absorbed dermally
in exposed guinea pigs, and limited information on inhalation exposure indicates that 40 percent
of the dose was absorbed in the lungs of dogs (U.S. EPA 1997). Additionally, the absorption of
mercuric salts varies with the solubility of the specific salt. For example, the less soluble sulfide
salt is more poorly absorbed as mercuric sulfide than the more soluble chloride salt as mercuric
chloride (U.S. EPA 1997). Divalent mercury distributes widely throughout the body; however, it
cannot cross the blood-brain or placental barriers.
The metabolism and distribution of mercuric chloride (HgCl2) has been described in dairy
cows and rats. Potter et al. (1972) orally administered 344 |j,Ci of radio-labeled mercuric
chloride by gelatin capsule using balling gum to 2 Holstein cows. After 6 days, 94.87 percent of
the dose was excreted in feces, 0.044 percent in urine, and 0.0097 percent in milk, for a total
excretion of 94.92 percent of the dose. The biological half-life was calculated as 28.5 hours.
Rats dosed by intravenous injection with 1 mg[Hg]/kg[body weight] mercuric chloride excreted
15.2 percent of the dose in feces and 16.3 percent in urine over 4 days for a total excretion (fecal
and urinary) of 31.5 percent of the administered dose in 4 days (Gregus and Klaassen 1986).
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The metabolism and distribution of mercuric nitrate [Hg(NO3)2] have also been described
for dairy cows and rats. Four Holstein dairy cows were given an oral dose of 1.7 mCi radio-
labeled Hg(NO3)2 in a gelatin capsule via balling gum. Urine, feces, and milk were collected for
10 days and analyzed. Results indicated that 74.91 percent of the administered dose was
excreted in feces, 0.08 percent in urine, and 0.01 percent in milk with a total excretion of
75 percent of the dose in 10 days (Mullen et al. 1975). Mullen et al. (1975) also reported a
biological half-life for the transfer of orally ingested mercury to milk of 5 days. Transfer of
mercury to feces was slightly more complicated with an initial half-life of 15 hr (probably
reflecting the unabsorbed dose), then a decrease in elimination time which resulted in a 3 day
half-life (probably representing excretion of the absorbed dose) (Mullen et al. 1975). Rothstein
and Hayes (1960) dosed 7 Wistar rats with 50 \ig (0.2 mg[Hg]/kg[body wt]) radio-labeled
mercury as Hg(NO3)2 via intravenous injection. After 52 days the cumulative percent excretion
was 25 percent of the administered dose in urine and 37 percent in feces for a total excretion of
62 percent of the injected dose over 52 days (Rothstein and Hayes 1960). In another study,
6 Holtzman rats were dosed by subcutaneous injection with 20 [id of radio-labeled Hg(NO3)2,
and 0.018 percent of the dose was recovered in the hair 20 days after administration (Mansour
et al. 1973). For pregnant female rats, a clearance half-time of 16.2 days was also reported (18
measurements over a 3-week period).
Fitzhugh et al. (1950) exposed rats (20/dose group) to mercuric acetate in the diet at
concentrations of 0.5, 2.5, 10, 40, and 160 ppm Hg. The average intake of Hg in a 24-hour
period was 7.5, 37.5, 150, 600, and 2,400 [ig per rat, and the 24-hour excretion was 52, 40, 43,
47, and 43 percent of those doses, respectively, in feces and 4.8, 1.0, 0.5, 0.37, and 1.7 percent,
respectively, in urine (Fitzhugh et al. 1950).
Divalent mercury is poorly absorbed from the GI tract (20 percent, see above), therefore,
elimination rates obtained from oral or dietary exposure may be misleading. Hayes and
Rothstein (1962) reported an initial half-life for fecal elimination of inorganic mercury of 0.6
days in Holstein cows. Later, the half-life increased to 3 days, as in the study by Mullen et al.
(1975). This indicates that a large proportion of the dose is initially excreted via the feces due
to lack of absorption. In the current TRIM.FaTE library, the elimination rate constant for
terrestrial wildlife represents the elimination of both absorbed and unabsorbed mercury in feces
(and urine). As long as the concentration of mercury in the tissues of the wildlife (birds and
mammals) is not needed for the risk assessment, these elimination rates can be used for purposes
of estimating the transfer of ingested mercury from wildlife to surface soil and water by setting
the assimilation efficiency property in the wildlife compartment to 1.0. However, if the
concentration of Hg in the animals tissues is needed for the risk assessment (e.g., to track risks to
humans that consume meat from deer or cows in the modeling region), it would be necessary to
determine a true assimilation efficiency to estimate the proportion of the ingested mercury that is
absorbed by the animal. Then, separate rate constants and algorithms would be needed to track
separately the elimination of the absorbed mercury and unabsorbed mercury.
Rates of excretion of divalent mercury by mammals (rats and cows) are summarized in
Table 3 (mean values for excretion to urine and feces, lactation, and excretion to hair presented
in Table 1). No information on excretion by avian species is available.
JULY 2005 A-62 TRIM.FATE EVALUATION REPORT VOLUME II
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Table 3
Excretion of Divalent Mercury in Mammals
Test
Species a
Cow-
Holstein
Cow-
Holstein
Form
HgCl2
Hg(N03)2
Dose
344 |iCi
l.VmCi
Dose
Route b
oral
oral
Elimination
Route
urine + feces
urine + feces
Rat-SD
Rat-Wistar
HgCl2
Hg(N03)2
1 mg/kg
50 ng
iv
iv
urine + feces
urine + feces
Rat
Rat
Rat
Rat
Rat
mercuric
acetate
mercuric
acetate
mercuric
acetate
mercuric
acetate
mercuric
acetate
7.5 Hg
37.5 ^g
150 |ig
600 [ig
2400 |ig
diet
diet
diet
diet
diet
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
Cow-
Holstein
Cow-
Holstein
HgCl2
Hg(N03)2
344 [id
l.VmCi
oral
oral
milk
milk
Rat-Holtz-
man
Hg(N03)2
20 jig
sc inj
hair
Percent
of Dose
94.91
74.99
Days
6
10
x + SE
31.5
62
4
52
x + SE
56.8
41.0
43.5
47.37
44.7
1
1
1
1
1
x + SE
0.0097
0.01
6
10
x + SE
0.018
20
Rate
(Day1)
0.49632
0.13859
Dose
Vehi-
cle
gel cap
gel cap
Source
Potter etal. 1972
Mullen etal. 1975
0.31745 + 0.17886
0.09458
0.01861
saline
sol
sodium
chloride
Gregus & Klaassen
1986
Rothstein & Hayes
1960
0.05660 + 0.03798
0.83933
0.52763
0.57093
0.64188
0.59240
food
food
food
food
food
Fitzhugh et al. 1950
Fitzhugh et al. 1950
Fitzhugh et al. 1950
Fitzhugh et al. 1950
Fitzhugh et al. 1950
0.63443 + 0.05443
0.00002
0.00001
gel cap
gel cap
Potter etal. 1972
Mullen etal. 1975
0.00001 + 0.000003
0.00001
injec-
tion
Mansouretal. 1973
a Rat-SD = Sprague Dawley rat.
biv = intravenous injection and sc inj = subcutaneous injection.
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Organic Mercury
Organic mercury is by far the most studied species of mercury. It is rapidly and
extensively absorbed through the GI tract (95 percent of the dose in humans) and is distributed
throughout the body via carrier-mediated transport (U.S. EPA 1997). Like elemental mercury,
organic mercury can cross the blood-brain and placental barriers.
Radio-labeled methylmercuric chloride was intravenously injected into 6 pregnant
Holtzman rats at a dose of 10 |j,Ci; after 20 days, 0.21 percent of the administered dose was
transferred to hair. The whole-body clearance half-life was reported to be 8.4 days (Mansour et
al. 1973). Gregus and Klaassen (1986) also administered radio-labeled methylmercuric chloride
via intravenous injection to Sprague-Dawley rats at a dose of 1 mg[Hg]/kg[ body wt]. Within 4
days, 5.6 percent of the injected dose was excreted in feces and 0.5 percent in urine for a total
excretion of 6.1 percent of the administered dose. Additionally, 2-hr biliary excretion was 0.7,
0.9, 0.7, and 0.5 percent of doses at 0.1, 0.3, 1.0, and 3.0 mg[Hg]/kg[body wt], respectively
(Gregus and Klaassen 1986). Syrian Golden hamsters (N = 9) were given an oral dose of 0.32
mg[Hg]/kg[body wt] as radio-labeled methylmercury chloride, and the elimination rate was
found to follow a first-order rate equation with a half-life of 6.9 days (Nordenhall et al. 1995).
Nordenhall et al. (1995) estimated that approximately 5 percent of the oral dose administered to
the dams was transferred to pups via milk over 21 days. Four days after oral administration of
methylmercury chloride, 20 percent of the mercury in milk was inorganic (Nordenhall et al.
1995). Sell and Davison (1975) dosed via intraruminal injection, 1 Nubian goat and 1 Guernsey
cow with 100 and 500 |j,Ci radio-labeled methylmercury chloride, respectively. After 13 days,
0.28, 31.18, and 1.45 percent of the dose administered to the goat were excreted in milk, feces,
and urine, respectively. Conversely, none of the dose was excreted in cow milk, 25.32 percent
was excreted in cow feces, and 1.28 percent was excreted in cow urine after 7 days.
Takeda and Ukita (1970) exposed Donryu rats to 20 |ag[Hg]/kg[body wt] as radio-labeled
ethyl-mercuric chloride dissolved in olive oil by subcutaneous injection. Cumulative excretion
during 8 days post-exposure was 10.52 percent of dose in urine and 6.01 percent of dose in feces.
In urine, 41.9 percent and 58.1 percent of the total mercury was organic and inorganic,
respectively, on day 8. In contrast, 65 percent of fecal mercury was organic and 35 percent was
inorganic on day 8 (Takeda and Ukita 1970). Fang and Fallin (1973) orally dosed 14 rats with 3
|j,mol radio-labeled ethyl-mercuric chloride in corn oil. Mercury content was measured in 1-2
rats on days 0.25, 1, 2, 3, 4, 5, 7, 10, and 14 after dosing. Fourteen days after dosing, 32.5
nmole/g hair had accumulated in the fur. Wistar rats have an estimated 3 g of fur (Talmage
1999), therefore, approximately 3.25 percent of the original dose was excreted in fur.
Fitzhugh et al. (1950) exposed rats (20/dose group) to phenyl mercuric acetate in the diet
at doses of 0.5, 2.5, 10, 40, and 160 ppm [Hg]. The average intake of Hg in a 24-hour period
was 7.5, 37.5, 150, 600, and 2,400 [ig and the 24-hour excretion was 44, 35, 27, 35, and 30
percent of those doses, respectively, in feces and 9.2, 4.5, 6.2, 4.3, and 2.4 percent, respectively,
in urine (Fitzhugh et al. 1950).
Humans also have been used as subjects for studying the metabolism of methylmercury.
Three subjects were given an oral dose of 2.6 [id radio-labeled methylmercuric nitrate (Aberg et
al. 1969). Mean cumulative mercury excretion values 10 days post-exposure were 13.6 percent
JULY 2005 A-64 TRIM.FATE EVALUATION REPORT VOLUME II
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(13.6, 13, and 14.2 percent) of the dose in feces and 0.24 percent (0.18, 0.26, and 0.27 percent)
in urine. After 49 days, 34.1 percent (33.4 and 34.7 percent) of the initial dose was excreted via
feces and 3.31 percent (3.29 and 3.33 percent) via urine (Aberg et al. 1969). Aberg et al. (1969)
also reported the biological half-life of methylmercuric chloride to be 70.4, 74.2, and 73.7days
(x = 72.8 days) for the three subjects and measured approximately 0.12 percent of the initial
dose in hair approximately 45 days (range 40-50 days) after exposure.
Two papers contained data suitable for use in determining excretion rates for avian
species. In the first study, Lewis and Furness (1991) orally dosed black-headed gulls with 200,
100, or 20 |J,L methylmercuric chloride using gelatin capsules. The cumulative excretion of
mercury for the 200 |J,L group was 26.4 percent of the dose in urine/feces and 51.2 percent in
feathers for a total of 77.5 percent eliminated from the body over 13 days. At the 100 |^L dose, a
total of 80.3 percent of the dose was eliminated (37.8 and 44.2 percent in urine/feces and
feathers, respectively) in 13 days. Finally, only 56.3 percent of the low dose was measured in
urine/feces and feathers, with 11 percent of the dose in urine/feces and 52.6 percent in feathers
after 13 days (Lewis and Furness 1991).
In the second study, 4 white-leghorn chickens and 4 Japanese quail were administered 20
ppm Hg as methylmercuric chloride in the diet for 21 days (Sell 1977). During the first 7 days
of this dosing period, chickens and quail were also given an oral dose of 2 [id of radio-labeled
methylmercuric chloride (Sell 1977). The rate calculations reported in Table A-18 assume that
the author accounted for the total intake of radio-labeled mercury from both sources when
reporting percent of dose excreted in feces and eggs. Chickens excreted 64 percent of the dose
in urine/feces and 22 percent of the dose in eggs produced during the 21 days post-exposure,
while quail excreted 41 and 54 percent of the dose in urine/feces and eggs, respective, during
the same 21 day post-exposure period (Sell 1977).
Rates of excretion of organic mercury by mammals (humans, goats, cows, and rats) are
summarized in Table 4, and rates of excretion by birds are summarized in Table 5 (mean values
for excretion to urine and feces, fur, feathers, and eggs presented in Table 1). No information on
excretion by avian species is available.
JULY 2005 A-65 TRIM.FATE EVALUATION REPORT VOLUME II
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Table 4
Excretion of Organic Mercury in Mammals
Test
Species a
Human
Human
Goat-
Nubian
Goat-
Nubian
Goat-
Nubian
Goat-
Nubian
Goat-
Nubian
Cow-
Guernsey
Cow-
Guemsey
Cow-
Guernsey
Cow-
Guemsey
Form
methylmercuric
nitrate
methylmercuric
nitrate
CH3-HgCl
CH3-HgCl
CH3-HgCl
CH3-HgCl
CH3-HgCl
CH3-HgCl
CH3-HgCl
CH3-HgCl
CH3-HgCl
Dose
2.6 |iCi
2.6 [id
100 |iCi
100 [id
100 |iCi
100 [id
100 |iCi
500 \id
500 |iCi
500 \id
500 |iCi
Dose
Route b
oral
oral
ir inj
irinj
ir inj
irinj
irinj
irinj
irinj
irinj
irinj
Elimination
Route
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
Rat-SD
Rat-Donryu
CH3-HgCl
ethyl-HgC!2
1 mg/kg
20 |ig/kg
iv
sc inj
urine + feces
urine + feces
Rat
Rat
Rat
Rat
Rat
phenyl
mercuric acetate
phenyl mercuric
acetate
phenyl mercuric
acetate
phenyl mercuric
acetate
phenyl mercuric
acetate
7.5 jig
37.5 ^g
150 |ig
600 [ig
2400 |ig
diet
diet
diet
diet
diet
urine + feces
urine + feces
urine + feces
urine + feces
urine + feces
Percent
of Dose
13.84
37.41
0.67
17.19
22.62
25.72
31.63
4.80
18.86
23.05
26.60
Days
10
49
1
3
5
7
13
1
3
5
7
x + SE
6.1
16.53
4
8
x + SE
53.2
39.5
33.2
39.3
32.4
1
1
1
1
1
x + SE
Rate
(Day1)
0.01490
0.00956
0.00672
0.06287
0.05129
0.04248
0.02925
0.04919
0.06966
0.05240
0.04418
Dose
Vehicle
aq sol
aqsol
ethanol
ethanol
ethanol
ethanol
ethanol
ethanol
ethanol
ethanol
ethanol
Source
Abergetal. 1969
Abergetal. 1969
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
0.03932 + 0.00644
0.01573
0.02259
saline sol
olive oil
Gregus &
Klaassenl986
Takeda & Ukita
1970
0.01916 + 0.00343
0.75929
0.50253
0.40347
0.49923
0.39156
food
food
food
food
food
Fitzhugh et al.
1950
Fitzhugh et al.
1950
Fitzhugh et al.
1950
Fitzhugh et al.
1950
Fitzhugh et al.
1950
0.51121 + 0.06621
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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Table 4 (continued)
Excretion of Organic Mercury in Mammals
Test
Species a
Goat-
Nubian
Goat-
Nubian
Goat-
Nubian
Goat-
Nubian
Form
CH3-HgCl
CH3-HgCl
CH3-HgCl
CH3-HgCl
Dose
100 |iCi
100 [id
100 |iCi
100 [id
Dose
Route b
ir inj
irinj
ir inj
irinj
Elimination
Route
milk
milk
milk
milk
Human
Rat-Wistar
Rat-Wistar
Rat-Wistar
Rat-Wistar
Rat-Wistar
Rat-Wistar
Rat-Wistar
Rat-Wistar
Rat-Wistar
methylmercuric
nitrate
ethyl-HgC!2
ethyl-HgC!2
ethyl-HgC!2
ethyl-HgC!2
ethyl-HgC!2
ethyl-HgC!2
ethyl-HgC!2
ethyl-HgC!2
ethyl-HgC!2
2.6 [id
3 |-imole
3 |imole
3 |-imole
3 |imole
3 |-imole
3 |imole
3 |-imole
3 |imole
3 |-imole
oral
oral
oral
oral
oral
oral
oral
oral
oral
oral
hair
hair
hair
hair
hair
hair
hair
hair
hair
hair
Rat-
Holtzman
CH3-HgCl
lOjiCi
iv
hair
Percent
of Dose
0.08
0.14
0.19
0.28
Days
3
5
7
13
x + SE
0.12
0.05
0.14
0.18
0.52
0.59
0.67
1.08
2.25
5.50
45
0.25
1
2
3
4
5
7
10
14
x + SE
0.21
20
Rate
(Day1)
0.00027
0.00028
0.00027
0.00022
Dose
Vehicle
ethanol
ethanol
ethanol
ethanol
Source
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
Sell & Davison
1975
0.00026 + 0.00001
0.00003
0.00200
0.00140
0.00090
0.00174
0.00148
0.00134
0.00155
0.00228
0.00404
aqsol
corn oil
corn oil
corn oil
corn oil
corn oil
corn oil
corn oil
corn oil
corn oil
Aberg et al.
1969
Fang & Fallin
1973
Fang & Fallin
1973
Fang & Fallin
1973
Fang & Fallin
1973
Fang & Fallin
1973
Fang & Fallin
1973
Fang & Fallin
1973
Fang & Fallin
1973
Fang & Fallin
1973
0.00168 + 0.00033
0.00011
Mansour et al.
1973
"Rat-SD = Sprague Dawley rat.
bir = intraruminal injection, iv = intravenous injection, and sc inj = subcutaneous injection.
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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Table 5
Excretion of Organic Mercury in Birds
Test
Species a
Gull-BH
Gull-BH
Gull-BH
Form
methyl-
HgCl
methyl-
HgCl
methyl-
HgCl
Dose
200 |iL
100 |iL
20 |iL
Dose
Route
oral
oral
oral
Elimination
Route
feces
feces
feces
Chicken-
WL
Quail-
Japanese
methyl-
HgCl
methyl-
HgCl
20 ppm + 2
jiCi
20 ppm + 2
I^Ci
diet/oral
diet/oral
feces
feces
Gull-BH
Gull-BH
Gull-BH
methyl-
HgCl
methyl-
HgCl
methyl-
HgCl
200 |iL
100 |iL
20 |iL
oral
oral
oral
feathers
feathers
feathers
Chicken-
WL
Quail-
Japanese
methyl-
HgCl
methyl-
HgCl
20 ppm + 2
I^Ci
20 ppm + 2
jiCi
diet/oral
diet/oral
eggs
eggs
Percent
of Dose
26.4
37.7
11
Days
13
13
13
x + SE
64
32
21
21
x + SE
51.2
44.2
52.6
13
13
13
x + SE
21.88
54.08
21
21
x + SE
Rate
(Day1)
0.02358
0.03640
0.00896
Dose
Vehicle
gel cap
gel cap
gel cap
Source
Lewis & Furness
1991
Lewis & Furness
1991
Lewis & Furness
1991
0.02298 + 0.00793
0.04865
0.01836
food
food
Sell 1977
Sell 1977
0.03351 + 0.01514
0.05519
0.04488
0.05743
gel cap
gel cap
gel cap
Lewis & Furness
1991
Lewis & Furness
1991
Lewis & Furness
1991
0.05593 + 0.00075
0.01176
0.03706
food
food
Sell 1977
Sell 1977
0.02441 + 0.01265
1 Gull-BH = black-headed gull, Chicken-WL = white-leghom chicken.
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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References for Attachment A-3
Aberg, B., L. Ekman, R. Falk, U. Greitz, G. Persson, and J. Snihs. 1969. Metabolism of methyl
mercury (203Hg) compounds in man. Arch. Environ. Health 19:478-484.
ATSDR. 1997. Agency for Toxic Substances and Disease Registry. Toxicological Profile for
Mercury. Draft for public comment (Update). ATSDR-7P-97-7 (Draft). U.S. Department of
Health and Human Services.
Cherian, M.G., J.B. Hursh, T.W. Clarkson, and J. Allen. 1978. Radioactive mercury distribution
in biological fluids and excretion in human subjects after inhalation of mercury vapor. Arch.
Environ. Health. 33(May/June): 109-114.
Fang, S.C. and E. Fallin. 1973. Uptake, distribution, and metabolism of inhaled ethylmercuric
chloride in the rat. Arch. Environ. Contam. Toxicol. 1:347-361.
Fitzhugh, O.G., A.A. Nelson, E.P. Laug, and F.M. Kunze. 1950. Chronic oral toxicities of
mercuri-phenyl and mercuric salts. Arch. Indust. Hyg. Occup. Med. 2:433-442.
Gregus, Z, and C.D. Klaassen. 1986. Disposition of metals in rats: A comparative study of
fecal, urinary, and biliary excretion and tissue distribution of eighteen metals. Toxicol. Applied
Pharm. 85:24-38.
Hayes, A.D, and A. Rothstein. 1962. The metabolism of inhaled mercury vapor in the rat
studied by isotope techniques. J. Pharm. Exper. Therap. 138:1-10.
Lewis, S.A. and R.W. Furness. 1991. Mercury accumulation and excretion in laboratory reared
black-headed gull Larus ridibundus chicks. Arch. Environ. Contam. Toxicol. 21:316-320.
Mansour, M.M., N.C. Dyer, L.H. Hoffman, A.R. Schulert, and A.B. Brill. 1973. Maternal-fetal
transfer of organic and inorganic mercury via placenta and milk. Environ. Res. 6:479-484.
Mullen, A.L., R.E. Stanley, S.R. Lloyd, and A.A. Moghissi. 1975. Absorption, distribution and
milk secretion of radionuclides by the dairy cow IV. Inorganic radiomercury. Health Physics.
28:685-691.
Nordenhall, K., L. Dock and M. Vahter. 1995. Lactational exposure to methylmercury in the
hamster. Arch Toxicol. 69:235-241.
Potter, G.D., D.R. Mclntyre, and G.M. Vattuone. 1972. Metabolism of 203Hg administered as
HgCl2 in the dairy cow and calf. Health Physics. 22:103-106.
Rothstein, A. and A.D. Hayes. 1960. The metabolism of mercury in the rat studied by isotope
techniques. J. Pharm. Exper. Therap. 130:166-176.
Sell, J.L. 1977. Comparative effects of selenium on metabolism of methylmercury by chickens
and quail: tissue distribution and transfer into eggs. Poultry Sci. 56:939-948.
JULY 2005 A-69 TRIM.FATE EVALUATION REPORT VOLUME II
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Sell, J.L. and K.L. Davison. 1975. Metabolism of mercury, administered as methylmercuric
chloride or mercuric chloride, by lactating ruminants. J. Agric. Food Chem. 23:803-808.
Takeda, Y. and T. Ukita. 1970. Metabolism of ethylmercuric chloride-203Hg in rats. Toxicol.
Applied Pharm. 17:181-188.
Talmage, S. 1999. Personal communication. Oak Ridge National Laboratory. February.
Teisinger, J. and V. Fiserova-Bergerova. 1965. Pulmonary retention and excretion of mercury
vapors in man. Indust. Med. Surgery. July:581-584.
U.S. EPA. 1997. U.S. Environmental Protection Agency. Mercury Study Report to Congress.
Volume V: Health effects of mercury and mercury compounds. Office of Air Quality Planning
and Standards and Office of Research and Development.
JULY 2005 A-70 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
-------
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Appendix B
DETAILED RESULTS FOR EMISSION CASE B 1
B.I Mass Accumulation Tables
Table # Title of Table Corresponds to
Table B-l Overall Distribution of Total Mercury Mass (g) in Exhibit 3-2
Modeling System
Table B-2 Distribution of Total Mercury Mass (g) in Abiotic Exhibit 3-3
Compartments
Table B-3 Distribution of Total Mercury Mass (g) in Biotic Exhibit 3-4
Compartment Groups
Table B-4 Distribution of Total Mercury Mass (g) in Air, Surface Exhibit 3-5
Soil, and Terrestrial Plant Compartments
Table B-5 Distribution of Total Mercury Mass (g) in Surface Exhibit 3-6
Water, Sediment, and Aquatic Biota Compartments
Table B-6 Distribution of Total Mercury Mass (g) in Surface Soil N/A
and Terrestrial and Semi-aquatic Animal
Compartments
1 All data for emission case B (both elemental mercury and divalent mercury emitted from source only, no
boundary contributions or initial concentrations), 11-23-03 model run.
JULY 2005 B-l TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Table B-l
Overall Distribution of Total Mercury Mass (g) in Modeling System
Compartment/Sink Group
All Abiotic Compartments
All Biotic Compartments
Total in Modeling Region
Air Sinks
All Other Sinks
Total in Sinks
Total Mass in System
Year
Initial
0
0
0
0
0
0
1
2.2E+02
2.5E+01
2.5E+02
6.4E+04
7.0E+01
6.4E+04
5
2.2E+03
4.5E+01
2.3E+03
5.8E+05
7.2E+02
5.8E+05
10
4.6E+03
4.5E+01
4.6E+03
1 .2E+06
1.6E+03
1 .2E+06
20
9.0E+03
4.5E+01
9.0E+03
2.5E+06
3.6E+03
2.5E+06
30
1.3E+04
4.5E+01
1.3E+04
3.8E+06
5.9E+03
3.8E+06
0| 6.5E+04| 5.8E+05| 1.2E+06| 2.5E+06| 3.8E+06
' All values other than initial are annual averages for the specified year.
Table B-2
Distribution of Total Mercury Mass (g) in Abiotic Compartments
Compartment Type
Air
Surface Soil
Root Zone Soil
Vadose Zone Soil
Ground Water
Surface Water
Sediment
Total in Abiotic Compartments
Total Mass in System
Year
Initial
0
0
0
0
0
0
0
0
1
1.4E+01
2.0E+02
4.4E+00
8.1E-04
2.9E-11
9.0E-01
7.5E+00
2.2E+02
5
1.3E+01
2.1E+03
4.2E+01
5.4E-02
1 .2E-08
1.1E+00
6.9E+01
2.2E+03
10
1.3E+01
4.3E+03
8.0E+01
2.2E-01
1.0E-07
1.4E+00
1.5E+02
4.6E+03
20
1.3E+01
8.5E+03
1.5E+02
7.7E-01
7.8E-07
1.8E+00
3.3E+02
9.0E+03
30
1.3E+01
1 .2E+04
2.1E+02
1.5E+00
2.4E-06
2.2E+00
5.5E+02
1.3E+04
0| 6.5E+04| 5.8E+05| 1.2E+06| 2.5E+06| 3.8E+06
' All values other than initial are annual averages for the specified year.
JULY 2005
B-2
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Table B-3
Distribution of Total Mercury Mass (g) in Biotic Compartment Groups'
Compartment Group
Terrestrial Plants
Terrestrial/Semi-aquatic
Animals
Aquatic Plants b
Aquatic Animals
Total in Biotic Compartments
Total Mass in System
Year
Initial
0
0
0
0
0
1
2.5E+01
1.0E-01
1.6E-01
9.6E-04
2.5E+01
5
4.5E+01
1.9E-01
2.1E-01
4.4E-03
4.5E+01
10
4.5E+01
1.9E-01
2.4E-01
8.2E-03
4.5E+01
20
4.5E+01
1.9E-01
3.2E-01
1 .6E-02
4.5E+01
30
4.5E+01
1.9E-01
4.0E-01
2.6E-02
4.5E+01
0| 6.5E+04| 5.8E+05| 1.2E+06| 2.5E+06| 3.8E+06
a All values other than initial are annual averages for the specified year.
b Macrophyte compartments only; algae not included in this grouping because they are modeled as a phase of surface
water (not as a distinct compartment type).
Table B-4
Distribution of Total Mercury Mass (g) in Air, Surface Soil, and
Terrestrial Plant Compartments a
Compartment Type
Air
Surface Soil
Leaf
Particle on Leaf
Rootb
Stemb
Total Mass in System
Year
Initial
0
0
0
0
0
0
0
1
1.4E+01
2.0E+02
2.5E+01
4.2E-03
2.5E-06
5.5E-02
6.5E+04
5
1.3E+01
2.1E+03
4.5E+01
5.6E-03
2.0E-04
1.3E-01
5.8E+05
10
1.3E+01
4.3E+03
4.4E+01
5.6E-03
9.6E-04
1.3E-01
1 .2E+06
20
1.3E+01
8.5E+03
4.5E+01
5.6E-03
4.1E-03
1.3E-01
2.5E+06
30
1.3E+01
1.2E+04
4.4E+01
5.6E-03
9.1E-03
1.3E-01
3.8E+06
a All values other than initial are annual averages for the specified year.
b Because of methodology limitations, only modeled in the four volume elements with grasses/herbs vegetation type
(versus 19 volume elements modeled for leaf and particle on leaf).
JULY 2005
B-3
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Table B-5
Distribution of Total Mercury Mass (g) in Surface Water, Sediment, and
Aquatic Biota Compartments a
Compartment Type
Surface Water
Macrophyte
Water-column Herbivore
Water-column Omnivore
Water-column Carnivore
Sediment
Benthic Invertebrate
Benthic Omnivore
Benthic Carnivore
Total Mass in System
Year
Initial
0
0
0
0
0
0
0
0
0
1
9.0E-01
1.6E-01
3.6E-04
8.3E-05
2.7E-05
7.5E+00
3.1E-04
6.1E-06
5.0E-07
5
1.1E+00
2.1E-01
5.9E-04
2.3E-04
3.1E-04
6.9E+01
2.8E-03
8.5E-05
2.3E-05
10
1.4E+00
2.4E-01
8.1E-04
3.5E-04
4.9E-04
1.5E+02
5.9E-03
1 .9E-04
6.0E-05
20
1.8E+00
3.2E-01
1.3E-03
5.7E-04
8.2E-04
3.3E+02
1 .3E-02
4.1E-04
1 .4E-04
30
2.2E+00
4.0E-01
1.7E-03
7.9E-04
1.1E-03
5.5E+02
2.1E-02
6.7E-04
2.3E-04
0| 6.5E+04) 5.8E+05| 1.2E+06) 2.5E+06) 3.8E+06
1 All values other than initial are annual averages for the specified year.
Table B-6
Distribution of Total Mercury Mass (g) in Surface Soil and Terrestrial and
Semi-aquatic Animal Compartments a
Compartment Type
Surface Soil
Soil Arthropod
Earthworm
White-tailed Deer
Meadow Vole b
Mouse
Black-capped Chickadee
Short-tailed Shrew
Long-tailed Weasel
Red-tailed Hawk
MinkD
Bald Eagle
Raccoon b
Tree Swallow
Mallard c
Common Loon c
Total Mass in System
Year
Initial
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2.0E+02
1.8E-09
8.7E-06
9.8E-02
3.2E-04
2.6E-03
4.2E-05
1 .2E-05
4.8E-06
9.2E-06
1 .4E-06
3.9E-07
7.5E-07
3.9E-06
1 .8E-04
2.0E-08
6.5E+04
5
2.1E+03
1 .2E-07
8.4E-05
1.8E-01
5.2E-04
4.8E-03
7.7E-05
1.1E-04
8.9E-06
1.7E-05
2.3E-06
8.1E-07
3.5E-06
1.0E-05
4.1E-04
6.2E-08
5.8E+05
10
4.3E+03
5.4E-07
1 .6E-04
1.8E-01
5.2E-04
4.8E-03
7.7E-05
2.2E-04
9.2E-06
1.7E-05
2.4E-06
9.2E-07
6.7E-06
1.8E-05
4.1E-04
9.7E-08
1 .2E+06
20
8.5E+03
2.2E-06
2.9E-04
1.8E-01
5.3E-04
4.8E-03
7.7E-05
4.3E-04
9.8E-06
1.8E-05
2.5E-06
1.1E-06
1.3E-05
3.5E-05
4.1E-04
1.7E-07
2.5E+06
30
1 .2E+04
4.9E-06
4.2E-04
1.8E-01
5.3E-04
4.8E-03
7.7E-05
6.1E-04
1.0E-05
1.9E-05
2.6E-06
1 .3E-06
2.0E-05
5.4E-05
4.1E-04
2.4E-07
3.8E+06
a All values other than initial are annual averages for the specified year.
b Voles only present in two volume elements, raccoons and mink only present in 14 volume elements (versus 17 for
other land-based animals).
0 Mallards and loons assigned to surface water volume elements rather than surface soil; thus, only present in five
volume elements.
JULY 2005
B-4
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
B.2 Concentration Tables and Charts
Table # Title of Table
Table B-7 Annual Average Concentration of Total Mercury in
Air at Increasing Distance from the Source
Table B-8a Annual Average Concentration of Total Mercury: Soil
and Soil Biota in SW2
Table B-8b Annual Average Concentration of Total Mercury: Soil
and Soil Biota in SSE4
Table B-9a Annual Average Concentration of Total Mercury:
Terrestrial Plants in SW2
Table B-9b Annual Average Concentration of Total Mercury:
Terrestrial Plants in SSE4
Table B-9c Annual Average Concentration of Total Mercury:
Terrestrial Plants in W2
Table B-lOa Annual Average Concentration of Total Mercury: All
Compartments in Swetts Pond
Table B-lOb Annual Average Concentration of Total Mercury: All
Compartments in Brewer Lake
Table B-lla Annual Average Concentration of Total Mercury:
Terrestrial and Land-based Semi-aquatic Animals in
SW2
Table B-llb Annual Average Concentration of Total Mercury:
Terrestrial and Land-based Semi-aquatic Animals in
SSE4
Corresponds to
Exhibit 3-8
N/A
N/A
Exhibit 3-10
N/A
N/A
Exhibit 3-11
N/A
Exhibit 3-13
N/A
Chart # Title of Chart
Chart B-la Total Mercury Concentration in Air vs. Time at Increasing Distance
(Southeast) from the Source
Chart B-lb Total Mercury Concentration in Air vs. Time at Increasing Distance
(West) from the Source
Chart B-2a Total Mercury Concentration in Soil and Soil Biota vs. Time: SW
Chart B-2b Total Mercury Concentration in Soil and Soil Biota vs. Time: SSE4
JULY 2005
B-5
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart # Title of Chart
Chart B-3a Total Mercury Concentration in Air, Soil, and Plants vs. Time: SW2
(grasses/herbs)
Chart B-3b Total Mercury Concentration in Air, Soil, and Plants vs. Time: SSE4
(coniferous forest)
Chart B-3c Total Mercury Concentration in Air, Soil, and Plants vs. Time: W2
(coniferous forest)
Chart B-4a Total Mercury Concentration in Water-column and Related Biotic
Compartments vs. Time: Swetts Pond
Chart B-4b Total Mercury Concentration in Water-column and Related Biotic
Compartments vs. Time: Brewer Lake
Chart B-5a Total Mercury Concentration in Benthic and Related Biotic
Compartments vs. Time: Swetts Pond
Chart B-5b Total Mercury Concentration in Benthic and Related Biotic
Compartments vs. Time: Brewer Lake
Chart B-6a Total Mercury Concentration in Land-based Semi-aquatic Biotic
Compartments vs. Time: SW2
Chart B-6b Total Mercury Concentration in Land-based Semi-aquatic Biotic
Compartments vs. Time: SSE4
Chart B-7a Total Mercury Concentration in Shrew and Mouse Compartments vs.
Time: SW2
Chart B-7b Total Mercury Concentration in Shrew and Mouse Compartments vs.
Time: SSE4
Chart B-8a Total Mercury Concentration in Terrestrial Herbivore and Omnivore
Compartments vs. Time: SW2
Chart B-8b Total Mercury Concentration in Terrestrial Herbivore and Omnivore
Compartments vs. Time: SSE4
Chart B-9a Total Mercury Concentration in Terrestrial Carnivore (Weasel and
Hawk) Compartments vs. Time: SW2
Chart B-9b Total Mercury Concentration in Terrestrial Carnivore (Weasel and
Hawk) Compartments vs. Time: SSE4
JULY 2005 B-6 TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Table B-7
Annual Average Concentration of Total Mercury in Air at
Increasing Distance from the Source
Air Compartment
SSE1
SSE2
SSE3
SSE4
SSE5
WNW1
WSWI
W2
W3
Units
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
Year
Initial
0
0
0
0
0
0
0
0
0
1
8.8E-10
3.3E-10
2.0E-10
1.3E-10
1.1E-10
3.5E-10
4.6E-10
1.4E-10
8.1E-11
5
9.2E-10
3.3E-10
2.0E-10
1.3E-10
1.0E-10
4.7E-10
5.4E-10
1.7E-10
1.1E-10
10
9.2E-10
3.3E-10
1.9E-10
1.3E-10
1.0E-10
4.7E-10
5.4E-10
1.7E-10
1.1E-10
20
9.2E-10
3.3E-10
2.0E-10
1.3E-10
1.0E-10
4.7E-10
5.4E-10
1.8E-10
1.1E-10
30
9.2E-10
3.3E-10
1.9E-10
1.3E-10
1.0E-10
4.7E-10
5.4E-10
1.7E-10
1.1E-10
Table B-8a
Annual Average Concentration of Total Mercury: Soil and Soil Biota in SW2
Compartment
Surface Soil
Root Zone Soil
Vadose Zone Soil
Ground Water
Soil Arthropod
Earthworm
Units
g/g dry wt
g/g dry wt
g/g dry wt
g/L
g/kg wet wt
g/kg wet wt
Year
Initial
0
0
0
0
0
0
1
2.4E-10
6.4E-14
6.6E-18
1 .OE-22
1.0E-13
2.0E-12
5
1.9E-09
6.1E-13
4.8E-16
4.5E-20
5.4E-12
2.0E-11
10
4.0E-09
1.2E-12
1.9E-15
3.7E-19
2.3E-11
3.8E-11
20
7.7E-09
2.1E-12
6.5E-15
2.7E-18
9.4E-11
6.9E-11
30
1.1E-08
3.1E-12
1.3E-14
8.2E-18
2.1E-10
1.0E-10
Table B-8b
Annual Average Concentration of Total Mercury: Soil and Soil Biota in SSE4
Compartment
Surface Soil
Root Zone Soil
Vadose Zone Soil
Ground Water
Soil Arthropod
Earthworm
Units
g/g dry wt
g/g dry wt
g/g dry wt
g/L
g/kg wet wt
g/kg wet wt
Year
Initial
0
0
0
0
0
0
1
5.3E-11
3.7E-14
4.2E-18
6.5E-23
2.1E-14
1.2E-12
5
5.2E-10
2.9E-13
2.3E-16
2.1E-20
1.4E-12
9.6E-12
10
1.1E-09
5.3E-13
9.0E-16
1.8E-19
6.3E-12
1.7E-11
20
2.1E-09
8.5E-13
3.0E-15
1.3E-18
2.6E-11
2.8E-11
30
3.1E-09
1.1E-12
5.6E-15
3.7E-18
5.7E-11
3.7E-11
JULY 2005
B-7
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Table B-9a
Annual Average Concentration of Total Mercury: Terrestrial Plants in SW2
Compartment
Leaf - Grasses/Herbs 3
Particle on Leaf- Grasses/Herbs 3
Stem - Grasses/Herbs
Root - Grasses/Herbs
Surface Soil
Air - SSW2b
Air - SSW3b
Units
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/g dry wt
g/m3
g/m3
Year
Initial
0
0
0
0
0
0
0
1
1 .2E-07
6.8E-07
7.0E-09
1.2E-13
2.4E-10
3.0E-10
1.9E-10
5
2.8E-07
1 .6E-06
1.9E-08
7.2E-12
1.9E-09
3.0E-10
1.8E-10
10
2.8E-07
1 .6E-06
1.9E-08
3.3E-11
4.0E-09
3.0E-10
1.8E-10
20
2.8E-07
1 .6E-06
1.9E-08
1.3E-10
7.7E-09
3.0E-10
1.8E-10
30
2.8E-07
1 .6E-06
1.9E-08
3.0E-10
1.1E-08
3.0E-10
1.8E-10
a Each annual average data point shown for leaf and particle on leaf is the average of values during the days (May 13
- September 29 for each year) for which leaves were modeled as present during the entire day (i.e., represents a
growing season average).
b Because of the differences in the air and surface parcel layouts, there is not one single air parcel whose boundaries
match those of the SW2 surface parcel (see Exhibits 2-1 and 2-2). However, air parcels SSW2 and SSW3 do
completely overlay the SW2 surface parcel (among the air parcels, SSW2 has the most overlap with surface parcel
SW2).
Table B-9b
Annual Average Concentration of Total Mercury: Terrestrial Plants in SSE4
Compartment
Leaf- Coniferous Forest
Particle on Leaf- Coniferous Forest
Stem - Coniferous Forest3
Root - Coniferous Forest3
Surface Soil
Air - SSE4b
Air-SSE3b
Air-ESE3b
Air - ESE4b
Units
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/g dry wt
g/m3
g/m3
g/m3
g/m3
Year
Initial
0
0
N/A
N/A
0
0
0
0
0
1
8.1E-08
1 .8E-06
N/A
N/A
5.3E-11
1.3E-10
2.0E-10
2.8E-10
1.9E-10
5
1.3E-07
3.0E-06
N/A
N/A
5.2E-10
1.3E-10
2.0E-10
2.8E-10
1.9E-10
10
1.3E-07
2.9E-06
N/A
N/A
1.1E-09
1.3E-10
1.9E-10
2.8E-10
1.9E-10
20
1.3E-07
3.0E-06
N/A
N/A
2.1E-09
1.3E-10
2.0E-10
2.8E-10
1.9E-10
30
1.3E-07
2.9E-06
N/A
N/A
3.1E-09
1.3E-10
1.9E-10
2.8E-10
1.9E-10
a Root and stem not modeled for coniferous forest.
b Because of the differences in the air and surface parcel layouts, there is not one single air parcel whose boundaries
match those of the SSE4 surface parcel (see Exhibits 2-1 and 2-2). However, air parcels SSE4, SSE3, ESE3, and
ESE4 do completely overlay the SSE4 surface parcel (among the air parcels, SSE4 has the most overlap with
surface parcel SSE4).
JULY 2005
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Table B-9c
Annual Average Concentration of Total Mercury: Terrestrial Plants in W2
Compartment
Leaf- Coniferous Forest
Particle on Leaf- Coniferous Forest
Stem - Coniferous Forest a
Root - Coniferous Forest3
Surface Soil
Air -W2 (100% overlap)
Units
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/g dry wt
g/m3
Year
Initial
0
0
N/A
N/A
0
0
1
2.2E-07
4.8E-06
N/A
N/A
1.1E-10
1.4E-10
5
5.5E-07
1 .2E-05
N/A
N/A
1 .4E-09
1.7E-10
10
5.5E-07
1 .2E-05
N/A
N/A
3.1E-09
1.7E-10
20
5.6E-07
1 .2E-05
N/A
N/A
6.3E-09
1.8E-10
30
5.5E-07
1 .2E-05
N/A
N/A
9.2E-09
1.7E-10
aRoot and stem not modeled for coniferous forest.
Table B-lOa
Annual Average Concentration of Total Mercury: All Compartments in Swetts Pond
Compartment
Surface Water
Macrophyte
Water-column Herbivore
Water-column Omnivore
Water-column Carnivore
Sediment
Benthic Invertebrate
Benthic Omnivore
Benthic Carnivore
Mallard
Common Loon
Units
g/L
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/g dry wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
Year
Initial
0
0
0
0
0
0
0
0
0
0
0
1
2.2E-11
1 .6E-08
4.3E-08
2.2E-08
2.2E-08
2.2E-11
1.3E-09
5.1E-10
3.7E-10
1.3E-07
1 .4E-08
5
3.3E-11
2.5E-08
9.0E-08
8.8E-08
3.2E-07
2.3E-10
1 .4E-08
8.1E-09
1.9E-08
1.8E-07
6.4E-08
10
4.5E-11
3.5E-08
1 .4E-07
1.5E-07
6.4E-07
5.9E-10
3.4E-08
2.1E-08
5.7E-08
1.8E-07
1 .2E-07
20
7.1E-11
5.5E-08
2.4E-07
2.8E-07
1 .2E-06
1.5E-09
9.1E-08
5.7E-08
1 .6E-07
1.8E-07
2.3E-07
30
9.8E-11
7.6E-08
3.4E-07
4.1E-07
1 .8E-06
2.8E-09
1 .6E-07
1.0E-07
3.0E-07
1.9E-07
3.4E-07
Table B-lOb
Annual Average Concentration of Total Mercury: All Compartments in Brewer Lake
Compartment
Surface Water
Macrophyte
Water-column Herbivore
Water-column Omnivore
Water-column Carnivore
Sediment
Benthic Invertebrate
Benthic Omnivore
Benthic Carnivore
Mallard
Common Loon
Units
g/L
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/g dry wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
Year
Initial
0
0
0
0
0
0
0
0
0
0
0
1
1.6E-11
1 .2E-08
3.9E-08
2.9E-08
3.1E-08
1.5E-11
9.4E-10
4.3E-10
3.6E-10
1.3E-07
2.0E-08
5
2.0E-11
1 .5E-08
5.9E-08
7.2E-08
3.3E-07
1.4E-10
8.1E-09
5.1E-09
1.3E-08
1.7E-07
5.4E-08
10
2.4E-11
1.9E-08
7.8E-08
1.0E-07
4.8E-07
3.2E-10
1.9E-08
1 .2E-08
3.5E-08
1.8E-07
7.9E-08
20
3.4E-11
2.6E-08
1 .2E-07
1 .6E-07
7.6E-07
7.4E-10
4.4E-08
2.8E-08
8.7E-08
1.8E-07
1.3E-07
30
4.3E-11
3.4E-08
1.5E-07
2.1E-07
1 .OE-06
1 .2E-09
7.3E-08
4.8E-08
1.5E-07
1.8E-07
1.8E-07
JULY 2005
B-9
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Table B-lla
Annual Average Concentration of Total Mercury: Terrestrial and Land-based
Semi-aquatic Animals in SW2
Compartment
White-tailed Deer
Meadow Vole
Mouse
Black-capped Chickadee
Short-tailed Shrew
Long-tailed Weasel
Red-tailed Hawk
Tree Swallow
Raccoon
Mink
Bald Eagle
Surface Soil
Leaf - Grasses/Herbs a
Units
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/g dry wt
g/kg wet wt
Year
Initial
0
0
0
0
0
0
0
0
0
0
0
0
0
1
3.2E-08
6.3E-08
6.5E-08
1 .2E-07
1 .4E-08
9.3E-09
1.8E-08
2.9E-09
1 .4E-09
1.3E-08
7.6E-09
2.4E-10
1 .2E-07
5
7.7E-08
1.5E-07
1 .6E-07
3.0E-07
1.0E-07
2.3E-08
4.4E-08
6.1E-09
6.7E-09
2.9E-08
2.1E-08
1.9E-09
2.8E-07
10
7.7E-08
1.5E-07
1 .6E-07
3.0E-07
2.1E-07
2.7E-08
5.0E-08
8.7E-09
1.3E-08
3.0E-08
2.6E-08
4.0E-09
2.8E-07
20
7.7E-08
1.5E-07
1 .6E-07
3.0E-07
4.0E-07
3.5E-08
6.1E-08
1 .2E-08
2.3E-08
3.2E-08
3.4E-08
7.7E-09
2.8E-07
30
7.8E-08
1.5E-07
1 .6E-07
3.0E-07
5.7E-07
4.2E-08
7.0E-08
1.5E-08
3.2E-08
3.3E-08
4.1E-08
1.1E-08
2.8E-07
' Each annual average data point shown for
each year) for which leaves were modeled
leaf is the average of values
as present (i.e., represents a
during the days (May 13 - September 29
growing season average).
Table B-lIb
Annual Average Concentration of Total Mercury: Terrestrial and Land-based
Semi-aquatic Animals in SSE4
Compartment
White-tailed Deer
Mouse
Black-capped Chickadee
Short-tailed Shrew
Long-tailed Weasel
Red-tailed Hawk
Tree Swallow
Raccoon
Mink
Bald Eagle
Surface Soil
Leaf- Coniferous Forest
Units
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/kg wet wt
g/g dry wt
g/kg wet wt
Year
Initial
0
0
0
0
0
0
0
0
0
0
0
0
1
1.9E-07
3.8E-07
7.5E-07
3.5E-09
3.2E-08
8.0E-08
1.8E-09
1 .2E-09
4.9E-08
4.6E-08
5.3E-11
8.1E-08
5
3.2E-07
6.4E-07
1 .2E-06
2.8E-08
5.6E-08
1 .4E-07
8.2E-09
5.4E-09
8.9E-08
1.1E-07
5.2E-10
1.3E-07
10
3.2E-07
6.4E-07
1 .2E-06
5.7E-08
5.8E-08
1 .4E-07
1.9E-08
1.1E-08
9.4E-08
1.5E-07
1.1E-09
1.3E-07
20
3.2E-07
6.4E-07
1 .2E-06
1.1E-07
6.0E-08
1 .4E-07
4.8E-08
2.6E-08
1.1E-07
2.4E-07
2.1E-09
1.3E-07
30
3.2E-07
6.4E-07
1 .2E-06
1 .6E-07
6.2E-08
1.5E-07
8.6E-08
4.3E-08
1 .2E-07
3.3E-07
3.1E-09
1.3E-07
JULY 2005
B-10
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
1.0E-09
ChartB-1a
Total Mercury Concentration in Air vs. Time at Increasing Distance
(South-Southeast) from the Source
O.OE+OO
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-ll
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
6.0E-10
5.0E-10
E
s
§ 4.0E-10
s
0)
o
o>
O)
s
re
3.0E-10
2.0E-10
1.0E-10
O.OE+00
s
Chart B-1b
Total Mercury Concentration in Air vs. Time at Increasing Distance
(West) from the Source
\
s
-Air in
WNW1
•Air in
WSW1
Air in
W2
•Air in
W3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-12
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-2a - Log Scale
Total Mercury Concentration in Soil and Soil Biota vs. Time: SW2a
1.0E-07
1.0E-18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
' Ground water not shown; maximum value is 8.2E-18 g/L at year 30.
JULY 2005
B-13
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-2b - Log Scale
Total Mercury Concentration in Soil and Soil Biota vs. Time: SSE4a
1.0E-08
1.0E-18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
J Ground water not shown; maximum value is 3.7E-18 g/L at year 30.
JULY 2005
B-14
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-3a - Log Scale
Total Mercury Concentration in Air, Soil, and Plants vs. Time: SW2 (grasses/herbs)
ra
ra
1 .OE-05
1 .OE-06
1 .OE-07 -
1 .OE-08
— « 1.0E-09
S
o
o
I
1 .OE-10
1 .OE-1 1
1 .OE-12
1 .OE-13
1 .OE-08
S u rface
Soil
P a rticle o n
Leaf a
Air - b
SSW2
1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 19 20 21 22 23 24 25 26 27 28 29 30
-10
a Each annual average data point shown for leaf and leaf particle is the average of values during the three months (June to August) for which
leaves were modeled as present during the entire month.
b Because of the differences in the air and surface parcel layouts, the boundaries of the SSW2 air parcel do not match those of the SW2 parcel (see
Exhibits 2-1 and 2-2), but this air parcel does have substantial overlap with the surface parcel (among air parcels, SSW2 has the most overlap with
surface parcel SW2).
JULY 2005
B-15
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-3b - Log Scale
Total Mercury Concentration in Air, Soil, and Plants vs. Time: SSE4 (coniferous forest)
1.0E-05
1.0E-06
1.0E-07
1.0E-08
o
O
1.0E-09
1.0E-10
1.0E-11
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-09
1.0E-10 _
<
-Surface soil
-Leaf
Particle on
Leaf
—• Air - SSE4 '
1.0E-11
a Root and stem not modeled for coniferous forest.
b Because of the differences in the air and surface parcel layouts, the boundaries of the SSE4 air parcel do not match those of the SSE4 surface
parcel (see Exhibits 2-1 and 2-2), but this air parcel does have substantial overlap with the surface parcel (among air parcels, SSE4 has the most
overlap with surface parcel SSE4).
JULY 2005
B-16
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-3c - Log Scale
Total Mercury Concentration in Air, Soil, and Plants vs. Time: W2 (coniferous forest)
1.0E-04
1.0E-05
£•
•o
"3)
I
£ 1.0E-06 -
1
1
t =
•2 ° 1.0E-07 -
P
13
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-4a - Log Scale
Total Mercury Concentration in Water-column and Related Biotic Compartments
vs. Time: Swetts Pond
1.0E-05
o>
1.0E-11
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-18
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-4b - Log Scale
Total Mercury Concentration in Water-column and Related Biotic Compartments
vs. Time: Brewer Lake
1.0E-05
o>
&
o
IE
1.0E-06
1.0E-07
o>
.2 | 1.0E-08
4-i O
0)
o
c
o
o
0)
O)
s
0)
re
1.0E-09
1.0E-10
1.0E-11
-Surface water
-Common Loon
Macrophyte
-Water-column
Herbivore
•Water-column
Omnivore
-Water-column
Carnivore
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-19
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-5a - Log Scale
Total Mercury Concentration in Benthic and Related Biotic Compartments
vs. Time: Swetts Pond
1.0E-06
Benthic
Invertebrate
Benthic
Omnivore
Benthic
Carnivore
Sediment
a
Raccoon
Tree
Swallow3
1.0E-11
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
' Results shown for compartment SSE4, where semi-aquatic animals feed from Swetts Pond.
JULY 2005
B-20
TRDVLFATE EVALUATION REPORT VOLUME II
-------
o
IE
£
1
o>
a
0)
go
11
O "
o
0)
O)
s
0)
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-5b - Log Scale
Total Mercury Concentration in Benthic and Related Biotic Compartments
vs. Time: Brewer Lake
1.0E-06
1.0E-07
1.0E-08
1.0E-09
1.0E-10
1.0E-11
Benthic
Invertebrate
—©— Benthic Omnivore
Benthic Carnivore
—•—Sediment
--••--• Raccoon3
—*—Tree Swallow3
12345678 9101112131415161718192021222324252627282930
Year
' Results shown for compartment ESE4, where semi-aquatic animals feed from Brewer Lake.
JULY 2005
B-21
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-6a - Log Scale
Total Mercury Concentration in Land-based Semi-aquatic Biotic Compartments
vs. Time: SW2
1.0E-07
re
= 1.0E-08
o
O) (A
£ 0>
tl
O =
•^ «
So
c "-
o5 ?
o >
0)
O)
s
0)
I
re
1.0E-09
1.0E-10
Surface Soil
-©—Mink
Raccoon
-•— Bald Eagle
• •* - -Tree Swallow
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-22
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-6b - Log Scale
Total Mercury Concentration in Land-based Semi-aquatic Biotic Compartments
vs. Time: SSE4
1.0E-06
o>
re"
o
II-
1
1.0E-07
^ o
O) (A
:* Q) 1.0E-08
O) O
re
g 1 1.0E-09
§£
O ^
o>
O)
s
£ 1.0E-10
re
C
C
1.0E-11
Surface Soil
-8—Mink
Raccoon
-•— Bald Eagle
• ••- - -Tree Swallow
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-23
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-7a - Log Scale
Total Mercury Concentration in Shrew and Mouse Compartments vs. Time: SW2
1.0E-06
Surface Soil
—e—Short-tailed
Shrew
Mouse
—•—Soil Arthropod
- - ••- - - Earthworm
• Leaf (grasses/
herbs) a
1.0E-13
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
a Each annual average data point shown for leaf and particle on leaf is the average of values during the days (May 13 to September 29 each year) for
which leaves were modeled as present (i.e., represents a growing season average).
JULY 2005
B-24
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-7b - Log Scale
Total Mercury Concentration in Shrew and Mouse Compartments vs. Time: SSE4
1.0E-06 -1
Short-tailed
Shrew
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-14
JULY 2005
B-25
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-8a - Log Scale
Total Mercury Concentration in Terrestrial Herbivore and Omnivore
Compartments vs. Time: SW2
1.0E-06
1
s
o
1
+J
I
o
o
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-8b - Log Scale
Total Mercury Concentration in Terrestrial Herbivore and Omnivore
Compartments vs. Time: SSE4
1.0E-05
o>
1.0E-06
0)
u
c
O
O
0)
O)
2
0)
re
1.0E-07
1.0E-08
• Leaf (conifer)
—&— Black-capped
Chickadee
White-tailed
Deer
—•— Mouse
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-27
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-9a - Log Scale
Total Mercury Concentration in Terrestrial Carnivore (Weasel and Hawk)
Compartments vs. Time: SW2
1.0E-06
1.0E-09
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-28
TRDVLFATE EVALUATION REPORT VOLUME II
-------
APPENDIX B
DETAILED RESULTS FOR EMISSION CASE B
Chart B-9b - Log Scale
Total Mercury Concentration in Terrestrial Carnivore (Weasel and Hawk)
Compartments vs. Time: SSE4
1.0E-05
• Black-capped
Chickadee
-Short-tailed
Shrew
Mouse
— Long-tailed
Weasel
- - Red-tailed Hawk
1.0E-09
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
JULY 2005
B-29
TRDVLFATE EVALUATION REPORT VOLUME II
-------
-------
APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
-------
-------
APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Appendix C
STEADY-STATE: INPUTS AND DETAILED RESULTS
C.I Estimation of Steady-state Inputs for Mercury Test Case
As discussed in Chapter 4, the steady-state mode of TRIM.FaTE requires that no model
inputs can be assigned time-varying values. Therefore, all time-varying inputs in a dynamic
scenario must be replaced with representative constant values to generate steady-state results for
that scenario. In the dynamic scenarios for the mercury test case, the following inputs were
assigned time-varying values:
Air temperature;
Wind speed;
• Wind direction;
• Mixing height;
• Precipitation rate;
• isDay (0 at night, 1 during the day);
• AllowExchange (0 during non-growing season, 1 during growing season);
Litter fall rate (for deciduous forest and grasses/herbs, user-specified rate during litter fall
and zero at all other times); and
River flush rate (i.e., flow) and current velocity.
In order to provide a sound basis of comparison with the results from the dynamic
simulations, constant values for these inputs were calculated such that the resulting steady-state
simulation closely approximated the system modeled in the dynamic simulations. Many of these
time-varying inputs combine and interact in complex ways within TRIM.FaTE to affect the
magnitude of internally calculated variables and ultimately the ways pollutant mass is moved
within the modeled system. In order to capture the impact of interactions between these time-
varying inputs, the processes affected by time-varying inputs were considered when developing
the constant values for these inputs.
C.I.I Estimating Constant Values for Time-varying Inputs
Steady-state models have typically used arithmetic means for inputs that vary over time
and space; however, it is not clear whether arithmetic means are appropriate for setting up a
steady-state simulation in a way that is representative of- and comparable to - a specific
dynamic scenario. By evaluating the hourly-based values of time-varying inputs and exploring
different ways for calculating long-term central tendency of the data, this analysis found that not
only are the hourly data not normally distributed but different calculation methods can result in
differing results. This is illustrated in the following text box.
JULY 2005 C-1 TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
Example of Potential Problems in Representing Dynamic Data with Constant Values
In its simplest form, the advective transfer factor (in units of I/day) across an interface
between two neighboring air parcels is given by:
TF =
y
v s
where:
fs^r = flow of air across the interface from sending compartment s to receiving
compartment r, in units of mVday. It is calculated as the product of flux (in m/day)
and interfacial area (in m2) across the interface.
Vs = volume of the sending compartment s, in units of m3.
The arithmetic mean of TF^^^. (i.e., AM[TFs^r]) can be calculated in two ways.
The first approach calculates TFs_>r for each hour of met data and then estimates the long-term
average of these hourly transfer factors as:
1 n
= -lLTF^r
= v '
where:
j = hour
n = total number of hours (i.e., n = 8,760 for one year of meteorological data).
Alternatively, given Equation 1, the average values of/^r and Vs can be calculated from the
meteorological data and spatial coordinates of the air parcels, and then the AM[TFs^r] can be
estimated as the quotient of the two average values such that:
r i (3)
Approach! AM\VS I
However, if the hourly values for flow and/or volume are not normally distributed (which they
typically are not), the two approaches give different answers.
To evaluate the magnitude of this potential difference, both methods were applied to the five-
year meteorological data set used as the basis of the meteorological input data in the dynamic
mercury test case simulation. Advective transfer factors were calculated across a total of 124 defined
air parcel boundaries. On average, Approach 1 (Equation 2) resulted in steady-state transfer factors
that were approximately five percent greater than Approach 2 (Equation 3) and the difference
between the two approaches for any given air parcel boundary ranged from -40 percent to +20
percent. This difference is illustrated on a log scale in Exhibit C-l.
JULY 2005 C-2 TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Exhibit C-l
Comparison of Long-term Advective Transfer Factors Calculated Using the Average of the
Hourly Transfer Factors (y-axis) (Eq. 2) and the Daily Average Volumetric Flux
Normalized to the Average Sending Cell Volume (x-axis) (Eq. 3) a
1000
4-
01
<
|
o
c
o
TJ
i
CO
100 H
10 -
10 100
Based on Total Annual Volumetric Flux Normalized to Average Sending Cell Volume
1000
' Plot represents 124 air parcel boundaries.
These types of uncertainties were considered during the development of the constant
approximations for dynamic inputs. To be consistent with existing modeling approaches, this
analysis used arithmetic means to represent central tendency of the time-varying inputs. In
general, the steady-state inputs were estimated by first identifying the processes affected by each
time-varying input and determining how the various inputs interact or combine to influence these
processes. Next, long-term arithmetic means were calculated for each input and correlations
between related inputs were estimated. It is important to note that the steady-state inputs
described in this appendix are designed to be representative of long-term average transfers for
the mercury test case, and may not be representative of day-to-day or hour-to-hour variations or
for other TRIM.FaTE applications.
The remainder of this appendix describes how steady-state values were developed for
each of these time-varying inputs. For the purposes of this discussion, these inputs are grouped
into the following three categories:
Meteorological inputs (i.e., air temperature, wind speed, wind direction, mixing height,
and precipitation rate);
JULY 2005
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APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
Plant inputs (i.e., isDay, Allow Exchange., and litter fall rate); and
• Water body flow inputs (i.e., river flush rate and river current velocity).
C.1.2 Meteorological Inputs
There are five time-varying meteorological inputs used in the mercury test case:
• Air temperature;
• Wind direction;
• Wind speed;
• Mixing height; and
Precipitation rate.
The methods used to estimate constant values for these inputs are described below.
Air Temperature
Based on a review of the meteorological data and relevant TRIM.FaTE algorithms, it was
determined that the long-term arithmetic mean of the time-varying air temperature values (i.e.,
280 K) was appropriate for this application of TRIM.FaTE's steady-state mode.
Wind Direction
Due to the relationship between wind speed and direction, using constant input values in
the algorithm used to estimate air-to-air advective transfers could potentially result in a much
different modeled system than that modeled in the dynamic simulations - in particular, a much
different spatial distribution of the mass. In light of this, a new algorithm that does not require
wind speed and direction as inputs was developed and used for modeling air-to-air advective
transfers in steady-state mode. The only input required by this algorithm is an estimate of the
steady-state transfer (a first-order rate constant, in units of "per day") across each air-to-air
interface in the modeling area. That is,
TFs^r = AdvectiveTFs^r ^
TRIM.FaTE calculates the transfer of pollutant mass across each air-to-air interface by
multiplying TFs^r by the moles of pollutant in air compartment s. This algorithm is also used to
estimate the transfers of pollutant mass from the air compartments bounding the modeling
domain to air advection sinks.
The constant steady-state advective transfer factors in Equation (4) were estimated using
TRIM.FaTE-generated hourly air-to-air advective transfers for each interface for the case B
dynamic simulation and calculating the arithmetic mean of these transfers over the five-year
meteorological input data period for each interface. The resulting steady-state advective transfer
factors are provided in Exhibit C-2. As described in Section 1 of this appendix, these transfer
JULY 2005 C-4 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Exhibit C-2
Steady-state Advective Transfer Factors
Air-to-Air Interface
Air_ENE1 to Air_ENE2
Air_ENE1 to Air_ESE1
Air_ENE1 to Air_NNE1
Air_ENE1 to Air_Source
Air_ENE2toAir_ENE1
Air_ENE2to Air_ENE3
Air_ENE2toAir_ESE2
Air_ENE2to Air_NNE2
Air_ENE3toAir_ENE2
Air_ENE3to Air_ENE4
Air_ENE3toAir_ESE3
Air_ENE3to Air_NNE3
Air_ENE4toAir_ENE3
Air_ENE4to Air_ENE5
Air_ENE4toAir_ESE4
Air_ENE4 to Sink 29 for Air_ENE4
Air_ENE5toAir_ENE4
Air_ENE5toAir_ESE5
Air_ENE5 to Sink 36 for Air_ENE5
Air_ENE5 to Sink 37 for Air_ENE5
Air_ESE1 toAir_ENE1
Air_ESE1 toAir_ESE2
Air_ESE1 toAir_Source
Air_ESE1 toAir_SSE1
Air_ESE2toAir_ENE2
Air_ESE2toAir_ESE1
Air_ESE2toAir_ESE3
Air_ESE2 to Air_SSE2
Air_ESE3toAir_ENE3
Air_ESE3toAir_ESE2
Air_ESE3toAir_ESE4
Air_ESE3 to Air_SSE3
Transfer Factor
(1/day)
1.81e+02
1 .59e+02
1 .29e+02
6.23e+00
1 .38e+01
9.96e+01
4.99e+01
4.20e+01
1 .90e+01
8.24e+01
2.75e+01
2.23e+01
2.06e+01
3.74e+01
2.78e+01
2.976+01
2.046+01
5.566+01
1.716+01
5.806+01
1 .39e+02
1 .83e+02
5.556+00
1 .34e+02
4.156+01
1 .36e+01
9.896+01
4.256+01
2.296+01
1 .88e+01
8.346+01
2.246+01
Air-to-Air Interface
Air_NNW3toAir_W3
Air_NNW3 to Sink 20 for Air_NNW3
Air_Source to Air_ENE1
Air_Source to Air_ESE1
Air_Source to Air_NNE1
Air_Source to Air_NNW1
Air_Source to Air_SSE1
Air_Source to Air_SSW1
Air_Source to Air_WNW1
Air_Source to Air_WSW1
Air_SSE1 toAir_ESE1
Air_SSE1 toAir_Source
Air_SSE1 toAir_SSE2
Air_SSE1 toAir_SSW1
Air_SSE2 to Air_ESE2
Air_SSE2toAir_SSE1
Air_SSE2 to Air_SSE3
Air_SSE2 to Air_SSW2
Air_SSE3 to Air_ESE3
Air_SSE3 to Air_SSE2
Air_SSE3 to Air_SSE4
Air_SSE3 to Air_SSW3
Air_SSE4toAir_ESE4
Air_SSE4toAir_SSE3
Air_SSE4toAir_SSE5
Air_SSE4toAir_SSW4
Air_SSE5 to Air_ESE5
Air_SSE5 to Air_SSE4
Air_SSE5 to Sink 39 for Air_SSE5
Air_SSE5 to Sink 40 for Air_SSE5
Air_SSW1 to Air_Source
Air_SSW1 to Air_SSE1
Transfer Factor
(1/day)
2.306+01
6.486+01
2.796+02
2.356+02
2.046+02
1 .96e+02
2.346+02
2.586+02
9.336+01
8.736+01
2.186+02
1 .32e+01
1 .84e+02
6.206+01
6.596+01
2.836+01
8.406+01
1 .99e+01
3.686+01
3.916+01
7.696+01
1 .09e+01
2.496+01
3.146+01
5.826+01
6.966+00
2.846+01
4.106+01
2.406+01
4.136+01
1 .49e+01
1 .78e+02
JULY 2005
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APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
Exhibit C-2 (continued)
Steady-state Advective Transfer Factors
Air-to-Air Interface
Air_ESE4 to Air_ENE4
Air_ESE4 to Air_ESE3
Air_ESE4 to Air_ESE5
Air_ESE4 to Air_SSE4
Air_ESE5 to Air_ENE5
Air_ESE5 to Air_ESE4
Air_ESE5 to Air_SSE5
Air_ESE5 to Sink 42 for Air_ESE5
Air_NNE1 to Air_ENE1
Air_NNE1 to Air_NNE2
Air_NNE1 to Air_NNW1
Air_NNE1 to Air_Source
Air_NNE2 to Air_ENE2
Air_NNE2 to Air_NNE1
Air_NNE2 to Air_NNE3
Air_NNE2 to Air_NNW2
Air_NNE3 to Air_ENE3
Air_NNE3 to Air_NNE2
Air_NNE3 to Air_NNW3
Air_NNE3 to Sink 18 for Air_NNE3
Air_NNW1 to Air_NNE1
Air_NNW1 to Air_NNW2
Air_NNW1 to Air_Source
Air_NNW1 to Air_WNW1
Air_NNW2 to Air_NNE2
Air_NNW2 to Air_NNW1
Air_NNW2 to Air_NNW3
Air_NNW2 to Air_W2
Air_NNW3 to Air_NNE3
Air_NNW3 to Air_NNW2
Transfer Factor
(1/day)
1.52e+01
1.40e+01
5.916+01
1.426+01
1.016+01
1.076+01
1.016+01
4.286+01
2.796+02
1.486+02
5.956+01
1.676+01
8.206+01
3.396+01
6.396+01
1.956+01
4.966+01
5.116+01
1.076+01
6.506+01
1.886+02
1.476+02
1.796+01
1.316+02
5.576+01
3.076+01
6.646+01
4.10e+01
2.936+01
5.176+01
Air-to-Air Interface
Air_SSW1 to Air_SSW2
Air_SSW1 to Air_WSW1
Air_SSW2 to Air_SSE2
Air_SSW2 to Air_SSW1
Air_SSW2 to Air_SSW3
Air_SSW2 to Air_W2
Air_SSW3 to Air_SSE3
Air_SSW3 to Air_SSW2
Air_SSW3 to Air_SSW4
Air_SSW3 to Air_W3
Air_SSW4 to Air_SSE4
Air_SSW4 to Air_SSW3
Air_SSW4 to Sink 31 for
Air_W2 to Air_NNW2
Air_W2 to Air_SSW2
Air_W2 to Air_W3
Air_W2 to Air_WNW1
Air_W2 to Air_WSW1
Air_W3 to Air_NNW3
Air_W3 to Air_SSW3
Air_W3 to Air_W2
Air_W3 to Sink 23 for Air_W3
Air_WNW1 to Air_NNW1
Air_WNW1 to Air_Source
Air_WNW1 to Air_W2
Air_WNW1 to Air_WSW1
Air_WSW1 to Air_Source
Air_WSW1 to Air_SSW1
Air_WSW1 to Air_W2
Air_WSW1 to Air_WNW1
Transfer Factor
(1/day)
1.826+02
1.296+02
5.376+01
2.596+01
8.586+01
4.24e+01
2.976+01
4.00e+01
7.736+01
2.226+01
4.826+01
8.04e+01
8.086+01
3.266+01
4.316+01
3.106+01
1.696+01
1.68e+01
1.756+01
2.326+01
5.306+01
2.896+01
2.226+02
1.99e+01
7.376+01
1.676+02
1.886+01
2.956+02
7.436+01
1.366+02
JULY 2005
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APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
factors can also be estimated based on the daily average volumetric flux from the sending air
compartment to the receiving air compartment normalized to the volume of the sending air
compartment. It is not clear that one method is preferable, and thus the method using the
arithmetic means of the hourly transfer factors was used because these data were readily
available as outputs of TRIM.FaTE, whereas the other method would have required additional
calculations outside of TRIM.FaTE.
Because wind direction was already used in the dynamic model run to generate the
hourly advective transfer factors, a constant wind direction was not required to run TRIM.FaTE
in steady-state mode.
Wind Speed
Although wind speed is not required to calculate steady-state air-to-air advective
transfers because of the use of advective transfer factors (described above), it is still a required
input used in calculating diffusion between air and surface water and between air and leaves.
Based on a review of its use in these algorithms, it was determined that the long-term arithmetic
mean of the time-varying wind speed values (i.e., 3.64 m/s) was appropriate for this application
of TRIM.FaTE's steady-state mode.
Mixing Height
Based on a review of the meteorological data and relevant TRIM.FaTE algorithms, it was
determined that the long-term arithmetic mean of the time-varying mixing height (i.e., 887 m)
was appropriate for this application of TRIM.FaTE's steady-state mode.
Precipitation Rate
Based on a review of the meteorological data and relevant TRIM.FaTE algorithms, it was
determined that the long-term arithmetic mean of the time-varying precipitation rate (i.e., 0.0041
m/day) was appropriate for this application of TRIM.FaTE's steady-state mode.
C.1.3 Plants Inputs
There are five time-varying plant-related inputs used in the mercury test case:
• AllowExchange (two inputs);
isDay (two inputs); and
• Litter fall rate.
The methods used to estimate constant values for these inputs are described below.
AllowExchange
AllowExchange is used as a seasonal on/off "switch" to account for the presence or
absence of leaf and particle-on-leaf compartments in the chemical mass balance. In the mercury
test case simulation, it is set to 1.0 when the leaf and particle-on-leaf compartments can
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APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
exchange pollutant mass with other compartments (i.e., the growing season) and zero when they
cannot (i.e., the non-growing season). It is used in every algorithm involving leaf and particle-
on-leaf compartments.
For coniferous plants, AllowExchange is set to 1.0 for the duration of each dynamic
simulation because coniferous leaf and particle-on-leaf compartments are assumed to exchange
pollutant mass throughout the year. Because coniferous leaf and particle-on-leaf compartments
are assigned a constant value for AllowExchange in the dynamic simulations, there was no need
to develop separate steady-state values.
For deciduous and grasses/herbs plants, AllowExchange is set to 1.0 from May 12 until
September 30 (i.e, the growing season for the test case) and to zero from September 30 until May
12 (i.e., the non-growing season). In developing the constant value for AllowExchange for use in
TRIM.FaTE's steady-state mode, all of the algorithms involving AllowExchange were analyzed
to determine if there are complex interactions between AllowExchange and any other time-
varying inputs. The results of this analysis indicated that, because of the relationship between
AllowExchange and the mixing height, two different values of AllowExchange are appropriate
for deciduous and grasses/herbs leaf and particle-on-leaf compartment, one for algorithms that
transport mass from air to plants and another for the other plant-related algorithms.
For algorithms involving deciduous and grasses/herbs leaf and particle-on-leaf
compartments that do not involve the mixing height (e.g., leaf-to-stem transfers, ingestion of
leaves by biota), the constant value for AllowExchange for use in steady-state simulations was
assigned the long-term average of the dynamic AllowExchange value (0.386).
For algorithms involving deciduous and grasses/herbs leaf and particle-on-leaf
compartments that transport mass from air to plants, a more involved analysis was required due
to the relationship between AllowExchange and mixing height. As described in Section 2 of this
appendix, the constant value for mixing height used in the steady-state simulations was 887 m.
However, as illustrated in Exhibit C-3, the mixing height is generally lower when
AllowExchange is 1.0 than when it is zero. The lower mixing height when AllowExchange is 1.0
leads to an increase in the transfer of pollutant mass due to dry particle and dry gaseous
deposition due to the lower volume of the sending air compartment. Thus, a steady-state value
for AllowExchange for deciduous and grasses/herbs leaf and particle-on-leaf compartments was
developed to account for the differences between the long-term average mixing height (887 m)
and the average mixing height when AllowExchange is equal to 1.0 (803 m). This value was
calculated by multiplying the long-term average value for AllowExchange (0.386) by the ratio of
the long-term average mixing height to the average mixing height when AllowExchange is 1.0
(887/803 = 1.1), resulting in an AllowExchange value of 0.426 for algorithms that transport
pollutant mass from air to plants.
isDay
When run in dynamic mode, TRIM.FaTE uses the isDay property to determine if plant
stomata are open. It is set to zero at night (when stomata are closed) and 1.0 during the day
JULY 2005 C-8 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Exhibit C-3
Distributions of Hourly Mixing Heights for Different Values of
AllowExchange and isDay
100% -,
All Data
AllowExchange & IsDay = 1
AllowExchange=1
AllowExchange=0
E
o 25% H
0%
0 500 1000 1500 2000
Rural Mixing Height (m)
2500 3000
(when stomata are open). The isDay and AllowExchange properties are both used in calculating
diffusion from air to plant leaves, and thus their interactions were considered in calculating the
steady-state value for isDay.
Because of the relationship between isDay and AllowExchange and the fact that two
constant values were developed for AllowExchange (as described above), two values were also
developed for isDay, one for algorithms that transport pollutant mass from air to plants and one
for algorithms that transport pollutant mass from plants to air. To calculate the isDay value for
use in algorithms that transport pollutant mass from air to plants (0.552), the long-term average
of the product ofisDay and AllowExchange (0.235) was divided by the constant AllowExchange
value developed for algorithms that transport pollutant mass from air to plants (0.426). To
calculate the isDay value for use in the other plant-related algorithms (0.609), the long-term
average of the product ofisDay and AllowExchange (0.235) was divided by the AllowExchange
value developed for the other plant-related algorithms (0.386).
Litter Fall Rate
For this test case, the litter fall rate for deciduous and grasses/herbs plants is configured
in the dynamic simulations such that 99 percent of the pollutant mass in leaf and particle-on-leaf
compartments is transferred to surface soil in 32 days each year, starting on September 27 and
ending on October 29. The constant value for litter fall rate for use in steady-state simulations
was developed such that 99 percent of the pollutant mass is transferred to surface soil at a
JULY 2005
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APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
constant rate over the course of the year (i.e., 365 days). This value was calculated using the
following equation:
0.01 = exp(- Llitter x 365) (5)
Solving Equation 12 for Llitter gives a steady-state litter fall rate for deciduous and grasses/herbs
leaf and particle-on-leaf compartments of 0.013/day. Because coniferous leaf and particle-on-
leaf compartments are assigned constant litter fall rates in the dynamic simulations, there was no
need to develop separate steady-state values.
C.1.4 Water Body Flow Inputs
There are two time-varying inputs related to water body flow used in the mercury test
case:
• River current velocity; and
• River flush rate (i.e., flow).
The methods used to estimate constant values for these inputs are described below. Note that the
other water bodies included in the mercury test case were, unlike the river, assigned constant
values for current velocity and flush rate/flow, and thus it was not necessary to develop steady-
state values for these water bodies.
River Current Velocity
Based on a review of the meteorological data and relevant TRIM.FaTE algorithms, it was
determined that the time-weighted average current velocity of the time-varying river current
velocity (i.e., 0.166 m/s) was appropriate for this application of TRIM.FaTE's steady-state
mode.
River Flush Rate
The constant value for river flush rate developed for this application of TRIM.FaTE's
steady-state mode was calculated based on the constant river current velocity (described above),
the depth and width of the river at the downstream end, and the volume of the river using the
following formula.
Current velocity (m I yr) x width (m) x depth (m)
Flush rate (1 / yr) = — —j- (6)
Volume (m )
The resulting constant value for river flush rate was 531.24/yr.
JULY 2005 C-10 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
C.2 Compartment-specific Steady-state Results
Compartment Type
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Air
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Volume Element
Air_ENE1
Air_ENE2
Air_ENE3
Air_ENE4
Air_ENE5
Air_ESE1
Air_ESE2
Air_ESE3
Air_ESE4
Air_ESE5
Air_NNE1
Air_NNE2
Air_NNE3
Air_NNW1
Air_NNW2
Air_NNW3
Air_Source
Air_SSE1
Air_SSE2
Air_SSE3
Air_SSE4
Air_SSE5
Air_SSW1
Air_SSW2
Air_SSW3
Air_SSW4
Air_W2
Air_W3
Air_WNW1
Air_WSW1
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
Steady-state
Mass (g)
6.1e-01
1.3e+00
1.5e+00
1.5e+00
5.7e-01
6.1e-01
1.5e+00
1.8e+00
2.66+00
2.86+00
4.26-01
8.66-01
5.56-01
2.86-01
4.86-01
2.66-01
2.56-01
4.96-01
1.2e+00
1.3e+00
1.5e+00
8.66-01
3.56-01
6.86-01
6.06-01
2.76-01
6.96-01
3.36-01
2.16-01
2.26-01
3.76-04
1.36-04
3.76-04
4.1e-04
1.86-04
1.26-04
2.46-04
3.26-04
3.36-04
1.56-04
Steady-state Concentration
Value
6.86-10
3.26-10
2.06-10
1.46-10
8.26-11
7.36-10
3.86-10
2.56-10
1.76-10
9.36-11
S.Oe-10
1.96-10
7.86-11
3.76-10
1.16-10
3.66-11
4.46-09
6.16-10
2.86-10
1.76-10
LOe-10
5.16-11
4.46-10
1.56-10
7.96-11
4.66-11
7.66-11
2.26-11
2.86-10
2.96-10
5.36-07
6.16-08
2.86-07
1.26-07
9.46-08
5.26-08
9.16-08
2.26-07
1.26-07
4.16-07
Units
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/m3
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
JULY 2005
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APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Bald Eagle
Benthic Carnivore
Benthic Carnivore
Benthic Carnivore
Benthic Carnivore
Benthic Carnivore
Benthic Invertebrate
Benthic Invertebrate
Benthic Invertebrate
Benthic Invertebrate
Benthic Invertebrate
Benthic Omnivore
Benthic Omnivore
Benthic Omnivore
Benthic Omnivore
Benthic Omnivore
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Volume Element
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
Sed_Brewer
Sed_Fields
Sed_River
Sed_Swetts
Sed_Thurston
Sed_Brewer
Sed_Fields
Sed_River
Sed_Swetts
Sed_Thurston
Sed_Brewer
Sed_Fields
Sed_River
Sed_Swetts
Sed_Thurston
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
Steady-state
Mass (g)
8.2e-05
5.3e-04
4.6e-04
2.56-04
1.1e-04
1.36-04
2.4e-04
4.86-08
8.5e-06
8.76-08
1.3e-05
7.66-06
8.6e-06
1.36-07
9.0e-08
1.56-07
1.9e-07
6.46-06
4.5e-07
1.26-07
2.1e-05
6.56-06
6.9e-08
3.66-07
3.5e-02
8.26-03
3.3e-04
9.66-03
7.3e-03
2.96+00
7.9e-01
2.76-02
8.7e-01
6.56-01
9.6e-02
2.46-02
9.1e-04
2.86-02
2.1e-02
1.56-06
7.8e-06
2.66-06
Steady-state Concentration
Value
4.3e-08
S.Oe-07
2.0e-07
8.76-08
5.6e-08
9.46-08
7.6e-08
3.46-07
1.9e-05
3.26-07
1.9e-05
1.96-05
1.9e-05
2.46-07
2.9e-07
2.66-07
2.6e-06
1.66-05
1.3e-06
2.66-07
3.6e-05
1.66-05
2.5e-07
5.66-07
4.5e-05
5.56-05
3.9e-07
1.16-04
5.4e-05
2.16-05
3.0e-05
1.86-07
5.9e-05
2.76-05
1.4e-05
1.86-05
1.2e-07
3.76-05
1.7e-05
1.76-06
2.8e-06
1.66-06
Units
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
JULY 2005
C-12
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Black-capped Chickadee
Common Loon
Common Loon
Common Loon
Common Loon
Common Loon
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Leaf- Coniferous Forest
Leaf- Coniferous Forest
Leaf- Coniferous Forest
Leaf- Coniferous Forest
Volume Element
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
SW_Brewer
SW_Fields
SW_River
SW_Swetts
SW_Thurston
RootSoil_E1
RootSoil_E4
RootSoil_ESE2
RootSoil_ESE3
RootSoil_ESE4
RootSoil_ESE5
RootSoil_N1
RootSoil_N2
RootSoil_NE2
RootSoil_NE3
RootSoil_SE1
RootSoil_SE6
RootSoil_SSE2
RootSoil_SSE3
RootSoil_SSE4
RootSoil_SSE5
RootSoil_SW2
RootSoil_W1
RootSoil_W2
SurfSoil_E4
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_SE1
Steady-state
Mass (g)
4.5e-05
2.4e-05
S.Oe-07
LOe-06
2.1e-06
2.06-06
1.9e-05
5.56-07
4.1e-05
1.76-06
1.2e-05
5.76-07
7.2e-07
2.46-05
1.7e-05
3.26-06
2.2e-07
3.46-06
2.5e-06
9.26-03
3.3e-03
9.26-03
LOe-02
4.4e-03
2.96-03
3.8e-03
5.96-03
8.0e-03
8.16-03
3.6e-03
2.06-03
1.3e-02
1.16-02
6.2e-03
2.76-03
3.2e-03
6.26-03
5.9e-03
4.56+00
2.6e+01
1.46+01
1.1e+01
Steady-state Concentration
Value
1.16-05
LOe-05
2.86-07
3.1e-07
1.16-06
5.6e-07
4.36-05
2.3e-07
1.96-05
5.9e-07
3.46-06
2.3e-07
4.26-07
6.2e-06
2.26-05
2.3e-05
2.76-07
4.3e-05
1.96-05
8.8e-08
LOe-08
4.7e-08
2.16-08
1.6e-08
8.66-09
LOe-07
1.56-08
3.6e-08
1.96-08
6.8e-08
7.16-09
5.0e-08
3.46-08
1.4e-08
9.36-09
1.6e-08
8.26-08
1.3e-08
3.16-07
1.2e-06
1.16-06
4.7e-06
Units
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
JULY 2005
C-13
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Leaf- Coniferous Forest
Leaf- Coniferous Forest
Leaf- Coniferous Forest
Leaf- Deciduous Forest
Leaf- Deciduous Forest
Leaf- Deciduous Forest
Leaf- Deciduous Forest
Leaf- Deciduous Forest
Leaf- Deciduous Forest
Leaf- Deciduous Forest
Leaf- Deciduous Forest
Leaf - Grasses/Herbs
Leaf - Grasses/Herbs
Leaf - Grasses/Herbs
Leaf - Grasses/Herbs
Leaf Particle - Coniferous Forest
Leaf Particle - Coniferous Forest
Leaf Particle - Coniferous Forest
Leaf Particle - Coniferous Forest
Leaf Particle - Coniferous Forest
Leaf Particle - Coniferous Forest
Leaf Particle - Coniferous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Grasses/Herbs
Leaf Particle - Grasses/Herbs
Leaf Particle - Grasses/Herbs
Leaf Particle - Grasses/Herbs
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Volume Element
SurfSoil_SSE2
SurfSoil_SSE4
SurfSoil_W2
SurfSoil_E1
SurfSoil_ESE2
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE3
SurfSoil_SE6
SurfSoil_SSE3
SurfSoil_SSE5
SurfSoil_N1
SurfSoil_NE2
SurfSoil_SW2
SurfSoil_W1
SurfSoil_E4
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_SE1
SurfSoil_SSE2
SurfSoil_SSE4
SurfSoil_W2
SurfSoil_E1
SurfSoil_ESE2
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE3
SurfSoil_SE6
SurfSoil_SSE3
SurfSoil_SSE5
SurfSoil_N1
SurfSoil_NE2
SurfSoil_SW2
SurfSoil_W1
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
Steady-state
Mass (g)
2.4e+01
7.0e+00
1.4e+01
1.56+00
3.1e+00
9.76-01
1.1e+00
2.36+00
6.5e-01
1.66+00
6.1e-01
5.76-01
2.8e+00
9.26-01
4.7e-01
3.46-04
1.9e-03
lOe-03
8.36-04
1.8e-03
5.26-04
LOe-03
7.46-05
1.6e-04
S.Oe-05
5.4e-05
1.26-04
3.3e-05
8.16-05
3.1e-05
6.86-04
1.8e-04
6.16-05
4.9e-04
2.66-06
1.8e-06
2.76-06
7.3e-06
3.66-06
9.7e-07
1.86-06
2.5e-06
Steady-state Concentration
Value
2.16-06
3.6e-07
6.76-07
1.1e-06
1.26-06
2.2e-07
2.06-07
4.0e-07
1.76-07
3.5e-07
1.66-07
1.1e-06
9.26-07
3.4e-07
4.66-07
6.7e-06
2.56-05
2.4e-05
LOe-04
4.4e-05
7.76-06
1.4e-05
6.86-06
7.5e-06
1.46-06
1.3e-06
2.66-06
1.1e-06
2.36-06
1.0e-06
1.26-04
5.1e-06
1.96-06
4.1e-05
1.26-06
2.6e-07
6.56-07
6.9e-07
5.96-07
1.4e-07
2.26-07
5.1e-07
Units
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
JULY 2005
C-14
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Long-tailed Weasel
Macrophyte
Macrophyte
Macrophyte
Macrophyte
Macrophyte
Mallard
Mallard
Mallard
Mallard
Mallard
Meadow Vole
Meadow Vole
Mink
Mink
Mink
Mink
Mink
Mink
Mink
Mink
Mink
Mink
Mink
Mink
Mink
Mink
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Volume Element
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
SW_Brewer
SW_Fields
SW_River
SW_Swetts
SW_Thurston
SW_Brewer
SW_Fields
SW_River
SW_Swetts
SW_Thurston
SurfSoil_NE2
SurfSoil_SW2
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
Steady-state
Mass (g)
2.5e-06
2.9e-06
6.4e-07
7.56-06
3.2e-06
2.96-06
8.2e-07
9.66-07
4.0e-06
2.06+01
S.Oe+00
2.16-01
5.3e+00
4.0e+00
8.6e-05
2.26-05
9.6e-05
2.36-05
1.8e-05
7.16-04
2.5e-04
9.76-08
1.4e-05
9.26-06
3.9e-06
1.16-05
2.3e-07
1.66-07
6.8e-07
3.46-06
1.5e-06
1.56-07
1.2e-05
3.56-06
LOe-07
1.2e-04
S.Oe-04
1.9e-04
2.86-03
1.5e-03
5.96-05
8.2e-05
Steady-state Concentration
Value
2.86-07
2.5e-06
LOe-07
1.4e-06
4.46-07
3.2e-07
1.36-07
2.2e-07
4.06-07
3.6e-06
4.86-06
3.4e-08
8.96-06
4.2e-06
2.26-06
3.0e-06
2.36-06
5.5e-06
2.76-06
5.4e-07
2.16-07
1.9e-07
3.96-06
4.3e-06
3.56-06
3.0e-06
8.76-08
1.4e-07
2.56-06
3.3e-06
1.26-06
8.5e-08
7.36-06
3.3e-06
7.26-08
1.1e-06
1.56-06
9.3e-07
5.56-06
5.2e-06
1.76-07
2.0e-07
Units
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
JULY 2005
C-15
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Mouse
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Raccoon
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Red-tailed hawk
Root - Grasses/Herbs
Volume Element
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
SurfSoil_N1
Steady-state
Mass (g)
1.6e-04
1.5e-04
1.2e-03
4.16-05
2.6e-03
1.46-04
7.9e-04
4.46-05
5.5e-05
1.56-03
1.8e-05
5.46-04
3.2e-04
1.26-04
4.0e-04
1.76-05
1.7e-05
7.06-06
1.4e-04
2.66-05
2.3e-05
4.86-04
1.4e-04
9.66-06
2.9e-06
2.76-06
3.2e-06
1.26-05
6.2e-06
1.16-06
2.0e-06
2.86-06
2.9e-06
S.Oe-06
7.3e-07
1.26-05
3.6e-06
4.46-06
9.3e-07
1.16-06
6.7e-06
1.1e+00
Steady-state Concentration
Value
6.7e-07
3.46-07
2.2e-05
1.46-07
9.9e-06
4.06-07
1.8e-06
1.56-07
2.7e-07
3.26-06
1.3e-06
5.96-06
6.0e-06
4.36-06
4.2e-06
2.56-07
5.7e-07
lOe-06
5.26-06
7.7e-07
5.36-07
1.1e-05
5.36-06
2.7e-07
1.76-06
4.9e-07
9.66-07
1.5e-06
1.36-06
2.0e-07
3.16-07
7.5e-07
4.06-07
5.5e-06
1.56-07
2.8e-06
6.36-07
6.0e-07
1.96-07
3.2e-07
8.56-07
9.1e-07
Units
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
JULY 2005
C-16
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Root - Grasses/Herbs
Root - Grasses/Herbs
Root - Grasses/Herbs
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
Sediment
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Short-tailed Shrew
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Volume Element
SurfSoil_NE2
SurfSoil_SW2
SurfSoil_W1
Sed_Brewer
Sed_Fields
Sed_River
Sed_StreamN
Sed_StreamS
Sed_Swetts
Sed_Thurston
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
RootSoil_E1
RootSoil_E4
RootSoil_ESE2
RootSoil_ESE3
RootSoil_ESE4
RootSoil_ESE5
RootSoil_N1
RootSoil_N2
RootSoil_NE2
RootSoil_NE3
RootSoil_SE1
RootSoil_SE6
RootSoil_Source
RootSoil_SSE2
RootSoil_SSE3
Steady-state
Mass (g)
2.2e+00
8.7e-01
1.7e+00
7.16+04
1.9e+04
6.56+02
1.8e+03
6.46+03
2.1e+04
1.66+04
8.4e-04
S.Oe-04
8.4e-04
9.46-04
4.0e-04
2.66-04
5.4e-04
7.66-04
7.4e-04
3.36-04
1.9e-04
1.26-03
LOe-03
5.7e-04
2.56-04
3.0e-04
5.46-04
4.2e+03
1.56+03
4.3e+03
4.86+03
2.1e+03
1.36+03
1.8e+03
2.76+03
3.7e+03
3.86+03
1.7e+03
9.46+02
8.0e+02
6.16+03
5.3e+03
Steady-state Concentration
Value
3.26-07
1.4e-07
7.26-07
3.6e-07
5.26-07
3.1e-09
9.96-07
7.3e-07
LOe-06
4.7e-07
2.76-05
3.1e-06
1.46-05
6.3e-06
4.86-06
2.6e-06
4.66-06
1.1e-05
5.86-06
2.1e-05
2.26-06
1.5e-05
lOe-05
4.46-06
2.8e-06
4.96-06
3.9e-06
2.76-09
3.1e-10
1.46-09
6.3e-10
4.76-10
2.6e-10
3.16-09
4.6e-10
1.16-09
5.8e-10
2.16-09
2.1e-10
1.96-08
1.5e-09
1.0e-09
Units
g/kg wet
g/kg wet
g/kg wet
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
JULY 2005
C-17
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Volume Element
RootSoil_SSE4
RootSoil_SSE5
RootSoil_SW2
RootSoil_W1
RootSoil_W2
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N1
SurfSoil_N2
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_Source
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W1
SurfSoil_W2
VadoseSoil_E1
VadoseSoil_E4
VadoseSoil_ESE2
VadoseSoil_ESE3
VadoseSoil_ESE4
VadoseSoil_ESE5
VadoseSoil_N1
VadoseSoil_N2
VadoseSoil_NE2
VadoseSoil_NE3
VadoseSoil_SE1
VadoseSoil_SE6
VadoseSoil_Source
VadoseSoil_SSE2
VadoseSoil_SSE3
VadoseSoil_SSE4
VadoseSoil_SSE5
Steady-state
Mass (g)
2.9e+03
1.3e+03
1.5e+03
2.96+03
2.7e+03
1.36+04
4.5e+03
1.36+04
1.4e+04
6.06+03
3.9e+03
5.46+03
8.1e+03
1.16+04
1.1e+04
S.Oe+03
2.8e+03
2.46+03
1.8e+04
1.66+04
8.5e+03
3.76+03
4.5e+03
8.86+03
8.1e+03
2.26+02
8.2e+01
2.26+02
2.5e+02
1.16+02
7.0e+01
9.26+01
1.4e+02
2.06+02
2.0e+02
8.76+01
5.0e+01
4.26+01
3.1e+02
2.76+02
1.5e+02
6.76+01
Steady-state Concentration
Value
4.4e-10
2.86-10
4.7e-10
2.56-09
3.8e-10
5.26-07
6.0e-08
2.76-07
1.2e-07
9.16-08
5.0e-08
6.36-07
8.8e-08
2.26-07
He-07
4.06-07
4.1e-08
3.66-06
3.0e-07
2.06-07
8.5e-08
5.56-08
9.5e-08
S.Oe-07
7.4e-08
7.86-11
9.3e-12
4.26-11
1.9e-11
1.46-11
7.8e-12
9.16-11
1.4e-11
3.36-11
1.7e-11
6.06-11
6.5e-12
5.46-10
4.4e-11
2.96-11
1.3e-11
8.56-12
Units
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
g/g dry
JULY 2005
C-18
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Soil - Vadose Zone
Soil -Vadose Zone
Soil -Vadose Zone
Stem - Grasses/Herbs
Stem - Grasses/Herbs
Stem - Grasses/Herbs
Stem - Grasses/Herbs
Surface water
Surface water
Surface water
Surface water
Surface water
Surface water
Surface water
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Tree swallow
Water-column Carnivore
Water-column Carnivore
Water-column Carnivore
Water-column Carnivore
Water-column Carnivore
Water-column Herbivore
Water-column Herbivore
Water-column Herbivore
Water-column Herbivore
Water-column Herbivore
Water-column Omnivore
Volume Element
VadoseSoil_SW2
VadoseSoil_W1
VadoseSoil_W2
SurfSoil_N1
SurfSoil_NE2
SurfSoil_SW2
SurfSoil_W1
SW_Brewer
SW_Fields
SW_River
SW_StreamN
SW_StreamS
SW_Swetts
SW_Thurston
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
SW_Brewer
SW_Fields
SW_River
SW_Swetts
SW_Thurston
SW_Brewer
SW_Fields
SW_River
SW_Swetts
SW_Thurston
SW_Brewer
Steady-state
Mass (g)
7.8e+01
1.5e+02
1.4e+02
1.36-02
6.1e-02
2.16-02
LOe-02
1.46+02
1.7e+01
2.06+00
7.5e-02
1.36-01
1.4e+01
1.06+01
3.2e-06
1.66-03
6.3e-06
2.46-03
9.8e-04
1.16-03
1.2e-05
6.86-06
1.3e-05
1.76-06
1.3e-03
7.86-06
LOe-05
4.16-03
1.3e-03
6.16-06
1.4e-05
6.66-02
1.1e-02
9.86-04
LOe-02
7.26-03
9.4e-02
2.16-02
1.2e-03
2.26-02
1.6e-02
4.56-02
Steady-state Concentration
Value
1.4e-11
7.26-11
1.16-11
6.36-08
5.1e-08
1.96-08
2.6e-08
4.66-09
6.2e-09
8.36-11
He-08
8.06-09
1.2e-08
5.56-09
I.Oe-07
1.66-05
I.Oe-07
1.66-05
1.16-05
1.16-05
9.5e-08
9.66-08
I.Oe-07
9.96-08
1.4e-05
9.66-08
9.5e-08
S.Oe-05
1.4e-05
9.56-08
9.4e-08
I.Oe-04
8.4e-05
1.46-06
1.4e-04
6.36-05
1.6e-05
1.86-05
1.8e-07
3.36-05
1.5e-05
2.16-05
Units
g/g dry
g/g dry
g/g dry
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/L
g/L
g/L
g/L
g/L
g/L
g/L
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
JULY 2005
C-19
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX C
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Water-column Omnivore
Water-column Omnivore
Water-column Omnivore
Water-column Omnivore
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
Volume Element
SW_Fields
SW_River
SW_Swetts
SW_Thurston
SurfSoil_E1
SurfSoil_E4
SurfSoil_ESE2
SurfSoil_ESE3
SurfSoil_ESE4
SurfSoil_ESE5
SurfSoil_N2
SurfSoil_NE2
SurfSoil_NE3
SurfSoil_SE1
SurfSoil_SE6
SurfSoil_SSE2
SurfSoil_SSE3
SurfSoil_SSE4
SurfSoil_SSE5
SurfSoil_SW2
SurfSoil_W2
Steady-state
Mass (g)
7.8e-03
6.5e-04
7.7e-03
5.56-03
2.7e-03
1.86-02
5.3e-03
lOe-01
5.66-02
1.7e-03
1.96-03
4.2e-03
4.06-03
4.5e-02
1.16-03
9.6e-02
S.Oe-03
2.8e-02
1.16-03
1.5e-03
5.66-02
Steady-state Concentration
Value
1.9e-05
2.86-07
3.3e-05
1.56-05
3.4e-07
7.36-07
3.5e-07
2.76-06
2.6e-06
6.66-08
6.4e-08
2.56-07
1.2e-07
1.16-05
5.2e-08
4.86-06
1.2e-07
8.56-07
4.9e-08
9.36-08
1.6e-06
Units
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
g/kg wet
JULY 2005
C-20
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
C.3 Detailed Comparison of Steady-state and Dynamic Simulation Results
Compartment Type
Air3
Soil - Surface
Soil - Root Zone
Soil - Vadose Zone
Surface water
Sediment
Leaf- Deciduous Forest3
Leaf- Coniferous Forest3
Leaf- Grasses/Herbs3
Leaf Particle - Deciduous Forest3
Leaf Particle - Coniferous Forest3
Leaf Particle - Grasses/Herbs3
Root - Grasses/Herbs
Stem - Grasses/Herbs3
Macrophyte
Earthworm
Arthropod
Short-tailed Shrew
Meadow Vole3
White-tailed Deer3
Black-capped Chickadee3
Mouse3
Long-tailed Weasel
Red-tailed hawk
Tree swallow
Mallard
Mink3
Raccoon
Common Loon
Bald Eagle3
Steady-state Cone : Year 30 Dynamic Cone
Max
Ratio
1.1
30
570
3,900
120
360
7.6
3.3
5.7
7.4
3.2
5.5
980
1.4
120
570
1,700
31
2.4
3.2
4.1
5.1
19
16
350
29
110
260
130
130
Parcel
w/ Max
ESE4
SSE3
SSE3
SSE3
Swetts
Swetts
SSE3
ESE3
NE2
SSE3
ESE3
NE2
NE2
NE2
Swetts
SSE3
SSE3
SSE3
NE2
ESE3
SSE3
SSE3
SSE3
SSE3
SSE4
Swetts
SSE5
SSE4
Brewer
ESE5
Min
Ratio
0.2
1.6
40
390
3.6
7.6
1.5
1.3
1.4
1.5
1.3
0.4
460
0.4
2.4
120
350
6.5
1.7
0.6
0.8
0.9
2.0
1.7
4.5
0.2
2.0
6.5
8.8
2.0
Parcel
w/ Min
NNW3
Source
Source
Source
River
River
N2
W2
N1
N2
W2
N1
SW2
N1
River
N2
N2
N2
SW2
N2
N2
N2
W2
W2
E1
River
N2
N2
River
W2
Arith
Mean
Ratio
0.7
16
290
2,000
77
230
5.8
2.5
3.3
5.6
2.4
2.6
640
0.8
87
300
1,000
18
2.0
2.3
2.5
2.8
7.0
6.1
130
15
32
110
97
45
Steady-state Cone : Year 30 Dynamic w/
Steady-state Inputs Cone
Max
Ratio
1.0
5.4
150
1,700
25
75
1.0
1.0
1.0
1.0
1.0
1.0
300
1.0
24
150
310
5.4
1.2
1.2
1.6
1.8
4.7
4.4
74
13
34
44
23
27
Parcel
w/ Max
All
SSE3
SSE3
W1
Thurston
Thurston
All
All
All
All
All
All
W1
All
Thurston
SSE3
SSE3
SSE3
NE2,
SW2
SSE3
SSE3
SSE3
SSE3
SSE3
SE6
Swetts
SSE5
SE6
Thurston
SSE5
Min
Ratio
1.0
2.7
70
750
2.5
3.1
1.0
1.0
1.0
1.0
1.0
1.0
150
1.0
2.2
70
150
2.7
1.2
1.0
1.0
1.0
1.3
1.2
2.8
1.1
1.0
2.7
3.3
1.1
Parcel
w/ Min
All
Source
SE6
SE6
River
River
All
All
All
All
All
All
NE2
All
River
SE6
N2
N2
NE2,
SW2
Multiple
Multiple
Multiple
SE1
SE1
E1
River
SE1,
SSE2
SE1
River
SE1
Arith
Mean
Ratio
1.0
3.3
90
1,000
12
37
1.0
1.0
1.0
1.0
1.0
1.0
220
1.0
14
91
170
3.2
1.2
1.1
1.2
1.3
2.2
2.1
23
7.2
9.6
17
15
8.9
Year 30 Dynamic with Steady-state Inputs
Cone : Year 30 Dynamic Cone
Max
Ratio
1.0
9.2
5.5
3.2
7.8
8.0
7.6
3.3
5.6
7.4
3.2
5.5
6.4
1.4
7.8
5.5
9.3
9.2
2.0
3.2
3.2
3.2
4.7
4.2
7.7
2.2
4.0
8.3
8.3
6.9
Parcel
w/ Max
Multiple
ESE3
ESE3
ESE3
Swetts
Fields
SSE3
ESE3
NE2
SSE3
ESE3
NE2
NE2
NE2
Fields
ESE3
ESE3
ESE3
NE2
ESE3
ESE3
ESE3
ESE2
ESE2
E4
Swetts
ESE5
ESE3
Swetts
SSE4
Min
Ratio
0.2
0.6
0.5
0.4
1.4
2.4
1.5
1.3
1.4
1.5
1.3
0.4
1.9
0.4
1.1
1.5
2.2
2.2
1.4
0.5
0.5
0.6
1.4
1.3
1.6
0.2
1.2
2.3
2.7
1.4
Parcel
w/ Min
NNW3
Source
Source
Source
River
River
N2
W2
N1
N2
W2
N1
W1
N1
River
W2
W2
W2
SW2
N2
N2
N2
W2
W2
E1
River
N2
N2
River
W2
Arith
Mean
Ratio
0.6
5.1
3.3
2.1
5.9
6.1
5.8
2.5
3.2
5.6
2.4
2.6
3.3
0.8
5.6
3.5
5.8
5.7
1.7
2.1
2.1
2.2
3.1
2.9
3.8
1.7
2.7
5.4
6.3
3.6
JULY 2005
C-21
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX c
STEADY-STATE: INPUTS AND DETAILED RESULTS
Compartment Type
Water-column Herbivore
Water-column Omnivore
Water-column Carnivore
Benthic Invertebrate
Benthic Omnivore
Benthic Carnivore
Steady-state Cone : Year 30 Dynamic Cone
Max
Ratio
100
99
98
360
360
370
Parcel
w/ Max
Brewer
Brewer
Brewer
Swetts
Swetts
Swetts
Min
Ratio
8.2
9.2
9.7
7.6
7.8
8.2
Parcel
w/ Min
River
River
River
River
River
V
Arith
Mean
Ratio
78
69
67
260
260
260
Steady-state Cone : Year 30 Dynamic w/
Steady-state Inputs Cone
Max
Ratio
19
15
14
74
74
78
Parcel
w/ Max
Thurston
Thurston
Thurston
Thurston
Thurston
Thurston
Min
Ratio
3.1
3.3
3.4
3.2
3.2
3.3
Parcel
w/ Min
River
River
River
River
River
River
Arith
Mean
Ratio
12
10
10
41
41
42
Year 30 Dynamic with Steady-state Inputs
Cone : Year 30 Dynamic Cone
Max
Ratio
8.2
8.4
8.4
8.0
8.0
8.0
Parcel
w/ Max
Swetts
Swetts
Swetts
Fields
Fields
Fields
Min
Ratio
2.6
2.8
2.8
2.4
2.4
2.5
Parcel
w/ Min
River
River
River
River
River
River
Arith
Mean
Ratio
6.2
6.4
6.3
5.9
5.9
5.9
a Indicates dynamic concentration was average of years 26-30 rather than year 30 (all leaf and particle on leaf averaged for entire year, zeros included, to facilitate comparison to
steady-state).
JULY 2005
C-22
TRIM.FATE EVALUATION REPORT VOLUME II
-------
APPENDIX D
DETAILED RESULTS FOR SENSITIVITY ANALYSIS
-------
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
AirDensity g cm3
AirTemperature K
AlgaeCarbonContentDryWt
AlgaeDensity g m3
AlgaeDensityinWaterColumn g L
AlgaeGrowthRate
AlgaeRadius
AlgaeUptakeRate
AlgaeUptakeRate
Algae WaterC ontent
AllowExchange Steady State forAir
AllowExchange_SteadyState_forAir
AllowExchange Steady State forAir
AllowExchange Steady State forAir
AllowExchange Steady State forAir
AllowExchange Steady State forAir
AllowExchange Steady State forAir
AllowExchange Steady State forAir
AllowExchange Steady State forOther
AllowExchange Steady State forOther
AllowExchange Steady State forOther
AllowExchange Steady State forOther
AllowExchange Steady State forOther
AllowExchange Steady State forOther
AllowExchange Steady State forOther
AllowExchange Steady State forOther
ArealDensity Freshweight
ArthropodSoilPartitioning TimetoReachAlphaofEquilibrium
ArthropodSoilPartitioning TimetoReachAlphaofEquilibrium
ArthropodSoilPartitioning TimetoReachAlphaofEquilibrium
Arthropod SoilPartitionCoefficient
Arthropod SoilPartitionCoefficient
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
Chemical
All
All
All
All
All
All
All
Hg2
MHg
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Hg2
HgO
MHg
Hg2
MHg
Hg2
Hg2
Hg2
Hg2
Hg2
HgO
HgO
Object Type
Compartment
Scenario
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Air
FullSS
Surface water
Surface water
Surface water
Surface water
Surface water
Surface water
Surface water
Surface water
Leaf- Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Leaf Particle - Coniferous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Grasses/Herbs
Root - Grasses/Herbs
Stem - Grasses/Herbs
Leaf- Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Leaf Particle - Coniferous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Grasses/Herbs
Root - Grasses/Herbs
Stem - Grasses/Herbs
Earthworm
Arthropod
Arthropod
Arthropod
Arthropod
Arthropod
Black-capped Chickadee
Mallard
Mouse
Red-tailed hawk
Short-tailed Shrew
Black-capped Chickadee
Mallard
Number
of VEs b
30
N/a
7
7
7
7
7
7
7
7
7
8
4
7
8
4
4
4
7
8
4
7
8
4
4
4
19
17
17
17
17
17
17
5
17
17
17
17
5
Mean
Input
0.0012
280
0.465
l.OOE+06
0.0025
0.7
2.5
4.00E-11
7.07E-11
0.9
1
0.426
0.426
1
0.426
0.426
0.426
0.426
1
0.35
0.35
1
0.35
0.35
0.35
0.35
0.045
21
21
21
0.46
2.9
1
1
1
1
1
1
1
JULY 2005
D-l
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromArthropods
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
Chemical
HgO
HgO
HgO
MHg
MHg
MHg
MHg
MHg
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Mouse
Red-tailed hawk
Short-tailed Shrew
Black-capped Chickadee
Mallard
Mouse
Red-tailed hawk
Short-tailed Shrew
Bald Eagle
Benthic Carnivore
Benthic Omnivore
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
Water-column Carnivore
Water-column Herbivore
Water-column Omnivore
Bald Eagle
Benthic Carnivore
Benthic Omnivore
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
Water-column Carnivore
Water-column Herbivore
Number
of VEs b
17
17
17
17
5
17
17
17
17
5
5
17
5
17
5
2
14
14
17
17
17
5
5
5
17
5
5
17
5
17
5
2
14
14
17
17
17
5
5
Mean
Input
1
1
1
1
1
1
1
1
1
0.04
0.04
1
1
1
1
1
1
1
1
1
1
0.04
0.04
0.04
1
0.04
0.04
1
1
1
1
1
1
1
1
1
1
0.04
0.04
JULY 2005
D-2
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromPlants
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
Chemical
HgO
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
Hg2
Hg2
Hg2
Hg2
Hg2
HgO
HgO
HgO
HgO
HgO
MHg
MHg
MHg
MHg
MHg
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Water-column Omnivore
Bald Eagle
Benthic Carnivore
Benthic Omnivore
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
Water-column Carnivore
Water-column Herbivore
Water-column Omnivore
Black-capped Chickadee
Mallard
Meadow Vole
Mouse
White-tailed Deer
Black-capped Chickadee
Mallard
Meadow Vole
Mouse
White-tailed Deer
Black-capped Chickadee
Mallard
Meadow Vole
Mouse
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Number
of VEs b
5
17
5
5
17
5
17
5
2
14
14
17
17
17
5
5
5
17
5
2
17
17
17
5
2
17
17
17
5
2
17
17
17
17
17
5
2
14
17
Mean
Input
0.04
1
0.2
0.2
1
1
1
1
1
1
1
1
1
1
0.2
0.2
0.2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
JULY 2005
D-3
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
Chemical
Hg2
Hg2
Hg2
Hg2
Hg2
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Number
of VEs b
14
17
17
17
17
17
17
17
5
2
14
17
14
17
17
17
17
17
17
17
5
2
14
17
14
17
17
17
17
17
17
5
17
5
2
14
17
14
17
Mean
Input
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
JULY 2005
D-4
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWater
AssimilationEfficiencyFromWonns
AssimilationEfficiencyFromWorms
AssimilationEfficiencyFromWorms
AssimilationEfficiencyFromWorms
AssimilationEfficiencyFromWonns
AssimilationEfficiencyFromWorms
AttenuationF actor
AttenuationF actor
AttenuationF actor
AverageLeafArealndex No Time Dependence
Chemical
Hg2
Hg2
Hg2
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
Hg2
Hg2
HgO
HgO
MHg
MHg
All
All
All
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Raccoon
Short-tailed Shrew
Raccoon
Short-tailed Shrew
Raccoon
Short-tailed Shrew
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Leaf- Coniferous Forest
Number
of VEs b
17
17
17
17
17
5
17
5
2
14
17
14
17
17
17
17
17
17
5
17
5
2
14
17
14
17
17
17
17
14
17
14
17
14
17
7
8
4
7
Mean
Input
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2.9
2.9
2.9
5
JULY 2005
D-5
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
AverageLeafArealndex No Time Dependence
AverageLeafArealndex No Time Dependence
AverageVerticalVelocity
AverageVerticalVelocity
AverageVerticalVelocity
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BW
BiomassPerArea kg m2
BiomassPerArea kg m2
Boundary Lay erThicknessAboveSediment
BulkWaterFlowRate Volumetric
BulkWaterFlowRate Volumetric
BulkWaterFlowRate Volumetric
BulkWaterFlowRate Volumetric
BulkWaterFlowRate Volumetric
ChlorideConcentration mg L
ChlorideConcentration mg L
ChlorophyllConcentration mg L
CorrectionExponent
CorrectionExponent
CorrectionExponent
CorrectionExponent
Chemical
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Link
Link
Link
Link
Link
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Arthropod
Bald Eagle
Benthic Carnivore
Benthic Omnivore
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
Water-column Carnivore
Water-column Herbivore
Water-column Omnivore
White-tailed Deer
Arthropod
Macrophyte
Surface water
from SW Brewer, to SW Fields
from SW Fields, to SW StreamN
from SW StreamN, to SW River
from SW Streams, to SW Brewer
from SW Thurston, to SW StreamS
Surface water
Surface water - River
Surface water
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Root - Grasses/Herbs
Stem - Grasses/Herbs
Number
of VEs b
8
4
20
20
20
17
17
5
5
17
5
17
5
2
14
17
14
17
17
17
5
5
5
17
17
5
7
1
1
1
1
1
6
1
7
8
4
4
4
Mean
Input
3.4
5
6.00E-04
6.00E-04
6.00E-04
1.31E-04
4.74
2
0.25
0.0108
4.134
0.147
1.134
0.0441
0.8315
0.02
6.35
1.126
0.022
0.0201
2
0.025
0.25
74.8
3.01E-04
1.5
0.02
4.30E+04
5.30E+04
7.41E+04
1.62E+04
8,060
2.8
3.4
0.0053
0.76
0.76
0.76
0.76
JULY 2005
D-6
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
Current Velocity
CurrentVelocity
Current Velocity
D pureair
D pureair
D pureair
D purewater
D purewater
D purewater
DegreeStomatalOpening
DegreeStomatalOpening
DegreeStomatalOpening
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
DemethylationRate
Density
DiffusiveExchangeCoefficient
DimensionlessViscousSublayerThickness
DistanceBetweenMidpoints
Chemical
All
All
All
Hg2
HgO
MHg
Hg2
HgO
MHg
All
All
All
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
All
All
All
All
Object Type
Compartment
Compartment
Compartment
Chemical
Chemical
Chemical
Chemical
Chemical
Chemical
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Link
Compartment
Link
Object Name / Link Information
Surface water - River
Surface water - Stream N
Surface water - Stream S
Divalent Mercury
Elemental Mercury
Methy IMercury
Divalent Mercury
Elemental Mercury
Methy IMercury
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Bald Eagle
Black-capped Chickadee
Common Loon
Groundwater
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Sediment
Short-tailed Shrew
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Stem - Grasses/Herbs
Surface water
Tree swallow
White-tailed Deer
Macrophyte
Various
Surface water
from SW Brewer, to SW Fields
Number
of VEs b
1
1
1
N/a
N/a
N/a
N/a
N/a
N/a
7
8
4
17
17
5
20
7
8
4
17
5
2
14
17
14
17
7
17
20
20
20
4
7
17
17
5
10
7
1
Mean
Input
0.166
0.736
0.0247
0.4784
0.4784
0.456
5.54E-05
5.54E-05
5.28E-05
1
1
1
0.09
0.09
0.09
0.06
0.03
0.03
0.03
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.0501
0.09
0.06
0.06
0.06
0.03
0.013
0.09
0.09
1
2.25E-04
4
2,230
JULY 2005
D-7
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
DistanceBetweenMidpoints
DistanceBetweenMidpoints
DistanceBetweenMidpoints
DistanceBetweenMidpoints
DistanceBetweenMidpoints
DistanceBetweenMidpoints
DistanceBetweenMidpoints
DistanceBetweenMidpoints
DistanceBetweenMidpoints
DragCoefficient
DustDensity
DustLoad
elevation
emissionRate
emissionRate
FlowRateofTranspiredWaterperAreaofLeafSurface
Flushes per year
Flushes per year
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FoodlngestionRate
FractionOrganicMatteronParticulates
FractionPhloemRatewithTranspirationFlowRate
FractionofAreaAvailableforRunoff
Fractionofareaavailableforerosion
Fractionofareaavailableforverticaldiffusion
HenryLawConstant
HenryLawConstant
HenryLawConstant
Chemical
All
All
All
All
All
All
All
All
All
All
All
All
All
Hg2
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Hg2
HgO
MHg
Object Type
Link
Link
Link
Link
Link
Link
Link
Link
Link
Compartment
Compartment
Compartment
Source
Source
Source
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Chemical
Chemical
Chemical
Object Name / Link Information
from SW Brewer, to SW StreamS
from SW Fields, to SW Brewer
from SW Fields, to SW StreamN
from SW River, to SW StreamN
from SW StreamN, to SW Fields
from SW StreamN, to SW River
from SW StreamS, to SW Brewer
from SW StreamS, to SW Thurston
from SW Thurston, to SW StreamS
Surface water
Air
Air
Facility
Facility
Facility
Stem - Grasses/Herbs
Surface water - River
Surface water - Swetts
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Air
Stem - Grasses/Herbs
Soil - Surface
Soil - Surface
Soil - Surface
Divalent Mercury
Elemental Mercury
MethylMercury
Number
of VEs b
1
1
1
1
1
1
1
1
1
7
30
30
N/a
N/a
N/a
4
1
1
17
17
5
17
5
2
14
17
14
17
17
17
17
30
4
20
20
20
N/a
N/a
N/a
Mean
Input
3,015
2,230
4,195
3,715
4,195
3,715
3,015
2,075
2,075
0.0011
1,400
6.15E-08
0.01
17.66
335.6
0.0048
531.2
4.31
0.12
0.74
0.23
0.0735
0.1
0.097
0.14
0.2
0.11
0.12
0.47
0.198
0.05
0.2
0.05
1
1
1
7.19E-05
719.4
0.04762
JULY 2005
D-8
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
horizontalWindSpeed
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
Chemical
All
Hg2
Hg2
Hg2
Hg2
Hg2
HgO
HgO
HgO
HgO
HgO
MHg
MHg
MHg
MHg
MHg
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
Object Type
Scenario
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
FullSS
Benthic Carnivore
Benthic Omnivore
Water-column Carnivore
Water-column Herbivore
Water-column Omnivore
Benthic Carnivore
Benthic Omnivore
Water-column Carnivore
Water-column Herbivore
Water-column Omnivore
Benthic Carnivore
Benthic Omnivore
Water-column Carnivore
Water-column Herbivore
Water-column Omnivore
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Number
of VEs b
N/a
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
17
17
5
17
5
2
14
17
14
17
17
17
17
17
17
5
17
5
2
14
17
14
17
Mean
Input
3.64
3
3
3
3
3
3
3
3
3
3
1
1
1
1
1
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
JULY 2005
D-9
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps A
InhalationProps B
InhalationProps B
InhalationProps B
InhalationProps B
InhalationProps B
InhalationProps B
InhalationProps B
InhalationProps B
InhalationProps B
InhalationProps B
Chemical
HgO
HgO
HgO
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Number
of VEs b
17
17
17
17
17
5
17
5
2
14
17
14
17
17
17
17
17
17
5
17
5
2
14
17
14
17
17
17
17
17
17
5
17
5
2
14
17
14
17
Mean
Input
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.409
0.409
0.409
0.5458
0.409
0.5458
0.5458
0.5458
0.5458
0.409
0.5458
0.409
0.5458
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
JULY 2005
D-10
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
InhalationProps B
InhalationProps B
InhalationProps B
InputCharacteristicDepth m
InputCharacteristicDepth m
InputCharacteristicDepth m
InputCharacteristicDepth m
InputCharacteristicDepth m
InputCharacteristicDepth m
InputCharacteristicDepth m
InputCharacteristicDepth m
InputCharacteristicDepth m
isDay Steady State forAir
isDay Steady State forOther
K ow
K ow
K ow
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
Kd
LeafWettingFactor
LengthofLeaf
LengthofLeaf
LengthofLeaf
Chemical
All
All
All
Hg2
Hg2
Hg2
HgO
HgO
HgO
MHg
MHg
MHg
All
All
Hg2
HgO
MHg
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
HgO
HgO
HgO
HgO
HgO
HgO
MHg
MHg
MHg
MHg
MHg
MHg
All
All
All
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Scenario
Scenario
Chemical
Chemical
Chemical
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Short-tailed Shrew
Tree swallow
White-tailed Deer
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
FullSS
FullSS
Divalent Mercury
Elemental Mercury
MethylMercury
Groundwater
Sediment
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Surface water
Groundwater
Sediment
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Surface water
Groundwater
Sediment
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Surface water
Leaf - Deciduous Forest
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Number
of VEs b
17
17
17
20
20
20
20
20
20
20
20
20
N/a
N/a
N/a
N/a
N/a
20
7
20
20
20
7
20
7
20
20
20
7
20
7
20
20
20
7
8
7
8
4
Mean
Input
0.8
0.8
0.8
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.552
0.609
3.33
4.15
1.7
5.80E+04
5.80E+04
5.80E+04
5.80E+04
5.80E+04
l.OOE+05
1,000
3,000
1,000
1,000
1,000
1,000
7,000
3,000
7,000
7,000
7,000
l.OOE+05
3.00E-04
0.01
0.1
0.05
JULY 2005
D-ll
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
LipidContent
LipidContent
LipidContent
LipidContent
LitterFallRate
LitterFallRate
LitterFallRate
Methy lationRate
Methy lationRate
Methy lationRate
Methy lationRate
Methy lationRate
Methy lationRate
NumberofFishperSquareMeter
NumberofFishperSquareMeter
NumberofFishperSquareMeter
NumberofFishperSquareMeter
NumberofFishperSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
Chemical
All
All
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Root - Grasses/Herbs
Stem - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Groundwater
Sediment
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Surface water
Benthic Carnivore
Benthic Omnivore
Water-column Carnivore
Water-column Herbivore
Water-column Omnivore
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink -El
Mink-E4
Mink - ESE3
Mink - ESE4
Mink - ESE5
Mink-N2
Mink-NE2
Mink - SE1
Mink - SE6
Mink - SSE2
Mink - SSE3
Mink - SSE4
Mink - SSE5
Mink - SW2
Mouse
Number
of VEs b
8
4
4
4
7
8
4
20
7
20
20
20
7
5
5
5
5
5
17
17
5
17
5
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
17
Mean
Input
0.00224
0.00224
0.011
0.00224
0.0021
0.013
0.013
0.001
l.OOE-04
0.001
0.001
0.001
0.001
1.07E-04
0.00755
8.95E-05
0.06584
0.00234
1.30E-08
3.50E-05
4.90E-08
6.50E-06
9.30E-06
0.006
2.72E-07
6.02E-07
2.29E-07
2.10E-07
6.02E-07
3.70E-07
2.72E-07
2.72E-07
1.96E-07
2.72E-07
2.72E-07
2.10E-07
1.96E-07
3.70E-07
0.0023
JULY 2005
D-12
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
NumberoflndividualsPerSquareMeter
OrganicCarbonContent
OrganicCarbonContent
OrganicCarbonContent
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
Chemical
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Raccoon - El
Raccoon - E4
Raccoon - ESE3
Raccoon - ESE4
Raccoon - ESE5
Raccoon - N2
Raccoon - NE2
Raccoon - SE1
Raccoon - SE6
Raccoon - SSE2
Raccoon - SSE3
Raccoon - SSE4
Raccoon - SSE5
Raccoon - SW2
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Groundwater
Soil - Root Zone
Soil - Vadose Zone
Air
Bald Eagle
Benthic Carnivore
Benthic Omnivore
Black-capped Chickadee
Common Loon
Groundwater
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Long-tailed Weasel
Macrophyte
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Number
of VEs b
1
1
1
1
1
1
1
1
1
1
1
1
1
1
17
17
17
17
20
20
20
30
17
5
5
17
5
20
7
8
4
17
5
5
2
14
17
14
17
Mean
Input
9.06E-07
2.01E-06
7.65E-07
6.99E-07
2.01E-06
1.24E-06
9.06E-07
9.06E-07
6.52E-07
9.06E-07
9.06E-07
6.99E-07
6.52E-07
1.24E-06
6.70E-07
6.10E-04
7.00E-04
4.60E-05
0.01
0.01664
0.00128
0.00385
1
l.OOE+06
l.OOE+06
1
1
l.OOE-08
l.OOE+06
l.OOE+06
l.OOE+06
1
l.OOE+09
1
1
1
1
1
1
JULY 2005
D-13
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
pH
phi
PhloemDensity
Porosity
Rain
ReductionRate
ReductionRate
ReductionRate
ReductionRate
ReductionRate
ReductionRate
rho
rho
rho
rho
rho
rho
Root RootZonePartitioningBulkSoil PartitionCoefficient
Root RootZonePartitioningBulkSoil PartitionCoefiicient
Root RootZonePartitioningBulkSoil TimetoReachAlphaofSS
Root RootZonePartitioningBulkSoil TimetoReachAlphaofSS
Root RootZonePartitioningBulkSoil TimetoReachAlphaofSS
SedimentDeposition Velocity
SedimentPartitioning PartitionCoefficient
SedimentPartitioning PartitionCoefficient
SedimentPartitioning PartitionCoefficient
SedimentPartitioning TimeToReachAlphaofEquilibrium
SedimentPartitioning TimeToReachAlphaofEquilibrium
SedimentPartitioning TimeToReachAlphaofEquilibrium
SoillngestionRate
SoillngestionRate
SoillngestionRate
SoillngestionRate
Chemical
HgO
HgO
HgO
HgO
HgO
HgO
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
All
All
All
All
All
All
Hg2
MHg
Hg2
HgO
MHg
All
Hg2
HgO
MHg
Hg2
HgO
MHg
All
All
All
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Scenario
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Short-tailed Shrew
Tree swallow
Water-column Carnivore
Water-column Herbivore
Water-column Omnivore
White-tailed Deer
Surface water
Sediment
Stem - Grasses/Herbs
Groundwater
FullSS
Groundwater
Sediment
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Surface water
Groundwater
Sediment
Soil - Root Zone
Soil - Surface
Soil - Vadose Zone
Surface water
Root - Grasses/Herbs
Root - Grasses/Herbs
Root - Grasses/Herbs
Root - Grasses/Herbs
Root - Grasses/Herbs
Surface water
Benthic Invertebrate
Benthic Invertebrate
Benthic Invertebrate
Benthic Invertebrate
Benthic Invertebrate
Benthic Invertebrate
Mallard
Meadow Vole
Mouse
Raccoon
Number
of VEs b
17
17
5
5
5
17
7
7
4
20
N/a
20
7
20
20
20
7
20
7
20
20
20
7
4
4
4
4
4
7
5
5
5
5
5
5
5
2
17
14
Mean
Input
1
1
l.OOE+06
l.OOE+06
l.OOE+06
1
6.8
0.6
1000
0.2
0.0041
3.25E-06
l.OOE-06
3.25E-06
1.25E-05
3.25E-06
0.0075
2,600
2,650
2,600
2,600
2,600
2,650
0.18
1.2
21
21
21
2
0.0824
0.0824
5.04
14
14
14
0.0033
0.0023
0.02
0.094
JULY 2005
D-14
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
SoillngestionRate
SoillngestionRate
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Chemical
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Type
Compartment
Compartment
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Object Name / Link Information
Short-tailed Shrew
White-tailed Deer
from Air ENEl,toAir ENE2
from Air ENEl,toAir ESE1
from Air ENEl,toAir NNE1
from Air ENEl,toAir Source
from Air ENE2,toAir ENE1
from Air ENE2, to Air ENE3
from Air ENE2, to Air ESE2
from Air ENE2, to Air NNE2
from Air ENE3,toAir ENE2
from Air ENE3,toAir ENE4
from Air ENE3,toAir ESE3
from Air ENE3,toAir NNE3
from Air ENE4, to Air ENE3
from Air ENE4, to Air ENE5
from Air ENE4, to Air ESE4
from Air ENE5,toAir ENE4
from Air ENE5,toAir ESE5
from Air ESEl,toAir ENE1
from Air ESEl,toAir ESE2
from Air ESEl,toAir SSE1
from Air ESEl,toAir Source
from Air ESE2, to Air ENE2
from Air ESE2,toAir ESE1
from Air ESE2,toAir ESE3
from Air ESE2,toAir SSE2
from Air ESE3,toAir ENE3
from Air ESE3,toAir ESE2
from Air ESE3,toAir ESE4
from Air ESE3,toAir SSE3
from Air ESE4, to Air ENE4
from Air ESE4,toAir ESE3
from Air ESE4,toAir ESE5
from Air ESE4,toAir SSE4
from Air ESE5,toAir ENE5
from Air ESE5,toAir ESE4
from Air ESE5,toAir SSE5
from Air NNEl,toAir ENE1
Number
of VEs b
17
17
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean
Input
0.0611
0.001
60.61
128.5
260.5
17.32
38.41
33.18
39.22
90.04
52.84
27.46
21.87
45.5
57.34
12.45
21.81
56.95
43.85
167.1
61.1
208.2
15.48
51.09
37.9
33.16
69.81
27.83
52.01
27.87
36.04
18.79
38.77
19.66
22.7
12.47
29.83
15.39
133.1
JULY 2005
D-15
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Chemical
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Type
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Object Name / Link Information
from Air NNE1, to Air NNE2
from Air NNEl,toAir NNW1
from Air NNEl,toAir Source
from Air NNE2, to Air ENE2
from Air NNE2,toAir NNE1
from Air NNE2, to Air NNE3
from Air NNE2, to Air NNW2
from Air NNE3,toAir ENE3
from Air NNE3,toAir NNE2
from Air NNE3,toAir NNW3
from Air NNW1, to Air NNE1
from Air NNWl,toAir NNW2
from Air NNW1, to Air Source
from Air NNWl,toAir WNW1
from Air NNW2, to Air NNE2
from Air NNW2, to Air NNW1
from Air NNW2, to Air NNW3
from Air NNW2, to Air W2
from Air NNW3,toAir NNE3
from Air NNW3, to Air NNW2
from Air NNW3,toAir W3
from Air SSEl,toAir ESE1
from Air SSEl,toAir SSE2
from Air SSEl,toAir SSW1
from Air SSEl,toAir Source
from Air SSE2,toAir ESE2
from Air SSE2,toAir SSE1
from Air SSE2,toAir SSE3
from Air SSE2,toAir SSW2
from Air SSE3,toAir ESE3
from Air SSE3,toAir SSE2
from Air SSE3,toAir SSE4
from Air SSE3,toAir SSW3
from Air SSE4,toAir ESE4
from Air SSE4,toAir SSE3
from Air SSE4,toAir SSE5
from Air SSE4,toAir SSW4
from Air SSE5,toAir ESE5
from Air SSE5,toAir SSE4
Number
of VEs b
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean
Input
181.9
165.6
13.38
36.8
26.85
77.83
54.32
23.46
40.63
29.76
62.52
181.6
14.34
215.5
18.58
24.2
81.89
65.1
9.78
40.53
35.86
133.6
146.9
170.8
16
38.08
34.29
66.39
55.36
21.75
47.84
60.7
30.27
14.81
38.49
46.58
19.37
17.76
49.64
JULY 2005
D-16
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Chemical
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Type
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Link
Object Name / Link Information
from Air SSWl,toAir SSE1
from Air SSWl,toAir SSW2
from Air SSWl,toAir Source
from Air SSWl,toAir WSW1
from Air SSW2,toAir SSE2
from Air SSW2,toAir SSW1
from Air SSW2,toAir SSW3
from Air SSW2,toAir W2
from Air SSW3,toAir SSE3
from Air SSW3,toAir SSW2
from Air SSW3,toAir SSW4
from Air SSW3,toAir W3
from Air SSW4,toAir SSE4
from Air SSW4,toAir SSW3
from Air Source, to Air ENE1
from Air Source, to Air ESE1
from Air Source, to Air NNE1
from Air Source, to Air NNW1
from Air Source, to Air SSE1
from Air Source, to Air SSW1
from Air Source, to Air WNW1
from Air Source, to Air WSW1
from Air W2, to Air NNW2
from Air W2, to Air SSW2
from Air W2,toAir W3
from Air W2,toAir WNW1
from Air W2,toAir WSW1
from Air W3,toAir NNW3
from Air W3,toAir SSW3
from Air W3,toAir W2
from Air WNW1 , to Air NNW1
from Air WNWl,toAir Source
from Air WNW1 , to Air W2
from Air WNWl,toAir WSW1
from Air WSWl,toAir SSW1
from Air WSWl,toAir Source
from Air WSWl,toAir W2
from Air WSWl,toAir WNW1
from Air ENE4, to Sink 29 for Air ENE4
Number
of VEs b
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean
Input
59.93
144.6
18.03
272.2
17.94
31.69
67.11
87.31
9.92
49.47
61.2
45.73
16.05
98.3
93.05
49.93
247.5
237.7
187.7
207.1
259.9
243
19.51
20.18
86.07
5.64
5.62
10.66
10.89
17.65
127.8
6.61
205.2
132.1
134.9
6.26
206.2
166.8
21.11
JULY 2005
D-17
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
Steady State AdvectiveTransfer
StomatalAreaNormalizedEffectiveDiffusionPathLength
StomatalAreaNormalizedEffectiveDiffusionPathLength
StomatalAreaNormalizedEffectiveDiffusionPathLength
SuspendedSedimentconcentration
SuspendedSedimentconcentration
TSCF
TSCF
TotalErosionRate kg m2 day
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
Chemical
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Hg2
MHg
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
HgO
Object Type
Link
Link
Link
Link
Link
Link
Link
Link
Link
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
from Air ENE5, to Sink 36 for Air ENE5
from Air ENE5, to Sink 37 for Air ENE5
from Air ESE5, to Sink 42 for Air ESE5
from Air NNE3, to Sink 18 for Air NNE3
from Air NNW3, to Sink 20 for Air NNW3
from Air SSE5, to Sink 39 for Air SSE5
from Air SSE5, to Sink 40 for Air SSE5
from Air SSW4, to Sink 31 for Air SSW4
from Air W3, to Sink 23 for Air W3
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Surface water
Surface water - River
Stem - Grasses/Herbs
Stem - Grasses/Herbs
Soil - Surface
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Number
of VEs b
1
1
1
1
1
1
1
1
1
7
8
4
6
1
4
4
20
17
17
5
17
5
2
14
17
14
17
17
17
17
17
17
5
17
5
2
14
17
14
Mean
Input
5.77
41.25
14.3
79.84
79.42
18.94
45.85
91.12
80.16
200
200
200
0.0018
0.015
0.5
0.2
2.89E-04
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
JULY 2005
D-18
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalRunoffRate m3 m2 day
TransferFactortoLeaf
TransferFactortoLeaf
TransferFactortoLeaf
TransferFactortoLeaf
TransferFactortoLeaf
TransferFactortoLeaf
TransferFactortoLeaf
TransferFactortoLeaf
TransferFactortoLeaf
TransferFactortoLeafParticle
TransferFactortoLeafParticle
TransferFactortoLeafParticle
TransferFactortoLeafParticle
TransferFactortoLeafParticle
TransferFactortoLeafParticle
TransferFactortoLeafParticle
TransferFactortoLeafParticle
TransferFactortoLeafParticle
VaporDryDepositionVelocity m day
VaporDryDepositionVelocity m day
VaporDryDepositionVelocity m day
Chemical
HgO
HgO
HgO
HgO
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
MHg
All
Hg2
Hg2
Hg2
HgO
HgO
HgO
MHg
MHg
MHg
Hg2
Hg2
Hg2
HgO
HgO
HgO
MHg
MHg
MHg
Hg2
Hg2
HgO
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Soil - Surface
Leaf Particle - Coniferous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Grasses/Herbs
Leaf Particle - Coniferous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Grasses/Herbs
Leaf Particle - Coniferous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Soil - Surface
Surface water
Soil - Surface
Number
of VEs b
17
17
17
17
17
17
5
17
5
2
14
17
14
17
17
17
17
20
7
8
4
7
8
4
7
8
4
7
8
4
7
8
4
7
8
4
20
7
20
Mean
Input
0.05
0.05
0.05
0.05
0.086
0.086
0.086
0.26
0.086
0.26
0.26
0.26
0.26
0.086
0.26
0.086
0.26
0.00101
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
0.002
864
864
8.64
JULY 2005
D-19
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
VaporWashoutRatio
VaporWashoutRatio
vdep
vdep
vdep
VolumeParticlePerAreaLeaf
VolumeParticlePerAreaLeaf
VolumeParticlePerAreaLeaf
WashoutRatio
WaterColumnDissolvedPartitioning PartitionCoefficient
WaterColumnDissolvedPartitioning PartitionCoefficient
WaterColumnDissolvedPartitioning PartitionCoefficient
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
WaterContent
WaterContent
WaterContent
WaterContent
WaterContent
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps A
WaterlngProps B
WaterlngProps B
WaterlngProps B
WaterlngProps B
WaterlngProps B
WaterlngProps B
Chemical
Hg2
HgO
Hg2
HgO
MHg
All
All
All
All
Hg2
HgO
MHg
Hg2
HgO
MHg
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Type
Chemical
Chemical
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Divalent Mercury
Elemental Mercury
Air
Air
Air
Leaf Particle - Coniferous Forest
Leaf Particle - Deciduous Forest
Leaf Particle - Grasses/Herbs
Air
Macrophyte
Macrophyte
Macrophyte
Macrophyte
Macrophyte
Macrophyte
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Root - Grasses/Herbs
Stem - Grasses/Herbs
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Bald Eagle
Black-capped Chickadee
Common Loon
Long-tailed Weasel
Mallard
Meadow Vole
Number
of VEs b
N/a
N/a
30
30
30
7
8
4
30
5
5
5
5
5
5
7
8
4
4
4
17
17
5
17
5
2
14
17
14
17
17
17
17
17
17
5
17
5
2
Mean
Input
1.60E+06
1,200
500
500
500
l.OOE-09
l.OOE-09
l.OOE-09
2.00E+05
0.883
0.883
4.4
18
18
18
0.8
0.8
0.8
0.8
0.8
0.059
0.059
0.059
0.099
0.059
0.099
0.099
0.099
0.099
0.059
0.099
0.059
0.099
0.67
0.67
0.67
0.9
0.67
0.9
JULY 2005
D-20
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.I. Input Properties Assessed in the Mercury Test Case Sensitivity Analysis
Property a
WaterlngProps B
WaterlngProps B
WaterlngProps B
WaterlngProps B
WaterlngProps B
WaterlngProps B
WaterlngProps B
WaterTemperature K
Water content
WetDensity
WetDensity
WetDensity
WetDensity
WetDensity
WetDepInterceptionFraction UserSupplied
WetDepInterceptionFraction UserSupplied
WetDepInterceptionFraction UserSupplied
WetMassperArea
WetMassperArea
WetMassperArea
WetMassperArea
WetMassperArea
WormSoillnteraction t alpha
WormSoillnteraction t alpha
WormSoillnteraction t alpha
WormSoilPartitionCoefficient dryweight
WormSoilPartitionCoefficient dryweight
WormSoilPartitionCoefficient dryweight
XylemDensity
Chemical
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Hg2
HgO
MHg
Hg2
HgO
MHg
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Volume Element
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link Information
Mink
Mouse
Raccoon
Red-tailed hawk
Short-tailed Shrew
Tree swallow
White-tailed Deer
Various
Earthworm
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Root - Grasses/Herbs
Stem - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Deciduous Forest
Leaf - Grasses/Herbs
Root - Grasses/Herbs
Stem - Grasses/Herbs
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Earthworm
Stem - Grasses/Herbs
Number
of VEs b
14
17
14
17
17
17
17
7
19
7
8
4
4
4
7
8
4
7
8
4
4
4
19
19
19
19
19
19
4
Mean
Input
0.9
0.9
0.9
0.67
0.9
0.67
0.9
293.2
0.84
820
820
820
820
830
0.2
0.2
0.2
2
0.6
0.6
1.4
0.24
21
21
21
0.36
0.36
0.36
900
aFor additional description of the input properties used in TRIM.FaTE, along with a key between common names used for properties and their TRIM.FaTE code names, see Module 16
of the TRIM.FaTE User's Guide and the TRIM.FaTE technical support documents.
b Value indicates number of volume elements (VEs) a property applies to (and was varied in at the same time in the sensitivity analysis). For link object types, the number of links is
reported, not the number of VEs.
JULY 2005
D-21
TRIM.FATE EVALUATION REPORT VOLUME II
-------
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Air Compartment ESE1
Property
emissionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
FullSS
Divalent Mercury
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.999
-0.444
0.397
-0.349
0.333
-0.255
-0.228
0.199
0.189
0.187
-0.149
-0.140
-0.139
-0.131
-0.121
-0.118
-0.118
0.114
0.100
-0.099
0.099
-0.082
-0.079
-0.070
-0.051
0.050
0.049
0.048
-0.048
-0.045
Sensitivity Score
0.999
-0.038
0.065
-0.043
0.037
-0.030
-0.024
0.049
0.021
0.026
-0.016
-0.015
-0.022
-0.014
-0.015
-0.014
-0.354
0.018
0.011
-0.011
0.016
-0.010
-0.009
-0.008
-0.008
0.007
0.006
0.008
-0.005
-0.005
Elemental Mercury in Air Compartment ESE1
Property
emissionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.997
-0.458
0.399
-0.360
0.338
-0.271
-0.245
0.194
0.183
0.183
-0.146
-0.140
-0.138
-0.127
0.122
-0.117
0.117
-0.110
0.095
-0.089
-0.086
-0.068
-0.066
-0.062
-0.056
0.056
-0.056
0.055
0.054
0.053
Sensitivity Score
0.997
-0.039
0.066
-0.044
0.038
-0.031
-0.026
0.027
0.045
0.020
-0.016
-0.022
-0.015
-0.014
0.020
-0.014
0.013
-0.012
0.015
-0.010
-0.010
-0.007
-0.007
-0.007
-0.005
0.008
-0.005
0.007
0.009
0.009
July 2005
D-23
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Air Compartment ESE1
Property
rho
AirTemperature K
emissionRate
HenryLawConstant
Kd
MethylationRate
DimensionlessViscousSublayerThickness
horizontalWindSpeed
Rain
VaporWashoutRatio
SedimentDepositi on Velocity
D_pureair
SteadyState AdvectiveTransfer
WaterTemperature K
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DragCoefficient
ReductionRate
SteadyState AdvectiveTransfer
rho
SuspendedSedimentconcentration
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
Chemical
All
All
HgO
MHg
MHg
Hg2
All
All
All
Hg2
All
MHg
All
All
All
Hg2
All
All
All
All
All
Hg2
All
All
All
All
All
HgO
All
HgO
Object Name
Surface water
FullSS
Facility
MethylMercury
Surface water
Surface water
Surface water
FullSS
FullSS
Divalent Mercury
Surface water
MethylMercury
Link from Air, to Air
Surface water
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Soil-Surface
Link from Air, to Air
Soil-Surface
Surface water
Link from Air, to Air
Link from Air, to Air
Macrophyte
Macrophyte
Macrophyte
Elasticity
6.901
-1.671
0.992
0.748
-0.693
0.664
-0.646
0.642
0.563
0.561
-0.554
0.500
-0.441
-0.432
-0.424
-0.389
0.370
-0.359
-0.358
0.322
0.322
-0.305
0.303
-0.298
-0.297
-0.292
-0.266
-0.263
0.262
0.262
Sensitivity Score
0.345
-0.002
0.992
0.748
-0.693
0.664
-0.194
0.642
0.069
1.683
-0.166
0.025
-0.038
-0.432
-0.052
-0.389
0.040
-0.039
-0.038
0.036
0.096
-0.305
0.050
-0.015
-0.089
-0.034
-0.029
-0.790
0.261
0.785
Divalent Mercury in Air Compartment SSE1
Property
emissionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
FullSS
Divalent Mercury
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.999
0.458
-0.434
-0.330
0.217
0.195
-0.174
-0.147
-0.140
-0.133
-0.129
0.123
-0.116
0.116
-0.099
-0.098
-0.098
-0.094
0.089
-0.075
0.072
-0.063
0.056
-0.055
-0.047
-0.043
-0.041
-0.040
-0.039
-0.038
Sensitivity Score
0.999
0.051
-0.046
-0.037
0.035
0.017
-0.029
-0.016
-0.016
-0.016
-0.016
0.014
-0.013
0.014
-0.011
-0.012
-0.295
-0.012
0.013
-0.008
0.010
-0.007
0.014
-0.006
-0.005
-0.007
-0.005
-0.004
-0.005
-0.005
July 2005
D-24
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Air Compartment SSE1
Property
emissionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.998
-0.456
0.431
-0.333
0.228
0.199
-0.174
-0.153
-0.144
-0.141
0.134
0.132
-0.129
-0.113
-0.112
0.096
-0.086
0.080
-0.079
-0.075
-0.072
-0.063
-0.059
-0.057
0.056
0.045
-0.045
0.045
-0.044
-0.043
Sensitivity Score
0.998
-0.049
0.048
-0.037
0.037
0.017
-0.029
-0.018
-0.016
-0.017
0.016
0.015
-0.016
-0.012
-0.013
0.014
-0.011
0.011
-0.009
-0.008
-0.008
-0.007
-0.007
-0.007
0.014
0.007
-0.005
0.006
-0.007
-0.005
Methyl Mercury in Air Compartment SSE1
Property
rho
AirTemperature K
emissionRate
HenryLawConstant
Kd
DimensionlessViscousSublayerThickness
horizontalWindSpeed
MethylationRate
SteadyState AdvectiveTransfer
Rain
VaporWashoutRatio
SedimentDepositi on Velocity
D_pureair
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
WaterTemperature K
DragCoefficient
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SuspendedSedimentconcentration
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
Flushes per year
SteadyState AdvectiveTransfer
Chemical
All
All
Hg2
MHg
MHg
All
All
Hg2
All
All
Hg2
All
MHg
All
All
Hg2
All
All
Hg2
All
All
All
All
All
All
HgO
All
HgO
All
All
Object Name
Surface water
FullSS
Facility
MethylMercury
Surface water
Surface water
FullSS
Surface water
Link from Air, to Air
FullSS
Divalent Mercury
Surface water
MethylMercury
Link from Air, to Air
Link from Air, to Air
Surface water
Surface water
Surface water
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Macrophyte
Macrophyte
Macrophyte
Surface water
Link from Air, to Air
Elasticity
6.517
-1.638
0.993
0.733
-0.685
-0.651
0.647
0.642
-0.616
0.572
0.571
-0.544
0.490
-0.426
0.371
-0.355
-0.333
0.324
-0.314
-0.307
-0.283
0.270
-0.258
0.257
-0.241
-0.240
0.239
0.239
-0.233
-0.231
Sensitivity Score
0.326
-0.002
0.993
0.733
-0.685
-0.195
0.647
0.641
-0.066
0.070
1.713
-0.163
0.025
-0.046
0.046
-0.354
-0.332
0.097
-0.314
-0.015
-0.031
0.023
-0.077
0.041
-0.030
-0.721
0.239
0.717
-0.070
-0.027
July 2005
D-25
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Air Compartment SSE3
Property
emissionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
FullSS
Divalent Mercury
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.996
0.507
-0.323
0.307
-0.290
-0.290
-0.263
0.248
-0.246
-0.245
-0.214
0.185
-0.167
0.162
0.155
-0.135
0.134
-0.118
-0.113
-0.108
-0.108
-0.108
0.107
-0.099
0.098
0.095
-0.094
-0.088
0.086
0.086
Sensitivity Score
0.996
0.057
-0.036
0.034
-0.035
-0.869
-0.029
0.027
-0.030
-0.027
-0.023
0.029
-0.019
0.026
0.019
-0.016
0.019
-0.020
-0.012
-0.013
-0.012
-0.013
0.017
-0.011
0.014
0.008
-0.012
-0.011
0.012
0.007
Elemental Mercury in Air Compartment SSE3
Property
emissionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.993
0.488
-0.346
-0.293
0.287
-0.277
-0.260
0.224
-0.218
-0.194
0.193
0.177
0.176
-0.155
0.152
-0.141
-0.131
-0.124
-0.121
-0.115
0.109
0.107
-0.104
0.103
-0.096
-0.094
-0.092
-0.090
0.089
0.087
Sensitivity Score
0.993
0.055
-0.039
-0.032
0.032
-0.030
-0.032
0.025
-0.023
-0.022
0.031
0.028
0.021
-0.017
0.022
-0.016
-0.014
-0.014
-0.020
-0.014
0.018
0.015
-0.012
0.009
-0.010
-0.011
-0.011
-0.011
0.013
0.010
July 2005
D-26
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Air Compartment SSE3
Property
rho
AirTemperature K
emissionRate
HenryLawConstant
Kd
DimensionlessViscousSublayerThickness
horizontalWindSpeed
MethylationRate
SedimentDepositi on Velocity
Rain
VaporWashoutRatio
D_pureair
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Flushes per year
DragCoefficient
SuspendedSedimentconcentration
ReductionRate
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
SteadyState AdvectiveTransfer
Chemical
All
All
Hg2
MHg
MHg
All
All
Hg2
All
All
Hg2
MHg
All
All
All
All
All
All
Hg2
All
Hg2
All
All
All
All
All
HgO
All
HgO
All
Object Name
Surface water
FullSS
Facility
MethylMercury
Surface water
Surface water
FullSS
Surface water
Surface water
FullSS
Divalent Mercury
MethylMercury
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Surface water
Surface water
Surface water
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Soil - Surface
Macrophyte
Macrophyte
Macrophyte
Link from Air, to Air
Elasticity
6.618
-1.745
0.993
0.779
-0.754
-0.741
0.735
0.691
-0.606
0.571
0.570
0.521
0.512
-0.448
-0.422
-0.383
0.368
-0.361
-0.324
-0.315
-0.306
-0.304
0.270
-0.266
0.248
0.248
-0.221
0.220
0.220
0.206
Sensitivity Score
0.331
-0.002
0.993
0.779
-0.754
-0.222
0.735
0.690
-0.182
0.070
1.709
0.026
0.057
-0.049
-0.051
-0.115
0.110
-0.108
-0.324
-0.034
-0.306
-0.015
0.030
-0.030
0.074
0.074
-0.663
0.220
0.660
0.023
Divalent Mercury in Soil - Surface in SurfSoil SSE4
Property
emissionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WetDepInterceptionFraction UserSupplied
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WaterContent
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf - Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Leaf- Coniferous Fores!
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.995
0.608
0.607
-0.569
-0.569
0.367
-0.366
-0.366
0.265
-0.227
0.223
-0.193
-0.187
-0.178
-0.173
-0.167
-0.163
0.141
0.140
0.134
-0.129
-0.120
-0.116
-0.112
-0.109
-0.108
0.105
0.105
0.096
-0.096
Sensitivity Score
0.995
0.074
1.822
-0.171
-0.171
0.041
-0.365
-0.018
0.029
-0.025
0.025
-0.021
-0.020
-0.022
-0.019
-0.019
-0.163
0.022
0.022
0.019
-0.015
-0.013
-0.013
-0.034
-0.012
-0.012
0.053
0.015
0.016
-0.010
July 2005
D-27
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Soil - Surface in SurfSoil SSE4
Property
HenryLawConstant
Kd
AirTemperature K
Fractionofareaavailableforverticaldiffiision
emissionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
D_pureair
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WetDepInterceptionFraction UserSupplied
Chemical
HgO
HgO
All
All
Hg2
All
Hg2
All
All
Hg2
All
HgO
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Elemental Mercury
Soil - Surface
FullSS
Soil - Surface
Facility
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Elemental Mercury
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Leaf - Coniferous Fores!
Elasticity
-1.009
1.000
0.998
-0.962
0.906
0.596
0.553
-0.518
-0.518
0.509
0.508
-0.484
-0.478
0.370
0.264
-0.230
0.221
-0.194
-0.191
-0.181
-0.175
-0.169
-0.159
0.143
0.141
0.135
-0.129
-0.122
-0.117
-0.113
Sensitivity Score
-1.009
1.000
0.001
-0.289
0.906
0.073
1.659
-0.156
-0.156
0.508
0.025
-0.024
-0.143
0.041
0.029
-0.025
0.025
-0.021
-0.021
-0.022
-0.019
-0.02
-0.159
0.023
0.023
0.019
-0.015
-0.013
-0.014
-0.034
Methyl Mercury in Soil - Surface in SurfSoil SSE4
Property
emissionRate
DemethylationRate
MethylationRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WetDepInterceptionFraction UserSupplied
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WaterContent
SteadyState AdvectiveTransfer
Chemical
Hg2
MHg
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Soil - Surface
Soil - Surface
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Leaf - Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Leaf - Coniferous Forest
Link from Air, to Air
Elasticity
0.995
-0.991
0.982
0.608
0.607
-0.570
-0.570
0.367
-0.366
-0.365
0.265
-0.227
0.223
-0.193
-0.187
-0.178
-0.173
-0.167
-0.163
0.141
0.14
0.134
-0.129
-0.120
-0.116
-0.112
-0.109
-0.108
0.105
0.105
Sensitivity Score
0.995
-0.990
0.981
0.074
1.822
-0.171
-0.171
0.041
-0.365
-0.018
0.029
-0.025
0.025
-0.021
-0.020
-0.022
-0.019
-0.019
-0.163
0.022
0.022
0.019
-0.015
-0.013
-0.013
-0.034
-0.012
-0.012
0.053
0.015
July 2005
D-28
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Soil - Surface in SurfSoil SW2
Property
emissionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
rho
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
Hg2
All
All
All
Object Name
Facility
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.997
0.685
0.685
-0.556
-0.556
-0.386
-0.380
-0.380
-0.309
0.247
0.166
-0.158
-0.139
-0.132
-0.102
0.093
0.092
0.084
0.084
-0.083
0.082
-0.078
-0.073
0.070
0.070
-0.064
0.061
0.058
-0.048
0.047
Sensitivity Score
0.997
0.084
2.055
-0.167
-0.167
-0.042
-0.019
-0.379
-0.036
0.035
0.018
-0.017
-0.016
-0.015
-0.012
0.028
0.013
0.012
0.010
-0.009
0.009
-0.01
-0.009
0.008
0.011
-0.008
0.061
0.005
-0.008
0.005
Elemental Mercury in Soil - Surface in SurfSoil SW2
Property
HenryLawConstant
Kd
AirTemperature K
Fractionofareaavailableforverticaldiffusion
emissionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
D_pureair
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
HgO
HgO
All
All
Hg2
All
Hg2
All
All
Hg2
All
HgO
HgO
All
All
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
Object Name
Elemental Mercury
Soil - Surface
FullSS
Soil - Surface
Facility
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Elemental Mercury
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
-1.009
1.000
0.998
-0.978
0.933
0.674
0.641
-0.520
-0.520
0.513
0.512
-0.492
-0.486
-0.384
-0.321
0.256
0.169
-0.160
-0.142
-0.136
-0.106
0.095
0.087
0.086
-0.085
0.085
0.083
-0.078
-0.075
0.071
Sensitivity Score
-1.009
1.000
0.001
-0.293
0.933
0.082
1.923
-0.156
-0.156
0.512
0.026
-0.025
-0.146
-0.042
-0.037
0.036
0.019
-0.017
-0.016
-0.016
-0.012
0.013
0.026
0.012
-0.010
0.010
0.009
-0.01
-0.009
0.008
July 2005
D-29
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Soil - Surface in SurfSoil SW2
Property
emissionRate
DemethylationRate
MethylationRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
Chemical
Hg2
MHg
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
Hg2
Object Name
Facility
Soil - Surface
Soil - Surface
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
from Air, to Air
Elasticity
0.997
-0.992
0.983
0.685
0.685
-0.556
-0.556
-0.386
-0.380
-0.379
-0.309
0.247
0.166
-0.158
-0.139
-0.132
-0.102
0.093
0.092
0.084
0.084
-0.083
0.082
-0.078
-0.073
0.070
0.070
-0.064
0.061
0.058
Sensitivity Score
0.997
-0.991
0.982
0.084
2.055
-0.167
-0.167
-0.042
-0.379
-0.019
-0.036
0.035
0.018
-0.017
-0.016
-0.015
-0.012
0.028
0.013
0.012
0.010
-0.009
0.009
-0.01
-0.009
0.008
0.011
-0.008
0.061
0.005
Divalent Mercury in Worm in RootSoil SSE4
Property
Water content
ReductionRate
rho
WormSoilPartitionCoefficient dryweight
emissionRate
Average VerticalVelocity
Kd
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
Chemical
All
Hg2
All
Hg2
Hg2
All
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
MHg
All
Object Name
Worm
Soil - Root Zone
Soil - Root Zone
Worm
Facility
Soil - Surface
Soil - Surface
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Soil - Surface
Link from Air, to Air
Elasticity
-5.250
-1.009
-1.009
1.000
0.995
0.956
-0.822
0.608
0.607
-0.569
-0.569
0.367
-0.366
-0.366
0.265
-0.227
0.223
-0.193
-0.187
-0.178
-0.173
-0.167
-0.163
0.141
0.140
0.134
-0.129
-0.120
-0.117
-0.116
Sensitivity Score
-5.245
-1.008
-0.050
3.000
0.995
0.287
-0.822
0.074
1.822
-0.171
-0.171
0.041
-0.365
-0.018
0.029
-0.025
0.025
-0.021
-0.020
-0.022
-0.019
-0.019
-0.163
0.022
0.022
0.019
-0.015
-0.013
-0.117
-0.013
July 2005
D-30
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Worm in RootSoil SSE4
Property
Water content
HenryLawConstant
WormSoilPartitionCoefficient dryweight
Kd
AirTemperature K
emissionRate
Fractionofareaavailableforverticaldiffiision
D_pureair
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
ReductionRate
rho
SteadyState AdvectiveTransfer
Average VerticalVelocity
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
HgO
HgO
HgO
All
Hg2
All
HgO
All
Hg2
All
All
All
HgO
Hg2
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
Object Name
Worm
Elemental Mercury
Worm
Soil - Root Zone
FullSS
Facility
Soil - Surface
Elemental Mercury
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Comp
Comp
Soil - Surface
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
-5.250
-1.011
1.000
1.000
0.999
0.927
-0.732
-0.611
0.599
0.566
-0.531
-0.531
0.369
-0.364
0.299
0.299
0.264
0.263
-0.230
0.221
-0.203
-0.193
-0.190
-0.180
-0.174
-0.169
-0.160
0.142
0.141
0.135
Sensitivity Score
-5.245
-1.011
3.000
1.000
0.001
0.927
-0.219
-0.031
0.073
1.698
-0.159
-0.159
0.041
-0.109
0.299
0.015
0.029
0.079
-0.025
0.025
-0.203
-0.021
-0.021
-0.022
-0.019
-0.02
-0.160
0.023
0.023
0.019
Methyl Mercury in Worm in RootSoil SSE4
Property
Water content
DemethylationRate
ReductionRate
rho
WormSoilPartitionCoefficient dryweight
MethylationRate
emissionRate
Average VerticalVelocity
Kd
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
MHg
Hg2
All
MHg
Hg2
Hg2
All
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Worm
Soil - Root Zone
Soil - Root Zone
Soil - Root Zone
Worm
Soil - Root Zone
Facility
Soil - Surface
Soil - Surface
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf - Coniferous Fores!
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Elasticity
-5.250
-1.010
-1.009
-1.009
1.000
1.000
0.995
0.956
-0.822
0.608
0.607
-0.569
-0.569
0.367
-0.366
-0.366
0.265
-0.227
0.223
-0.193
-0.187
-0.178
-0.173
-0.167
-0.163
0.141
0.140
0.134
-0.129
-0.120
Sensitivity Score
-5.245
-1.009
-1.008
-0.050
3.000
0.999
0.995
0.287
-0.822
0.074
1.822
-0.171
-0.171
0.041
-0.365
-0.018
0.029
-0.025
0.025
-0.021
-0.020
-0.022
-0.019
-0.019
-0.163
0.022
0.022
0.019
-0.015
-0.013
July 2005
D-31
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Worm in Root Soil SW2
Property
Water content
WormSoilPartitionCoefficient dryweight
emissionRate
ReductionRate
rho
Average VerticalVelocity
Kd
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
DemethylationRate
MethylationRate
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
Hg2
Hg2
Hg2
All
All
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
MHg
MHg
Hg2
All
Hg2
All
All
All
All
All
Object Name
Worm
Worm
Facility
Soil - Root Zone
Soil - Root Zone
Soil - Surface
Soil - Surface
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
-5.250
1.000
0.997
-0.976
-0.976
0.956
-0.823
0.685
0.685
-0.556
-0.556
-0.386
-0.380
-0.380
-0.309
0.247
0.166
-0.158
-0.139
-0.132
-0.118
-0.108
0.107
-0.102
0.093
0.092
0.084
0.084
-0.083
0.082
Sensitivity Score
-5.245
3.000
0.997
-0.975
-0.049
0.287
-0.823
0.084
2.055
-0.167
-0.167
-0.042
-0.379
-0.019
-0.036
0.035
0.018
-0.017
-0.016
-0.015
-0.118
-0.108
0.107
-0.012
0.028
0.013
0.012
0.010
-0.009
0.009
Elemental Mercury in Worm in RootSoil SW2
Property
Water content
HenryLawConstant
WormSoilPartitionCoefficient dryweight
Kd
AirTemperature K
emissionRate
Fractionofareaavailableforverticaldiffusion
Rain
VaporWashoutRatio
D_pureair
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
ReductionRate
rho
Average VerticalVelocity
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
InputCharacteristicDepth m
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
HgO
HgO
HgO
All
Hg2
All
All
Hg2
HgO
All
All
All
HgO
All
Hg2
All
All
All
Hg2
All
All
All
All
HgO
All
All
Hg2
All
All
Object Name
Worm
Elemental Mercury
Worm
Soil - Root Zone
FullSS
Facility
Soil - Surface
FullSS
Divalent Mercury
Elemental Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Elasticity
-5.250
-1.011
1.000
1.000
0.999
0.948
-0.744
0.677
0.652
-0.617
-0.529
-0.529
-0.384
-0.370
-0.318
0.299
0.299
0.261
0.254
-0.201
0.169
-0.160
-0.141
-0.135
0.125
-0.105
0.094
0.089
0.086
-0.085
Sensitivity Score
-5.245
-1.011
3.000
1.000
0.001
0.948
-0.223
0.083
1.955
-0.031
-0.159
-0.159
-0.042
-0.111
-0.037
0.299
0.015
0.078
0.036
-0.201
0.019
-0.017
-0.016
-0.016
0.125
-0.012
0.013
0.027
0.012
-0.010
July 2005
D-32
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Worm in RootSoil SW2
Property
Water content
DemethylationRate
WormSoilPartitionCoefficient dryweight
MethylationRate
emissionRate
rho
ReductionRate
Average VerticalVelocity
Kd
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
DemethylationRate
MethylationRate
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
MHg
MHg
Hg2
Hg2
All
Hg2
All
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
MHg
MHg
Hg2
All
Hg2
All
All
All
Object Name
Worm
Soil - Root Zone
Worm
Soil - Root Zone
Facility
Soil - Root Zone
Soil - Root Zone
Soil - Surface
Soil - Surface
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
-5.250
-1.008
1.000
0.998
0.997
-0.976
-0.976
0.956
-0.822
0.685
0.685
-0.556
-0.556
-0.386
-0.380
-0.380
-0.309
0.247
0.166
-0.158
-0.139
-0.132
-0.118
-0.109
0.108
-0.102
0.093
0.092
0.084
0.084
Sensitivity Score
-5.245
-1.007
3.000
0.997
0.997
-0.049
-0.975
0.287
-0.822
0.084
2.055
-0.167
-0.167
-0.042
-0.379
-0.019
-0.036
0.035
0.018
-0.017
-0.016
-0.015
-0.118
-0.108
0.107
-0.012
0.028
0.013
0.012
0.010
Divalent Mercury in Leaf - Coniferous Forest in Coniferous Forest in SurfSoil_SSE4
Property
AllowExchange SteadyState forAir
emissionRate
WetMassperArea
WetDepInterceptionFraction UserSupplied
WaterContent
SteadyState AdvectiveTransfer
LitterFallRate
AllowExchange SteadyState forOther
TransferFactortoLeafParticle
SteadyState AdvectiveTransfer
VaporWashoutRatio
Rain
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AttenuationFactor
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
Hg2
All
All
All
All
All
All
Hg2
All
Hg2
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Leaf - Coniferous Forest
Facility
Leaf- Coniferous Fores!
Leaf- Coniferous Forest
Leaf - Coniferous Forest
Link from Air, to Air
Leaf- Coniferous Forest
Leaf- Coniferous Forest
Leaf - Coniferous Forest
Link from Air, to Air
Divalent Mercury
FullSS
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf - Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Sink
Elasticity
1.010
0.994
-0.819
0.696
-0.651
0.534
-0.506
-0.496
-0.478
0.416
0.354
0.354
-0.352
0.273
0.261
-0.257
0.216
-0.215
-0.214
-0.202
-0.191
-0.181
-0.180
0.178
0.168
0.145
-0.144
-0.144
0.144
-0.143
Sensitivity Score
1.009
0.994
-0.819
0.209
-0.326
0.060
-0.506
-0.495
-1.434
0.047
1.062
0.043
-0.039
0.082
0.029
-0.031
0.024
-0.023
-0.023
-0.025
-0.021
-0.021
-0.019
0.028
0.168
0.021
-0.017
-0.016
0.023
-0.014
July 2005
D-33
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Leaf - Coniferous Forest in Coniferous Forest in SurfSoil_SSE4
Property
AllowExchange SteadyState forOther
WetDensity
isDay_SteadyState_forOther
AllowExchange SteadyState forAir
AverageLeafArealndex No Time Dependence
isDay SteadyState forAir
emissionRate
HenryLawConstant
AirTemperature K
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
All
All
HgO
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Leaf - Coniferous Forest
Leaf - Coniferous Fores!
FullSS
Leaf- Coniferous Forest
Leaf - Coniferous Forest
FullSS
Facility
Elemental Mercury
FullSS
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Elasticity
-1.010
-1.010
-1.010
1.000
0.994
0.994
0.992
-0.950
0.940
0.515
0.403
-0.385
-0.262
0.246
-0.244
-0.238
-0.220
-0.214
-0.214
0.199
-0.188
0.174
0.162
-0.161
-0.159
-0.157
0.153
0.153
0.148
-0.147
Sensitivity Score
-1.009
-1.010
-1.009
0.999
0.994
0.993
0.992
-0.950
0.001
0.058
0.045
-0.043
-0.032
0.027
-0.027
-0.026
-0.024
-0.026
-0.026
0.022
-0.020
0.028
0.023
-0.018
-0.017
-0.018
0.024
0.024
0.024
-0.014
Methyl Mercury in Leaf - Coniferous Forest in Coniferous Forest in SurfSoil_SSE4
Property
rho
AllowExchange SteadyState forOther
isDay SteadyState forAir
AverageLeafArealndex No Time Dependence
emissionRate
WetDensity
AllowExchange SteadyState forAir
isDay_SteadyState_forOther
AirTemperature K
Kd
MethylationRate
DimensionlessViscousSublayerThickness
horizontalWindSpeed
SedimentDepositi on Velocity
D_pureair
SteadyState AdvectiveTransfer
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
WaterTemperature K
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DragCoefficient
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
rho
Chemical
All
All
All
All
Hg2
All
All
All
All
MHg
Hg2
All
All
All
MHg
All
All
Hg2
All
All
Hg2
All
All
All
All
All
All
All
Hg2
All
Object Name
Surface water
Leaf - Coniferous Forest
FullSS
Leaf- Coniferous Forest
Facility
Leaf - Coniferous Forest
Leaf- Coniferous Forest
FullSS
FullSS
Surface water
Surface water
Surface water
FullSS
Surface water
MethylMercury
Link from Air, to Air
FullSS
Divalent Mercury
Link from Air, to Air
Link from Air, to Air
Surface water
Surface water
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Soil - Surface
Elasticity
7.707
-1.006
0.998
0.993
0.992
-0.949
0.948
-0.942
-0.800
-0.755
0.728
-0.691
0.687
-0.614
0.588
0.569
0.551
0.549
-0.487
0.427
-0.405
-0.391
-0.368
-0.364
0.344
-0.318
-0.307
0.3
-0.292
-0.290
Sensitivity Score
0.385
-1.005
0.997
0.993
0.992
-0.949
0.947
-0.941
-0.001
-0.755
0.727
-0.207
0.687
-0.184
0.029
0.064
0.067
1.647
-0.054
0.048
-0.405
-0.391
-0.040
-0.044
0.103
-0.035
-0.037
0.037
-0.291
-0.014
July 2005
D-34
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Leaf - Grasses/Herbs in Grasses/Herbs in SurfSoil_SW2
Property
emissionRate
AllowExchange SteadyState forAir
LitterFallRate
WetMassperArea
WetDepInterceptionFraction UserSupplied
Rain
VaporWashoutRatio
WaterContent
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
AttenuationFactor
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
FullSS
Divalent Mercury
Leaf- Grasses/Herbs
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Leaf- Grasses/Herbs
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.997
0.987
-0.950
-0.866
0.827
0.638
0.638
-0.532
-0.486
0.389
-0.314
-0.283
0.253
-0.210
0.170
0.139
0.134
0.134
-0.132
0.118
-0.11
0.101
-0.089
0.080
-0.079
0.075
-0.071
-0.070
-0.064
-0.062
Sensitivity Score
0.997
0.986
-0.073
-0.866
0.248
0.078
1.915
-0.266
-0.056
0.055
-0.034
-0.032
0.028
-0.024
0.021
0.042
0.134
0.019
-0.014
0.013
-0.013
0.011
-0.010
0.011
-0.010
0.008
-0.008
-0.008
-0.008
-0.010
Elemental Mercury in Leaf - Grasses/Herbs in Grasses/Herbs in SurfSoil_SW2
Property
AllowExchange SteadyState forOther
WetDensity
isDay_SteadyState_forOther
AllowExchange SteadyState forAir
emissionRate
AverageLeafArealndex No Time Dependence
isDay SteadyState forAir
HenryLawConstant
AirTemperature K
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
K ow
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
HgO
All
All
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
HgO
All
All
All
All
All
All
Object Name
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
FullSS
Leaf- Grasses/Herbs
Facility
Leaf- Grasses/Herbs
FullSS
Elemental Mercury
FullSS
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elemental Mercury
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
-1.010
-1.010
-1.010
1.000
0.995
0.991
0.991
-0.949
0.940
-0.525
0.412
-0.382
-0.307
0.230
-0.208
0.190
-0.155
0.131
-0.114
-0.105
0.100
0.097
0.090
0.088
-0.083
-0.080
0.075
-0.073
-0.070
0.070
Sensitivity Score
-0.029
-1.010
-1.009
0.999
0.995
0.991
0.990
-0.949
0.001
-0.061
0.059
-0.042
-0.034
0.025
-0.024
0.023
-0.017
0.019
-0.014
-0.012
0.011
0.011
0.013
0.265
-0.009
-0.009
0.008
-0.009
-0.009
0.011
July 2005
D-35
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Leaf - Grasses/Herbs in Grasses/Herbs in SurfSoil_SW2
Property
DemethylationRate
Kd
emissionRate
TSCF
AllowExchange SteadyState forOther
MethylationRate
WetDensity
rho
ReductionRate
AverageLeafArealndex No Time Dependence
isDay_SteadyState_forOther
FlowRateofTranspiredWaterperAreaofLeafSurfacc
AllowExchange SteadyState forOther
HenryLawConstant
Average VerticalVelocity
D_pureair
AirTemperature K
DegreeStomatalOpening
StomatalAreaNormalizedEffectiveDiffusion
Kd
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
MHg
MHg
Hg2
MHg
All
Hg2
All
All
Hg2
All
All
All
All
MHg
All
MHg
All
All
All
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
Object Name
Soil - Root Zone
Soil - Root Zone
Facility
Stem - Grasses/Herbs
Leaf- Grasses/Herbs
Soil - Root Zone
Leaf- Grasses/Herbs
Soil - Root Zone
Soil - Root Zone
Leaf- Grasses/Herbs
FullSS
Stem - Grasses/Herbs
Stem - Grasses/Herbs
MethylMercury
Soil - Surface
MethylMercury
FullSS
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
Soil - Surface
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
-0.998
-0.998
0.997
0.988
-0.988
0.988
-0.970
-0.967
-0.966
0.963
-0.963
0.959
0.958
-0.951
0.946
-0.941
0.931
-0.871
-0.871
-0.814
0.684
0.684
-0.548
-0.548
-0.386
-0.379
-0.379
-0.313
0.248
0.166
Sensitivity Score
-0.997
-0.998
0.997
2.965
-0.028
0.987
-0.970
-0.048
-0.965
0.963
-0.962
0.959
0.027
-0.951
0.284
-0.047
0.001
-0.870
-0.871
-0.814
0.083
2.052
-0.164
-0.164
-0.042
-0.379
-0.019
-0.036
0.035
0.018
Divalent Mercury in Sediment in River
Property
rho
Flushes per year
emissionRate
Rain
VaporWashoutRatio
SuspendedSedimentconcentration
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Average VerticalVelocity
SteadyState AdvectiveTransfer
Chemical
All
All
Hg2
All
Hg2
All
Hg2
All
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Facility
FullSS
Divalent Mercury
Surface water
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Soil - Surface
Link from Air, to Air
Elasticity
1.209
-1.008
0.995
0.732
0.731
-0.603
0.394
0.369
0.369
-0.363
-0.363
-0.251
-0.088
0.076
-0.076
-0.075
-0.074
-0.059
-0.056
0.046
0.046
-0.043
-0.037
0.037
-0.035
-0.034
0.031
-0.030
-0.030
-0.030
Sensitivity Score
0.060
-0.302
0.995
0.089
2.194
-0.181
0.394
0.111
0.111
-0.363
-0.018
-0.027
-0.011
0.023
-0.008
-0.008
-0.008
-0.007
-0.007
0.007
0.007
-0.005
-0.004
0.005
-0.004
-0.004
0.003
-0.004
-0.009
-0.003
July 2005
D-36
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Sediment in River
Property
rho
Flushes per year
emissionRate
Kd
SuspendedSedimentconcentration
HenryLawConstant
phi
SedimentDepositi on Velocity
emissionRate
AirTemperature K
rho
ReductionRate
D_pure water
Rain
VaporWashoutRatio
CurrentVelocity
SteadyState AdvectiveTransfer
horizontalWindSpeed
DimensionlessViscousSublayerThickness
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
D_pureair
Chemical
All
All
HgO
HgO
All
HgO
All
All
Hg2
All
All
Hg2
HgO
All
Hg2
All
All
All
All
Hg2
All
All
Hg2
All
All
All
All
All
All
HgO
Object Name
Surface water
Surface water
Facility
Surface water
Surface water
Elemental Mercury
Sediment
Surface water
Facility
FullSS
Sediment
Sediment
Elemental Mercury
FullSS
Divalent Mercury
Surface water
Link from Air, to Air
FullSS
Surface water
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elemental Mercury
Elasticity
1.399
-0.991
0.653
0.614
-0.583
-0.512
-0.480
-0.380
0.347
0.343
0.320
0.320
0.297
0.266
0.255
0.241
-0.197
0.136
-0.136
0.126
0.126
0.126
-0.123
-0.123
-0.118
-0.117
-0.102
-0.102
0.092
0.091
Sensitivity Score
0.070
-0.297
0.653
0.614
-0.175
-0.512
-0.144
-0.114
0.347
0.000
0.016
0.319
0.089
0.033
0.764
0.041
-0.021
0.136
-0.041
0.126
0.038
0.038
-0.123
-0.006
-0.013
-0.014
-0.012
-0.011
0.013
0.005
Methyl Mercury in Sediment in River
Property
rho
Flushes per year
DemethylationRate
emissionRate
MethylationRate
Rain
VaporWashoutRatio
SuspendedSedimentconcentration
ReductionRate
rho
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
phi
DemethylationRate
MethylationRate
SteadyState AdvectiveTransfer
rho
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SedimentDepositi on Velocity
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
FractionofAreaAvailableforRunoff
TotalRunoffRate m3 m2 day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
MHg
Hg2
Hg2
All
Hg2
All
Hg2
All
Hg2
All
All
All
All
MHg
Hg2
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Sediment
Facility
Sediment
FullSS
Divalent Mercury
Surface water
Soil - Surface
Soil - Surface
Surface water
Soil - Surface
Soil - Surface
Link from Air, to Air
Sediment
Soil - Surface
Soil - Surface
Link from Air, to Air
Sediment
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Elasticity
1.117
-1.001
-0.998
0.995
0.910
0.732
0.731
-0.527
-0.370
-0.370
0.359
0.353
0.353
-0.254
0.120
-0.089
0.088
-0.088
-0.082
0.078
-0.075
-0.075
-0.074
0.068
-0.058
-0.055
0.048
0.048
0.046
-0.045
Sensitivity Score
0.056
-0.300
-0.997
0.995
0.909
0.089
2.194
-0.158
-0.369
-0.018
0.359
0.106
0.106
-0.028
0.036
-0.089
0.088
-0.011
-0.004
0.023
-0.008
-0.008
-0.008
0.020
-0.007
-0.007
0.014
0.014
0.007
-0.005
July 2005
D-37
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Sediment in Sed_Swetts
Property
rho
emissionRate
Kd
Flushes per year
Rain
VaporWashoutRatio
WaterTemperature K
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
SuspendedSedimentconcentration
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
Hg2
Hg2
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
HgO
All
HgO
All
All
All
Object Name
Surface water
Facility
Surface water
Surface water
FullSS
Divalent Mercury
Surface water
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Macrophyte
Macrophyte
Macrophyte
Surface water
Link from Air, to Air
Link from Air, to Air
Elasticity
6.975
0.993
0.795
-0.788
0.607
0.605
-0.539
0.385
0.324
0.324
-0.318
-0.318
0.270
-0.232
0.226
-0.201
-0.196
-0.195
-0.187
-0.167
-0.161
0.147
0.143
-0.141
-0.140
0.140
0.140
-0.136
0.134
-0.130
Sensitivity Score
0.349
0.993
0.795
-0.236
0.074
1.815
-0.539
0.043
0.097
0.097
-0.318
-0.016
0.030
-0.025
0.025
-0.200
-0.021
-0.021
-0.023
-0.019
-0.018
0.023
0.023
-0.140
-0.419
0.140
0.419
-0.041
0.019
-0.015
Elemental Mercury in Sediment in Sed_Swetts
Property
rho
phi
SuspendedSedimentconcentration
emissionRate
SedimentDepositi on Velocity
ReductionRate
rho
Flushes per year
Kd
WaterTemperature K
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DimensionlessViscousSublayerThickness
horizontalWindSpeed
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
Chemical
All
All
All
Hg2
All
Hg2
All
All
Hg2
All
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Sediment
Surface water
Facility
Surface water
Sediment
Sediment
Surface water
Surface water
Surface water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
FullSS
Link from Air, to Air
Leaf - Coniferous Fores!
Elasticity
7.838
-1.305
-1.072
0.993
-0.938
0.872
0.870
-0.796
0.732
-0.689
0.607
0.605
0.385
0.324
0.324
-0.318
-0.318
0.270
-0.232
0.226
-0.196
-0.195
-0.187
-0.167
-0.161
0.147
0.144
-0.144
0.143
-0.141
Sensitivity Score
0.392
-0.392
-0.322
0.993
-0.281
0.871
0.043
-0.239
0.732
-0.688
0.074
1.815
0.043
0.097
0.097
-0.317
-0.016
0.030
-0.025
0.025
-0.021
-0.021
-0.023
-0.019
-0.018
0.023
0.043
-0.144
0.023
-0.140
July 2005
D-38
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Sediment in Sed_Swetts
Property
rho
DemethylationRate
emissionRate
MethylationRate
Kd
Flushes per year
Rain
VaporWashoutRatio
WaterTemperature K
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
SuspendedSedimentconcentration
Chemical
All
MHg
Hg2
Hg2
Hg2
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
HgO
All
HgO
All
Object Name
Surface water
Sediment
Facility
Sediment
Surface water
Surface water
FullSS
Divalent Mercury
Surface water
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf - Coniferous Forest
Macrophyte
Macrophyte
Macrophyte
Surface water
Elasticity
6.960
-1.007
0.993
0.991
0.790
-0.787
0.607
0.605
-0.539
0.385
0.324
0.324
-0.318
-0.318
0.270
-0.232
0.226
-0.200
-0.196
-0.195
-0.187
-0.167
-0.161
0.147
0.143
-0.141
-0.140
0.139
0.139
-0.134
Sensitivity Score
0.348
-1.006
0.993
0.990
0.790
-0.236
0.074
1.815
-0.538
0.043
0.097
0.097
-0.318
-0.016
0.030
-0.025
0.025
-0.200
-0.021
-0.021
-0.023
-0.019
-0.018
0.023
0.023
-0.141
-0.419
0.139
0.418
-0.040
Divalent Mercury in Surface Water in River
Property
Flushes per year
emissionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
rho
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Average VerticalVelocity
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
Hg2
All
Hg2
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Facility
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Surface water
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Elasticity
-1.010
0.995
0.732
0.731
0.372
0.372
-0.362
-0.362
-0.251
0.215
-0.087
0.076
-0.076
-0.075
-0.074
-0.059
-0.056
-0.046
0.045
-0.043
-0.038
0.037
-0.035
-0.034
0.031
-0.030
-0.030
-0.030
-0.028
-0.028
Sensitivity Score
-0.303
0.995
0.089
2.194
0.112
0.112
-0.361
-0.018
-0.027
0.011
-0.011
0.023
-0.008
-0.008
-0.008
-0.007
-0.007
-0.005
0.007
-0.005
-0.004
0.005
-0.004
-0.004
0.003
-0.004
-0.009
-0.003
-0.003
-0.003
July 2005
D-39
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Surface Water in River
Property
Flushes per year
emissionRate
HenryLawConstant
AirTemperature K
CurrentVelocity
D_pure water
horizontalWindSpeed
DimensionlessViscousSublayerThickness
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
D_pureair
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DragCoefficient
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Rain
Chemical
All
HgO
HgO
All
All
HgO
All
All
All
All
All
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Facility
Elemental Mercury
FullSS
Surface water
Elemental Mercury
FullSS
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elemental Mercury
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
FullSS
Elasticity
-0.983
0.957
-0.750
0.503
0.353
0.352
0.201
-0.200
-0.171
-0.149
-0.138
0.134
-0.132
0.117
-0.114
-0.101
0.101
-0.093
-0.086
0.083
-0.083
-0.064
-0.064
-0.060
-0.058
0.057
-0.057
-0.052
0.050
0.048
Sensitivity Score
-0.295
0.957
-0.750
0.001
0.060
0.106
0.201
-0.060
-0.019
-0.017
-0.015
0.007
-0.015
0.017
-0.012
-0.012
0.030
-0.011
-0.009
0.012
-0.009
-0.008
-0.008
-0.007
-0.007
0.007
-0.006
-0.006
0.007
0.006
Methyl Mercury in Surface Water in River
Property
emissionRate
DemethylationRate
MethylationRate
Flushes per year
Rain
VaporWashoutRatio
ReductionRate
rho
Kd
FractionofAreaAvailableforRunoff
TotalRunoffRate m3 m2 day
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SedimentDepositi on Velocity
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
rho
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SuspendedSedimentconcentration
MethylationRate
Average VerticalVelocity
Chemical
Hg2
MHg
Hg2
All
All
Hg2
Hg2
All
MHg
All
All
All
All
All
All
All
Hg2
All
All
All
All
Hg2
All
MHg
All
All
All
All
Hg2
All
Object Name
Facility
Soil - Surface
Soil - Surface
Surface water
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Surface water
Link from Air, to Air
Soil - Surface
Link from Air, to Sink
Link from Air, to Air
Surface water
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Surface water
Soil - Surface
Elasticity
0.998
-0.940
0.931
-0.929
0.732
0.732
-0.437
-0.436
-0.310
0.285
0.285
-0.279
0.187
0.187
-0.111
-0.096
0.095
-0.076
-0.069
0.065
-0.064
0.059
-0.055
-0.054
0.051
-0.048
-0.044
-0.041
0.038
-0.036
Sensitivity Score
0.998
-0.939
0.930
-0.279
0.089
2.195
-0.436
-0.022
-0.310
0.086
0.086
-0.03
0.056
0.056
-0.033
-0.012
0.028
-0.008
-0.008
0.003
-0.007
0.059
-0.007
-0.054
0.008
-0.006
-0.005
-0.012
0.038
-0.011
July 2005
D-40
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Surface Water in SW_Swetts
Property
rho
emissionRate
Flushes per year
Rain
VaporWashoutRatio
WaterTemperature K
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
Hg2
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
HgO
All
HgO
All
All
All
All
Object Name
Surface water
Facility
Surface water
FullSS
Divalent Mercury
Surface water
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Macrophyte
Macrophyte
Macrophyte
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Elasticity
6.064
0.993
-0.789
0.607
0.605
-0.538
0.385
0.325
0.325
-0.318
-0.318
0.270
-0.232
0.226
-0.201
-0.196
-0.195
-0.187
-0.167
-0.161
0.147
0.143
-0.141
-0.140
0.140
0.140
0.134
-0.130
-0.119
-0.115
Sensitivity Score
0.303
0.993
-0.237
0.074
1.815
-0.538
0.043
0.097
0.097
-0.318
-0.016
0.030
-0.025
0.025
-0.201
-0.021
-0.021
-0.023
-0.019
-0.018
0.023
0.023
-0.140
-0.421
0.140
0.420
0.019
-0.015
-0.013
-0.013
Elemental Mercury in Surface Water in SW_Swetts
Property
rho
WaterTemperature K
emissionRate
Flushes per year
ReductionRate
Rain
VaporWashoutRatio
DimensionlessViscousSublayerThickness
horizontalWindSpeed
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
D_pure water
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DragCoefficient
AirDensity g cm3
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
phi
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
Hg2
All
Hg2
All
Hg2
All
All
HgO
All
HgO
All
All
All
Hg2
All
HgO
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Facility
Surface water
Surface water
FullSS
Divalent Mercury
Surface water
FullSS
Macrophyte
Macrophyte
Macrophyte
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Elemental Mercury
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Sediment
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
6.351
-2.163
0.989
-0.878
0.631
0.608
0.602
0.446
-0.446
0.440
-0.439
-0.439
0.386
0.323
0.323
-0.316
-0.316
-0.296
0.271
-0.232
0.226
-0.223
-0.221
-0.196
-0.195
-0.187
-0.182
-0.167
-0.161
0.147
Sensitivity Score
0.318
-2.161
0.989
-0.263
0.630
0.074
1.807
0.134
-0.446
1.319
-0.439
-1.317
0.043
0.097
0.097
-0.316
-0.016
-0.089
0.030
-0.025
0.025
-0.067
-0.011
-0.021
-0.021
-0.023
-0.055
-0.019
-0.018
0.023
July 2005
D-41
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Surface Water in SW Swetts
Property
rho
emissionRate
Flushes per year
MethylationRate
Kd
SedimentDepositi on Velocity
Rain
VaporWashoutRatio
SuspendedSedimentconcentration
WaterTemperature K
SteadyState AdvectiveTransfer
ReductionRate
rho
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DemethylationRate
MethylationRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
DemethylationRate
Chemical
All
Hg2
All
Hg2
MHg
All
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
MHg
Hg2
All
All
All
All
All
Hg2
All
All
All
MHg
Object Name
Surface water
Facility
Surface water
Surface water
Surface water
Surface water
FullSS
Divalent Mercury
Surface water
Surface water
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Sediment
Elasticity
4.932
0.994
-0.714
0.647
-0.637
-0.627
0.607
0.606
-0.520
-0.400
0.381
-0.328
-0.328
0.278
0.278
0.269
-0.231
0.225
-0.209
0.207
-0.195
-0.193
-0.185
-0.167
-0.163
-0.159
0.145
-0.145
0.142
-0.139
Sensitivity Score
0.247
0.994
-0.214
0.647
-0.637
-0.188
0.074
1.817
-0.156
-0.400
0.043
-0.328
-0.016
0.083
0.083
0.030
-0.025
0.025
-0.208
0.206
-0.021
-0.021
-0.023
-0.019
-0.018
-0.159
0.023
-0.145
0.023
-0.139
Divalent Mercury in Benthic Carnivore in River
Property
WaterTemperature K
rho
Flushes per year
HowMuchFasterHgEliminationlsThanForMHg
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
emissionRate
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
phi
Rain
VaporWashoutRatio
SuspendedSedimentconcentration
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
rho
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
BW
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
All
All
Hg2
All
Hg2
All
All
All
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Surface water
Benthic Carnivore
Benthic Invertebrate
Benthic Omnivore
Facility
Benthic Carnivore
Benthic Omnivore
Sediment
FullSS
Divalent Mercury
Surface water
Surface water
Soil - Surface
Soil - Surface
Sediment
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Benthic Omnivore
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
-2.918
1.210
-1.008
-0.998
0.994
0.994
0.991
0.974
-0.966
-0.897
0.729
0.729
-0.603
0.392
0.368
0.368
0.363
-0.362
-0.362
-0.251
-0.087
0.085
0.076
-0.076
-0.075
-0.075
-0.059
-0.056
-0.047
0.046
Sensitivity Score
-2.915
0.061
-0.302
-0.998
2.983
0.993
0.991
0.973
-0.966
-0.269
0.089
2.186
-0.181
0.392
0.110
0.110
0.018
-0.018
-0.361
-0.027
-0.011
0.025
0.023
-0.008
-0.008
-0.008
-0.007
-0.007
-0.005
0.007
July 2005
D-42
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Benthic Carnivore in River
Property
WaterTemperature K
rho
phi
OxidationRate
OxidationRate
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
Flushes per year
rho
emissionRate
Kd
SuspendedSedimentconcentration
HenryLawConstant
SedimentDepositi on Velocity
emissionRate
AirTemperature K
ReductionRate
D_pure water
Rain
VaporWashoutRatio
CurrentVelocity
SteadyState AdvectiveTransfer
BW
BW
horizontalWindSpeed
DimensionlessViscousSublayerThickness
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
Chemical
All
All
All
HgO
HgO
HgO
HgO
HgO
All
All
HgO
HgO
All
HgO
All
Hg2
All
Hg2
HgO
All
Hg2
All
All
All
All
All
All
Hg2
All
All
Object Name
Surface water
Surface water
Sediment
Benthic Carnivore
Benthic Omnivore
Benthic Carnivore
Benthic Invertebrate
Benthic Omnivore
Surface water
Sediment
Facility
Surface water
Surface water
Elemental Mercury
Surface water
Facility
FullSS
Sediment
Elemental Mercury
FullSS
Divalent Mercury
Surface water
Link from Air, to Air
Benthic Omnivore
Benthic Carnivore
FullSS
Surface water
Surface water
Soil - Surface
Soil - Surface
Elasticity
29.640
1.399
-1.383
-1.010
-1.010
1.000
1.000
0.999
-0.991
0.683
0.653
0.614
-0.583
-0.512
-0.380
0.347
0.343
0.320
0.297
0.266
0.255
0.241
-0.197
-0.152
-0.151
0.136
-0.136
0.126
0.126
0.126
Sensitivity Score
29.610
0.070
-0.415
-1.009
-1.009
0.999
3.000
0.998
-0.297
0.034
0.653
0.614
-0.175
-0.512
-0.114
0.347
0.000
0.319
0.089
0.033
0.764
0.041
-0.021
-0.045
-0.045
0.136
-0.041
0.126
0.038
0.038
Methyl Mercury in Benthic Carnivore in River
Property
WaterTemperature K
rho
Flushes per year
SedimentPartitioning PartitionCoefficient
DemethylationRate
AssimilationEfficiencyFromFood
emissionRate
HowMuchFasterHgEliminationlsThanForMHg
MethylationRate
phi
Rain
VaporWashoutRatio
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
SuspendedSedimentconcentration
ReductionRate
rho
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
NumberofFishperSquareMeter
BW
rho
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
BW
DemethylationRate
MethylationRate
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
Chemical
All
All
All
MHg
MHg
MHg
Hg2
MHg
Hg2
All
All
Hg2
MHg
MHg
All
Hg2
All
Hg2
All
All
All
All
All
All
All
All
MHg
Hg2
All
Hg2
Object Name
Surface water
Surface water
Surface water
Benthic Invertebrate
Sediment
Benthic Omnivore
Facility
Benthic Carnivore
Sediment
Sediment
FullSS
Divalent Mercury
Benthic Carnivore
Benthic Omnivore
Surface water
Soil - Surface
Soil - Surface
Surface water
Soil - Surface
Soil - Surface
Benthic Omnivore
Benthic Omnivore
Sediment
Link from Air, to Air
Benthic Carnivore
Benthic Carnivore
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Elasticity
-1.592
1.117
-1.001
1.000
-0.998
0.996
0.995
-0.975
0.910
-0.778
0.732
0.731
0.722
-0.688
-0.527
-0.370
-0.370
0.359
0.353
0.353
0.315
0.302
0.282
-0.254
-0.245
-0.160
-0.089
0.088
-0.088
0.078
Sensitivity Score
-1.590
0.056
-0.300
3.000
-0.997
0.995
0.995
-0.975
0.909
-0.233
0.089
2.194
0.722
-0.688
-0.158
-0.369
-0.018
0.359
0.106
0.106
0.315
0.091
0.014
-0.028
-0.245
-0.048
-0.089
0.088
-0.011
0.023
July 2005
D-43
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Benthic Carnivore in Sed_Swetts
Property
rho
WaterTemperature K
emissionRate
HowMuchFasterHgEliminationlsThanForMHg
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
phi
Kd
Flushes per year
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SuspendedSedimentconcentration
SteadyState AdvectiveTransfer
Chemical
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
All
Hg2
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Facility
Benthic Carnivore
Benthic Omnivore
Benthic Invertebrate
Benthic Carnivore
Benthic Omnivore
Sediment
Surface water
Surface water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Sediment
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Elasticity
6.989
-2.999
0.993
-0.991
0.983
0.980
0.975
-0.952
-0.835
0.794
-0.788
0.607
0.605
0.385
0.324
0.324
0.323
-0.318
-0.318
0.270
-0.232
0.226
-0.200
-0.196
-0.195
-0.187
-0.167
-0.161
-0.151
0.147
Sensitivity Score
0.349
-2.996
0.993
-0.991
0.982
2.940
0.974
-0.952
-0.250
0.794
-0.236
0.074
1.815
0.043
0.097
0.097
0.016
-0.318
-0.016
0.030
-0.025
0.025
-0.199
-0.021
-0.021
-0.023
-0.019
-0.018
-0.045
0.023
Elemental Mercury in Benthic Carnivore in Sed_Swetts
Property
WaterTemperature K
rho
phi
rho
SuspendedSedimentconcentration
OxidationRate
OxidationRate
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
emissionRate
SedimentDepositi on Velocity
ReductionRate
Flushes per year
Kd
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
All
HgO
HgO
HgO
HgO
HgO
Hg2
All
Hg2
All
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Sediment
Sediment
Surface water
Benthic Carnivore
Benthic Omnivore
Benthic Carnivore
Benthic Omnivore
Benthic Invertebrate
Facility
Surface water
Sediment
Surface water
Surface water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
29.160
7.838
-2.215
1.230
-1.072
-1.010
-1.010
1.000
0.999
0.999
0.993
-0.938
0.872
-0.796
0.732
0.607
0.605
0.385
0.324
0.324
-0.318
-0.318
0.270
-0.232
0.226
-0.196
-0.195
-0.187
-0.167
-0.161
Sensitivity Score
29.131
0.392
-0.665
0.062
-0.322
-1.009
-1.009
0.999
0.998
2.996
0.993
-0.281
0.871
-0.239
0.732
0.074
1.815
0.043
0.097
0.097
-0.317
-0.016
0.030
-0.025
0.025
-0.021
-0.021
-0.023
-0.019
-0.018
July 2005
D-44
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Benthic Carnivore in Sed_Swetts
Property
rho
WaterTemperature K
DemethylationRate
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
emissionRate
MethylationRate
HowMuchFasterHgEliminationlsThanForMHg
phi
Kd
Flushes per year
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
BW
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
MHg
MHg
MHg
Hg2
Hg2
MHg
All
Hg2
All
MHg
MHg
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
Hg2
All
All
Object Name
Surface water
Surface water
Sediment
Benthic Invertebrate
Benthic Omnivore
Facility
Sediment
Benthic Carnivore
Sediment
Surface water
Surface water
Benthic Carnivore
Benthic Omnivore
FullSS
Divalent Mercury
Link from Air, to Air
Benthic Omnivore
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Benthic Omnivore
Sediment
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Benthic Carnivore
Surface water
Link from Air, to Air
Link from Air, to Air
Elasticity
6.960
-1.086
-1.007
0.999
0.996
0.993
0.991
-0.954
-0.804
0.790
-0.787
0.731
-0.667
0.607
0.605
0.385
0.335
0.324
0.324
-0.318
-0.318
0.318
0.302
0.270
-0.232
0.226
-0.216
-0.200
-0.196
-0.195
Sensitivity Score
0.348
-1.085
-1.006
2.998
0.995
0.993
0.990
-0.954
-0.241
0.790
-0.236
0.730
-0.667
0.074
1.815
0.043
0.335
0.097
0.097
-0.318
-0.016
0.095
0.015
0.030
-0.025
0.025
-0.216
-0.200
-0.021
-0.021
Divalent Mercury in Benthic Invertebrate in River
Property
rho
Flushes per year
SedimentPartitioning PartitionCoefficient
emissionRate
phi
Rain
VaporWashoutRatio
SuspendedSedimentconcentration
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
Hg2
Hg2
All
All
Hg2
All
Hg2
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Benthic Invertebrate
Facility
Sediment
FullSS
Divalent Mercury
Surface water
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Sediment
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
1.209
-1.008
1.000
0.995
-0.895
0.732
0.731
-0.603
0.394
0.369
0.369
-0.363
-0.363
0.361
-0.251
-0.088
0.076
-0.076
-0.075
-0.074
-0.059
-0.056
0.046
0.046
-0.043
-0.037
0.037
-0.035
-0.034
0.031
Sensitivity Score
0.060
-0.302
3.000
0.995
-0.268
0.089
2.194
-0.181
0.394
0.111
0.111
-0.363
-0.018
0.018
-0.027
-0.011
0.023
-0.008
-0.008
-0.008
-0.007
-0.007
0.007
0.007
-0.005
-0.004
0.005
-0.004
-0.004
0.003
July 2005
D-45
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Benthic Invertebrate in River
Property
rho
phi
SedimentPartitioning PartitionCoefficient
Flushes per year
rho
emissionRate
Kd
SuspendedSedimentconcentration
HenryLawConstant
SedimentDepositi on Velocity
emissionRate
AirTemperature K
ReductionRate
D_pure water
Rain
VaporWashoutRatio
CurrentVelocity
SteadyState AdvectiveTransfer
horizontalWindSpeed
DimensionlessViscousSublayerThickness
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
HgO
All
All
HgO
HgO
All
HgO
All
Hg2
All
Hg2
HgO
All
Hg2
All
All
All
All
Hg2
All
All
Hg2
All
All
All
All
All
All
Object Name
Surface water
Sediment
Benthic Invertebrate
Surface water
Sediment
Facility
Surface water
Surface water
Elemental Mercury
Surface water
Facility
FullSS
Sediment
Elemental Mercury
FullSS
Divalent Mercury
Surface water
Link from Air, to Air
FullSS
Surface water
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
1.399
-1.383
1.000
-0.991
0.683
0.653
0.614
-0.583
-0.512
-0.380
0.347
0.343
0.320
0.297
0.266
0.255
0.241
-0.197
0.136
-0.136
0.126
0.126
0.126
-0.123
-0.123
-0.118
-0.117
-0.102
-0.102
0.092
Sensitivity Score
0.070
-0.415
3.000
-0.297
0.034
0.653
0.614
-0.175
-0.512
-0.114
0.347
0.000
0.319
0.089
0.033
0.764
0.041
-0.021
0.136
-0.041
0.126
0.038
0.038
-0.123
-0.006
-0.013
-0.014
-0.012
-0.011
0.013
Methyl Mercury in Benthic Invertebrate in River
Property
rho
Flushes per year
SedimentPartitioning PartitionCoefficient
DemethylationRate
emissionRate
MethylationRate
phi
Rain
VaporWashoutRatio
SuspendedSedimentconcentration
ReductionRate
rho
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
SteadyState AdvectiveTransfer
DemethylationRate
MethylationRate
SteadyState AdvectiveTransfer
WaterTemperature K
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SedimentDepositi on Velocity
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
FractionofAreaAvailableforRunoff
TotalRunoffRate m3 m2 day
Chemical
All
All
MHg
MHg
Hg2
Hg2
All
All
Hg2
All
Hg2
All
Hg2
All
All
All
All
MHg
Hg2
All
All
Hg2
All
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Benthic Invertebrate
Sediment
Facility
Sediment
Sediment
FullSS
Divalent Mercury
Surface water
Soil - Surface
Soil - Surface
Surface water
Soil - Surface
Soil - Surface
Sediment
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Surface water
Soil - Surface
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Soil - Surface
Elasticity
1.117
-1.001
1.000
-0.998
0.995
0.910
-0.778
0.732
0.731
-0.527
-0.370
-0.370
0.359
0.353
0.353
0.282
-0.254
-0.089
0.088
-0.088
-0.082
0.078
-0.075
-0.075
-0.074
0.068
-0.058
-0.055
0.048
0.048
Sensitivity Score
0.056
-0.300
3.000
-0.997
0.995
0.909
-0.233
0.089
2.194
-0.158
-0.369
-0.018
0.359
0.106
0.106
0.014
-0.028
-0.089
0.088
-0.011
-0.082
0.023
-0.008
-0.008
-0.008
0.020
-0.007
-0.007
0.014
0.014
July 2005
D-46
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Benthic Invertebrate in Sed_Swetts
Property
rho
SedimentPartitioning PartitionCoefficient
emissionRate
phi
Kd
Flushes per year
Rain
VaporWashoutRatio
WaterTemperature K
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
WaterColumnDissPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
Chemical
All
Hg2
Hg2
All
Hg2
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
HgO
All
HgO
Object Name
Surface water
Benthic Invertebrate
Facility
Sediment
Surface water
Surface water
FullSS
Divalent Mercury
Surface Water
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Sediment
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Macrophyte
Macrophyte
Macrophyte
Elasticity
6.975
0.996
0.993
-0.813
0.795
-0.788
0.607
0.605
-0.553
0.385
0.324
0.324
-0.318
-0.318
0.308
0.270
-0.232
0.226
-0.201
-0.196
-0.195
-0.187
-0.167
-0.161
0.147
0.143
-0.141
-0.140
0.140
0.140
Sensitivity Score
0.349
2.987
0.993
-0.244
0.795
-0.236
0.074
1.815
-0.553
0.043
0.097
0.097
-0.318
-0.016
0.015
0.030
-0.025
0.025
-0.200
-0.021
-0.021
-0.023
-0.019
-0.018
0.023
0.023
-0.140
-0.419
0.140
0.419
Elemental Mercury in Benthic Invertebrate in Sed_Swetts
Property
rho
phi
rho
SuspendedSedimentconcentration
SedimentPartitioning PartitionCoefficient
emissionRate
SedimentDepositi on Velocity
ReductionRate
Flushes per year
Kd
WaterTemperature K
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DimensionlessViscousSublayerThickness
horizontalWindSpeed
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
HgO
Hg2
All
Hg2
All
Hg2
All
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Sediment
Sediment
Surface water
Benthic Invertebrate
Facility
Surface water
Sediment
Surface water
Surface water
Surface Water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
FullSS
Link from Air, to Air
Elasticity
7.838
-2.215
1.230
-1.072
0.999
0.993
-0.938
0.872
-0.796
0.732
-0.702
0.607
0.605
0.385
0.324
0.324
-0.318
-0.318
0.270
-0.232
0.226
-0.196
-0.195
-0.187
-0.167
-0.161
0.147
0.144
-0.144
0.143
Sensitivity Score
0.392
-0.665
0.062
-0.322
2.996
0.993
-0.281
0.871
-0.239
0.732
-0.702
0.074
1.815
0.043
0.097
0.097
-0.317
-0.016
0.030
-0.025
0.025
-0.021
-0.021
-0.023
-0.019
-0.018
0.023
0.043
-0.144
0.023
July 2005
D-47
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Benthic Invertebrate in Sed_Swetts
Property
rho
DemethylationRate
SedimentPartitioning PartitionCoefficient
emissionRate
MethylationRate
phi
Kd
Flushes per year
WaterTemperature K
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
WaterColumnDissPartitioning TimeToReachAlphaofEquil
Chemical
All
MHg
MHg
Hg2
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
Hg2
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
HgO
Object Name
Surface water
Sediment
Benthic Invertebrate
Facility
Sediment
Sediment
Surface water
Surface water
Surface Water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Sediment
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf - Coniferous Forest
Macrophyte
Elasticity
6.960
-1.007
0.999
0.993
0.991
-0.804
0.790
-0.787
-0.608
0.607
0.605
0.385
0.324
0.324
-0.318
-0.318
0.302
0.270
-0.232
0.226
-0.200
-0.196
-0.195
-0.187
-0.167
-0.161
0.147
0.143
-0.141
-0.140
Sensitivity Score
0.348
-1.006
2.998
0.993
0.990
-0.241
0.790
-0.236
-0.607
0.074
1.815
0.043
0.097
0.097
-0.318
-0.016
0.015
0.030
-0.025
0.025
-0.200
-0.021
-0.021
-0.023
-0.019
-0.018
0.023
0.023
-0.141
-0.419
Divalent Mercury in Benthic Omnivore in River
Property
WaterTemperature K
rho
Flushes per year
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
emissionRate
HowMuchFasterHgEliminationlsThanForMHg
phi
Rain
VaporWashoutRatio
SuspendedSedimentconcentration
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
rho
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
BW
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
Chemical
All
All
All
Hg2
Hg2
Hg2
Hg2
All
All
Hg2
All
Hg2
All
All
All
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
Object Name
Surface Water
Surface water
Surface water
Benthic Invertebrate
Benthic Omnivore
Facility
Benthic Omnivore
Sediment
FullSS
Divalent Mercury
Surface water
Surface water
Soil - Surface
Soil - Surface
Sediment
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Benthic Omnivore
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Benthic Omnivore
Elasticity
-1.378
1.210
-1.008
0.994
0.994
0.991
-0.966
-0.897
0.729
0.729
-0.603
0.392
0.368
0.368
0.363
-0.362
-0.362
-0.251
-0.087
0.085
0.076
-0.076
-0.075
-0.075
-0.059
-0.056
-0.047
0.046
-0.043
0.043
Sensitivity Score
-1.377
0.061
-0.302
2.983
0.993
0.991
-0.966
-0.269
0.089
2.186
-0.181
0.392
0.110
0.110
0.018
-0.018
-0.361
-0.027
-0.011
0.025
0.023
-0.008
-0.008
-0.008
-0.007
-0.007
-0.005
0.007
-0.005
0.043
July 2005
D-48
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Benthic Omnivore in River
Property
WaterTemperature K
rho
phi
OxidationRate
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
Flushes per year
rho
emissionRate
Kd
SuspendedSedimentconcentration
HenryLawConstant
SedimentDepositi on Velocity
emissionRate
AirTemperature K
ReductionRate
D_pure water
Rain
VaporWashoutRatio
CurrentVelocity
SteadyState AdvectiveTransfer
BW
horizontalWindSpeed
DimensionlessViscousSublayerThickness
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
Chemical
All
All
All
HgO
HgO
HgO
All
All
HgO
HgO
All
HgO
All
Hg2
All
Hg2
HgO
All
Hg2
All
All
All
All
All
Hg2
All
All
Hg2
All
All
Object Name
Surface Water
Surface water
Sediment
Benthic Omnivore
Benthic Invertebrate
Benthic Omnivore
Surface water
Sediment
Facility
Surface water
Surface water
Elemental Mercury
Surface water
Facility
FullSS
Sediment
Elemental Mercury
FullSS
Divalent Mercury
Surface water
Link from Air, to Air
Benthic Omnivore
FullSS
Surface water
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Elasticity
16.110
1.399
-1.383
-1.010
1.000
0.999
-0.991
0.683
0.653
0.614
-0.583
-0.512
-0.380
0.347
0.343
0.320
0.297
0.266
0.255
0.241
-0.197
-0.152
0.136
-0.136
0.126
0.126
0.126
-0.123
-0.123
-0.118
Sensitivity Score
16.094
0.070
-0.415
-1.009
3.000
0.998
-0.297
0.034
0.653
0.614
-0.175
-0.512
-0.114
0.347
0.000
0.319
0.089
0.033
0.764
0.041
-0.021
-0.045
0.136
-0.041
0.126
0.038
0.038
-0.123
-0.006
-0.013
Methyl Mercury in Benthic Omnivore in River
Property
rho
Flushes per year
SedimentPartitioning PartitionCoefficient
DemethylationRate
AssimilationEfficiencyFromFood
emissionRate
MethylationRate
phi
Rain
VaporWashoutRatio
HowMuchFasterHgEliminationlsThanForMHg
WaterTemperature K
SuspendedSedimentconcentration
ReductionRate
rho
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
NumberofFishperSquareMeter
BW
rho
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
SteadyState AdvectiveTransfer
BW
DemethylationRate
MethylationRate
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
Chemical
All
All
MHg
MHg
MHg
Hg2
Hg2
All
All
Hg2
MHg
All
All
Hg2
All
Hg2
All
All
All
All
All
MHg
All
All
All
MHg
Hg2
All
Hg2
All
Object Name
Surface water
Surface water
Benthic Invertebrate
Sediment
Benthic Omnivore
Facility
Sediment
Sediment
FullSS
Divalent Mercury
Benthic Omnivore
Surface Water
Surface water
Soil - Surface
Soil - Surface
Surface water
Soil - Surface
Soil - Surface
Benthic Omnivore
Benthic Omnivore
Sediment
Benthic Carnivore
Benthic Carnivore
Link from Air, to Air
Benthic Carnivore
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Elasticity
1.117
-1.001
1
-0.998
0.996
0.995
0.91
-0.778
0.732
0.731
-0.688
-0.558
-0.527
-0.37
-0.37
0.359
0.353
0.353
0.315
0.302
0.282
-0.281
-0.281
-0.254
-0.239
-0.089
0.088
-0.088
0.078
-0.075
Sensitivity Score
0.056
-0.3
3
-0.997
0.995
0.995
0.909
-0.233
0.089
2.194
-0.688
-0.557
-0.158
-0.369
-0.018
0.359
0.106
0.106
0.315
0.091
0.014
-0.28
-0.281
-0.028
-0.072
-0.089
0.088
-0.011
0.023
-0.008
July 2005
D-49
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Benthic Omnivore in Sed_Swetts
Property
rho
WaterTemperature K
emissionRate
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
HowMuchFasterHgEliminationlsThanForMHg
phi
Kd
Flushes per year
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SuspendedSedimentconcentration
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
Chemical
All
All
Hg2
Hg2
Hg2
Hg2
All
Hg2
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Surface Water
Facility
Benthic Omnivore
Benthic Invertebrate
Benthic Omnivore
Sediment
Surface water
Surface water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Sediment
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Leaf - Coniferous Forest
Elasticity
6.989
-1.618
0.993
0.983
0.980
-0.952
-0.835
0.794
-0.788
0.607
0.605
0.385
0.324
0.324
0.323
-0.318
-0.318
0.270
-0.232
0.226
-0.200
-0.196
-0.195
-0.187
-0.167
-0.161
-0.151
0.147
0.143
-0.141
Sensitivity Score
0.349
-1.616
0.993
0.982
2.940
-0.952
-0.250
0.794
-0.236
0.074
1.815
0.043
0.097
0.097
0.016
-0.318
-0.016
0.030
-0.025
0.025
-0.199
-0.021
-0.021
-0.023
-0.019
-0.018
-0.045
0.023
0.023
-0.140
Elemental Mercury in Benthic Omnivore in Sed_Swetts
Property
WaterTemperature K
rho
phi
rho
SuspendedSedimentconcentration
OxidationRate
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
emissionRate
SedimentDepositi on Velocity
ReductionRate
Flushes per year
Kd
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
BW
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
All
HgO
HgO
HgO
Hg2
All
Hg2
All
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface Water
Surface water
Sediment
Sediment
Surface water
Benthic Omnivore
Benthic Omnivore
Benthic Invertebrate
Facility
Surface water
Sediment
Surface water
Surface water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Benthic Omnivore
Link from Air, to Air
Elasticity
15.540
7.838
-2.215
1.230
-1.072
-1.010
0.999
0.999
0.993
-0.938
0.872
-0.796
0.732
0.607
0.605
0.385
0.324
0.324
-0.318
-0.318
0.270
-0.232
0.226
-0.196
-0.195
-0.187
-0.167
-0.161
-0.152
0.147
Sensitivity Score
15.524
0.392
-0.665
0.062
-0.322
-1.009
0.998
2.996
0.993
-0.281
0.871
-0.239
0.732
0.074
1.815
0.043
0.097
0.097
-0.317
-0.016
0.030
-0.025
0.025
-0.021
-0.021
-0.023
-0.019
-0.018
-0.046
0.023
July 2005
D-50
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Benthic Omnivore in Sed_Swetts
Property
rho
DemethylationRate
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
emissionRate
MethylationRate
phi
Kd
Flushes per year
HowMuchFasterHgEliminationlsThanForMHg
Rain
VaporWashoutRatio
WaterTemperature K
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
BW
rho
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
BW
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
MHg
MHg
MHg
Hg2
Hg2
All
Hg2
All
MHg
All
Hg2
All
All
All
All
All
Hg2
All
All
All
MHg
All
All
All
All
All
Hg2
All
All
Object Name
Surface water
Sediment
Benthic Invertebrate
Benthic Omnivore
Facility
Sediment
Sediment
Surface water
Surface water
Benthic Omnivore
FullSS
Divalent Mercury
Surface Water
Link from Air, to Air
Benthic Omnivore
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Benthic Omnivore
Sediment
Benthic Carnivore
Benthic Carnivore
Link from Air, to Air
Link from Air, to Air
Benthic Carnivore
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Elasticity
6.960
-1.007
0.999
0.996
0.993
0.991
-0.804
0.790
-0.787
-0.667
0.607
0.605
-0.499
0.385
0.335
0.324
0.324
-0.318
-0.318
0.318
0.302
-0.272
-0.272
0.270
-0.232
-0.231
0.226
-0.200
-0.196
-0.195
Sensitivity Score
0.348
-1.006
2.998
0.995
0.993
0.990
-0.241
0.790
-0.236
-0.667
0.074
1.815
-0.499
0.043
0.335
0.097
0.097
-0.318
-0.016
0.095
0.015
-0.272
-0.272
0.030
-0.025
-0.069
0.025
-0.200
-0.021
-0.021
Divalent Mercury in Common Loon in River
Property
rho
WaterTemperature K
TotalExcretionRate
Flushes per year
FoodlngestionRate
emissionRate
phi
Rain
VaporWashoutRatio
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
SuspendedSedimentconcentration
HowMuchFasterHgEliminationlsThanForMHg
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
DemethylationRate
MethylationRate
Kd
ReductionRate
rho
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
HowMuchFasterHgEliminationlsThanForMHg
SteadyState AdvectiveTransfer
DemethylationRate
TotalExcretionRate
BW
Chemical
All
All
Hg2
All
All
Hg2
All
All
Hg2
Hg2
Hg2
Hg2
All
Hg2
MHg
MHg
MHg
MHg
Hg2
Hg2
Hg2
All
All
All
All
MHg
All
MHg
MHg
All
Object Name
Surface water
Surface Water
Common Loon
Surface water
Common Loon
Facility
Sediment
FullSS
Divalent Mercury
Common Loon
Benthic Invertebrate
Benthic Omnivore
Surface water
Benthic Omnivore
Benthic Invertebrate
Common Loon
Benthic Omnivore
Sediment
Sediment
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Sediment
Benthic Omnivore
Link from Air, to Air
Common Loon
Common Loon
Benthic Omnivore
Elasticity
1.153
-1.019
-1.010
-0.990
0.984
0.978
-0.834
0.719
0.719
0.572
0.569
0.569
-0.563
-0.553
0.412
0.412
0.410
-0.410
0.374
0.373
-0.360
-0.360
0.356
0.356
0.324
-0.284
-0.254
0.203
-0.203
0.173
Sensitivity Score
0.058
-1.018
-3.030
-0.297
0.295
0.978
-0.250
0.088
2.158
0.571
1.708
0.568
-0.169
-0.553
1.236
0.411
0.410
-0.410
0.373
0.373
-0.359
-0.018
0.107
0.107
0.016
-0.284
-0.028
0.202
-0.203
0.052
July 2005
D-51
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Common Loon in River
Property
InhalationProps B
InhalationAssimilationEfficiency
InhalationProps A
emissionRate
OxidationRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
BW
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
HgO
All
HgO
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Common Loon
Common Loon
Common Loon
Facility
Common Loon
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Common Loon
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
1.126
0.997
0.997
0.995
-0.962
-0.524
0.412
-0.382
-0.306
0.229
-0.208
-0.202
0.190
-0.155
0.131
-0.113
-0.105
0.100
0.097
0.090
-0.083
-0.080
0.075
-0.073
-0.070
0.069
-0.061
0.060
-0.058
-0.057
Sensitivity Score
0.338
0.299
0.299
0.995
-0.961
-0.061
0.058
-0.042
-0.034
0.025
-0.024
-0.060
0.023
-0.017
0.019
-0.014
-0.012
0.011
0.011
0.013
-0.009
-0.009
0.008
-0.009
-0.009
0.011
-0.008
0.005
-0.009
-0.006
Methyl Mercury in Common Loon in River
Property
rho
Flushes per year
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
FoodlngestionRate
DemethylationRate
AssimilationEfficiencyFromFood
emissionRate
MethylationRate
phi
Rain
VaporWashoutRatio
HowMuchFasterHgEliminationlsThanForMHg
WaterTemperature K
SuspendedSedimentconcentration
DemethylationRate
TotalExcretionRate
ReductionRate
rho
Kd
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
NumberofFishperSquareMeter
BW
rho
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
SteadyState AdvectiveTransfer
BW
DemethylationRate
Chemical
All
All
MHg
MHg
All
MHg
MHg
Hg2
Hg2
All
All
Hg2
MHg
All
All
MHg
MHg
Hg2
All
Hg2
All
All
All
All
All
MHg
All
All
All
MHg
Object Name
Surface water
Surface water
Benthic Invertebrate
Common Loon
Common Loon
Sediment
Benthic Omnivore
Facility
Sediment
Sediment
FullSS
Divalent Mercury
Benthic Omnivore
Surface Water
Surface water
Common Loon
Common Loon
Soil - Surface
Soil - Surface
Surface water
Soil - Surface
Soil - Surface
Benthic Omnivore
Benthic Omnivore
Sediment
Benthic Carnivore
Benthic Carnivore
Link from Air, to Air
Benthic Carnivore
Soil - Surface
Elasticity
1.116
-1.001
0.999
0.998
0.998
-0.997
0.995
0.995
0.909
-0.777
0.732
0.731
-0.687
-0.558
-0.527
-0.514
-0.491
-0.370
-0.370
0.359
0.353
0.353
0.315
0.301
0.282
-0.280
-0.280
-0.254
-0.238
-0.090
Sensitivity Score
0.056
-0.300
2.997
0.997
0.299
-0.996
0.994
0.995
0.908
-0.233
0.089
2.194
-0.687
-0.558
-0.158
-0.513
-0.491
-0.369
-0.018
0.359
0.106
0.106
0.315
0.090
0.014
-0.280
-0.280
-0.028
-0.071
-0.090
July 2005
D-52
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Common Loon in SW_Swetts
Property
rho
WaterTemperature K
TotalExcretionRate
FoodlngestionRate
emissionRate
phi
Kd
Flushes per year
Rain
AssimilationEfficiencyFromFood
VaporWashoutRatio
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
HowMuchFasterHgEliminationlsThanForMHg
DemethylationRate
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
MethylationRate
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
rho
SteadyState AdvectiveTransfer
HowMuchFasterHgEliminationlsThanForMHg
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
Chemical
All
All
Hg2
All
Hg2
All
Hg2
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
MHg
MHg
MHg
MHg
Hg2
All
All
All
Hg2
All
All
All
MHg
All
All
Hg2
Object Name
Surface water
Surface Water
Common Loon
Common Loon
Facility
Sediment
Surface water
Surface water
FullSS
Common Loon
Divalent Mercury
Benthic Omnivore
Benthic Invertebrate
Benthic Omnivore
Sediment
Common Loon
Benthic Invertebrate
Benthic Omnivore
Sediment
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Sediment
Link from Air, to Air
Benthic Omnivore
Link from Air, to Air
Link from Air, to Air
Surface water
Elasticity
6.977
-1.177
-1.010
0.998
0.993
-0.822
0.792
-0.788
0.607
0.605
0.605
0.596
0.592
-0.577
-0.395
0.393
0.393
0.392
0.388
0.385
0.324
0.324
-0.318
-0.318
0.315
0.270
-0.263
-0.232
0.226
-0.200
Sensitivity Score
0.349
-1.176
-3.030
0.299
0.993
-0.247
0.792
-0.236
0.074
0.605
1.815
0.595
1.776
-0.577
-0.394
0.393
1.179
0.392
0.388
0.043
0.097
0.097
-0.318
-0.016
0.016
0.030
-0.263
-0.025
0.025
-0.200
Elemental Mercury in Common Loon in SW_Swetts
Property
rho
OxidationRate
InhalationProps B
InhalationAssimilationEfficiency
InhalationProps A
emissionRate
SteadyState AdvectiveTransfer
WaterTemperature K
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
BW
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
emissionRate
SteadyState AdvectiveTransfer
AssimilationEfficiencyFrom Water
WaterlngProps A
WaterlngProps B
SteadyState AdvectiveTransfer
Flushes per year
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
HgO
All
HgO
All
HgO
All
All
All
All
All
All
All
All
All
All
All
All
Hg2
All
HgO
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Common Loon
Common Loon
Common Loon
Common Loon
Facility
Link from Air, to Air
Surface Water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Common Loon
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Facility
Link from Air, to Air
Common Loon
Common Loon
Common Loon
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Elasticity
1.068
-0.962
0.940
0.832
0.832
0.828
0.471
-0.362
-0.285
0.284
-0.283
-0.263
-0.248
0.225
-0.223
-0.214
-0.189
0.185
0.172
0.171
0.168
0.168
0.159
0.149
-0.147
-0.147
0.145
-0.139
-0.129
-0.122
Sensitivity Score
0.053
-0.961
0.282
0.250
0.250
0.828
0.053
-0.362
-0.032
0.032
-0.031
-0.029
-0.030
0.025
-0.067
-0.023
-0.022
0.029
0.172
0.027
0.168
0.050
0.048
0.021
-0.044
-0.016
0.018
-0.016
-0.014
-0.014
July 2005
D-53
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Common Loon in SW_Swetts
Property
rho
DemethylationRate
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
FoodlngestionRate
AssimilationEfficiencyFromFood
emissionRate
MethylationRate
phi
Kd
Flushes per year
HowMuchFasterHgEliminationlsThanForMHg
Rain
VaporWashoutRatio
DemethylationRate
WaterTemperature K
TotalExcretionRate
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
BW
rho
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
BW
Chemical
All
MHg
MHg
MHg
All
MHg
Hg2
Hg2
All
Hg2
All
MHg
All
Hg2
MHg
All
MHg
All
All
All
All
Hg2
All
All
All
MHg
All
All
All
All
Object Name
Surface water
Sediment
Benthic Invertebrate
Common Loon
Common Loon
Benthic Omnivore
Facility
Sediment
Sediment
Surface water
Surface water
Benthic Omnivore
FullSS
Divalent Mercury
Common Loon
Surface Water
Common Loon
Link from Air, to Air
Benthic Omnivore
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Benthic Omnivore
Sediment
Benthic Carnivore
Benthic Carnivore
Link from Air, to Air
Link from Air, to Air
Benthic Carnivore
Elasticity
6.959
-1.006
0.999
0.998
0.998
0.995
0.993
0.990
-0.803
0.790
-0.787
-0.667
0.607
0.605
-0.514
-0.499
-0.491
0.385
0.335
0.324
0.324
-0.318
-0.318
0.318
0.302
-0.272
-0.272
0.270
-0.232
-0.231
Sensitivity Score
0.348
-1.005
2.996
0.997
0.299
0.994
0.993
0.989
-0.241
0.790
-0.236
-0.667
0.074
1.815
-0.513
-0.499
-0.491
0.043
0.335
0.097
0.097
-0.318
-0.016
0.095
0.015
-0.272
-0.272
0.030
-0.025
-0.069
Divalent Mercury in Water Column Carnivore in River
Property
WaterTemperature K
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
Flushes per year
HowMuchFasterHgEliminationlsThanForMHg
AlgaeUptakeRate
AssimilationEfficiencyFromFood
emissionRate
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
Rain
VaporWashoutRatio
Kd
SuspendedSedimentconcentration
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
ReductionRate
SteadyState AdvectiveTransfer
rho
BW
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
All
Hg2
Hg2
All
All
All
All
Hg2
All
All
All
All
All
Hg2
All
All
All
Object Name
Surface Water
Surface water
Surface water
Surface water
Surface water
Water Column Carnivore
Surface water
Water Column Herbivore
Facility
Water Column Carnivore
Water Column Omnivore
Water Column Herbivore
Water Column Omnivore
FullSS
Divalent Mercury
Surface water
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Surface water
Water Column Herbivore
Link from Air, to Air
Water Column Herbivore
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Elasticity
-4.336
-1.010
-1.010
-1.010
-1.010
-1.001
1.000
1.000
0.994
0.933
0.933
-0.931
-0.922
0.732
0.731
-0.604
-0.603
0.372
0.372
-0.362
-0.362
-0.251
0.215
0.113
-0.087
0.078
0.076
-0.076
-0.075
-0.074
Sensitivity Score
-4.332
-0.303
-0.303
-0.303
-0.303
-1.001
0.300
0.999
0.994
0.932
0.932
-0.931
-0.922
0.089
2.194
-0.604
-0.181
0.112
0.112
-0.018
-0.361
-0.027
0.011
0.034
-0.011
0.078
0.023
-0.008
-0.008
-0.008
July 2005
D-54
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Water Column Carnivore in River
Property
WaterTemperature K
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AlgaeUptakeRate
AssimilationEfficiencyFromFood
emissionRate
HowMuchFasterHgEliminationlsThanForMHg
DemethylationRate
MethylationRate
Flushes per year
Rain
VaporWashoutRatio
Kd
SuspendedSedimenteoncentration
NumberofFishperSquareMeter
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
BW
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
ReductionRate
rho
BW
Kd
FractionofAreaAvailableforRunoff
TotalRunoffRate m3 m2 day
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
Chemical
All
All
All
All
MHg
MHg
Hg2
MHg
MHg
Hg2
All
All
Hg2
MHg
All
All
MHg
MHg
All
All
MHg
MHg
Hg2
All
All
MHg
All
All
All
All
Object Name
Surface Water
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Facility
Water Column Carnivore
Soil - Surface
Soil - Surface
Surface water
FullSS
Divalent Mercury
Surface water
Surface water
Water Column Herbivore
Water Column Carnivore
Water Column Omnivore
Water Column Carnivore
Water Column Herbivore
Water Column Herbivore
Water Column Omnivore
Soil - Surface
Soil - Surface
Water Column Carnivore
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Elasticity
-1.998
-1.010
-1.010
-1.010
1.000
1.000
0.998
-0.983
-0.940
0.931
-0.929
0.732
0.732
-0.658
-0.644
0.534
0.493
0.485
-0.485
0.478
-0.470
-0.464
-0.437
-0.436
-0.363
-0.310
0.285
0.285
-0.279
0.187
Sensitivity Score
-1.996
-0.303
-0.303
-0.303
0.300
0.999
0.998
-0.983
-0.939
0.930
-0.279
0.089
2.195
-0.658
-0.193
0.534
0.492
0.485
-0.485
0.143
-0.470
-0.464
-0.436
-0.022
-0.109
-0.310
0.086
0.086
-0.030
0.056
Divalent Mercury in Water Column Carnivore in SW_Swetts
Property
rho
WaterTemperature K
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AlgaeUptakeRate
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
emissionRate
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
Flushes per year
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
All
All
Hg2
All
All
All
Hg2
All
All
All
All
Hg2
All
All
Hg2
All
All
Object Name
Surface water
Surface Water
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Water Column Carnivore
Facility
Water Column Carnivore
Water Column Omnivore
Water Column Herbivore
Water Column Omnivore
Surface water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Elasticity
6.064
-4.287
-1.010
-1.010
-1.010
1.000
1.000
-0.995
0.993
0.934
0.933
-0.923
-0.910
-0.789
0.607
0.605
0.385
0.325
0.325
-0.318
-0.318
0.270
-0.232
0.226
-0.201
-0.196
-0.195
-0.191
-0.187
-0.167
Sensitivity Score
0.303
-4.283
-0.303
-0.303
-0.303
0.300
0.999
-0.995
0.993
0.933
0.932
-0.923
-0.910
-0.237
0.074
1.815
0.043
0.097
0.097
-0.318
-0.016
0.030
-0.025
0.025
-0.201
-0.021
-0.021
-0.191
-0.023
-0.019
July 2005
D-55
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Water Column Carnivore in SW_Swetts
Property
rho
WaterTemperature K
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AssimilationEfficiencyFromFood
AlgaeUptakeRate
emissionRate
HowMuchFasterHgEliminationlsThanForMHg
Kd
Flushes per year
SuspendedSedimenteoncentration
MethylationRate
SedimentDepositi on Velocity
Rain
VaporWashoutRatio
NumberofFishperSquareMeter
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
BW
HowMuchFasterHgEliminationlsThanForMHg
NumberofFishperSquareMeter
HowMuchFasterHgEliminationlsThanForMHg
SteadyState AdvectiveTransfer
BW
ReductionRate
rho
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
All
MHg
MHg
Hg2
MHg
MHg
All
All
Hg2
All
All
Hg2
All
MHg
MHg
All
MHg
All
MHg
All
All
Hg2
All
All
All
All
Object Name
Surface water
Surface Water
Surface water
Surface water
Surface water
Water Column Herbivore
Surface water
Facility
Water Column Carnivore
Surface water
Surface water
Surface water
Surface water
Surface water
FullSS
Divalent Mercury
Water Column Herbivore
Water Column Carnivore
Water Column Omnivore
Water Column Herbivore
Water Column Herbivore
Water Column Carnivore
Water Column Omnivore
Link from Air, to Air
Water Column Carnivore
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Elasticity
4.932
-1.392
-1.010
-1.010
-1.010
1.000
1.000
0.994
-0.966
-0.791
-0.714
-0.673
0.647
-0.627
0.607
0.606
0.540
0.503
0.492
0.482
-0.464
-0.458
-0.454
0.381
-0.341
-0.328
-0.328
0.278
0.278
0.269
Sensitivity Score
0.247
-1.391
-0.303
-0.303
-0.303
0.999
0.300
0.994
-0.966
-0.791
-0.214
-0.202
0.647
-0.188
0.074
1.817
0.540
0.503
0.491
0.145
-0.464
-0.458
-0.454
0.043
-0.102
-0.328
-0.016
0.083
0.083
0.030
Divalent Mercury in Water Column Herbivore in River
Property
WaterTemperature K
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
Flushes per year
AlgaeUptakeRate
AssimilationEfficiencyFromFood
emissionRate
HowMuchFasterHgEliminationlsThanForMHg
Rain
VaporWashoutRatio
Kd
SuspendedSedimenteoncentration
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
ReductionRate
SteadyState AdvectiveTransfer
rho
BW
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
SteadyState AdvectiveTransfer
BW
Chemical
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
All
Hg2
Hg2
All
All
All
All
Hg2
All
All
All
All
All
Hg2
All
All
All
Hg2
All
All
All
Object Name
Surface Water
Surface water
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Facility
Water Column Herbivore
FullSS
Divalent Mercury
Surface water
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Surface water
Water Column Herbivore
Link from Air, to Air
Water Column Herbivore
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Water Column Omnivore
Water Column Omnivore
Link from Air, to Air
Water Column Omnivore
Elasticity
-1.459
-1.010
-1.010
-1.010
-1.010
1.000
1.000
0.994
-0.931
0.732
0.731
-0.604
-0.603
0.372
0.372
-0.362
-0.362
-0.251
0.215
0.113
-0.087
0.078
0.076
-0.076
-0.075
-0.074
-0.068
-0.068
-0.059
-0.058
Sensitivity Score
-1.458
-0.303
-0.303
-0.303
-0.303
0.300
0.999
0.994
-0.931
0.089
2.194
-0.604
-0.181
0.112
0.112
-0.018
-0.361
-0.027
0.011
0.034
-0.011
0.078
0.023
-0.008
-0.008
-0.008
-0.068
-0.068
-0.007
-0.017
July 2005
D-56
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Water Column Herbivore in River
Property
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AlgaeUptakeRate
AssimilationEfficiencyFromFood
emissionRate
DemethylationRate
MethylationRate
Flushes per year
Rain
VaporWashoutRatio
Kd
SuspendedSedimentconcentration
WaterTemperature K
NumberofFishperSquareMeter
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
BW
HowMuchFasterHgEliminationlsThanForMHg
BW
ReductionRate
rho
Kd
FractionofAreaAvailableforRunoff
TotalRunoffRate m3 m2 day
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SedimentDepositi on Velocity
SteadyState AdvectiveTransfer
Chemical
All
All
All
MHg
MHg
Hg2
MHg
Hg2
All
All
Hg2
MHg
All
All
All
MHg
All
All
MHg
All
Hg2
All
MHg
All
All
All
All
All
All
All
Object Name
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Facility
Soil - Surface
Soil - Surface
Surface water
FullSS
Divalent Mercury
Surface water
Surface water
Surface Water
Water Column Herbivore
Water Column Omnivore
Water Column Omnivore
Water Column Herbivore
Water Column Herbivore
Water Column Omnivore
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Surface water
Link from Air, to Air
Elasticity
-1.010
-1.010
-1.010
1.000
1.000
0.998
-0.940
0.931
-0.929
0.732
0.732
-0.658
-0.644
-0.548
0.534
-0.520
-0.520
0.478
-0.470
-0.442
-0.437
-0.436
-0.310
0.285
0.285
-0.279
0.187
0.187
-0.111
-0.096
Sensitivity Score
-0.303
-0.303
-0.303
0.300
0.999
0.998
-0.939
0.930
-0.279
0.089
2.195
-0.658
-0.193
-0.547
0.534
-0.520
-0.520
0.143
-0.470
-0.133
-0.436
-0.022
-0.310
0.086
0.086
-0.030
0.056
0.056
-0.033
-0.012
Divalent Mercury in Water Column Herbivore in SW_Swetts
Property
rho
WaterTemperature K
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AlgaeUptakeRate
AssimilationEfficiencyFromFood
emissionRate
HowMuchFasterHgEliminationlsThanForMHg
Flushes per year
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
Chemical
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
All
All
Hg2
All
All
All
Hg2
All
All
All
All
Hg2
All
All
Hg2
All
All
All
All
All
All
Object Name
Surface water
Surface Water
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Facility
Water Column Herbivore
Surface water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf - Coniferous Forest
Elasticity
6.064
-1.817
-1.010
-1.010
-1.010
1.000
1.000
0.993
-0.923
-0.789
0.607
0.605
0.385
0.325
0.325
-0.318
-0.318
0.270
-0.232
0.226
-0.201
-0.196
-0.195
-0.191
-0.187
-0.167
-0.161
0.147
0.143
-0.141
Sensitivity Score
0.303
-1.815
-0.303
-0.303
-0.303
0.300
0.999
0.993
-0.923
-0.237
0.074
1.815
0.043
0.097
0.097
-0.318
-0.016
0.030
-0.025
0.025
-0.201
-0.021
-0.021
-0.191
-0.023
-0.019
-0.018
0.023
0.023
-0.140
July 2005
D-57
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Water Column Herbivore in SW_Swetts
Property
rho
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AssimilationEfficiencyFromFood
AlgaeUptakeRate
emissionRate
Kd
Flushes per year
WaterTemperature K
SuspendedSedimentconcentration
MethylationRate
SedimentDepositi on Velocity
Rain
VaporWashoutRatio
NumberofFishperSquareMeter
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
BW
HowMuchFasterHgEliminationlsThanForMHg
BW
SteadyState AdvectiveTransfer
ReductionRate
rho
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DemethylationRate
Chemical
All
All
All
All
MHg
MHg
Hg2
MHg
All
All
All
Hg2
All
All
Hg2
All
MHg
All
All
MHg
All
All
Hg2
All
All
All
All
All
All
MHg
Object Name
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Surface water
Facility
Surface water
Surface water
Surface Water
Surface water
Surface water
Surface water
FullSS
Divalent Mercury
Water Column Herbivore
Water Column Omnivore
Water Column Omnivore
Water Column Herbivore
Water Column Herbivore
Water Column Omnivore
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Elasticity
4.932
-1.010
-1.010
-1.010
1.000
1.000
0.994
-0.791
-0.714
-0.694
-0.673
0.647
-0.627
0.607
0.606
0.540
-0.514
-0.514
0.482
-0.464
-0.437
0.381
-0.328
-0.328
0.278
0.278
0.269
-0.231
0.225
-0.209
Sensitivity Score
0.247
-0.303
-0.303
-0.303
0.999
0.300
0.994
-0.791
-0.214
-0.693
-0.202
0.647
-0.188
0.074
1.817
0.540
-0.513
-0.514
0.145
-0.464
-0.131
0.043
-0.328
-0.016
0.083
0.083
0.030
-0.025
0.025
-0.208
Divalent Mercury in Water Column Omivore in River
Property
WaterTemperature K
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
Flushes per year
AlgaeUptakeRate
AssimilationEfficiencyFromFood
emissionRate
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
Rain
VaporWashoutRatio
Kd
SuspendedSedimentconcentration
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
rho
ReductionRate
SteadyState AdvectiveTransfer
rho
BW
SteadyState AdvectiveTransfer
NumberofFishperSquareMeter
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
Chemical
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
All
Hg2
Hg2
All
All
All
All
Hg2
All
All
All
All
All
Hg2
All
All
All
Hg2
All
Object Name
Surface Water
Surface water
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Facility
Water Column Omnivore
Water Column Herbivore
Water Column Omnivore
FullSS
Divalent Mercury
Surface water
Surface water
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Surface water
Water Column Herbivore
Link from Air, to Air
Water Column Herbivore
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Water Column Carnivore
Water Column Carnivore
Elasticity
-2.717
-1.010
-1.010
-1.010
-1.010
1.000
1.000
0.995
0.933
-0.931
-0.922
0.732
0.731
-0.604
-0.603
0.372
0.372
-0.362
-0.362
-0.251
0.215
0.113
-0.087
0.078
0.076
-0.076
-0.075
-0.074
-0.067
-0.067
Sensitivity Score
-2.714
-0.303
-0.303
-0.303
-0.303
0.300
0.999
0.995
0.932
-0.931
-0.922
0.089
2.194
-0.604
-0.181
0.112
0.112
-0.018
-0.361
-0.027
0.011
0.034
-0.011
0.078
0.023
-0.008
-0.008
-0.008
-0.067
-0.067
July 2005
D-58
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Water Column Omivore in River
Property
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AlgaeUptakeRate
AssimilationEfficiencyFromFood
emissionRate
DemethylationRate
MethylationRate
Flushes per year
WaterTemperature K
Rain
VaporWashoutRatio
Kd
SuspendedSedimentconcentration
NumberofFishperSquareMeter
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
AssimilationEfficiencyFromFood
BW
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
ReductionRate
rho
BW
Kd
FractionofAreaAvailableforRunoff
TotalRunoffRate m3 m2 day
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
Chemical
All
All
All
MHg
MHg
Hg2
MHg
Hg2
All
All
All
Hg2
MHg
All
All
MHg
All
MHg
All
MHg
MHg
Hg2
All
All
MHg
All
All
All
All
All
Object Name
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Facility
Soil - Surface
Soil - Surface
Surface water
Surface Water
FullSS
Divalent Mercury
Surface water
Surface water
Water Column Herbivore
Water Column Carnivore
Water Column Carnivore
Water Column Omnivore
Water Column Herbivore
Water Column Herbivore
Water Column Omnivore
Soil - Surface
Soil - Surface
Water Column Carnivore
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Elasticity
-1.010
-1.010
-1.010
1.000
1.000
0.998
-0.940
0.931
-0.929
-0.796
0.732
0.732
-0.658
-0.644
0.534
-0.512
-0.512
0.485
0.478
-0.470
-0.464
-0.437
-0.436
-0.435
-0.310
0.285
0.285
-0.279
0.187
0.187
Sensitivity Score
-0.303
-0.303
-0.303
0.300
0.999
0.998
-0.939
0.930
-0.279
-0.796
0.089
2.195
-0.658
-0.193
0.534
-0.512
-0.512
0.485
0.143
-0.470
-0.464
-0.436
-0.022
-0.131
-0.310
0.086
0.086
-0.030
0.056
0.056
Divalent Mercury in Water Column Omivore in SW_Swetts
Property
rho
WaterTemperature K
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AlgaeUptakeRate
AssimilationEfficiencyFromFood
emissionRate
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
Flushes per year
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
Hg2
Hg2
All
All
Hg2
All
All
All
Hg2
All
All
All
All
Hg2
All
All
Hg2
All
All
All
All
Object Name
Surface water
Surface Water
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Facility
Water Column Omnivore
Water Column Herbivore
Water Column Omnivore
Surface water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
6.064
-2.795
-1.010
-1.010
-1.010
1.000
1.000
0.993
0.933
-0.923
-0.910
-0.789
0.607
0.605
0.385
0.325
0.325
-0.318
-0.318
0.270
-0.232
0.226
-0.201
-0.196
-0.195
-0.191
-0.187
-0.167
-0.161
0.147
Sensitivity Score
0.303
-2.792
-0.303
-0.303
-0.303
0.300
0.999
0.993
0.932
-0.923
-0.910
-0.237
0.074
1.815
0.043
0.097
0.097
-0.318
-0.016
0.030
-0.025
0.025
-0.201
-0.021
-0.021
-0.191
-0.023
-0.019
-0.018
0.023
July 2005
D-59
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Water Column Omivore in SW_Swetts
Property
rho
AlgaeDensity g m3
AlgaeGrowthRate
AlgaeRadius
AssimilationEfficiencyFromFood
AlgaeUptakeRate
emissionRate
Kd
Flushes per year
SuspendedSedimentconcentration
MethylationRate
SedimentDepositi on Velocity
Rain
VaporWashoutRatio
WaterTemperature K
NumberofFishperSquareMeter
AssimilationEfficiencyFromFood
NumberofFishperSquareMeter
AssimilationEfficiencyFromFood
BW
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
BW
SteadyState AdvectiveTransfer
ReductionRate
rho
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
MHg
MHg
Hg2
MHg
All
All
Hg2
All
All
Hg2
All
All
MHg
All
MHg
All
MHg
MHg
All
All
Hg2
All
All
All
All
All
Object Name
Surface water
Surface water
Surface water
Surface water
Water Column Herbivore
Surface water
Facility
Surface water
Surface water
Surface water
Surface water
Surface water
FullSS
Divalent Mercury
Surface Water
Water Column Herbivore
Water Column Carnivore
Water Column Carnivore
Water Column Omnivore
Water Column Herbivore
Water Column Herbivore
Water Column Omnivore
Water Column Carnivore
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Elasticity
4.932
-1.010
-1.010
-1.010
1.000
1.000
0.994
-0.791
-0.714
-0.673
0.647
-0.627
0.607
0.606
-0.548
0.540
-0.502
-0.502
0.492
0.482
-0.464
-0.454
-0.427
0.381
-0.328
-0.328
0.278
0.278
0.269
-0.231
Sensitivity Score
0.247
-0.303
-0.303
-0.303
0.999
0.300
0.994
-0.791
-0.214
-0.202
0.647
-0.188
0.074
1.817
-0.547
0.540
-0.501
-0.502
0.491
0.145
-0.464
-0.454
-0.128
0.043
-0.328
-0.016
0.083
0.083
0.030
-0.025
Divalent Mercury in Mouse in SurfSoil SSE4
Property
TotalExcretionRate
emissionRate
AssimilationEfficiencyFromSoils
FoodlngestionRate
FoodlngestionRate
BW
NumberoflndividualsPerSquareMeter
AssimilationEfficiencyFromSoils
AllowExchange SteadyState forAir
VaporWashoutRatio
Rain
SoillngestionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WaterContent
WetDepInterceptionFraction UserSupplied
LitterFallRate
AllowExchange SteadyState forOther
SteadyState AdvectiveTransfer
TransferFactortoLeafParticle
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
Chemical
Hg2
Hg2
Hg2
All
All
All
All
Hg2
All
Hg2
All
All
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
Hg2
Object Name
Mouse
Facility
Mouse
Mouse
White-tailed Deer
White-tailed Deer
White-tailed Deer
White-tailed Deer
Leaf - Coniferous Forest
Divalent Mercury
FullSS
Mouse
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Fores!
Leaf- Coniferous Forest
Leaf - Coniferous Forest
Leaf - Coniferous Forest
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Elasticity
-1.007
0.994
0.953
0.553
-0.508
-0.508
-0.508
-0.496
0.496
0.465
0.436
0.432
0.394
0.331
-0.320
0.313
-0.284
0.282
-0.273
0.270
0.263
-0.249
-0.249
-0.220
0.219
-0.191
-0.189
-0.186
-0.175
0.173
Sensitivity Score
-1.007
0.994
0.952
0.166
-0.152
-0.152
-0.508
-0.496
0.495
1.394
0.053
0.130
0.044
0.037
-0.160
0.094
-0.284
0.281
-0.030
0.810
0.029
-0.075
-0.075
-0.024
0.024
-0.023
-0.021
-0.020
-0.020
0.052
July 2005
D-60
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Mouse in SurfSoil SSE4
Property
rho
InhalationProps B
WaterlngProps B
OxidationRate
WaterTemperature K
emissionRate
InhalationAssimilationEfficiency
InhalationProps A
emissionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Rain
AssimilationEfficiencyFrom Water
WaterlngProps A
VaporWashoutRatio
SteadyState AdvectiveTransfer
Flushes per year
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AirTemperature K
HenryLawConstant
SteadyState AdvectiveTransfer
AssimilationEfficiencyFromSoils
SteadyState AdvectiveTransfer
Kd
SoillngestionRate
SteadyState AdvectiveTransfer
Chemical
All
All
All
HgO
All
HgO
HgO
All
Hg2
All
All
All
All
HgO
All
Hg2
All
All
All
All
All
All
All
HgO
All
HgO
All
HgO
All
All
Object Name
Surface water
Mouse
Mouse
Mouse
Surface Water
Facility
Mouse
Mouse
Facility
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
FullSS
Mouse
Mouse
Divalent Mercury
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
FullSS
Elemental Mercury
Link from Air, to Air
Mouse
Link from Air, to Air
Soil - Surface
Mouse
Link from Air, to Air
Elasticity
1.736
-1.712
-0.974
-0.960
-0.588
0.563
0.539
0.539
0.437
0.392
0.299
-0.286
0.276
0.272
0.272
0.267
0.256
-0.239
-0.238
0.210
-0.208
-0.201
0.191
-0.191
-0.191
0.189
-0.189
0.181
0.181
-0.177
Sensitivity Score
0.087
-0.514
-0.292
-0.959
-0.587
0.563
0.162
0.162
0.437
0.044
0.033
-0.032
0.034
0.272
0.082
0.800
0.028
-0.072
-0.026
0.023
-0.023
-0.025
0.000
-0.191
-0.020
0.189
-0.022
0.181
0.054
-0.019
Methyl Mercury in Mouse in SurfSoil SSE4
Property
emissionRate
AssimilationEfficiencyFromSoils
SoillngestionRate
DemethylationRate
MethylationRate
TotalExcretionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
DemethylationRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WetDepInterceptionFraction UserSupplied
Chemical
Hg2
MHg
All
MHg
Hg2
MHg
All
Hg2
All
All
All
Hg2
All
All
MHg
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
Mouse
Mouse
Soil - Surface
Soil - Surface
Mouse
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Mouse
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Leaf - Coniferous Fores!
Elasticity
0.995
0.930
0.930
-0.929
0.921
-0.745
0.608
0.607
-0.568
-0.568
0.367
-0.366
-0.365
0.265
-0.257
-0.227
0.223
-0.193
-0.187
-0.178
-0.173
-0.167
-0.163
0.141
0.140
0.134
-0.129
-0.120
-0.116
-0.112
Sensitivity Score
0.995
0.929
0.279
-0.928
0.920
-2.236
0.074
1.822
-0.170
-0.170
0.041
-0.365
-0.018
0.029
-0.256
-0.025
0.025
-0.021
-0.020
-0.022
-0.019
-0.019
-0.163
0.022
0.022
0.019
-0.015
-0.013
-0.013
-0.034
July 2005
D-61
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Mouse in SurfSoil SW2
Property
TotalExcretionRate
emissionRate
AssimilationEfficiencyFromSoils
SoillngestionRate
VaporWashoutRatio
Rain
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
rho
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
FoodlngestionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
LitterFallRate
AllowExchange SteadyState forOther
SteadyState AdvectiveTransfer
Chemical
Hg2
Hg2
Hg2
All
Hg2
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
Object Name
Mouse
Facility
Mouse
Mouse
Divalent Mercury
FullSS
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Mouse
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
Link from Air, to Air
Elasticity
-1.007
0.997
0.972
0.910
0.681
0.678
-0.512
-0.512
-0.380
-0.350
-0.350
-0.323
0.259
0.173
-0.156
-0.151
-0.138
-0.101
0.097
0.091
0.090
0.088
0.087
0.085
-0.082
-0.077
-0.076
-0.075
0.074
0.073
Sensitivity Score
-1.007
0.997
0.971
0.273
2.042
0.083
-0.154
-0.154
-0.041
-0.017
-0.349
-0.038
0.037
0.019
-0.017
-0.017
-0.016
-0.012
0.029
0.013
0.011
0.013
0.026
0.009
-0.009
-0.009
-0.009
-0.006
0.002
0.008
Elemental Mercury in Mouse in SurfSoil SW2
Property
InhalationProps B
OxidationRate
emissionRate
InhalationAssimilationEfficiency
InhalationProps A
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
HenryLawConstant
AirTemperature K
AssimilationEfficiencyFromSoils
SoillngestionRate
Kd
Fractionofareaavailableforverticaldiffusion
emissionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Rain
SteadyState AdvectiveTransfer
VaporWashoutRatio
BW
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
D_pureair
VaporDryDepositionVelocity m day
Chemical
All
HgO
HgO
HgO
All
All
All
All
All
HgO
All
HgO
All
HgO
All
Hg2
All
All
All
All
All
Hg2
All
All
All
Hg2
All
All
HgO
HgO
Object Name
Mouse
Mouse
Facility
Mouse
Mouse
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elemental Mercury
FullSS
Mouse
Mouse
Soil - Surface
Soil - Surface
Facility
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
FullSS
Link from Air, to Sink
Divalent Mercury
Mouse
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Elemental Mercury
Soil - Surface
Elasticity
-2.415
-0.960
0.775
0.760
0.760
-0.476
-0.382
0.375
-0.267
-0.242
0.239
0.238
0.237
0.237
-0.232
0.225
0.215
-0.191
0.165
0.162
-0.156
0.154
-0.152
-0.125
-0.125
0.123
0.123
0.121
-0.116
-0.115
Sensitivity Score
-0.725
-0.959
0.775
0.228
0.228
-0.055
-0.042
0.053
-0.030
-0.242
0.000
0.238
0.071
0.237
-0.069
0.225
0.024
-0.022
0.020
0.020
-0.017
0.463
-0.045
-0.038
-0.038
0.123
0.006
0.017
-0.006
-0.035
July 2005
D-62
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Mouse in SurfSoil SW2
Property
emissionRate
AssimilationEfficiencyFromSoils
SoillngestionRate
DemethylationRate
MethylationRate
TotalExcretionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
DemethylationRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
MHg
All
MHg
Hg2
MHg
All
Hg2
All
All
All
Hg2
All
All
MHg
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
Object Name
Facility
Mouse
Mouse
Soil - Surface
Soil - Surface
Mouse
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Mouse
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.997
0.933
0.933
-0.933
0.924
-0.745
0.685
0.685
-0.556
-0.556
-0.386
-0.380
-0.379
-0.309
-0.257
0.247
0.166
-0.158
-0.139
-0.132
-0.102
0.093
0.092
0.084
0.084
-0.083
0.082
-0.078
-0.073
0.070
Sensitivity Score
0.997
0.932
0.280
-0.932
0.923
-2.235
0.084
2.055
-0.167
-0.167
-0.042
-0.379
-0.019
-0.036
-0.256
0.035
0.018
-0.017
-0.016
-0.015
-0.012
0.028
0.013
0.012
0.010
-0.009
0.009
-0.010
-0.009
0.008
Divalent Mercury in Raccoon in SurfSoil SSE4
Property
rho
TotalExcretionRate
emissionRate
Rain
VaporWashoutRatio
FoodlngestionRate
SedimentPartitioning PartitionCoefficient
AssimilationEfficiencyFromFood
phi
Kd
Flushes per year
SoillngestionRate
AssimilationEfficiencyFromSoils
SteadyState AdvectiveTransfer
ReductionRate
rho
WaterTemperature K
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
Hg2
Hg2
All
Hg2
All
Hg2
Hg2
All
Hg2
All
All
Hg2
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Raccoon
Facility
FullSS
Divalent Mercury
Raccoon
Benthic Invertebrate
Raccoon
Sediment
Surface water
Surface water
Raccoon
Raccoon
Link from Air, to Air
Soil - Surface
Soil - Surface
Surface Water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Sediment
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
3.965
-1.010
0.994
0.607
0.606
0.564
0.536
0.536
-0.472
0.451
-0.447
0.435
0.433
0.377
-0.339
-0.339
-0.328
0.268
-0.230
0.225
-0.194
-0.192
-0.183
0.182
-0.167
-0.166
-0.150
0.144
0.141
0.134
Sensitivity Score
0.198
-1.010
0.994
0.074
1.818
0.169
1.609
0.536
-0.142
0.451
-0.134
0.130
0.432
0.042
-0.338
-0.017
-0.327
0.030
-0.025
0.025
-0.021
-0.021
-0.022
0.009
-0.019
-0.018
-0.150
0.023
0.023
0.019
July 2005
D-63
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in Raccoon in SurfSoil SSE4
Property
rho
phi
rho
SuspendedSedimenteoncentration
emissionRate
AssimilationEfficiencyFromFood
SedimentPartitioning PartitionCoefficient
FoodlngestionRate
OxidationRate
SedimentDepositi on Velocity
ReductionRate
Flushes per year
Kd
WaterTemperature K
Rain
VaporWashoutRatio
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
Hg2
HgO
HgO
All
HgO
All
Hg2
All
Hg2
All
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
Object Name
Surface water
Sediment
Sediment
Surface water
Facility
Raccoon
Benthic Invertebrate
Raccoon
Raccoon
Surface water
Sediment
Surface water
Surface water
Surface Water
FullSS
Divalent Mercury
Link from Air, to Air
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
7.779
-2.197
1.220
-1.063
0.991
0.991
0.990
0.990
-0.962
-0.930
0.865
-0.790
0.726
-0.699
0.606
0.604
0.385
0.318
0.318
-0.312
-0.312
0.270
-0.232
0.226
-0.196
-0.195
-0.187
-0.167
-0.161
0.147
Sensitivity Score
0.389
-0.659
0.061
-0.319
0.991
0.990
2.971
0.297
-0.961
-0.279
0.864
-0.237
0.726
-0.698
0.074
1.811
0.043
0.095
0.095
-0.312
-0.016
0.030
-0.025
0.025
-0.021
-0.021
-0.023
-0.019
-0.018
0.023
Methyl Mercury in Raccoon in SurfSoil SSE4
Property
rho
emissionRate
DemethylationRate
SedimentPartitioning PartitionCoefficient
MethylationRate
AssimilationEfficiencyFromFood
FoodlngestionRate
TotalExcretionRate
phi
Kd
Flushes per year
Rain
VaporWashoutRatio
WaterTemperature K
SteadyState AdvectiveTransfer
ReductionRate
rho
rho
SteadyState AdvectiveTransfer
DemethylationRate
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
Hg2
MHg
MHg
Hg2
MHg
All
MHg
All
Hg2
All
All
Hg2
All
All
Hg2
All
All
All
MHg
All
All
All
All
All
All
All
Hg2
All
All
Object Name
Surface water
Facility
Sediment
Benthic Invertebrate
Sediment
Raccoon
Raccoon
Raccoon
Sediment
Surface water
Surface water
FullSS
Divalent Mercury
Surface Water
Link from Air, to Air
Soil - Surface
Soil - Surface
Sediment
Link from Air, to Air
Raccoon
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
Link from Air, to Air
Link from Air, to Air
Elasticity
6.377
0.994
-0.922
0.915
0.907
0.906
0.906
-0.748
-0.736
0.724
-0.721
0.607
0.605
-0.539
0.384
-0.322
-0.322
0.277
0.270
-0.258
0.249
0.249
-0.232
0.226
-0.195
-0.194
-0.186
-0.184
-0.167
-0.162
Sensitivity Score
0.319
0.994
-0.921
2.746
0.906
0.905
0.272
-2.245
-0.221
0.724
-0.216
0.074
1.816
-0.538
0.043
-0.322
-0.016
0.014
0.030
-0.258
0.075
0.075
-0.025
0.025
-0.021
-0.021
-0.023
-0.183
-0.019
-0.018
July 2005
D-64
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in Raccoon in SurfSoil SW2
Property
TotalExcretionRate
SoillngestionRate
emissionRate
AssimilationEfficiencyFromSoils
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Kd
Chemical
Hg2
All
Hg2
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
Hg2
Object Name
Raccoon
Raccoon
Facility
Raccoon
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Elasticity
-1.010
0.998
0.997
0.994
0.685
0.685
-0.554
-0.554
-0.386
-0.380
-0.380
-0.309
0.247
0.166
-0.158
-0.139
-0.132
-0.102
0.093
0.092
0.084
0.084
-0.083
0.082
-0.078
-0.072
0.070
0.070
-0.063
0.061
Sensitivity Score
-1.010
0.299
0.997
0.993
0.084
2.055
-0.166
-0.166
-0.042
-0.379
-0.019
-0.036
0.035
0.018
-0.017
-0.016
-0.015
-0.012
0.028
0.013
0.012
0.010
-0.009
0.009
-0.010
-0.009
0.008
0.011
-0.008
0.061
Elemental Mercury in Raccoon in SurfSoil SW2
Property
OxidationRate
HenryLawConstant
AirTemperature K
AssimilationEfficiencyFromSoils
Kd
SoillngestionRate
Fractionofareaavailableforverticaldiffusion
emissionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
ReductionRate
rho
D_pureair
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
emissionRate
InhalationProps B
SteadyState AdvectiveTransfer
InhalationAssimilationEfficiency
InhalationProps A
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Water content
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
HgO
HgO
All
HgO
HgO
All
All
Hg2
All
Hg2
All
All
Hg2
All
HgO
HgO
All
All
All
HgO
All
All
HgO
All
All
All
All
All
All
All
Object Name
Raccoon
Elemental Mercury
FullSS
Raccoon
Soil - Surface
Raccoon
Soil - Surface
Facility
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Soil - Surface
Soil - Surface
Elemental Mercury
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Facility
Raccoon
Link from Air, to Air
Raccoon
Raccoon
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Worm
Link from Air, to Air
Link from Air, to Air
Elasticity
-0.962
-0.815
0.796
0.757
0.757
0.757
-0.756
0.747
0.540
0.514
-0.398
-0.398
0.387
0.387
-0.379
-0.375
-0.373
-0.342
0.272
0.253
0.240
0.167
0.163
0.163
-0.163
-0.155
-0.146
-0.105
-0.102
0.099
Sensitivity Score
-0.961
-0.815
0.001
0.757
0.757
0.227
-0.227
0.747
0.066
1.542
-0.119
-0.119
0.387
0.019
-0.019
-0.113
-0.041
-0.040
0.039
0.253
0.072
0.019
0.049
0.049
-0.018
-0.017
-0.017
-0.105
-0.012
0.012
July 2005
D-65
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in Raccoon in SurfSoil SW2
Property
emissionRate
AssimilationEfficiencyFromSoils
SoillngestionRate
DemethylationRate
MethylationRate
TotalExcretionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
DemethylationRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
MHg
All
MHg
Hg2
MHg
All
Hg2
All
All
All
Hg2
All
All
MHg
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
Object Name
Facility
Raccoon
Raccoon
Soil - Surface
Soil - Surface
Raccoon
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Raccoon
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.997
0.985
0.984
-0.978
0.969
-0.748
0.686
0.686
-0.542
-0.542
-0.384
-0.379
-0.379
-0.305
-0.258
0.244
0.163
-0.157
-0.138
-0.131
-0.101
0.093
0.091
0.083
0.083
-0.082
0.081
-0.079
-0.071
0.070
Sensitivity Score
0.997
0.984
0.295
-0.977
0.968
-2.245
0.084
2.057
-0.163
-0.163
-0.042
-0.379
-0.019
-0.035
-0.258
0.035
0.018
-0.017
-0.015
-0.015
-0.012
0.028
0.013
0.012
0.010
-0.009
0.009
-0.010
-0.009
0.011
Divalent Mercury in White-tailed Deer in SurfSoil SSE4
Property
TotalExcretionRate
emissionRate
AllowExchange SteadyState forAir
BW
NumberoflndividualsPerSquareMeter
WaterContent
WetDepInterceptionFraction UserSupplied
SteadyState AdvectiveTransfer
LitterFallRate
AllowExchange SteadyState forOther
TransferFactortoLeafParticle
SteadyState AdvectiveTransfer
VaporWashoutRatio
SteadyState AdvectiveTransfer
Rain
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AttenuationFactor
AssimilationEfficiencyFromSoils
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
Hg2
All
All
All
All
All
All
All
All
Hg2
All
Hg2
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
Hg2
All
All
Object Name
White-tailed Deer
Facility
Leaf- Coniferous Forest
White-tailed Deer
White-tailed Deer
Leaf - Coniferous Fores!
Leaf- Coniferous Forest
Link from Air, to Air
Leaf - Coniferous Forest
Leaf - Coniferous Forest
Leaf- Coniferous Forest
Link from Air, to Air
Divalent Mercury
Link from Air, to Air
FullSS
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
White-tailed Deer
Link from Air, to Air
Link from Air, to Air
Elasticity
-1.010
0.994
0.925
-0.839
-0.839
-0.597
0.589
0.500
-0.468
0.465
0.446
0.412
0.372
-0.339
0.325
0.261
0.256
-0.241
0.217
-0.215
-0.207
-0.200
-0.191
-0.181
-0.180
0.171
0.154
0.145
0.144
0.144
Sensitivity Score
-1.010
0.994
0.924
-0.252
-0.839
-0.298
0.177
0.056
-0.468
0.465
1.338
0.046
1.116
-0.038
0.040
0.029
0.077
-0.029
0.024
-0.023
-0.023
-0.024
-0.021
-0.019
-0.021
0.027
0.154
0.145
0.020
0.023
July 2005
D-66
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Elemental Mercury in White-tailed Deer in SurfSoil SSE4
Property
rho
WaterlngProps B
InhalationProps B
WaterTemperature K
OxidationRate
emissionRate
AssimilationEfficiencyFrom Water
WaterlngProps A
emissionRate
Flushes per year
InhalationAssimilationEfficiency
InhalationProps A
SteadyState AdvectiveTransfer
Rain
VaporWashoutRatio
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
DimensionlessViscousSublayerThickness
horizontalWindSpeed
WaterColumnDissPartitioning TimeToReachAlphaofEquil
BiomassPerArea kg m2
WaterColumnDissolvedPartitioning PartitionCoefficient
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
All
All
All
All
HgO
Hg2
HgO
All
HgO
All
HgO
All
All
All
Hg2
Hg2
All
All
All
All
All
All
HgO
All
HgO
All
All
All
All
All
Object Name
Surface water
White-tailed Deer
White-tailed Deer
Surface Water
White-tailed Deer
Facility
White-tailed Deer
White-tailed Deer
Facility
Surface water
White-tailed Deer
White-tailed Deer
Link from Air, to Air
FullSS
Divalent Mercury
Surface water
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Surface water
FullSS
Macrophyte
Macrophyte
Macrophyte
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
3.206
1.920
1.488
-1.090
-0.962
0.537
0.504
0.504
0.463
-0.443
0.439
0.439
0.393
0.331
0.327
0.318
-0.264
0.259
0.247
-0.238
0.225
-0.225
0.222
-0.221
-0.221
0.213
-0.206
-0.199
-0.192
-0.185
Sensitivity Score
0.160
0.576
0.446
-1.089
-0.961
0.537
0.504
0.151
0.463
-0.133
0.132
0.132
0.044
0.040
0.982
0.318
-0.029
0.029
0.028
-0.026
0.068
-0.225
0.665
-0.221
-0.664
0.024
-0.022
-0.024
-0.021
-0.021
Methyl Mercury in White-tailed Deer in SurfSoil SSE4
Property
emissionRate
AssimilationEfficiencyFromSoils
SoillngestionRate
DemethylationRate
MethylationRate
TotalExcretionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
DemethylationRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
AllowExchange SteadyState forAir
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
WetDepInterceptionFraction UserSupplied
Chemical
Hg2
MHg
All
MHg
Hg2
MHg
All
Hg2
All
All
All
Hg2
All
All
MHg
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
Object Name
Facility
White-tailed Deer
White-tailed Deer
Soil - Surface
Soil - Surface
White-tailed Deer
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
White-tailed Deer
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Leaf- Coniferous Forest
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Leaf - Coniferous Fores!
Elasticity
0.995
0.985
0.985
-0.979
0.970
-0.748
0.608
0.607
-0.557
-0.557
0.367
-0.365
-0.365
0.265
-0.258
-0.227
0.223
-0.193
-0.187
-0.178
-0.173
-0.167
-0.162
0.141
0.140
0.134
-0.129
-0.120
-0.116
-0.112
Sensitivity Score
0.995
0.984
0.295
-0.978
0.969
-2.245
0.074
1.822
-0.167
-0.167
0.041
-0.365
-0.018
0.029
-0.258
-0.025
0.025
-0.021
-0.020
-0.022
-0.019
-0.019
-0.162
0.022
0.022
0.019
-0.015
-0.013
-0.013
-0.034
July 2005
D-67
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Divalent Mercury in White-tailed Deer in SurfSoil SW2
Property
TotalExcretionRate
emissionRate
VaporWashoutRatio
Rain
AssimilationEfficiencyFromSoils
SoillngestionRate
AllowExchange SteadyState forAir
LitterFallRate
AllowExchange SteadyState forOther
SteadyState AdvectiveTransfer
TransferFactortoLeafParticle
WetDepInterceptionFraction UserSupplied
SteadyState AdvectiveTransfer
BW
NumberoflndividualsPerSquareMeter
SteadyState AdvectiveTransfer
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
WaterContent
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
rho
ReductionRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
FoodlngestionRate
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
Hg2
Hg2
Hg2
All
Hg2
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
All
All
All
Hg2
All
All
All
All
Hg2
All
All
Object Name
White-tailed Deer
Facility
Divalent Mercury
FullSS
White-tailed Deer
White-tailed Deer
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
Link from Air, to Air
Leaf- Grasses/Herbs
Leaf- Grasses/Herbs
Link from Air, to Air
White-tailed Deer
White-tailed Deer
Link from Air, to Air
Soil - Surface
Soil - Surface
Leaf- Grasses/Herbs
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
Link from Air, to Sink
White-tailed Deer
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Elasticity
-1.010
0.996
0.661
0.644
0.610
0.534
0.454
-0.440
0.433
-0.392
0.369
0.361
-0.353
-0.324
-0.323
0.313
-0.300
-0.300
-0.247
0.206
-0.206
-0.205
-0.205
-0.169
-0.146
0.145
0.124
0.114
0.107
0.099
Sensitivity Score
-1.010
0.996
1.982
0.079
0.609
0.160
0.453
-0.034
0.012
-0.045
1.108
0.108
-0.038
-0.097
-0.323
0.044
-0.090
-0.090
-0.123
0.023
-0.023
-0.010
-0.205
-0.020
-0.016
0.043
0.015
0.034
0.015
0.011
Elemental Mercury in White-tailed Deer in SurfSoil SW2
Property
InhalationProps B
OxidationRate
emissionRate
InhalationAssimilationEfficiency
InhalationProps A
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
BW
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
HenryLawConstant
SteadyState AdvectiveTransfer
AirTemperature K
SteadyState AdvectiveTransfer
AssimilationEfficiencyFromSoils
SoillngestionRate
Kd
SteadyState AdvectiveTransfer
emissionRate
SteadyState AdvectiveTransfer
Chemical
All
HgO
HgO
HgO
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
All
HgO
All
All
All
HgO
All
HgO
All
Hg2
All
Object Name
White-tailed Deer
White-tailed Deer
Facility
White-tailed Deer
White-tailed Deer
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
White-tailed Deer
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elemental Mercury
Link from Air, to Air
FullSS
Link from Air, to Air
White-tailed Deer
White-tailed Deer
Soil - Surface
Link from Air, to Air
Facility
Link from Air, to Air
Elasticity
3.106
-0.962
0.926
0.915
0.915
-0.508
0.399
-0.381
-0.293
0.224
-0.202
-0.188
0.181
-0.155
0.128
-0.110
-0.105
0.099
0.095
0.090
-0.083
-0.083
0.081
-0.077
0.075
0.074
0.074
-0.074
0.074
0.073
Sensitivity Score
0.932
-0.961
0.926
0.275
0.275
-0.059
0.057
-0.042
-0.033
0.025
-0.023
-0.056
0.022
-0.017
0.018
-0.013
-0.012
0.011
0.010
0.013
-0.083
-0.009
0.000
-0.008
0.075
0.022
0.074
-0.009
0.074
0.008
July 2005
D-68
TRIM.FaTE Evaluation Report Volume II
-------
Appendix D.2. Sensitivity of Predicted Concentration to TRIM.FaTE Input Properties
(top 30 properties for each compartment examined, ranked by absolute elasticity)3
Methyl Mercury in White-tailed Deer in SurfSoil SW2
Property
AssimilationEfficiencyFromSoils
SoillngestionRate
emissionRate
DemethylationRate
MethylationRate
TotalExcretionRate
Rain
VaporWashoutRatio
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
SteadyState AdvectiveTransfer
ReductionRate
rho
SteadyState AdvectiveTransfer
DemethylationRate
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
VaporDryDepositionVelocity m day
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
SteadyState AdvectiveTransfer
Chemical
MHg
All
Hg2
MHg
Hg2
MHg
All
Hg2
All
All
All
Hg2
All
All
MHg
All
All
All
All
All
All
Hg2
All
All
All
All
All
All
All
All
Object Name
White-tailed Deer
White-tailed Deer
Facility
Soil - Surface
Soil - Surface
White-tailed Deer
FullSS
Divalent Mercury
Soil - Surface
Soil - Surface
Link from Air, to Air
Soil - Surface
Soil - Surface
Link from Air, to Air
White-tailed Deer
Link from Air, to Air
Link from Air, to Air
Link from Air, to Sink
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Soil - Surface
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Link from Air, to Air
Elasticity
0.999
0.999
0.997
-0.991
0.982
-0.748
0.685
0.685
-0.556
-0.556
-0.386
-0.38
-0.379
-0.309
-0.258
0.247
0.166
-0.158
-0.139
-0.132
-0.102
0.093
0.092
0.084
0.084
-0.083
0.082
-0.078
-0.073
0.07
Sensitivity Score
0.998
0.3
0.997
-0.99
0.981
-2.245
0.084
2.055
-0.167
-0.167
-0.042
-0.379
-0.019
-0.036
-0.258
0.035
0.018
-0.017
-0.016
-0.015
-0.012
0.028
0.013
0.012
0.01
-0.009
0.009
-0.01
-0.009
0.008
a For additional description of the input properties used in TRIM.FaTE, along with a key between common names used for properties and their TRIM.FaTE
code names, see Module 16 of the TRIM.FaTE User's Guide and the TRIM.FaTE technical support documents.
July 2005
D-69
TRIM.FaTE Evaluation Report Volume II
-------
-------
Appendix D.3. Properties with Absolute Elasticities > 0.5
Property a
EmissionRate
VaporWashoutRatio
Rain
rho
WaterTemperature K
EmissionRate
SedimentDeposition Velocity
Flushes_per year
SuspendedSedimentconcentration
HenryLawConstant
Kd
Demethy lationRate
MethylationRate
AirTemperature K
phi
Fractionofareaavailableforerosion
TotalErosionRate kg m2 day
Demethy lationRate
Kd
MethylationRate
MethylationRate
Steady State AdvectiveTransfer
SedimentPartitioning PartitionCoefficient
SedimentPartitioning PartitionCoefficient
rho
ReductionRate
SedimentPartitioning PartitionCoefficient
Kd
AlgaeDensity_g m3
AlgaeGrowthRate
AlgaeRadius
AlgaeUptakeRate
AlgaeUptakeRate
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
Chemical
Hg2
MHg
All
All
All
HgO
All
All
All
HgO
Hg2
MHg
Hg2
All
All
All
All
MHg
MHg
Hg2
Hg2
All
Hg2
MHg
All
Hg2
HgO
HgO
All
All
All
Hg2
MHg
Hg2
Hg2
MHg
MHg
Hg2
Hg2
Object Type
Source
Chemical
Scenario
Compartment
VolumeElement
Source
Compartment
Compartment
Compartment
Chemical
Compartment
Compartment
Compartment
Scenario
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Link
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link
Information
Facility
Divalent Mercury
FullSS
Surface water
Surface water
Facility
Surface water
Surface water
Surface water
Elemental Mercury
Surface water
Soil - Surface
Soil - Surface
FullSS
Sediment
Soil - Surface
Soil - Surface
Sediment
Surface water
Sediment
Surface water
from Air, to Air
Benthic Invertebrate
Benthic Invertebrate
Sediment
Sediment
Benthic Invertebrate
Surface water
Surface water
Surface water
Surface water
Surface water
Surface water
Benthic Omnivore
Water-column Herbivore
Benthic Omnivore
Water-column Herbivore
Benthic Omnivore
Water-column Herbivore
Freq"
78
71
71
47
38
15
13
48
29
12
19
11
11
13
25
20
20
11
11
11
8
10
9
9
8
5
7
4
12
12
12
6
6
6
6
6
6
6
6
Compartment
Types c
17
17
17
14
12
11
11
10
10
10
9
8
8
7
6
6
6
6
6
6
6
5
5
5
5
5
4
4
3
3
3
3
3
3
3
3
3
3
3
Mean
Absolute
Elasticity d
0.98
0.65
0.65
4.69
3.63
0.84
0.73
0.88
0.67
0.80
0.74
0.96
0.95
1.07
1.13
0.55
0.55
1.00
0.72
0.95
0.66
0.53
0.85
0.99
0.98
0.87
1.00
0.61
1.01
1.01
1.01
1.00
1.00
0.85
1.00
1.00
1.00
0.83
0.93
JULY 2005
D-71
TRIM.FATE EVALU ALIGN REPORL VOLUME II
-------
Appendix D.3. Properties with Absolute Elasticities > 0.5
Property a
HowMuchFasterHgEliminationlsThanForMHg
NumberofFishperSquareMeter
Fractionofareaavailableforverticaldiffusion
D pureair
AverageVerticalVelocity
Kd
ReductionRate
rho
HenryLawConstant
AllowExchange SteadyState forAir
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
DimensionlessViscousSublayerThickness
HowMuchFasterHgEliminationlsThanForMHg
OxidationRate
horizontalWindSpeed
isDay SteadyState forOther
AssimilationEfficiencyFromFood
Demethy lationRate
Kd
Methy lationRate
isDay SteadyState forAir
BW
NumberoflndividualsPerSquareMeter
WaterContent
WetDepInterceptionFraction UserSupplied
Water content
FoodlngestionRate
FoodlngestionRate
SoillngestionRate
SoillngestionRate
SoillngestionRate
D_pureair
AllowExchange SteadyState forAir
AllowExchange SteadyState forOther
AllowExchange SteadyState forOther
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
Chemical
MHg
All
All
MHg
All
Hg2
Hg2
All
MHg
All
Hg2
HgO
All
Hg2
HgO
All
All
MHg
MHg
HgO
Hg2
All
All
All
All
All
All
All
All
All
All
All
HgO
All
All
All
Hg2
Hg2
Hg2
Object Type
Compartment
Compartment
Compartment
Chemical
Compartment
Compartment
Compartment
Compartment
Chemical
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Scenario
Scenario
Compartment
Compartment
Compartment
Compartment
Scenario
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Chemical
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link
Information
Benthic Omnivore
Water-column Herbivore
Soil - Surface
Methyl Mercury
Soil - Surface
Soil - Surface
Soil - Root Zone
Soil - Root Zone
Methyl Mercury
Leaf - Coniferous Forest
Water-column Omnivore
Benthic Omnivore
Surface water
Water-column Omnivore
Benthic Omnivore
FullSS
FullSS
Water-column Carnivore
Soil - Root Zone
Soil - Surface
Soil - Root Zone
FullSS
White-tailed Deer
White-tailed Deer
Leaf - Coniferous Forest
Leaf - Coniferous Forest
Worm
Common Loon
Raccoon
Mouse
Raccoon
White-tailed Deer
Elemental Mercury
Leaf - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Grasses/Herbs
Benthic Carnivore
Common Loon
Water-column Carnivore
Freq"
6
6
5
3
5
5
5
5
4
4
4
4
4
4
4
4
4
3
3
3
3
3
2
2
2
2
6
4
3
3
3
3
2
2
2
2
2
2
2
Compartment
Types c
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean
Absolute
Elasticity d
0.68
0.54
0.83
0.68
0.95
0.82
0.99
0.99
0.80
0.97
0.93
1.00
0.68
0.92
1.01
0.68
0.98
0.51
1.01
0.92
1.00
0.99
0.67
0.67
0.62
0.64
5.25
0.99
0.82
0.92
0.91
0.84
0.61
0.99
1.01
1.00
0.97
0.59
0.93
JULY 2005
D-72
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.3. Properties with Absolute Elasticities > 0.5
Property a
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AverageLeafArealndex No Time Dependence
AverageLeafArealndex No Time Dependence
DemethylationRate
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
InhalationAssimilationEfficiency
InhalationAssimilationEfficiency
InhalationProps A
InhalationProps A
InhalationProps B
InhalationProps B
InhalationProps B
Kd
NumberofFishperSquareMeter
NumberofFishperSquareMeter
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
ReductionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
WetDensity
WetDensity
Chemical
HgO
MHg
MHg
MHg
Hg2
MHg
MHg
All
All
MHg
Hg2
Hg2
MHg
MHg
HgO
HgO
All
All
All
All
All
HgO
All
All
HgO
HgO
HgO
HgO
HgO
Hg2
Hg2
Hg2
Hg2
Hg2
MHg
MHg
MHg
All
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link
Information
Benthic Carnivore
Benthic Carnivore
Common Loon
Water-column Omnivore
Mouse
Mouse
White-tailed Deer
Leaf - Coniferous Forest
Leaf - Grasses/Herbs
Common Loon
Benthic Carnivore
Water-column Carnivore
Benthic Carnivore
Water-column Carnivore
Common Loon
Mouse
Common Loon
Mouse
Common Loon
Mouse
White-tailed Deer
Soil - Root Zone
Water-column Carnivore
Water-column Omnivore
Benthic Carnivore
Common Loon
Mouse
Raccoon
White-tailed Deer
Soil - Surface
Common Loon
Mouse
Raccoon
White-tailed Deer
Mouse
Raccoon
White-tailed Deer
Leaf - Coniferous Forest
Leaf - Grasses/Herbs
Freq"
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Compartment
Types c
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean
Absolute
Elasticity d
1.00
0.73
1.00
0.52
0.96
0.93
0.99
0.99
0.98
0.51
0.99
1.00
0.96
0.97
0.91
0.65
0.91
0.65
1.03
2.06
2.30
1.00
0.51
0.52
1.01
0.96
0.96
0.96
0.96
0.51
1.01
1.01
1.01
1.01
0.75
0.75
0.75
0.98
0.99
JULY 2005
D-73
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.3. Properties with Absolute Elasticities > 0.5
Property a
WomSoilPartitionCoefficient dry weight
WormSoilPartitionCoefficient dryweight
WormSoilPartitionCoefficient dryweight
rho
AllowExchange SteadyState forOther
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromWater
DegreeStomatalOpening
FlowRateofTranspiredWaterperAreaofLeafSurface
FoodlngestionRate
FoodlngestionRate
InhalationAssimilationEfficiency
InhalationProps A
Kd
LitterFallRate
LitterFallRate
ReductionRate
StomatalAreaNormalizedEffectiveDiffusionPathLength
TSCF
WaterContent
WaterlngProps A
WaterlngProps B
WaterlngProps B
WetDepInterceptionFraction UserSupplied
WetMassperArea
WetMassperArea
Chemical
Hg2
HgO
MHg
All
All
Hg2
HgO
MHg
Hg2
Hg2
HgO
MHg
HgO
All
All
All
All
HgO
All
MHg
All
All
Hg2
All
MHg
All
All
All
All
All
All
All
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link
Information
Worm
Worm
Worm
Soil - Surface
Stem - Grasses/Herbs
Raccoon
Raccoon
Raccoon
Raccoon
White-tailed Deer
Raccoon
Raccoon
White-tailed Deer
Leaf - Grasses/Herbs
Stem - Grasses/Herbs
Mouse
White-tailed Deer
White-tailed Deer
White-tailed Deer
Soil - Root Zone
Leaf - Coniferous Forest
Leaf - Grasses/Herbs
Surface water
Leaf - Grasses/Herbs
Stem - Grasses/Herbs
Leaf - Grasses/Herbs
White-tailed Deer
Mouse
White-tailed Deer
Leaf - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Grasses/Herbs
Freq"
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Compartment
Types c
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean
Absolute
Elasticity d
1.00
1.00
1.00
0.51
0.96
0.54
0.99
0.91
0.99
0.61
0.76
0.98
0.50
0.87
0.96
0.55
0.51
0.92
0.92
1.00
0.51
0.95
0.63
0.87
0.99
0.53
0.50
0.97
1.92
0.83
0.82
0.87
a For additional description of the input properties used in TRIM.FaTE, along with a key between common names used for properties and their TRIM.FaTE code names, see Module 16
of the TRIM.FaTE User's Guide and the TRIM.FaTE technical support documents.
b Number of outputs (maximum of 93) for which the property has absolute elasticity greater than 0.5.
c Number of compartment types (maximum of 17) for which the property has absolute elasticity greater than 0.5.
d Calculated using only those elasticities with absolute values greater than 0.5.
JULY 2005
D-74
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.4. Properties with Absolute Sensitivity Scores > 0.5
Property a
EmissionRate
VaporWashoutRatio
WaterTemperature K
EmissionRate
HenryLawConstant
Kd
Demethy lationRate
MethylationRate
Demethy lationRate
Kd
MethylationRate
MethylationRate
ReductionRate
SedimentPartitioning PartitionCoefficient
SedimentPartitioning PartitionCoefficient
Kd
phi
SedimentPartitioning PartitionCoefficient
WaterColumnDissolvedPartitioning PartitionCoefficient
WaterColumnDissolvedPartitioning TimeToReachAlphaofEquil
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
NumberofFishperSquareMeter
TransferFactortoLeafParticle
AllowExchange SteadyState forAir
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
Demethy lationRate
HenryLawConstant
HowMuchFasterHgEliminationlsThanForMHg
Kd
Kd
MethylationRate
Chemical
Hg2
Hg2
All
HgO
HgO
Hg2
MHg
Hg2
MHg
MHg
Hg2
Hg2
Hg2
Hg2
MHg
HgO
All
HgO
HgO
HgO
Hg2
Hg2
MHg
MHg
Hg2
Hg2
MHg
All
Hg2
All
Hg2
HgO
MHg
MHg
MHg
Hg2
Hg2
HgO
Hg2
Object Type
Source
Chemical
VolumeElement
Source
Chemical
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Chemical
Compartment
Compartment
Compartment
Compartment
Object Name / Link
Information
Facility
Divalent Mercury
Surface water
Facility
Elemental Mercury
Surface water
Soil - Surface
Soil - Surface
Sediment
Surface water
Sediment
Surface water
Sediment
Benthic Invertebrate
Benthic Invertebrate
Surface water
Sediment
Benthic Invertebrate
Macrophyte
Macrophyte
Benthic Omnivore
Water-column Herbivore
Benthic Omnivore
Water-column Herbivore
Benthic Omnivore
Water-column Herbivore
Benthic Omnivore
Water-column Herbivore
Leaf - Coniferous Forest
Leaf - Coniferous Forest
Water-column Omnivore
Benthic Omnivore
Water-column Carnivore
Soil - Root Zone
Methyl Mercury
Water-column Omnivore
Soil - Surface
Soil - Surface
Soil - Root Zone
Freq"
78
81
38
15
12
19
11
11
11
11
11
8
5
9
11
4
4
7
6
6
6
6
6
6
6
6
6
6
3
4
4
4
3
3
4
4
5
3
3
Compartment
Types c
17
17
12
11
10
9
8
8
6
6
6
6
5
5
5
4
4
4
4
4
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
Mean
Absolute
Score d
0.98
1.81
3.63
0.84
0.80
0.74
0.96
0.95
0.99
0.72
0.95
0.66
0.87
2.55
2.65
0.61
0.66
2.99
0.83
0.83
0.85
1.00
0.99
1.00
0.83
0.93
0.68
0.54
1.19
0.97
0.93
1.00
0.51
1.00
0.80
0.92
0.82
0.92
0.99
JULY 2005
D-75
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.4. Properties with Absolute Sensitivity Scores > 0.5
Property a
NumberoflndividualsPerSquareMeter
OxidationRate
ReductionRate
horizontalWindSpeed
isDay Steady State forAir
isDay SteadyState forOther
AllowExchange SteadyState forAir
AllowExchange SteadyState forOther
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromFood
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromSoils
AssimilationEfficiencyFromWater
AverageLeafArealndex No Time Dependence
AverageLeafArealndex No Time Dependence
DegreeStomatalOpening
DemethylationRate
FlowRateofTranspiredWaterperAreaofLeafSurface
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
HowMuchFasterHgEliminationlsThanForMHg
InhalationProps B
InhalationProps B
Kd
Kd
Chemical
All
HgO
Hg2
All
All
All
All
All
Hg2
Hg2
Hg2
Hg2
HgO
HgO
MHg
MHg
MHg
MHg
Hg2
Hg2
Hg2
HgO
MHg
MHg
MHg
HgO
All
All
All
MHg
All
Hg2
Hg2
MHg
MHg
All
All
HgO
MHg
Object Type
Compartment
Compartment
Compartment
Scenario
Scenario
Scenario
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link
Information
White-tailed Deer
Benthic Omnivore
Soil - Root Zone
SteadyState
SteadyState
SteadyState
Leaf - Grasses/Herbs
Leaf - Coniferous Forest
Benthic Carnivore
Common Loon
Raccoon
Water-column Carnivore
Benthic Carnivore
Raccoon
Benthic Carnivore
Common Loon
Raccoon
Water-column Omnivore
Mouse
Raccoon
White-tailed Deer
Raccoon
Mouse
Raccoon
White-tailed Deer
White-tailed Deer
Leaf - Coniferous Forest
Leaf - Grasses/Herbs
Leaf - Grasses/Herbs
Common Loon
Stem - Grasses/Herbs
Benthic Carnivore
Water-column Carnivore
Benthic Carnivore
Water-column Carnivore
Mouse
White-tailed Deer
Soil - Root Zone
Soil - Root Zone
Freq"
2
4
5
4
3
4
2
2
2
2
1
2
2
1
2
2
1
2
2
1
1
1
2
1
2
1
2
2
1
2
1
2
2
2
2
2
1
2
1
Compartment
Types c
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean
Absolute
Score d
0.67
1.01
0.99
0.68
0.99
0.98
0.99
1.01
0.97
0.59
0.54
0.93
1.00
0.99
0.73
1.00
0.91
0.52
0.96
0.99
0.61
0.76
0.93
0.98
0.99
0.50
0.99
0.98
0.87
0.51
0.96
0.99
1.00
0.96
0.97
0.62
0.93
1.00
1.00
JULY 2005
D-76
TRIM.FATE EVALUATION REPORT VOLUME II
-------
Appendix D.4. Properties with Absolute Sensitivity Scores > 0.5
Property a
LitterFallRate
NumberofFishperSquareMeter
NumberofFishperSquareMeter
OxidationRate
OxidationRate
OxidationRate
OxidationRate
OxidationRate
ReductionRate
ReductionRate
StomatalAreaNonnalizedEffectiveDiffusionPathLength
TSCF
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TotalExcretionRate
TransferFactortoLeafParticle
WaterlngProps B
Water content
WetDensity
WetDensity
WetMassperArea
WetMassperArea
WormSoilPartitionCoefficient dry weight
WormSoilPartitionCoefficient dry weight
WormSoilPartitionCoefficient dry weight
Chemical
All
All
All
HgO
HgO
HgO
HgO
HgO
Hg2
Hg2
All
MHg
Hg2
Hg2
Hg2
Hg2
MHg
MHg
MHg
Hg2
All
All
All
All
All
All
Hg2
HgO
MHg
Object Type
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Compartment
Object Name / Link
Information
Leaf - Coniferous Forest
Water-column Carnivore
Water-column Omnivore
Benthic Carnivore
Common Loon
Mouse
Raccoon
White-tailed Deer
Soil - Surface
Surface water
Leaf - Grasses/Herbs
Stem - Grasses/Herbs
Common Loon
Mouse
Raccoon
White-tailed Deer
Mouse
Raccoon
White-tailed Deer
Leaf - Grasses/Herbs
White-tailed Deer
Worm
Leaf - Coniferous Forest
Leaf - Grasses/Herbs
Leaf - Coniferous Forest
Leaf - Grasses/Herbs
Worm
Worm
Worm
Freq"
1
2
2
2
2
2
2
2
2
1
1
1
2
2
2
2
2
2
2
1
1
6
2
2
1
1
2
2
2
Compartment
Types c
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Mean
Absolute
Score d
0.51
0.51
0.52
1.01
0.96
0.96
0.96
0.96
0.51
0.63
0.87
2.96
3.03
1.01
1.01
1.01
2.24
2.25
2.25
1.11
0.58
5.24
0.98
0.99
0.82
0.87
3.00
3.00
3.00
a For additional description of the input properties used in TRIM.FaTE, along with a key between common names used for properties and their TRIM.FaTE code names, see Module 16
of the TRIM.FaTE User's Guide and the TRIM.FaTE technical support documents.
b Number of outputs (maximum of 93) for which the property has absolute elasticity greater than 0.5.
c Number of compartment types (maximum of 17) for which the property has absolute elasticity greater than 0.5.
d Calculated using only those elasticities with absolute values greater than 0.5.
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRIM.FATE
COMPARISON
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
E.I Description of 3MRA Processes
3MRA is an environmental modeling system designed to facilitate site-based human and
ecological risk assessments at local, regional, and national scales. 3MRA combines data bases
containing chemical, climatological, and site data with a series of 17 science-based simulation
models within a fully integrated software architecture to provide a user the ability to execute
Monte-Carlo-based assessment methodologies. Exhibit E-l illustrates the systems design of
3MRA as applied to the assessment of national risks resulting from the disposal of solid waste in
land-based waste management units, with components used in this model comparison
highlighted.
A series of system processors collectively manage the execution of the 3MRA modeling
system. Various system processors interact with the user to develop science module input data
files, manage the execution of the individual system components, and process modeling outputs
to form risk summaries. The primary 3MRA system processors are listed below.
System User Interface (SUP: represents the user access point to the technology. Via the
SUI, the user selects combinations of sites, waste management units, chemicals, and
constituent concentrations in waste streams to be simulated, plus the number of Monte-
Carlo simulations to be executed per site. The SUI also allows the user to configure the
computer directory structure where individual components of the system are stored.
Finally, the SUI manages the overall execution of the user defined national assessment.
Site Definition Processor (SDP): performs all data retrieval from the site, regional,
national, and chemical data bases and organizes it into a series of "site simulation files"
that contain the input data for each of the 17 science models.
• Multi-media Simulation Processor (MMSP): manages the invocation, execution, and
error handling associated with the 17 individual science models that simulate source
release, multimedia fate and transport, foodweb dynamics, and human/ecological
exposure and risk.
Chemical Properties Processor (CPP): accesses the chemical properties data base and
either transfers or calculates all requested data. The CPP represents a single location
within the modeling system where chemical data are made available.
• Exit Level Processor I (ELP I): assimilates the individual site risk results and builds a
risk summary data base containing data used to assess national protection criteria.
Exit Level Processor II (ELP II): reads the ELP I derived risk summary data base and
generates, based on regulatory criteria, specific national exemption levels.
• Risk Visualization Processor (RVP): presents risk summary results in graphical form.
Exhibit 6-1 (in Chapter 6) illustrates the 3MRA multimedia model design contained
within the MMSP, highlighting the modules that were used as part of this model comparison
study. Individual science models are included for each of five land-based waste management
JULY 2005 E-l TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
units that simulate the release of contaminants to air, water, and soil media. Fate and transport of
contaminants through the multimedia environment are simulated by the atmospheric, watershed,
surface water, vadose zone, and aquifer modules. Contaminant uptake through the food web is
simulated by the farm food chain, terrestrial food web, and aquatic food web modules. Human
and ecological exposure modules estimate doses to human and ecological receptors, and the risk
modules apply toxicity data to the doses to derive estimates of risk. The modules included in
3MRA represent a "linked media" model, meaning that individual simulation modules,
representing each element of a risk assessment, are executed in a logical sequence from source to
fate and transport to food web to exposure and risk.
To download the 3MRA model and access a series of documents describing the 3MRA
modeling system in detail, the reader is referred to the following web sites:
• http://www.epa.gov/ceampubl/mmedia/index.htm (modeling system); and
• http://www.epa.gov/epaoswer/hazwaste/id/hwirwste/risk.htm (documentation).
JULY 2005 E-2 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Exhibit E-l. 3MRA Modeling System Design a
,» Waste Management Facility Loop (1 Site)
Waste Management Unit Loop (5 Source Types)
Sampled Input Data Iteration Loop (n,.)
,*\ Chemical Loop
Cw= Waste stream concentration
(Mercury)
Cw Loop
I
i
\/
List of Sites
o
Key
User Interface
Data File
Processor
Database
i
t
Header Info from SUI
Warnings/Errors to SUI
Multimedia
Multipathwaj
Simulation
Processor
Chemical
Properties
Processor
Site Input Data
Site Definition
Multimedia Multipathway
Simulation
Cw Exit Level Processing
13MRA components used in this model comparison are shaded.
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
E.2 Detailed Results for 3MRA - TRIM.FaTE Comparison1
Chart # Title of Chart
Chart E-la Divalent Mercury Concentration in Air vs. Time: Swetts Pond Watershed
Chart E-lb Divalent Mercury Concentration in Air vs. Time: Near Source, Northwest
Chart E-lc Divalent Mercury Concentration in Air vs. Time: Near Source, Southwest
Chart E-ld Divalent Mercury Concentration in Air vs. Time: Adjacent to Source,
Southeast
Chart E-2a Mercury Deposition Flux to Soil Surface vs. Time: Swetts Pond Watershed
Chart E-2b Mercury Deposition Flux to Soil Surface vs. Time: Near Source, Southwest
Chart E-2c Mercury Deposition Flux to Soil Surface vs. Time: Far From Source,
Southeast
Chart E-3 Divalent Mercury Concentration in Leaves (grasses/herbs) vs. Time: Near
Source, Southwest
Chart E-4a Divalent Mercury Concentration in Surface Soil vs. Time: Swetts Pond
Watershed
Chart E-4b Divalent Mercury Concentration in Surface Soil vs. Time: Near Source,
Northwest
Chart E-4c Divalent Mercury Concentration in Surface Soil vs. Time: Near Source,
Northeast
Chart E-4d Divalent Mercury Concentration in Surface Soil vs. Time: Near Source,
Southwest
Chart E-4e Divalent Mercury Concentration in Surface Soil vs. Time: Adjacent to
Source, Southeast
Chart E-5 Divalent and Total Mercury Concentration in Roots (grasses/herbs) and
Associated Soil vs. Time: Near Source, Southwest
Chart E-6a Divalent and Total Mercury Concentration in Earthworms and Associated
Soil vs. Time: Swetts Pond Watershed
Chart E-6b Divalent Mercury Concentration in Earthworms and Associated Soil vs.
Time: Near Source, Northwest
Chart E-6c Divalent Mercury Concentration in Earthworms and Associated Soil vs.
Time: Near Source, Northeast
JA11 TRIM.FaTE data for emission case A (divalent mercury emitted from source only, no boundary
contributions or initial concentrations), 11-17-03 model run.
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-6d Divalent and Total Mercury Concentration in Earthworms and Associated
Soil vs. Time: Near Source, Southwest
Chart E-7a Mercury Concentration in Surface Water vs. Time: Swetts Pond
Chart E-7b Mercury Concentration in Surface Water vs. Time: Brewer Lake
Chart E-8a Mercury Concentration in Sediment vs. Time: Swetts Pond
Chart E-8b Mercury Concentration in Sediment vs. Time: Brewer Lake
Chart E-9a Methyl Mercury Concentration in Fish vs. Time: Swetts Pond
Chart E-9b Methyl Mercury Concentration in Fish vs. Time: Brewer Lake
Chart E-lOa Total Mercury Concentration in Small Birds vs. Time: Swetts Pond
Watershed
Chart E-lOb Total Mercury Concentration in Small Birds vs. Time: Near Source,
Southwest
Chart E-lla Total Mercury Concentration in Omniverts vs. Time: Swetts Pond
Watershed
Chart E-llb Total Mercury Concentration in Omniverts vs. Time: Near Source,
Northwest
Chart E-llc Total Mercury Concentration in Omniverts vs. Time: Near Source,
Northeast
Chart E-lld Total Mercury Concentration in Omniverts vs. Time: Near Source,
Southwest
Chart E-12a Total Mercury Concentration in Small Mammals vs. Time: Swetts Pond
Watershed
Chart E-12b Total Mercury Concentration in Small Mammals vs. Time: Near Source,
Northwest
Chart E-12c Total Mercury Concentration in Small Mammals vs. Time: Near Source,
Northeast
JULY 2005 E-5 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-1a - Log Scale
Divalent Mercury Concentration in Air vs. Time: Swetts Pond Watershed8
1.0E-10
+J
§ 1.0E-11
u
o
o
o
O)
2
0)
1.0E-12
3 4
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Hg2+: 3MRA Location 4 •
•Hg2+: TRIM.FaTE SSE3 -H-Hg2+: TRIM.FaTE SSE4
3 Annual average for TRIM.FaTE is based on instantaneous estimates every two hours throughout the year and represents an average
concentration over a volume that extends from the ground to the mixing height. 14-year average for 3MRA (based on instantaneous estimates
every hour throughout the period) is applied to entire period and is a point concentration at ground level.
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-1b - Log Scale
Divalent Mercury Concentration in Air vs. Time: Near Source, Northwest8
1.0E-10
o
§ 1.0E-11
Ǥ
0)
O)
2
0)
1.0E-12
5 6
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Hg2+: 3MRA Location 14
•Hg2+:TRIM.FaTEW2
•Hg2+:TRIM.FaTENNW2
a Annual average for TRIM.FaTE is based on instantaneous estimates every two hours throughout the year and represents an average
concentration over a volume that extends from the ground to the mixing height. 14-year average for 3MRA (based on instantaneous estimates
every hour throughout the period) is applied to entire period and is a point concentration at ground level.
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-1c - Log Scale
Divalent Mercury Concentration in Air vs. Time: Near Source, Southwest8
1.0E-10
-2
o
+-
TO
+j
§ 1.0E-11
c
O
o
0)
O)
TO
0)
1.0E-12
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
7 8
•Hg2+: 3MRA Location 11
•Hg2+:TRIM.FaTEW2
•Hg2+: TRIM.FaTE SSW2
a Annual average for TRIM.FaTE is based on instantaneous estimates every two hours throughout the year and represents an average
concentration over a volume that extends from the ground to the mixing height. 14-year average for 3MRA (based on instantaneous estimates
every hour throughout the period) is applied to entire period and is a point concentration at ground level.
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-1d - Log Scale
Divalent Mercury Concentration in Air vs. Time: Adjacent to Source, Southeast8
1 .OE-09
fO
o
1
§ 1.0E-10
o
o
o
o
O)
P
1.0E-11
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Hg2+: 3MRA Location 10
•Hg2+:TRIM.FaTESSE1
•Hg2+: TRIM.FaTE ESE1
a Annual average for TRIM.FaTE is based on instantaneous estimates every two hours throughout the year and represents an average
concentration over a volume that extends from the ground to the mixing height. 14-year average for 3MRA (based on instantaneous estimates
every hour throughout the period) is applied to entire period and is a point concentration at ground level.
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-2a - Log Scale
Mercury Deposition Flux to Soil Surface vs. Time: Swetts Pond Watershed'
1 .OE-07
^ 1.0E-08
* 1.0E-09
.o
! 1.0E-10
0)
01
2
0)
"* 1.0E-11
1.0E-12
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Hg2+: TRIM.FaTE Dry Particle Deposition Flux from Air to Soil SSE4 —•— Hg2+: TRIM.FaTE Dry Vapor Deposition Flux from Air to Soil SSE4
-Hg2+: TRIM.FaTE Wet Particle Deposition Flux from Air to Soil SSE4 X Hg2+: TRIM.FaTE Wet Vapor Deposition Flux from Air to Soil SSE4
•Hg2+: 3MRA Wet Vapor Deposition Flux from Air to Soil Location 4
a Annual average for TRIM.FaTE based on instantaneous estimates every two hours throughout the year. 14-year average for 3MRA (based on
instantaneous estimates every hour throughout the period) applied to entire period.
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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1.0E-07
1.0E-08
f
ro
5
"s 1.0E-09
o
o
Q
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-2c - Log Scale
Mercury Deposition Flux to Soil Surface vs. Time: Far From Source, Southeast3
1 .OE-07
1 .OE-08
1 .OE-09
X
3
o 1.0E-10
o
Q.
0)
o) 1.0E-11
O)
ro
1.0E-12
1.0E-13
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
X Hg2+: TRIM.FaTE Dry Particle Deposition Flux from Air to Soil SE6 —•— Hg2+: TRIM.FaTE Dry Vapor Deposition Flux from Air to Soil SE6
—A— Hg2+: TRIM.FaTE Wet Particle Deposition Flux from Air to Soil SE6 —K— Hg2+: TRIM.FaTE Wet Vapor Deposition Flux from Air to Soil SE6
: 3MRA Wet Vapor Deposition Flux from Air to Soil Location 1
a Annual average for TRIM.FaTE based on instantaneous estimates every two hours throughout the year. 14-year average forSMRA (based on
instantaneous estimates every hour throughout the period) applied to entire period.
JULY 2005
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TRIM.FATE EVALUATION REPORT VOLUME II
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I
O)
J£
3
c
g
is
1.0E-07
o
o
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-4a - Log Scale
Divalent Mercury Concentration in Surface Soil vs. Time: Swetts Pond
Watershed
1 .OE-08
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-11
•Hg2+: 3MRA Location 4 -»-Hg2+: TRIM.FaTE SSE4
JULY 2005
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APPENDIX E
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1 .OE-07
1.0E-11
Chart E-4b - Log Scale
Divalent Mercury Concentration in Surface Soil vs. Time: Near Source,
Northwest
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
•Hg2+: 3MRA Location 14 -»-Hg2+: TRIM.FaTE N2 -A-Hg2+: TRIM.FaTE W2
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
1 .OE-06
1.0E-07
£<
•o
-*
c
~ 1.0E-08
2
+j
0)
u
o
o
g, 1.0E-09
2
a)
Chart E-4c - Log Scale
Divalent Mercury Concentration in Surface Soil vs. Time: Near Source,
Northeast
3
C
1.0E-10
1.0E-11
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
•Hg2+: 3MRA Location 9 -B-Hg2+: TRIM.FaTE NE2 -A-Hg2+: TRIM.FaTE E1
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-4d - Log Scale
Divalent Mercury Concentration in Surface Soil vs. Time: Near Source,
Southwest
1 .OE-07
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-10
•Hg2+: 3MRA Location 11 -»-Hg2+: TRIM.FaTE SW2
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-4e - Log Scale
Divalent Mercury Concentration in Surface Soil vs. Time: Adjacent to Source,
Southeast
1 .OE-07
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-10
•Hg2+: 3MRA Location 10 -B-Hg2+: TRIM.FaTE SE1 -»-Hg2+: TRIM.FaTE E1
JULY 2005
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1 .OE-05
Chart E-5 - Log Scale
Divalent and Total Mercury Concentration in Roots (grasses/herbs) and
Associated Soil vs. Time: Near Source, Southwest
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-13
•Hg2+: 3MRA Habitat 3 - Plant Root
-Total Hg: TRIM.FaTE SW2 - Plant Root (Grasses/Herbs)
-Hg2+: TRIM.FaTE SW2 - Root Zone Soil
-Hg2+: TRIM.FaTE SW2 - Plant Root (Grasses/Herbs)
•Hg2+: 3MRA Location 11 - Top 5 cm Soil
-Total Hg: TRIM.FaTE SW2 - Root Zone Soil
JULY 2005
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1 .OE-06
Chart E-6a - Log Scale
Divalent and Total Mercury Concentration in Earthworms and Associated Soil
vs. Time: Swetts Pond Watershed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-15
•Hg2+: 3MRA Habitat 11 - Earthworm
-Total Hg: TRIM.FaTE SSE4 - Earthworm
-Hg2+: TRIM.FaTE SSE4 - Root Zone Soil
-Hg2+: TRIM.FaTE SSE4 - Earthworm
•Hg2+: 3MRA Location 4 - Top 5 cm Soil
•Total Hg: TRIM.FaTE SSE4 - Root Zone Soil
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-6b - Log Scale
Divalent Mercury Concentration in Earthworms and Associated Soil vs. Time:
Near Source, Northwest
1 .OE-06
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-15
•Hg2+: 3MRA Habitat 8 - Earthworm -A-Hg2+: TRIM.FaTE N2 - Earthworm
•Hg2+: 3MRA Location 14 -Top 5 cm Soil —X— Hg2+: TRIM.FaTE N2 - Root Zone Soil
•Hg2+: TRIM.FaTE W2 - Earthworm
• Hg2+: TRIM.FaTE W2 - Root Zone Soil
JULY 2005
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APPENDIX E
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Chart E-6c - Log Scale
Divalent Mercury Concentration in Earthworms and Associated Soil vs. Time:
Near Source, Northeast
1 .OE-06
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-15
•Hg2+: 3MRA Habitat 9 - Earthworm -»-Hg2+: TRIM.FaTE E1 - Earthworm
•Hg2+: 3MRA Location 9/10 -Top 5 cm Soil —X— Hg2+: TRIM.FaTE E1 - Root Zone Soil
•Hg2+: TRIM.FaTE NE2 - Earthworm
• Hg2+: TRIM.FaTE NE2 - Root Zone Soil
JULY 2005
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1 .OE-05
Chart E-6d - Log Scale
Divalent and Total Mercury Concentration in Earthworms and Associated Soil:
Near Source, Southwest
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-14
•Hg2+: 3MRA Habitat 3 - Earthworm
•Hg2+: 3MRA Location 11 - Top 5 cm Soil
•Hg2+: TRIM.FaTE SW2 - Earthworm
- Hg2+: TRIM.FaTE SW2 - Root Zone Soil
-Total Hg: TRIM.FaTE SW2 - Earthworm
-Total Hg: TRIM.FaTE SW2 - Root Zone Soil
JULY 2005
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1 .OE-09
1.0E-14
Chart E-7a - Log Scale
Mercury Concentration in Surface Water vs. Time: Swetts Pond
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Total Hg: TRIM.FaTE Swetts Pond —0— Hg2+: TRIM.FaTE Swetts Pond
-Total Hg: 3MRA Location (1,7) —•—Hg2+: 3MRA Location (1,7)
HgO: TRIM.FaTE Swetts Pond
•HgO: 3MRA Location (1,7)
-MHg: TRIM.FaTE Swetts Pond
•MHg: 3MRA Location (1,7)
JULY 2005
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SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
1.0E-10
1.0E-11
o
I
1.0E-12
< 1.0E-13
1.0E-14
Chart E-7b - Log Scale
Mercury Concentration in Surface Water vs. Time: Brewer Lake
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Total Hg: TRIM.FaTE Brewer Lake —0— Hg2+: TRIM.FaTE Brewer Lake
•Total Hg: 3MRA Location (1,11) —•—Hg2+: 3MRA Location (1,11)
HgO: TRIM.FaTE Brewer Lake
•HgO: 3MRA Location (1,11)
•MHg: TRIM.FaTE Brewer Lake
•MHg: 3MRA Location (1,11)
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
1 .OE-08
1.0E-14
Chart E-8a - Log Scale
Mercury Concentration in Sediment vs. Time: Swetts Pond
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Total Hg: TRIM.FaTE Swetts Pond —A— Hg2+: TRIM.FaTE Swetts Pond
-Total Hg: 3MRA Location (1,7) —A— Hg2+: 3MRA Location (1,7)
•HgO: TRIM.FaTE Swetts Pond
•HgO: 3MRA Location (1,7)
-MHg: TRIM.FaTE Swetts Pond
•MHg: 3MRA Location (1,7)
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-8b - Log Scale
Mercury Concentration in Sediment vs. Time: Brewer Lake
1 .OE-08
—A—A—A—A—A—A
—A—A—A—
1-OE-13
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-15
Total Hg: TRIM.FaTE Brewer Lake —A— Hg2+: TRIM.FaTE Brewer Lake
-Total Hg: 3MRA Location (1,11) —A— Hg2+: 3MRA Location (1,11)
•HgO: TRIM.FaTE Brewer Lake
•HgO: 3MRA Location (1,11)
-MHg: TRIM.FaTE Brewer Lake
•MHg: 3MRA Location (1,11)
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
1 .OE-05
1.0E-10
Chart E-9a - Log Scale
Methyl Mercury Concentration in Fish vs. Time: Swetts Pond
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
•MHg:3MRAT4Fish
•MHg: TRIM.FaTE Water-column Carnivore
•MHg: TRIM.FaTE Benthic Carnivore
•MHg: TRIM.FaTE Largemouth Bass/Smallmouth Bass/Northern Pike
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
1 .OE-05
1.0E-10
Chart E-9b - Log Scale
Methyl Mercury Concentration in Fish vs. Time: Brewer Lake
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
•MHg:3MRAT4Fish
•MHg: TRIM.FaTE Water-column Carnivore
•MHg: TRIM.FaTE Benthic Carnivore
•MHg: TRIM.FaTE Largemouth Bass/Smallmouth Bass/Northern Pike
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-10a - Log Scale
Total Mercury Concentration in Small Birds vs. Time: Swetts Pond Watershed
1 .OE-05
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-10
•Total Hg: 3MRA Habitat 11 - Small Bird -A-Total Hg: TRIM.FaTE SSE4 - Tree Swallow -*-Total Hg: TRIM.FaTE SSE4 - Chickadee
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-10b - Log Scale
Total Mercury Concentration in Small Birds vs. Time: Near Source, Southwest
1 .OE-05
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-10
•Total Hg: 3MRA Habitat 3 - Small Bird -A-Total Hg: TRIM.FaTE SW2 - Tree Swallow -X-Total Hg: TRIM.FaTE SW2 - Chickadee
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-11a - Log Scale
Total Mercury Concentration in Omniverts vs. Time: Swetts Pond Watershed
1 .OE-06
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-10
•Total Hg: 3MRA Habitat 11 - Omivert
-Total Hg: TRIM.FaTE Swetts - Mallard
•Total Hg: TRIM.FaTE SSE4 - Raccoon
-Total Hg: TRIM.FaTE SSE4 - Long-tailed Weasel
-Total Hg: TRIM.FaTE SSE4 - Mink
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-11b - Log Scale
Total Mercury Concentration in Omniverts vs. Time: Near Source, Northwest
1 .OE-06
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-10
—•—Total Hg: 3MRA Habitat 8 - Omnivert
-A-Total Hg: TRIM.FaTE W2 - Long-tailed Weasel
-*-Total Hg: TRIM.FaTE N2 - Raccoon
-Total Hg: TRIM.FaTE N2 - Long-tailed Weasel
-Total Hg: TRIM.FaTE N2 - Mink
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
1 .OE-06
Chart E-11c - Log Scale
Total Mercury Concentration in Omni verts vs. Time: Near Source, Northeast
1.0E-10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Total Hg: 3MRA Habitat 9 - Omnivert
-Total Hg: TRIM.FaTE E1 - Long-tailed Weasel
-Total Hg: TRIM.FaTE E1 - Mink
-Total Hg: TRIM.FaTE E1 - Raccoon
-Total Hg: TRIM.FaTE NE2 - Long-tailed Weasel
-Total Hg: TRIM.FaTE NE2 - Mink
-Total Hg: TRIM.FaTE NE2 - Raccoon
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-11d - Log Scale
Total Mercury Concentration in Omni verts vs. Time: Near Source, Southwest
1 .OE-07
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-10
•Total Hg: 3MRA Habitat 3 - Ominvert
-Total Hg: TRIM.FaTE SW2 - Mink
-Total Hg: TRIM.FaTE SW2 - Long-tailed Weasel
•Total Hg: TRIM.FaTE SW2 - Raccoon
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
1 .OE-06
Chart E-12a - Log Scale
Total Mercury Concentration in Small Mammals vs. Time: Swetts Pond
Watershed
1.0E-07
O)
o
g 1.0E-08
o
O
o
O)
= 1.0E-09
c
c
1.0E-10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Year
•Total Hg: 3MRA Habitat 11 - Small Mammal -•-Total Hg: TRIM.FaTE SSE4 - Mouse -A-Total Hg: TRIM.FaTE SSE4 - Short-tailed Shrew
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
1 .OE-05
1 .OE-09
Chart E-12b - Log Scale
Total Mercury Concentration in Small Mammals vs. Time: Near Source,
Northwest
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
•Total Hg: 3MRA Habitat 8 - Small Mammal
-Total Hg: TRIM.FaTE W2 - Mouse
-Total Hg: TRIM.FaTE W2 - Short-tailed Shrew
-Total Hg: TRIM.FaTE N2 - Mouse
-Total Hg: TRIM.FaTE N2 - Short-tailed Shrew
JULY 2005
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APPENDIX E
SUPPLEMENTAL MATERIALS FOR 3MRA - TRDVLFATE COMPARISON
Chart E-12c - Log Scale
Total Mercury Concentration in Small Mammals vs. Time: Near Source,
Northeast
1 .OE-05
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1.0E-10
—•—Total Hg: 3MRA Habitat 9 - Small Mammal
-A-Total Hg: TRIM.FaTE E1 - Mouse
-K-Total Hg: TRIM.FaTE NE2 - Short-tailed Shrew
-Total Hg: TRIM.FaTE NE2 - Mouse
-Total Hg: TRIM.FaTE E1 - Short-tailed Shrew
•Total Hg: TRIM.FaTE NE2 - Meadow Vole
JULY 2005
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
Appendix F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
This appendix provides a summary of the off-site mercury monitoring data sets identified for the
test case site. Off-site measurement data for comparison were identified for air, soil, lake/pond
surface water and sediment, river surface water and sediment, and various biota. However, only
data that were used for comparison to TREVI.FaTE modeling results are presented in this
appendix (see Chapter 7 for detailed comparison of modeling and monitoring results).
Note: Data sources have not been listed here to retain the anonymity of the case study site.
F.I Off-site Air Monitoring Data
Environmental Medium: Ambient air
Number of Data Points: Approximately 29,000 data points from 3 continuous monitoring
stations. Data quality flags are included indicating automatic calibration, power failure, valid
measurement, standard addition, maintenance and manual calibrations, equipment failure or
malfunction, no peak (i.e., below detection limit), overload (beyond analyzer range), and suspect
data (based on quality assurance measures).
Measurement Endpoint (Units): One-hour average total gaseous mercury concentration
(ng/m3)
Sampling Date(s): (1) AA1: August 27, 1998 to November 1, 1999
(2) AA2: August 26, 1998 to September 27, 1999
(3) AA3: September 4, 1998 to September 24, 1999
(hourly samples throughout period)
Sample Location(s): (1) AA1: Approximately 1,500 feet southeast of facility
(2) AA2: Approximately 4,300 feet north-northwest of the facility
(3) AA3: Approximately 6,400 feet north-northwest of the facility
Purpose of Monitoring: To provide data to the state environmental agency as a result of a
consent agreement enforcement order
Overall Range: (1) AA1: 0.774 - 526 ng/m3
(2)AA2: 0.259-90.1 ng/m3
(3)AA3: 0.461 - 34.2 ng/m3
Mean:3 (1) Overall mean for AA1: 8.63 ng/m3
(2) Overall mean for AA2: 2.25 ng/m3
(3) Overall mean for AA3: 1.78 ng/m3
a Standard deviation not reported in source.
JULY 2005 F-1 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
Raw Data:
Summary of 1-Hour Average TGM (ng/m3) Collected at the AA1 Site
Statistic
minimum
25th percentile
median
75th percentile
maximum
arithmetic mean
1st Quarter
(Sept - Dec)
0.826
1.56
3.53
11.5
157
9.96
2nd Quarter
(Jan - Mar)
0.798
0.940
1.93
9.32
153
8.33
3rd Quarter
(Apr - Jun)
0.786
0.860
1.10
6.71
171
7.47
4th Quarter
(Jul - Oct)
0.774
0.860
1.02
5.56
526
8.33
Overall
0.774
0.878
1.80
8.80
526
8.63
Summary of 1-Hour Average TGM (ng/m3) Collected at the AA2 Site
Statistic
minimum
25th percentile
median
75th percentile
maximum
arithmetic mean
1st Quarter
(Sept - Dec)
0.993
1.45
1.72
2.66
25.8
2.48
2nd Quarter
(Jan - Mar)
1.01
1.31
1.47
1.70
24.5
1.85
3rd Quarter
(Apr - Jun)
0.722
1.17
1.43
2.21
25.8
2.34
4th Quarter
(Jul - Sept)
0.259
0.77
1.09
1.91
90.1
2.20
Overall
0.259
1.25
1.50
2.19
90.1
2.25
Summary of 1-Hour Average TGM (ng/m3) Collected at the AA3 Site
Statistic
minimum
25th percentile
median
75th percentile
maximum
arithmetic mean
1st Quarter
(Sept - Dec)
0.461
1.03
1.34
1.73
14.8
1.76
2nd Quarter
(Jan - Mar)
0.525
0.677
1.36
1.61
26.4
1.58
3rd Quarter
(Apr - Jun)
0.603
1.09
1.28
1.56
29.4
1.87
4th Quarter
(Jul - Sept)
0.481
0.794
0.98
1.51
34.2
1.91
Overall
0.461
0.893
1.26
1.62
34.2
1.78
JULY 2005
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
Other Information: Corresponding meteorological data are also available from an on-site
monitoring station, including approximately 8,760 data points each for (1) average hourly wind
speed (mph), (2) average hourly wind direction (°N), (3) average hourly ambient temperature
(°C), and (4) average hourly solar radiation (W/m2) from 1 continuous monitoring station from
November 1, 1998 to November 1, 1999. [The data set of meteorological parameters being used
as inputs to TREVLFaTE is from two monitoring stations near the facility (within 20 km),
supplemented by data from a continuous monitoring station roughly 150 km southwest of the
facility. This data set includes approximately 8,760 hourly averaged measurements from each
year from 1987 to 1991 for wind speed (m/s), wind direction (degrees), rural and urban mixing
height (m), precipitation rate (mm/hour), precipitation type (unitless), ambient temperature (K),
stability class (unitless), friction velocity (m/s), monin-obukhov length (m), and surface
roughness length (m).]
A wind rose was generated from the on-site hourly wind speed and direction data and presented
in the final report with the on-site air monitoring data. This wind rose is included below for
reference.
Raw Data:
Total Meteorological Data Collected at the Facility
Statistic
available
periods
observed
periods
data recovery
5-min average
WS (mph)
105,058
98,244
93.5
5-min average
WD (°N)
105,058
101,546
96.7
5-min average
Temp. (°F)
105,058
97,725
93.0
5-min average
SR (W/m2)
105,058
68,042
64.8
Quarterly data summaries are also provided in the Final Report (December 1999).
JULY 2005
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
Wind Rose - On-site Meteorological Station
(included in final report with on-site air monitoring)
[South .. •••
JULY 2005
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
F.2 Off-site Soil Monitoring Data
Environmental Medium: Surface soil
Number of Data Points: 4 data points from 4 locations
Measurement Endpoint(s) (Units): Total mercury dry weight concentration (mg/kg)
Sampling Date(s): June 6-7, 1995 and October 27, 1997
Sample Location(s): Park (2200 m to SSW)
Purpose of Monitoring: 1995 and 1997 site investigations
Range: 0.18 - <0.32 mg/kg, dry weight
Mean and Standard Deviation: N/A (only one value above detection limit)
Raw Data:
Park Sample Site
SSS-018
SSS-054
SSS-055
SSS-056
Year
1995
1997
1997
1997
Total Hg (mg/kg)
0.18
<0.32
<0.30
<0.30
Other Information: The value for sample code SSS-018 was obtained during 1995 earthworm
sampling and concurrent soil samples from earthworm collection sites. Values for SSS-054,
SSS-055, and SSS-056 were determined during 1997 soil sampling and mercury analysis.
[Corresponding on-site data also available].
JULY 2005
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
F.3 Off-site Sediment Monitoring Data
Environmental Medium: Sediment
Number of Data Points: 4 data points, including a single measurement each from 4 different
off-site ponds and lakes.
Measurement Endpoint(s) (Units): Total mercury concentration in the upper 2 cm of the
sediment in the deepest part of the water body (mg/kg, dry weight)
Sampling Date(s): (1) September 19, 1996
(2) September 26, 1996
(3) September 20, 1996
(4) September 20, 1996
Sampling Location(s): (1) Swetts Pond, (2) Thurston Pond, (3) Brewer Lake, and (4) Fields
Pond. Deepest part of each water body.
Purpose of Monitoring: To determine if lakes and ponds are measurably affected by small,
local air emission sources of mercury
Range: N/A (single value)
Mean and Standard Deviation: N/A
Raw Data: (1) Swetts Pond: 0.319 mg/kg
(2) Thurston Pond: 0.157 mg/kg
(3) Brewer Lake: 0.201 mg/kg
(4) Fields Pond: 0.132 mg/kg
JULY 2005 F-6 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
F.4 Off-site Biota Monitoring Data
Environmental Medium: Earthworm at Park
Number of Data Points: 1 data point
Measurement Endpoint(s) (Units): (1) Percent moisture in earthworm
(2) Total mercury concentration in earthworm
sample (mg/kg, wet weight)
(3) Mercury concentration in concurrent soil sample
(mg/kg, dry weight)
Sampling Date: 1995
Sampling Location: Park (2200 m to SSW)
Purpose of Monitoring: unknown
Range: N/A (single value)
Mean and Standard Deviation: N/A
Raw Data: (1) 87.9 percent moisture in earthworm
(2) 0.044 (mg/kg, wet weight), mercury concentration in earthworm sample
(3) 0.18 (mg/kg, dry weight), mercury concentration in soil sample
Corresponding on-site data also available.
Environmental Medium: Short-tailed Shrew (Blarina brevicaudd)
Number of Data Points: 1 data point
Measurement Endpoint(s) (Units): (1) Total mercury concentration (mg/kg, wet weight)
(2) Percent moisture (%)
Sampling Date(s): June 1995
Sample Location(s): Park (2200 m to SSW)
Purpose of Monitoring: 1995 Site Investigation
Range: N/A (single value)
Mean and Standard Deviation: N/A
JULY 2005 F-7 TRIM.FATE EVALUATION REPORT VOLUME II
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
Raw Data: (1) 0.064 mg/kg, wet weight
(2)73.4%
Environmental Medium: Deer Mouse
Number of Data Points: 2 data points
Measurement Endpoint(s) (Units): (1) Total mercury (mg/kg, wet weight)
(2) Percent moisture
Sampling Date(s): 1995
Sample Location(s): Park (2200 m to SSW)
Purpose of Monitoring: Mercury risk assessment
Range: N/A
Mean and Standard Deviation:
Raw Data:
(1) 0.0515 ± 0.05, total mercury (mg/kg, wet weight)
(2) 75.4 ± 2.68, percent moisture
Sample
Park
Park
Percent Moisture
77.3
73.5
Total Hg (mg/kg wet weight)
0.087
0.016
Similar on-site data also available.
Environmental Medium: White perch from lakes in area of case study
Number of Data Points: 35 mercury concentration and fish length data points from 4
waterbodies, including (1) 10 data points from Swetts Pond, (2) 8 data points from Fields Pond,
(3) 11 data points from Thurston Pond, and (4) 6 data points from Brewer Lake
Measurement Endpoint(s) (Units): (1) Total mercury concentration in skinless fillet
(mg/kg, wet weight)
(2) Fish length (mm)
JULY 2005
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
Sampling Date(s):
(1) September 19, 1996
(2) September 20, 1996
(3) September 26, 1996
(4) September 20, 1996
Sample Location(s): (1) Swetts Pond
(2) Fields Pond
(3) Thurston Pond
(4) Brewer Lake
Purpose of Monitoring: To determine if lakes and ponds are measurably affected by small,
local air emission sources of mercury
Range: (1) 0.50 - 1.31 mg/kg, wet weight and 240 - 350 mm in length
(2) 0.28 - 0.72 mg/kg, wet weight and 135 - 270 mm in length
(3) 0.60 - 2.20 mg/kg, wet weight and 186 - 305 mm in length
(4) 0.32 - 0.53 mg/kg, wet weight and 185 - 202 mm in length
Mean and Standard Deviation:
(1) 0.98 ± 0.25 mg/kg, wet weight and 308 ± 32 mm in
length
(2) 0.45 ± 0.14 mg/kg, wet weight and 224 ± 48 mm in
length
(3) 1.07 ± 0.43 mg/kg, wet weight and 231 ± 34 mm in
length
(4) 0.41 ± 0.08 mg/kg, wet weight and 195 ± 8 mm in
length
Raw Data:
Sample Point
Swetts Pond-5544
Swetts Pond-5544
Swetts Pond-5544
Swetts Pond-5544
Swetts Pond-5544
Swetts Pond-5544
Swetts Pond-5544
Swetts Pond-5544
Swetts Pond-5544
Swetts Pond-5544
Fields Pond- 4282
Fields Pond- 4282
Fields Pond- 4282
Date
9/19/96
9/19/96
9/19/96
9/19/96
9/19/96
9/19/96
9/19/96
9/19/96
9/19/96
9/19/96
9/20/96
9/20/96
9/20/96
Length (mm)
320
340
330
350
320
300
295
285
295
240
270
258
265
Total Hg (mg/kg ww)
1.29
1.00
0.86
1.31
0.97
0.99
1.15
0.73
1.04
0.50
0.72
0.45
0.57
JULY 2005
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APPENDIX F
SUMMARY OF AVAILABLE OFF-SITE MONITORING DATA
Sample Point
Fields Pond- 4282
Fields Pond- 4282
Fields Pond- 4282
Fields Pond- 4282
Fields Pond- 4282
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
ThurstonPond-4321
Brewer Lake-4284
Brewer Lake-4284
Brewer Lake-4284
Brewer Lake-4284
Brewer Lake-4284
Brewer Lake-4284
Date
9/20/96
9/20/96
9/20/96
9/20/96
9/20/96
9/26/96
9/26/96
9/26/96
9/26/96
9/26/96
9/26/96
9/26/96
9/26/96
9/26/96
9/26/96
9/26/96
9/20/96
9/20/96
9/20/96
9/20/96
9/20/96
9/20/96
Length (mm)
240
240
215
170
135
305
260
235
243
245
240
186
200
210
213
200
202
191
201
185
187
201
Total Hg (mg/kg ww)
0.43
0.49
0.31
0.28
0.37
2.20
1.26
1.11
1.26
1.02
1.04
0.60
1.00
0.75
0.82
0.67
0.53
0.43
0.32
0.48
0.37
0.32
JULY 2005
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United States Office of Air Quality Planning and Standards Publication No. EPA-453/R-05-002
Environmental Protection Emissions Standards & Air Quality Strategies and Standards Divisions July 2005
Agency Research Triangle Park, NC
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