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       Rural Scenarios

       For the rural scenarios considered, exposure to divalent mercury accounted for approximately
90% of the total mercury exposure.  The primary exposure pathway is from plant products which
account for 70-90% of the total mercury exposure.  Most of the exposure through plant products is
from consumption of fruits.  Most of the mercury predicted to occur in fruits is the results of air-to-
plant transfer. There is a great deal of uncertainty regarding the magnitude of air to plant transfer of
mercury (see Appendix A).

       The rural subsistence farmer receptors are predicted to have about twice as much exposure to
mercury as the rural home gardener.  Thus, although there  are many differences between the two rural
scenarios as parameterized here, the end results are not substantially different  because exposure is
dominated  by plant  pathways,  and the differences between  scenarios are not as significant for these
pathways.

       Exposure to mercury from milk (dairy) dominates exposure from animal products for the high
end rural scenario considered (total of seven types of animal products are assumed to be consumed).

       Urban Scenarios

       The results for the urban scenarios are independent of the water body results. For the urban
average scenario, the only exposure pathways considered are inhalation and ingestion of soil.  For the
urban high end scenario, the ingestion of home grown  produce is considered as well, although with
lower contact fractions than  for the rural home gardener scenario.

       For the urban average  scenarios, exposure to mercury from the inhalation route dominated
exposure.  The urban high-end scenario included a small garden to the urban average scenario, with
the result that similar contributions to the total divalent mercury and methylmercury exposures
occurred as for the rural home gardeners. The  urban high-end adult receptor had a predicted mercury
exposure of about one-third that of the rural home gardener.  The high end urban child scenario
consisted of a pica child assumed to ingest 7.5  grams of soil per day.  The exposure rate is then
proportional to the assumed  soil concentration,  which in this case is 50 ng/g.

       Fish Ingestion Scenarios

       It was assumed that the high-end fish consumer eats fish from the affected freshwater  lake on
a daily basis; that is, seasonal consumption rate variation was not addressed. This individual is the
most exposed person to methylmercury in this assessment,  and was predicted to be exposed to
approximately twice the level of methylmercury to which the recreational angler is exposed to. On a
gram per bodyweight basis, the high-end fish-consuming child was the maximally exposed
subpopulation.  This is based on the consumption rate  and the bodyweight, and is consistent with the
data presented in Chapter 3 and Appendix H.

        For the fish ingestion scenarios, intake of mercury was mainly the methylmercury species.
Although intake of methylmercury via plants and soil is considered in the subsistence fisher scenario,
it accounts for less than 0.5% of the total methylmercury intake.  For the high end scenario, however,
in which home-grown vegetables are consumed, divalent mercury primarily from green plant
consumption  was predicted to  account for about 5% of the total mercury intake.  The recreational
angler was  assumed to be exposed to mercury via fish, soil and water consumption. Exposure via soil
and water however, accounted for less than 0.1% of the total mercury intake.
June 1996                                    4-25                        SAB REVIEW DRAFT

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       4.3.3.2  Predicted Methylmercury Intakes for Wildlife Receptors

       The exposure of three birds and two mammals to methylmercury through ingestion of
methylmercury-contaminated fish was estimated, and the results are shown in Table 4-21. The
exposure of these wildlife receptors to mercury may occur through other routes as well; e.g.,  the
ingestion of mercury-contaminated drinking water and food sources other than fish, inhalation of
atmospheric mercury, and dermal uptake through soil  and water. Fish consumption is thought to be
the dominant mercury exposure pathway for piscivorous accounting for most, exposure to mercury,
because methylmercury bioaccumulates to such a great extent in their food source, fish.  Consequently,
an analysis of these wildlife receptors' methylmercury contact rate based only on the daily ingestion
rate of fish is logical and appropriate.  The piscivorous bird's or mammal's estimated methylmercury
contact rate from fish consumption were based on two important factors:  the methylmercury
concentration in the fish and the daily amount of fish eaten.

       The ratio of grams fish consumed per  day to piscivore body weight is important in estimating
methylmercury  exposure on a g/Kg Bw/day basis.  The greater this ratio the higher the resulting
mercury exposure, assuming mercury concentrations in fish consumed are constant.

       By using the relationship for mercury described by the four-tier trophic food chain model (i.e.,
the BAFs specific for fish in trophic levels 3 and 4), the estimates of the animal's daily fish
consumption rates from each trophic level and the body weight  of the animal, the rates of
methylmercury  exposure (in mg/Kg BW/day) for the animals in this hypothetical environment can be
ranked.  This ranking is independent of the actual fish methylmercury concentrations as long  as the
predator-prey factor is the same. The piscivore exposure ranking from highest to lowest is the
following:
                      Kingfisher > River Otter > Osprey.Mink > Bald Eagle

       The ranking demonstrates the importance of three factors:  the trophic level which the
piscivore consumes, the daily consumption rate and the ratio of daily fish consumption rate to body
weight.  Despite consuming a comparatively small amount of the trophic level 3 fish, the kingfisher
ranks first as having the highest mercury exposure per individual.  This is attributable to the high food
consumption per bodyweight for this species.

4.4    Summary of IEM2 Model Results

       In this chapter the watershed, water body, food chain and  exposure  models of EEM2 were run
using a mercury air concentration of 1.6 ng/m3, a mercury deposition rate of 10 ug/m2/yr, and
watershed mercury soil concentration of 50 ng/g.  These values had been measured in the environment.

       Environmental Media Modeling

       The predicted surface water and benthic sediment mercury concentration at both the eastern
and western site were within the range of measured mercury concentrations for these media.  At both
sites, most (about 80%) of the input of mercury to the water body was predicted to be from erosion
and runoff rather than direct" deposition.  Similarly,  burial of the mercury  in the water body bed was
predicted to be  the dominant loss process of mercury, accounting for  more than 85% of the mercury
outflow at both sites.  Volatilization of mercury from the water body was predicted to be slightly
larger at the western site  due to the differences in climate.
June 1996                                    4-26                        SAB REVIEW DRAFT

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        Biota Modeling

        The predicted concentrations and speciations of mercury in green plants were within the range
of measured concentrations. The mercury concentrations in green plants were the result of direct
deposition onto exposed foliar surfaces, air-to-plant transfer and soil-to-plant transfer.  As modeled in
this assessment, most of the mercury predicted to occur in green plants was the result of air-to-plant
transfer for those types of green plants for which this route of transfer was deemed appropriate. The
plants for which air-to-plant transfer was not considered were potatoes and root vegetables.  The
contribution of air-to-plant transfer in the overall plant  burden of mercury  is uncertain.  Of the types  of
green plants considered, this uncertainty is largest for fruits and fruiting vegetables.  The predicted
concentrations for these plant types were at or slightly  above the upper end of the range of observed
values.

        The predicted mercury concentrations in all animal products except fish were low.  This was
the result of generally low concentrations in plants and small plant-to-animal and soil-to-animal
biotransfer factors.  The dominant exposure pathway for animals was predicted to occur through
ingestion of plant products.

        Mercury concentrations in fish were predicted to be the highest of the  biota considered.
Mercury concentrations in fish were the product of the bioaccumulation factor and the dissolved
concentration of mercury in surface water.  The predicted mercury concentrations in fish were within
the range of reported values.  The predicted concentrations of mercury in fish  at the two  sites were
consistent with the mean of reported values in Bahnick et al. (1992). There was a great deal of
uncertainty and variability associated with uptake of mercury by fish.

        Human and Wildlife Exposure Modeling

        Human exposure to mercury was predicted to be dominated by indirect routes of exposure
except for the hypothetical average urban dweller. This individual was assumed to be exposed to
mercury from inhalation and soil ingestion only.  Furthermore, exposure tends to be dominated by
either divalent or methylmercury species.  For the agricultural  scenario, divalent mercury dominates
mercury exposure, and for scenarios that include fish ingestion, methylmerucry dominates mercury
exposure.

        For those hypothetical individuals exposed through consumption of both green plants and
animal products, mercury  exposure through consumption of green plants was greater than through
consumption of animal products. This was the result of low plant-to-animal and soil-to-animal
biotransfer factors when compared to  the air-to-plant biotransfer factors. Of the green plants
considered, exposure to mercury from fruits and fruiting vegetables was the largest.  There is
uncertainty in this result due to uncertainty in the role air-to-plant transfer plays for uptake of mercury
for these plant products.  Of the animal products considered, exposure to mercury was predicted to be
the largest from dairy products.

        Those hypothetical humans  who were assumed to consume fish had the highest exposures.
This was a result of the bioaccumulation factor for fish. Methylmercury was the primary species to
which these individuals were exposed. On a per body  weight  basis, children were predicted to be
more exposed than adults.

        The piscivorous animals with the highest fish ingestion rate (per body weight) generally had
the highest methylmercury intakes, except in cases where a piscivorous species was assumed to prey
more heavily on trophic level 4 fish.


June 1996                                    4-28                        SAB  REVIEW DRAFT

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5.      LONG RANGE TRANSPORT ANALYSIS

5.1     Description of the Analytic Approach

5.1.1    Objectives

        The goal of this analysis was to model the emission, transport, and fate of airborne mercury
over the continental U.S. using the meteorologic data for the year of 1989. The results of the
simulation were intended to be used to answer a number of fundamental questions.  Probably the most
general question was "How much mercury is emitted to the air annually over the United States, and
how much of that is then deposited back to U.S. soils and water bodies?" A second question was that
of the contribution by source category to the total amount of mercury emitted and the amount
deposited  within the U.S.  In order to  answer the questions about the source relative depositions,
information  on chemical and physical  forms of the mercury emissions from the various source
categories was  needed since these characteristics determine the rate and location of the wet and dry
deposition processes for mercury.

        The intent of the analysis was to determine which geographical areas of the United States
having the highest and lowest amounts of deposition from sources using  the overall results of the long-
range transport modeling effort nation-wide. This analysis was expected to contribute understanding
of the key variables, such as source location, chemical/physical form of emission, or meteorology, that
might contribute to the outcomes.  These long-range modeling efforts were also  intended to be used
for comparison with local impact modeling results, essentially to estimate the effects of hypothetical
new local  sources in relation to the estimated effects from long-range transport.

5.1.2    Description of the Long-range Transport Model Used

        To estimate the nationwide concentration and deposition patterns for airborne mercury, the
Regional Lagrangian Model of Air Pollution (RELMAP) was adapted to simulate the emission,
transport and diffusion, and wet and dry deposition of elemental mercury vapor (Hg°), divalent
oxidized mercury gases (Hg2+), and particle-bound mercury (Hgp).  The  RELMAP was originally
designed to  simulate sulfur dioxi'de (SO2) and sulfate (SO2").  An evaluation of the model was
performed for those pollutant species (Eder et al., 1986). The RELMAP provides a continual
accounting of pollutant mass balances  during the simulation process so that the user is assured that no
pollutant mass is being created or destroyed by systematic numerical errors.

        The RELMAP simulates  long-range pollutant transport and diffusion in terms of individual
pollutant puffs  generated at regular time intervals at their source location with each puff moving
independently within a predefined horizontal area. The puff motion is determined by predefined wind
fields, with the wind flow vectors defined on a latitude-longitude grid covering the modeled area.
Pollutant deposition to the surface by precipitation (wet deposition) is estimated using observed hourly
precipitation data that  are also analyzed on this latitude-longitude grid. For the RELMAP mercury
simulations, the modeling grid resolution was set to  Vi degree longitude by Va degree latitude to
approximate a 40-km square. Pollutant concentrations and depositions to the Earth's surface were
calculated for each rectangular shaped area, or grid cell, defined by the array of grid points in the
latitude-longitude grid.                                       „

        For large modeling domains, such as that in this study for the lower 48 United States, the
pollutant emissions from the many individual sources within each grid cell were totaled, and a single
pollutant puff was generated at the cell center to represent all sources in that cell.  These puffs were

June 1996                                     5-1                        SAB REVIEW DRAFT

-------
generated on a regular time interval, in this case three hours, and the mass of the pollutant in the puff
was the total of all emissions in the grid cell  during that three-hour model time step.  The puff was
moved horizontally by the wind, horizontal diffusion was modeled by puff expansion, and the
pollutant mass in the puff was distributed vertically in four layers between the Earth's surface and  the
top of the Planetary Boundary Layer (PBL).  The PEL is the lowest portion of the atmosphere and is
the layer most directly affected by physical and chemical interactions with the Earth's surface.  As the
puff moves, it is acted upon by precipitation  (wet deposition) and dry deposition processes which
remove some of the pollutant mass and deposit it to the surface.  There may also be chemical
processes within the puff which create or destroy particular pollutants.  Due to  the long atmospheric
lifetime of mercury emissions compared to the sulfur compounds the RELMAP was originally
designed to simulate, a PBL venting process  was included to simulate the gradual leakage of pollutants
into the free atmosphere above.  The venting, dry deposition, wet deposition, and chemical
parameterizations used in this modeling effort are described in  detail in Appendix D.  When the
pollutant mass in a puff  was depleted to  a predefined limit, the puff was dropped from the" model and
any remaining pollutant  was ignored for the remainder of the simulation.  Also, if the puff was moved
out of the horizontal model domain by the wind it was dropped from the simulation and its remaining
pollutant load was ignored.

        U.S. EPA's modifications to the RELMAP for atmospheric mercury simulation were  heavily
based on recent Lagrangian model developments in Europe (Petersen et al., 1995).  The mercury
version of RELMAP was developed to handle the three types of mercury mentioned above, as well as
carbon soot.  Recent experimental work indicates that ozone and  carbon soot are both important in the
wet deposition of Hg°.  Carbon soot, or total carbon aerosol, was included as a modeled pollutant in
the mercury version of RELMAP to provide  necessary information for the  Hg° wet deposition
parameterization. The Agency was able to obtain observed O3 air concentration data from its
Aerometric Information Retrieval System (AIRS) and from the ACIDMODES field study.  Thus, O3
was not included as an explicitly modeled pollutant.  Methylmercury was not included in the mercury
version of RELMAP because it is not yet known if it has a primary natural or anthropogenic  source or
if it is produced  in the atmosphere.

        For the RELMAP mercury  modeling  study, each of the source types modeled was assumed to
emit mercury in  a-particular distribution of chemical and physical forms.  These form distributions, or
speciation factors, define the estimated fraction  of mercury emitted as Hg°, Hg2+, or Hg , these were
adopted from Petersen et al. (1995). Since there remains considerable uncertainty about Hg2+
attachment to particles, an alternate emission speciation was also  simulated to measure the  sensitivity
of the RELMAP results to this uncertainty. The point source type definitions and their mercury
emission speciation factors for the base and alternate scenarios are described in detail in Appendix F.
       Global oceanic and terrestrial emissions were accounted for by using a background
atmospheric concentration of elemental mercury gas of 1.6 ng/m3. The wet and dry deposition
parameterizations described in Appendix D were used to simulate the deposition of Hg  given a
constant concentration of 1.6 ng/m3 throughout the entire three-dimensional model domain to estimate
the deposition of elemental mercury vapor from this global atmospheric reservoir.

5.1.3   Description of the Mercury Emissions Data Used

       The atmospheric mercury emission inventory used for this modeling study is described in
detail in Volume II of this report.  Data from this inventory were used to compile estimates of the
mercury emissions from 7 major stationary source types (point sources) and from a group of minor
source types for  which individual emission site locations were not available (.area sources).  The 8

June 1996                                     5-2                         SAB REVIEW DRAFT

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emission source types resolved for input to the RELMAP mercury model were;  electric-utility fossil-
fueled boilers, non-utility fossil-fueled boilers, municipal solid waste combustion, medical waste
incineration, chlor-alkali factories, non-ferrous metal smelting, all other point sources, and area
sources.  For each of the 7 point source types, an estimate of the mercury emission speciation profile
was made to define the most likely chemical and physical  forms of the emissions.  In addition to these
base-case emission profiles, alternate profiles were defined to allow model sensitivity tests to be
performed.

       The base-case and alternate mercury emission speciation profiles for the 7 point source types
are shown in Table 5-1. The area sources were modeled as emissions of elemental mercury vapor
only.  The area source data used for input to the RELMAP model simulation included estimates of the
emission of mercury from latex paints,  which has not been included in the
                                           Table 5-1
                Emission Speciation Profiles for the Point Source Types Defined
Point Source Type
Electric Utility Boilers
Non-utility Fossil Fuel
Combustion
Municipal Waste
Combustion
Medical Waste
Incineration
Non-ferrous Metal
Smelting
Chlor-alkali Plants
Other Point Sources
Base-Case Speciation (%)
Hg°
50
20
85
70
80
Hg2+
30
60
10
30
10
HgpOc
20
20
5
0
10
Alternate Speciation (%)
Hg°a
50
20
85
70
80
Hg2+b
0
0
0
0
0
H«pc
50
80
15
30
20
a Hg° = Elemental Mercury
b Hg2+ = Divalent Vapor-phase Mercury
c Hg  = Particle Bound/Mercury
June 1996
5-3
SAB REVIEW DRAFT

-------
assessment of emissions described in emissions inventory in Volume II.  These latex paint emission
estimates totaled 4 metric  tons per year for the lower 48 states.  The analyses differs because the
inventory reflects that mercury has been removed from paint since the early 90's. however, emissions
may still be occurring. A second discrepancy is that the long-range transport analysis modeled 3.25
tons of Hg from oil-fired utility boilers.  A more recent analysis of the mercury content of residue oil
necessitated the revision of the estimate  in the  inventory to  0.25 tons/yr from oil-fired utility boilers.
The compiled nationwide patterns of elemental, divalent, and particle-bound mercury emissions from
all point and area source types using the base-case emission speciation profiles are illustrated in
Figures 5-1, 5-2 and 5-3, respectively.  Figure  5-4 shows the pattern of particle-bound mercury
emissions using the alternate speciations profiles.

       Table 5-2 shows the results of applying the base-case emission speciation profiles to the eight
mercury emission source types resolved  by the RELMAP mercury model.  These emissions are for the
lower 48 states only and the total mercury mass figures differ slightly  from the national totals shown
in Volume  II. The RELMAP analysis indicates that of the total anthropogenic  emissions from the
lower 48 states, 26% is from medical waste incineration, 22% is from  municipal waste combustion,
22% is from electric utility boilers,  13% is  from fossil fuel combustion other than that by electric
utilities, 4% is from non-ferrous  metal smelting, and 3% is from chlor-alkali  factories.  As a whole,
large-scale  fossil fuel combustion represents about 35% of the total anthropogenic mercury emissions
to the atmosphere in the lower 48 states.  The atmospheric emissions from all other point source types
represent 7% of the total emission and area sources represent 3% of all anthropogenic emissions in the
lower 48 states.  Because of differences  in the  behavior of the various chemical and physical forms of
mercury in the atmosphere, it is necessary to determine their relative contributions to the total mass of
mercury emitted.  Table 5-2 indicates that, based on the base-case emission speciation profiles, about
41% of all  anthropogenic mercury emitted to the air from the lower 48 states is in the form of Hg°
vapor, 41% is in the form of Hg2+ vapor, and 17% is emitted as particle-bound mercury.
                                           Table 5-2
                  Mercury Emissions Inventory Totaled by Source Type Using
                  Base-case Emission Speciation Profiles (metric tons per year)
Source Type
Medical Waste Incineration
Municipal Waste Combustion
Electric Utility Boilers
Non-Utility Fossil Fuel
Non-Ferrous Smelting
Chlor-alkali Factories
Other Point Sources
Area Sources
Total
Hg°a
11.7
10.0
24.3
14.3
7.4
4.6
13.0
6.9
92.0
Hg2+b
35.1
29.9
14.6
8.6
0.9
1.9
1.6
0.0
92.6
Hgp<
11.7
10.0
9.7
5.7
0.4
0.0
1.6
0.0
39.1
Total
Mercury
58.6
49.8
48.5
28.5
8.7
6.5
16.2
6.9
223.8
               a Hg° = Elemental Mercury
               b Hg2"1" = Divalent Vapor-phase Mercury
               c Hg  = Particle-Bound/Mercury
June 1996
5-4
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5.2     An Interpretive Analysis of the Results

5.2.1    Mass Balances of Mercury within the Long-range Model Domain

        The general mass balance of elemental mercury gas, divalent mercury gas, and particle-bound
mercury from the RELMAP simulation results using the base-case emission speciation profiles are
shown in Table 5-3.  The mass-balance accounting for the simulation using the meteorologic data from
the year 1989 shows a total of 223.8 metric tons of mercury emitted to the atmosphere from
anthropogenic sources. This simulated emission total differs from the national totals indicated in
Volume II since the states of Alaska and Hawaii are not within the model domain and latex paint
emissions are not considered.  Using the base-case emission speciation profiles, the simulation
indicates that 77.9 metric tons of anthropogenic mercury emissions are deposited within the model
domain and 0.6 metric tons remain in the air within the model domain at the end of the simulation.
The remainder,  about 145.3 metric tons, is transported outside the model domain and probably diffuses
into the global atmospheric  reservoir.  The simulation also indicates that 33.0  metric tons of mercury
is deposited within the model domain from this global atmospheric reservoir, suggesting that about
four times as much mercury is being added to the global reservoir as is being  deposited from it.  The
total amount of mercury deposited in the model domain annually from U.S. anthropogenic emissions
and from the global background concentration is estimated to be 111.0 metric  tons, or about one-half
of the total atmospheric emissions from anthropogenic sources in the lower 48 United States.
                                           Table 5-3
                    Modeled Mercury Mass Budget in Metric Tons for 1989
                       Using the Base-Case Emission Speciation Profiles
Source/Fate
Total U.S. anthropogenic emissions
Mass advected from model domain
Dry deposited anthropogenic emissions
Wet deposited anthropogenic emissions
Remaining in air at end of simulation
Total deposited anthropogenic emissions
Deposition from background Hg°
Mercury deposited from all sources
Hg°a
92.0
90.4
0.0
1.2
0.4
1.2
33.0
34.2
Hg2+b
92.6
29.9
39.0
23.6
0.1
62.6
0.0
62.6
Hgpc
39.1
25.0
0.6
13.4
0.1
14.1
0.0
14.1
Total
Mercury
223.8
145.3
39.6
38.3
0.6
77.9
33.0
111.0
(All figures rounded to the nearest tenth of a metric ton)
       a Hg  = Elemental Mercury
       b Hg2+ = Divalent Vapor-phase Mercury
       0 Hg  = Particle-Bound/Mercury
       As shown in Table 5-4, the alternate case emission speciation profiles result in a noticeably
different mass balance. By assuming that all of the Hg2"1" emitted becomes attached to ambient
paniculate matter, the  total deposition of anthropogenic Hg to the surface is reduced by
June 1996
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                                          Table 5-4
                    Modeled Mercury Mass Budget in Metric Tons for 1989
                        Using the Alternate Emission Speciation Profiles
Source/Fate
Total U.S. anthropogenic emissions
Mass advected from model domain
Dry deposited anthropogenic emissions
Wet deposited anthropogenic emissions
Remaining in air at end of simulation
Total deposited anthropogenic emissions
Deposition from background Hg°
Mercury deposited from all sources
Hg°a
92.0
90.4
0.0
1.2
0.4
1.2
33.0
34.2
Hg2+b
0.0
0.0
0.0
. 0.0
0.0
0.0
0.0
0.0
Hgpc
131.7
84.5
2.1
44.9
0.2
47.0
0.0
47.0
Total
Mercury
223.8
174.9
2.1
46.1
0.6
48.2
33.0
81.3
(All figures rounded to the nearest tenth of a metric ton)
       a Hg°  = Elemental Mercury
         Hg~+ = Divalent Vapor-phase Mercury
       c     = Particle-Bound/Mercury
about 40%.  This is primarily due to the fact that the dry deposition velocity of paniculate matter in
the size range typical for continental air masses with moderate urban influences (-0.3 micron
diameter) is much smaller than the dry deposition velocity  assumed for Kg2"1" vapor. Less efficient wet
scavenging of paniculate versus gaseous Hg2+ also contributes to the lower total deposition using the
alternate emission speciation profiles. While the assumption of total Hg2+ attachment to particles is
only intended as a bounding exercise, the results show the  importance of an accurate determination of
the mass of paniculate Hg emitted and that formed during  transport.

        Of the total anthropogenic mercury mass deposited to the surface in the model domain, 80% is
estimated by the RELMAP to come from Hg2+ emissions,  18% from Hgp emissions and 2% from Hg°
emissions  when the base-case emission speciation profiles  are used.  When the deposition of Hg° from
the global background is considered in addition to anthropogenic sources in the lower 48 states, the
species  fractions become 56% Hg2+, 31% Hg° and 13% Hgp. The vast majority of mercury already in
the global atmosphere is in the form of Hg° and, in general, the anthropogenic Hg° emissions do not
greatly increase the existing Hg° concentration. Although  Hg° is removed from the atmosphere very
slowly,  the global background reservoir is large and extraction of mercury from it is significant in
terms of the total deposition. It should be noted here that  dry deposition of Hg° is significant only at
very high  concentrations and has not been included in the  RELMAP simulations.  Wet deposition is
the only major pathway for removal of Hg° from the atmosphere.  This removal pathway simulated by
the RELMAP involves oxidation of mercury by ozone in an aqueous solution; thus, the Hg that is
extracted from the atmosphere by the modeled precipitation process would  actually be deposited
primarily in the form of Hg2"1".

        Results from the RELMAP simulation show that of the 92.0 metric tons of anthropogenic Hg°
emitted in the lower 48 states, only 1.2 tons (1.3%) is deposited within the model domain, while of the
92.6 metric tons of Hg2+ emitted, about 62.6 tons (67.6%) is deposited. Ninety-eight percent of the
deposited  anthropogenic mercury was emitted in the form  of Hg   or Hgp.  Thus, a strong argument
June 1996
5-10
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can be made that the combined Hg~+ and Hgp component of anthropogenic mercury emissions can be
used as an indicator of eventual deposition of those emissions to the lower 48 states and surrounding
areas.  The emission inventory and base-case chemical/physical speciations profiles indicate that of all
combined Hg2+ and Hgp emissions, about 36% is from medical waste incineration, 30% is from
municipal waste combustion, 18% is from electric  utility boilers, 11% is from combustion of fossil
fuel other than by electric utilities,  1% is from chlor-alkali factories,  1% is from non-ferrous metal
smelting, and 2% is from all other sources.

5.2.2    Qualitative Description of Mercury Concentration Results

        Annual average surface-level concentration fields for elemental mercury, divalent mercury, and
particulate mercury have been obtained from the RELMAP simulation using the meteorologic data for
the year 1989.  Figure 5-5 shows the annual average elemental mercury (Hg°) concentration at ground
level from anthropogenic sources obtained by using the estimates of Petersen et al. (1995) for the
source-based emission speciation profiles (base case). It shows that anthropogenic Hg° concentrations
remain less than 0.1 ng/m3 over most of the investigation area.  The areas where the average
anthropogenic  Hg° concentrations exceed 0.1 ng/m3 are  mostly confined to the highly industrialized
regions of the eastern Mid-west and the
background concentration of 1.6 ng/m3, i
anthropogenic emissions is rather small.
regions of the eastern Mid-west and the North-east. Compared to the estimated average global
background concentration of 1.6 ng/m3, this 0.1 ng/m3 elevation of Hg° concentration by
       Figure 5-6 shows annual average divalent mercury vapor (Hg2+) air concentrations, also using
the base case emissions.  These values are significantly lower than for anthropogenic Hg°, and there
are some new areas of maximum concentration.  The higher concentration areas have values from 0.05
to just over 0.1  ng/m3 and are mostly confined to Florida, the Midwest and  the Northeast corridor.
The background atmospheric mercury loading is assumed to be completely in the elemental form, so
there is no background contribution to the Hg2"1"  concentrations. In most areas, the anthropogenic
component of the Hg° concentrations shown in Figure 5-5 are  3 to 5 times higher than the Hg2+
concentrations shown in Figure 5-6.  For the  base-case emission speciation,  Hg2"1" vapor is a minor
component of the total mercury  emissions from some source types, but it is  a significant part  of the
total mercury emissions from waste incineration  (60%) and fossil fuel combustion (30%).  Since the
total Hg2+ emissions are about equal to those for Hg°, these much lower average annual Hg2+
concentrations cannot be attributed to the emissions.  The lower simulated air concentrations of Hg2+
vapor are due to more rapid depletion of atmospheric mercury  from wet and dry deposition.

       The RELMAP Hg° and  Hg2+ air concentration results taken together with the assumed
background Hg° concentration of 1.6 ng/m3 agree well with observations of vapor-phase Hg air
concentration in Minnesota by Fitzgerald et al. (1991), in Vermont by Burke et al. (1994) and in
Wisconsin by Lambourg et al. (In press).  These works showed that annual  average vapor-phase Hg
concentrations were near the levels found over other remote locations in the northern hemisphere, from
1.6 to 2.0  ng/m3,  Measurements taken for a two-week period at three sites  in Broward County,
Florida, (Dvonch et al., 1995) show slightly elevated vapor-phase Hg air concentrations for two of
those sites downwind of industrial activities.  These two sites had average vapor-phase Hg air
concentrations of 3.3 and 2.8 ng/m3.  The RELMAP simulation results for the Fort Lauderdale area
show only about a 0.2 ng/m3 elevation of the annual average vapor-phase Hg (Hg° plus Hg2+)
concentration over the 1.6 ng/m3 background value assumed. The measurements of Dvonch et al.
(1995), however, did not extend for a significant portion of
June 1996                                    5-11                        SAB REVIEW DRAFT
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the year and there was no discrimination between Hg  and Hg2+ forms.  The third site for their
observations had an average vapor-phase air concentration of 1.8 ng/m3, which is what the RELMAP
simulation suggests. A more comprehensive air monitoring program is required before an evaluation
of the RELMAP results in Florida can be performed.

       Paniculate  mercury (Hgp) emissions are thought to be a small  fraction of the total for most
source types.  For the base-case emission speciation, 20% is the largest paniculate fraction of mercury
emissions for any source type.  Figure 5-7 shows that the simulated annual average Hgp concentrations
were even lower than those for Hg"+ vapor.  The maximum  annual average values are around 50-100
pg/m3 (0.05-0.1 ng/m3) in the urban centers of the Northeast. Keeler et al. (1994) found instantaneous
Hgp concentrations in urban Detroit during March of 1992 of over 1 ng/m3 and average concentrations
over an  18-day period of 94 pg/m3. Given the 40-km horizontal scale of the RELMAP computational
grid, however, one cannot expect the simulation to reflect these extreme local-scale measurement
results.  The RELMAP simulation suggests an annual average Hgp concentration in the Detroit area of
about  50 pg/m3.  Dvonch et al. (1995) found average Hgp concentrations in Broward County, Florida,
of between 34 and  51 pg/m3  at three sites  from 25 August to 7 September of 1993. The RELMAP
simulation results agree well  with these observations around the city of Fort Lauderdale.  Keeler et al.
(1994) found  annual average Hgp air concentrations  of 10.5 pg/m3 in Pellston, Michigan, 22.4 pg/m3
in South Haven,  Michigan, and 21.9 pg/m3 in Ann Arbor, Michigan, from April 1993 to April  1994,
and 11.2 pg/m3 in Underhill, Vermont, for the year of  1993.  The RELMAP simulation results agree
quite well with these observations.

       Table 5-5 shows a percentile analysis of the  simulated concentration results from the RELMAP
grid cells within  the lower 48 United States. This table shows that the Hg° concentrations never
exceeded the  assumed background level of 1.6 ng/m3 by a large relative amount. It also  shows that
Hg2"1" and to a lesser degree Hgp air concentrations were highly elevated in only a few grid cells.
There is an order of magnitude difference in the Hg2+ concentrations at the 90th percentile level and
the maximum level, with a factor of 5 difference for Hgp.

       For the alternate emission speciation tests, the Kg2"*"  vapor emission fraction was redistributed
to the Hgp fraction. This was done to simulate the complete attachment of Hg2+ vapor to ambient
paniculate matter.  The annual average Hg° and Hgp concentration fields from this  test (not shown)
indicate that,  as one would expect, there was no change to the Hg° results, but the Hgp concentrations
were increased nearly to the level of anthropogenic Hg°, with maximum concentrations over 100
pg/m3 (0.1 ng/m3) over the larger urban areas of Florida and the Midwest and over nearly all of the
Washington, D.C. to Boston corridor.

5.2.3   Description of Mercury Wet Deposition Simulation Results

       Figure 5-8  shows the total simulated wet deposition of Hg° from anthropogenic sources using
the meteorologic data for the year 1989 using the base emission speciation factors.  Figure 5-9 shows
the total simulated  wet deposition of Hg° assuming only a non-depleting global background
concentration of  1.6 ng/m .  Both of these wet deposition results are influenced by  ozone  and soot
concentrations due  to the chemical transformations modeled  by the RELMAP.  Emission patterns
influence the  primary anthropogenic Hg° wet deposition pattern,  and it is obvious that total annual
precipitation is a strong factor in  wet deposition from the global  background concentration with
heaviest wet deposition in areas with the highest annual
June 1996                                    5-14                       SAB REVIEW DRAFT
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                                          Table 5-5
   Percentile Analysis of RELMAP Simulated Concentration Results for the Continental U. S.
                           Using the Base-Case Emissions Speciation
Variable
Full Area
Hg°a concentration (ng/m3)
Hg2+b concentration (pg/m3)
Hgpc concentration (pg/m3)
Total mercury (ng/m )
East of 90°W longitude
Hg°a concentration (ng/m3)
Hg2+b concentration (pg/m3)
Hgpc concentration (pg/m3)
Total mercury (ng/m )
West of 90°W longitude
Hg°a concentration (ng/m3)
Hg2+b concentration (pg/m3)
Hgpc concentration (pg/m3)

Total mercury (ng/m3)
Min

1.602
0.265
0.391
1.603

1.615
1.486
4.058
1.622

1.602
0.265
0.391
1.603

10th

1.606
0.804
1.426
1.608

1.638
4.745
8.815
1.655

1.605
0.687
1.243
1.607

50th

1.619
3.368
5.183
1.627

1.665
9.966
14.88
1.691

1.612
1.909
3.327
1.618

90th

1.681
14.72
19.18
1.715

1.705
25.25
28.01
1.755

1.636
6.303
8.565
1.651

Max

1.899
149.1
99.33
2.114

1.899
149.1
99.33
2.114

1.818
29.80
28.73
1.860

       3 Hg°  = Elemental Mercury
       b Hg2"1" = Divalent Vapor-phase Mercury
       0 Hgp  = Particle-Bound/Mercury
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precipitation.  It is widely accepted that deposition of measurable quantities of mercury occurs on
continental and global scales, and the RELMAP simulation shows areas of Hg° wet deposition
occurring in remote areas.  The base-case wet deposition results for Hg~r vapor shown in Figure 5-10
show high deposition areas that are much more local to the emission source areas.  There are many
model cells in urban areas with wet deposition totals of Hg2* vapor over 20 ug/m2 while most of the
cells in the non-urban areas have wet depositions of less than 5 ug/m2.  This indicates the Hg2+ vapor
wet deposits more on the local scale and not on regional or global scales and that its wet removal of
Hg2"1" from the atmosphere is much more rapid than for Hg°.  This is an expected result due  to the
higher water solubility of most mercuric salts compared to mercury in the elemental form.  Figure 5-
11 shows that for the base emission speciation the maximum simulated wet deposition of Hgp is about
half of that for Hg2+ vapor.  This is partly due to differences in the total mass of Hgp emitted
compared to Hg2+, but it is also due to the less efficient wet scavenging that is assumed for  Hgp
versus Hg2"1".  The areas of Hgp wet deposition are also more widely distributed than for Hg2+ due to
the slower wet scavenging of Hgp and, thus, a greater opportunity for long-range transport.

       The total wet deposition of mercury emitted in all three forms is shown in Figure 5-12.  This
illustration shows significant wet deposition of mercury over most of the eastern half of the U.S.  For-
the simulated meteorological year of 1989, nearly the entire eastern half of the nation has a wet
deposition total of over 5 ug/m2 and values exceed 20 ug/m2 over much of the urban northeast U.S.
In fact, the largest simulated wet deposition exceeded 100 ug/m2 in the grid cell containing New York
City.  Figure 5-12 was not  designed to highlight these maximum wet deposition results because at this
time such extreme wet deposition rates for total  mercury cannot be substantiated by observations.  In
the RELMAP simulation the most impacted areas are subjected to wet deposition of mercury mainly
from  emissions of Hg2+ vapor.  It is likely that the RELMAP model for mercury may still be
significantly incomplete, and that other chemical and/or physical transformations may occur which
moderate the wet deposition of Hg2+ vapor and possibly Hgp.

       There exist only limited data with which to compare the RELMAP simulation results.
Measurements of mercury wet deposition at three locations in northeastern Minnesota during 1989 by
Glass et  al. (1991) indicated annual wet deposition rates of 6.5  ug/m2 at Duluth, 13.5 ug/m2  at Marcell
and 41.9 ug/m2 at Ely. A later study by Sorensen et al. (1994) measuring annual wet deposition of
mercury  during 1990,  1991 and 1992 at Ely, Duluth and seven  other sites in Minnesota, upper
Michigan and northeastern North Dakota found all annual wet deposition totals to be within  the range
of 3.8 to 9.7 ug/m2, bringing into question the Ely observation of 41.9 ug/m2 in 1989 by Glass et al.
(1991).  Measurements by Fitzgerald et al. (1991) at Little Rock Lake, in northern Wisconsin, of
mercury  in snow during February and  March, 1989, and in rain from May to August, 1989, have been
used to estimate annual mercury depositions in rain and snow of 4.5 and 2.3 ug/m2, respectively.  This
suggests  a total annual mercury wet deposition of 6.8 ug/m2 at Little Rock Lake.  Measurements at
Presque Isle,  also in northern Wisconsin, from 1993 to 1994 by Lambourg et al. (In press) suggested a
wet deposition rate for total mercury of 5.2 ug/m2/yr, somewhat less than the measurements  by
Fitzgerald et al. (1991).  The extremely heavy rainfall during the summer of 1993 in the mid-west
states to  the south and west of Presque Isle may be responsible for the lower wet deposition.  The
RELMAP simulation results using the  meteorologic data for 1989 indicate 2 to 10 ug/m2 wet
deposition of total mercury over most  of the area represented by these studies; the major exception
was the Minneapolis-Saint Paul metropolitan area where the RELMAP indicates over 20 ug/m2.
June 1996                                     5-19                        SAB REVIEW DRAFT
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       There were also some mercury wet deposition measurement programs conducted during the
early 1990"s in somewhat less remote sites in Michigan and Vermont.  Observations by Hoyer et al.
(1995) during two years of event precipitation sampling at three sites in Michigan show evidence for
a north-to-south gradient in mercury wet deposition.  From March 1992 to March 1993, the total
mercury wet deposition observed at South Haven, in southwest Michigan, was 9.45  ug/nr.  At
Pellston.  in the northern part of the lower peninsula of Michigan, the wet deposition was 5.79 ug/nr.
At Dexter, in southeast Michigan about 100 km west of Detroit, the wet deposition  was 8.66 ug/m2.
From March  1993 to March 1994,  wet deposition at South Haven was  12.67 ug/m2, significantly
higher than for the previous year, while measurements at Pellston and Dexter remained about constant
at 5.54 and 9.11 ug/m2, respectively.   Hoyer et al. (1995) attribute the higher second-year wet
deposition at South Haven to an increased precipitation rate and cite the measurements by Burke et al
(1995) at  Underbill, Vermont, as further evidence of the importance of precipitation amount. From
December 1992 to December 1993, the average volume-weighted mercury concentration at Underbill
(8.3  ng/L) was  similar to that observed at Pellston (7.9 ng/L). However, with more precipitation
during that period the total mercury wet deposition at Underbill  was 9.26 ug/m2, significantly higher
than at Pellston.  The RELMAP simulation results show 5-10 ug/m2 wet deposition of total mercury at
the Pellston site which  agrees well  with the 1992 to 1994 observations there.  At  Underbill, the
RELMAP simulation indicates 10-20 ug/m2 wet deposition for 1989 which is slightly larger than the
observation in 1993. At the South Haven and Dexter sites, the RELMAP appears to be estimating
nearly 20 fag/m2 wet deposition of  total mercury for 1989 which is significantly larger than the
measurements of  1992  to  1994.
       The very large total mercury wet deposition values (>50 ug/m2) from the RELMAP simulation
for some of the larger urban centers in the Ohio Valley and Northeast regions cannot be evaluated
thoroughly due to a lack of long-term precipitation event sampling at those locations. A study by
Dvonch et al. (1995) describes precipitation event sampling from 19 August to 7 September of 1993 at
4 sites in Broward County, Florida, in and around the city of Fort Lauderdale. During the 20-day
sampling period, total mercury mean concentrations in precipitation were 35, 57, 40 and 46 ng/L at the
4 sites.  Given the average annual precipitation of 150 cm per year typical of that area, the resulting
annual wet deposition estimates at these 4 sites would 52.5, 85.5, 60 and 69 ug/m2.  Since most of the
annual rainfall in Broward County occurs in warm tropical conditions of the March to October wet
season, this extrapolation from 20 days during the wet season to an annual estimate is inappropriate.
Additional urban measurement studies are required to allow any credible evaluation of RELMAP wet
deposition results in heavily populated, industrialized area.

       Figure 5-13 shows the wet deposition of total mercury from the alternate emission speciation
and offers some  measure of the sensitivity of the RELMAP simulation results to the emission
speciation estimates used.  This figure shows basically the same large-scale pattern as for the base-case
emissions, but in general the amount of wet deposition is  increased.  Since the wet scavenging ratio
for Hgp is less than one-third of that for Hg2+, it would be expected to be reduced when using the
alternate emission speciation which reallocate all Hg2+ vapor emissions to the Hgp form.  This
sensitivity is the result of an interaction of the wet deposition processes with those for dry deposition.
As discussed in the next sub-section of this report, the alternate emission speciation profiles result in
greatly reduced dry deposition compared to the base-case.  This results in very little depletion of
pollutant puffs by dry deposition, and provides a longer opportunity for wet deposition to occur.  The
slower dry deposition of Hgp versus Hg2+ vapor more than offsets the effect of lower wet scavenging
ratios for Hgp  versus Hg2+ vapor.
June 1996                                     5-23                        SAB REVIEW DRAFT
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       The percentile analysis of the wet deposition simulation results in Table 5-6 show that for the
base-case emission speciation only 10 percent of the land area of the continental U.S. has an annual
wet deposition of total Hg of more than about 16 ug/m , and 50 percent of the land area
has less than 3.4 ug/m2.  However, due to rapid wet deposition of Hg2"1" and Hgp there are select areas
where wet deposition may be significantly higher.  In the eastern U.S., east of 90 degrees west
longitude, the 50th and 90th percentile levels for total  Hg wet deposition are considerably higher than
those for the entire continental U.S., about 12 and 25 ug/m2, respectively.
                                           Table 5-6
      Percentile Analysis of RELMAP Simulated Wet Deposition for the Continental U. S,
                            Using the Base-Case Emissions Speciation
Variable
Full Area
Hg°a wet dep. (ug/m2/yr)
Hg2+b wet dep. (ug/m2/yr)
Hgpc wet dep. (ug/nr/yr)
Total mercury (ug/m2/yr)
East of 90°W longitude
Hg°a wet dep. (ug/m2/yr)
Hg2+b wet dep. (ug/m2/yr)
Hgpc wet dep. (ug/m2/yr)
Total mercury (ug/m2/yr)
West of 90°W longitude
Hg0* wet dep. (ug/m2/yr)
Hg2+b wet dep. (ug/m2/yr)
Hgpc wet dep. (ug/m2/yr)
Total mercury (ug/m2/yr)
Min

0.022
0.002
0.001
0.025

0.540
0.242
0.191
0.979

0.022
0.002
0.001
0.025
10th

0.590
0.087
0.067
0.792

3.099
1.837
1.252
6.846

0.512
0.067
0.050
0.686
50th

2.143
0.749
0.502
3.365

5.382
4.269
2.607
12.40

1.337
0.313
0.253
1.993
90th

6.306
6.217
3.618 '
15.85

7.406
12.40
6.482
25.42

3.995
1.767
1.146
6.936
Max

10.66
125.1
37.72
173.5

10.66
125.1
37.72
173.5

7,854
13.93
6.550
23.87
       a Hg°  = Elemental Mercury
       b Hg  = Divalent Vapor-phase Mercury
       c Hg  = Particle-Bound/Mercury
5.2.4   Qualitative Description of Mercury Dry Deposition Results

       As described in the section on the RELMAP mercury model parameterizations, it was assumed
that Hg was not effectively dry deposited due to its high vapor pressure and very low water solubility
at normal atmospheric temperatures.  Therefore, only Hgz+ vapor and Hgp were dry deposited using
the base emission speciation, and only Hgp using the alternate emission speciation. The percentile
analysis of the simulated dry deposition using the base-case emission speciation profiles shown in
                                                    ,2+
Table 5-7 indicates the strong local dry deposition of Hgz+ vapor as
June 1996
5-25
SAB REVIEW DRAFT
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                                           Table 5-7
       Percentile Analysis of RELMAP Simulated Dry Deposition for the Continental U. S.
                            Using the Base-Case Emission Speciation
Variable
Full Area
Hg2+a dry dep. (ug/m2/yr)
Hgpb dry dep, (ug/m2/yr)
Total mercury (ug/m2/yr)
East of 90°W longitude
Hg2+a dry dep. (ug/m2/yr)
Hgpb dry dep. (ug/m2/yr)
Total mercury (ug/m2/yr)
West of 90°W longitude
Hg2+a dry dep. (ug/m2/yr)
Hgpb dry dep. (ug/m2/yr)
Total mercury (ug/m2/yr)
Min

0.113
0.002
0.117

0.434
0.017 '
0.451

0.113
0.002
0.117
10th

0.412
0.010
0.425

2.649
0.049
2.699

0.342
0.009
0.352
50th

1.641
0.035
1.669

6.263
0.104
6.373

0.923
0.024
0.948
90th

8.500
0.130
8.629

15.53
0.189
15.73

3.614
0.063
3.679
Max

153.5
0.749
154.2

153.5
0.749
154.2

29.85
0.236 -'
30.03
       a Hg"+ = Divalent Vapor-phase Mercury
       b Hg  = Particle-Bound/Mercury
parameterized in the RELMAP mercury model.  There is considerable uncertainty regarding the dry
deposition velocity of Hg2+ and in the extremely high local depositions indicated from the simulation.

       Figure 5-14 shows the simulated annual dry deposition totals for Hg"+ using the base emission
speciation.  Dry deposition of Hg2+ appears to occur primarily on the local scale, within one or two
grid cells from the source (40-80 km), much like the wet deposition. The magnitude of the dry
deposition of Hg2"1" is similar to that for wet deposition, with urban areas showing values in excess of
20 ug/m2.  As was the case for wet deposition, dry deposition of Hg2"1"  vapor in heavily populated
urban centers is very intense, exceeding 100 ug/m2 in the model grid cell containing New York City.
Again, it must be stressed that dry deposition of Hg2+ vapor is not well  understood. The simulation
used nitric  acid vapor data as a surrogate for Hg2"1" vapor based on similar water solubilities.  The
Agency has been unable to find observations of the dry deposition of Hg2+ vapor with which to
compare to the RELMAP simulation results.  Dry deposition rates for vapor-phase Hg have been
estimated from vertical eddy flux  calculations at a single site (Lindberg  et al., 1992), but these
calculations estimate the combined effects of both Hg" and Hg2"1" vapors. The relatively high solubility
and reactivity of Hg2+ compounds suggests that dry deposition of total vapor-phase mercury may  be
strongly driven by the fractional of Hg2+  in the  total vapor-phase mercury concentration.
June 1996
5-26
SAB REVIEW DRAFT
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        Figure 5-15 shows the simulated annual dry deposition totals for Hgp using the base emission
speciation. As described in Appendix D, the dry deposition velocity estimates for Hgp have been
made based on the assumption that the paniculate mass is concentrated around a 0.3 jam diameter size.
The patterns show less intense local dry deposition of Hgp than for Hg2+, but the dry deposition  still
appears to occur primarily within a few hundred km of the source areas.  This slower dry deposition
combined with relatively smaller quantities of Hgp emission result in maximum dry deposition values
of only around 0.5 ug/m2. In urban areas  where larger particle sizes are more prevalent, these
estimates of Hgp dry deposition are probably too low, but the RELMAP could treat only one particle
size.  Since the focus of this modeling  was on the regional scale, 0.3 urn wais chosen as the most
appropriate diameter size.

        Figures 5-16 and 5-17 show the simulated annual dry deposition for all forms of mercury
using the base-case and alternate emission speciations, respectively.   A comparison of these figures
clearly demonstrates the sensitivity of the modeling results to changes in the emission speciation
profiles. Dry deposition is not a major pathway for removal of the atmospheric mercury burden  when
the alternate emission speciation profiles are employed.  This result  also indicates that dry deposition
would be much less important if significant transfer of Hg2+ to Hgp is occurring through particle
adsorption or condensation.  Thus, it is very important that our understanding of the physical
transformations of Hg in the atmosphere be complete and  accurate.

5.2.5    Qualitative Description of Total Mercury Deposition Results

        Since both wet and dry deposition of mercury can affect human and ecosystem health, an
analysis of the  simulated total deposition of all forms of mercury has been performed for both the
base-case and  alternate emission speciations.  Table 5-8 shows a percentile  analysis of total deposition
of mercury in  all modeled forms. The  strong bias toward mercury deposition in the eastern U.S.  is
immediately obvious. Also obvious is  the order of magnitude difference between the 90th percentile
level and the maximum values in the nationwide and eastern U.S. analyses.  The extremely high
simulated deposition totals over heavily populated urban centers cannot be substantiated by
observations at this time. Due to the high degree of uncertainty regarding the emission speciations and
possible rapid  chemical and physical transformations immediately after emission, it is recommended
that these maximum simulated deposition values should be considered highly uncertain until further
research is conducted to reduce these uncertainties.

        Figure 5-18 shows the base-case total deposition of mercury to the Earth's surface from the
RELMAP simulation.  These results show deposition totals of over 20 ug/m2 throughout most of the
Northeast and  Ohio Valley regions and in  various urban areas nationwide, with values over 50 ug/m2
for the northeast corridor and at other major urban centers. Figure 5-19 shows the total mercury
deposition results using the alternate emission speciations.  For the alternate case, the areas with
simulated deposition greater than 20 ug/m2 are noticeably reduced,  and the  alternate case results  show
few areas with annual mercury deposition  totals over 50 |ag/m2. The sensitivity of the model
deposition results to uncertainties in the emission speciation profiles appears to be greatest in  areas of
high mercury deposition.
June 1996                                     5-28                        SAB REVIEW DRAFT
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                                          Table 5-8
     Percentile Analysis of RELMAP Simulated Total Depositions for the Continental U. S.
                    Using the Base-Case and Alternate Emission Speciations
Variable

Full Area
Base-Case
Alternate
East of 90°W
longitude

Base-Case
Alternate
West of 90°W
lohgitude

Base-Case
Alternate
Min
(ug/m2/yr)

0.538
0.122


1.717
1.256



0.538
0.122

10th
(Hg/m2/yr) •

1.470
0.922
»


9.837
8.252



1.251
0.800

50th
(|jg/m2/yr)

5.133
3.796

•\
18.88
14.65



2.941
2.303

90th
(|iig/nr/yr)

24.59
18.12


39.97
28.67



10.58
7.916

Max
(|ag/m'/yr)

327.6
151.0


327.6
151.0



49.21
23.35

53    General Data Interpretations of the RELMAP Modeling

       At this time there is significant uncertainty regarding the chemical arid physical forms of
emissions and their chemical and physical transformations in the atmosphere This long-range
modeling effort did not provide for a complete new model development and evaluation; the modeling
effort has relied heavily on the assumptions of Petersen et al.  (1995) regarding emission speciation and
chemical and physical pathways  for mercury depositioa  The model parameterizations for the
Lagrangian puff model were developed under the European Monitoring and Evaluation Programme
(EMEP). These mercury modeling results were compared to measurements of Hg° and Hgp air
concentration and  wet deposition in northern Europe.  The comparison showed their model results
agreed with measurements to within a factor of 2 in nearly all cases.  While the climate of northern
Europe may be quite different from that  of some locations in  North America, it has been  assumed that
the predominant chemical and physical mechanisms for mercury transport, transformation and
deposition should  be the same for both regions.

       The wet deposition results from  the RELMAP simulation of atmospheric mercury seem to
agree with actual measurements  within a factor of 2 in most cases. The RELMAP estimates of more
than 100 ug/m2 wet deposition using the meteorologic data for the year 1989 in the grid cell
containing New York City seems a bit too high, but there are very few measurements which can be
compared to these results. The RELMAP dry deposition results indicate that the importance of dry
versus wet deposition processes may be  dependent on the fraction of emitted mercury that eventually
becomes particle-bound before deposition.  Very few direct measurements of the dry deposition of
gaseous and paniculate Hg have been made to date.  Vertical concentration
June 1996
•5-32
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gradients and eddy flux correlations have been used to estimate the dry flux or' total gaseous mercury
by Lindberg et al. (1992), but no discrimination was made between Hg° and Hg  + forms.

       Many  of the measurement studies performed up until the 1980's are now suspected of having
been subject to laboratory contamination. It is only recently that, by employing ultra-clean laboratory
techniques, mercury measurement studies have been able to assess accurately atmospheric
concentrations and deposition quantities of mercury in near-background conditions.  Even now. it is
very difficult to obtain an accurate assessment of the chemical forms of mercury in typical ambient air
samples.  The  RELMAP  air concentration results seem quite plausible, with the vast majority of
atmospheric mercury estimated to be in the elemental vapor form,  but the precise concentrations of
Hg2"1" and Hgp cannot be  simulated with much confidence until a more complete understanding is
established of  all pertinent chemical and physical processes in the  atmosphere.

       There  are some limitations of the RELMAP and other Lagrangian puff models that may
negatively affect the accuracy of atmospheric  mercury modeling.  The simulated pollutant puff must
move as  an integral volume, and differences in wind direction or speed at various heights above the
surface are not treated. The pollutant puff is currently simulated with a predefined vertical top,
through which turbulent exchanges of air and  pollutants are set at  any arbitrary value.  For pollutants
such as Hg° that remain in the atmosphere for a long  time, significant transfer of mass between the
PEL and the rest of the atmosphere is inevitable.  These exchanges can be  attributed not only to
turbulent processes but also larger-scale vertical atmospheric motions, both  rising and sinking.  Finally,
Lagrangian puff models have no straightforward way to treat the horizontal boundary flux  of pollutant
into the model domain.  Hg° vapor is known to be transported in the atmosphere on a global scale, but
adequate methods are unavailable to model its transport from other parts of the earth into the model
domain.  The U.S. EPA is working to develop a general purpose air-quality model employing an
Eulerian  reference frame  which should prove more suitable for mercury transport and deposition
simulations; completion of this model is still a year or more away. A more rigorous discussion of
these and other scientific  caveats is provided in Appendix D.

5.4    Potential Impacts of Long Range  Transport

       In this section the results of the regional air modeling  are used in conjunction with the IEM2
model  and the hypothetical exposure scenarios to predict environmental concentrations and exposure
rates for  humans and piscivorous wildlife. The 50th and 90th percentiles of the mercury air
concentrations and annual deposition rates that are predicted by RELMAP for the western and eastern
halves  of the U.S. were used as inputs to the modeling. The potential impact of predicted atmospheric
mercury concentrations and deposition rates associated with areas of the eastern U.S. considered to be
remote from anthropogenic influences was also included in the long range transport  analysis.  See
Figure 1-2.

       The sites and the exposure scenarios utilized in the long range transport analysis have been
discussed in Chapter 4 of this Volume and are briefly discussed here.  The  IEM2 model is described
in Chapter 4 of this Volume and in Appendix  D; only  the inputs to and the results of the DEM2
modeling are described here.
June 1996                                     5-35                        SAB REVIEW DRAFT
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5.4.1    Description of Hypothetical Sites and Watersheds
        Two generic sites .-art considered:  a humid site east of 90 degrees west longitude, and a more
arid site west of 90 degrees west longitude (these are described in Appendix B). The primary
differences between the two sites as parameterized were the assumed erosion characteristics for the
watershed and the amount of dilution flow from the water body. The eastern site had generally
steeper terrain in the watershed than for the other site.  A circular drainage lake with a diameter of
1.78 km and average depth of 5 m, with a 2 cm benthic sediment depth was modeled at both sites.
The watershed area was 37.3 km2.

        In the long range transport  analysis both the eastern and western sites were assumed to be
impacted by mercury from only the RELMAP modeling.  The 50th and 90th percentiles of the
mercury air concentrations and annual deposition rates for the eastern and western halves of the U.S.
were used as inputs to the IEM2 modeling for both sites.  An additional eastern site with the  same
characteristics  as previously described was assumed to be remote and\>nly impacted by long range
transport.  The impacts of a separate atmospheric mercury concentration and annuah deposition rate
were modeled for this site.

5.4.2   Description of Hypothetical Exposure Scenarios

        For the analyses that were conducted for this report, three types of settings were considered:
rural (agricultural), lake, and urban. These three settings were selected because they encompass a wide
variety and each is expected to provide a "high-end" mercury concentration in  environmental  media of
concern for either human or wildlife species exposure (e.g.,  elevated mercury concentrations are
expected in the waters of the lake setting).

        Exposure to environmental  mercury is the result of mercury concentrations at specific exposure
points (e.g., ingested fish). For each setting in each location of the long range transport analysis,
individuals representing several specific subpopulations were considered.  For a more detailed
description of the parameter values chosen for the exposure  assessment see Appendix A.  Table 5-9
summarizes the hypothetical scenarios considered as well as the exposure  pathways considered in each
scenario.

        Table 5-10 shows  the default values for the scenario-independent parameters for both the child
and adult receptors, and Table 5-11 shows the default values for the scenario-dependent exposure
parameters.  The technical bases for these values are in Appendices A and B.

        Both high-end and average rural scenarios were evaluated.  The high-end scenario consisted of
a subsistence farmer and child who consumed elevated levels of locally-grown food products.  It was
assumed that each farm was located on approximately 10 acres.  The subsistence fanner was  assumed
to raise  livestock and to consume home-grown animal tissue and animal products, including chickens
and eggs as well as beef and dairy  cattle.  All chicken feed  was assumed to be derived from non-local
sources.  For bovine consumption of contaminated feed, 100% of the hay  arid corn used for feed was
assumed to be from the affected area.   It was also assumed that the subsistence farmer collected
rainwater in cisterns for drinking.  The average rural home gardener was assumed to consume garden
fruits and vegetables.  There was no consumption of locally-raised animals or locally-collected water.

        In the urban high end scenario, it was assumed that  the person had a small garden similar in
size to that of the average rural scenario. To address the fact that home-grown fruits and
June 1996                                     5-36                       SAB REVIEW DRAFT
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                                         Table 5-10
                 Default Values of Scenario-Independent Exposure Parameters
      All human consumption rates except for soil and water are reported as dry weight.
Parameter
Body weight (kg)
Exposure duration (years)
Inhalation rate (nrVday)
Vegetable consumption rates (g/kg BW/day)
Leafy vegetables
Grains and cereals
Legumes
Potatoes
Fruits
Fruiting vegetables
Animal Product Consumption rates (g/kg BW/day)
Beef (excluding liver)
Beef liver
Dairy
Pork
Poultry
Eggs
Lamb
Soil Ingestion rates (g/day)
Water ingestion rate (L/day)
Default Valuea
Adult
70
30
20

0.028
1.87
0.381
0.17
0.57
0.064

0.341
0.066
0.599
0.169
0.111
0.093
0.057
0.1
2
Child
17
18
16

0.008
3.77
0.666
0.274
0.223 -
0.12

0.553
0.025
2.04
0.236
0.214
0.073
0.061
Scenario-
dependent
1
 See Appendix A for details regarding these parameter values.
June 1996
5-38
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vegetables generally make up a smaller portion of the diet in urban areas, trie contact fractions were
based on weight ratios of home-grown to total fruits and vegetables consumed for city households.
These fractions can be up to 10 times smaller than the values for rural households, depending on food
plant type (see Table 5-11 and Appendix A). Exposure duration for inhalation was 24 hours per day.
Also considered as part of the high-end urban scenario was a pica child.  The average urban scenario
consisted of an adult who worked outside of the immediate area. The exposure duration for
inhalation, therefore, was only  16 hours a day compared to the 24 hours a day for the rural and high-
end urban scenarios.  The only other pathway considered for this scenario was ingestion of average
levels of soil.

        Three human fish consumption scenarios were associated with the hypothetical  lake:  (1) an
adult high-end fish consumer scenario, in which an individual was assumed to ingest large amounts  of
locally-caught fish as well as home-grown garden produce (plant ingestion parameters identical to the
rural home gardener scenario) and consume  drinking water from the affected lake;  (2) a child of a
high-end local fish consumer, assumed to ingest local  fish, garden produce, and soil as well as inhale
the air; and (3)  a recreational angler scenario, in which the exposure pathways evaluated were fish
ingestion, inhalation,  and soil ingestion. These consumption scenarios were thought to represent
identified fish-consuming subpopulations in the U.S.  No commercial distribution of locally caught fish
was assumed Fish consumption rates for the three fish consuming subpopulations were derived from
the Columbia River Inter-Tribal Fish Commission Report (1994) (See Table 5-12).
                                          Table 5-12
                          Fish Consumption Rates used in this Study
                        (Columbia River Inter-Tribal Commission, 1994)
Subpopulation
High-end Adult
High-end Child
Recreational Angler
Fish Consumption Rate (g/day)
60
20
30
       Five piscivorous species of birds and mammals are assumed to inhabit areas around the
hypothetical lakes and be exposed to mercury through consumption of fish from the lakes. The
piscivorous wildlife exposure scenarios are described in Chapter 4 of this Volume.  Table 5-13 lists
the assumed animal body weights, fish consumption rates and the trophic level of the fish consumed
(U.S. EPA, 1993).  Dermal, inhalation, and drinking water exposures for the wildlife species were not
modeled in this assessment.  Other food sources were also not considered in this assessment.
June 1996
5JAR RFVTFW DRAFT
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                                          Table 5-13
       Fish Consumption Rates for Piscivorous Birds and Mammals (from U.S. EPA, 1993)
Animal
Bald Eagle
Osprey
Kingfisher
River Otter
Mink
Body Weight
(kg)
4.6
1.5
0.15
7.4
.0.8
Total Ingestion
Rate (g/day)
500
300
75
1220
178
% of Diet
from Trophic
Level 3 Fish
74
100
100
80
t
90
% of Diet
from Trophic
Level 4 Fish
18
0
0
20
0
% of Diet
from Non-
aquatic
sources
8
0
0
0
10
5.4.3   Results of IEM2 Modeling in the Long Range Transport Analysis

        For each of the sites described above the IEM2 models  were run using the atmospheric
mercury concentrations and the annual deposition rates predicted by RELMAP as inputs. The
predicted mercury concentrations in soil, water and fish are presented in Table 5-14, and the predicted
intakes  are presented in subsequent tables.

        The predicted concentrations in soil, water and fish were in the range  of measured values
reported in Chapter 2 of this volume. The predicted soil  concentrations at the "two" eastern sites and
the western sites range from 5.8 to 88 ng/g. The predicted concentrations are the result of
                                          Table 5-14
    Predicted Media Concentrations using RELMAP Results Only in Conjunction with IEM2

Remote Site in East
Eastern Site
50th Percentile
90th Percentile
Western Site
50th Percentile
90th Percentile
Total Mercury
Air
Concentration
(ng/m3)
1.7e+00

1 .Te-i-00
1.8e+00

1.6e+00
1.7e+00
Total Mercury
Deposition
Rate
(Mg/m2/yr)
4.56-t-Ol

1.9e-f-01
4.0e+01

2.9e+00
l.le+01
Total Mercury
Unfilled Soil
Concentration
(ng/g)
8.8e+01

3.7e+01
7.8e+01

5.8e+00
2.1e+01
Total Mercury
Surface Water
Concentration
(ng/D
2.2e+00

9.1e-01
1.9e+00

1.7e-01
5.6e-01
Predicted Methylmercury
Fish Concentration (ug/g)
Trophic
Level 3
l.Oe-01

4.3e-02
9.0e-02

8.5e-03
2.9e-02
Trophic
Level 4
S.le-01

2.2e-01
4.5e-01

4.3e-02
1.4e-01
the modeled mercury deposition rates based on the RELMAP simulation that used meteorologic data
from the year 1989.  Sites subjected to higher deposition rates, such as the remote eastern site and the
June 1996
5-41
SAB REVIEW DRAFT
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90th percentile site in the east have higher deposition rates than the other sites.  The predicted soil
concentration in the 50th percentile eastern site was higher than the 90th percentile western site: again.
this is a direct result of deposition  estimated in the RELMAP simulation.

       The predicted concentrations  in the surface waters were primarily the result of transport from
the watershed  soils and direct deposition onto the water body.  The predicted mercury concentrations
in the water bodies, which range from 0.17 to 2.2 ng/L, were comparable to mercury concentrations
measured in surface waters by several authors; these are reviewed in Chapter 2 of this Volume.
Again, as evidenced in Table 5-14, the higher the deposition rate  the greater the predicted surface
water concentrations.  Similarly, the concentration of dissolved mercury in the water body depends in
this modeling effort largely on the  total amount of mercury  deposited.  As a result, the predicted
methylmercury concentrations in fish, which are the product of the total dissolved mercury
concentration in the water body and the methylmercury bioaccumulation factor, are heavily influenced
by the total annual deposition rate.  The highest concentrations are predicted in trophic level 4 fish in
the remote east and 90th percentile eastern sites.  The predicted methylmercury concentrations in fish,
which range from approximately 0.03 ug/g to 0.5 ug/g,  are clearly in the range of measured
concentrations which extends up to approximately 9 ug/g. The average fish methylmercury
concentrations reported in Bahnick et al.,  1994 and Lowe et al., 1985 are 0.26 and 0.11 ug/g,
respectively.  Other studies have shown higher concentrations in fish in the eastern (particularly the
northeastern) U.S. (Simonin et al.,  1994; Mills et al,  1994 and NJDEPE, 1994).

       The mercury concentrations predicted in soils, plants and  animal products describe a range of
mercury concentrations in items potentially ingested by humans based on the results of the RELMAP
model. The mercury intakes presented in Chapter 3 provide a basis of comparison. The results
predicted by the IEM2 model for human ingestion of mercury are comparable to estimates presented in
recent reports, given the differences in consumption rates. No data exist to compare predicted wildlife
mercury exposures.  Note that the consumption rates and contact  fractions of the various  hypothetical
scenarios are not designed to represent the general population, and all pathways not considered were
assumed to result in no exposure to mercury.

       Using  the predicted air concentrations at the Western and two Eastern locations, the inhalation
route was never predicted to be the dominant pathway of total mercury  exposure, except  for the urban
average scenario. Because this  scenario assumes no plant or animal consumption and exposure
through dermal routes was not considered in this Report, the only mercury exposure routes are soil
ingestion and inhalation.  Low predicted concentrations in soil and low  soil ingestion rates result in  the
prevalence of the inhalation route.  The insignificance of exposure through the inhalation route when
compared to ingestion routes was described previously by the WHO (WHO, 1990).

       The results for the hypothetical adult rural subsistence fanner scenario are comparable to the
general population exposure estimates presented in Chapter  3 since most sources of food for
consumption are assumed to be derived from a mercury-impacted area.  For the Remote Site analysis,
"impacted sources" represent mercury concentrations in foods that may be comparable to
concentrations in commercial foodstuffs.  For the scenario, daily mercury ingestion levels for all
locations were about 3.0 ug/day for adults, assuming a bodyweight of 70 kg.  As noted in Chapter 4,
most (approximately 90%) of this mercury was predicted to be the divalent species. For the  •
agricultural scenarios, predicted mercury intake was dominated by consumption of plant products, and
the air-to-plant pathway is the primary pathway that plants are predicted to take up mercury from the
environment.  As a result there  is little difference between the results across sites because the air
concentrations are similar.
June 1996                                     5-42                        SAB REVIEW DRAFT
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        The hypothetical high-end fisher has a high fish consumption rate but has lower contact
 fractions for food ingestion than hypothetical  subsistence farmers.  For purposes of this comparison to
 previous general population exposure estimates, the mercury intake from non-fish foodstuffs was  .
 underestimated.  Total mercury ingestion rates for the high-end fishers across the three eastern
 locations vary from about  15 to 35 ug/day.  These  estimates are two to three times higher than the
 estimates presented in Chapter 3 for the general population.  Predicted mercury ingestion rates for the
 hypothetical recreational angler range from  about 6.5-15 ug/day.  The difference between the total
 mercury ingestion rates predicted for the hypothetical recreational angler and the hypothetical high-end
 fisher is primarily due to the lower fish ingestion rate of the hypothetical recreational angler.

 5.4.4   Summary of Potential Impacts of Long Range Transport

        Air Modeling

        The predicted air concentrations used for the two sites are at or slightly above the air
 concentration of 1.6 ng/m3 used as a representative air concentration in Chapter 4. The predicted
 deposition rates are higher for the eastern site, due to a combination of different climate conditions and
 the distribution  of mercury emissions sources in the two geographic areas.

        Environmental Media Modeling

        The predicted soil  and  surface water body concentrations of mercury were most heavily
 influenced by the predicted total mercury deposition rate.  The predicted soil and surface water body
 mercury concentrations were all within the range of measured mercury concentrations  for these media-.
differences between the hypothetical watersheds of the eastern and western sites account for the
 variation in water body loading from mercury deposited onto the watershed.

        Biota Modeling

        The mercury concentrations in green plants were the result of direct deposition and  air-to-plant
 transfer onto exposed foliar surfaces, as well as soil-to-plant transfer.   As modeled in this assessment,
 small amounts of mercury  in green plants were predicted to result from direct deposition and
 soil-to-plant transfer.   Most of the mercury predicted to occur in green plants  was the result of
 air-to-plant transfer for those types of green plants  for which this route of transfer was  deemed •
 appropriate.  The predicted concentrations and speciation of mercury in green plants are within the
 range of measured concentrations.  The predicted speciation of mercury in green plants was consistent
 with the reported values.

        The mercury  concentrations in all animal products except fish  were predicted to be  low.  This
 was the result of generally low concentrations in plants and small plant-to-animal and  soil-to-animal
 biotransfer factors. Few data are available for comparison.

        Mercury concentrations in fish were predicted to be the highest of the biota considered.
 Mercury concentrations in  fish were the product of the bioaccumulation factor and the dissolved
 concentration of mercury in surface water.  The predicted mercury concentrations in fish were within
 the range of reported values. There is a great deal of uncertainty and variability  associated  with the
 uptake of mercury  by fish.
June 1996                                     5-43                        SAB REVIEW DRAFT
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       Human and Wildlife Exposure Modeling

       Human exposure to anthropogenic mercury was predicted to be dominated by indirect routes
of exposure except for the hypothetical average urban dweller as indicated in Table 5-15 and 5-16.
This individual was assumed to be exposed to emitted mercury from inhalation and soil ingestion only.
For all other exposure scenarios, except those including fish consumption, the divalent species was
predicted to be the primary species to which humans were exposed.

       Those hypothetical humans who were assumed to consume fish had the highest exposures.
This was a result of the bioaccumulation factor into fish.  Methylmercury was  the primary species to
which these individuals were exposed.  On a per body weight basis, children were predicted to be
more exposed than adults.

       The animals with the highest fish ingestion rate (per body weight) generally had the highest
methylmercury intakes, except in cases where a piscivorous species was assumed to prey more heavily
on trophic level 4 fish as indicated in Table 5-17.
June 1996                                    5-44                       SAB REVIEW DRAFT
-------
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6.      LOCAL IMPACT ANALYSIS

6.1     Description of Approach

        The results of two separate analyses are presented in Chapter 6. The first analysis shows the
predictions of mercury fate, transport, and exposure that result from individual emission sources (see
Figure 1-3).  These model predictions are confined to the 50 km area around an individual
anthropogenic mercury emission source, which is defined here to be the local area around the
emissions source.  In the second analysis the regional mercury modeling results RELMAP from
Chapter 5 are combined with the results of the first analysis and the materials presented in Appendix
H to estimate complete mercury exposure.  See Figure 1-4.

6.1.1    Rationale and Utility of Model Plant Approach

        Mercury is generally present as a low-level  contaminant in combustion materials (e.g., coal or
municipal solid waste) and industrial material  (for more information on mercury in emissions refer to
Volume II of this Report).  During combustion and high-temperature industrial processes, mercury is
volatilized from these materials.  Because of its high volatility, it is difficult to remove mercury from
the post-combustion air stream.  As a consequence, mercury is released to the atmosphere.  As noted
previously, anthropogenic mercury emissions are not the only source of mercury to the atmosphere.
Mercury may be introduced into the atmosphere through volatilization from natural sources such as
lakes and soils. Consequently, it is difficult to trace the source(s) of mercury concentrations in
environmental media and biota.  For this reason it is also difficult to  gain an understanding of
contribution to those concentrations.

       For the purposes of the analysis in  this Chapter, exposure to mercury is defined as chemical
contact with  the outer boundary of an organism (also called a receptor). An organism's contact with
mercury may occur through several different exposure routes including dermal, inhalation, and oral.
Assessment of mercury exposure is complicated by  the physical and chemical properties of this
element. These include, the different physical  forms of mercury which  are manifested in the
environment, the different oxidative states it exhibits, and the duality of its environmental behavior as
both an inorganic and an organic compound.  In addition, the uncertain accuracy of analytical
techniques, particularly at low environmental concentrations, and problems with contamination during
environmental sampling further complicate  the assessment of mercury exposure.

       For this assessment it was not possible to model the emission impact of every mercury
emission source in each selected industrial  and combustion class.  Consequently, the actual  mercury
emission data and  facility characteristics for any specific source were not modeled. Instead, a model
plant approach, as  described in Appendix F, was utilized to develop facilities which represent actual
sources.  Model plants were developed to represent  six source  categories; namely municipal waste
combustors, coal and oil-fired boilers, medical waste incinerators, chlor-alkali plants,  primary copper
smelters and  primary lead smelters. The model plants were designed to characterize the mercury
emission rates as well as the atmospheric release processes exhibited by actual facilities in the source
class.  The modeled facilities were not designed to exhibit extreme sources (e.g., the facility with the
highest mercury emission rate) but rather to serve as a representative of the industrial/combustion
source class.

       This  assessment took as its starting point the results of measured mercury emissions from
selected anthropogenic sources.  Using a series of fate, transport and exposure models and hypothetical
constructs, mercury concentrations in environmental media, pertinent biota and ultimately mercury

June 1996'                                     6-1 .                       SAB REVIEW DRAFT
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contact with human and wildlife receptors were predicted. A.n effort was made to estimate the amount
of receptor contact with mercury as well as the oxidative state and form of mercury contacted. No
attempt was made to estimate an internal dose for either wildlife or human receptors.

       In taking the model plant approach, it was realized that there would be a great deal of
uncertainty surrounding the predicted fate  and transport of mercury as well as the ultimate estimates of
exposure. The uncertainty can be divided into modeling uncertainty arid parameter uncertainty.
Parameter uncertainty can be further subdivided into uncertainty and variability depending on the level
to which a particular model parameter is understood.  A limited quantitative analysis of uncertainty is
presented.  It is also hoped that the direction  of future research can be influenced toward reducing the
identified uncertainties which significantly impact key results.

6.1.2   Phase and Oxidation State of Emitted Mercury

       The literature describes several forms of mercury detected in the emissions from the  selected
sources.  Primarily, these include elemental mercury (Hg°) and inorganic mercuric (Hg2+). Generally,
only total mercury has been measured in emission analyses.  The reports of methylmercury in
emissions are imprecise.  It is believed that, if methylmercury is emitted from industrial  processes and
combustion sources, the quantities emitted are much smaller than emissions of Hg° and Hg2"*".  Only
Hg° and  Hg2+ were considered in this air dispersion modeling.

       The two types of mercury species  considered in the emissions are  expected to behave quite
differently once emitted from the stack. Hg°, due to its high vapor pressure and  low water solubility,
is not expected to deposit close to the facility.  In contrast, Hg2+, because of differences in these
properties, is expected to deposit in greater quantities closer  to the emission sources.

       At the point of stack emission and during atmospheric transport, the contaminant is partitioned
between two physical phases:  vapor and particle-bound.  The  mechanisms of transport of these two
phases are quite  different.  Particle-bound contaminants can be removed from the atmosphere by both
wet deposition (precipitation scavenging) and dry deposition (gravitational settling, Brownian
diffusion).  Vapor phase contaminants may also be depleted  by these processes, although historically
their main impacts were considered to be through absorption into plant tissues  (air-to-leaf transfer) and
human exposure occurred through inhalation.

       For the present analysis, the vapor/particle  (V/P) ratio in the local atmosphere was assumed to
be equal  to the V/P ratio as it would exist in  stack emissions.  It is recognized that this is a
simplification of reality, as the ratio when emitted from the stack is likely to change as the distance
from the stack increases.  The air concentration used for inhalation was taken as  the sum of  the vapor
and particle air concentrations.

       The particle size distribution may differ from one combustion process to another, depending on
the type of furnace  and design of combustion chamber, composition of feed/fuel, paniculate  matter
removal efficiency and design of air pollution control  equipment, and amount of air in excess of
stoichiometric amounts that is used to sustain the temperature of combustion.   The particle size
distribution used is  an estimate of the distribution within an  ambient air aerosol mass and not at stack
tip. Based on this assumption,  an aerosol particle distribution based on data, collected by Whitby
(1978) was used. This distribution was split  between  two modes: accumulation and coarse particles.
The geometric mean diameter of several hundred measurements indicates that the accumulation mode
dominates particle size, and a representative particle diameter for this mode is  0.3 microns.   The coarse
particles  are formed largely from mechanical  processes that suspend dust,  sea spray and soil  particles

June 1996                            .          6-2                         SAB REVIEW DRAFT
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in the air.  A representative diameter for coarse panicles is 5.7 microns. The fraction of particle
emissions assigned to each particle class was approximated based on the determination of the density
of surface area of each representative particle size relative to total surface area of the aerosol  mass.
Using this method, approximately 93% and 1% of the total surface area was estimated to be in the 0.3
and 5.7 micron diameter particles, respectively.
                                            Table 6-1
                       Representative Particle Sizes and Size Distribution
                      Assumed for Divalent Mercury Paniculate Emissions
Representative Particle Size
(microns)*
0.3
5.7
Assumed Fraction of
Particle Emissions in Size
Category
0.93
0.07
                       * These values are based on the geometric means of aerosol particle distribution measurements
                       as described in Whitby (1978).
The speciation estimates for the model plants were made from thermal-chemical modeling of mercury
compounds in flue gas, from the interpretation of bench and pilot scale combustor experiments and
from interpretation of available field test results.  The amount of uncertainty surrounding the emission
rates data vary for each source.  There was also uncertainty with respect to the species of mercury
emitted.

       Although the speciation may change with distance from the local source, for this analysis  it
was assumed that  there were no plume reactions  that significantly modified the speciation at the local
source. Because of the differences in deposition  characteristics of the two forms of mercury
considered, the assumption of no plume chemistry was  a particularly  important source of uncertainty.

6.1.3  Modeling  the Deposition of Mercury

       Once emitted from a source, the mercury may be deposited to the ground via two main
processes:  wet and dry deposition. Wet deposition refers  to the mass transfer of dissolved gaseous  or
suspended  particulate mercury species from the atmosphere to the earth's surface by precipitation,
while dry deposition refers to such mass transfer  in the absence of precipitation.

       The deposition properties of the two species of mercury addressed in stack emissions,
elemental and divalent mercury, are considered to be quite different.  Due to its higher solubility,
divalent mercury vapor was thought to deposit much more rapidly than elemental mercury.  At this
time, however, no conclusive data exist to support accurate quantification of the deposition rate of
divalent mercury vapor.  In this analysis, nitric acid vapor  was used as a surrogate  for Hg2"1" vapor
based on their similar solubility in water.  Whether a pollutant is in the vapor form or particle-bound
is also important for estimating deposition, and each is  treated separately.
June 1996
6-3
SAB REVIEW DRAFT
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        Dry deposition was estimated by multiplying the predicted air concentration at ground level by
a deposition velocity. For particles, the dry deposition velocity was estimated using the CARB
algorithms (CARB 1986) that represent empirical relationships for transfer resistances as a function of
particle size, density, surface area, and friction velocity.  For the  vapor phase fraction, the dry
deposition velocities  were specified for each atmospheric stability class.  (Atmospheric stability is a
measure of the amount of turbulance in the atmosphere: stable conditions inhibit dispersion and
unstable conditions enhance dispersion.  See Appendix D.)  For this analysis, elemental mercury vapor
was assumed to not dry deposit or to deposit at negligible rates.  Divalent mercury vapor was assumed
to dry deposit in a manner similar to nitric acid, for which deposition velocities were  available (See
Appendix D for more information). For stability classes A-C, a dry deposition velocity of 1 cm/s was
assumed for divalent mercury vapor,  while for classes D-F a dry  deposition velocity of 0.3 cm/s was
assumed.  The lower value was assumed for classes D-F is made because it has been  observed that dry
deposition is lower during nighttime conditions, and classes D-F  occur predominantly at  night (N.B.
Classes A-C by definition can occur only during -daytime conditions).

        Wet deposition was estimated according to the method of Slinn (1984) and later  modified by
PEI and Cramer (1986).  The wet deposition rate was characterized by a scavenging coefficient that
can depend on precipitation intensity and particle size.  For particles, the scavenging coefficients  were
from PEI  and  Cramer (1986).  For the vapor phase fraction, a scavenging coefficient was also used,
but it was calculated using estimates  for the washout ratio, which is the ratio of the concentration of
the chemical in surface-level precipitation to the concentration in surface-level air.  Because of its
higher  solubility, divalent mercury vapor was assumed to be washed out at higher rates than elemental
mercury vapor. The washout ratio for divalent mercury vapor was  selected based on  an  assumed
similarity  between scavenging for divalent mercury and gaseous nitric acid. This is based" on Petersen
(1995),  and the value used for the washout ratio for divalent vapor  was 1.6xl06 (see Appendix D of
this volume for more details).

6.1.4    Development and Description of Model Plants

        Model plants representing six source classes were developed to represent a range of mercury
emissions sources. The source categories were selected for the exposure assessment based on their
estimated  annual mercury emissions as a class or their potential to be localized point sources of
concern. The  categories selected were these:

        •       municipal waste  combustors (MWCs),
        •       medical waste incinerators (MWIs),
        •       utility boilers,
        •       chlor-alkali plants (CAP),
        •       primary copper smelters (PCS), and
        •       primary lead smelters (PLS).

        Parameters for each model plant were selected after evaluation of the characteristics of a given
source category and  current knowledge of mercury emissions from  that source category.  Important
variables for the mercury risk assessment included mercury emission rates, mercury speciation and
mercury transport/deposition rates. Important model plant parameters included stack height, stack
diameter, stack volumetric flow rate, stack gas temperature, plant capacity factor (relative average
operating hours per year), stack mercury concentration, and mercury speciation. Emission estimates
were assumed to represent typical emission levels emitted from existing sources. Table 6-2 shows the
process parameters assumed for each model plant considered in this analysis (for details  regarding
these values, see Appendix F).

June 1996                                     6-4                        SAB REVIEW DRAFT
-------
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6.1.5   Hypothetical Locations of Model Plants

        There are a variety of geographic aspects that can influence the impacts of mercury emissions
from an anthropogenic source.  These aspects include factors that affect the environmental chemistry
of a pollutant and the physics of plume dispersion.  Environmental  chemistry can include factors such
as the amount of wet deposition in a given area.  Factors atfecting  plume dispersion include terrain,
wind direction and average wind speed.

        Because wet deposition may be an important factor leading to mercury exposures, especially
for the more soluble species emitted, the meteorology of a location was used as a selection  criterion.
Two different types of meteorology were deemed necessary to characterize the environmental  fate and
transport of mercury: an arid/semi-arid site and a humid site.  The humidity of an area was based on
total'yearly rainfall.  (See Appendix B).
                   •\
        Terrain features refer to the variability of the receptor height with respect to a local source.
Broadly speaking, there were two main types of terrain used in the modeling:  simple, and complex.
Simple  terrain is defined as a study area that is relatively level and well below stack top (rather, the
effective stack height).  Complex terrain referred to terrain that is not simple, such as source located in
a valley or a source located near a hill.  This included receptors that are above or below the top of the
stack of the source.  Complex terrain can effect concentrations, plume trajectory, and deposition. Due
to the complicated nature of plume flow in complex terrain, it is  probably not possible to predict
impacts in complex terrain as accurately as for simple terrain (for a description of the air model used
and how it addressed complex terrain, see Appendix D). In view of the wide range of uncertainty
inherent in accurately modeling the deposition of the mercury species considered, the impacts  posed by
complex terrain were not incorporated in the local scale analysis. However, a limited exercise
investigating the extent consideration of complex terrain may affect the air modeling results was
performed.

6.1.6   Hypothetical Exposure Scenarios and Location of Receptors Relative to Local Source

        Three types local plant settings were utilized:  urban,  rural  and lacustrine.  These have been
described in section 4.2.2.

        There are conceivably an infinite number of possible receptor locations (relative to the source)
which could have been used for this analysis.  For the purpose of this analysis, a watershed/water body
configuration was assumed to be located at 2.5 km, 10 km, and 25  km from the local- source,  as shown
in Figure 6-1.

        Area-averaged deposition rates and air concentrations were computed for both the water body
and watershed. The watershed value was used to estimate soil and vegetation concentrations.

6.2     Results of Local Scale Modeling

        In this section the possible effects  of a local source on receptors within 50 km are considered.
The air  modeling alone is one of the most uncertain and controversial aspects of the entire analysis.
There are at present no validated models for estimating the deposition of mercury close to an
anthropogenic source.

        For each hypothetical model plant, COMPDEP (the local atmospheric dispersion and
deposition model used in this assessment)  was run using weather data from 1989, the same year used

June 1996 .                                    6-6                         SAB REVIEW DRAFT
-------
                                           Figure 6-1
       Configuration of Hypothetical Water Body and Watershed Relative to Local Source
                          Local
                          Source
               Center of lake
               at 2.5 km, 10km,
               or 25 km from source
                         Prevailing Downwind Direction
                                                                        Watershed
for the RELMAP analysis. The predicted values for air concentration and deposition rate were then
used as inputs for the IEM2 model.  Separate area-averaged values were used for both the water body
and watershed. These values were assumed to be representative for 30 years, the assumed typical
lifetime of a facility.  During this 30 year period, the mercury concentration in soil was allowed to
build-up, taking into account basic loss processes such as leaching, runoff and erosion.  The calculated
values at the end of the 30 year period were then used as input to the water portion of the IEM2
model, which calculated steady-state water concentrations based on the 30 year values.  The only
difference between the results presented here and the results in Section 4.3 are the air concentrations
and deposition rates used.

6.2.1    Air Concentrations

        In analyzing the air concentrations predicted by the COMPDEP model, it is important to
observe that in a typical year the predicted air concentration due to the local source at any receptor is
zero a rather substantial fraction of the time.  There  are two basic reasons for this.  First,  in order for
there to be a non-zero air concentration for a given hour the receptor must be in the downwind
direction.  This means that the wind  must be blowing in a direction within 90 degrees of the receptor
itself.  For most sites this only occurs about 50% of the time for the direction with the highest
frequency.   Second, even if the receptor is downwind, the predicted air concentration will be
significant  only if the wind is blowing in a direction within about 10 degrees of the receptor's
direction relative to the facility.  For most sites this occurs for the prevailing downwind direction only
about 10 to 15 percent of the time. Because the air  concentrations are averaged over the  year, this
results in (usually) low average air concentrations.

        Figures 6-2 and 6-3 show the predicted air concentrations  (area-averaged over the watershed)
for all facilities for  both the Eastern  and Western site.  The results are virtually identical for both sites,
and none of the predicted air concentrations exceed 4 ng/m3.
June 1996                                      6-7                         SAB REVIEW DRAFT
-------
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        The differences in predicted air concentrations across source classes depend primarily on three
key parameters: the total mercury emission rate, the stack height and the exit velocity of the plume
from the source.  The sensitivity of the air model to the emission rate is to be expected because the
predicted air concentrations  are linear with the total mercury emission rate, and the model plants are
assumed to have a wide range of emission rates (from 2 kg/yr up to over 1330 kg/yr; see Table 6-6).
Both the stack height and exit velocity were used in calculating the effective stack  height, which is the
height  to which the plume rises  from the stack top.  The importance of the effective stack height on
air concentrations is well  known and is demonstrated in this assessment by the predicted air
concentrations for the chlor-alkali plant, which clearly dominates the values as a whole.  See Figures
6-2 and 6-3.  This is due  to an assumed combination of a low stack height (10 feet) and slow stack
gas exit velocity (0.1 m/s) (this results in a low effective stack height),  and a comparatively high
assumed total mercury emissions rate of about 280 kg/yr.   The low stack parameters result in predicted
low plumes  that are not as vertically dispersed at the receptor  when compared with the facilities with
higher  stacks, thereby enhancing air concentrations.
                              \
        In general, the predicted average concentrations of atmosphere mercury were quite low from
the model plants. The only source class for which significantly elevated air concentrations were
predicted is  the chlor-alkali  plant.  This is due to a very low stack height coupled with a high assumed
mercury emission rate.  The low stack height resulted in predicted plumes that were close to the
receptors considered, and so there was less predicted dispersion of the plume compared to the other
facilities.

6.2.2    Deposition  Rates

        In contrast to the  predicted air concentrations, the annual deposition rates are cumulative; they
represent the sum of any deposition that occurs during the year, and hence are not affected by long
periods of little deposition.  Furthermore, the COMPDEP  model predicts that significant deposition
events  occur infrequently, and it is these relatively rare events that are responsible for  the majority of
the annual deposition rate.

        Because dry deposition is calculated by multiplying the predicted air concentration for the hour
by the  deposition velocity, significant dry deposition events only occur  when, for the reasons discussed
above, there is  a "spike" of  predicted high  air concentration for a given hour.  Annual  dry deposition
tended to be dominated by these peak values when the wind is blowing within a few degrees of the
receptor's direction.

        For the Eastern site  which had a higher rate of precipitation than the Western site, wet
deposition can dominate the total deposition for receptors  close to the source. Single wet deposition
events  were predicted to deposit 300 times more mercury  than a high dry deposition event.   These
high wet deposition events based on the modeling are even rarer than elevated dry  deposition events
because not only must the wind direction be within a few degrees of the receptor's direction, but
precipitation must be occurring as well.

        Table 6-3 shows the deposition rate parameters for the emitted pollutants addressed in this
study.
June 1996                                      6-10                        SAB REVIEW DRAFT
-------
                                             Table 6-3
       Comparison of Assumed Deposition Parameters for Elemental and Divalent Mercury
Parameter
Dry Deposition Velocity (cm/s)
Washout Ratio (unitless)
Wet Deposition Scavenging
coefficient (/s)a
Elemental
Mercury Vapor
0
i.eoxio4
3xlO'6 to 1x10" 5
Divalent
Mercury Vapor
0.3 - 1.0
i.eoxio6
3xlO'4 to IxlO'3
Divalent
Mercury Particulate
0.2 - 1.4b
NA
2.2xlO"4 (light precipitation) to
1.46x10 (heavy precipitation)
a For elemental and divalent vapor, this is calculated by L = W P / L, where W is the washout ratio, P is the representative
precipitation intensity for the hour, and L is the predicted mixing height for the hour. Due to the dependence on mixing
height, the upper end values of the ranges shown routinely occur even for light precipitation.
b Based on particle density of 1.8 g/cm3, particle diameter of 2 |om, surface roughness of 0.3 m, and ambient air temperature
of 295 K.
        Although such wet deposition events may be rare for a fixed receptor, anytime precipitation
occurs a significant deposition event will occur in the downwind direction for that hour.  Because of
this, the wet deposition rates may be more uniform with respect to direction than one may expect.

        In general, only the divalent form of mercury is predicted to have significant deposition rates.
This is  because the assumed atmospheric removal (wet and dry deposition) of divalent mercury is
significantly larger for divalent mercury than for elemental mercury.  For dry deposition of divalent
vapor, the deposition velocities were either 0.3 cm/s or  1 cm/s, depending on the stability class for a
given hour, while it was assumed that elemental mercury vapor does not dry deposit.  Although wet
deposition of elemental mercury  vapor is addressed, the difference in solubilities of the two forms of
mercury resulted in the divalent  vapor being predicted to wet deposit at a higher rate than elemental
vapor.

        Table 6-4 shows a breakdown of the total deposition rate in terms of the amount that was
predicted to occur via wet deposition and fraction of total deposition that is paniculate.

        There are several general features noted here. With the current assumptions regarding mercury
deposition and receptor terrain, wet deposition dominated the predicted deposition rates. For most of
the facilities, wet deposition accounted for most of the total deposition, and the contribution of wet
deposition decreases with distance from the source.  As expected, wet deposition played a larger role
at the more humid site in the east.

        The predicted dry deposition rates depend ultimately on the predicted air concentrations and
the speciation of the mercury in  the local atmosphere.  For this reason, dry deposition accounted for
most of the total deposition for the facility with the highest predicted air concentrations, the chlor-
alkali plant.  In complex terrain,  dry deposition can play a larger but uncertain role than in the results
presented here.  This  is discussed in more detail below.

        Although for  some facilities particulates comprised more than 50% of the total divalent
emissions, it played a smaller role  in terms of total  local deposition.  This indicates the faster removal
from the atmosphere of the vapor form.
June 1996
6-11
SAB REVIEW DRAFT
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        Figures 6-4 and 6-5 show the predicted total mercury deposition rates for all facilities at both
sites.  Although there is uncertainty in the deposition rates, especially near the source, there are no
data currently available to support or refute the predicted deposition rates at 2.5 km.

6.2.3   Media Concentrations

        Soil.  The predicted soil concentrations are directly proportional to total deposition.  A
comparison between Figure 6-4 (total deposition) and Figures 6-6 and 6-7 (soil concentrations)  does
not show any differences, save for a change of scale and units.  Mercury that deposits was predicted to
speciate as 98% Hg2+ and 2% methylmercury  (see appendix A) and to  mix evenly  throughout the
unfilled (1 cm) or tilled (20 cm) depths. The predicted tilled soil concentration was therefore, 20 times
less  than the untilled concentration. Any factor changing total deposition behavior has the identical
effect on soil concentrations.

        Green Plants.  Both the soil and air concentrations (root and foliar uptake)  were used to
calculate the mercury concentrations in grains, beans, fruits, and  fruiting vegetables.  Only atmospheric
uptake was assumed to occur for leafy vegetables and only root uptake for potatoes and root
vegetables.  Additionally, unprotected plants (non-grains, non-rooting plants) we  assumed to
accumulate mercury though direct deposition onto the edible portion of the plant.

        Shown in Figures 6-8 and 6-9 are the predicted mercury  concentrations in leafy vegetables,
and  shown in Figures 6-10 and 6-11 are values for fruits.  Leafy vegetables accumulate slightly more
mercury than fruiting vegetables; most of the difference arises because  of the. higher interception
fraction of leafy vegetables (resulting in more  efficient direct deposition:  Appendix A). Since  air
uptake dominated plant mercury accumulation  for both types of plants,  the trends in these figures
follow those for predicted air concentrations very closely.

        Mercury concentrations in grains and legumes were much lower than  the concentrations
predicted in leafy  vegetables but followed the same overall trends among model plants and distance
from the source.  The lower concentrations were a result of smaller interception fractions for grains
and  legumes and the reduction in the air-to-plant biotransfer factor that accounted for lower
concentrations in the edible parts of grains and legumes.  Fruits were modeled with the same uptake
parameters as fruiting vegetables, and had the same mercury concentrations.  Potatoes and rooting
vegetables had lower mercury concentrations than plant types which also accumulated mercury  from
the air, due to the low tilled soil concentrations.  For all plants, Hg2+ always accounted for the  bulk of
the plant mercury.  (See Chapter 4; section 4.3.2.2.)

        Beef. As  with the plant concentrations, the predicted beef concentrations (Figures 6-12  and
6-13) were rather low, mainly due to a low transfer coefficient (see Appendix A).  Beef were assumed
to get mercury from both vegetation and soil they consume.

        Surface Water.  Surface water concentrations, as with soil concentrations, followed total
deposition patterns; however, a comparison between total deposition and surface  water concentrations
indicates noticeable differences in trends. The deposition rate was the most critical factor in
determining surface water concentrations.

       Fish.  The fish concentrations were estimated by multiplying  the predicted  total mercury
dissolved surface water concentration by the trophic-level-specific bioaccumulation factor (BAP). The
bioaccumulation factor related total dissolved mercury in water to methylmercury concentrations in
fish tissue.  There is considerable uncertainty in the BAF, but the perdicted concentrations of mercury

June 1996                                      6-13                        SAB REVIEW DRAFT
-------
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-------
                                      Table 6-11
   Predicted Intakes for Wildlife Receptors for the Eastern Site based on COMPDEP Results
Percent of surface water concentration
dissolved 70 5%
Facility

Mercury Surface Water
Concentration (ng/1)
Methylmercury Fish
Concentrations (ug/g)
Trophic 3
Trophic 4
25 km











Large Municipal Waste Combustor
Small Municipal Waste Combustor
Continuous Medical Waste Incinerator
Intermittent MWI
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkah plant
Primary Copper Smelter
Primary Lead Smelter
1 9E+01
2.9E+00
2.3E+00
8 OE-02
5.7E-01
45E-01
8 1E-02
1 4E-02
9.0E+00
1 3E+00
7.2E+00
8.7E-01
1 4E-01
1.1E-01
3 7E-03
2.7E-02
2 1E-02
3.8E-03
6 6E-04
4.2E-01
6 1E-02
3.4E-01
4.4E+00
6 9E-01
5 4E-01
1.9E-02
1.3E-01
1.1E-01
1.9E-02
3 3E-03
2.1E+00
3 1E-01
1 7E+00
10 tan











Large Municipal Waste Combustor
Small Municipal Waste Combustor
Continuous Medical Waste Incinerator
Intermittent MWI
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkali plant
Primary Copper Smelter
Primary Lead Smelter
2.6E+00
4 5E-01
3.6E-01
1 2E-02
1.1E-01
7 3E-02
1.3E-02
2 1E-03
9.7E-01
2 1E-01
1.2E+00
1 2E-01
2.1E-02
1.7E-02
5.4E-04
5.0E-03
3 4E-03
5.9E-04
9.8E-05
4.5E-02
9 8E-03
5.5E-02
6 2E-01
1 1E-01
8 6E-02
2.8E-03
2.5E-02
1.7E-02
3.0E-03
4.9E-04
2.3E-01
4.9E-02
2.8E-01
25 km











Large Municipal Waste Combustor
Small Municipal Waste Combustor
Continuous Medical Waste Incinerator
Intermittent MWI
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkali plant
Primary Copper Smelter
Primary Lead Smelter
6 OE-01
1 2E-01
9 OE-02
2.8E-03
2.1E-02
1.6E-02
3.0E-03
4.7E-04
2.1E-01
5.1E-02
2 9E-01
2.8E-02
5 4E-03
4.2E-03
1 3E-04
9.9E-04
7 3E-04
1 .4E-04
2.2E-05
9.8E-03
2.4E-03
14E-02
1 4E-01
2 7E-02
21E-02
6.5E-04
5 OE-03
3.7E-03
7.1E-04
1.1E-04
4.9E-02
1.2E-02
7 OE-02
Predicted Methylmercury Intakes ^g/kg/day)
Bald Eagle
Osprey
Kingfisher
River Otter
Mink

1.6E-01
2 5E-02
1.9E-02
6.7E-04
4.8E-03
3.8E-03
6.8E-04
1 2E-04
7.5E-02
1.1E-02
6 OE-02
1.7E-01
2.7E-02
2.1E-02
7 5E-04
5 3E-03
4.2E-03
7 5E-04
1.3E-04
8.4E-02
1 2E-02
6.7E-02
4.4E-01
6.8E-02
5 3E-02
1.9E-03
1.3E-02
1.1E-02
1.9E-03
3 3E-04
2.1E-01
3 OE-02
1.7E-01
2.6E-01
4 1E-02
3 2E-02
1 1E-03
8.0E-03
6 3E-03
1 1E-03
2 OE-04
1 3E-01
1.8E-02
1. OE-01
1.7E-01
2 7E-02
2 1E-02
7.5E-04
5 3E-03
4 2E-03
7.5E-04
1 3E-04
8 4E-02
1.2E-02
6 7E-02

2.2E-02
3 8E-03
3.0E-03
9.8E-05
8.9E-04
6.2E-04
1.1E-04
1.8E-05
8.1E-03
1 8E-03
9 9E-03
2.5E-02
4.2E-03
3 4E-03
1.1E-04
9 9E-04
6.9E-04
1.2E-04
2.0E-05
9 OE-03
2.0E-03
1.1E-02
6.1E-02
1.1E-02
8.5E-03
2.7E-04
2.5E-03
1.7E-03
2.9E-04
4.9E-05
2.3E-02
4.9E-03
2.7E-02
3 7E-02
6 3E-03
5.1E-03
1.6E-04
1.5E-03
1. OE-03
1.8E-04
2 9E-05
1.3E-02
2.9E-03
1.6E-02
2.5E-02
4 2E-03
3 4E-03
1.1E-04
1. OE-03
6.9E-04
1.2E-04
2.0E-05
9 OE-03
2 OE-03
1.1E-02

5 1 E-03
9 7E-04
76E-04
2.3E-05
1.8E-04
1 3E-04
2.5E-05
4 OE-06
1.8E-03
4.3E-04
2.5E-03
5 6E-03
1 1E-03
8 4E-04
2.6E-05
2.0E-04
1.5E-04
2.8E-05
4.4E-06
2.0E-03
4 8E-04
2.8E-03
1 4E-02
2 7E-03
2.1 E-03
6 5E-05
5.0E-04
3.7E-04
7.0E-05
1 IE-OS
4.9E-03
1.2E-03
6 9E-03
8 4E-03
1.6E-03
1.3E-03
3 9E-05
3 OE-04
2.2E-04
4.2E-05
6.6E-06
2 9E-03
7 1E-04
4.1 E-03
5 6E-03
1.1E-03
8 4E-04
2 6E-05
2.0E-04
1 5E-04
2 8E-05
4 4E-06
2.0E-03
4.8E-04
2 8E-03
June 1996
6-37
SAB REVIEW DRAFT
-------
                                       Table 6-12
                      Predicted Intakes for Wildlife Receptors for the
                         Western Site Based on COMPDEP Results
Percent of surface water concentration dissolved- 11%
Plant
Mercury Surface Water
Concentration (ng/1)
Methylmercury Fish
Concentrations (ug/gj
Trophic 3
Trophic 4
25 km
Large Municipal Waste Combustor
Small Municipal Waste Combustor
Continuous Medical Waste Incinerator
Intermittent Medical Waste Incinerator
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkah plant
Primary Copper Smelter
Primary Lead Smelter
7.1E+UO
1.3E+00
1.6E+00
6.0E-02
2.2E-01
1 6E-01
3.2E-02
4.6E-03
8 2E+00
4 4E-01
2 6E+00
3 6E-01
6.7E-02
7.9E-02
31E-03
1.1E-02
S.OE-03
1 6E-03
2.3E-04
4.2E-01
2.2E-02
1 3E-01
1 8E+00
3.4E-01
40E-01
1.6E-02
5 7E-02
4.0E-02
8.2E-03
1.2E-03
2.1E+00
1.1E-01
6.8E-01
10 km
Large Municipal Waste Combustor
Small Municipal Waste Combustor
Continuous Medical Waste Incinerator
Intermittent Medical Waste Incinerator
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkah plant
Primary Copper Smelter
Primary Lead Smelter
1.7E+00
3 4E-01
3 2E-01
1 OE-02
6 6E-02
3.9E-02
8.0E-03
1.2E-03
1 .OE+00
1 1E-01
6 5E-01
8.9E-02
1.7E-02
1.6E-02
5.3E-04
3.4E-03
2.0E-03
4.1E-04
5.9E-05
5.1E-02
5.5E-03
3.3E-02
4.5E-01
8.7E-02
8.3E-02
2.7E-03
1.7E-02
1. OE-02
2.1E-03
3.0E-04
2.6E-01
2.8E-02
1.7E-01
25 km
Large Municipal Waste Combustor
Small Municipal Waste Combustor
Continuous Medical Waste Incinerator
Intermittent Medical Waste Incinerator
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Odor-alkali plant
Primary Copper Smelter
Primary Lead Smelter
6 3E-01
1 2E-01
9 5E-02
2.9E-03
2.1E-02
1 2E-02
2.8E-03
4.0E-04
2.6E-01
3.8E-02
2.4E-01
3.2E-02
6.0E-03
4.8E-03
1.5E-04
1 1E-03
6 3E-04
1.4E-04
2.1E-05
1.3E-02
2.0E-03
1.2E-02
1.6E-01
3.0E-02
2.4E-02
7.4E-04
5.3E-03
3.2E-03
7.3E-04
l.OE-04
6.8E-02
9.9E-03
6.1E-02
Predicted Methylmercury Intakes ^g/kg/dayj
Bald Eagle
Osprey
Kingfisher
River Otter
Mink

6.5E-02
1.2E-02
1.4E-02
5.5E-04
2.0E-03
1 4E-03
2 9E-04
4 2E-05
7.5E-02
4 OE-03
2 4E-02
7 2E-02
1 3E-02
1 6E-02
61E-04
2.3E-03
1.6E-03
3 2E-04
4 6E-05
8 3E-02
4 5E-03
2 7E-02
I 8E-01
3.4E-02
4.0E-02
1.5E-03
5.7E-03
4.0E-03
8.1E-04
L2E-04
2 1E-01
1 1E-02
6 7E-02
1.1E-01
2.0E-02
2 4E-02
9.2E-04
3 4E-03
2.4E-03
4.8E-04
6 9E-05
1 2E-01
6.7E-03
4 OE-02
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1.3E-02
1 6E-02
61E-04
2.3E-03
1.6E-03
3 2E-04
4.7E-05
8.3E-02
4 5E-03
2 7E-02

1.6E-02
3.1E-03
3.0E-03
9.6E-05
6.1E-04
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1. IE-OS
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1.7E-03
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1.1E-03
8.7E-04
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1.9E-04
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2.6E-05
3.7E-06
2.4E-03
3.5E-04
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6 4E-03
1.2E-03
9.7E-04
2.9E-05
2.1E-04
1 3E-04
2.9E-05
4.1E-06
2.7E-03
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2.4E-03
; 6E-02
3.0E-03
2 4E-03
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5.3E-04
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7.2E-05
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9.8E-04
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1.8E-03
1.4E-03
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3.1E-04
1.9E-04
4.3E-05
6.2E-06
4.0E-03
5.9E-04
3.6E-03
6 4E-03
1.2E-03
9 7E-04
2 9E-05
2.1E-04
1.3E-04
2.9E-05
4 1E-06
2 7E-03
3.9E-04
2.4E-03
June 1996
6-38
SAB REVIEW DRAFT
-------
Animals with the highest ingestion rate (per body weight) generally had the highest methylmercury
intakes, except in cases where a species prefers preying on trophic 4 fish.  For example, the river otter
eats less on a per weight basis than the osprey but eats more highly contaminated fish,  and receives a
higher mercury exposure as a result.

6.2.6    Mass  Balances within the Local-Scale Domain

        In this section the fraction of the mercury emitted from each hypothetical facility that is
predicted to deposit within 50 km is estimated.  The area-averaged wet and dry deposition rates are
also estimated based on the fraction from the single source that is predicted to deposit within 50 km.

        Tables 6-13 and 6-14 show the results for all facilities at both sites.  These results were
obtained by using a total of 480 receptors for each facility and site.  The receptors were placed in 16
directions around the facility and 30 distances, from 0.5 km to 50  km. The difference between the
lower bound and upper bound estimates is due to the method of interpolation between receptors.

        In general,  1-25% of the total mercury emitted is predicted to deposit within 50 km at the
humid site in  flat terrain, while 0.5-18% is predicted to deposit at the arid  site.  This implies that at
least 75% of the total mercury emissions is transported more than 50 km from any of the sources
considered,  and is consistent with the RELMAP results that predict that mercury may be transported
across large distances.

        The differences between the results for the two sites  are due primarily to the differences in the
frequency and intensity of precipitation, although one can expect other  differences due to the
frequency of each particular stability class during the year.  As shown in Table 6-15, at the humid site,
precipitation occurs about 12% of the year, with  about 5% of this precipitation of moderate intensity
(0.11  to 0.30 in/hr). At the arid site, precipitation occurs about 3% of the  year, with about 2% of the
precipitation of moderate intensity.

        Wet deposition is predicted to be an effective removal mechanism  within 50 km of the source
for some facilities.  For the humid site in the year considered, precipitation occurs for about  12.5% of
the year.. This value is to be compared to the percent  of total emissions wet deposited within 50 km.
For some facilities, 6-10%  of the total emissions is predicted to wet deposit within 50 km at the humid
site, implying that 50-80%  of the plume is being washed out within 50 km during periods of
precipitation.

        Wet deposition is predicted to dominate total deposition except for those facilities with low
stacks:  the  medical waste incinerators and the chlor-alkali plant.   This  is because the predicted air
concentrations are higher, and hence more dry deposition is predicted to occur. In general, the fraction
of total emissions that is predicted to wet deposit within 50 km is positively correlated  with the
fraction of emissions that are divalent mercury.  This is due to the assumed higher solubility of the
divalent species compared to that of elemental mercury.

        The percentage of mercury deposited within 50 km depends on two main factors: facility
characteristics that  influence effective stack height (stack height plus plume rise) and the fraction, of
mercury emissions  that is divalent mercury.  In most cases, the effective stack height affects only the
air concentrations, and hence dry deposition.  It does not affect predicted wet deposition because the
precipitation is assumed to originate above and pass through the entire  plume.
June 1996                                      6-39                        SAB REVIEW DRAFT
-------
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                                           Table 6-15
                     Precipitation Frequencies at the Humid and Arid Sites
Site
Humid Site
Arid Site
Hours of Precipitation
(% of year)
1098 (12.5%)
220 (2.5%)
Breakdown of Precipitation Hours (% of precipitation)
Trace to 0.1 in/hr
1044 (95%)
214 (97%)
0.11 to 0.3 in/hr
50 (4.5%)
5 (2.3%)
> 0.3 in/hr
4 (0.4%)
1 (0.5%)
Exceptions occur when the effective stack height is above the mixing height, in which case no
deposition is predicted to occur, or when the receptor .is located above the effective stack height (see
Appendix D for details on how terrain is addressed). The former can happen routinely for facilities
with extremely high stacks and large exit gas velocities (e.g., large coal-fired utility boiler).

        The differences between the results for the LMWC and SMWC are primarily due to
differences in the parameters used to estimate  the effective stack height (stack height plus plume rise):
stack height, stack diameter, and exit temperature (please see Table 6-6 on page 6-6-5 of this volume
for a summary of all model plant characteristics).  The effective stack height is used to estimate dry
deposition.  The lower plumes predicted for the SMWC result in higher air concentrations, and hence
higher predicted dry deposition. About twice  as much of the emitted mercury is predicted to dry
deposit for the SMWC than for the LMWC. This difference is roughly the same as the ratio of the
stack heights (LMWC  stack is about twice as  high as that of the SMWC). Wet deposition is only
affected by differences in wind speed at stack  top, and in this case the ultimate effects are minimal.
The wind speed at stack top is  extrapolated  from the height at which it was  measured using  wind
profile exponents (see  Appendix D).

        In addition  to the total mercury emission rate, which does not affect the fraction deposited
within 50 km, the only difference between the CMWI and the IMWI is the assumed stack diameter:
2.8 m for the CMWI and 1.2 m for the IMWI. The stack diameter is used to calculate air
concentrations and dry deposition rates. The predicted wet deposition rates  do not depend on the stack
diameter and so there is no difference between the predicted wet deposition  for the two facilities.
There are differences in the predicted dry deposition, and these differences must be due solely to the
difference in stack diameter.  Because the assumed exit temperature of the facilities is so high (1500
F), buoyancy forces are always predicted to dominate the plume's rise from  the stack.  The buoyancy
flux is estimated using the method of Briggs (1975), and this estimate is positively correlated with
stack diameter (see Appendix D).  The larger  diameter for the CMWI results in a larger buoyancy
flux, which results in slightly higher plumes than that for the IMWI.  This results in lower air
concentrations and dry deposition rates for the CMWI than that of the IWMI.

        Differences between the results for the utility boilers are due primarily to the difference in
stack heights.  This is  also the reason for  the differences between the results for the PCS  and PLS.
For all utility boilers, less than 10% of the total mercury emitted is predicted to deposit within 50 km.
Again, this is a reflection of the high effective stack heights associated with this source class.
June 1996
6-42
SAB REVIEW DRAFT
-------
        The deposition rates averaged over the entire 50 km radius region surrounding each facility are
given in Tables 6-16 and 6-17.  These values  are comparable to or well below typically reported
deposition rates (see Section 2).

6.2.7   Summary of Local Impact Analysis Results

        Air Modeling

        The predicted average atmospheric mercury concentrations that result from the emissions of
the model plants were generally low.  Only mercury emissions from the chlor-alkali model plant
markedly elevated predicted air concentrations at the receptors considered.  Using the COMPDEP
model and assuming flat terrain at the two hypothetical sites, 75% or more of the emitted mercury was
predicted to be transported beyond the 50 Km modeling domain of the local impact analysis. The
percentage of emitted mercury  predicted to deposit within 50 Km of the local source ranged from
0.5% to 25% for all model plants at both sites.  Deposition of mercury emitted to the atmosphere from
the model plants occurred through both wet and dry  mechanisms. Removal of atmospheric mercury
through precipitation was, in general, predicted to dominate total mercury deposition near the model
plants.  The wet deposition of mercury was predicted to be greater at the more  humid eastern site than
at the more arid western site.  The predicted dry deposition rate depended on the predicted
atmospheric concentrations of vapor-phase divalent mercury and, to a lesser extent, particle-bound
divalent mercury.  As the distance of the receptor from the stack of the model plant increased, the
percentage of total deposition which resulted from dry deposition was predicted to increase.  At this
time no suitable data exist for comparing the predicted deposition patterns around the model plants.

        Environmental Media Modeling

        The predicted soil and  surface water body concentrations of mercury were most heavily
influenced by the predicted total mercury deposition rate from a local source. The predicted soil and
surface water body mercury concentrations were all within the range of measured mercury
concentrations for these media.  Differences between the hypothetical watersheds of the eastern and
western sites account for the variation in water body loading from mercury deposited  onto the
watershed.

        Biota Modeling

        The mercury concentrations in green plants were the result of direct deposition and air-to-plant
transfer onto exposed foliar surfaces, and soil-to-plant transfer.  As modeled in  this assessment, small
amounts of mercury in green plants were predicted to result from direct deposition and soil-to-plant
transfer. Most of the mercury predicted  to occur in green plants was the result of air-to-plant transfer
for those types of green plants  for which this route of transfer was deemed appropriate.  Since
air-to-plant transfer is primarily the product of the concentration of the pollutant in the air and the
air-to-plant biotransfer factor, model plants with the highest predicted atmospheric mercury
concentrations have the highest predicted mercury concentrations in green plants. Although the data
are not extensive particularly near many  of these anthropogenic sources, the predicted concentrations
in green plants are within the range of measured concentrations. The predicted speciation of mercury
in green plants was consistent  with the reported values.
June 1996                                     6-43                         SAB REVIEW DRAFT
-------
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        The mercury concentrations in all animal products except fish were predicted to be low.  This
was the result of generally low concentrations in plants and small plant-to-animal and soil-to-animal
biotransfer factors.  The predicted concentrations and speciation of mercury in animal products was
consistent with the reported values.

        Mercury concentrations in fish were predicted to be the highest of the biota considered.
Mercury concentrations in fish were the product of the bioaccumulation factor and the dissolved
concentration of mercury in surface water. The predicted mercury  concentrations in fish were within
the range of reported values.  There is a great deal of uncertainty and variability associated with the
uptake of mercury by fish.

        Human and Wildlife Exposure Modeling

        Human exposure to anthropogenic mercury was predicted to be dominated by indirect routes
of exposure except for the hypothetical average urban dweller. This individual was assumed to be
exposed to emitted mercury from inhalation and soil  ingestion only.  For all other exposure scenarios,
except those including fish consumption, the divalent species  was predicted to be the primary species
to which humans  were exposed.  For those hypothetical individuals exposed through consumption of
both green plants  and animal products, mercury  exposure through consumption of green plants was
greater than through consumption of animal products. This was the result of low plant-to-animal and
soil-to-animal biotransfer factors when compared to the air-to-plant biotransfer factors.

        Those hypothetical humans who were assumed to consume fish had the highest exposures.
This was a result  of the  bioaccumulation factor into fish.  Methylmercury was the primary species  to
which these individuals were exposed. On a per body weight basis, children were predicted to be
more exposed than adults.

        The animals with the highest  fish ingestion rate (per body weight) generally had the highest
methylmercury intakes, except in cases where a  piscivorous species was assumed to prey more heavily
on trophic level 4 fish.  For example, the  river otter was assumed to consume less fish on a per weight
basis  than the osprey but was assumed to  consume larger fish from a higher trophic level which had
higher methylmercury body burdens.  As a result the otter was predicted to have a higher mercury
exposure.

6.2.8    Additional Analysis Based  on Emission  Guidelines for Existing MWCs and New Source
        Performance Standards

        The exposure analysis for MWCs presented in the local impact analysis was based on the
model plant parameters described in Appendix F.  The emission rate  was  490 ug/dscm for the large
MWC and  700 ug/dscm  for the small MWC model plant.  The emission rate for the large MWC was
based on a combination  spray dryer/fabric filter  achieving 30  percent mercury control.  The small
MWC model plant was assumed to not have any appreciable  mercury control and was modeled at  an
emission rate of 700 ug/dscm.  Since this analysis was performed,  the U.S. EPA has finalized emission
guidelines for existing MWCs and New Source Performance Standards for new facilities (October  31,
1995).  The final rules require new and existing MWCs that combust more than 39 tons of waste per
day, to reduce their  mercury emissions to no more than 80 ug/dscm.  To reflect this regulation, an
additional analysis was performed which evaluated the large and small  MWC model plants at this
emission rate.
June 1996                                    6-46                        SAB REVIEW DRAFT
-------
additional analysis was performed which evaluated the large and small MWC model plants at this
emission rate.

        To (achieve this emission reduction it is likely that most facilities will use activated carbon
injection as  a control measure.  As explained in Appendix F, activated carbon injection effectively
captures Hg 2+ with the result that the percentage of Hg° as a fraction of total mercury increases.  In
addition, the fraction of mercury associated with paniculate matter (Hg (PM)) also decreases. To
address this change in the mercury speciation profile, three emission scenarios were modeled. These
are illustrated in Table 6-18.

        In the three emission speciation profiles the percentage of elemental mercury released ranges
from 30 to 90 percent. The remaining mercury  emissions are assumed to be divalent and in the vapor-
phase.  The velocity and temperature of the exit gas assumed for the model  plants were unchanged
from the local impact analysis.

        As described in the previous sections, the two types of mercury species considered in the
emissions are expected to behave quite differently once emitted from the stack.  Elemental mercury is
not expected to deposit close to  the facility and vapor phase divalent mercury is expected to deposit in
greater quantities closer to the emission sotirces. Because of the assumptions made pertaining to the
atmospheric chemistry of mercury (i.e.,  no dry  atmospheric chemistry of mercury in the local
atmosphere) and atmospheric partitioning of mercury (i.e.,  the  vapor/particle (V/P) ratio in the local
atmosphere was assumed to be equal to the V/P ratio as it would exist in stack  emissions), the
mandated decreases in total mercury emitted and the resulting decreases in  the amount of divalent
mercury emitted are predicted to result in decreased concentrations in environmental media  and biota.
These  predicted decreases are presented in Tables 6-19 and 6-20.

        If the elemental mercury emissions comprise 30% of he total mercury emitted, then the
legislated decreases are predicted to result in a  decrease of at least 80% of the total mercury air
concentration and total mercury  deposition rate at 2.5 Km downwind from the plant, when compared
to the  predictions from the model plants developed for the previous section. The corresponding
predicted concentrations  in fish are also decreased at least 80% at this distance.  If the elemental
mercury emissions comprise 90% of the total mercury emitted, then the legislated decreases  are
predicted to result in a decrease  of at least 90% of the total mercury concentration in the air and the
total mercury deposition  rate  when compared to the predictions from the model plants developed for
the previous section. The corresponding predicted concentrations in fish are also decreased at least
90%. That the decreases  in the amount of mercury emitted and in the amount of divalent mercury
emitted result in decreased local deposition, is most evident in emission scenario C, in which 90% of
the emissions are assumed to be elemental.
June 1996                                     6-47                        SAB REVIEW DRAFT
-------
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                                          Table 6-19
    Mercury Concentrations Predicted in Media and Biota As a Result of Mercury Emissions
                     From Municipal Waste Combustors After Imposition
                   of the MACT Standards in the Hypothetical Western Site
Plant
LMWC


SMWC


LMWC


SMWC


LMWC


SMWC


Distance
(Km)
2.5





10





25





Species
Hgo/Hg^
30/70
60/40
90/10
30/70
60/40
90/10
30/70
60/40
90/10
30/70
60/40
90/10
30/70
60/40
90/10
30/70
60/40
90/10
Watershed Air
Concentration
(ug/m3)
0.04
0.04
0.04
0.01
0.01
0.01
0.03
0.03
0.03
0.07
0.07
0.07
0.02
0.02
0.02
0.03
0.03
0.03
Watershed
Total
Deposition
(ug/m2 /yr)
16.1
9.3
2.5
2.2
1.3
0.3
4.7
2.7
0.8
0.7
0.4
0.1
1.9
1.1
0.3
0.3
0.2
0.04
Unfilled Soil
Concentration
(ng/g)
32.2
18.6
5.1
4.4
2.6
0.7
9.3
5.4
1.6
1.3
0.8
0.2
3.8
2.2
0.6
0.5
0.3
0.09
Surface Water
Concentration
(ng/L)
1.2
0.7
0.2
0.2
0.1
0.02
0.3
0.2
0.05
0.05
0.04
0.02
0.1
0.06
0.02
0.02
0.01
0.002
Tier3
Fish
(ug/g)
0.06
0.04
0.01
0.008
0.005
0.001
0.02
0.009
0.002
0.002
0.001
0.0003
0.005
0.003
0.0009
0.0007
0.0004
0.0001
Tier 4
Fish
(ug/g)
0.31
0.18
0.05
0.04
0.02
0.007
0.07
0.04
0.01
0.01
0.006
0.002
0.03
0.02
0.005
0.004
0.002
0.0006
LMWC = Large Municipal
SMWC = Small Municipal
Hg° = Elemental Mercury
Hg4^ = Divalent Mercury
Waste Combustor
Waste Combustor
June 1996
                           6-49
SAB REVIEW DRAFT
-------
                                         Table 6-20
             Mercury Concentrations Predicted in Media and Biota As a Result of
            Mercury Emissions From Municipal Waste Combustors After Imposition
                   of the MACT Standards in the Hypothetical Eastern Site
Plant
LMWC


SMWC


LMWC


SMWC


LMWC


SMWC


Distance
(Km)
2.5





10





25




V
Species
Hg'VHg**
30/70
60/40
90/10
30/70
60/40
90/10
30/70
60/40
90/10
30/70
60/40
90/10
30/70
60/40
90/10
30/70
60/40
90/10
Watershed Air
Concentration
(ug/m3)
0.04
0.04
0.04
0.01
0.01
0.01
0.04
0.04
0.04
0.01
0.01
0.01
0.02
0.02
0.02
0.003
0.004
0.004
Watershed
Total
Deposition
(ug/m2 /yr)
44.3
25.9
7.4
5.0
2.9
0.8
7.6
4.6
1.6
1.0
0.6
0.2
1.9
1.2
0.4
0.3
0.2
0.1
Unfilled
Soil
Concentration
(ng/g)
86.3
50.4
14.5
9.8
5.7
1.7
14.9
9.0
3.0
1.9
1.1
0.4
3.7
2.3
0.9
0.5
0.3
0.1
Surface
Water
Concentration
(ng/L)
3.2
1.9
0.5
0.4
- 0.2
0.1
0.4
0.2
0.1
0.1
0.03
0.01
0.1
0.1
0.02
0.01
0.01
0.003
Tier 3
Fish
(ug/g)
0.15
0.09
0.03
0.02
0.01
0.003
0.02
0.01
0.004
0.002
0.001
0.0005
0.004
0.003
0.001
0.0006
0.0004
0.0001
Tier 4
Fish
(ug/g)
0.76
0.44
0.13
0.09
0.05
0.01
0.1
0.06
0.02
0.01
0.007
0.002
0.02
0.01
0.01
0.003
0.002
0.0007
6.3    Results of Combining Local and Regional Models

       In this section the results are presented for combining the local and regional impacts of
anthropogenic sources.  For both the eastern and western sites, the 50th and 90th percentile of the
predicted air concentrations and  deposition rates by the regional air model are used in conjunction with
the air concentrations and deposition rates predicted by the local scale model for each plant to obtain
estimates of environmental concentrations and possible exposure for both human and wildlife species.
June 1996
6-50
SAB REVIEW DRAFT
-------
6.3.1    Air Concentrations. Deposition, and Water Concentrations

        In this section the air concentrations,' deposition rates, and water concentrations predicted using
the local and regional  scale model results are presented.  The predicted concentrations for other media
are given in appendix  G.

        Tables 6-21 and 6-22 show the predicted air concentration, deposition rates and surface water
concentrations for both sites and each facility, using both the 50th and 90th percentiles of the
RELMAP values.  The predicted air concentrations are typically dominated by the regional values,
even for the watersheds relatively close to the facility. The only exception to this is the chlor-alkali
plant, for which larger air concentrations are predicted (this is due to the low stack height and  assumed
stack gas exit velocity).

        Except for the utility boilers, the predicted deposition due to the local source was in general
larger than the regional contribution at the closest watershed considered. The importance of the
regional contribution increased as a function of distance from the source, with the regional sources
predicted to dominate  total deposition for all facilities at 25 km in the Eastern site.    The patterns
for the predicted surface water concentrations, and hence fish concentrations, are similar to the
deposition results. However, the contribution of the regional sources is slightly smaller than for
deposition because the deposition rate to the water body itself is also considered in the local analysis.
This rate is larger than that for the watershed because the water body is closer to the source, and so
the relative contribution of the regional source is smaller.

6.3.2    Human Exposure

        Tables 6-23 through 6-36 show the predicted human intake for each exposure scenario  and
site. The regional contribution for inhalation intake is similar to the contribution to air concentrations
described above.

        The intake for the agricultural exposure  scenarios, the rural subsistence farmer and the  home
gardener, was dominated in general by the regional sources.  The total intakes were in fact comparable
to the predicted  intakes using a typical air concentration of 1.6 ng/m3 as described in Chapter 4.  The
regional sources are predicted to dominate exposure because these sources  dominate the air
concentrations, and the plant and animal concentrations are driven by the predicted air concentrations
due to  the air-to-plant  transfer coefficients used  (see section 4.3 for detailed assessment of the
contribution to plant and animal concentrations from the various routes).

        For the agricultural scenarios, most of the mercury intake was divalent mercury, and the
dominant pathway was the consumption of fruits and fruiting vegetables. The mercury in potatoes and
root vegetables results solely from root uptake since no air uptake was  assumed to occur for these
plants (Appendix A).   For leafy vegetables, all the mercury is predicted to  be from air uptake since no
root uptake was  assumed to occur. For grains, legumes, fruits and fruiting  vegetables the bulk of
mercury was also modeled to result from air uptake of atmospheric elemental mercury and
transformation to other species.  Fish consumption was the dominant mercury exposure pathway for
those hypothetical individuals modeled to consume fish.
June 1996                                      6-51                         SAB REVIEW DRAFT
-------
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6.3.5    Combining the Results of Local and Regional Models: Summary Conclusions

        Except for the chlor-alkali plant, the mercury air concentrations that were predicted to result
from the emissions of the model plants were generally dominated by the regional sources.  The larger
contribution to the air concentration from the chlor-alkali plant was due to the predicted lower
effective stack heights for this source.  For the two hypothetical  sites considered, the regional
contribution was estimated using  either the 50th or 90th  percentile air concentration and deposition
rate as predicted by RELMAP. The regional contribution was larger at the hypothetical eastern U.S.
site than the western site.

        Except for the utility boilers, mercury deposition rates at the closest receptor considered (2.5
Km from the local source) were predicted to be dominated by the emissions from the local source (i.e.,
the model plant), with the impact of the regional contribution increasing as a function of distance from
the source.  The predicted deposition rates were lower for the utility boilers due to lower mercury
emission rates associated with the model plants developed to categorize this source class and the
predicted nigh effective stack heights,  which disperse the mercury to a greater degree than the other
source classes.

        The predicted green plant concentrations followed the air concentration patterns, and  so the
relative contributions  of the regional and local sources were similar to that for the  air concentrations.
The predicted concentrations of mercury in soil, surface  water and fish followed deposition patterns,
and the relative contributions of the regional and local sources were similar to that for deposition.

        For the agricultural scenarios, the ingestion of plants was the primary route of exposure to
mercury, and hence the contributions of regional and local sources were similar to that for the air
concentrations. For the human fish ingestion scenarios and  piscivorous wildlife exposure  scenarios,
the predicted intakes follow deposition patterns.  Since deposition from local sources generally
dominated total deposition close to  the model plants, exposure through fish consumption followed
local deposition patterns.  As the predicted deposition from  the local source decreased with increasing
distance and regional  deposition as  a fraction of total deposition  increased, the impact deposition of
mercury predicted to result from regional source emissions increased.

        Because of the hypothetical nature of both the individual humans and the sites that were
considered, estimates  of exposures to mercury resulting from the consumption of non-local fish, from
occupation or from background sources, other than the atmosphere, were not added to the exposure
estimates  developed in Chapter 6 of this  volume. These sources of mercury exposure may be
significant and for a site-specific  assessment it may be appropriate to consider these for members of
the population.

        Piscivorous wildlife may  also be exposed to mercury from other sources.  For example, they
may be exposed through the non-fish portion of their diet or through consumption  of drinking water.
It may be appropriate to  consider these sources for a specific site assessment.

6.4     Uncertainty and Sensitivity Analyses

        As has been noted previously,  the behavior of atmospheric mercury close to the point of
release has not been studied extensively.  This alone results in a significant degree of uncertainty
implicit in the preceding  modeling exercises. In this section, several of these assumptions along with
other possible behaviors  are examined  to illustrate the implications of these potential properties of
atmospheric mercury  in the near-field.

June 1996                                     6-71                         SAB REVIEW  DRAFT
-------
6.4.1    Drv Deposition

        In this study, divalent mercury vapor was assumed to deposit in a manner similar to nitric
acid, and elemental mercury was assumed to dry deposit at a negligible rate.  In particular, a dry
deposition velocity of 1 cm/s during daytime conditions and 0.3 cm/s during nighttime conditions was
assumed for divalent mercury vapor, and a dry. deposition velocity of 0 cm/s was used for elemental
mercury vapor.  It is noted that under certain conditions dry deposition velocities of nitric acid have
been estimated to be as high as 4 cm/s (see Appendix D).

        In this section the results using this assumption are compared to the results using the
assumption that all mercury vapor dry deposits with a deposition velocity of 0.06 cm/s, a rate based on
the work of Lindberg et al., (1991).

        Lindberg et al., (1991) calculated dry deposition velocities for total mercury vapor (assumed to
be all Hg°) to a  forest canopy in eastern Tennessee.  It was assumed that the dry deposition of
mercury vapor to plant canopies is based exclusively on the leafs physiology and biochemistry.
Dominant processes for mercury uptake are gas exchange at the leafs surface via stomata followed by
mercury assimilation at the gas-liquid interface within the leaf mesophyll.  Lindberg et al., (1991)
modified the  "big leaf' aerodynamic resistance (resistance the leaf provides to transport of atmospheric
pollutants to the leaf interior) model of Hicks et al., (1987) to infer mean hourly dry deposition
velocities for mercury vapor.  Detailed hourly  meteorological data and canopy measurements were
used in this model to calculate the mean total resistance and its reciprocal, the dry deposition velocity.
Weekly mean Hg° dry  deposition velocities ranged from 0.006 in winter to 0.12 cm/s in summer, from
which  an average value of 0.06 cm/s is taken.  This value is probably a low-end value for mercury
vapor.   Lindberg's group reported in Hanson et al., (1994) that dry deposition to the forest canopy
may only occur when mercury concentrations exceeded a "compensation concentration" around 15
ng/m3.  This  means overall deposition may not occur under typical ambient concentrations of gaseous
mercury, and illustrates the uncertainties in calculation and measurement of 'dry deposition velocities
for mercury.

        The alternate deposition rate used in the present analysis based on Lindberg et al., (1991) was
chosen more  to provide a lower-end bound for the dry deposition modeling rather than represent an
accurate approximation to the actual mercury deposition process near an anthropogenic source.
Additionally, Lindberg  et al., (1991) recognize that Hg2+ is thought to deposit much more readily than
Hg° and the dry deposition velocities reported  do not necessarily apply to Hg24".

        When a  single  dry deposition velocity  for all vapor-phase mercury emissions is used, the
vapor-phase dry deposition flux is the result of the mass of vapor-phase mercury emissions and the
stack characteristics. The assumption of a single dry deposition velocity for  vapor-phase mercury ties
the total dry deposition rate more closely to the mass of mercury vapor emitted from the stack
compared to the method that estimates different deposition velocities for vap>or phase Hg and Hg  .

        Table 6-39 shows the results for select facilities using both assumptions.
June 1996                                     6-72                        SAB REVIEW DRAFT
-------
                                            Table 6-39
                       Comparison of Results using Different Assumptions
                          Regarding Dry Deposition of Mercury Vapor
Plant
Large Municipal Waste Combustor
Small Municipal Waste Combustor
Continuous Medical Waste Incinerator
Intermittent Medical Waste Incinerator
Large Coal-fired Utility Boiler
Chlor-alkali Plant
Primary Copper Smelter
Primary Lead Smelter
Base Emissions Speciation ("o of Total Hg
Emissions)
HgO Vapor
20
20
20
20
50
70
85
85
Hg:+ Vapor
60
60
60
60
30
30
10
10
Hg2+ Paniculate
20
20
20
20
20
0
' 5
5
Predicted
2.5 km Watershed
#1A #2B
5.59 26.28
2.25 8.82
4.78 15.36
0.20 0.62
0.03 0.13
70.78 82.70
0.73 0.92
7.66 7.93
Dry Deposition Rate (
10 km Watershed
#1A #2B
4.79 17.16
1.34 4.44
1.73 4.89
0.06 0.16
0.04 0.14
15.13 14.08
0.75 0.64
6.91 5.07
ug/m~/yr)
25 km W.iter
-------
mercury, in this section the possible impacts of uncertainty in wet deposition of vapor are briefly
discussed.

        Due to its higher solubility, divalent mercury is thought to wet deposit at much higher rates
than that of elemental mercury vapor.  Determination of washout ratios for divalent mercury vapor is
precluded by the limitations of current analytical measurement techniques:  ii has not been possible to
obtain measurements of divalent vapor air concentrations, and hence there are no reported values in
the peer-reviewed literature.  For this reason, the washout ratio used for divalent mercury vapor is
based on an assumed similarity between divalent mercury and nitric acid, for which washout ratios are
available (Petersen 1995).  In particular, a value of 1.6xl06 is used.  Comparisons of concentrations  in
precipitation calculated using this value agree quite well for nitric acid. However, the applicability of
this value for divalent mercury vapor is uncertain, as there may be other processes specific to  mercury
that would result in a smaller washout ratio.  It seems that a lower bound for this value is on the order
of 104, which is based on measured values for elemental mercury (Appendix D).

        Because the washout ratio is used to calculate a scavenging coefficient, the effect of the
uncertainty in the washout ratio is not strictly linear (see Appendix D) using three different  washout
ratios.  A larger washout ratio results in a larger scavenging coefficient, which results in more of the
plume being depleted closer to the source.  Thus, at larger distances from the source, the predicted wet
deposition may be higher using a smaller washout ratio.  In the example here, this happens  at  about
22 km when comparing 1.6xl06 and 1.6X105, and at about 40 km when comparing 1.6xl06 and
1.6xl04

        Thus,  these results imply that the uncertainty in the washout ratio will primarily affect
predictions of deposition close to the facility.  These predictions are of course the most critical,  and  at
present cannot be validated due to a lack of available measured data near the facilities of concern.

6.4.3    Effect of Terrain on Results of Local Scale Modeling

        In the previous sections, mercury stack emissions  from hypothetical model plants,
characteristic of industrial source categories, were evaluated with the COMPDEP air dispersion and
deposition model. These model plants were placed in simple terrain, and in either dry or humid
climate conditions. In reality, many of these emission sources may actually be located  in rolling
topography, which may ultimately affect the predicted media concentrations near the facility.  In this
study EPA chose to ignore quantitatively the possible ambient air and deposition impacts posed by
terrain.  This was done to simplify the air modeling analysis in view of the wide range  of uncertainty
inherent in accurately modeling the deposition of the various mercury species.  Moreover, EPA
currently does not have refined air dispersion and deposition models capable of accurately modeling
plume behavior in elevated terrain.  The uncertainty lies in the predicted magnitude of the result.
However, because elevated terrain can cause an increase in the predicted ambient air  and deposition
impacts in comparison to flat terrain, EPA has undertaken a limited  modeling exercise to investigate to
what extent consideration of  elevated terrain may affect the air modeling results. The results of this
exercise are summarized in this section.

        In general, terrain refers to  the height of a receptor with respect to a local source.  However, it
is actually the height of the receptor with respect to the effective stack height (stack height  plus plume
rise) that is important in the calculations.  There are three types of terrain:  simple, intermediate, and
complex.  Simple terrain consists of receptors located at the same elevation as the stack base.
Complex terrain refers to receptors  at or above the effective stack height, and intermediate terrain
refers to receptors between these two extremes.

June 1996                                     6-74                        SAB REVIEW DRAFT
-------
        For calculating air concentrations and dry deposition rates,  non-simple terrain is addressed by
the COMPDEP model in a standard manner by reducing the effective stack height and, in stable
conditions for receptors  above stack top, by scaling the predicted values by a height-dependent
correction factor.  The first method reflects the fact that the receptor is  closer to the center of the
plume, thereby resulting in higher concentrations and rates.  The specific amount that the effective
stack height is reduced is determined based on the methods described in Briggs (1973) and Egan
(1975),  and are discussed in Appendix D.  The effective stack height used is always  at least half of the
effective stack height calculated independent of terrain (see section D.2.1.6 of Appendix D).  This
results in a steady increase as a function of receptor height up to half of the effective stack height,
after which the calculated value is  essentially constant.  It is not actually constant because in stable
conditions (atmospheric  stabilities E and F) for receptors located above the effective  stack height the
calculated values are scaled by  an additional  "correction factor" that further reduces the predicted
values.  Although most of the dry deposition occurs during unstable and neutral conditions, this
additional reduction can result in the predicted deposition diminishing slightly for receptors that  are
above the effective stack height.

        The general approach taken in  this exercise was to compute the dry deposition as a function of
receptor height above ground level. This was done for each facility at  distances of 2.5 and 25 km
from the source. Facilities from each source class were included because the effect of terrain on the
predicted deposition rates can differ substantially depending on the source characteristics that affect the
plume height.

        Tables 6-40 and 6-41 show the effects of receptor height on the predicted dry deposition rate
for all facilities at the two different distances from the source.  These tables show the dimensionless
ratio of the predicted value at a given height and the predicted value for a receptor at the same
elevation as the stack base.

        The magnitude of the ratios is  different across facilities  because the effective stack height
depends on the source characteristics, and the distance between  the receptor and the center of the
plume depends on the calculated effective stack height. For facilities with lower stacks (e.g., waste
incinerators, chlor-alkali plant), there is little change for the receptor heights considered because the
receptors are so much higher than the effective stack height, and the slight diminishing effect
discussed above can even  be observed.  At 2.5 km, these results show that the effect of receptor height
can be substantial for receptors close to the source: there can be an increase of an order of magnitude
in the predicted dry deposition rates. At 25 km, the difference as a function of receptor height is not
as extreme because more dispersion has occurred:  the vertical change in air concentrations is not as
great as it is for closer receptors, thereby resulting in less deposition.
June 1996                                      6-75                         SAB REVIEW DRAFT
-------
                                          Table 6-40
   Illustration of Effect of Receptor Height on Dry Deposition at a Distance of 2.5 km from the
          Source:  Ratio of Predicted Value with Value for Receptor in Simple Terrain
Plant
Large MWC
Continuous MWI
Large Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkali plant
Primary Lead Smelter
25
1.4
2.2
1.1
1.4
1.3
1.0
1.3
50
2.1
2.2
1.2
2.2
1.9
1.0
1.9
Receptor
75
4.4
2.0
1.4
3.6
3.0
1.0
2.8
Height (m)
100
6.6
2.0
1.7
6.7
5.8
0.9
4.7
125
9.9
1.9
2.0
9.0
9.8
0.9
8.4
150
11.1
1.8
2.5
9.0
16.6
0.8
11.7
                                          Table 6-41
   Illustration of Effect of Receptor Height on Dry Deposition at a Distance of 25 km from the
          Source: Ratio of Predicted Value with Value for Receptor in Simple Terrain
Plant
Large MWC
Continuous MWI
Large Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkali plant
Primary Lead Smelter
25
1.2
1.7
1.1
1.2
1.2
1.1
1.1
50
1.4
1.3
1,2
1.4
1.4
1.0
1.3
Receptor Height (m)
75 100
2.6 2.8
1.0 1.0
1.3 1.5
1.6 2.7
1.7 3.1
1.0 1.0
1.6 1.8
125
2.9
0.9
1.7 '
2.8
3.3
0.9
3.1
150
2.6
0.9
2.0
2.3
3.5
0.9
3.2
       The predicted wet deposition rates (not shown) are not significantly impacted by change in
receptor height. This is because the only height-dependent aspect of the method of wet deposition
calculation is when the receptor is above the effective stack height, in which case sector-averaging is
performed (see Appendix D).

       The results of this exercise show that the deposition flux of divalent mercury in elevated
terrain can increase up to 15 times over simple terrain at a receptor 2.5 km from the source, and up to
4 times at 25 km.
June 1996
6-76
SAB REVIEW DRAFT
-------
7.      CONCLUSIONS

The following conclusions are  presented in approximate order of degree of certainty in the
conclusion, based on the quality of the underlying database. The conclusions progress from
those with greater certainty to those with lesser certainty.

•       The present study in conjunction with available scientific knowledge, supports a plausible link
        between mercury emissions from anthropogenic combustion and industrial sources and
        mercury concentrations  in air, soil, water and sediments.  The critical variables contributing to
        this linkage are these:

        a)      the species of mercury that are emitted from the sources, with elemental mercury
               (Hg°) mostly contributing to concentrations in ambient air and divalent mercury (Hg2+)
               mostly contributing to concentrations in soil, water and sediments;

        b)      the overall amount of mercury emitted from a combustion source; and

        c)      the climate conditions.

•       The present study, in conjunction with available scientific knowledge, supports a plausible link
        between mercury emissions from anthropogenic combustion and industrial sources and
        methylmercury concentrations in freshwater fish.  The critical variables contributing to this
        linkage are the following:

        a)      the species of mercury that are emitted, with emitted divalent mercury mostly
               depositing into local watershed areas and, to a lesser extent the atmospheric conversion
               of elemental mercury to divalent species which are deposited over greater distances;

        b)      the overall amount of mercury emitted from a source;

        c)      the extent of mercury methylation in the water body; and

        d)      the climate conditions.

•       There is a lack of adequate mercury measurement data near the anthropogenic atmospheric
        mercury sources considered in this report.  The lack of such measured data preclude a
        comparison of the modeling results with measured data around these sources. These data
        include measured mercury deposition rates as well as measured concentrations in the
        atmosphere, soils, water bodies and biota.

•       From the RELMAP analysis of mercury  deposition and on a comparative basis, a facility
        located in a humid climate has a higher annual rate of mercury deposition than a facility
        located in an arid climate. The critical variables are the estimated washout ratios of elemental
        and divalent mercury, as well as the annual amount of precipitation.  Precipitation removes
        various forms of mercury from the atmosphere and deposits mercury to the surface of the
        earth.

•       On a  national scale, an apportionment between sources of mercury and mercury in
        environmental media and biota cannot be described in quantitative terms with the current
        scientific understanding  of the environmental fate and transport of this pollutant.


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       Consumption of fish is the dominant pathway of exposure to methylmercury for fish-
       consuming humans and wildlife.  There is a great deal of variability among individuals in
       these populations with respect to  food sources  and fish consumption rates.  As a result, there is
       a great deal of variability in exposure to methylmercury in these populations. The
       anthropogenic contribution to the total amount of methylmercury in fish is, in part, the result
       of anthropogenic mercury releases from industrial and combustion sources increasing mercury
       body burdens in fish.  As a consequence of human and wildlife consumption of the alfected
       fish, there is an incremental increase in exposure to  methylmercury.

       Due to differences in fish consumption rates per body weight and differences in body weights
       among species,  it is likely that piscivorous  birds and mammals have much higher
       environmental exposures to methylmercury than humans through the consumption of
       contaminated fish.  This is true even in the case of fish consumption by humans who consume
       above average amounts of fish. The critical variables contributing to these outcomes are these:
               V
       a)     the fish consumption rate;

       b)     the body weight of the individual in relation to the fish consumption rate; and

       c)     the rate of biomagnification between trophic levels within the aquatic food-chain.

       The results of the assessment of current exposure of the U.S. population from fish
       consumption as described in Appendix H indicate that exposure to methylmercury from
       contaminated fish results in an incremental increase  in most U.S. fish-consumers.
       Methylmercury  exposure rates on a per body weight basis among fish-consuming children are
       predicted to be higher than for fish-consuming adults. The exposure rates among fish-
       consuming children under the age of 15 are estimated to average between 0.12  and 0.16
       micrograms of methylmercury per kilogram of body weight per day. The exposure rates
       among fish-consuming adults are estimated to average between 0.07 and 0.08 micrograms of
       methylmercury per kilogram of body weight per day.  Human adult fish consumption rates
       vary from 0 to greater than 300 grams per day.

       From the modeling analysis and a review of field measurement studies, it is concluded that
       mercury deposition appears to be ubiquitous across  the continental U.S., and at, or above,
       detection limits when measured with current analytic methods.

       Based on the RELMAP modeling analysis and a review of recent measurement data published
       in peer-reviewed scientific literature, there is predicted to be a  wide range of mercury
       deposition rates across the continental U.S.  The highest predicted rates (i.e., above 90th
       percentile)  are more than 50 times higher than the lowest predicted rates (i.e., below the 10th
       percentile). Three principal factors contribute to these modeled and observed deposition
       patterns:

              a)      emission source locations;

              b)      amount of divalent and paniculate mercury emitted or  formed in the
                      atmosphere; and

              c)      climate and meteorology.
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•       Based on the modeling analysis of the transport and deposition of stationary point source and
        area source air emissions of mercury from the continental U.S., it is concluded that the
        following geographical areas have the highest annual rate of deposition of mercury in all forms
        (above the levels predicted at the 90th percentile):

               a)     The southern.Great Lakes and Ohio River Valley.

               b)     The Northeast and southern New  England.

               c)     Scattered areas in the South with  the most elevated deposition occurring in the
                      Miami and Tampa areas.

        Measured deposition estimates  are limited,  but are available for certain geographic regions.
        The data that are available corroborate the RELMAP modeling results for specific areas.

•       Based on modeling analysis of the transport and deposition of stationary point source and area
        source air emissions of mercury from the continental U.S., it is concluded that the following
        geographical areas have the lowest annual rate of deposition of mercury in all forms (below
        the levels predicted at the 10th percentile).

               a)     The less populated areas of the Great Basin, including southern  Idaho,
                      southeastern  Oregon, most of southern  and western Utah, most of Nevada, and
                      portions of western New Mexico, and

               b)     Western Texas other than near El Paso, and most of northeastern Montana.

•       Based on limited monitoring data, the RELMAP model predictions of atmospheric mercury
        concentrations  and wet deposition across the U.S. are comparable with typically measured
        data.

•       EPA concludes that the selected major anthropogenic sources as  modeled and parameterized
        for this assessment, can be ranked by predicted deposition rate at 2.5 Km in flat terrain, on a
        relative basis from high to low, as follows:

        Municipal waste combustors
        Chlor-alkali plants
        Lead smelters
        Copper smelters
        Medical waste  incinerators
        Utility boilers

        The critical variables impacting the ranking are these:

        a)      estimated amounts of divalent and paniculate mercury emitted; and

        b)      parameters that influence the plume height, primarily the stack height and stack exit
               gas velocity.

        This ranking may be sensitive to differences in the distance from the source (distances other
        than 2.5 Km) and the topography of the terrain.


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•      From the analysis of deposition and on a comparative basis, the deposition of divalent mercury
       close to  an emission source is greater for receptors in elevated terrain (i.e., terrain above the
       elevation of the stack base) than from receptors located in flat terrain  (i.e., terrain below the
       elevation of the stack base).  The critical variables are parameters that influence the plume
       height, primarily the stack height and stack exit gas velocity.

•      In terms of methylmercury intake on a per body weight basis, the five wildlife species
       considered in this analysis can be ranked from high to low as follows:

               Kingfisher
               River Otter
       --      Mink, Osprey
               Bald eagle

       Methylmercury exposures for the most exposed wildlife species (the kingfisher) may be up  to
       two orders of magnitude higher than human exposures from contaminated freshwater fish (on a
       kilogram fish consumed per body weight basis).  This assumes that the fish within different
       trophic levels of a given lake are contaminated with the same concentrations of
       methylmercury.

•      Modeling estimates of the transport and deposition of stationary point source and area source
       air emissions of mercury from the continental U.S. have revealed the following partial mass
       balance.

               Of the total amount of elemental mercury vapor that is emitted, about 1 percent (1.2
               metric tons/yr) may be atmospherically transformed into  divalent mercury by
               tropospheric ozone and adsorbed to particulate soot in the air  and subsequently
               deposited in rainfall and snowfall to the surface of the continental U.S.  The vast
               majority of emitted elemental mercury does not readily deposit and is transported
               outside the U.S. or vertically diffused to the free  atmosphere to become part of the
               global cycle.

               Nearly  all of the elemental mercury  vapor emitted' from other sources around the globe
               also enters the global cycle and can  be deposited slowly  to the U.S. Nearly 30 times as
               much elemental mercury vapor is deposited from these other sources than from
               stationary point sources and area sources within the continental U.S.

               Of the total amount of divalent mercury  vapor that is emitted, about 68 percent (62.6
               metric tons/year deposits to the surface through wet or dry processes within the
               continental U.S. The remaining 32 percent is transported outside the U.S.  or is
               vertically diffused to the free atmosphere to become part of the global cycle.

               Of the total amount of particulate mercury that is emitted, about 36 percent (14.1
               metric tons/year deposits to the surface through wet or dry processes within the
               continental U.S. The remaining 64 percent is transported outside the U.S.  or is
               vertically diffused to the free atmosphere to become part of the global cycle.
                                                                                         U
•      Assuming these deposition efficiencies are correct (namely; elemental mercury -  1%, divalent
       mercury vapor  - 68%,  and particulate mercury -  36%) the relative source contributions to the
       total anthropogenic mercury that is deposited to the continental U.S. are ranked as follows:


June 1996                                      7-4                        SAB REVIEW DRAFT
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               Medical waste incineration 36% (28 Megagrams (Mg) of 78 Mg)
               Municipal waste combustion 31% (24 Mg of 78 Mg)
               Coal-tired electric utility boilers 17% (13 Mg of 78 Mg)
               Industrial and residential fossil fuel use 10% (8 Mg of 78 Mg)
               Chlor-alkali factories 2% (1 Mg of 78 Mg)
               Non-ferrous metal smelting 1% (1 Mg of 78 Mg)
               Oil-fired electric utility boilers 1% (1 Mg of 78 Mg)

•       Based on the local scale atmospheric modeling results in flat terrain, at least 75% of the
        emitted mercury from each facility is predicted to be transported more than 50 km from the
        facility.

•       The models used in the exposure analysis indicate that, except for utility boilers and
        intermittent medical waste incinerators, deposition within 10 Km of a facility is generally
        dominated by emissions from the local source rather than from emissions transported from
        regional mercury emissions sources.

To improve the quantitative exposure assessment component of the risk assessment for mercury
and mercury compounds, U.S. EPA would need more and better mercury emissions data and
measured mercury data near sources of concern, as well as a better quantitative understanding
of mercury chemistry in the emissions plume, the atmosphere,  soils, water bodies and biota.
Specific needs include these.

        Mercury in the Atmosphere

               •      aqueous oxidation-reduction kinetics in atmospheric water droplets;

               •      physical adsorption and condensation of divalent mercury gas to ambient
                      paniculate matter

               •      photolytic reduction of particle-bound divalent mercury by sunlight

               •      convincing evidence that gas-phase oxidation of mercury is insignificant

        Mercury in Soils and Water Bodies

               •      uptake and release kinetics of mercury from terrestrial  plants

               •      biogeochemical mercury transport and transformation kinetics in benthic
                      sediments

               •      methylation and demethylation kinetics in water bodies

               •      sorption coefficients to soils, suspended solids and benthic solids

               •      complexation to organic matter in water bodies

        Information Leading to an Improved Quantitative Understanding of Aquatic Bioaccumulation
        Processes and Kinetics
June 1996                                    7-5                        SAB REVIEW DRAFT
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               •       uptake kinetics b*y aquatic plants and phytoplankton

               •       partitioning and binding behavior of mercury species within organisms

               •       metabolic transformations of mercury, and the effect on uptake, internal
                      distribution, and excretion

       Information that will facilitate the development of a dynamic, linked terrestrial-aquatic mass
       balance modeling framework  that includes realistic mercury chemistry and the aquatic food
       web as an integral component

               •       More  measurements of methylmercury concentrations in fish for better
                      identification  of the range in fish species,

               •       Surveys of fish consumption among potential high-end fish consumers which
                      examine specific biomarkers indicating mercury exposure (e.g.,  blood mercury
                      concentrations and hair mercury concentrations), and

               •       A pharmacokinetic-based understanding of mercury partitioning in humans and
                      wildlife particularly the interactions of different forms of mercury and different
                      uptake routes.
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8.      RESEARCH NEEDS

        During the development of the mercury exposure assessment, many areas of uncertainty and
significant data gaps were identified.  Many of these have been identified in the document, and several
are presented in the following list.

1.       Improved analytical techniques for measuring speciated mercury air emissions as well as total
        mercury emissions from major point sources. Laboratory evidence suggests that divalent
        mercury gas emissions will wet and dry deposit much more readily than elemental mercury
        gas. Particle-bound mercury is also likely to deposit relatively quickly.  Current stack
        sampling methods do not provide sound information about the fraction of mercury emissions
        that are in oxidized form. While filters are used to  determine paniculate mercury fractions,
        high temperature stack samples may not be indicative of the fraction of mercury that is bound
        to particles  after dilution and cooling in the  first few seconds after emission to the atmosphere.
        Methods for determination of the chemical and physical forms of mercury air emissions after
        dilution and cooling need to be developed and used to characterize all known major point
        sources.

2.       Evaluated Local and Regional Atmospheric Fate and Transport Models are needed. These
        models should treat all important chemical and physical transformations  which take place  in
        the atmosphere.  The  development of these models will require comprehensive field
        investigations to determine the important atmospheric transformation pathways (e.g., aqueous
        cloud chemistry, gas-phase chemistry, particle attachment, photolytic reduction) for various
        climatic regions.  The evaluation of these models will require long-term national (possibly
        international) monitoring networks to quantify the actual air concentrations and surface
        deposition rates for the various chemical and physical forms of mercury.

3.       Better understanding of mercury transport from watershed to water body including the soil
        chemistry of mercury, the temporal aspects of the soil equilibrium, the impact of low  levels of
        volatile mercury species'in surface soils and water bodies on total mercury concentrations and
        equilibrium.

4.       Better understanding of foliar uptake of mercury and plant/mercury chemistry.  (The most
        important questions:  do plants convert elemental or divalent mercury into forms of mercury
        that are more readily  bioaccumulated?  Do plants then emit these different forms to the air?)
        A better understanding of the condensation point for mercury is needed.

5.       Better understanding of mercury movement from plant into  soil (detritus).  May need to refine
        the models used to account for movement of mercury in leaf litter to soil.

6.       The impact  of anthropogenic mercury on the "natural," existing mercury levels and species
        formed in soil, water, and sediments needs better understanding.  How does the addition of
        anthropogenic mercury affect "natural" soil and water mercury cycles?  Natural emission
        sources need to be studied better and their impacts better  evaluated.

7.       Improved understanding of mercury flux in water bodies and impact of plant and animal
        biomass are needed.  Unlike many other pollutants,  most  of the methylmercury in a water
        body appears to be in the biological compartment. The sedimentation rate as well as  benthic
        sediment:water partition coefficient require field evaluation.  Important to consider rivers and
        other larger water bodies in these flux analyses.

June 1996                          '           8-1                         SAT* PPVTPW no ACT
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8.     The BAF contains a substantial level of uncertainty.  A more appropriate BAF can probably be
       developed when the data base upon which the estimate is based is enlarged; i.e., need data
       from more than four studies.  The availability of more data would enable the possible
       development of lake-type adjustment factors for the mercury BAF possibly based on color,
       acidification susceptibility,  etc., or species-specific BAF adjustment factors for freshwater
       species most commonly consumed.  Also need a time analysis of fish mercury uptake which
       could lead to the development of a dynamic fish model. A mercury BAF for saltwater fish is
       needed.

9.     Better estimates of fish consumption rates for high-end consumers (subsistence) as well as
       recreational anglers are needed.  Fish species-specific consumption rates are also needed.

10.    Need to improve the biotransfer factors for mercury from soil and plants to beef.
i	inn*                                     8.0                         SAR RFVTFW DRAFT
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           APPENDIX A

    PARAMETER JUSTIFICATIONS
SCENARIO INDEPENDENT PARAMETERS
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                               TABLE OF CONTENTS
DISTRIBUTION NOTATION  	iii

A.     SCENARIO INDEPENDENT PARAMETERS	A-l
       A.1    Chemical Independent Parameters	A-l
              A.I.I  Basic Constants	A-l
              A.1.2  Receptor Parameters	A-l
                     A.l.2.1        Body Weight	A-2
                     A.l.2.2        Exposure Duration	A-2
              A.1.3  Agricultural Parameters  	A-3
                     A.I.3.1        Interception Fraction  	A-3
                     A.l.3.2        Length of Plant Exposure	A-4
                     A.l.3.3        Plant Yield	A-5
                     A.l.3.4        Plant Ingestion by Animals	A-6
                     A.1.3.5        Soil  Ingestion by Animals  	A-7
              A. 1.4  Exposure Parameters 	A-7
                     A. 1.4.1        Inhalation Rate  	A-8
                     A.l.4.2        Consumption Rates  	A-9
                     A. 1.4.3        Soil  Ingestion Rate  	A-10
                     A.l.4.4        Groundwater Ingestion Rate	A-ll
                     A.l.4.5        Fish Ingestion Rate  	A-12
                     A.l.4.6        Contact Fractions	A-14
       A.2    Chemical Dependent Parameters	A-15
              A.2.1  Basic Chemical Properties	A-15
                     A.2.1.1        Molecular Weight  	A-15
                     A.2.1.2        Henry's Law Constant	A-15
                     A.2.1.3        Soil-Water Partition Coefficient	A-16
                     A.2.1.4        Sediment-to-Water Partition Coefficient	A-16
                     A.2.1.5        Suspended Sediment-Water Partition Coefficient	A-17
                     A.2.1.6        Soil  and Water Loss Degradation Constants	A-18
                     A.2.1.7        Equilibrium Fraction for Chemical in Soil  	A-18
                     A.2.1.8        Equilibrium Fraction for Chemical in Water	A-19
              A.2.2  Biotransfer Factors	A-20
                     A.2.2.1        Plant-Soil BCF  	A-21
                     A.2.2.2        Air-Plant BCF	A-24
                     A.2.2.3        Animal BTF	A-28
                     A.2.2.4        Fish Bioaccumulation Factor	A-32
                     A.2.2.5        Plant Surface Loss Coefficient  	A-33
                     A.2.2.6        Fraction of Wet Deposition Adhering	A-33
       A.3    References	A-35
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                                  LIST OF TABLES
A-l    Chemical Independent Constants  	'	A-l
A-2    Ranges of Values for Suspended Sediment-to-Water Partition Coefficient	A-17
A-3    Reported Values for Fraction of Total Mercury that is Methylmercury in Water  	A-20
A-4    Soil-to-Plant Transfer Coefficients for Mercury (from Cappon, 1987
       and Cappon, 1981)		.	A-22
A-5    Other Values for Soil-to-Plant Transfer Coefficients for Hg2"1"	A-23
A-6    Relative Concentration of Mercury in Different Parts of Edible Plants  	A-26
A-7    Mercury Speciation in Various Plants (Cappon, 1981,  1987)	A-27
A-8    Mercury Concentrations  in Specific Beef Tissue Media Per Test Group and
       Dose (from Vreman et al, 1986)	A-29
A-9    Animal Biotransfer Factors Derived from Vreman et al. (1986)	A-30
A-10   Mercury Concentrations  and Resulting BTFs in Lamb Muscle  Tissue Per
       Test Group and Dose (from van der Veen and Vreman 1986)  	A-31
A-l 1   Values From Hoffman et al. (1992) and the Values of Fw Estimated Using
       Those Values	 A-34
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                                 DISTRIBUTION NOTATION
        A comprehensive uncertainty analysis was not conducted as part of this study.  Initially,
preliminary parameter probability distributions were developed.  These are listed in Appendicies A and
B.  These were not utilized in the generation of quantative exposure estimates.  They are provided as a
matter of interest for the reader.

        Unless noted otherwise in the text, distribution notations are presented as follows.


         Distribution                               Description

          Log (A,B)       Lognormal  distribution with mean A and standard deviation B

          Log*(A,B)      Lognormal  distribution, but A and B are mean and standard
                          deviation of underlying normal distribution.

         Norm (A,B)      Normal distribution with mean A and standard deviation B

           U (A,B)       Uniform distribution over the range (A,B)

          T (A,B,C)       Triangular distribution over the range (A,C) with mode of B
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A.
SCENARIO INDEPENDENT PARAMETERS
       This appendix describes the scenario-independent parameters used in the exposure modeling
for the Mercury Study Report to Congress.  Scenario independent parameters are variables whose
values are independent of a particular site and are constant among various site-specific situations.
Examples of scenario independent parameters are air density, the average height of an adult, or the
average crop yield of a particular food item.  These scenario independent parameters may be either
chemical independent or chemical dependent.  The following sections present the chemical
independent and chemical dependent parameters used in this study.

A.I    Chemical Independent Parameters

       Chemical independent parameters are. variables that remain constant despite the specific
contaminant being evaluated.  The chemical independent variables used in this study are described in
the following sections.

A.1.1  Basic Constants
       Table A-l lists the chemical independent constants used in the study, their definitions, and
values.
               Parameter
           Table A-l
Chemical Independent Constants


     Description
                                                           Value
              R

              pa

              ua

              Psed

              Cdrag

              K
                     ideal gas constant

                     air density

                     viscosity of air

                     solids density

                     drag coefficient

                     Von Karman's coefficient

                     boundary thickness
                          8.21E-5 m3-atm/mole-K

                          1.19E-3 g/cm3

                          1.84E-4 g/cm-second

                          2.7 kg/L

                          1.1E-3

                          7.40E-1

                          4.0
A.1.2  Receptor Parameters

       Receptor parameters are variables that reflect information about potential receptors modeled in
the study. These parameters include body weight, exposure duration, and other characteristics of
potential receptors.

       A. 1.2.1        Body Weight

Parameter:     BWa, BWc

Definition:     Body weights (or masses) of individual human receptors
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Units:
                       Receptor
    Default Value (kg)
                       Child

                       Adult
            17

            70
       Technical Basis:

       The default values for children and adults are those assumed in U.S. EPA, 1990.


A. 1.2.2        Exposure Duration

Parameter:     ED

Definition:     Length of time that exposure occurs.

Units:          years
Receptor
Child
Adult
Default Value
(years)
18
30
Distribution
U(l,18)
U(7,70)
Range
(years)
1-18
7-70
Technical Basis:

       The 18-year exposure duration for the child is based on U.S. EPA guidance for this study.  For
adults, the 30-year duration is the assumed lifetime of the facility (U.S. EPA, 1990). It should be
noted for noncarcinogenic chemicals the exposure duration is not used in the calculations.  The range
and distribution are arbitrary to determine the relative sensitivity of this variable, when appropriate.
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A. 1.3   Agricultural Parameters

        A.I.3.1         Interception Fraction

Parameter:    RPi

Definition:     The fraction of the total deposition within a unit area that is initially intercepted by
               vegetation.
 Units:
unitless
Crop
Leafy vegetables
Legume vegetables
Fruiting vegetables
Rooting vegetables
Grains and cereals
Forage
Silage
Fruits
Potatoes
Default
Value
0.15
0.008
0.05
0
0
0.47
0.44
0.05
0
Distribution
Log (0.16, 0.10)
Log(0.008, 6.004)
Log(0.05, 0.05)
N/A
N/A
Norm(0.47, 0.3)
Log (0.44, 0.3)
Log (0.05, 0.05)
N/A
Range
0.08 - 0.38
0.005 - 0.01
0.004 - 0.08
N/A
N/A
0.02 - 0.89

0.004 - 0.08
N/A
Technical Basis:

        For leafy vegetables, Baes et al. (1984) obtained an average interception fraction of 0.15 where
it was emphasized that this value represents a theoretical average over the United States. This value
was calculated assuming a logistic growth pattern for leafy vegetables and taking into account a
distribution of field spacings (for details see Baes et al., p.68). The associated distribution and ranges
shown in the previous table were calculated based on Baes's analyses by Belcher and Travis (1989).

        For legumes and fruits, Belcher and Travis (1989)  used the exposed produce equation that
relates the  interception fraction to the standing crop biomass (also called productivity) and crop
biomass values from Shor et al. (1982) to obtain the range of values given in the previous table. The
values for fruiting vegetables are assumed to be the same as for fruits.

        The distribution for forage is based on the work of Hoffman and Baes (1979), who determined
that the values are normally distributed with the parameters presented in the previous table.

        The value for silage was calculated in Baes et al. (1984) and is based essentially on sorghum
and corn plantings (Knott, 1957; Rutledge, 1979).

        Potatoes, root vegetables and grains are assumed to equal zero since the edible portion of the
plant is protected from direct deposition (grains have a protective husk).
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       A. 1.3.2        Length of Plant Exposure

Parameter:     TPi

Definition:     The amount of time that the edible part of an exposed plant is exposed to direct
               deposition.

Units:          years
Plant Type
Leafy vegetables
Legume vegetables
Fruiting vegetables
Forage
Silage
Fruits
Default Value
(years)
0.157
0.123
0.123i
0.123
0.123
0.123
Distribution
U(0.082,0.247)
U(0.082,0.247)
11(0.082,0.247)
U(0.082,0.247)
U(0.082,0.247)
11(0.082,0.247)
Range
(years)
0.082-
0.082-
0.082 -
0.082 -
0.082 -
0.082 -
0.247
0.247
0.247
0.247
0.247
0.247
Technical Basis:

       Bounding estimates were obtained by assuming an average time between successive harvests
of 30 and 90 days. This range is based on the values in Baes et al. (1984) of 60 to 90 days and the
reported values by the South Coast Air Quality Management District (SCAQMD) (1988) of 45 days
for tomatoes and 30-85 days for lettuce.

       The default value for leafy vegetables is the midpoint of the range for lettuce.  The values for
legumes, fruits and fruiting vegetables are based on the value of 45 days for tomatoes.   The value for
forage and  silage is the average time between successive hay harvests and successive grazings by
cattle (Baes et al., 1984).
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        A.l.3.3
Parameter:     YPi
Plant Yield
Definition:     Yield of the z'th plant per unit area.

Units:         kg (dry weight)/m2
Type of Crop
Leafy vegetables
Legume vegetables
Fruiting vegetables
Rooting vegetables
Grains and cereals
Forage
Fruits
Potatoes
Silage
Default Value (kg
(dry weight)/m2)
0.177
0.104
0.107
0.334
0.3
0.31
0.107
0.48
0.84
Range (kg (dry
weight)/m2)
0.091 - 0.353
' 0.077 -0.130
0.012 - 0.253
0.090 - 0.434
0.14-0.45
0.02- 0.75
0.012 - 0.253
0.405 - 0.555
0.3- 1.34
Distribution
Log (0.177, 0.086)
Log (0.104, 0.038)
Log(0.107, 0.093)
Log(0.334, 0.142)
Log (0.30, 0.09)
0.84482993969
Log(0.107, 0.093)
Log (0.48, 0.106)
Log(0.84,0.26)
Technical Basis:

        The distributions and ranges shown for all but the silage values are those used in Belcher and
Travis (1989).  The distributions selected were chosen based on a probability plot for leafy vegetables
with data in Shor et al. (1982). The default values are the means of the distributions.  Silage was not
considered in Belcher and Travis (1989), but the same method by which the default values and
distributions were calculated there were replicated using data from Shor et al. (1982) for the purpose
of this assessment.
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       A. 1.3.4        Plant Ingestion by Animals

Parameter:     QPij

Definition:     The daily consumption of plants by livestock.

Units:          kg dry weight/day
Livestock
of
Beef/Beef



Dairy



Pork


Consumption
Plants
Liver
grain
forage
silage

grain
forage
silage

grain
silage
Default Value
(kg dry weight/day)

' 0.97
8.80
2.50

2.60
11.0
3.30

3.0
1.3
Distribution

U(0.5,6.5)
U(2.0,9.0)
U(l,5)

U(0.5,6.5)
U(7,15)
U(l,5)

U(2,4)
U(0.5,3)
Range
(kg dry weight/day)

0.5-6.5
2.0-9.0
1.0-5.0

0.5 - 6.5
7.0-15.0
1.0-5.0

2.0-4.0
0.5-3.0
    Sheep (lamb)
   Poultry/Eggs
                    forage
                      grain
 1.1
0.08
   U(0,2)


U(0.04,0.10)
0.0 - 2.0
0.04-0.10
Technical Basis:

       With the exception of the beef liver, egg and lamb-forage values, the default values are from
U.S. EPA (1990). The value for beef liver is assumed to be the same as for cattle, and the value for
eggs is assumed to be the same as for poultry.  The value for lamb-forage is from the National
Academy of Sciences  (NAS, 1987).

       The ranges shown are based on a combination of the ranges determined by Belcher and Travis
(1989), the U.S. EPA  (1990) values, and the objective of capturing all of the most likely values.

       Although lognormal distributions were chosen in Belcher and Travis (1989),  this was not
based on the actual distribution of the available data; that is, no probability plots were done.  For that
reason, uniform distributions are suggested here.
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        A. 1.3.5        Soil Ingestion by Animals

Parameter:     QSj

Definition:     Quantity of soil ingested daily by the a specific animal.

Units:         kg/day
Livestock
Beef/beef liver
Dairy
Pork
Sheep (lamb)
Poultry/eggs
Default Value
(kg/day)
0.39
0.41
0.034
0.05
0.009
Range
(kg/day)
0.1 - 0.72
0.1 - 0.72
0.0 - 0.0688
0.01 -0.15
0.006 - 0.012
Technical Basis:

        The values for beef cattle and dairy cattle are from McKone and Ryan (1989).  The value for
beef liver is assumed to be the same as for beef. The value for pork is the mean of the distributions
used in Belcher and Travis (1989) and are based on values in Fries (1987). The sheep  value is from
Fries (1982). The value for poultry is the mean of the distribution used in the Hanford Environmental
Dose Reconstruction Project (HEDR, 1992) and is based on values for free-ranging chickens.  The
range is that used in HEDR (1992).

        For beef, dairy and pork, the ranges are from Belcher and Travis (1989).

        The range for sheep is based on the values  reported in Fries  (1982). The lower end of the
range is for sheep that are fed in a lot, in which case they eat little soil.  The upper end is based on
sheep grazing on poor pasture land.

A. 1.4   Exposure Parameters

        Exposure parameters are variables that directly affect an individual's dose or intake of a
contaminant. Such parameters include inhalation and ingestion rates of air, water and crops and the
surface area of skin for the purposes of dermal contact scenarios.
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       A. 1.4.1        Inhalation Rate

Parameter:     INH

Definition:     Rate of inhalation of air containing contaminants.

Units:          m3/day
                         Receptor    Default Value      Distribution
                                        (m3/day)
Infant
Child
Adult
5.14
16
20
7(1.7,5.14,15.4)
7(2.9,16,53.9)
7(6,20,60)
Technical Basis:

       The default value for infants is the central value of the distribution used for 1 year olds in
Hanford Environmental Dose Reconstruction Project (HEDR) (1992) and is from Roy and Courtay
(1991). The default value for children is based on U.S. EPA (1990).  The default value for adults is
that recommended in U.S. EPA (1991), which states that this value represents a reasonable upper
bound for individuals that spend a majority of time  at home.

       The range for infants is that used for 1 year olds in HEDR (1992)  and was determined  by
scaling the value 5.14 by 0.3 and 3.0, respectively.  The range for children is  the smallest range
containing the values used for 5-, 10-, and 15-year-old children in HEDR (1992).  The range for the
adult was obtained by scaling the default value by the same numbers used for infants of 0.3 and 3.0
(we note that HEDR, 1992 used a slightly higher central value of 22 nvVday).

       To prevent a bias towards upper-end inhalation rates, triangular distributions were considered
more appropriate than more arbitrary uniform distributions, with a most likely value equal to the
default value.
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        A. 1.4.2        Consumption Rates

 Parameter:    CPi, CAj

 Definition:     Consumption rate of food product per kg of body weight per day.

 Units:         g dry weight/kg BW/day
Food Type
Leafy Vegetables
Grains and cereals
Legumes
Potatoes
Fruits
Fruiting vegetables
Rooting Vegetables
Beef, excluding liver
Beef liver*
Dairy (milk)
Pork
Poultry
Eggs
Lamba
Child (gDW/kgBW/day)
0.008
3.77
0.666
0.274
0.223
0.120
0.036
0.553
0.025
2.04
0.236
0.214
0.093
0.061
Adult (g DW/kg BW/day)
0.0281
1.87
0.381
0.170
0.570
0.064
0.024
0.341
0.066
0.599
0.169
0.111
0.073
0.057
  Only the 95-100 percentile of the data from TAS (1991) was nonzero.
 Technical Basis:

        All of the values reported above are given on a gram dry weight per kg of body weight per
 day basis.  With the exception of the ingestion rates for adults for leafy vegetables and  fruits, the
 values are either the 50-55 percentile (or the 95-100 percentile if the median was zero)  of the data
 from Technical Assessment Systems, Inc. (TAS). The values for the percentiles were reported in g
 DW/kg of body weight per day.

        TAS conducted this analysis of food consumption habits of the total population and five
 population subgroups in the United States. The data used were the results of the Nationwide Food
 Consumption Survey (NFCS) of 1987-88  conducted by the United States Department of Agriculture.
 The information in the NFCS was collected during  home visits by trained interviewers using one-day
 interviewer-recorded recall and a two-day self-administered record.  A stratified area-probability
 sample of households was drawn in the 48 contiguous states from April 1987 to 1988.   More than
 10,000 individuals provided information for the basic survey.
.June 1996                                    A-9                        SAB REVIEW DRAFT
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       Each individual's intake of food was averaged across the 3 days of the original NFCS survey,
and food consumption for each food group was determined for each individual.  Percentiles were then
computed for six population subgroups:

               U.S. population
               males >: 13 years
               females  > 13 years
               children 1-6 years
               children 7-12 years
               infants < 1 year.

       The values for children in the previous table are based on the data for children between 7 and
12 year of age, while the adult values are for males older than 12 years of age.  The males older than
12 years  of age were chosen to represent the adult since rates  for females are lower; this is
recoganized to be somewhat conservative.  The United States population rates include the rates of
children which were considered inappropriate for the hypothetical adult receptors modeled in  this
analysis.

       The values for leafy vegetables and fruits for adults are from (IJSU.S, EPA 1989).

       A. 1.4.3        Soil Ingestion Rate

Parameter:     Cs

Definition:     Amount of soil ingested daily.

Units:          g/day
Receptor
Pica Child
Child
Adult
Default Value (g/day)
7.5
0.2
0.1
Distribution
U(5,10)
11(0.016,0.2)
U(0.016,0.1)
Range (g/day)
5-10
0.016-0.2
0.016-0.1
Technical Basis:

       Soil ingestion may occur inadvertently through hand-to-mouth contact or intentionally in the
case of a child who engages in pica.  The default values for adults and non-pica children are those
suggested for use in U.S. EPA (1989).  More recent studies have found that these values are rather
conservative. For example,  Calabrese and Stanek (1991) found that average soil intake by children
was found to range from 0.016 to 0.055 g/day.  This range, in conjunction with the suggested U.S.
EPA values, was used to obtain the ranges shown.

       Several studies suggest that a pica child may ingest up to 5 to 10 g/day (LaGoy,  1987, U.S.
EPA, 1989).  This range was selected, and the midpoint was chosen as the default value.
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        A. 1.4.4        Groundwater Ingestion Rate

Parameter:    Cw

Definition:     The amount of water consumed each day.

Units:         L/day
                        Receptor    Default Values       Distribution
                                        (L/day)
Child
Adult
1.0
2.0
Log*(0.378; 0.079)
Log*(0.1; 0.007)
Technical Basis:

        The default values for children and adult are those also suggested in U.S. EPA (1989) and
were first published by the Safe Drinking Water Committee of the National Academy of Sciences
(NAS, 1977).

        The distributions are those computed in Roseberry and Burmaster (1992).  In that paper,
lognormal distributions were fit to data collected in a national survey for both total water intake and
tap water intake by children and adults.  These data were  originally gathered in the 1977-1978
Nationwide Food Consumption Survey of the United States Department of Agriculture and were
analyzed by Ershow and Cantor (1989).

        In Roseberry and Burmaster (1992), distributions were fit to the intake rates for humans ages
0-1 year, 1-11 years, 11-20 years, 20-65  years and older than 65 years.  The distribution for children
ages 1-11 was chosen for the child's distribution given in the previous table and the distribution for
adults ages 20-65 was used for the adult.  For the purpose of the present analysis, the tap water intake
was deemed more appropriate than total water intake. The total water intake included water intrinsic in
foods that are accounted for in the agricultural pathways, while the tap water intake was the sum of
water consumed directly as a  beverage and water added to foods and beverages during preparation.

        The minima and maxima were selected as the 2.5  and 97.5 percentiles, respectively.
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       A. 1.4.5        Fish Ingestion Rate

Parameter:     Cf

Definition:     Quantity of locally - caught fish ingested per day.

Units:         g/day
                               Receptor            Default Value (g/day)

                      High End Fisher                        60

                      Child of high end fisher                 20

                      Recreational Angler                     30
Technical Basis:
       Because of the bioaccumulation of methylmercury in fish, the fish ingestion rate is an
important parameter for modeling mercury exposure. Fish consumption rates are difficult to determine
for a general population study because individual fish ingestion rates vary widely across the United
States. This animal protein source may be readily  consumed or avoided on a seasonal, social,
economic or demographic basis.  Ideally, for an actual site, specific surveys identifying the type,
source, and quantity of fish consumed by  area residents would be used.  Within the context of this
study, it is not possible Jo characterize this variability completely (Please see Appendix H for a more
complete discussion of reported fish consumption rate variability).

       For this part of the assessment, individuals in three broad groups of exposed populations will
be considered:  high end fishers, recreational anglers and the general population.  For the general
population, no commercial distribution of locally caught fish was assumed.  All consumers of locally-
caught fish were assumed to be recreational anglers or subsistence fishers.

       In U.S. EPA's 1989 Exposure Factors  Handbook, fish consumption  data from Puffer (1981)
and Pierce et al. (1981) are suggested as most appropriate for fish consumption of recreational anglers
from large water bodies. The median of this subpopulation is 30 g/day with a 90th percentile of 140
g/day (340 meals/year). The median was  used as the surrogate value for recreational  anglers.

       For subsistence fishers, human fish consumption data were obtained from the report of the
Columbia River Inter-Tribal Fish Commission (1994), which estimated fish  consumption rates for
members of four tribes inhabiting the Columbia River Basin.  The estimated fish consumption rates
were based on interviews with 513 adult tribe  members who lived on or near the reservation. The
participants had been selected from patient registration lists provided by the Indian Health Service.
Adults interviewed provided information on fish consumption for themselves and for 204 children
under 5 years of age.

       During the study fish were consumed by over 90%  of the population with only 9% of the
respondents reporting no fish consumption.  Monthly variations in consumption rates were reported.
The average daily consumption  rate  during the two highest  intake months was  107.8 grams/day,  and
the daily consumption rate during the two lowest consumption months was 30.7 grams/day. Members
who were aged 60 years and older had an average  daily consumption rate of 74.4 grams/day. During
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the past two decades, a decrease in fish consumption was generally noted among respondents in this
survey. The maximum daily consumption rate for fish reported for this group was 972 grams/day.

       The mean daily fish consumption rate for the total adult population (aged 18 years and older)
was reported to be 59 grams/day. The mean daily fish consumption rate for the adult females
surveyed was 56 g/day and the mean daily fish consumption rate for the adult males surveyed was 63
grams. A value of 60 grams of fish per day was selected for the subsistence angler modeled in this
report.

       Other fish consumption rate studies  for specific subpopulations (i.e., anglers and subsistence
consumers) have been conducted. These studies are briefly described  in Appendix H.  These studies
demonstrate the wide range of fish consumption rates exhibited across the U.S. population.  They also
tend to corroborate the estimates to be used in this analysis.  These analyses also illustrate the
difficulty in determining average and high-end consumption rates for subpopulations considered to be
more likely to consume more fish.
                       \
       In the lacustrine scenarios of this assessment, all fish  were assumed to originate from the
lakes, which are considered to represent several small lakes that may be present in a hypothetical
location.

       The effects of fish preparation for food on extant mercury levels in fish have also been
evaluated (Morgan et al., 1994).  Total mercury levels in walleye  were found to be constant before
and after preparation; however, mercury concentrations in the cooked fish were increased 1.3 to 2.0
times when compared to mercury levels in the raw fish. It was suggested that this increase was
probably due to water and fat loss during  cooking and fish skin removal.  A preparation factor
adjustment was noted but not implemented in this analysis because human consumption levels were
measured on uncooked fish.  (For more information see Appendix H.)
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        A. 1.4.6
Contact Fractions
Parameter:     FPi, Faj

Definition:      that fraction of the food type grown or raised on contaminated land

Units:          Unitless
Food
Subsistence
Farmer
Rural Home Urban Gardener
Gardener/
Subsistence
Fisher
Comment
   Grains
   Legumes
   Potatoes
   Root Vegetables
   Fruits


   Fruiting
   Vegetables

   Leafy Vegetables
                     0.667


                      0.8


                     0.225


                     0.268



                     0.233


                     0.623


                     0.058
0.195         Values are for corn from
             Table 2-7 in U.S. EPA
             (1989)

 0.5          Values are for peas from
             Table 2-7 in U.S. EPA
             (1989).

0.031         Values are for total fresh
             potatoes from Table 2-7 in
             U.S. EPA (1989).

0.073         Values are for carrots from
             Table 2-7 in U.S. EPA
             (1989).

             Values are for Total non-
0.076         citrus fruit from Table 2-7
             in U.S. EPA (1989).

0.317         Values are for tomatoes
             from Table 2-7 in U.S.
             EPA (1989).

0.026         Values are for lettuce from
             U.S. EPA (1989)'
Beef 1
Beef liver 1
Dairy 1
Pork 1
Poultry 1
Eggs 1
Lamb 1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Technical Basis:

        The values for the subsistence farmer are consistent with the assumptions regarding this
scenario. The values for the gardeners are from U.S. EPA (1989), per U.S. EPA guidance.  Because it
is assumed  that only the subsistence farmers will consume contaminated animal products, the contact
fractions for gardeners is 0 for consumption of local animal products.
June  1996
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A.2    Chemical Dependent Parameters

       Chemical dependent parameters are variables that change depending on the specific
contaminant being evaluated. The chemical dependent variables used in this study are described in the
following sections.

A.2.1  Basic Chemical Properties

       The following sections list the chemical properties used in the study, their definitions, and
values.

       A.2.1.1         Molecular Weight

Parameter:    Mw

Definition:     The mass in grams of one mole of molecules of a compound.

Units:         g/mole
                              Chemical
        Default Value (g/mole)
                      Hg°, Hg2+

                      Methylmercury

                      Methyl mercuric chloride

                      Mercuric chloride
                 201

                 216

                 251

                 272
       A.2.1.2       Henry's Law Constant

Parameter:    H

Definition:    Provides a measure of the extent of chemical partitioning between air and water at
              equilibrium.

Units:         atm-m3/mole
                         Chemical
                    Hg2+ (HgCl2)
                    Methylmercury
Technical Basis:
Default Value (atm-m /mole)
                                                   v.ixicr3
         T.lxlO'10
          4.7x10'
       The higher the Henry's Law Constant, the more likely a chemical is to volatilize than to
remain in the water. The value for Hg° is from Iverfeldt and Persson (1985), while the other values
are from Lindquist and Rodhe (1985).
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       A.2.1.3        Soil-Water Partition Coefficient

Parameter:    Kd

Definition:     Equilibrium concentration in dry soil divided by concentration in water.

Units:         mL/g
                              Chemical       Default Value (mL/g)

                           Hg2+                     53,700

                           Methylmercury            53,700
Technical Basis:
       The values in the previous table are the geometric mean of calibrated values (see Appendix C
of Volume HI).

       A.2.1.4        Sediment-to-Water Partition Coefficient

Parameter:    Kdb

Definition:     Equilibrium concentration in dry sediment divided by concentration in water.

Units:         mL/g
                              Chemical       Default Value (mL/g)

                          Hg2+                     157,000

                          Methylmercury            157,000
Technical Basis:
       The values in the previous table are the geometric mean of calibrated values (see Appendix C
of Volume III).
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        A.2.1.5        Suspended Sediment-Water Partition Coefficient
                                             •

Parameter:     Kdw

Definition:      Suspended sediment-water partition coefficient.

Units:         L/kg
                    Chemical      Default Value (L/kg)          Range

                 Hg2+                     95000              1340-188,000

                 Methylmercury            650000           320,000 - 1,000,000
Technical Basis:
       For divalent mercury, data were available from three studies, and are shown in Table A-2.
The default value is the midpoint of the range.
                                          Table A-2
                      Ranges of Values for Suspended Sediment-to-Water
                                     Partition Coefficient
                       Range (L/kg)               Reference

                      1380-188,000     Moore and Ramamodoray (1984)

                      118,000          Glass  et al. (1990)

                      86,800-113,000   Robinson and Shuman (1989)
For methylmercury, the only data found that specifically address suspended material are those in
Bloom et al. (1991).  In particular, they report that "Regardless of pH, for over three orders of
magnitude, the log Kd for seston [suspended matter]  was in the range of 5.5 to 6.0."  The range listed
in the previous table corresponds to this range. The  midpoint of the observed range is used as the
default value.
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       A.2.1.6
       Soil and Water Loss Degradation Constants
Parameter:

Definition:


Units:
ksg and kwg

Soil and water body loss of the contaminant due to biotic and abiotic degradation and
aqueous hydrolysis, respectively.

/yr
Chemical
Hg°
Hg2+
Methylmercury
Default Value (year)
0.0
0.0
0.0
Range
N/A
N/A
N/A
Technical Basis:

       Data indicate that equilibrium is established between different species of mercury rather than a
degradation/breakdown process.  Parks et al., (1989) found that "In water, methylmercury and
inorganic appear to be in quasi-equilibrium, as the methylmercury/total mercury ratio in river water is
independent of contact time with sediments, the atmosphere, and the theoretical residence time of
waters."  For this reason, it appears reasonable simply to assume no net loss with time if any mercury
species occurs in either soil or water.

       A.2.1.7        Equilibrium Fraction for Chemical in Soil

Parameter:     fspecs

Definition:     For all chemicals tied together in soil equilibrium, the fraction which is chemical i is
               given by fspec.
Units:
unitless
Chemical Default Value
Hg°
Hg2+
Methylmercury
0
0.98
0.02
Distribution
None
7(0.9,0.98,0.9998)
!-%Hg2+
Technical Basis:

       Akagi et al. (1979) reported methylmercury fractions of .02, .072 and .089 for sand,
silt/woodchips, and woodchip sediments as compared to total mercury. Wilken and Hintelmann (1991)
reported that 0.10 of the total mercury in sediments from the River Elbe in Germany is methylated,
although they pointed out that others had reported maximums of 0.01  and 0.02. Hildebrand et al.
(1980) found methylmercury fractions of .0002 - .0005 in sediments from the Holston River, VA.
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                               A-18
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        The measurements in the previous table did not distinguish between Hg2"1" and Hg  in the
remaining fractions, leaving the partitioning of these species in soil uncertain.  It is known that Hg
can be formed from .reduction of Hg2+ in the soil environment, a fraction of which will volatilize and
a fraction of which can be bound to organic matter.  Both processes depend strongly on soil conditions
(Nriagu, 1979).  At the redox potential normally found in soils, however, Hg2+ complexes  are
expected to be predominant than Hg°.

        Cappon (1984) found that percent of methylmercury over total mercury for nonamended soils
is 2.6%. This is an upper bound on values from unpublished data reported by several authors
(Lindqvist et al., 1991).

        A.2.1.8        Equilibrium Fraction for Chemical in Water

Parameter:     fspecw

Definition:     For all chemicals tied together in water equilibrium, the fraction which is chemical i is
               given by fspecw.

Units:         unitless
Chemical Default Value
Hg°
Hg2+
Methylmercury
0.02
0.83
0.15
Distribution
NA
1 - (Methyl+Hg° %)a
Log(0.14,1.0)
Range
0.007 -
0.04
.31 - .96
.03 - .65
       a The distribution is 1 minus the methylmercury concentration and elemental mercury
       concentration dissolved in the water.

Technical Basis:

       The default value given for methylmercury is that suggested in U.S. EPA (1993).  In well
oxygenated water, the remaining fraction (i.e., non-methylated) will be mainly Hg2+ complexes
(Nriaga, 1979). There will be a small fraction of total  mercury in water that will be Hg° due to
reduction of Hg2+ by humic acid and microorganisms (Nriaga, 1979; Alberts et al., 1974).

       Fitzgerald et al. (1991) measured the concentration of total dissolved gaseous mercury in
various lake waters  and found in all cases that it consisted mainly of elemental mercury (> 97%).
Much of these measurements were taken at both basins of Little Rock Lake, WI, from which total
mercury concentrations for the acid-treatment and reference basins  are known from the work of Watras
and Bloom (1992).  Comparing the concentrations within each basin gives a possible range for the
percent mercury in water that is Hg° of 0.7 - 4%,  the  midpoint of which (2%) we use as the default
equilibrium percentage of mercury in the water column that is elemental mercury.

       There are a  wealth of data on the Methylmercury/Total mercury in the water column.  Table
A-3 lists the values  found reported in the literature.  These values were used to determine the range
given previously for methylmercury. The range for Hg2+ is then given by subtracting the
contributions from methylmercury and elemental mercury from the total.  (For more details see
Volume V of this Report.)

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                                           Table A-3
         Reported Values for Fraction of Total Mercury that is Methylmercury in Water


                             Values                        Reference

                  0.26, 0.11, 0.07, 0.07, 0.15        Bloom et al. (1991)

                  0.01, 0.022, 0.019, 0.054,         Parks et al. (1989)
                  0.055, 0.052, 0.049, 0.064

                  0.32, 0.48, 0.57                  Akagi et al. (1979)

                  0.12,0.05                        Watras and Bloom (1992)

                  <:0.025                          Bloom and Watras (1989)

                  0.04-0.05                        Lee et al. (1990)

                  0.26-0.46                        Kudo et al. (1982)

                  0.01-0.89                        Gill and Bruland (1990)

                  0.036-0.273                      Bloom and Effler (1990)

                  0.036-0.053	Lee and Hultberg (1990.)	


 A.2.2  Biotransfer Factors

        Biotransfer factors reflect the extent of chemical partitioning between a biological medium
 (plants, meats or fish) and an external medium (air, soil or water).  The following sections describe the
 BCFs used in this study.

        It is necessary to note the uncertainty inherent in determining BCFs for mercury species with
 regard to plant uptake.  In general, there seems to be no consensus in the literature on plant
 bioconcentration factors for mercury, as values for each crop vary widety among studies. Further, in
 many studies the mercury speciation is not determined.  In deriving BCFs for plant absorption of
 mercury species fromthe air and soil, it was, therefore sometimes necessary to make assumptions about
 certain behaviors ofmercury based on whatever information was at hand, as opposed to established
 scientific knowledg, which was lacking.  These assumptions are described in each Technical Basis
 section that follows, but it is useful at this time to identify some of the general uncertainties regarding
 plant uptake of mercury.

        (1)    Plants both absorb and release mercury to the environment.  Hanson et al.  (1994)
               demonstrates clearly that at ambient air concentrations forest foliage usually acts as a
               source of elemental mercury to the atmosphere; deposition (plant absorption) only
               occurs above a "compensation concentration"  at air mercury  levels well above
               background.  It is not yet known from where the mercury released by the plants
               originates (air uptake during periods of high mercury air concentrations, root uptake,
               Hg(II) absorption,  etc.).  Similarly, Mosbaek (1988) found that for a given period of
               time more elemental mercury was released from a plant-soil  system than was absorbed
               by the plant. These  cases, however, in no way indicate that  mercury is not
               bioconcentrated in plants; the above behaviors are consistent with mercury being
               collected by plants only to certain levels, after which any mercury absorbed is simply
               released.

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        (2)     It is usually not known from where the mercury that is found in plants originated (air
               vs. soil).  Only one study determined the fractions of total mercury in plants which
               came from air and soil (Mosbaek, 1988); in this study, soil was isotopicly labelled
               with 203Hg.  After some time the specific activity in the plant was compared to that in
               the soil to ascertain how much of the mercury in the plant came from the soil.
               Although the  experiment worked well, isotopic equilibrium in the soil was never
               achieved, and the number of plants studied was limited.

        (3)     The speciation of mercury in plants is often not known. If it is known, it is still very
               unclear as to how the speciation occurred.  The plant speciation may be simply a result
               of direct uptake of different mercury species from the environment (but from air or
               soil?).  It has been shown, however, that a few plants have the ability to change the
               species of mercury initially taken up from the environment (Fortmann et al., 1978).
               Such behavior may have to be accounted for regarding plant uptake of mercury.

        A.2.2.1        Plant-Soil BCF
Parameter:
BRi
Definition:     The ratio of the contaminant concentration in plants (based on dry weight) to that in
               the soil.
Units:
Unitless
Crop
Leafy vegetables
Legume vegetables
Fruiting vegetables
Rooting vegetables
Grains and cereals
Forage
Fruits
Potatoes
Silage

Default
Value
0
0.015
0.018
0.036
0.0093
0
0.018
0.1
0
Hg2+
Distribution
None
17(0.00026,
0.157)
11(0.007,0.059)
17(0.011,0.073)
17(0.0024,0.057)
None
17(0.007-0.059)
17(0.05,0.2)
None
Methylmercury
Default
Value
0
0.031
0.024
0.099
0.019a
0
0.024
0.2a
0
Distribution
None
17(0.0,
0.090)
17(0.0,0.11)
17(0.013,0.29)
17(0.0048,0. ll)a
None
17(0.0,0.11)
U(0.1,0.4)a
None
  Hg2+ values multiplied by 2
Technical Basis:

       Mosbaek (1988) convincingly showed that for leafy, above-ground parts of plants virtually all
of the mercury uptake was from air; therefore, for leafy vegetables, forage and silage no root uptake
June 1996
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was modeled.

       Values in Cappon-(1987) and Cappon (1981) were the only data located which measured
methylmercury concentrations in plants, and methylmercury plant-soil BCF's were determined for
rooting vegetables, fruiting vegetables,  and legumes.  Values were determined for crops grown on
compost (Cappon 1987) and sludge-treated soils (Cappon 1981), and those values considering edible
portions of plants are shown in Table A-4.

                                          Table A-4
                        Soil-to-Plant Transfer Coefficients for Mercury
                            (from Cappon, 1987 and Cappon, 1981)
Crop
'
Beet
Carrot
Onion, Yellow
Onion, Spanish
Red Radish
White Radish
Turnip
1987 Values
Hg2*
Rooting
.055
.026
.073
- -
.056
-
.026
Methylmercury
Vegetables
.227
.118
.288
-
.092
-
.013
Hg2*

.017
.014
.053
.047
.018
.011
-
1981 Values
Methylmercury

.11
.048
.042
.030
.066
.060
-
                                      Fruiting Vegetables
Cucumber, slicing
Cucumber, pickle
Pepper
Zucchini
Summer Squash
Acorn Squash
Spaghetti Squash
Pumpkin
Tomato

Green Bush Beans
Yellow Bush Beans
Lima Beans
-
.007
.019
.021
-
-
-
-
.059
Legumes
.011
-
-
-
0
.022
0

-
-
-
.105

0
-
-
.015
.015
.016
.014
.007
.016
.016
.008
.020

.014
.017
.017
0
.006
.042
.018
0
.012
.024
.006
.072

.020
.015
.090
       It has been shown, however, that mercury taken up into plants from the environment can be
transformed into other mercury species, especially to organomercuric forms such as methylmercury
(Fortmann et al., 1978).  The methylmercury in plants, therefore, may not have been directly absorbed
from the environment. For the purposes of this study, considering root uptake, methylmercury
concentrations in plants were treated as though they originated from the soil.  It is also important to
June 1996
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note that air-to-plant transfer jnay have occurred, but the Cappon (1981, 1987) study was not designed
to measure air-uptake.

        Table A-5 shows additional soil-to-plant transfer coefficients for Hg2+ species (it was assumed
that all the mercury in the soil is Hg2+, which at worst would result in an error of a few percent in the
Hg2+ soil-to-plant transfer coefficients) determined from a number of studies.  Temple and Linzon
(1977) sampled garden produce in  the vicinity of a chlor-alkali plant.  Lenka et al. (1992) also
measured mercury concentrations in soil and plants near a chlor-alkali plant.  Somu et al. (1985)
determined mercury uptake in wheat and beans grown on HgCl2 contaminated soil.  John (1972)
determined mercury concentrations in plants grown on soil artificially contaminated with HgCl2.
Wiersma et al. (1986) measured soil and plant total mercury concentrations from major growing areas
in the Netherlands.  Belcher and Travis (1989) compiled data  from EPA (1985). Mosbaek (1988)
studied plant concentrations  from soil and  air uptake under background conditions.  For studies
reporting wet weight plant concentrations,  wet weight to dry weight conversion factors in Baes et al.
(1984) were used to convert to dry weight based concentrations.
                                        .   Table A-5
                  Other Values for Soil-to-Plant Transfer Coefficients for Hg2+
         Crop
              Values
         References
  Legume vegetables
  Fruiting vegetables
0.157-1.79,  0.00026-0.0003,
0.0005, 0.003-0.03

0.013-0.33,  0.127-1.36,  0.0078-
0.028
  Rooting vegetables     0.09-0.33, 0.090-0.149, 0.0065-
                        0.013,  0.05-0.2, 1.6-1.9


  Grains and cereals     0.0024-0.0093,  0.0033, 0.00038-
                        0.057
  Fruits

  Potatoes
0.0078-0.028

0.05-0.2
Lenka et al. (1992), Somu et
al. (1985), John (1972),
Belcher and Travis (1989).

Temple and Linzon (1977),
Lenka et al. (1992), Belcher
and Travis  (1989).

Temple and Linzon (1977),
Lenka et al. (1992), John
(1972),  Belcher and Travis
(1989),  Mosbaek (1988)

Somu et al.  (1985),  John
(1972),  Belcher and Travis
(1989).

Belcher and Travis (1989).

Belcher and Travis (1989).
        When possible, default values were chosen based on experiments under reasonable or
background conditions, as opposed to experiments where the soil was "spiked" with large amounts of
mercury or measurements were taken from severely polluted areas.  This is actually a conservative
approach;  although plants from mercury  polluted areas will have greater contaminate levels, the
efficiency of accumulation (quantified in the transfer coefficients) tends to decrease with increasing
contaminate concentrations.  Values from Cappon (1987) and Cappon (1981) were used when possible,
since these experiments were conducted under reasonable garden conditions, edible portions of plants
were analyzed separately, and different mercury species were measured.  Cappon (1981) analyzed
plants grown in control soil (total mercury soil content of 120 ng/g  with 4.2% methylmercury) in
June 1996
                      A-23
             SAB REVIEW DRAFT
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addition to the sludged soil (330 ng/g with 5.1% methylmercury, which is comparable to the 1987
soil levels of 430 ng/g with 5.3% methylmercury).  The control soil data were not used since the
methylmercury levels were often undetectable.  Note that the compost and sludge-amended soils,
although elevated in  mercury, are nonetheless at reasonable concentrations.  For fruiting vegetables,
rooting vegetables and legumes values from Cappon (1987) and values derived from the edible potions
of plants grown on sludged soil from Cappon (1981) were pooled and averaged; the results were used
as the defaults for these plant types.

       Default Hg2+ values for grains and cereals are from Somu (1985); the methylmercury values
were assumed to be twice as great in accordance with the overall average trend noted in plants from
the pooled Cappon data.  The default values for fruits were assumed to be the same as for fruiting
vegetables. The default Hg2+ value for potatoes was taken from Belcher and Travis (1989); the
methylmercury value for potatoes was assumed to be twice the Hg2+ value.
       A.2.2.2
       Air-Plant BCF
Parameter:
El
Definition:     The ratio of the contaminant concentration in plants (based on dry weight) to that in
               the air.
Units:
Unitless
Crop
Leafy vegetables
Legume vegetables
Fruiting vegetables
Rooting vegetables
Grains and cereals
Forage
Fruits
Potatoes
Silage

Default
Value
18000
1050
22000
0
1050
18000
22000
0
18000
Kg2**
Distribution
U[ 12000,24000]
U[700,1400]
U[14000,29000]
NA
U[700,1400]
U[ 12000,24000]
U[ 14000,29000]
NA
U[ 12000,24000]
Methylmercury3
Default
Value
5000
100
1200
0
100
5000
1200
0
5000
Distribution
U[3300,6800]
U[65,130]
U[780,1600]
NA
U[65,130]
U[3300,6800]
U[780,1600]
NA
U[3300,6800]
a Based on elemental mercury air concentration, and speciation of divalent and methylmercury species
based on Cappon (1981,1987).

Technical Basis:

       Mosbaek (1988) determined that mercury concentration in the above-ground, leafy parts of
plants is  almost entirely the result of air-to-plant transfer of mercury. Cappon (1987,1981), however,
found only divalent and methylmercury in these types of plants.  Fitzgerald (1986) noted that up to
99% of the total airborne mercury is Hg° vapor (Fitzgerald, 1986).  It was assumed that any
June 1996
                              A-24
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atmospheric elemental mercury taken up by the plant is converted into Hg2+ and methyl mercury in the
plant tissue.  This is not unreasonable: it has been shown that mercury taken up into plants from the
environment can be transformed into other mercury species (Fortmann et al., 1978).

        A strong correlation between mercury soil concentration and concentration in rooting
vegetables has been established (John, 1972; Lenka et al., 1992; Lindberg et al., 1979), and the
Mosbaek study (1988) demonstrated that much of the mercury in rooting vegetables  was from the soil.
As a result, air-to-plant uptake of mercury was not modeled for rooting vegetables and potatoes.

        For grains, fruits, legumes and fruiting vegetables, little correlation between mercury plant
concentrations and either air or soil concentrations has been found; however, non-negligible
concentrations of mercury species in these plants are routinely observed. For this reason, both air-to-
plant and soil-to-plant uptake  was modeled for these plants.  Using a conservative approach, the
transfer factors for each accumulation pathway were calculated as if all of the mercury  in the plant
came only from that pathway.  This has the effect of possibly double-counting  the amount of mercury
in the plant tissue.  There is a great deal of uncertainty due to the lack of applicable data.

        The range of  air-plant bioconcentration factors based on Mosbaek et al. (1988) was found to
be 15,000 - 31,000, based on  total mercury concentration in the plant tissue. Mosbeak et al. (1988)
determined average mercury concentrations due to air uptake in lettuce, radish tops, and grass.
Concentrations were converted to dry weight according to Baes et al. (1984), and the overall range of
air-plant bioconcentration factors based on total mercury in the plant tissue was found to be 15,000 -
31,000.  Air to plant bioconcentration factors can be-derived from other studies only indirectly (by
making a reasonable estimate  of the air concentration and assuming all the mercury in plant tissue
comes from air), and the values arrived at for various plant species generally fall into the previous
range. Due to the limited data, it was decided to use the midpoint of the Mosbeak et al. (1988)
bioconcentration values (23,000) as the starting default for all plant species  assumed to  accumulate
mercury from the air.

        This approach was adjusted for the consideration of portions of grains and legumes that are not
directly exposed to the atmosphere.  Although atmospherically absorbed mercury can translocate
throughout different portions of the plant, data indicate internal portions  of grains and legumes (the
edible portions) do not appear to accumulate mercury to the same degree as plant leaves or vines.
Somu et al. (1985), John (1972), and Cappon (1981) determined mercury concentrations from different
portions  of the same plants. Table A-6 below shows the relative concentrations of total mercury found
in plant parts from the portions of these studies representative of noncontaminated conditions.
June 1996                                     A-25                        SAB REVIEW DRAFT
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                                           Table A-6
              Relative Concentration of Mercury in Different Parts of Edible Plants
Legumes
vines
stalks
pods
seeds
Grains
leaves
stalks
husks
grain
Beans
(Somu et al. 1985)

1.0

0.060
Wheat
(Somu et al. 1985)

1.0

0.14
Peas
(John 1972)
1.0
•
0.045
0.0091
Oats
(John 1972)
1.0
0.063
0.61
0.051
Beans
(Cappon 1981)


1.0
0.028 - 0.089





                                                       i
       A clear trend of decreasing mercury concentrations is seen proceeding from leafy to seed
portions of the plants.  Based on these data, it was decided to decrease the default air-to-plant
biconcentration factor of 23,000 by a factor of 20 (to 1200) to account for the decreasing accumulation
of airborne mercury for the edible portions of these plants as compared to the leafy portions (for which
the biconcentration factor of 23,000 is applicable). Airborne mercury uptake by fruits may also be
overestimated with the default bioconcentration  factor.  However, no data are available to explore this
possibility.

       The product of the bioconcentration factors and the  atmospheric mercury concentration is the
total mercury in the plant tissue resulting from accumulation of airborne elemental mercury.  Plant-
specific speciation estimates from Cappon (1981,1987) were used to partition the total mercury
bioconcentration factor (and corresponding range) in order to model the relative fractions of
methylmercury and Hg2+ found in the plant; these are shown in Table A-7;  note that  the rest of the
mercury was found to be divalent mercury.
       Thus, for leafy, fruiting and legume vegetables, the default values for the bioconcentration of
methylmercury based on the elemental mercury concentration in air were assumed to be 23,000 or
1200 multiplied by the average methylmercury percentages in Table A-6; the  Hg2+ values were
derived similarly  (Hg2+ fraction x 23,000).  The values for fruits were assumed to be the same as for
fruiting vegetables. The values for forage and silage were assumed the same  as for leafy vegetables,
and the values for grains were assumed to be the same as for legumes (beans).
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                                      Table A-7
                          Mercury Speciation in Various Plants
Plant Type

Head lettuce
Leaf lettuce
Spinach ,
Swiss chard, Fordhook
Swiss chard, Ruby Red
Broccoli3
Late Cabbage
Red Cabbage
Savoy King Cabbage
Jersey Wakefield Cabbagea
Cauliflower
Collards
Average

Green Bush Beans
Yellow Bush Beans
Lima Beans
Average

Cucumber, slicing
Cucumber, pickle
Pepper
Zucchini
Summer Squash
Acom Squash
Spaghetti Squash
Pumpkin
Tomato
Average
" These were classified as
% Methylmercury Cappon % Mefhylmercury Cappon (1987)
(1981)
Leafy vegetables
8.8 21.4
16.5 18
19.8 23.1
30.2 14.8
28.6
33.1 17.8
28.8
22.4
25.2
18
21.2
22.8
21.8
Legume vegetables
0 7.2
4.3
22.4
8.5
Fruiting vegetables
0
2.1 0
12.5 6.1
6.7 0
0
4.1
7.4
4.0
16.0 9.1
5.2
"cole" in Cappon (1987).
June 1996
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       A.2.2.3        Animal RTF

Parameter:     BAj

Definition:     The equilibrium concentration of a pollutant in an animal divided by the average daily
               intake of the pollutant.

Units:          day/kg DW
Livestock
beef
beef liver
dairy

pork
poultry
eggs
lamb
Default Value (day/kg DW)
0.02
0.05
0.02

0.00013
0.11
0.11
0.09
Distribution
11(0.0008,0.04)
11(0.02,0.1)
U(0.003,0.09)
\
U(0.00005, 0.00026)
U( 0.094,0. 13)
U( 0.094,0. 13)
11(0.009,0.3)
Technical Basis:

       Biotransfer factors measure pollutant transfer from the environment to animal tissues and
products.  They are defined as the ratio of pollutant concentration in animal tissue to the daily
pollutant intake of an animal.  The biotransfer factors for mercury to cattle tissues were estimated
based on data found in Vreman et al. (1986), and biotransfer factors for mercury to  lamb were based
on data found in van der Veen and Vreman (1986).

       The data collected from Vreman et al. (1986) and van der Veen and Vreman (1986) are not
from single pollutant and single route ingestion studies; rather, the animals in these  studies were
generally dosed with elevated levels of several metals in a single wafer.  This is not the ideal set of
studies for assessing the transfer of mercury primarily from ingested grass and soil.  These studies,
however  are multiple dose and long-term experiments which should provide data more representative
of the desired equilibrium situation than a single, very large dose experiment.

       In two experiments, Vreman et al. (1986) measured transfer of mercury from diet to tissues
and milk of dairy cattle. In the first experiment 12 lactating cows/group were placed on pasture in 2
groups for 3 months.  The control group was fed uncontaminated wafers and, based on mercury levels
in the pasture grass, were estimated to ingest 0.2 mg mercury/day.  The exposed group received wafers
treated with a solution of mercury acetate, lead, cadmium and arsenic pentoxi.de; the daily mercury
ingestion rate for the exposed group was  1.7 ing/day.  During the experiment mercury levels in milk
were measured.  After three months on test, four cows/group were slaughtered, and  mercury levels
were measured in liver, kidney and muscle samples. In the second  study, lactating cows were kept
indoors and divided into 4 groups of 8  for up to 28 months.  In  addition to the control group, the diets
of 3 other groups were supplemented with the following: wafers containing the same metals (1.7 mg
mercury/day), sludge delivering dietary levels of 3.1 mg mercury/day, and sludge delivering dietary
levels of 1.2mg mercury/day.  Two cows from each group were slaughtered at study termination
June 1996                                    A-28                        SAB REVIEW DRAFT
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(except for the group receiving 3.1 mg mercury/day from sludge in which only one cow was
sacrificed).  Mean milk mercury concentrations in the groups were reported, and mercury levels in the
slaughtered cows were measured in liver, kidney and muscle samples.

        Shown in Table A-8 are data from Vreman et al. (1986) that are relevant to deriving beef and
dairy biotransfer, factors.  The  tissue mercury concentrations presented are in wet weight.
                                           Table A-8
                         Mercury Concentrations in Specific Beef Tissue
                   Media Per Test Group and Dose (from Vreman et al, 1986)
Test Group
Pasture Control
Pasture Treated
Indoor Control
Indoor Wafer
Indoor
Dose
(mg mercury/day)
0.2
1.7
0.2
1.7
3.1
Mercury in Milk
(ug/Kg WWA)
2.3
0.9
<0.5
0.6
2.4
Mercury in
Muscle
(ug/Kg WWA)
3
4
2
2
1
Mercury in Liver
(ug/Kg WWA)
7
10
3
26
14
  High-Level Sludge

  Indoor
  Low-Level Sludge
1.2
1.3
A Wet weight

       The data in Table A-8 can be easily converted into milk, beef and liver biotransfer factors by
converting the tissue concentrations  to dry weight and dividing the tissue concentrations by the daily
intake of mercury (after converting the intake from mg/day to ug/day).  The moisture content of the
above tissues are reported in Baes et al. (1984): 0.87 for whole milk, 0.615 for beef and 0.70 for liver.
The biotransfer factors derived are shown in Table A-9.
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                                          Table A-9
                 Animal Biotransfer Factors Derived from Vreman et al. (1986)


                                           Biotransfer Factor (day/kg DW)

        Test Group             Dairy              Beef                 Beef Liver
Pasture Control
Pasture Treated
Indoor Control
Indoor Wafer
Indoor High-Level
0.09
0.004
0.02
0.003
0.006
0.04
0.006
0.03
0.003
0.0008
0.1
0.02
0.05
0.05
0.02
  Sludge

  Indoor Low-Level               0.008               0.004                   0.03
  Sludge
       Using the number of animals sampled for each value in Table A-9, weighted averages for the
Dairy, Beef and Beef Liver Biotransfer factors can be derived.  These are chosen as the default values,
with the ranges taken from Table A-9.

       In a experiment very similar to Vreman et al. (1986), van der Veen and Vreman (1986)
measured transfer of mercury from diet to tissues of 10 week old fattening lambs. Two groups of 8
lambs were placed on pasture for 3  months.  The control group was fed uncontaminated feed
concentrate and based on mercury levels in the pasture grass and uncontaminated feed were estimated
to ingest <0.02 mg mercury/Kg dry feed-day. The exposed group received feed concentrate treated
with a solution of mercury acetate, lead, cadmium and arsenic pentoxide; the daily mercury ingestion
rate for the exposed group  was 0.08 mg/Kg dry feed. Another four groups of 8 lambs were kept
indoors and were fed hay and feed concentrate.  A control group was fed uncontaminated feed
concentrate, and were estimated to ingest <0.02 mg mercury/Kg dry feed-day.  The 3 other groups
were fed feed concentrate contaminated with, respectively,   a soluble solution of the metals, harbor
sludge and sewage sludge.  Daily mercury ingestion rates for these groups ranged from 0.14 - 0.27
mg/Kg dry feed. After three months all lambs were slaughtered and mercury levels were measured in
liver, kidney, brain and muscle samples.

       Shown in Table A-10 are data from van der Veen and Vreman et al. (1986) and the biotransfer
factors derived from these data.
June 1996                                   A-30                       SAB REVIEW DRAFT
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                                          Table A-10
              Mercury Concentrations and resulting BTFs in Lamb Muscle Tissue
                Per Test Group and Dose (from van der Veen and Vreman 1986)
Test Group
Pasture Control
Pasture Treated
Indoor Control
Indoor Wafer
Indoor
Dose (mg
mercury/Kg dry
feed-day)
<0.02
0.08
<0.02
0.14
0.27
Feed Amount
(Kg DW/day)
1.36
1.36
1.3
1.28
1.39
mercury in
Muscle
(ug/Kg WW)
1
3
2
1
1
Muscle
Dry %
32.3
32.8
30.5
29.5
30.5
BTFA
(day/Kg DW)
0.2
0.08
0.3
0.02
0.009
  High-Level Sludge

  Indoor
  Low-Level Sludge
0.17
1.38
29.1
0.02
        A Biotransfer Factor (BTF)
        To calculate the biotransfer factors listed from the data in Table A-10,  the daily mercury
intake was calculated from the mercury concentration in dry feed and daily intake of dry feed,  van
der Veen and Vreman (1986) reported the dry weight fractions of the muscle samples, and the mercury
concentration in muscle was calculated on a dry weight basis.  The biotransfer  factor for each group of
lambs was then determined.  The average over all groups was  chosen as the default value,  with the
ranges taken from Table A-10.

        In U.S. EPA (1993b), uptake slopes were developed for a number of pollutants found in
sludge including mercury.  For pork and poultry, U.S. EPA (1993b) reviewed the literature on
concentrations of metals in meat from studies in which livestock were fed known concentrations of the
metals in feed.  These values were used to  obtain the default values (after convening wet-weight
values to dry-weight).
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       A.2.2.4        Fish Bioaccumulation Factor

Parameter:    Tier 3 Fish BAF (BAF3)
              Tier 4 Fish BAF (BAF4)

Definition:     The concentration of the methylmercury in fish divided by the concentration of total
              dissolved mercury in water  ,

Units:         L/kg
                                                          Percentiles (L/kg)
Fish Type
Tro'phic Level 3 Fish
Trophic Level 4 Fish
Default
Value (L/kg)
66,200
335,000
5th
Percentile
6,400
22,700
Median
662,000
335,000
95th
Percentile
684,000
4,700,000
Technical Basis:

       For a more complete discussion, the reader is referred to Volume V of this Report.  The
methylmercury value is most important since virtually 100% of mercury in fish tissue is
methylmercury.

       The BAFs for methylmercury is defined as the ratio of the methylmercury concentration in fish
flesh divided by the concentration of total dissolved mercury (organic plus inorganic forms) in the
water column. As virtually 100% of the mercury in fish flesh is in the methyl form, the definition of
the BAF used here is equivalent to the definition of a  total mercury BAF as found in the literature.
The BAF represents the accumulation of mercury in fish of a specific trophic level from both water
intake and predation on contaminated organisms, the latter being the dominant pathway.  In this report
BAFs for methylmercury are estimated for trophic level 3 (forage fish)  and trophic level 4 (piscivorous
fish) designated as BAF3 and BAF4, respectively.  The BAFs are intended to be representative of the
random selection of a fish  from a random lake in a random geographical location.

       The BAFs were estimated by probabilistic Monte Carlo simulation methods  as described in
Appendix A to Volume V  of this report.  Distributions were constructed from a  limited number of
available studies for BAF3 and a  predator-prey factor for trophic level 4 (PPF4).  PPF4 represents the
bioaccumulation of mercury for piscivorous trophic level 4 fish feeding on trophic level 3 fish.  BAF4
is the product of BAF3 and PPF4. Five studies were available for the estimation of BAF3 with values
ranging from  10,000 to 350,000.  PPF4 is  based on 12 studies with values ranging from 1.2 to 15.5.
A sensitivity analysis shows that  BAF3 has the greatest effect on the variance of the BAF4 output,
contributing 75% of the variance.  A major source of variability in the BAF estimates is the
dependence of PPF4 and to some extent, BAF3, on the age (and consequently the size) of the fish.
Because fish accumulate mercury throughout their lives, the predator-prey and bioaccumulation factors
increase with age, particularly for trophic level 4 fish.   There is uncertainty as to whether a single
BAF value is appropriate for derivation of water concentration when the fish-size range of the fish-
consuming populations is known.  For example, kingfishers feed on smaller fish while human
recreational anglers primarily consume large fish.  Because of the large variance in the BAF
distributions and the lack of distinction between uncertainty and variability, the current
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recommendation is to apply BAFs derived from valid data collected at the site of concern.  Otherwise.
it is recommended that the mean values of the BAF distributions, rather than upper or lower
percentiles, be used for exposure assessment.

        A.2.2.5        Plant Surface Loss Coefficient

Parameter:     kp

Definition:     A measure of the loss of contaminants deposited on plant surfaces over time as a result
               of environmental processes.

Units:         /yr
Chemical
Hg°
Hg2+
Methylmercury
Default Value
(per year)
40.41
40.41
40.41
Distribution
Log(40.41, 17.39)
Log(40.41, 17.39)
Log(40.41, 17.39)
Range
28.11 -52.7
28.11-52.7
28.11-52.7
Technical Basis:

        The values in the previous table were taken from Belcher and Travis (1989), although no
speciation was provided. The values for all species were assumed to be the same.  The default value
is the mean of the lognormal distribution used in Belcher and Travis  (1989).  The choice of a
lognormal distribution was based on the work of Miller and Hoffman (1983).

        A.2.2.6        Fraction of Wet Deposition Adhering

Parameter:     Fw

Definition:     Fraction of wet deposition that adheres to plant (i.e., is not washed  off).

Units:         unitless


                 Default Value           Distribution               Range

                       0.6                 7(0.1,0.6,0.8)               0.1-0.8
Technical Basis:

       The unitless parameter Fw represents the fraction of the pollutant in wet deposition that
adheres to the plant, is not washed off by precipitation and is used to estimate plant pollutant levels.
A value of 1 is the most conservative; this implies that all of the pollutant which deposits onto the
plant via wet deposition will adhere to the plant.  U.S. EPA (1990) originally used a value of 0.02,
which significantly diminishes the impact of this pathway. A more recent study by Hoffman et al.


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(1992) suggests an answer between these extremes for both dissolved pollutants and suspended
particulates in simulated rain drops.

       Hoffman et al. (1992) attempted to quantify the amount of radiolabeled beryllium (Be) and
Iodine (I) as well as particles of sizes 3, 9, and 25 urn that adhered to three plant types (fescue, clover,
and a typical weeded plot). The radiolabeled pollutants were dissolved or suspended in water, which
was then showered upon the different types of vegetation to simulate precipitation. Two precipitation
intensities were modeled in the experiment:  moderate (1-4 cm/hour) and high (4-12 cm/hour).  Due to
experimental complications, total deposition and pollutant retention upon the vegetation were estimated
by the authors; these estimates were termed the interception fraction in the Hoffman report. For
example, in the experiment.  Beryllium in the form of BeCl2 was dissolved in toe water and then
showered upon the vegetation.  For the moderate and high intensity precipitation events simulated, the
mean interception fractions were estimated to be 0.28 and 0.15, respectively.

       The 1993 Addendum to the Indirection Exposure Methodology (U.S. EPA, 1993a) models
deposition and retention as the product of the interception fraction (Rpj) and Fw. In terms of  the U.S.
EPA model, the Hoffman report estimates the product Rp-pFw. To obtain estimates for Fw, the values
reported in Hoffman et al. (1992) were  divided by the interception fraction for forage used in  this
assessment (0.47; Baes et al.,  1984).  This  provides estimates of 0.60 and 0.32 for Fw for the  moderate
and high precipitation intensities, respectively (see Table A-ll).

       Table A-10 shows the Hoffman et  al. (1992) estimates for the interception and adhesion of
dissolved pollutants and suspended particles in simulated moderate and high intensity precipitation.
Based on the Hoffman estimates and the assumption of an interception fraction for forage of 0.47, the
Fw for the two pollutants and three particle sizes were estimated for the precipitation intensities
studied, and the means were calculated.  No  attempt has been made to  adjust  the final estimate for
frequency of the two precipitation intensities; however, since moderate precipitation intensities are
more common, the unadjusted means are probably an underestimate.
                                          Table A-ll
                              Values From Hoffman et al. (1992)
                      and the Values of Fw Estimated Using Those Values
Compound
I
Beryllium
3 um
9 um
25 um
Rpj x Fw for
Moderate
Intensity
0.08
0.28
0.30
0.33
0.37
Rpj x Fw for
High
Intensity
0.05
0.15
0.24
0.26
0.31
Fw Estimate
for Moderate
Intensity
0.17
0.60
0.64
0.70
0.79
Fw Estimate
for High
Intensity
0.11
0.32
0.51
0.55
0.66
Fw Mean
0.14
0.46
0.58
0.63
0.72
                                                                           2+
       The Fw estimated for beryllium was used as a surrogate for mercury. Be  , as a cation, is
                                         ,2+
assumed to behave in a manner similar to Hg  during deposition. Because the moderate intensity is
expected to be more common than the heavy intensity, an Fw of 0.60 is assumed to be a reasonable
estimate of Fw for divalent mercury. This value is higher than the range of 0.1-0.3 presented in
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McKone and Ryan (1989).  For beryllium, Hoffman noted the appearance of a strong  attraction
between the cation and the plant surface, which was assumed to be negatively charged.  Beryllium is
believed to adsorb to cation exchange sites in the leaf cuticle.  Once dried on the plant surface,
beryllium was not easily removed by subsequent precipitation events.  Divalent mercury is assumed  to
exhibit a similar behavior. The range of 0.1-0.8 was used to estimate the sensitivity of this parameter.

        The adjusted Hoffman data indicate that the greater the intensity of the precipitation, the
smaller the Fw estimate for both dissolved pollutants and suspended particles. This is intuitively
appealing given the understanding of the physical process.  Hoffman et al. (1992) noted that the
intensity and amount of rainfall had approximately the same impact on the estimated values. It should
also be noted that the data indicate that the value of Fw for pollutants that deposit as anions (e.g., I)
may be significantly lower than cations.

A.3    References

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          APPENDIX B

   PARAMETER JUSTIFICATIONS
SCENARIO-DEPENDENT PARAMETERS
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                              TABLE OF CONTENTS
                                                                                    Paee
B.     SCENARIO DEPENDENT PARAMETERS  	B-l
       B.I    Time of Concentration	B-l
       B.2    Average Air Temperature	B-2
       B.3    Watershed Area  	B-2
       B.4    Average Annual Precipitation	B-3
       B.5    Average Annual Irrigation  	B-3
       B.6    Average Annual Runoff	,	B-4
       B.7    Average Annual Evapotranspiration	B-4
       B.8    Wind Speed  	B-5
       B.9    Soil Density	B-5
       B.10   Mixing Depth in Watershed Area  	B-6
       B.ll   Mixing Depth for Soil Tillage	B-6
       B.12   Soil Volumetric Water Content	B-7
       B.13   Soil Erosivity Factor	B-8
       B.14   Soil Erodability Factor	B-8
       B.15   Topographic Factor	B-9
       B.16   Cover Management Factor	 B-10
       B.17   Sediment  Delivery Ratio to Water Body  	 B-ll
       B.18   Pollutant Enrichment Factor	 B-l 1
       B.19   Water Body Surface Area	 B-12
       B.20   Water Body Volume  	 B-12
       B.21   Long-Term Dilution How   	 B-13
       B.22   Suspended Solids Deposition Rate	 B-14
       B.23   Benthic Sediment Concentration	 B-14
       B.24   Upper Benthic Sediment Depth	 B-15
       B.25   Aquatic Plant Biomass	 B-15
       B.26   Total Fish Biomass	 B-16
       B.27   References 	 B-16
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                                LIST OF TABLES

                                                                                 Page

B-l   Water Content Per Soil Type	B-7
B-2   Representative Soil Types For Each Site		B-8
B-3   Cover Factor Values of Undisturbed Forest Land
      (from WQAM,  1985; original citation Wischmeier and Smith, 1978)	 B-10
B-4   Long-Term Dilution Flow in In/Yr	 B-13
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                                 DISTRIBUTION NOTATION
        A comprehensive uncertainty analysis was not conducted as part of this study. Initially,
preliminary parameter probability distributions were developed. These are listed in Appendices A and
B.  These parameter probability distributions were not utilized to generate quantitative exposure
estimates.  They are provided as a matter of interest for the reader.
        Unless noted otherwise in the text, distribution notations are presented  as follows.
         Distribution
       Description
          Log (A,B)      Lognormal distribution with mean A and standard deviation B
          Log* (A,B)      Lognormal distribution, but A and B are mean and standard
                          deviation of underlying normal distribution.
          Norm (A,B)      Normal distribution with mean A and standard deviation B
           U (A,B)       Uniform distribution over the range  (A,B)
          T (A,B,C)      Triangular distribution over the range (A,C) with mode of B
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B.     SCENARIO DEPENDENT PARAMETERS

       This appendix describes the scenario dependent parameters used in the exposure modelling for
the Mercury Study Report to Congress.  Scenario dependent parameters are variables whose values are
dependent on a particular site and may differ among various site-specific  situations.  For this
assessment, three settings are being evaluated:  (1) rural, (2) lacustrine, and (3) urban. The receptors
differ for each of these scenarios, as do the parameters. These scenario dependent parameters may be
either chemical independent or chemical dependent.  The following sections present  the chemical
independent and chemical dependent parameters used in this assessment.

       Chemical independent parameters are variables that remain constant despite the specific
contaminant being evaluated.  The chemical independent variables used in this assessment are
described in the following sections.

       Site physical data include information such as the environmental setting, vegetative cover,
presence of surface water or groundwater, area of source and meteorological and climatological data.
These parameters are described in the  following sections.

B.I    Time of Concentration

Parameter:     Tc

Definition:      Number of years that the air concentration at the above level persists; equal to the
               facility lifetime for calculations from anthropogenic sources

Units:          yrs


                    Scenario               Default Value(s)           Distribution
                                                (years)

                       All                        30                    None
Technical Basis:

       The time of concentration is the same as the assumed facility lifetime. The generic value is 30
years.  It is noted that this assumption is made only for estimation of  soil concentrations.  The water
concentrations are calculated assuming steady-state has been attained, with the flux due to
runoff/erosion based on the 30-year soil concentrations.
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B.2    Average Air Temperature

Parameter:    Ta

Definition:     Average air temperature of microscale area

Units:         °C
                                             Default Value
                   Location               •  (Years value is           Distribution
                                            based upon) (°C)
Eastern Location
Western Location
11.9 (25)
13.4 (47)
U (8,16)
U (9,17)
Technical Basis:

       The values for local airports are reported in the section "U.S. Local Climatological Data
Summaries for 288 Primary Stations throughout the U.S." on CDROM by WeatherDisc Associates
(1992).  The distributions are arbitrary to explore the sensitivity of this parameter.

B.3    Watershed Area

Parameter:    WAI

Definition:     Area of contamination which drains into a water body

Units:         Km2
                    Location             Default Value (Km2)

          Eastern Location                        37.3

          Western Location                       37.3

Technical Basis:

        The values for the fish ingestion pathways are based on hypothetical watershed/waterbody
surface area ratio of 15 and a lake diameter of 1.78 km.  This parameter was used only to calculate the
erosion and runoff load to the water body.
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B.4    Average Annual Precipitation

Parameter:     P

Definition:     Average annual precipitation

Units:         cm/yr
Location
Eastern Location
Western Location
Default Value
(cm/yr)
102
21
Distribution
, 7(82,102,122)
7(1,21,41)
Technical Basis:               \

       All values are for local airports as reported in the section "U.S. Local Climatological Data
Summaries for 288 Primary Stations throughout the U.S." on CDRom by WeatherDisc Associates
(1992).   These were considered the "best estimates" of a triangular distribution, with a range of 20
in/yr above and below the mode.

B.5    Average Annual Irrigation

Parameter:     I

Definition:     Average annual irrigation of plants

Units:          cm/yr
Location
Eastern Location
Western Location
Default Value
(cm/yr)
12.5
57.5
Distribution
U(0,25)
U(50,65)
Technical Basis:

       The ranges were approximated from Figure 4.25 in Baes et al. (1984). The tentative default
values are the midpoint of this range. It was assumed that both the farmer and home gardener will
irrigate the same amount if they are in the same area of the country (i.e., irrigation rate does not
depend on size of plot).
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B.6    Average Annual Runoff

Parameter:     Ro

Definition:     Average annual runoff

Units:         cm/yr
Location
Eastern Location
Western Location
Default Value
(cm/yr)
'18
1
Distribution
U(9,27)
U(0,2)
Technical Basis:

       The default values for the eastern location are from Geraghty et al. (1973).  The total runoff
values given in that report include groundwater recharge, direct runoff, and shallow interflow.
Following U.S. EPA  (1993), this number was reduced by one-half to represent surface runoff.
Because of the difficulty of hydrologic modelling in the western location, the PRZM-2 model (Carsel,
1984) was used to estimate the runoff for this area.  The estimated value was 1 cm/yr. The
distributions are arbitrary to determine the sensitivity of this parameter.

B.7    Average Annual Evapotranspiration

Parameter:     Ev

Definition:     Average annual loss of water due to evaporation

Units:         cm/yr


                     Location               Default Value          Distribution
                                               (cm/yr)

            Eastern Location                      65                U(60,70)

            Western Location                     13                 U(8,18)


Technical Basis:

       For the eastern location, the ranges are based on estimates from isopleths given in Figure 4.24
in Baes et al. (1984).  The values presented there were estimated based on local data (average
temperature and precipitation) as well as the maximum possible sunshine for the area. The default
value is the  midpoint of this range.  For the western location, the model PRZM-2 was used to estimate
the values given previously.
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B.8     Wind Speed

Parameter:     W

Definition:     Wind speed

Units:         m/s
                    Location             Default Value (m/s)        Distribution

           Eastern Location                       4.3                   U(l,7)

           Western Location                      4.0                   U(l,7)
Technical Basis:

        All values were collected for local airports and reported in the section "U.S. Local
Climatological Data Summaries for 288 Primary Stations throughout the U.S." on CDROM by
WeatherDisc Associates (1992).  The primary use of this parameter is for estimating volatilization
from soil and water bodies.  -The distributions are  arbitrary to explore the sensitivity of this parameter.

B.9     Soil Density

Parameter:     BD

Definition:     Soil density

Units:          g/cm3


               Location       Default Value (g/cm3)     Distribution        Range

               All Sites                 1.4             Log(1.4,0.15)     0.93-1.84
Technical Basis:

       The distribution is from Belcher and Travis (1989) and is based on a probability plot using
data from Hoffman and Baes (1979).  There is little variation in the parameter, despite the fact that
more than 200 data points were used.  The default value is the mean of the distribution.
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B.10   Mixing Depth in Watershed Area

Parameter:     Zd

Definition:     The depth that contaminants are incorporated into soil (no tillage)

Units:         cm


                      Location       Default Value (cm)      Distribution

                      All Sites	IX)	U(0.5,5)	


Technical Basis:

       The default value is based on U.S. EPA (1990).  The distribution is arbitrary to determine the
relative sensitivity of the parameter.

B.ll   Mixing Depth for Soil Tillage

Parameter:     Ztill

Definition:     The depth that contaminants are incorporated into tilled soil

Units:         cm
                      Location       Default Value (cm)      Distribution

                      All Sites              20                U( 10,30)
Technical Basis:
       .The default value is based on U.S. EPA (1990).  The distribution is arbitrary to determine the
sensitivity of this parameter.
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 B.12   Soil Volumetric Water Content

 Parameter:     Theta.O

 Definition:     Amount of water that a given volume of soil can hold
 Units:
ml/cm
Location
Eastern Location
Western Location
Default Value
(ml/cm3)
0.30
0.36
Distribution
11(0.15,0.42)
U(0.15,0.42)
Technical Basis:

       Values for water content can range from 0.003 to 0.40 ml/cm3 depending on the type of soil
(Hoffman and Baes, 1979).  Table B-l demonstrates the dependency of values on the hydrologic soil
type.  These values were derived from the PATRIOT software system (Imhoff et al., 1994), which can
be obtained from the Center for Exposure Assessment Modeling at the U.S. Environmental Protection
Agency, Athens, Georgia.
                                          Table B-l
                                 Water Content Per Soil Type
Soil Type
A
B
C
D
Water Content
0.15
0.22
0.30
0.42
       Representative soil types for both sites are shown in Table B-2 and were determined from
Carsel (1984). The soil types were used in conjunction with the previous table to determine the
default value for the soil water content, with the value for the western location being the average of
the values for types C and D.
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                                           Table B-2
                            Representative Soil Types For Each Site
                                   Location
Soil Type
                          Eastern Location
                          Western Location
    C
   C/D
The distribution for all sites is a uniform distribution over the range over all soil types.

B.13   Soil Erosivity Factor

Parameter:    R

Definition:    Quantifies local rainfall's ability to cause erosion

Units:
kg/knr-yr
Location
Eastern Location
Western Location
Default Value
(kg/km2-yr)
200
53
Distribution
U( 100,300)
U(30,75)
Technical Basis:

       The ranges were determined based on an isopleth map for the region in USD A (1978). The
upper and lower bounds were determined from this map by finding extremes within a  300-mile radius.

B.14   Soil Erodability Factor

Parameter:     K

Definition:     Quantifies soil's susceptibility to erosion

Units:          tons/acre
Location
Eastern Location
Western Location
Default Value
(tons/acre)
0.30
0.28
Distribution
U(0.12,0.48)
U(0.08,0.48)
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Technical Basis:

        Based on similar soil near the eastern location (loamy sand, loam, and silt loam) and using
Table A2-2 in U.S. EPA (1989), a range of 0.12 to 0.48 was obtained.  A similar analyses has not
been performed for the other sites, but the ranges listed in the previous table are apparently the
maximum range possible based on Table A2-2 in U.S. EPA (1990); therefore,  these-ranges encompass
all likely values  and can be used for sensitivity analyses.  The default values are the midpoint of these
ranges.

B.15    Topographic Factor

Parameter:    LS

Definition:    Provides a measure of the  length and steepness of the land slope
                                                                                       \
Units:        unitless


                      Location             Default Value         Distribution

             Eastern Location                     2.5               U(0.25,5)

             Western Location                   0.4                U(0.1,1.2)
Technical Basis:

       The length and steepness of the land slope substantially affect the rate of soil erosion.
Table A2-3 in U.S. EPA (1989) contains LS values for various slopes and slope lengths and was used
in conjunction with United States Geological Survey (USGS) maps to obtain the ranges given in the
previous table.  A 1:24000 map was available for the humid/east/complex I site while only a 1:250000
USGS map was available for all other sites.  The default value was chosen as representative of the
most common slope and length in the area.
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B.16   Cover Management Factor

Parameter:    C

Definition:    The ratio of soil loss from land cropped under local conditions to the corresponding
              loss from clean tilled fallow                       •

Units:         unitless

              	Location	Default Value	
                Eastern Location                            0.006
                Western Location	0.1	

                                                                         \
Technical Basis:

       The lower end of the range for areas having forests (0.001) is the lower of two values
suggested for woodlands in U.S. EPA (1988).  For those areas lacking forests (i.e., western site), the
value of 0.1 given for grass in U.S. EPA (1993) was used.

       For the watershed,  it was decided to use a cover fraction representative of undisturbed grass or
forested areas, although high-end values were used. It was noted that the cover fraction can vary by
several orders of magnitude, depending on the land use type and soil type.  Table B-3 shows estimates
of cover factor values for undisturbed forest land (Wischmeier and Smith, 1978).
                                          Table B-3
                       Cover Factor Values of Undisturbed Forest Land
              (from WQAM,  1985; original citation Wischmeier and Smith, 1978)
€
Percent of Area Covered by
Canopy of Trees and
Undergrowth
75-100
45-70
20-40
Percent of Area Covered by
Duff (litter) at least 5 cm deep
90-100
75-85
40-70
Cover Management
Factor Value
0.0001-0.001
0.002-0.004
0.003-0.009
Based on the above values and the objectives of this exposure assessment, it was decided that the high-end
values (of those above) would be  appropriate; a nominal value of 0.006 (the midpoint of the high-end
range) was chosen.
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B.17    Sediment Delivery Ratio to Water Body

Parameter:     Sdel

Definition:     Sediment delivery ratio to water body

Units:         unitless
                       Location         Default Value        Distribution

                     Both Locations             0.2             U(0.14,0.23)
Technical Basis:

        The sediment delivery ratio is the fraction of soil eroded from the watershed that reaches the water
body.   It can  be calculated based  on the watershed  surface area using an  approach proposed  by
Vanoni (1975):

                                          Sdel = a WA'Lb

where WAL is watershed area in m2, b is  an empirical slope coefficient (-0.125) and a is an empirical
intercept coefficient that varies with watershed area. A graph of the sediment delivery ratio as a function
of watershed area is  given in the Water Quality Assessment Manual (Mills et  al. 1985, pp. 177,178).
        *
B.18    Pollutant Enrichment Factor

Parameter:     EF

Definition:     The pollutant enrichment factor accounts for the fact that the lighter particles susceptible
               to erosion tend to have a greater concentration of pollutants attached per mass than what
               the average soil concentration may suggest.

Units:         unitless
                     Location         Default Value          Distribution

                   Both Locations             2                 U(1.5,2.6)
Technical Basis:
       Enrichment refers to the fact that erosion favors the lighter soil particles, which have higher
surface area to volume ratios and are higher in organic matter content. Concentrations of hydrophobic
pollutants would be expected to be higher in eroded soil as compared to in-situ soil. While enrichment
is best ascertained with sampling or site-specific expertise, generally it has been assigned values in the
range  of 1 to 5 for organic matter, phosphorus,  and other soil-bound  constituents  of concern.
Mullins et al. (1993, p.6-22) describe the following equation for calculating enrichment ratio for storm
events:

                                      EF =  2 + 0.2 ln(X/4J


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where Xe is the mass of soil eroded, in metric tons (1 metric ton =  1000 kg), and Aw is watershed area,
in hectares (1  hectare = 10,000 m2).   Experience suggests  that typical values range from 1.5 to 2.0,
reflecting erosion events from 0.08 to 1.0 tonnes per hectare.  A very large erosion event of 20 tonnes per
hectare would  have a predicted enrichment ratio of 2.6.  The default value assumed here is 2.

B.19   Water Body Surface Area

Parameter:    Waw

Definition:     Water body surface area

Units:         km2
                       Location        Default Value        Distribution

                    Both Locations         2.49               U(1.5,3)


Technical Basis:

       For the purpose of this assessment, it was assumed that the hypothetical water body has a diameter
of 1.78 km, from which the default surface area is calculated.

B.20   Water Body Volume

Parameter:     Vw

Definition:      Water body volume

Units:          m3
                      Location        Default Value        Distribution

                   Both Locations       1.24xl07             Constant
Technical Basis:

       For the purpose of this assessment, it was assumed that the hypothetical water body has a diameter
of 1.78 km and mean depth of 5 m.  The corresponding volume assuming a disk of height 5 m and radius
0.89 km is then given by 1.24xl07 m3  (using the formula volume=n r h).
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B.21   Long-Term Dilution Flow

Parameter:     Q

Definition:     Long term dilution flow

Units:         m3/yr
                              Location            Default Value (m/yr)

                     Eastern Location                     1.44xl07

                     Western Location                    1.44xl05
Technical Basis:

       The long-term dilution flow can be estimated from Tables in U.S. EPA (1985).  The values in
in/yr are  given in Table B-4. These were multiplied by the watershed area of 3.3xl07 m2 to obtain the
default values.
                                          Table B-4
                              Long-Term Dilution Flow In In/Yr
                               Location               Value (in/yr)

                      Eastern Location                       15
                      Western Location                    0.15
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 B.22   Suspended Solids Deposition Rate

 Parameter:    Ssdep

 Definition:     Suspended solids deposition rate

 Units:         m/dav
                          Scenario             Default Value (m/day)

                        Both Locations                     0.5
 Technical Basis:

        Stokes equation can be used to calculate the terminal velocity of a sediment particle settling
•through the water column, as described in Ambrose et. al. (1988):
                                    Vs =
 where:

        Vs is Stokes velocity for a particle with diameter dp and density =FEp, m/day, g is acceleration
        of gravity =3D 981 cm/sec2, = E6 is absolute viscosity of water =3D 0.01 poise (g/cm3-sec) at
        20 =F8C, = FEp is density of the solid, g/cm3, = FEw is density of water, 1.0 g/cm3, and dp is
        particle diameter, mm.

        Values of Vs for a range of particle sizes and densities are provided in Table 3.1.  Deposition
 velocities should be set at or below the Stoke's velocity corresponding to the median suspended particle
 size, keeping in mind that pollutants tend to sorb more to the smaller silts and clays than to large silt and
 sand particles =  20.  The deposition velocity here represents net deposition over time and so will be
 smaller for systems experiencing periodic scour = 20. The value chosen here is an order of magnitude
 below the Stoke's velocity calculated for medium silt particles.

 B.23   Benthic  Sediment Concentration

 Parameter:    BS

 Definition:     Benthic sediment concentration

 Units:         kg/L
                               Scenario           Default Value (kg/L)

                            Both Locations               1 kg/L


 Technical Basis:


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       Benthic sediment concentration is related to the densities of sediment particles, water, and the bulk
sediment:


                                      ^ _  FEp (FEb - FEw)
                                             (FEp - FE\v)
where

       FEp is the particle density in g/cm3,  FEw  is the water density in g/cm3, and =FEB is the
       sediment bulk density in g/cm3.

Typical particle densities in sediments range between 2.6 and 2.7 k/cm3, and at 20 degrees Celcius water
density is close to  1.0*g/cm3.  For these properties, a bulk density value o/ 1.6 g/cm3 corresponds to a
sediment concentration of 1.0 g/cm3 (or kg/L) and a porosity of 0.65, which represents consolidated
benthic sediment. An analysis of 1680 measured bulk densities in marine sediments exhibited a range from
1.25 to 1.8 g/cm3 and an average particle density of 2.7 (Richards et al.,1974). Some waterbodies contain
an upper unconsolidated layer of sediment with bulk densities of 1.1 to 1.3, which correspond to porosities
of 0.94 to 0.82 and sediment concentrations of 0.16 to 0.48 g/cm3. In this study, we represent pollutant
storage in consolidated beds.

B.24  Upper Benthic Sediment Depth

Parameter:     Db

Definition:     Benthic  sediment concentration
Units:         m
                      Scenario        Default Value (m)       Distribution

                   Both Locations            0.02             U(0.01,0.03)

Technical Basis:
       The total benthic sediment depth can vary from essentially zero in rocky streams to hundreds of
meters in oceans.  In the lake environments being modeled here, the total benthic sediment depth usually
exceeds a few centimeters.  Here we are modeling only the upper layer that is in partial contact with the
water column through physical mixing  and bioturbation.  Although bioturbation can descend to tens or
even hundreds of centimeters, only the top few centimeters would be in significant contact with the water
column.  Because this model assumes chemical equilibrium between the upper sediment layer and the
water column, a shallow depth of 2 cm was chosen.

B.25  Aquatic Plant Biomass

2 mg/L

Technical Basis:

    Aquatic biomass can include phytoplankton and, in shallow areas, benthic algae and rooted aquatic
plants.  Phytoplankton biomass,  as measured by chlorophyll a, can range from less than 1  E 6g/L in


June 1996                                    B-15                        SAB REVIEW DRAFT
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oligotrophic lakes to higher than 200 E 6g/L during blooms = in eutrophic lakes. Given a typical carbon
to chlorophyll ratio of 30 (Ambrose et al., 1988), and carbon to biomass ratio of approximately 0.4 (Bowie
et al.,  1985), the range of aquatic phytoplankton biomass is from 0.08 to 15 mg/L.  A yearly average
chlorophyll a value of 25 E6g/L gives an estimated biomass of about
2 mg/L, which was used in this study.

B.26  Total Fish Biomass

    Bioenergetics typically dictate that biomass declines each trophic level by a factor of 10.  Trophic
level  3 fish supported by 2  mg/L  aquatic biomass,  then, would be supported at about 0.02 mg/L.
Additionally, trophic level 4 fish and fish supported by external energy sources (such as insects) can be
present.  A total fish biomass  is estimated to be 0.05 mg/L.

B.27   References
                                                      \
Baes, C.F. and R.D. Sharp. 1983.  A proposal for estimation of soil leaching constants for use in
assessment models. J. Environ. Qual. 12: 17-28.

Baes, C.F., R.D. Sharp, A.L. Sjoreen, and R.W. Shor.  1984. A Review and Analysis of Parameters
for Assessing Transport of Environmentally Released Radionuclides through Agriculture.  Oak Ridge
National Laboratory, ORNL-5786.

Belcher,  G.D. and  C.C. Travis (1989).  Modelling Support for the Rural and Municipal Waste
Combustion Projects: Final Report on Sensitivity and Uncertainty Analysis for the Terrestrial Food
Chain  Model.  Prepared for the U.S. EPA.

Bowie, G.L. et al.  1985. Rates, Constants, and Kinetics Formulations in Surface Water Quality
Modeling (Second Edition). EPA/600/3-85/040. U.S. EPA, Athens, GA.

Carsel, R.F., C.N. Smith, L.A. Mulkey, J.D. Dean, and P. Jowise (1984).  User's Manual for the
Pesticide Root Zone Model (PRZM) Release 1. U.S. EPA, Athens, GA.  EPA-600/3-84-109.

Geraghty, J. J.,  D.  W. Miller,  F. V.  Der Leenden, and F. L. Troise, Water Atlas of the United States.
A Water Information Center Publication, Port Washington, N.Y. (1973).

Hoffman, F.O.  and D.F. Baes (1979).  A Statistical Analysis of Selected Parameters for Predicting
Food  Chain Transport and Internal Dose of Radionudeotides.  ORNL/NUREG/TM-882.

Imhoff, J.C., P.R. Hummel, J.L. Kittle, and R.F. Carsel. 1994. PATRIOT - A Methodology and
Decision Support System for Evaluating the Leaching  Potential of Pesticides. EPA/600/S-93/010. U.S.
Environmental Protection Agency, Athens, Georgia.

Mills,  W.B., et  al.  1985. Water Quality Assessment: A Screening Procedure for Toxic and
Conventional Pollutants in Surface and Ground Water. Part 1. EPA/600/6-85/002a. U.S. Environmental
Protection Agency, Athens, Georgia.

Mullins,  J.A., R.F. Carsel, J.E. Scarbrough, and A.M. Ivery. 1993. PRZM-2, A Model for Predicting
Pesticide Fate in the Crop Root and Unsaturated Soil Zones: Users Manual  for Release 2.0.
EPA/600/R-93/046. U.S. Environmental Protection Agency, Athens, Georgia.

National Climatic Data Center. 1986. Local Climatological Data - Annual Summary with Comparative
Data - Cincinnati,  (Greater Cincinnati Airport) Ohio.  ISSN 0198-3911, Asheville, North Carolina.

June 1996                                   B-16                       SAB REVIEW DRAFT
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Richards, A.F., T.J. Hirst, and J.M. Parks. 1974. Bulk Density-Water Content Relationship in Marine
Silts and Clays. Journal of Sedimentary Petrology, Vol.44. No.4, p 1004-1009.

U.S. Department of Agriculture. (1978).  Handbook No. 537:  Predicting Rainfall Erosion Losses.
U.S. Government Printing Office, Washington, D.C.

U.S. EPA. 1988. Superfund Exposure Assessment Manual  Office of Remedial and Emergency
Response, Washington, D.C. EPA/540/1086/060.

U.S. EPA. 1985. Water Quality Assessment: A Screening Procedure for Toxic and Conventional
Pollutants in Surface and Ground Water (Pan 1).   Washington, D.C. EPA/600/6-85/002-A.

U.S. EPA. 1989. Development of Risk Assessment Methodology for Land Application and Distribution
and Marketing of Municipal Sludge.  Office of Health and Environmental Assessment, Washington,
D.C. EPA/600/6-89/001.
                                                           \
U.S. EPA. 1990. Methodology for Estimating Health Risks from Indirect Exposure to Combustor
Emissions.  Office of Health and Environmental  Assessment, Washington, D.C. EPA/600/6-690/003.

U.S. EPA. 1993. Indirect Exposure Assessment  Working Group Recommendations, DRAFT pending
review.

Vanoni, V.A., 1975.  Sedimentation Engineering. American Society of Civil Engineers, New York,
NY. pp. 460-463.

WeatherDisc Associates.  1992.  U.S. Local Climatological Data Summaries for 288 Primary Stations
throughout the U.S., on CDROM.

Wischmeier, W. and  D. Smith. (1978). Predicting Rainfall and Erosion Losses: A Guide to
Conservation Planning. U.S. Department of Agriculture, Agriculture Handbook No. 537.
June 1996                                  B-17                      SAB REVIEW DRAFT
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                 APPENDIX C




MERCURY PARTITION COEFFICIENT CALIBRATIONS
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                              TABLE OF  CONTENTS


C.I    Introduction	C-l

C.2    Background	C-l
       C.2.1  Parameters and Coefficients  .-	C-l
       C.2.2  Mercury	C-2

C.3    Calibrations	'C-4
       C.3.1  Description of Calibration Approach	C-4
       C.3.2  Parameters Constant for All Calibrations  	C-4
       C.3.3  Calibration Results   	C-6
                     C.3.3.1 Swedish Composite Lake	C-6
                     C.3.3.2 Fen at Tivedan National Park, Sweden 	C-9
                     C.3.3.3 Composite Minnesota Lake  	C-15

C.4    Limitations and Uncertainties	:	C-21

C.5    Conclusions	C-23

C.6    References	C-25
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                                  LIST OF TABLES
C-l    Parameters Constant for All Calibrations	C-5
C-2    Target Values for Swedish Lake Calibration	C-6
C-3    Site-Specific Parameter Values Used in Swedish Composite Lake Calibrations
       (Excluding Partition Coefficients)	C-7
C-4    Summary of Composite Swedish Lake Calibrations .  ,	 C-10
C-5    Soil Layers and mercury Concentrations Reported in Aastrup et al. 1991  .	C-ll
C-6    Soilwater Mercury Concentrations Reported in Aastrup et al., 1991  	'	C-12
C-7    Model Parameter Values Estimated from Aastrup et al., 1991  	 C-13
C-8    Calibrated Values for Soil-Water Partition Coefficients for Combined Soil Layers and
       Mor Layer Alone	 C-14
C-9    Parameter Values for 80 Minnesota Lakes Reported in Sorensen et al.,  1990	C-16
C-10   Parameter Values Used in Composite Minnesota Lake Calibrations (Excluding Partition
       Coefficients)	 C-17
C-ll   Results for Composite Minnesota Lake Calibrations ..."	 C-20
C-12   Summary of Calibration Results	'	 C-24
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C.I     Introduction

        For an assessment of mercury exposure, an accurate modelling of watershed chemistry is
critical.  One of the important parameters in this watershed chemistry model is the soil-water partition
coefficient (Kj)  and the benthic sediment-water partition coefficient (see Appendix F for a more
complete model  description). The method by which literature values were determined did not account
completely for the watershed transport of mercury. As a consequence, a calibration effort was
undertaken in which mercury watershed transport was assessed at specific sites and modeled in
Addendum to the Methodology for Assessing Health Risks Associated with Indirect Exposure to
Combustor Emissions (IEM2).  To estimate a general effective Kd value for mercury, the model was
calibrated at three sites. The geometric  mean of the generated estimates was selected as the final
value.

        As noted by  Dooley (1992), there is a difference of about three orders of magnitude between
the reported Kd values for mercury in soil-water systems and those in water-suspended solid systems.
Dooley indicated that this difference is not as large as other values in the literature suggest. In this
appendix evidence is presented in support of this hypothesis by means of a series  of calibrations of a
watershed model.  The calibrated partition coefficients are about  one order of magnitude lower than
the reported partition coefficients in water suspended solid systems.

C.2    Background

C.2.1   Parameters and Coefficients

        The parameters used to address  mobility properties are among the most important in
multimedia chemical fate and transport modeling. In many models, it is assumed  that the total
chemical mass is partitioned among several different compartments.  A common assumption is that
partitioning is linear; that is, the fraction in one compartment is directly proportional to the fraction in
another compartment.

        For soil-water systems, the constant of proportionality is  called the partition or distribution
coefficient and is usually denoted by Kd, with units of (mg/kg)/(mg/L) or L/kg.  The partition
coefficient is the ratio of the concentration sorbed onto soil panicles to that dissolved in soil water at
equilibrium; that is no net changes of amount of chemical in soil and water components.  The
adsorptive properties of a chemical can  depend on a variety of environmental factors; e.g.,  pH of soil,
amount of organic matter in the soil or water, percent of sand, silt or clay in soil,  other chemicals
present and even the magnitude of the chemical concentration in the water itself.  Because of the
complicated nature of the sorptive process, it is not surprising that reported values for the linear
partition coefficient can vary over many orders of magnitude for a given chemical.

        More complicated methods than linear partitioning exist for addressing sorption.  The
nonlinear Freundlich equation is an example of a more complex model in which the soil concentration
is assumed to be proportional to some power of the water  concentration.  The particular power, usually
denoted n and called the Freundlich exponent, affords a wider range of data fitting capabilities, but as
with the simpler approach and as noted  by Buchter et al., (1989), it does not provide much information
about the actual  processes involved.

        For long-term (years) estimates,  the simple linear approach is perhaps most applicable. The
equilibrium assumptions necessary are more likely to be achieved over a long period of time,  and the
variation that could be observed and expected for short-term simulations are more likely to be
adequately characterized by  a single representative value. It is more appropriate to call such a partition
coefficient an "effective" partition  coefficient to reflect its strong empirical nature.

                                                                               RFVTFW DRAFT
-------
        It is this kind of estimate that is appropriate for the methodology described in the Draft
 Addendum to the Methodology for Assessing Health Risks from Combustor Emissions (U.S. EPA
 1993).  The IEM2 model also requires partition coefficients for the suspended sediment-water and
 benthic sediment-water. The soil-water Kd is critical in determining the movement of mercury from
 land to water bodies, while the other coefficients partition the mercury once it arrives in the water
 body.

 C.2.2   Mercury

        Mercury (Hg) can exist in three oxidation states: Hg°  (metallic or elemental), Hg22+
 (mercurous), and Hg2+ (mercuric).  The properties and behavior of mercury depend strongly on the
 oxidation state.  Mercurous and mercuric mercury can form numerous inorganic and organic
 compounds; however, mercurous mercury is rarely stable under ordinary environmental conditions.
 Most of the mercury encountered in all environmental media except the atmosphere is in the form of
 inorganic mercuric salts and organomercurics. Organomercurics are defined by the presence of a
 covalent C-Hg bond.  The compounds most likely to be found'under environmental conditions are
 these:  the mercuric salts HgCl2, Hg(OH)2 and HgS; the methylmercury (MHg)  compounds CH3HgCl
 and CH3HgOH; and, in small fractions, other organomercurics (e.g., dimethy[mercury, phenylmercury
 and ethylmercury).

        A number of methods can be used to determine mercury concentrations in environmental
 media.  The concentrations of total mercury, elemental mercury, organic mercury compounds
 (especially methylmercury) and information on various Hg2+ compounds can be measured, although
 speciation among Hg2+ compounds is not usually attempted. Recently, significant improvements and
 standardizations in analytical methodologies enable reliable data on the concentration of
 methylmercury, elemental mercury and the Hg2+ fraction to be readily separated from the total
 mercury in environmental media.  It is possible to further speciate the Hg   fraction into reactive, non-
 reactive and particle-bound compounds, but it is not generally  possible to determine which Hg2+
 species is present (e.g., HgS or HgCl2).

        Most of the mercury in soil is thought to be in the form of Hg2+ species.  Soil conditions are
 typically favorable for the formation of inorganic Hg2+ compounds such as HgCl2, Hg(OH)2 and
 inorganic Hg2+ compounds complexed with organic anions (Schuster 1991).  Although inorganic Hg2+
 compounds are quite soluble and thus theoretically mobile, they form complexes with soil organic
' matter (mainly fulvic  and humic acids) and mineral colloids, with the former being the dominating
 process. This is due largely to the affinity of Hg2+ and its inorganic compounds for sulfur containing
 functional groups. This complexing behavior greatly limits the mobility of mercury in soil.  Much of
 the mercury in soil is bound to  bulk organic matter and is susceptible to elution in runoff only by
 being attached to suspended soil or humus.  However, some Hg  will be absorbed onto  dissolvable
 organic ligands and other forms of dissolved organic carbon (DOC) and may then partition to runoff in
 the dissolved phase.  Hg° can be formed in soil by reduction of Hg2+ compounds/complexes mediated
 by humic substances (Nriagu 1979). This Hg° will eventually vaporize and re-enter the atmosphere.
 methylmercury can be formed by various microbial processes acting  on Hg2+ substances.  Generally,
 approximately 1-3%  of the total mercury in surface soil is methylmercury, and as  is the case for Hg2+
 species, it will be largely bound to organic matter. The other 97-99% of total soil mercury can be
 considered largely Hg2+ complexes, although a small fraction of mercury in typical soil will be Hg°
 (Revis et al.  1990). The methylmercury percentage can exceed 3% (Cappon 1987) in garden soil with
 high organic content under slightly acidic conditions.  Contaminated  sediments may also contain
 higher methylmercury percentages compared to soils (Wilken and Hintelmann 1991; Parks et al. 1989).

        Values for soil-water partition coefficients for mercury are rarely reported in the  literature,
 regardless of species.   Reported values for mercury range from 10 ml/g (Baes et al., 1984) to 408 ml/g

 June 1996                    -                C-2                        SAB REVIEW DRAFT
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(Rai and Zachara,  1984).  For a Freundlich model, the partition coefficients range from 19-299 ml/g,
with the Freundlich exponent ranging from 0.5 to 2.2 (Buchter et al., 1989).  Although there is
considerable variability in these results, they suggest that typical values in soil-water systems are
between 10 and 500 ml/g  and are certainly less than 1000 ml/g. These values are based on laboratory
experiments under conditions typically not representative of ambient mercury concentrations.

        Values derived from measurement under real-world conditions are naturally most appropriate.
A determination of the soil-water partition coefficient requires a measurement of speciated  soil
mercury concentration and the speciated soil water dissolved phase mercury concentration.
Measurements of the speciated soil concentrations are typically reported in the literature, but  speciated
soil water dissolved phase mercury concentration are considerably harder to find.

        Data on the benthic sediment-water Kd that are based on measurements under realistic
conditions  are scarce as well.  Wiener et al., (1990) studied mercury partitioning at Little Rock Lake, a
clear water seepage lake in north-central Wisconsin. The mercury concentrations in the surficial
sediment ranged from 10 to about 170 ng/g (dry weight). Assuming that the reactive mercury values
reported represent  dissolved Hg2+, the dissolved water concentrations range from 0.29 to 0.59  ng/L.
Using these values results in a range for the benthic sediment partition coefficients for this  site from
16950 ml/g to 586200 ml/g.  There appears to be at least as much  uncertainty in the benthic Kd as the
soil-water  Kd.

        In  contrast, a number of values for the suspended-sediment Kd have been determined.  These
are for the most part based on measured data under realistic conditions, unlike the values for  the soil-
water and benthic-sediment partition  coefficients (Appendix A). These values range from 103 ml/g
(Moore and Ramamodoray 1984) to  106 ml/g (Bloom et al., 1991).

        Because of the need for realistic partition coefficients in the exposure assessment, several
calibrations were performed and are described here. Studies were found that include data on the
movement  and partitioning of mercury in and around watersheds.  The type of information  available
varies among these studies and can include soil, sediment, surface water, soil water, and runoff water
mercury concentrations, as well as lake outflow, lake inflow (runoff and erosion) and sedimentation
rates for mercury.  The main  purposes of these calibrations are twofold:  1) to determine values for the
soil-water partition coefficients and the benthic sediment-water partition coefficients that result in
mercury to water transport and partitioning behavior that are in reasonable  agreement with  available
mercury transport data; and 2) to  confirm that the IEM2 model is capable of correctly predicting the
complex process of mercury movement and partitioning in the soil and water environments with the
use of realistic parameters. This is one of the most critical aspects of mercury behavior addressed in
the exposure assessment.

        The modelling results were not as  sensitive to the suspended-sediment partition coefficients as
the benthic sediment-water coefficients in predicting mercury behavior in the lakes considered in the
calibrations, due to the clarity of the  water bodies considered. There is also enough reliable
information on the suspended-sediment partition coefficients to believe that the mid-point from
measured values should reasonably predict mercury partitioning in this study (see Appendix A).  Thus,
the suspended-sediment partition coefficients were not used in the calibration process.

        It is stressed that these calibrations are intended to be only semi-quantitative, with the  degree
of accuracy of the  calibration determined qualitatively; the calibrated results are  only to be  "consistent
with"  available data. There are doubtless other possible calibrations.  This  problem is an intractable
aspect of almost any calibration and is discussed in section C.4.
     1QQfi                                     r-T                        SAR REVIEW DRAFT
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C.3     Calibrations

C.3.1   Description of Calibration Approach

        For the model application described in Volume III, the models were nan in a "forward"
fashion:  the input parameters were specified, and the output values (e.g., media concentrations) were
obtained.  In the calibration effort described here, this process was reversed; the input parameters were
modified so that certain output values were within specific ranges.  The values for the partition
coefficients that yielded acceptable output values were then used as representative values in the  main
report when the models  were run in the "forward" fashion.

        The watershed model used, IEM2, uses atmospheric chemical loads and perform to mass
balances on a watershed soil element and a surface water element.  This mass balance tracks all
mercury specified in the background soil  concentrations and the mercury deposition rates.  The mass
balances are performed for total mercury, which is assumed to speciate into three components:  Hg°,
Hg2+, and methylmercury.  The fraction of mercury in each of these components is specified for the
soil and the surface water elements.  Loadings and chemical properties are given for the individual
mercury components,  and the overall mercury transport and loss rates are calculated by the model.
IEM2 first performs a terrestrial  mass balance to obtain mercury concentrations in watershed soils.
IEM2 next performs an  aquatic mass balance driven by direct atmospheric deposition along with
runoff and erosion fluxes (i.e., amount of the chemical transported from soil element to surface  water
element per unit time) from watershed soils. The water body output values of the IEM2 model  are
calculated based on the  assumption that steady-state conditions (i.e., fluxes out of' surface  water
element are equal to fluxes into element so that concentrations are independent of time) have been
achieved.

        There are two main assumptions made in these calibrations.  The first is that the measured
surface  water concentrations are  due to the estimated (or reported) fluxes to the water body.  Other
processes of mercury  influx and  outflux not considered here are assumed negligible. If these are
significant, it could significantly  modify the necessary calibrated values.  Second, it  is assumed that
conditions are approximately at steady-state.

        The calibrations were generally performed in three steps, depending on the particular data
available. First the soil-water partition coefficients were adjusted until the soil-water concentration
was within the  target  soil-water concentration range.  Then the runoff/erosion parameters were adjusted
until the fluxes to the water body were within the target range of values.  Finally, the benthic  sediment
partition coefficients were adjusted until the water concentrations and benthic sediment concentrations
were both within acceptable range (increasing the benthic sediment partition coefficient reduces  the
water concentration and increases the benthic sediment  concentration).

C.3.2   Parameters  Constant for All Calibrations

        Table C-l  shows the values for parameters that were the same for all calibrations.  For a more
complete description of these parameters  and the rationale for the values chosen, see Appendix A to
Volume III.
June 1996                                     C-4                        SAB REVIEW DRAFT
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                                                   Table C-l
                                 Parameters Constant for All Calibrations
Chemical-Dependent Parameters
Molecular weight (g/mole)
Henry's Constant (atm-m /mole)
Soil-water partition coefficient (ml/g)
Benthic-sediment partition coefficient
Suspended-sediment partition coefficient
Equilibrium fraction in soil
Equilibrium fraction in water
Constants
Ideal gas constant (m-atm/mole-K)
Air density (g/cm )
Solids density (kg/L=g/cm3)
Drag coefficient
Von Karman's Coefficient
Dimensionless boundary thickness
Run Options
Water body type
Suspended solids options switch
Equilibrium speciation option
Hg°
201
7.3X10-03
130a
130a
la
0
0.02
Value
8.21xlO-°5
1.19xlO'03
2.70
LlOxlO-03
7.40X10'01
4.00
Value
1
0
1
Hg2*
201
7.3xlO-10
Calibrated
Calibrated
9.50xl04 b
0.98
0.83
Methylmercury
216
4.7xlO-°7
Calibrated
Calibrated
6.50xl05 b
0.02
0.15
Comment
Used for volatilization from soil and
surface water
Used for water body calculations
Used to estimate speciation in waterbody
and concentration in benthos
Used for water body volatilization
calculations
Used for water body volatilization
calculations
Used for water body volatilization
calculations
Comment
Stagnant ponds, lakes
Use given sediment deposition rate to
calculate suspended solids concentration
Species are tied together in equilibrium
a Because it is assumed that the equilibrium fraction of elemental mercury in soil is 0 (see Appendix A), the soil-water
partition coefficient does not affect the calculations. Similarly, due to the low assumed equilibrium fraction in surface water
the other partition coefficients for elemental mercury does not significantly affect calculations and so it is not varied from the
value shown here.

  The suspended sediment partition coefficients were not as influential on the results used in the calibrations here, based on
the initial  sensitivity analyses. For this reason, they were assigned the values given in Appendix A.  These values are based
on these studies: Moore and Ramamodoray (1984), and Robinson and Shuman (1989).
June 1996
C-5
SAB REVIEW DRAFT
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C.3.3  Calibration Results

       C.3.3.1 Swedish Composite Lake

       In a series of papers, Meili investigated the mercury cycle through Swedish boreal forest
watersheds and lakes (Meili 1991a; Meili 1991b; Meili et al. 1991). The data in these papers, which
consist of a combination of summary values for Swedish lakes and predicted values, were used to
construct a model Swedish lake, from which many of the necessary IEM2 parameters could be
approximated. The output values in Meili (199la, 1991b)  that were used as target values in the
calibrations are shown in Table C-2.  Table C-3 shows the input values, excluding the partition
coefficients, used in the IEM2  model for the calibrations.
                                          Table C-2
                          Target Values for Swedish Lake Calibration
Output Parameter
Mercury Concentration in lake (ng/L)
Runoff load of mercury to lake,
ug/m2/yr or g/yr*
Mercury Concentration in Runoff (ng/L)
Outflow of mercury from lake, ug/m2/yr
or g/yr*
Sedimentation of mercury in lake,
ug/nV/yr or g/yra
Ratio of runoff load and direct
deposition
Surface sediment concentrau'on, ng/g
Mass balance of Loss Processes
Central Sweden
Southern Sweden
Value
2-3
4-8 Central Sweden.
6-11 Southern Sweden
3.7
2-5 Central Sweden,
3-7 Southern Sweden
7-20 Central Sweden,
10-30 Southern Sweden
0.6
150^60
% Sedimentation
80
79
Comment
For clearwater lake; Table I of Meili 1991b
(page 723)

Based on mercury/Carbon ratio of 0.25 ug/gB
in runoff water and organic carbon ^
concentration of 15 g/nr (Table I in Meili
1991b) I


For clearwater lakes (p.724 of Meili 1991b)
Range from Table I in Meili et al., (1991),
p.441 for characteristics of 25 study lakes in
1986
% Outflow
20 1
21
a Fluxes are given per unit lake surface area (Meili 1991b, p.722). Using the assumed surface area of 1 km for
a typical lake gives an estimate of the flux in grams per year.
June 1996
C-6
SAB REVIEW DRAFT
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        Because there is considerable uncertainty about the degree of volatilization from the surface
water body, two separate calibrations were performed.  In the first, volatilization from the surface
water body was considered as a loss process.  In the second, no volatilization was assumed.  The latter
is consistent with assumptions in Meili et al. (1991b), where volatilization was not considered due to
uncertainty. The calibration of the soil-water partition coefficients is the same in both calibrations
because it is not affected byloss processes from the surface water body.

        The first step was to  calibrate the soil-water partition coefficients so that the predicted total
mercury soil-water concentration was within range of the target values (3.75-5 ng/1).  Then the erosion
parameters were modified so that the fluxes to  the water body from runoff and erosion agreed.   In the
IEM2 model, the various erosion parameters (sediment  delivery ratio, pollutant enrichment factor,
erosivity factor, erodability factor, topographic  factor, cover management factor) are multiplied
together to obtain an estimate of the annual amount of soil erosion; thus,  there are many different
possible combinations of various values for these parameters that can yield the same general erosion
rate. Finally, the benthic sediment partition coefficients were modified so that the predicted surface-
water and benthic sediment concentrations  were'consistent with the values reported in Meili et al.   ^
1991.  The results of both calibrations are shown in the Table C-4.

        Although there is general agreement with the  target outfluxes, the high benthic sediment
concentration suggests that the assumption of no significant volatilization from the surface water may
not be true, unless there are processes not addressed here that serve to prevent the predicted high
benthic sediment concentrations from occurring.

        In summary, using the available data on 88 lakes in Sweden, the IEM2 watershed model was
calibrated using these available data.  The calibrated values of the benthic-sediment partition
coefficients depend on the significance  of the volatilization pathway from the  water body. The
calibrated benthic-sediment partition coefficients were found  to agree with the overall range reported in
Wiener et al. (1990).

        C.3.3.2 Fen at Tivedan National Park, Sweden

        Aastrup et al., (1991) describe mercury transport within a small segment (6%) of a larger
watershed area.  The results presented in this paper were used for calibrating the parameters involved
in estimating the runoff of mercury from the watershed.

        The study area was described as a minicatchment watershed consisting of a small forested
catchment in the Tivedan National Park located in southern Sweden.  The mercury budget was
estimated for a till formation on a slope, making up a funnel-shaped minicatchment of 0.014 km2.
This area drains into a fen (low land covered wholly or partly with water unless artificially drained).
Elevation in the minicatchment ranges from approximately 1.5 m at the point farthest from the fen to 0
m at the fen itself.  The catchment  was divided into three areas: an upper level with shallow soils, an
intermediate area, and a waterlogged area.
                                                                           C At} E>T7\/TT7YX; PlD
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        Mercury concentrations were reported for different soil layers .  It was noted that 41% of the
 total mercury estimated (8.8 kg/km2) was found in the highest humic layer.  Table C-5 shows the total
 mercury found in each soil layer analyzed.

                                           Table C-5
             Soil Layers and mercury Concentrations Reported in Aastrup et al. 1991
Soil Layer
Mor
E-Horizon
upper B-honzon
lower B-honzon
C-horizon
Total
Assumed Thickness (cm)
8 ,
6
6
NA
NA

Mercury Content (ng/g)
250
27
58
23
6
NA
Total Mercury
(g)
50
9
34
23
8
124
        Also reported were estimates of mercury concentrations in soilwater and groundwater. There
was a large amount of variation in the measured values. The values included in Table C-6 below
generally have a standard deviation as large as the mean, even  for those with as many as 30 samples.

        The soilwater mercury concentration calculated in the IEM2 model is assumed to be the
dissolved mercury, and hence does not consist of any particulate-bound mercury. For the purpose of
the calibration effort, it was decided that this calculated quantity would be bounded above by the
values for Hg-II (sum of reactive  and unreactive mercury) reported in Aastrup et al. and shown in
Table C-6.  These values, estimated in accordance with the standard Swedish sampling program
(Chapter 2 in Lindquist et al., 1991), are the sum of the dissolved Hg2+ plus some reactive particle
associations and some humic matter associations which may fall under paniculate mercury  (Hg-IIa and
Hg-IIb). The difference between  total mercury concentration (Hg-tot) and Hg-II was assumed to be
Hg2+ strongly bound to particulates (i.e., not dissolved). For more details regarding the mercury
notation of the Swedish Sampling Program see Section 2 of Appendix E  of this report.
        The fluxes out of the minicatchment area were estimated from Figure 4 (page 165) in Aastrup
et al. (as well as Figure 2 in Johansson et al., 1991).  The flux to the fen from the top 20 cm of soil is
2.6 g/km /yr.  Because the fluxes are normalized to the minicatchment area, multiplying by the area
(0.014 km2) yields a total flux of 0.0364 g/yr.  Similarly, for the top 8 cm  (called the mor layer,
which consists of humic matter distinct from mineral  soil), the flux is 1.5 g/km2/yr, corresponding to a
total flux of 0.02  g/yr.

        Two separate calibrations were performed.  In the first, the top 20 cm was treated as a single
layer, while in the second only the mor layer was used.  The parameters, their values, and the rationale
for their selection are shown in Table C-7.  Table C-8 shows the results for the both calibrations.
June 1996
C-ll
                                                                         SAB JAEVIEW DRAFT
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        Despite the low values used for the default soil erosion parameters, the loss due to erosion
becomes significant when the effective partition coefficient is increased. Whether this was actually
true at the Tividen site is not certain.  Nevertheless, the calibration for the mor layer alone provides an
upper bound for the partition coefficient, as the predicted soil-water concentration lies at the lower end
of the observed range.

        C.3.3.3 Composite Minnesota Lake

        These calibrations are based on the Minnesota lake characterization presented in Sorensen et
al., (1990) based on 80 lake  watersheds. Mercury concentrations in precipitation, lake water and
sediment were measured and analyzed along with watershed  data for 80 lake watersheds in the study
region of northeastern Minnesota. The summary values are shown in Table C-9.  Median values were
used when possible because  a number of large watersheds/ waterbodies biased the mean values.  The
values used in the IEM2 model are shown in Table C-10.

        The median value for evaporation reported in Sorensen et al., (1990) was 47.6; however, this
results in a negative net leach rate since leach rate is proportional to Precipitation +  Irrigation -
Runoff - Evapotranspiration.  For this  reason, the evapotranspiration  was set so that leach rate is 0.
This has little practical affect on  predicted values because  the background soil concentration  is not
subject  to these loss processes.

        Although no values are reported for the soil-water concentrations in Sorensen et al., (1990),
the values reported in Aastrup et al., (1991) for Swedish soil indicate that they may be approximated
by the surface water concentration. The target value for this effort was the median of the surface
water concentrations reported in Sorensen et al. of 2.3 ng/1.  After the soilwater concentrations were
calibrated, the topographic factor, used in estimating soil erosion, was set so that the indirect/direct
ratio was  consistent with that estimated from Sorensen et al., (1990).

        As in the calibrations performed for the composite Swedish lake, two separate calibrations
were performed, one with and one without volatilization from the surface water body. The results of
the calibrations are shown in Table C-ll.   These two calibrations provide a range for the benthic
sediment concentrations.

        Despite the uncertainties  introduced by using a  composite lake,  the  results are in general
agreement with previous calibrations.  As for the composite Swedish lake, the benthic-sediment
partition coefficients had to be substantially increased if volatilization from  the surface water body  was
not considered as a loss process.
Tiinp IQQfi                                     P-1S                        
-------
                              Table C-9
Parameter Values for 80 Minnesota Lakes Reported in Sorensen et al., 1990
Parameter
Lake concentration (ng/L)
Total organic carbon as C in surface water (mg/L)
Annual Direct deposition onto lake (ug/nv~/yr)
Deposition immediate ug/m (calculated deposition falling
directly on lake surface plus calculated runoff from
immediate watershed (assuming 100% mercury transport
to lake)
Indirect/Direct Deposition to Water Body
Median surface sediment concentration (ng/g)
Lake surface area (Ha)
Immediate watershed area (Ha)
Site elevation (m)
Topographic high immediate (m)
Annual precipitation (m/yr)
Annual evaporation, land (m/yr)
Annual runoff (m/yr) •
Lake renewal time (yr)
Total renewal time (yr)
Mean depth (m)
Lake volume (m )
% Forest
% Water
Median
2.30
6.76
12.7
24.8
0.95 (calculated using
median values, -but
not necessarily the
median)
154
328
650
432
476
0.665
0.476
0.196
49.1
2.18
5.70
1.5xl07
83
16.2
Range/Comment
0.90-7.00
3.53-14.3
10.4-15.4
14.8-58.4
This is a value calculated here by assuming
that the difference between the "deposition
immediate" and the direct deposition onto the
lake is the flux from the immediate
watershed (which turns out to be 12.1
ug/m /yr). Dividing this by the direct
deposition gives this ratio.
34-753
24-89400
55-168000
388-587
378-664
0.560-0.762
0.446-0.506
0.103-0.315
5.85-202
0.01-45.4
1.08-29.0
4.35X105 - 5.47xl09
46.2-94.7
4.10-38.5
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                                      Table C-ll
                    Results for Composite Minnesota Lake Calibrations
Parameter
Effective Soil-Water Partition
Coefficient (ml/g) for Hg2+ and
methylmercury
Effective Benthic Sediment Partition
Coefficient (ml/g) for Hg2+ and
Methylmercury
Total Mercury Soil Concentration after
one year (ng/g)
Total Mercury Soilwater
Concentration (ng/L)
Indirect/Direct Ratio
Total Load to Water body from
Catchment Region Considered (g/yr)
% Erosion
% Runoff
Total Water column concentration
(ng/L)
Steady-state outflows (g/yr)
Volatilization
Sedimentation
Dilution
Total Mercury Concentration in
Benthos (ng/g)
Volatilization
from Surface
Water Assumed
38200
100500
87.8
2.3
0.95
39.7
92.36
7.64
2.3

21.4 (26%)
44.0 (54%)
15.7 (20%)
214
No Volatilization
from Surface
Water Assumed
38200
149600
87.8
2.3
0.95
39.7
92.36
7.64
2.3

0
65.4 (80%)
15.7 (20%)
318
•Target Calibration
Value
NA
NA
87
2.30
0.95
NA
NA
NA
2.3




154 (Range 34-753)
June 1996
C-20
SAB REVIEW DRAFT
-------
C.4     Limitations and Uncertainties

        The calibrated partition coefficients derived here are intended to represent long-term retention
properties of the watershed systems for which they were derived.  An obvious limitation with any
calibration effort is that there may be other calibrations that also give the same qualitative agreement
but have significantly different values for the calibrated parameters.  The likelihood of such alternative
calibrations is increased when large data gaps exist.  Because the calibrations were performed in a
sequence of steps, these possibilities are discussed in turn for each step in the calibration process used
here.

        The soil-to-water partition coefficient calibrations seem the most defensible because there is
relatively little involved in calculating soilwater concentrations. Furthermore, only a relatively simple
argument is required in order to suggest that the typically reported values for the mercury partition
coefficients in soil-water systems are questionable.   In the EM2 model, the total concentration of each
mercury component in soil" is assumed to reach  equilibrium between  its paniculate and aqueous phases.
The concentration of species i dissolved in pore water is given by the following equation:

                                           ,
                                                 + Kdsi BD

The concentration in particulate phase is defined in equation (2).


                                                     Sc. Kd. BD
                                      -      •          »    *»
                                           .         6  +Kd.BD
                                                     S      S,l

where:
        SCj     =       total soil concentration of component "i" (|ag/g)
        6S      =       volumetric soil water content (Lwater/L)
        Kds j*  =       soil/water partition coefficient for component "i" (L/kg = ml/g)
        BD    =       soil bulk density (g/cm3)
        Cst j    =       total soil concentration of component "i" (mg/L)
        Cds J   =       concentration of "i" dissolved in pore water (mg/Lwater)
        C  j   =       concentration of "i" in particulate phase (mg/kg)

        The total soil concentration in ug/g is given by this equation.

                                                       ^
                                                 + Kd.   C
                                                                                              (2)
The value for the partition coefficient to achieve a given soil-water concentration is , thus,
                                                sc.     e
                                             = —L  - _JL                                    (4)
If the mercury soil-water concentrations reported in Meili et al, (1991) are accurate, then these values
indicate that the mercury in the soil-water represents only a small fraction of the total mercury per
volume.  For example, if a typical total soil concentration of 100 ng/g (0.10 ug/g) were completely
dissolved in a liter of water, and  assuming a typical soil density of 1.4 g/cm3, the resulting water


                                               p-91                        SAR RFVTFW DRAFT
-------
concentration would be  100000 ng/L (100 ug/L).  This is to be compared to the reported soil-water
concentrations in the range of 1-10 ng/L.  Thus, at most about 0.01% can be dissolved to achieve the
values observed, and the rest must be bound to particulates in the soil matrix. Even assuming a
volumetric soil water content of 1, using equation (4) above the partition coefficient must be about It)4
ml/a in order to have a dissolved  water concentration of 10 ng/L.  Achieving soil-water concentrations
of 2 ng/L requires  a partition coefficient of slightly less than 5xl04 ml/g.

       Because adequate speciation estimates were not available, there is uncertainty in the values for
the partition coefficients for methylmercury. For the purpose of this effort they were assumed to be
the same as for Hg2+. In sediment,  values between about 2% and 9%  methylmercury have been
reported (Agaki et al., 1979) for sand, silt/woodchips and woodchip sediments.  Cappon (1984) found
that percent methylmercury for  nonamended soils  was about 2.6% (this is an upper bound on values
from unpublished data reported by several authors as cited in  Water, Air and Soil Pollution  1991).  If
the speciation in soil-water is similar to  that sorbed onto soil particles, then the partition coefficients
for methylmercury would be similar to those for Hg2+.  Although there is considerable variability in
the percent of total mercury that is methylmercury in surface waters, the latest estimates (Bloom et al.,
1991; Watras and Bloom 1992) range from 5% to 25%  methylmercury.  If the fraction in soil water is
slightly larger than that sorbed onto  soil particles, as the data would indicate, then the required
calibrated partition coefficient for methylmercury would be correspondingly lower than that  for Hg2+
derived above.  This is because the fraction dissolved for methylmercury would be higher than that for
Hg2+.  However, for the purpose of this calibration effort, it was felt mat the data were not adequate to
justify separate calibrations for  both Hg2+ and methylmercury. The result of this assumption is that
the amount of methylmercury in the flux to the water body from runoff may be underestimated and
amount in soil  erosion overestimated.

       Calculation of the flux to  the water body boils down to  determining the a set of adequate
erosion/runoff parameters.  The total load due to runoff and erosion, denoted here by LE/Ri  (g/yr),  is
given by equation  (5).
                        Lm. = WAL(R C^ 10-2 + Xe SD ER C^ 1(T3 )  (5)
where:
        LE/R i  =      l°a(ito water body from surface runoff and soil erosion for component i (g/yr)
        WAL   =      watershed surface area (m2)
        R      =      average annual runoff (cm/yr)
        Cds j   =      concentration of "i" dissolved in pore water (mg/Lwater)
        Xe     =      unit soil loss (kg/m2/yr)
        SD    =      sediment delivery ratio (unitless)
        ER    =      contaminant enrichment ratio (unitless)
        Cps J   =      concentration of "i" in paniculate phase (mg/kg)

The unit soil loss rate is given by equation (6).

                                  X  = R  KLSCPS  907'18  (6)
                                                      0.00407
where
        R_     =      soil erosivity factor (kg/km2/yr)


June 1996                                     C-22                        SAB REVIEW DRAFT
-------
        K      =      - soil erodability factor (tons/acre)
        LS     =       topographic factor (unitless)
        C      =       cover management factor (unitless)
        PS     =       support practice factor (unitless)

        Values for the site-specific average annual runoff were available for the sites considered.
Values  for the soil erosion parameters (R_, K, LS, C, PS,  SD, ER) were not available.  This was
further  complicated by the composite nature of the sites considered for the calibration efforts. For this
reason,  the erosion parameters were calibrated so that the ultimate total  fluxes to the water body were
consistent with measured data.

        The calibrated benthic sediment partition coefficients are consistent with values reported
elsewhere (e.g., Wiener et al.,  1990).  Because there is apparently considerable uncertainty as to the
degree of volatilization from surface water bodies, two different calibrations were .performed, when
possible.  In the first the volatilization was  assumed to contribute to thetotal loss rate from the surface
water using a generic set of the relevant parameters (e.g., equilibrium speciation of the mercury species
in water,wind speed).  Another calibration was also performed assuming that volatilization was
negligible.  The differences in the calibrated partition coefficients are  well  within the range of the
usual variation of the partition coefficients themselves.  Calibration without volatilization requires that
sedimentation play a larger role as a loss process.  The higher benthic sediment partition coefficients
needed  to achieve this effect,  while resulting in high benthic sediment concentrations, provide upper
bounds  on the partition coefficients for the site under consideration.

C.5     Conclusions

        Calibrations of the IEM2 model were performed using three data sets, with the partition
coefficients serving as the primary calibration parameters.  Due to uncertainty as to the exact degree of
volatilization from the surface water body, two separate calibrations were performed with and without
volatilization.  The calibrated  partition coefficients are shown in Table C-12.

        The significance of volatilization as a mercury loss process from water bodies is currently
unclear. The  results derived here show that the assumption that volatilization  is negligible, while it
can be modelled by increasing the benthic sediment partition coefficients, results in benthic sediment
concentrations that are near or above the upper end of the measured values. This suggests that
volatilization may in fact be nonnegligible.

        Despite acknowledged uncertainties and limitations, the results derived here support the use of
soil-to-water partition coefficients that are higher than previously published values, when the
equilibrium assumption is considered appropriate.  The effective partition coefficients (soil and
benthic) determined from these calibrations are similar to values found for  suspended-sediment
partition coefficients, for which much reliable data are available.  Additionally, it was confirmed that
the IEM2 model could predict mercury movement and partitioning in  the soil  and water environments
with the use of realistic modeling parameters.  Thus, we have established that
     iQQfi                                      C-23                         SAB REVIEW DRAFT
-------
    42

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applying this part of the IEM2 model using partition coefficients representative of those in Table C-12
can be expected to result in reasonable predictions of mercury movement and behavior in and out of
watersheds.

       That soil-water partition coefficients larger than previously published values would be
necessary is consistent with the growing concern that watershed soils may be serving  as a significant
repository for mercury.  This repository can potentially act as a source of mercury to  water bodies
long after enhanced mercury deposition has occurred.

C.6    References

Aastrup, M., J.  Johnson, E. Bringmark,  I. Bringmark, and A. Iverfeldt (1991).  Occurrence and
Transport of Mercury  within a Small Catchment Area,  Water, Air, and Soil Pollution  56: 155-167.

Akagi H., D.C. Mortimer, and  D.R.  Miller (1979).  Mercury Methylation and Partition in Aquatic
Systems.  Bull.  Environ. Contain. Toxicol.  23:372-376.

Arnold, J.G., J. Williams, A. Nicks,  and N. Sammons,  1990.  SWRBB:  A Basin-Scale Simulation
Model for Soil  and Water Resources Management, Texas A & M University Press, College Station,
Texas.

Baes  et al., (1984) is cited in Appendix B Reference Section. It should also be cited'in Appendix C
Reference Section.

Bloom, N.S., C.J. Watras, and J.P. Hurley (1991).  Impact of Acidification on the Methylmercury
Cycle of Remote Seepage Lakes.  Water, Air,  and Soil Poll.  56:477-491.

Buchter, B., B.  Davidoff, M.C. Amacher, C. Hinz, I.K. Iskandar, and H.M. Selim  (1989).  Correlation
of Freundlich Kd and retention parameters with soils and elements.  Soil Science 148:370-379.

Cappon, C. (1984). Content and Chemical Form of mercury and selenium in soil, sludge and fertilizer
materials. Water, Air, Soil Pollut 22:95-104.

Cappon,  C.J. (1987).  Uptake and Speciation of  Mercury and Selenium in Vegetable CropsGrown on
Compost-Treated Soil.  Water,  Air, Soil Poll.  34:353-361.

Dooley, J. H. (1992).  Natural  Sources of Mercury in the Kirkwood-Cohansey Aquifer System of the
New Jersey  Coastal Plain. New Jersey  Geological Survey, Report 27.

Jensen, A. and Jensen, A.  Historical deposition  rates of mercury in Scandavia estimated by dating and
measurement of mercury in cores of peat bogs, Water, Air, and Soil Pollution  56:759-777.

Johansson, K., M.  Aastrup, A.  Andersson, L. Bringmark, and A. Iverfeldt (1991).  Mercury in Swedish
Forest Soils  and Waters - Assessment of Critical Load, Water, Air, and Soil Pollution 56:267-281.

Lindqvist, O., K. Johansson, M. Aastrup, A. Andersson, L. Bringmark, G. Hovsenius, L. Hakanson, A.
Iverfeldt, M. Meili, and B. Timm (1991). Mercury in the Swedish Environment - Recent Research  on
Causes, Consequences and Corrective Methods.  Water, Air and Soil Poll. 55:(all chapters)
                                             C-25                        SAB REVIEW DRAFT
-------
Meili, M. (199la).  The Coupling of Mercury and Organic Matter in the Biogeochemical Cycle -
Towards a Mechanistic Model for the  Boreal Forest Zone, Water, Air,  and Soil Pollution 56:333-347.

Meili, M., A: Iverfeldt and L. Hakanson (1991).  Mercury in the Surface Water of Swedish Forest
Lakes - Concentrations, Speciation and Controlling Factors, Water, Air, and Soil Pollution  56:439-
453.   .

Meili, M. (1991b).  Fluxes, Pools, and Turnover of Mercury in Swedish Forest Lakes, Water, Air, and
Soil Pollution  56:719-717.

Moore, J.W. and S. Ramomodoray (1984).  Heavy Metal in Natural Waters - Applied Monitoring in
Impact Assessment.  New York, Springer-Varlag.

Nriagu, J.O. (1979). The Biogeochemistry of Mercury in the Environment. Elsevier/North Holland.
Biomedical Press: New York.

Parks, J.W., A. Lutz, and J.A.  Sutton (1989).  Water Column Methylmercury in the Wabigoon/
English River-Lake System: Factors Controlling Concentrations, Speciation, and Net Production. Can.
J. Fisher. Aq. Sci. 46:2184-2202.

Rai, D.  and J.M. Zachara (1984).  Chemical attenuations, coefficients and constants in leachate
migration, v.l, A Critical Review.  EA-3356, v.l, Research Pro.

Revis, N.W., T.R. Osborne, G. Holdsworth, and C. Hadden (1990).  Mercury in Soil:  A Method for
Assessing Acceptable Limits.  Arch. Environ.  Contam. Toxicol  19:221-226.

Robinson, K.G. and M.S. Shuman (1989).  Determination of mercury in surface waters using an
optimized cold vapor spectrophotometric technique.  International Journal of Environmental Chemistry
36:111-123.

Sorensen, J., G.E. Glass, K.W. Schmidt, J.K Huber, and G.R. Rapp, Jr. (1990). Airborne Mercury
Deposition  and Watershed Characteristics in Relation to Mercury Concentrations in Water, Sediment,
Plankton, and Fish of Eighty Northern Minnesota Lakes, Environ. Sci. Technol. 24:1716-1727.

U.S. EPA.  1990.  Methodology for Assessing Health Risks Associated with Indirect Exposure to
Combustor Emissions. Interim final.  EPA/600/690/003.

U.S. EPA.  1993.  Draft  addendum to Methodology for Assessing Health Risks Associated with
Indirect Exposure to Combustor Emissions. EPA/600-93/003 November 1993.

U.S. EPA.  1994.  Personal communication with R.A. Ambrose, U.S. EPA, Athens, GA.

Watras, C.J. and N.S. Bloom (1992).  Mercury and Methylmercury in Individual Zooplankton:
Implications for Bioaccumulation. Limnol.  Oceanogr. 37(6):1313-1318.

Wiener, J.G., W.F. Fitzgerald, C.J. Watras, and R.G. Rada (1990).  Partitioning and Bioavailibility of
Mercury in an Experimentally Acidified Wisconsin Lake, Environmental Toxicology and Chemistry,
Vol.9, pp. 909-918.
June 1996                                   C-26                       SAB REVIEW' DRAFT
-------
 Wilken, R.D. and H. Hintelmann (1991).  Mercury and Methylmercury in Sediments and Suspended
 particles from the River Elbe, North Germany.  Water, Air and Soil Poll.  56:427-437.
June 1996                                 C-27                      SAB REVIEW DRAFT
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c
-------
          APPENDIX D




DESCRIPTION OF EXPOSURE MODELS
-------
-------
                             TABLE OF CONTENTS
                                                                                    Paae
D.I    Description of RELMAP Mercury Modelling	D-l
       D.I.I   History and Background Information	, D-l
       D.I.2   RELMAP Modeling Strategy for Atmospheric Mercury	D-l
       D.I.3   Model Parameterizations  	D-5
       D.1.4   Discussion of RELMAP Modeling Uncertainties	D-10

D.2    Description of COMPDEP Air Dispersion Model	D-ll
       D.2.1   Description of the COMPDEP Air Quality Model	D-ll
       D.2.2   Application of the COMPDEP Model for the-Exposure Assessment	 D-30

D.3    Description of the IEM2 Indirect Exposure Methodology 	D-38
       D.3.1   The Terrestrial Equations	 D-39
       D.3.2   The Aquatic Equations	 D-44
       D.3.3   Derivation of Select Equations  	D-60
       D.3.4   Summary of Notation	 D-64

D.4    References	 D-67
June 1996                                  D-i                      SAB REVIEW DRAFT
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                                  LIST OF TABLES
                                                                                       Page

D-l    Emission Speciation Profiles for the Point Source Types Defined	  D-3
D-2    Dry Deposition Velocity (cm/s) for Divalent Mercury (Hg2+)  	D-8
D-3    Wind Speeds Used for Each Pasquill Stability Category in the CARB Subroutine
       Calculations	D-9
D-4    Roughness Length Used for Each Land-Use Category in the CARB Subroutine
       Calculations	D-9
D-5    Classes of Atmospheric Stability and Associated Vertical Temperature Distribution  ....  D-12
D-6    Pasquill Turbulence Types and Corresponding Atmospheric Conditions
       (from Gifford, 1976)	:	.D-13
D-7    Meteorological Conditions Defining Pasquill Turbulence Types	  D-l3
D-8    Wind Profile Exponents Used In Scoping Study	  D-14
D-9    The Three Main Cases for Determining Air Concentration With Plume
       Depletion Effects	,	D-19
D-10   Parameters Used to Calculate Horizontal Dispersion Parameter s  in
       COMPDEP (Turner,  1970)	D-20
D-ll   Parameters Used to Calculate Vertical Dispersion Parameter sz in
       COMPDEP (Turner,  1970)	  D-21
D-12   Example of Precipitation Scavenging Coefficients (per second)
       in COMPDEP  	  D-24
D-l3   Terrain Adjustment Factors Used in Calculating Terrain-Dependent
       Effective Stack Height  	D-25
D-14   Coefficients Used to Calculate Lateral Virtual Distances for Pasquill
       Dispersion Rates	  D-28
D-15   Precipitation Intensities Considered by COMPDEP	  D-29
D-l6   Description of Meteorological Files Used to Make Input Files for COMP	D-32
D-17   Divalent Mercury Vapor Seasonally-Averaged Deposition Velocities (cm/s)  	D-33
D-l8   Air Modeling Parameter Values Used In the Exposure Assessment:
       Generic Parameters	  D-35
D-19   Model Plant Parameter Values Used in COMPDEP  	  D-36
D-20   Model Plant Mercury Speciation of Emissions	  D-37
June 1996                                   D-ii                       SAB REVIEW DRAFT
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                               LIST OF FIGURES
                                                                                  Page

D-l    Overview of the IEM2 Watershed Modules	  D-38
D-2    Overview of the IEM2 Soils Processes  	  D-40
D-3    Overview of the IEM2 Water Body Processes	  D-47
D-4    EEM2 Steady State Sedime_nt Ba'lance in Water Bodies	  D-48
    1QQ*                                D-iii                      SAB REVIEW DRAFT
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D.I    Description of RELMAP Mercury Modelling

D.I.I  History and Background Information

       During the mid-1970's, SRI International developed a Lagrangian puff air pollution model
called the European Regional Model of Air Pollution {EURMAP) for the Federal Environment Office
of the Federal Republic of Germany (Johnson et al.  1978). This regional model simulated monthly
sulfur dioxide (SO2) and sulfate (SO!}') concentrations, wet and dry deposition patterns, and generated
matrices  of international exchanges of sulfur for 13 countries of western and central Europe.  In the
late-1970's, the U.S. EPA sponsored SRI International to adapt and apply EURMAP to eastern  North
America.  The adapted version of this model, called Eastern North American Model of Air Pollution
(ENAMAP),  also calculated monthly SO2 and SO^" concentrations, wet and dry deposition patterns,
and generated matrices of interregional exchanges of sulfur for  a user-defined configuration of regions
(Bhumralker  et al., 1980; Johnson, 1983). In the early-1980's,  U.S. EPA modified and improved  the
ENAMAP model to increase its flexibility and scientific credibility.

       By 1985, simple parameterizations of processes involving fine (diameters «c2.5 jam)  and coarse
(2.5  um < diameters <  10.0 urn) particulate matter were incorporated into the model.  This version of
the model, renamed the Regional Lagrangian Model of Air Pollution (RELMAP),  is capable of
simulating concentrations and  wet  and dry deposition patterns of SO2, SO^  and fine and coarse
particulate matter and can also generate source-receptor matrices for user defined regions. In addition
to the main model program, the complete RELMAP modeling system includes 19  preprocessing
programs that prepare  gridded meteorological and emissions data for use in the main program.  The
RELMAP code was developed using FORTRAN on a Sperry-UNIVAC 1100/82 computing system.  It
has since been migrated and adapted to operate on other computing systems.  Currently, the  RELMAP
is operated by U.S. EPA's  Atmospheric Research and Exposure Assessment Laboratory on DEC VAX
and CRAY computing systems, and a test version has recently been installed on a DEC 3000 AXP
(Alpha) workstation. The simulations for the Mercury Study Report to Congress were performed  on a
CRAY Y-MP supercomputer at the National Environmental Supercomputing Center.  A complete
scientific specification of the RELMAP as used at U.S. EPA for atmospheric sulfur modeling is
provided in RELMAP:  A Regional Lagrangian  Model of Air Pollution - User's Guide (Eder et al.,
1986).  Section D.I.2.1 discusses the modifications made to the original sulfur version of RELMAP to
enable the simulation of atmospheric mercury.

D.I.2  RELMAP Modeling Strategy for  Atmospheric Mercury

       D.I.2.1         Introduction

       Previous versions of RELMAP have been described by  Eder et al. (1986)  and Clark  et al.
(1992).  The  goal of the current effort was to model the emission,  transport, and fate of airborne
mercury over the continental U.S. for the year of 1989. Modifications to the RELMAP for
atmospheric mercury simulation were heavily based on recent Lagrangian model developments in
Europe (Petersen et al., 1995).  The mercury version of RELMAP  was developed  to handle three
species of mercury:  elemental  (Hg°), divalent (the mercuric ion, Hg2+) and particulate mercury
(Hgpart), and  also carbon soot.  Recent experimental work indicates that ozone (Munthe, 1992) and
carbon soot (Iverfeldt, 1991; Brosset and  Lord,  1991; Lindqvist et  al., 1991) are both  important in
determining the wet deposition of Hg°. Carbon soot, or total carbon  aerosol, was  included as a
modeled  pollutant in the mercury version of RELMAP to provide necessary information for the Hg°
wet deposition parameterization. Observed O3 air concentration data were obtained from the Agency's
Aerometric Information Retrieval System (AIRS) data base, and it  was not necessary to include O3 as
an explicitly modeled pollutant.  Observed O3 air concentration data were objectively  interpolated in
time  and  space for each 3-hour timestep of the model simulation to produce grids  of O3 air

June 1996                                    D-l                        SAB REVIEW DRAFT
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concentration.  A minimum O3 air concentration value of 20 ppb was imposed.  Methyl mere ury was
not included in the mercury version of RELMAP because it is not yet known if it has a primary
natural or anthropogenic source, or if it is produced in the atmosphere.

       RELMAP may be run in either of two modes.  In the field mode, wet deposition, dry
deposition,  and air concentrations are eomputed at user-defined time intervals.  In the source-receptor
mode, RELMAP also computes the contribution  of each source cell to the deposition and concentration
at each receptor cell. For mercury, only the field mode of RELMAP operation was used.  With over
10,000 model cells in the high-resolution receptor grid and a significant fraction of these cells also
emitting mercury, the data accounting task of a source-receptor run for all mercury sources could  not
be performed with the computing resources and time available.

       Unless specified otherwise in the following sections, the modeling concepts and
parameterizations described by Eder et al. (1986) were preserved for the RELMAP mercury modeling
study.

       D.I.2.2        Physical Model Structure

       Because of the long atmospheric residence time of mercury,  long range transport of the
majority of mercury emitted was expected. RELMAP simulations were originally limited to the area
bounded by 25 and 55  degrees north latitude and 60 and 105  degrees west longitude and had a
minimum spatial resolution of 1 degree in both latitude and longitude.  For die Mercury Study Report
to Congress, the western limit of the RELMAP modeling domain was moved out to 130 degrees west
longitude, and the modeling grid resolution was reduced to l/i degree longitude by Vs degree latitude
(approximately 40 km square) to provide high-resolution coverage over the entire continental  U.S.

       Since the descriptive document by Eder et al.  (1986) was produced, the original 3-layer puff
structure  of the RELMAP has been replaced by a 4-layer structure.  The following model layer
definitions were used for the RELMAP mercury  simulations:

       Layer 1 top      -     30 to 50 meters above the surface (season-dependent)
       Layer 2 top      -     200 meters above the  surface
       Layer 3 top      -     700 meters above the  surface
       Layer 4 top      -     700 to 1500 meters above the surface (month-dependent)

       D.I.2.3        Mercury Emissions

       Area source emissions were introduced into the model in the lowest layer.  Point source
emissions were introduced into model layer 2 to  account for the effective stack height of the point
source type in question. Effective stack height is the  actual stack height plus the estimated plume rise.
The layer of emission is inconsequential during the daytime when complete vertical mixing is imposed
throughout  the 4 layers. At night, since there is  no vertical mixing,  area source emissions to  layer 1
are subject  to dry deposition while point source emissions to layer 2 are not. Large industrial emission
sources and sources with very hot stack emissions tend to have a larger plume rise, and their effective
stack heights might actually be larger than the top of layer 2. Since, however the layers of the
pollutant puffs remain vertically aligned during advection, the only significant process effected by the
layer of emission is nighttime dry deposition.

       Mercury emissions data were grouped into seven different point-source types and a general
area-source type.  The  area source emissions data describe those sources that are too small to be
accounted for individually in pollutant emission surveys.  For the  RELMAP mercury modeling study,
area sources were assumed to emit mercury entirely in the form of Hg° gas, while the seven point

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source types were each assigned particular mercury speciation profiles. These speciation profiles
defined the estimated fraction of mercury emitted as Hg , Hg~T, or Hg  t. Since there remains
considerable uncertainty as to the actual speciafion factors for each point source type, an alternate
emission speciation was simulated in addition to the base speciation in order to test the sensitivity of
the RELMAP results to the speciation profiles used.  The base-case and alternate speciation profiles
used for this study are shown in Table D-l.  The total (non-speciated) mercury emissions inventory
used is that described in Volume II of this Report.  Gridded fields of total Hg°,  Hg2+ and Hgpart point
source emission rates and Hg° area source emission rates were produced and used as input  to the
RELMAP model simulation.
                                          Table D-l
                Emission Speciation Profiles for the Point Source Types Defined
Point Source Type
Electric Utility Boilers
Non-utility Fossil Fuel
Combustion
Municipal Waste Combustion
Medical Waste Incineration
Non-ferrous Metal Smelting
Chlor-alkali Factories
Other Point Sources
Base-Case Speciation (%)
Hg°a
50
20
85
70
80
Hg2+b
30
60
10
30
10
HSp"
20
20
5
0
10
*
Alternate Speciation (%)
Hg°a
50
20
85
70
80
Hg2+b
0
0
0
0
0
H^PC
50
80
15
30
20
          Hg° symbolizes elemental mercury
          Hg~+ symbolizes divalent mercury
          Hg  symbolizes particle bound mercury
        Global-scale natural and recycled anthropogenic emissions were accounted for by assuming an
ambient atmospheric concentration of Hg° gas of 1.6 ng/m3. This use of a constant background
concentration to account for global-scale natural and anthropogenic emissions is the same technique
used by Petersen et al. (1995). The deposition parameterizations described in section D.I.3.1 were
used to simulate the scavenging of Hg" from this constant ambient concentration throughout the entire
3-dimensional model domain. The result was used as an estimate of the deposition of mercury from
all natural sources and anthropogenic sources outside the model domain.
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       D.I.2.4        Carbon Aerosol Emissions

       Penner et al. (1993) concluded that total carbon air concentrations are highly correlated with
sulfur dioxide (SO2) air concentrations from minor sources. They concluded that the emissions of
total carbon and S02 from minor point sources are correlated as well, since both pollutants result from
the combustion of fossil fuel.  Their data indicate a 35% proportionality constant for total carbon air
concentrations versus S02 air concentrations.  The RELMAP mercury model estimated total carbon
aerosol emissions using this 35% proportionality constant and SO2 emissions data for minor sources
obtained by the National Acidic Precipitation Assessment Program (NAPAP) for the year 1988.  Much
of these S02 emissions data had been previously analyzed for use by the Regional Acid Deposition
Model (RADM).  For the portion of the RELMAP mercury model domain not covered by the  RADM
domain, state by state totals of S02 emissions were apportioned to the county level on the basis of
weekday vehicle-miles-traveled data since recent air measurement studies have indicated  that aerosol
elemental carbon can be attributed mainly to transportation source types (Keeler et al., 1990).  The
county level data were  then apportioned by area to the individual RELMAP grid cells.  Total carbon
soot was assumed to be emitted into the lowest layer of the model.

       D.l.2.5        Ozone Concentration

       Ozone concentration data were obtained from U.S. EPA's Aerometric Information Retrieval
System (AIRS) and the Acidmodes experimental air sampling network.  AIRS and Acidmodes data
were available hourly.  Any observations of ozone concentration below 20 ppb  were treated as
missing.  For each RELMAP  grid cell, the ozone  concentrations were computed for the two mid-day
time steps by using the mean  concentration value  during two corresponding time periods  (1000-1300
and  1400-1600 local time). The mean of these two mid-day values was used to estimate the ozone
concentration for the time steps after 1600 local time and before 1000 local time the next morning.
This previous-day average was used at night since ground-level ozone data are not valid  for the levels
aloft, where the wet removal of elemental mercury was assumed to be occurring.  Finally, an objective
interpolation scheme was used to produce complete ozone concentration grids for each time step, with
a minimum value of 20 ppb imposed.

       D.I.2.6        Lagrangian Transport and Deposition

       In the model, each pollutant puff begins with an initial mass equal to the total emission rate of
all sources in the source cell multiplied by the model time-step length. For mercury, as for most other
pollutants, emission rates for each source cell were defined from input data, and a time step of three
hours was used.  The initial horizontal area of each puff was set to 1200 km2, instead of the standard
initial size of 2500 km  , in order to  accommodate the finer grid resolution used for the mercury
modeling study;  however, the standard horizontal  expansion rate of 339 km2 per hour was not
changed. Although each puff was defined with four separate vertical layers,  each layer of an
individual puff was advected through the model cell array by the same wind velocity field.  Thus, the
layers of each puff always remained vertically stacked.  Wind field initialization data for a National
Weather Service prognostic model, the Nested Grid Model (NGM), were obtained from the NOAA
Atmospheric Research  Laboratory for the entire year of 1989.  Wind analyses for the 
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Climatic Data Center, were used to estimate the wet removal of all pollutant species modeled.  Wet
and dry deposition mass totals were accumulated and average surface-level concentrations were
calculated on a monthly basis for each model  cell designated as a receptor. Except for cells in the far
southwest and eastern corners of the model domain where there were no wind data, all cells were
designated as receptors for the mercury simulation. When the mass of pollutant on a puff declines
through deposition, vertical diffusion or transformation to a user-defined minimum value, or when a
puff moves out of the model grid, the puff and its pollutant load is no longer tracked.  The amount of
pollutant in the terminated puff is taken into account in monthly mass balance calculations so that the
integrity of the model simulation is assured.  Output data from  the model includes monthly wet and
dry deposition totals  and monthly average air  concentration for  each modeled pollutant, in every
receptor cell.

D.I.3   Model Parameterizations

        D.I.3.1        Chemical Transformation and Wet Deposition

        The simplest type of pollutant to model with RELMAP is the inert type.  To model inert
pollutants, one can simply omit chemical transformation calculations for them, and not be concerned
with chemical interactions with the other chemical species in the model. In the mercury version of
RELMAP, particulate mercury and total carbon were each modeled explicitly as inert pollutant species.
Reactive pollutants are normally handled by a chemical transformation algorithm.   RELMAP was
originally developed  to simulate sulfur deposition, and the algorithm for transformation of sulfur
dioxide to sulfate was independent of wet deposition. For gaseous mercury, however, the situation is
more complex. Since there are no gaseous chemical reactions of mercury in the atmosphere which
appear to be significant (Petersen et al., 1995), for this modeling study mercury was assumed to be
reactive only in the aqueous medium.  Elemental mercury has a very  low solubility in water, while
oxidized forms of mercury and particle bound mercury readily find their way into the  aqueous  medium
through dissolution and particle scavenging, respectively. Worldwide observations of atmospheric
mercury, however, indicate that particulate mercury is generally a minor constituent of the total
mercury loading (Iverfeldt,  1991) and that gaseous elemental mercury (Hg°) is, by far, the major
component.  Swedish measurements of large north-to-south gradients  of mercury concentration in
rainwater without corresponding gradients of atmospheric mercury concentration suggest the presence
of physical and chemical interactions with other pollutants in the precipitation  scavenging process
(Iverfeldt, 1991).  Aqueous chemical  reactions incorporated into the mercury version of RELMAP
were based on research efforts in Sweden (Iverfeldt and Lindqvist, 1986; Lindqvist et  al., 1991;
Munthe et al., 1991;  Munthe and McEIroy, 1992; Munthe, 1992) and Canada (Schroeder and Jackson,
1987; Schroeder et al., 1991).

        Unlike other  pollutants that have been modeled with RELMAP, mercury has wet  deposition
and chemical transformation processes that are interdependent.  A combined transformation/wet-
removal scheme proposed by Petersen et al. (1995) was used.  In this scheme, the following aqueous
chemical processes were modeled when and where precipitation is  present.

        1)      oxidation of dissolved Hg° by ozone yielding Hg2+
       2)      catalytic reduction of this Hg2+ by sulfite ions
       3)      adsorption of Hg2+ onto carbon soot particles suspended in the aqueous medium
T	inn/c                                     r, c                        CAR i?T7\mJW
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Petersen et al. (1995) shows that these three simultaneous reactions can be considered in the
formulation of a scavenging ratio for elemental mercury gas as follows:
where,
       k, is the second order rate constant for the aqueous oxidation of Hg  by O3 equal to   . 4.7 x
107 M^s  ,
       k2 is the first order rate constant for the aqueous reduction of Hg2+ by sulfite ions  . .  , equal
to 4.0 x 10~4 s"1,
       HH is the dimensionless Henry's Law coefficient for Hg° (0.18 in winter, 0.22 in  ... spring
and autumn,  and 0.25 in summer as calculated from Sanemasa (1975)),
       [O3]   is the aqueous concentration of ozone,
       K3 is a model specific  adsorption equilibrium constant (5.0 x 10"6 m4g~1),
       csoot is the total  carbon soot aqueous concentration, and
       r is the assumed mean radius of soot particles (5.0 x  10"7 m).

[O3]aq is  obtained from this equation.
                                       •-  ljaq      rr
                                                   "03


where HO3 is the dimensionless Henry's Law coefficient for ozone (0.448 in winter, 0.382 in spring
and autumn, and 0.317 in summer as calculated from Seinfeld (1986)).  csoot is obtained from the
simulated atmospheric concentration of total  carbon aerosol  using a scavenging ratio of 5.0 x 103.

       The model used by Petersen et al. (1995) defined one-layer cylindrical puffs, and the Hg°
scavenging layer was defined as the entire vertical extent of the model.  The RELMAP defines 4-layer
puffs to allow special treatment of surface-layer and nocturnal inversion-layer processes.  It was
believed that, due to the low solubility of Hg° in water, the  scavenging process outlined above would
only take place effectively in the cloud regime, where the water droplet surface-area to volume ratio  is
high, and not in falling raindrops. Thus the Hg° wet scavenging process was applied only in the top
two layers on RELMAP, which extends from 200 meters above the surface to the model top.
       For the modeling study described in Petersen et al. (1995), the wet deposition of Hg~+ was
treated separately from that of Hg°. Obviously, any Hg2* dissolved into the water droplet directly
from the air could affect the reduction-oxidation balance between the total concentration of Hg° and
Hg  in the droplet.  Since the solubility and scavenging ratio for Hg    is much larger than that for
Hg , and since air concentrations of Hg  are typically larger than those of Hg   , separate treatment of
Hg2+ wet deposition was deemed acceptable.  Thus, p
moderating factor for the oxidation of dissolved Hg°.
  -'-'
Hg  wet deposition was deemed acceptable.  Thus, process 2 above was only considered as a
       In the exposure analysis in Volume III, there was no attempt to develop a new interacting
chemical mechanism for simultaneous Hg° and Hg2+ wet deposition.  Although Hg2+ was recognized
as a reactive species in aqueous phase redox reactions, it was, in essence, modeled as an inert species
just like particulate mercury and total carbon soot.  With the rapid rate at which the aqueous Hg2+
reduction reaction is believed to occur in the presence of sulfite, it is possible that an interactive cloud-
water chemical mechanism might  produce significant c
possible release of that Hg° into the gaseous medium.
water chemical mechanism might produce significant conversion of scavenged Hg2+ to Hg°, with
       Wet deposition of Hg2+, particulate mercury, and total carbon soot in the mercury version of
RELMAP were modeled with the same scavenging ratios used by Petersen et al. (1995). The gaseous
nitric acid scavenging ratio of 1.6 x 10"6 has been applied for Hg2+ since the water solubilities of these
two pollutant species are similar. For particulate mercury, a scavenging ratio of 5.0 x  10"5 was used,

June 1996                                     D-6                        SAB REVIEW DRAFT
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based on experiences in long-range modeling of lead in northern Europe. As previously mentioned, a
scavenging ratio of 5.0 x 10"5 was also used for total carbon soot.  These scavenging ratios for Hg~~,
particulate mercury, and total carbon soot were applied to all four layers of the RELMAP in the
calculation of pollutant mass scavenging  by precipitation.

        D.I.3.2        Dry Deposition

        Recent experimental data indicate that elemental mercury vapor does not exhibit a  net dry
depositional flux to vegetation until the atmospheric concentration exceeds a rather high compensation
point of around 10  ng/m3 (Hanson et al., 1994).  This compensation point is apparently dependent on
the surface or vegetation type and represents a balance between emission from humic soils and dry
deposition to leaf surfaces (Lindberg et al., 1992).  Since the emission of mercury from soils was
accounted for with  a global-scale ambient concentration and not an actual emission of Hg°, for
consistency, there was no explicit simulation of the dry deposition of Hg°.

        For Hg2+ during daylight hours, a dry deposition velocity table previously developed based on
HNO3 data (Walcek et al.,  1985; Wesely, 1986) was used.  The dry deposition characteristics of HNO3
and Hg2+ should be similar since their water solubilities are similar.  This dry deposition velocity data,
shown in Table D-2, provided season-dependent values for  11 land-use types under six  different
Pasquill stability categories.  Based on the predominant land-use type and climatological Pasquill
stability estimate of each RELMAP grid cell, and the season for the month  being modeled, the dry
deposition velocity  values shown in Table D-2 were used for the daytime only.  For nighttime, a  value
of 0.3 cm/s was used for all grid cells since the RELMAP does not have the capability  of applying
land-use dependent dry deposition at night. Since the nighttime dry deposition was applied only to the
lowest layer of the  model and no vertical mixing is assumed for nighttime hours, all Hg2+ modeled to
be quickly depleted from the lowest model layer by larger dry deposition velocities.

        For Hg  t,  Petersen et al. (1995) used a dry deposition velocity of 0.2 cm/s at all times and
locations. Lindberg et al. (1991) suggests that the  dry deposition of Hgpart seems to be dependent on
foliar activity.  During the daylight  hours of spring, summer, and autumn, a dry deposition velocity of
0.11 cm/s was used for Hg  t, except for model cells with predominant surface characteristics of
water, barren, and rocky terrain where 0.02 cm/s was used.  At night and at all hours during the
winter,  all cells used 0.02 cm/s as the dry deposition velocity for Hgpart.   Lindberg  et al. (1991)
suggested a value of 0.003  cm/s for non-vegetated  land, but since the RELMAP can not model land-
use dependent dry  deposition at night, the value of 0.02 cm/s was used for these cells by necessity.
June 1996                                     D-7                         SAB REVIEW DRAFT
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                                       Table D-2
                Dry Deposition Velocity (cm/s) for Divalent Mercury (Hg"+)
Season






Winter










Spring










Summer










Autumn





Land-Use Category

Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wedand
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Pasquill Stability Category

A
4.83
1.32
1.89
3.61
3.61
3.49
1.09
1.16
2.02
1.62
1.98
4.59
1.60
1.49
3.42
3.42
3.28
0.98
1.05
1.85
1.60
1.84
4.47
2.29
1.67
3.32
3.32
3.17
0.92
0.98
1.91
1.90
1.95
4.64
2.02
1.78
3.46
3.46
3.32
1.00
1.07
1.88
1.93
1.97

B
4.80
1.30
1.86
3.57
3.57
3.46
1.07
1.14
2.00
1.60
1.95
4.54
1.56
1.46
3.36
3.36
3.23
0.96
1.04
1.82
1.56
1.81
4.41
2.25
1.64
3.26
3.26
3.12
0.90
0.98
1.88
1.87
1.91
4.59
1.98
1.74
3.40
3.40
3.27
0.98
1.06
1.86
1.90
1.94

C
4.61
1.20
1.73
3.34
3.34
3.27
0.98
1.06
1.89
1.48
1.81
4.35
1.46
1.36
3.13
3.13
3.05
0.89
0.97
1.73
1.46
1.67
4.12
2.04
1.48
2.95
2.95
2.86
0.81
0.89
1.73
1.69
1.71
4.35
1.81
1.59
3.13
3.13
3.05
0.89
0.97
1.73
1.74
1.76

D
4.30
1.05
1.52
3.02
3.02
2.99
0.85
0.92
1.70
1.30
1.58
4.05
1.28
1.19
2.81
2.81
2.78
0.77
0.85
1.56
1.28
1.46
3.73
1.76
1.26
2.57
2.57
2.53
0.69
0.76
1.52
1.44
1.46
4.05
1.60
1.40
2.81
2.81
2.78
0.77
0.85
1.56
1.53
1.54

E
2.79
0.46
0.73
1.68
1.68
1.77
0.38
0.39
0.96
0.60
0.73
2.49
0.53
0.48
1.42
1.42
1.55
0.31
0.30
0.84
0.53
0.58
2.07
0.72
0.41
1.04
1.04
1.27
0.22
0.23
0.77
0.52
0.42
2.49
0.73
0.60
1.42
1.42
1.55
0.31
0.30
0.84
0.68
0.63

F
0.36
0.15
0.19
0.29
0.29
0.29
0.13
0.31
0.21
0.17
0.20
0.36
0.18
0.17
0.29
0.29
0.29
0.13
0.13
0.21
0.18
0.20
0.36
0.24
0.19
0.29
0.29
0.29
0.13
0.13
0.22
0.21
0.21
0.36
0.21
0.19
0.29
0.29
0.29
0.13
0.13
0.21
0.20
0.20
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        For total carbon soot, daytime dry deposition velocities were calculated using a FORTRAN subroutine
developed by the California Air Resources Board (CARB. 1987). A particle density of 1.0 g/cm3 and radius of
0.5 |jm was assumed. Table D-3 shows the wind speed (u) used for each Pasquill stability category in the
calculation of deposition velocity from the CARB subroutine, while Table D-4 shows the  roughness length (z0)
used for each land-use category.  For nighttime, a dry deposition velocity of 0.07 cm/s was used for all seasons
and land-use types.

                                             Table D-3
                        Wind Speeds Used for Each Pasquill Stability Category
                                 in the CARB Subroutine Calculations
Stability Category
A
B
C
$
E
F
Wind Speed (m/s)
10.0
5.0
5.0
2.5
2.5
1.0
                                             Table D-4
       Roughness Length Used for Each Land-Use Category in the CARB Subroutine Calculations
Land-Use Category
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed Forest/Wetland
Water
Barren Land
Non-forested Wetland
Mixed Agricultural/Range
Rocky Open Areas
Roughness Length (meters)
autumn-winter
0.5
0.15
0.12
0.5
0.5
0.4
10'6
0.1
0.2
0.135
0.1
spring-summer
0.5
0.05
0.1
0.5
0.5
0.4
10'6
0.1
0.2
0.075
0.1
        The RELMAP assumes instantaneous vertical mixing of all pollutants through the entire depth
of the model. For grid cells with significant emission rates, this results in an underestimation of the
ground-level concentration and therefore an underestimation of the dry deposition rate for mercury
species emitted near the ground. To remedy this, the model used a local dry deposition factor for
Hgpart in a similar manner as Petersen et al. (1995).  This local deposition factor was 0.5, meaning that
one-half of the Hgpart emissions from a  grid cell were assumed to dry deposit within that grid cell by
processes not otherwise simulated by the dry deposition parameterization.  There was no application of
a local deposition factor for Hg2+ since  the majority of its emission was assumed to be from elevated
point sources.
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        D.I.3.3        Vertical Exchange of Mass with the Free Atmosphere

        Due to the long atmospheric lifetime of mercury, the RELMAP was adapted to allow a
treatment of the exchange of mass between the surface-based mixed layer and the free atmosphere
above.  As a first approximation, a pollutant depletion rate of 5 percent, per 3-hour timestep was
chosen to represent this diffusive mass exchange. When compounded over a 24-hour period, this
depletion rate removes 33.6% of an inert, non-depositing pollutant.  Since all three forms of the
modeling mercury deposit to the surface to some degree, their  effective diffusion rate out of the top of
the model is less than 33.6% per day.

P. 1.4   Discussion of RELMAP Modeling Uncertainties

        D.I.4.1        Vertical Model Domain

        The RELMAP model top is defined to be the maximum vertical extent of the convectively
driven mixed layer. Vlonthly values defined from mixed-layer-height climatology are rough estimates
of a meteorological phenomenon tkat may not exist in many situations.  Although a surface-based
mixed layer may be well  defined, pollutants that persist in the  atmosphere for long periods of time are
certain to mix to some degree into the free atmosphere above the mixed-layer top. Chlorofluorocarbon
(CFC) compounds are an extreme example of this possibility.   Elemental mercury deposits relatively
slowly through precipitation processes due to its low water solubility, and its dry deposition appears to
be minimal since  it is in vapor form under normal atmospheric conditions. In fact, pollutant mass
balance accounting information from the RELMAP mercury simulation indicated that approximately
75% of all elemental mercury emitted was transported out of the model domain before it was wet or
dry deposited. Elemental mercury appears to be quite persistent in the atmosphere.

        Since the  RELMAP does not simulate the flux of air or pollutant through the height of the
mixed layer, which is fixed for each monthly simulation, the use of horizontally divergent/convergent
wind fields to define the motion of the pollutant puffs can sometimes result in unrealistic instantaneous
concentration fields.  Horizontally convergent winds  will tend to concentrate puffs at the point of
convergence, resulting in high modeled concentrations when the effects of the puffs are summed
together. Ordinarily, horizontal convergence in the surface-based mixed layer would push the mixed-.
layer top higher into  the atmosphere as  a result of the incompressible nature of air in general
atmospheric motion.  This higher mixed-layer top would compensate for the greater pollutant mass
loading per unit area from the  converging puffs, keeping the resulting  pollutant concentration more
constant. The RELMAP  was not designed to provide instantaneous realizations of pollutant
concentration fields.  Rather, it was designed for seasonal and  annual simulations where the total
effects of convergent and divergent wind fields can balance one another.  There does exist some
uncertainty, however, as to  whether this balance actually occurs in all  situations.

        D.I.4.2        Aqueous Chemistry

        The aqueous  reduction-oxidation chemistry mechanism in the mercury version of RELMAP
was applied only  to the Hg° dissolved from the ambient air into the water droplet. Where significant
concentrations of  Hg2+ from emissions exist in the ambient air, this Hg2+ could be dissolved into the
water droplet along with the Hg° and inhibit the scavenging of Hg°. The RELMAP results described
above indicate that Hg2+  air concentrations are certainly lower than those for Hg° at the length scales
of the RELMAP grid cells;  however,  the magnitude of the effect of ambient Hg2+ on the Hg°
oxidation scavenging is not yet well understood.

        Another source of modeling uncertainty in aqueous chemistry  relates to the fact that the
aqueous chemical mechanisms were invoked only when and where precipitation was known to occur,

June 1996                                    D-10                        SAB REVIEW DRAFT
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and precipitation fields were only defined over land areas where precipitation observations were
available. Significant wet transformation and removal of mercury may occur over oceanic areas  were
precipitation observations are not available, and it is possible that significant aqueous chemistry is
occurring in non-precipitating clouds.

        D.1.4.3        Transport and Diffusion

        Since the RELMAP simulates transport and diffusion only in the surface-based mixed layer
and vertical wind shear is small when the mixed-layer is well defined, under ideal conditions transport
and diffusion are handled adequately.  When the surface-based  mixed layer is not well  defined, vertical
gradients in the speed and/or direction of the wind may be present which cannot be represented by the
motion of individual Lagrangian puffs whose layers remain vertically stacked.  There are two
techniques that might be used to represent vertical wind shear in the RELMAP:  puff splitting and
wind-shear-dependent puff expansion. Due to  computational limits and scheduling constraints, these
were not attempted.  The most complete solution to the problem of vertical wind  shear is the use of a
Eulerian reference frame for numerical modeling.  The Atmospheric Characterization and Modeling
Division of U.S. EPA's Atmospheric Research and Exposure Assessment Laboratory has proposed  the
development of a Toxics Linear Chemistry Model (TLCM) using the Eulerian reference frame of the
Regional Acid Deposition Model  (RADM). The TLCM could  be operational within two years.

        D. 1.4.4        Boundary fluxes of pollutants

        Due to the fact that RELMAP simulates atmospheric pollutant loading as  the combined effect
of a population of discrete Lagrangian puffs, and the fact that elemental mercury gas has a long
residence time in the atmosphere, natural mercury emissions from the oceans and land surfaces could
not be explicitly modeled.  Given the  general west-to-east wind flow at the latitudes of the continental
U.S., simulated puffs of natural mercury emissions could  be emitted from all grid cells, but their
effects would be artificially concentrated in the eastern sections of the model domain.  The only  puffs
that could impact the western areas would be those originating  from the far western grid cells, while
the eastern areas could be impacted by puffs from all parts of the model domain.  The use of a
Eulerian-type model would allow the definition of boundary fluxes  of pollutant based on larger-scale
model  results or assumed background  concentration levels.

D.2     Description of COMPDEP Air Dispersion Model

D.2.1   Description of the CQMPDEP Air Quality Model

        General references for this section are Overcamp  (1977), Rao (1981) and  U.S. EPA (1992).

        The COMPDEP model uses hourly meteorological data to estimate air concentrations and
deposition fluxes from a  point source. In this section a summary description of the model is
presented.  In Section D.2.2.1, specific modifications made for this assessment are discussed.

        D.2.1.1         Atmospheric Stability and System Used in COMPDEP

        After pollutants are emitted from a source, they are diluted with ambient air. The degree of
dispersion is a function of wind speed and the level of turbulence.  In general, higher wind speeds  or
turbulence result in lower air concentrations. The amount of turbulence is quantified in terms of the
atmospheric stability.  A  stable atmosphere is one that suppresses vertical motions, hence mitigating
turbulence, while an unstable atmosphere is one that enhances turbulence. Atmospheric turbulence per
se is difficult and expensive to measure (Randerson, 1984), and it is usually estimated from other more
     1QQfi                                    D-11                        SAR RFVTFW DRAFT
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easily measurable quantities. In particular, the stability of the atmosphere is typically characterized by
the vertical temperature profile of the atmosphere.

       For an isolated parcel of air in which no heat is transferred in or out (adiabatic). one can show
using the first law of thermodynamic  and the hydrostatic equation (see, e.g., Hannat et al. 1982, pp. 2-
3) that there is a  1 degree (C) decrease for every 100 m increase in altitude, and a  1 degree increase
for every 100 m decrease.  This is called the adiabatic lapse rate.

       The atmosphere is not adiabatic as it is both heated and cooled.  This  results in temperature
profiles that differ from the adiabatic  profile, and it is this difference that is ultimately responsible for
a given atmospheric stability.  The three broad classes of  stability and their associated temperature
profiles are summarized in Table D-5.
                                            Table D-5
                               Classes of Atmospheric Stability and
                          Associated Vertical Temperature Distribution


   Vertical Temperature Profile           General Result                Class of Stability

 Increases with height, or. decreases                                            Stable
 less rapidly than adiabatic lapse      Vertical motions inhibited
 rate

 Nearly identical with adiabatic rate   No significant buoyant                    Neutral
                                     forces

 Decreases with height faster than                                             Unstable
 adiabatic lapse rate                  Vertical motions enhanced
        It should be noted that any time the temperature increases with altitude, the atmospheric
condition is termed an inversion.  Because of the associated stability, inversions tend to decrease
(Wark and Warner, 1981).

        The most widely used scheme of atmospheric stability classification, and that used in
COMPDEP, was developed by Pasquill (1961) and modified by Gifford (1961).  There are six stability
classes, denoted with the letters A through F.  In general, classes A through C indicate unstable
conditions, D is roughly neutral, and classes E and F represent stable conditions. Table D-6, from
Hanna et al., (1982), originally from Gifford (1976), shows the criteria for the different classes.
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                                          Table D-6
                                Pasquill Turbulence Types and
                            Corresponding Atmospheric Conditions
                                     (from Gifford, 1976)
Pasquill
Turbulence
Type
A
B
C
D
E
F
a Applicable to
Atmospheric
Stability Conditions
Extremely unstable
Moderately unstable'
Slightly unstable
Neutrala
Slightly stable
Moderately stable
heavy overcast day or night.
       The meteorological conditions that are used to determine the stability class are shown in
Table D-7.
                                          Table D-7
                 Meteorological Conditions Defining Pasquill Turbulence Types
                                     (from Gifford, 1976)

Surface wind
speed (m/sec)
<2
2-3
3-4
4-6
>6
Daytime Solar Radiation
Strong
A
A-B
B
C
C
Moderate
A-B
B
B-C
C-D
D
Slight
B
C
C
D
D
Nighttime conditions
Mostly Overcast

E
D
D
D
Mostly Clear

F
E
D
D
From this table, it can be seen that extremely unstable conditions (class A) occur during the day with
light winds and moderate to strong solar radiation (necessary conditions for the formation of an
unstable temperature profile; (Overcamp, 1977)).  Conversely, extremely stable conditions can occur
only at night with clear skies and light winds.  Hanna et al. (1982) note that some have filled in the
blank in Table D-7 with a "G" class, but this has not received wide acceptance (Hanna et al., 1982).
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                                          Table D-8
                       Wind Profile Exponents Used In The Assessment
Stability Category
A
B
C
D
E
F
Wind Profile Exponent
0.07
0.07
0.10
0.15
0.35
0.55
       Other stability classification schemes exist.  For example, M.E. Smith (1951) proposed a
classification scheme that is based on wind direction, and Cramer (1957) advocated a method based on
observed wind fluctuations at a height of 10m (often referred to the Brookhaven National Laboratory,
or BNL, stability classes).  In addition, Irwin (1979) proposed a method of allowing for a continuum
of stability, as  opposed to a discrete approach such as the Pasquill method.  Use of the Pasquill letter
classes is common due to their ease of use, and because they have produced satisfactory  results.

       For this assessment, the stability classes for each hour were estimated with the RAMMET
program (Catalano et al., 1987), using hourly surface meteorological data.

       Estimation of wind speed is important because higher wind speeds result in greater dispersion
and hence reduced concentrations of pollutants. Frictional forces cause the surface wind speed, which
is usually the value available, to be lower than the speed at the stack top. A power law wind speed
profile is typically used to calculate the change of wind speed with height, and takes the following
form:
where, uref     = wind speed at the reference height
       zrey     = reference height
       us      = wind speed at the release height
       Zj      = release height
       p       = wind speed profile exponent (dependent on atmospheric stability and is between 0
               and 1)

In general, a reference anemometer height of 10 m, the standard height for measuremen, of wind speed
and direction by the National Weather Service (Overcamp, 1977), is used.

       The wind profile exponents used in COMPDEP are given in Table D-8.  These are the default
values for rural settings in U.S. EPA (1992) and are based on Irwin (1979).  Although default values
for urban settings are available in U.S. EPA (1992) as well,  due to limitations of the COMPDEP

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model it was decided that their use was not warranted for this assessment.  The default values for
urban settings were about twice as high as the rural ones for classes A, B. and C, which resulted in
higher wind speeds at the stack top.

        Directional shear with height is not included,  which means that the direction of flow is
assumed to be the same at all heights over the region. The taller the effective height of a source, the
larger the expected error in direction of plume transport (Pierce and Turner, 1980).

        D.2.1.2        Plume Rise

        A general principle, borne out by the analytic solutions of the diffusion algorithms and known
since at least 1917 (Wells, 1917), is that the maximum ground level concentration is inversely
proportional to the height of release (Randerson,  1984). Due to the buoyant properties of the stack
gases and the velocity of the stack gases emitted, the  height of release from a modeling perspective is
usually higher than the actual physical height of the stack.  This height is called the effective stack
height and is the  sum of the physical stack height and the rise of the plume.

        Due to the sensitivity of the maximum concentrations  to the effective stack height, and
because the maximum downwind concentration  has been historically  the output of interest for
regulatory agencies, numerous methods exist for estimating plume rise in a variety of conditions.
Overcamp (1977) noted that over 50 different plume rise formulas had been published by 1977, and
Pasquill (1974) observed that there are many rival formulae from which to  choose.

        The method used in COMPDEP is based on Briggs (1969, 1972, 1975) and Bowers et  al.
(1979). With this approach, it must be determined whether thermal buoyancy or vertical momentum is
dominating the plume's motion.  Estimates of the buoyancy flux (Fb, units  of m4/s3) and momentum
flux (Fm, units of m4/s2) are based on Briggs (1975):


                                                •>(T-T\
                                                    4^
                                            2 ,2  *
where, g       - acceleration due to gravity (9.80616 m/s2)
       vs      = stack gas exit velocity (m/s)
       ds      = stack diameter (m)
       Ts      = stack gas temperature (K)
       Ta      = ambient air temperature (K).

       If the stack gas temperature is less than or equal to the ambient air temperature, it is assumed
that plume rise is dominated by momentum, in which case the effective stack height is given by this
formula:
where, hs      -      actual physical stack height (m)
       s       -      stability parameter (Briggs,  1970;  Hanna et al., 1982) and is only  used in
                      calculations for stable conditions (classes E and F):

where the constants 0.02 and 0.035 are default approximations of the derivative of the ambient
potential temperature with respect to height.
-------

mini 1.5  —£. |  , 3d,—
    [    (Usfi
                                                         Unstable or neutral
                                                         Stable
                                s =
   = _g^ I 0.020   StabilityClassE
          0.035   StabilityClassF
       If the stack gas temperature is greater than the ambient air temperature, then the determination
of which force is dominating is made by calculating a critical crossover temperature difference DTC
above which it is assumed that buoyancy dominates.  This critical value depends on the stack gas
temperature, atmospheric stability, and the magnitude of the buoyant flux itself in a chain of empirical
formulas (Briggs, 1969,  1972, 1975; Bowers  et al., 1979) as follows:
                                         .1/3
                                0.02971-^—   Unstable or neutralJF,<55
                                       *
                                         .2/3
0.00575T
                                               Unstable or neutral fh2 55
                              0.0195827;vjV/s   Stable
       If the difference between the stack gas temperature and the ambient air is less than the critical
temperature, then it is assumed that momentum dominates, and the equations above are used.
Otherwise, the effective stack height, is given by these equations:

                                  21.425—^—  Unstable or neutral^,,
                                          73/5
                                    38.71-
                 Unstable or neutral\F'
                                               Stable
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        Past a certain distance the plume is assumed to stop rising. This distance is called the distance
to final rise (Xf)  and is calculated in a similar method as for the plume rise.  The calculation is
dependent on which force dominates, the atmospheric  stability  class, and the magnitude of the buoyant
flux.  It is estimated by the following:
  49 F^  Buoyancy rise, Unstable or neutral, Fb<55

 119 F%5  Buoyancy rise, Unstable or neutral, Fb>55


2.0715—  Buoyancy rise, Stable


        0  Momentum rise, all stability classes
                   *fm
        The estimated distance-dependent plume rise is the minimum of the effective stack height for
final rise and the height based on that for buoyancy-dominated conditions (Briggs, 1972, p. 1030).
The distance-dependent plume effective stack height he(x) is this:
This is sometimes referred to as the "2/3 law" of plume rise (Briggs,  1970) and follows from the
assumptions that buoyancy is conserved and that the initial plume momentum is negligible for a very
buoyant plume in unstratified surroundings.  It is claimed (Hanna et al., 1982, p.14; Fay et al., 1969)
that the constant 1.60, based on the best fit to data in Table II of Briggs (1970), can be expected to be
accurate within 40% with variations due to downwash or local terrain effects.

        D.2.1.3        Estimation of Air Concentration Accounting for Plume Depletion

        The method used is that developed in Rao (1981).  All estimations of concentration and
deposition originate from the steady-state form of the atmospheric advection-diffusion equation:
where,  C(x,y,z) = pollutant concentration at (x,y,z)
        x      = downwind distance
        y      = horizontal crosswind distance
        z       = vertical distance
        U      = the constant average wind speed for the hour
        W      = the gravitational settling velocity (cm/g)
        K      = the eddy diffusivity  in the crosswind direction
        KZ      - the eddy diffusivity  in the vertical directions

        For a continuous point source  of strength Q located at (0,0,H), the assumed boundary
conditions are defined by this equation:

                                             n-17                        <;AR RFVTFW DRAFT
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                                                      8(y)
                              C(oo,y>z) = C(r,±«,z) = Cfey,-) = 0
                                     KC+WC\   =
                                        z     J:=0
where,  U      = wind speed (m/s)
        Vd     = depositional velocity (cm/s)
        H      = height above the ground

        The first condition is the limiting condition of the mass continuity equation at the source, with
d being the Dirac delta "function". This condition is implicated by the assumption that the source is
coming from an infinitely small point located at height H above the ground.

        The second condition (actually three separate boundary conditions) is equivalent to the
assumption that for all times the concentration of the pollutant is zero infinitely far away from the
source in all directions.

        The final  condition is the one that accounts for possible depletion of the plume.  It is the
mathematical formulation of the  assumption that at ground level (z=0) the sum of the turbulent transfer
of pollutant down the concentration gradient (Kz Cz) and the downward settling flux  due to the
particles' weight (W C) is balanced by the net flux of material to the surface resulting from an
exchange between the atmosphere and the surface (Rao and Satterfield,  1981).  The deposition velocity
Vd is the parameter that is assumed to characterize the interaction between the diffusing pollutant and
the surface.  If the deposition velocity is 0, then the lower boundary acts as a perfect reflector. If it is
infinite, it acts as a perfect sink.   If the deposition velocity is  equal to the settling velocity, then the
net deposition due to vertical diffusion is zero. For gases and small particles,  the settling velocity is
approximately 0,  while for particles the settling and deposition velocities are estimated using the
CARB algorithms (CARB,  1986) that represent empirical relationships for transfer resistances as a
function of particle size, density, surface roughness, and friction velocity.

        It is not difficult to derive an analytic expression for the solution of the advection-diffusion
equation satisfying the boundary conditions above, with the solution involving nothing more
complicated than  exponential and error functions.

        The eddy diffusion coefficients are expressed in terms of the standard  deviations of the
crosswind and vertical Gaussian  concentration distributions (sy and sz, respectively),  for which
extensive empirical data exist. In particular, for Fickian diffusion (Rao,  1981) the relationships are
these:

                             v     "if \  U            „     2,  \  U
                             K = °,to -r-      »     &t = °z(*) T:
In practice the dependence of the standard deviations on- the downwind distance is not usually
explicitly noted. Also, as is standard in the atmospheric dispersion literature, the partial differential
equation is solved  as if the eddy diffusion coefficients do not  depend on the  downwind distance x.  In
fact, the solution to the advection-diffusion equation would be different were this dependence

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considered, with the magnitude of difference between the two solutions depending on how
"nonconstant" the standard deviations are with respect to x (i.e., on the magnitude of the derivative of
the eddy diffusion coefficients with respect to x).

        It is assumed that the plume is allowed to travel in a potentially vertically bounded layer called
the mixing layer (sometimes called the Ekman layer: Pasquill, 1974). The height of this layer is called
the mixing height, denoted here by L.  If the effective stack height exceeds the mixing height, then the
plume is assumed to fully penetrate the elevated inversion and the ground level concentration is set to
zero. The mixing height is estimated based on twice-daily mixing heights  using the RAMMET
program, which uses the Holzworth (1972) procedures.  These mixing heights are considered
representative in rural areas only during periods of instability or neutral stability (stability classes A-
D).  The applicability of the Holzworth method to rural areas with stable atmospheric conditions is
considered questionable, because the minimum  mixing heights include the heat island effect for urban
areas.  In this case, unlimited vertical mixing is  assumed.

        Depending on the atmospheric stability class and mixing layer depth, the air concentration  was
estimated in three different ways, all of which are derived from the analytic solution of the original
advection-diffusion equation above.  The methods are summarized in Table D-9.
                                            Table D-9
                    The Three Main Cases for Determining Air Concentration
                                  With Plume Depletion Effects
              Condition                   Criteria of Determination          Method of Solution

                                      Pasquill classes E or F or
  Stable or unlimited mixing            unstable/neutral and L>5000 m     Analytic solution used.

                                                                        Multiple eddy reflections
  Unstable/neutral,                     Pasquill classes A-D,              from both the ground
  non-uniform mixing                 sz <1.6 L and L<5000 m           and stable layer aloft
                                                                        (plume is  "trapped")

                                                                        Non-uniform vertical
  Unstable/neutral, limited              Pasquill classes A-D,              term integrated with
  and uniform mixing                  sz >1.6 L and L<5000 m           limited mixing from
                                                                        height 0 to infinity

        Only the vertical diffusion field was modified by deposition, and for deposition velocities on
the order of a few centimeters per second, the shape of the vertical concentration profile was  modified
only slightly (Rao, 1980).

        D.2.1.4        Estimation of the Atmospheric Dispersion Parameters

        The dispersion parameters sy and sz were estimated using equations  that approximately fit the
Pasquill-Gifford curves (Turner, 1970) These equations approximately fit the Pasquill-Gifford curves
(Turner, 1970) and were  based on a rural setting:
     1QQA                                     r»_1Q                        QAR PPVTPW PlP APT
-------
                                  ay = 465.11628 x tan(fl(;e))
where x is the downwind distance (in km), and
                               ftfce) = 0.017453293( c - d lux).
The parameters c and d depend on the stability class and are given in Table D-10

                                        Table D-10
                          Parameters Used to Calculate Horizontal
                    Dispersion Parameter sy in COMPDEP (Turner, 1970)
Pasquill Stability Class
A
B
C
D
E
F
c
24.1670
18.3330
12.5000
8.3330
6.2500
4.1667
d
2.5334
1.8096
1.0857
0.72382
0.54287
0.36191
The vertical dispersion parameter is estimated by
                                         az = a x
where the parameters a and b depend on the stability class, and are given in Table D-ll.
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                                      Table D-ll
                     Parameters Used to Calculate Vertical Dispersion
                        Parameter sz in COMPDEP (Turner, 1970)
Pasquill Stability
Class
Aa








Ba


Ca
Da





Ea





x (km)
<0.10
0.10-0.15
0.16 - 0.20
0.21 - 0.25
0.26 - 0.30
0.31 - 0.40
0.41 - 0.50
0.51 - 3.11
>3.11
<0.20
0.21 - 0.40
>0.40
All
<0.30
0.31 - 1.00
1.01 - 3.00
3.01 - 10.00
10.01 - 30.00
>30.00
<0.10
0.11 - 0.30
0.31 - 1.00
1.01 - 2.00
2.01 - 4.00
4.01 - 10.00
a
122.800
158.080
170.220
179.520
217.410
258.890
346.750
453.850
b
90.673
98.483
109.300
61.141
34.459
32.093
32.093
33.504
36.650
44.053
24.260
23.331
21.628
21.628
22.534
24.703
b
0.94470
1.05420
1.09320
1.1262
1.26440
1.40940
1.72830
2.1160
b
0.93198
0.98332
1.09710
0.91465
0.86974
0.81066
0.64403
0.60486
0.56589
0.51179
0.83660
0.81956
0.75660
0.63077
0.57154
0.50527
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                                    Table D-ll (continued)
                       Parameters Used to Calculate Vertical Dispersion
                          Parameter sz in COMPDEP (Turner,  1970)
a If the calculated value of sz exceeds 5000 m, then it is set to 5000 m.
Pasquill Stability
Class
Aa



Fa









x (km)
<0.10
10.01 - 20.00
20.01 - 40.00
>40.00
<0.20
0.21 - 0.70
0.71 - 1.00
1.01 - 2.00
2.01 - 3.00
3.01 - 7.00 '
7.01 - 15.00
15.01 - 30.00
30.01 - 60.00
> 60.00
a
122.800
26.970
35.420
47.618
15.209
14.457
13.953
13.953
14.823
16.187
17.386
22.651
27.074
34.219
b
0.94470
0.46713
0.37615
0.29592
0.81558
0.78407
0.68465
0.63227
0.54503
0.46490
0.41507
0.32681
0.27436
0.21716
b sz is set to 5000 m.
       D.2.1.5
Deposition Processes
       COMPDEP addresses both wet and dry deposition, taking into account the fraction of an hour
during which precipitation occurs.

       The air concentrations are calculated accounting for plume depletion from dry deposition. The
dry deposition rate, in g/m2/time, is given by the product of the deposition velocity, the air
concentration and the fraction of the hour during which precipitation does not occur. For particles, the
settling and deposition velocities were estimated using the CARB algorithms (CARB, 1986) that
represent empirical relationships for transfer resistances as a function of particle size, density, surface
roughness and friction velocity.  In general, the deposition velocity has values that can range from zero
up to 180 cm/s (Sehmel, 1984).

       COMPDEP calculates the annual wet deposition flux according to the method developed by
Slinn (1984) and later modified by PEI and Cramer (1986).  The scavenging process consists of
T	inn/c
                                                                          CAD
-------
repeated exposures of particles and gases to cloud or precipitation elements with some chance of
collection on the element for each exposure (Engelmann, 1968). This has been addressed historically
as a first order decay process with .decay constant L, called the scavenging coefficient (units of  inverse
time). The concentration at any distance x downwind is then given by C(.\,y,z) exp(-L t), where
C(x,y,z) is  the (steady-state) concentration without scavenging and t is the time-since precipitation
began (Hanna et al., 1982). For the purpose of modelling, t was replaced with x/us (the travel time to
the receptor); thus, the decay is essentially accounting for previous scavenging upwind of the receptor.

        The wet deposition flux Dyw at  a given location is
                                              z
where z is the height from which the precipitation falls.  Because it whs assumed that the effects of
dry deposition and gravitational settling are negligible compared with precipitation scavenging, the
concentration used was that without deposition effects (deposition and settling velocities set to zero).
To facilitate evaluation of the integral, it was extended to infinity as an approximation.  The deposition
flux for a given hour then reduces to this equation:

                                                         -A-i
                                     Dyw=fA.QRDWe   "'
where,         /       = the fraction of the hour that precipitation occurs
               RDW   = the integrated vertical relative concentration (unit source strength) without
                       depletion effects
RDW can be calculated by these equations:

                                         }
                                             Simpleor intermediateterrain
                            xp(-0.5
                                             Complex terrain(sector-averaged)
As noted in PEI and Cramer (1986), there are several assumptions in deriving the equation for wet
deposition.

        1)      The intensity of precipitation is constant over the entire path between the source and
               receptor.

        2)      The precipitation originates  at a level above the top of the plume so that hydrometeors
               (i.e., products formed by condensation of water vapor) pass vertically through the
               entire plume.

        3)      The time duration of the precipitation over the  entire  path between the source and the
               receptor point is such that exactly / (f is defined as the fraction of the hour in which
               precipitation occurs) of the hourly emission is subject to a constant intensity  for the

June 1996                                      D-23                        SAB REVIEW DRAFT
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               entire travel time required to traverse the distance between the source and the receptor.
               The remaining fraction is subject only to dry deposition processes.

       In  COMPDEP, the scavenging coefficient may  be intensity- and particle-size dependent, in
which case the total wet deposition is the sum of the contributions of each category particle size
category.  For particles, example scavenging coefficients are from PEI and Cramer (1986) and are
shown in Table D-12.  Only a small fraction of the pollutants of concern for this exposure assessment
are paniculate or particulate-bound.
                                          Table D-12
          Example of Precipitation Scavenging Coefficients (per second) in COMPDEP
Precipitation Intensity
Heavy
Moderate
Light
Particle Size Category (mM)
Less than 2
1.46E-03
5.60E-04
2.20E-04
2 to 10
4.64E-03
8.93E-04
1.80E-04
Greater than 10
9.69E-03
9.69E-03
9.69E-03
Estimation of the scavenging coefficients for vapor phase pollutants are discussed in Section D. 2.2.1.

       D.2.1.6        Treatment of Terrain

       The "COMP" in the name COMPDEP refers to the capability of the model to estimate
concentrations and deposition at receptor locations at or above stack top.  This can be done in three
ways:  (1) the effective stack height may be modified based on the receptor height; (2)  the
concentrations may be reduced by a height-dependent correction factor for receptors above the stack
top; and (3) sector- averaging is used for receptors above stack top.

       The method of adjusting the effective stack height Ht is based on models developed by Briggs
(1973) and Egan (1975).  With this method the amount of reduction depends on the receptor height
and empirical terrain adjustment factors.
H
                                                    , Hter,
where, H      = the effective stack height calculated without considering terrain
       rh      = the height of the receptor above the stack base
       ter     = stability-class dependent terrain adjustment factor
       Hmin   = the minimum distance between the plume centerline and ground.

Following standard practice, Hmin=10 m.

       The terrain adjustment factors used in the exposure assessment are consistent with the method
of Egan (1975) and are shown in Table D-13.
June 1996
               D-24
SAB REVIEW DRAFT
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                                           Table D-13
                               Terrain Adjustment Factors Used in
                      Calculating Terrain-Dependent Effective Stack Height
Pasquill Stability Class
A
B
C
D
E
F
Terrain Adjustment Factor
(unitless)
0.5
0.5
0.5
0.5
0
0
 By choosing these terrain factors in neutral and unstable conditions the effective stack height is
 reduced by /y2 or H/2, whichever is  smaller.  It  should be noted that Briggs (1973) suggests that the
 stack height be reduced by rh or H/2, whichever is smaller.  Briggs' method will result in slightly
 higher ground-level concentrations for the  surface of small hills (Hanna et al., 1982).  It should also be
 noted that the reduction by H/2 is based on potential flow theory and wind-tunnel experiments (Hanna
 et al., 1982).  Both Egan's and Briggs' methods assume terrain factors of zero for stable conditions, in
 which case it is assumed that the plume maintains a constant elevation (and so the effective height is
 reduced by the receptor height). •

       For receptors whose  ground level elevation exceeds the effective stack height, the
 concentrations and deposition rates are multiplied by a  "correction" factor corr, given by this:
                          1(400-Z>i#)/400
                   corr = <      0
                                1
Q4QQm and.Stable conditions
 Unstable or neutral conditions
where Diff is the difference between receptor ground level elevation and the effective stack height.
Thus, in stable conditions (Pasquill stability classes E or F) the concentrations and deposition rates
were assumed to be zero if the receptor is 400 m above the effective stack height.  Although the exact
origin of this method is not clear, it is used in the Valley dispersion model (one of the precursors of
COMPDEP),  where it is observed  that the application of the correction factor in stable conditions
should not be inferred to represent pollutant decay or penetration into the terrain. This is an empirical
scheme intended as a general representation of the blocking of air flow by significant terrain features.
Furthermore, the concentrations calculated for the leeward side of a substantial hill  will not reflect this
attenuation upwind, and so such concentrations should be considered suspect.

        By setting the correction factor to one in unstable or neutral atmospheric stability conditions,
the plume was  assumed to  parallel the terrain feature at the terrain-dependent effect stack height as
calculated above.

        For terrain above the effective stack height, sector averaging was  used to calculate the air
concentration and was subsequently used for deposition.  This assumes that there is no crosswind
Tune, 1996
                                              D-25
                               SAB REVIEW DRAFT
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variation in concentration within an angular sector equal to the resolution of the wind direction data
(22.5 degrees for this assessment).  It should be noted that there was no technical basis for using
sector averaging for terrain above stack height rather than point estimates for the horizontal dispersion
parameters.  This decision was made consequent to personal communication with Donna Schwede (7-
20-94).

       D.2.1.7        Downwash

       Usually, emissions from an industrial source will rise due to a combination of their initial
vertical momentum and buoyancy.  Under certain peculiar conditions, however, they may be trapped  in
either the wake of the stack or nearby building, resulting in increased concentrations.  Two types of
these phenomena are addressed in COMPDEP:  stack-tip downwash and building downwash (building
wake effects).
      \
       Stack-tip Downwash.  In practice, it has been observed that if the stack exit velocity is low
relative to the wind speed, then the stack emissions may be pulled into the low pressure cavity in the
wake of the  stack.  The emissions are pulled down and may not rise further, resulting in higher ground
level concentrations  than if plume rise had occurred.  In COMPDEP, this phenomenon was assumed to
occur when the ratio of the stack exit velocity to the  wind speed at stack top was below 1.5.  This
value, which has survived without modification for 25 years, is that recommended by Briggs (1969)
and is based on wind tunnel experiments by Sherlock and Stalker (1941).  It should be noted that very
buoyant sources may accelerate fast enough to avoid any significant downwash (Overcamp, 1977;
Briggs, 1969).

       If stack-tip downwash occurs, then the physical stack height used is that of Briggs (1974):
From the above equation it can be seen that the maximum amount the stack height will be reduced by
this method is three times the diameter of the stack.
                                             •
       Building Downwash.  Building downwash can occur when the stack emittants are captured in
the wake of a nearby building. A long-standing  rule-of-thumb is that building effects should not occur
if the stack height is at least 2.5 times the height of any adjacent building.  Because this was
considered overly restrictive (from a design perspective) for tall, thin buildings, Briggs (1973)
proposed a modification of this rule in which building downwash was  assumed not to occur if the
stack height was greater than the sum of the building height and 1.5 times the minimum of the
building height and  width.

       In the use of COMPDEP for the mercury assessment, building downwash was considered if
the plume height, calculated from the sum of the stack height and the distance-dependent plume rise at
a distance of  two building heights, was greater than either (a) 2.5 times the building height, or (b) the
sum  of the building height and 1.5 times the building width.   If wake effects were predicted, then the
effective stack height was reduced by reducing the estimated plume rise. First a distance-dependent
plume rise is  estimated based on momentum-dominated conditions (Bowers el; al., 1979).

The  effective stack height was then set to the maximum of this value and the distance-dependent
buoyancy-dominated effective stack height:
     1QQ6                                    D-26                        SAB REVIEW DRAFT
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                    A^ = {
                            max
3F_
                                                      1/3
                                                           Unstable or neutral
                                                           Stable
                                he=hs + maxj A/zp , 1.60U
                                                         ,1/3*
        The dispersion parameters were also modified based on the dimensions of the building. These
modifications are based on Huber and Snyder building downvvash procedures (Huber, 1977; Huber,
and Snyder, 1976) and are principally based on the results of wind-tunnel experiments using a model
building with a crosswind double that of the building height.  Because the atmospheric turbulence
simulated in the wind-tunnel experiments was intermediate (between a slightly unstable Pasquill C
category and neutral D), the data upon which the formulas were based reflect a specific  stability,
building shape, and orientation with respect to the mean wind direction (U.S. EPA 1992, p. 1-20).

        The basic idea was to estimate modified lateral and vertical dispersion parameters, and  then
use the minimum of these and the dispersion parameters estimated without wake effects.   In general,
the ratio of the building width to building height plays a key  role.

        Setting

                            ,   f 0.35/4 + 0.067(.r-3/z2)
                          °y =
where hm is the minimum of the building width and height, and h1 and /i, are given by
and
                                            hh  else
and x  is the lateral virtual distance, given by

where the coefficients p and q depend on the stability class, and the coefficients cw and ch are given
by
                                                                            A t> r>rrwTr:vi7 r\r> A trr
-------
                                         [0.85  ifwjhb5
and
The virtual source location was calculated by requiring that sy'(10 ht) ~ .35 h1 + 0.5 h-, (Randerson,
1984; p.303).

       Table D-14 presents the coefficients used to calculate lateral virtual distances.
                                          Table D-14
                     Coefficients Used to Calculate Lateral Virtual Distances
                                  for Pasquill Dispersion Rates
Pasquill Stability
Class
A
B
C
D
E
F
P
209.14
154.46
103.26
68.26
51.06
33.92
q
0.890
0.902
0.917
0.919
0.921
0.919
The vertical dispersion term is modified similarly.
Tnnp. 19Q6
D-28
SAR RF.V1FAV DRAFT
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                                 O.U   + 0.067(*-3AJ
                          o, =1
                                                         ifxzlQh
where hm is the minimum of the building width and height, and xz is the vertical virtual distance,
given by
                                             1.2/z.
                                                      -h.
                                             The virtual source location xz is
where the coefficients a and £ are given in Table D-ll above.
calculated by requiring that sz'(10 hm) = 1.2 hm (Randerson, 1984; p.303)., and is added in order to
account for the enhanced initial plume growth caused by the building wake (U.S. EPA, 1992, p. 1-24)
        D.2.1.8
        Buoyancy-Induced Dispersion
        It has been observed that the initial dispersion of plumes may be augmented by turbulent
motion of the plume and turbulent entrainment of ambient air. ' This is addressed by increasing the
calculated standard deviations in the crosswind and vertical directions using the method of Pasquill
(1976)
where,
        "z

        DA

        D.2.1.9
=   the effective standard deviation of lateral concentration distributions (m) for
    buoyancy-induced dispersion
=   the effective standard deviation of vertical concentration distributions (m) for
    buoyancy-induced dispersion
=   the standard deviation of lateral concentration distributions (m) without buoyancy
    effects
=   the standard deviation of vertical concentration distributions (m) without buoyancy
    effects,
=   the estimated plume rise (m).

        Meteorological Data
        COMPDEP uses hourly meteorological data to estimate hourly concentrations and deposition
rates. If wet deposition is not to be modeled, then the only data file required is one containing hourly
values for average wind speed, wind direction, Pasquill stability class, mixing height, and ambient air
temperature.
Tune, 1996
                                              D.29
                                                            SAB REVIEW DRAFT
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       If wet deposition is to b'e modeled, then a data file containing a summary of the hourly
precipitation intensities and fraction of hour for which precipitation occurred is also required.
COMPDEP only considers four precipitation intensity classes.  These are summarized in Table D-15.

                                          Table D-15
                      Precipitation Intensities Considered  by COMPDEP
Intensity
Class
0
1
2
3
Precipitation Rate
(in/hr)
0
trace to 0.10
0.11 to 0.30
greater than 0.30
D.2.2  Application of the COMPDEP Model for the Exposure Assessment

       To estimate local mercury concentrations in environmental media, hypothetical sources (model
plants) were designed using available information.  Each model plant/control scenario/ emission
speciation estimate was placed in the hypothetical locations.  In this section modifications made to the
COMPDEP model for this exposure assessment, as well as parameter values used are discussed.
       D.2.2.1
Modifications of COMPDEP for the Exposure Assessment
       Several modifications were made to COMPDEP in order to address more effectively the
atmospheric deposition of mercury species. These modifications were necessary, because mercury
exists primarily in the vapor phase, and the previous version of COMPDEP (version 93340) could not
estimate deposition for vapor.

       Specification of Vapor phase/Particle-Bound Phase Ratio.  This modification consisted of
adjusting COMPDEP to allow the user to specify the fractions of the emissions of a particular
pollutant that are in vapor phase and particle-bound phase.  The modification was necessary because
the transport properties of the two phases can be quite different and the forms of mercury considered
in this report are primarily in the vapor phase.

       Dry Deposition of Vapor Phase Contaminants.  The mercury species assumed to be emitted
(elemental and divalent mercury) are predominantly in the vapor phase;  however, the algorithms in
COMPDEP for calculating deposition velocities can only be used for particles. For this reason,
COMPDEP was modified so that, for the vapor phase fraction of a pollutant, the user can specify
atmospheric stability class-dependent deposition velocities.  These were used in the concentration
algorithms with plume depletion, with the gravitational settling velocity set to zero as is recommended
for gases (Rao, 1981). Section D.2.2.3  gives a description of the deposition velocities used in the
exposure assessment.

       Wet Deposition of Vapor Phase Contaminants. To estimate wet deposition COMPDEP
requires a scavenging coefficient for each pollutant. This scavenging coefficient can depend on the
Tune. 1996
                       D-30
SAB REVIEW DRAFT
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precipitation intensity and particle size. No values are given for gases, for which the scavenging
coefficient will depend  strongly on the chemical properties of the pollutant in the vapor phase (e.g..
solubility).

        Modifications were made to COMPDEP to enable the user to specify a unitless washout ratio
W for the vapor phase portion of the pollutant. The washout ratio is the ratio of the concentration in
air to the concentration  in precipitation.

        Connection Between the Washout Ratio and Scavenging Coefficient. By definition, the
washout ratio W is the ratio of the concentration in surface-level precipitation to the concentration in
surface level air (Slinn, 1984).  Let Cw and Ca denote the concentrations in surface-level precipitation
and  surface-level air, respectively  (units of g/m3).  Then
 Using the scavenging coefficient to estimate air concentration during periods of precipitation, the
 concentration in air is given by this equation:
                                       ca = c
                                                      ,-At
where,  zs      = height of the receptor (m)
        x      - downwind distance to the receptor
        y      = crosswind distances to the receptor (m),
        t      = travel time (seconds) to the receptor
        L      = scavenging coefficient (units  of inverse seconds).


The concentration in precipitation can be approximated by the wet deposition flux divided by the
precipitation rate for the time period:
                                             H
                                          A  1C (x,y£) e'^ dz
                                    C  = —5:	
where,  H      = height from which the precipitation falls (m) and
        P      = precipitation rate (m/s).

The washout ratio is then given by these formulae:
June 199fi                                     D-31                         SAB REVIEW DRAFT
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A [C (x,
                                    H
                                  A fc (W) dz    (A fl)(l/#) fc (x,yj) dz
      W' =
            P C (jt,y,7) e
                         -At
                                                                 =   (A H) C(.r,y)
                                                                     P C (x,y^)
where Overline{Cf.T,y)} is the vertically averaged air concentration at (x,y). Assuming that
Over line {C(,x,y)}  is approximately equal to C(x,y, zs), and that the height H is the mixing height HL,
the equation reduces to

                                           W	
 •and conversely
                                            A =
                                                 WP
       Because only the intensity classes (0-3) of the precipitation were assumed to be in the
precipitation data file (see Section 7.2.8), the user must specify the representative precipitation rate for
each intensity class.

       For a discussion of the washout ratios used in this study see  D.2.2.3.

       D.I.2.2        Meteorological Data and Receptor Locations  Relative to Local Source

       For both of the locations, meteorological data were obtained.for one year (1989). The types of
data files and their use are described in Table D-16.
                                           Table D-16
                           Description of Meteorological Files Used to
                                Make Input Files for COMPDEP
Data File
Hourly surface observations
(CD144)
Mixing Height Data file
Use
Used by RAMMET program to create meteorological data
file.
Used by ORNL precipitation preprocessor to create
precipitation data file for wet deposition calculations.
Used by RAMMET to create meteorological data file.
June 1996
                                   D-32
SAB REVIEW DRAFT
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        D.2.2.3
Vapor Deposition Parameters
        Dry Deposition Velocities. The dry deposition velocities for divalent vapor were based on
those used by the RELMAP model and were estimated based on assumed similar deposition properties
of nitric acid.  Deposition velocities depend on the season, land use, time of day, and stability class.
Table D-17 shows the seasonally-averaged deposition velocities-as a function of land-use.  Dry
deposition rate is proportional to the dry deposition velocities.
                                           Table D-17
            Divalent Mercury Vapor Seasonally-Averaged Deposition Velocities (cm/s)
Land-use
Urban
Agricultural
Range
Deciduous Forest
Coniferous Forest
Mixed forest/wetland
Water
Barren Land
Non-forested wetland
Mixed agric./rangeland
Rocky open areas
Pasquill Stability Class
A
4.63
1.81
1.71
3.45
3.45
3.32
1.00
1.07
1.92
1.76
1.94
B
4.59
1.77
1.68
3.40
3.40
3.27
0.98
1.06
1.89
1.73
1.90
C
4.36
1.63
1.54
3.14
3.14
3.06
0.89
0.97
1.77
1.59
1.74
D *
4.03
1.42
1.34
2.80
2.80
2.77
0.77
0.85
1.59
1.39
1.51
E
2.46
0.61
0.56
1.39
1.39
1.54
0.31
0.31
0.85
0.58
0.59
F
0.36
0.20
0.19
0.29
0.29
0.29
0.13
0.18
0.21
0.19
0.20
In order to address the fact that deposition is lower during nighttime conditions, it was assumed that
the deposition velocity for divalent vapor was 0.3 cm/s for stability classes D-F, which occur primarily
at night, and 1  cm/s for stability classes A-C.

        Washout Ratios.  For this  assessment, it was assumed that both elemental and divalent mercury
species would be deposited via wet deposition.  Because of its higher solubility, the divalent form
would be washed out at significantly higher rates. The washout ratio is a function of the
concentration, total carbon, and  ozone air distribution.  For divalent vapor, a washout ratio of l.GxlO6
was used, while for the elemental  phase a value of 1.6xl04 was used; this was roughly the average of
the washout ratio for elemental vapor for both locations (see Section D.I).

        In order to calculate  the scavenging coefficient, the precipitation rate for the hour is required.
In the precipitation data file,  the precipitation rate is classified as either none, light  (trace to 0.1 in/hr),
moderate  (0.11  to 0.3 in/hr),  or heavy  (greater than 0.3 in/hr). Thus, a representative precipitation rate
is required and is specified by the version of COMPDEP modified for this assessment. For the light
and moderate categories, the  midpoint of the range was used, while for the heavy category the
representative rate was assumed equal  to 0.3 in/hr.

        An informal examination of the scavenging coefficients computed for divalent vapor (using the
representative rates discussed above) showed that they were in the same range as the upper end
T	i r\r\ /-
-------
scavenging coefficients for particles as estimated by PEI and Cramer (1986). Table D-16 presents
divalent mercury vapor deposition velocities.  It was assumed that the elemental mercury vapor has a
dry deposition velocity of zero.

       D.2.2.4        Other Parameters Used in Air Modeling

       This section presents trie values for all parameters  not already discussed that were used in the
air modeling for all of the model plants.  These are given in Tables  D-18 to D-20.
                                                                                                     €
June 1996                          .           D-34                        SAB REVIEW DRAFT
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                           Table D-18
            Air Modeling Parameter Values Used In
         the Exposure Assessment:  Generic Parameters
Parameter
Particle Density (g/cm3)
Surface Roughness Length (m)a
Anemometer Height (m)
Wind Speed Profile Exponents
Stability Class A
\
Stability Class B
Stability Class C
Stability Class D
Stability Class E
Stability Class F
Terrain Adjustment Factors
Stability Class A
Stability Class B
Stability Class C
Stability Class D
Stability Class E
Stability Class F
Distance Limit for Plume Centerline (m)
Model Run Options
Terrain Adjustment
Stack-tip Downwash
Building Wake Effects
Transitional Plume Rise
Buoyancy-induced dispersion
Calms Processing Option
Value Used in Exposure Assessment
1.8
0.30
10

0.07
0.07
0.10
0.15
0.35
0.55

0.5
0.5
0.5
0.5
0
0
10

Yes
No
No
Yes
Yes
No
This is used to estimate deposition velocities for particles.
                                                         SAR RFVTFW DRAFT
-------
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                                                   Table D-20
                                 Model Plant Mercury  Speciation of Emissions
Plant Type
»
Large Municipal Waste Combtistora
Small Municipal Waste Combustor
Continuous Medical Waste Incinerator
Intermittent Medical Waste Incinerator
Large Coal-fired Utility Boiler
Medium Coal-fired Utility Boiler
Small Coal-fired Utility Boiler
Medium Oil-fired Utility Boiler
Chlor-alkali Plant
Primary Copper Smelter
Primary Lead Smelter
Speciation of Emissions
% Elemental
Mercury
Vapor
20
20
20
20
50
50
50
50
70
85
85
% Divalent
Mercury
Vapor
60
60
60
60
So
30
30
30
30
10
10
% Divalent
Paniculate
20
20
20
20
20
20
20
20
0
5
5
Speciation of Emissions: "Error-Bounding
Estimate"
% Elemental
Mercury
Vapor
57.50
57.50
70.00
70.00
85.00
85.00
85.00
85.00
85.00
95.00
95.00
% Divalent
Mercury
Vapor
42.50
42.50
30.00
30.00
15.00
15.00
15.00
15.00
15.00
5.00
5.00
ac Divalent
Paniculate
0.0
0.0
0.0
0.0
0.0 \
0.0
0.0
0.0
0.0
0.0
0.0
a For the control scenario m which emissions are 90% of base, the assumed Speciation is the same as for the utility boilers.
June 1996
D-37
                                                                                         SAB REVIEW DRAFT
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D.3    Description of the IEM2 Indirect Exposure Methodology

       Atmospheric mercury concentrations  and deposition rates estimated from RELMAP and
COMPDEP drive the calculations of mercury in watershed soils and surface waters. The soil and
water concentrations, in turn, drive calculations of concentrations in the associated biota and fish,
which humans and other animals are assumed to consume. These "indirect" exposure calculations
were modified from the Indirect Exposure Document (IED; U.S. EPA,  1990) as updated in an
Addendum (in preparation).  Relevant  sections of the updated methodology, IEM2, are described
below. The equations were implemented in a spreadsheet and parameterized for several hypothetical
scenarios as described in Volume III of this report.

       IEM2 uses atmospheric chemical loadings  to perform mass balances on a watershed soil
element and a surface water element, as  illustrated in Figure D-l.  The mass balances were performed
for total mercury, which was assumed  to speciate into three components:  Hg°, Hg(II), and
methylmercury.  The fraction of mercury in each of these components was  specified for the soil and
the surface water elements. Loadings  and chemical properties were given for the individual mercury
components, and the overall mercury transport and loss rates are calculated by the methodology.

                                          Figure D-l
                          Overview of the IEM2 Watershed Modules
              '-'yds  '-'ydw
            Dry
                                                      '-'yds   "-*ydw   '-'
                                                                                    Advection
                                            Water column
                                               benthic
                                            transformation
   "soil
   "atm
   -*yds
    yws
                                    Definitions for Figure D-l
chemical concentration in upper soil
chemical concentration in water body
vapor phase chemical concentration in air
average dry deposition to watershed
average wet deposition to watershed
                                                     jjg/m3
rng/L
rng/L

mg/yr
nig/yr
June 1996
                            D-38
     SAB REVIEW DRAFT
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        IEM2 first performs a terrestrial mass balance to obtain mercury concentrations in watershed
 soils.  Soil concentrations were used along with vapor concentrations and deposition rates to calculate
 concentrations in various food plants. These were used, in turn, to calculate concentrations in animals.
 IEM2 next performs an aquatic mass balance driven by direct atmospheric deposition along with
 runoff and erosion loads from wateshed soils.  Methylmercury concentrations in fish were derived
• from total dissolved water concentrations using bioaccumulation factors (BAF).

        IEM2 was developed to handle individual chemicals, or chemicals linked by kinetic
 transformation reactions. The kinetic transformation rates affecting mercury components in soil, water,
 and sediments -- oxidation, reduction, methylation, and demethylation -- were considered too uncertain
 to implement in this study. For this study, the methodology was expanded to handle multiple
 chemical components in a steady-state relationship. The fraction of each chemical component in the
 soil and water column was specified by the user.  The methodology predicts the total chemical
 concentration in watershed soils and the water body based on loading  and dissipation rates specified
 for each of the  components.

        The nature of this methodology is basically steady with respect to time and homogeneous with
 respect to space.  While it tracks the buildup of watershed soil concentrations over the years given a
 steady depositional  load and long-term average hydrological behavior, it does not respond to unsteady
 loading or meteorological events.  There are, thus, limitations on the analysis and interpretations
 imposed by these simplifications.  The methodology cannot be used to predict the response to
 reduction  or elimination of loadings. The model's calculations of average water body concentrations
 are less reliable for unsteady environments, such as streams, than for more steady environments, such
 as lakes.

 D.3.1   The Terrestrial Equations

        The IEM2 framework for estimating watershed soil impacts from stack emissions calculates
 surface soil concentrations, including dissolved and sorbed phases, as illustrated in  Figure D-2. The
 model accounts for three routes of contaminant entry into the soil:  deposition of particle-bound
 contaminant through dryfall; deposition through wetfall; and diffusion of vapor phase contaminant into
 the soil surface. The model also accounts for five dissipation processes that remove contaminants
 from the surface soils:   decay of total contaminants (sorbed + dissolved) within the soil horizon;
 volatilization (diffusion of gas phase out of the soil surface); runoff of dissolved phase from the soil
 surface; leaching of the dissolved phase through the soil horizon; and erosion of paniculate phase from
 the soil surface. Key  assumptions in the watershed soil impact algorithm were  these:

        •       Soil concentrations within a depositional area are assumed to be uniform  within the
                area, and can be estimated by the following key parameters:  dry and wet contaminant
                deposition rates, a wind-driven gaseous exchange rate  with the atmosphere, a soil  *
                dissipation rate, a  soil bulk density, and a soil mixing depth.

        •       The partitioning of the contaminant within the soil/water matrices can be described by
                partition coefficients.
                                               r»_-?Q                          
-------
                                         Figure D-2
                            Overview of the IEM2 Soils Processes
                                'atm
                                                    D
                 Volatilization
    c,  •+
Background
                                  Diffusion
                                                      yds
                                         Dry Fall
                                                                    D
                                                     v
                                                                       yws
Wet Fall
                                                                     V
                                    >HRT
                 c:
                              'st
                          Transformation
                    st
                                                                  PS
                                                                      Runoff
                                                                     =>
                                                                                     Erosion
                                                                                    ==>
                                              0
                                           Leaching
 'atm
 yws
'st,
R
T
                    Definitions for Figure D-2

vapor phase chemical concentration in air  [ig/m
average dry deposition to watershed
average wet deposition to watershed
total chemical concentration in soil
reaction product concentration in soil
background chemical concentration in soil  ma/L
              chemical concentration in soil gas
              chemical concentration in soil water
              chemical concentration on soil particles
              Henry's Law constant
              universal gas constant
              temperature
              soil/water partition coefficient
                                                     jag/m
                                                                      mg/yr
                                                                      mg/yr
                                                                      mg/L
                                                                      IH2/L
                                                       mg/L
                                                        Hg/g
                                                 atm-m /mole
                                              atm-m3/mo]e-°K
                                                          °K
                                                        L/ka
-------
        D.3.1.1         Chemical Mass Balance in Watershed Soils

        A mass balance equation can be written for total mercury in watershed soils, balancing areal
deposition fluxes with chemical loss processes:
where:
        Sc     =       average watershed soil concentration after time period of deposition (ug
                       ppllutant/g soil)
        Lw    =       yearly average load of pollutant to watershed on an areal basic (a pollutant/m2-
                       yr)
        ks     =       total chemical loss rate constant from soil (yr  )
        Tc     =       total time period over which deposition has occurred (yr)
        Z      =       representative watershed mixing depth to which deposited pollutant is
                       incorporated (cm)
        BD    =       representative watershed soil bulk density (g/cm )
        100    =       units conversion factor (|ag-m2/g-cm2)
        Csb    =       background "natural" soil concentration (ug pollutant/g soil)

The first term in the equation represents the steady-state concentration achieved after a sufficient
period of constant loading.  The exponential term gives the fraction of the steady-state response
achieved after Tc years of loading.  The final term  gives the natural background concentration found
in soils.  The background soil concentration of mercury was assumed to be negligible in this study.
The major terms in this equation are discussed in sections below.

        D.3.1.2         Equilibrium Speciation in Watershed Soils

        Total mercury in the soil was assumed to be distributed among three components - Hg°,
Hg(II), and methylmercury.  The steady-state fraction of the total in each component is specified by
the user, so that:

                                         ScHg0 = Sc  -fst
where:                                              •
       Sc            =      soil concentration of total mercury (ug pollutant/g soil)
       ScHgO         ~      s°il concentration of elemental mercury (ug pollutant/g soil)
       ScHg(li)        =      soil concentration of divalent mercury (ug pollutant/g soil)
       ScMeHg        =      s°il concentration of methylmercury (ug pollutant /g soil)
       fsl            =      fraction of soil concentration that is elemental mercury
       fs2            =      fraction of soil concentration that is divalent mercury
       fs3            =      fraction of soil concentration that is methylmercury
 ,,„<> 1QQA                                      1-I-/11                           CAR RPVTPW FIR APT
-------
       The total concentration of each mercury component in soil was assumed to reach equilibrium
between its paniculate and aqueous phases according to the following equations:
                                             Sct Kd^ BD
                                      *'  ~  6, + Kd,, BD  •
         *                                   *     3,1

                                               Sct BD
                                      *-*  '"  6S + Kds. BD                                \

Where:
       SCj    =      total soil concentration of component "i" (ug/g)
       9S     =      volumetric soil water  content (Lwatet/L)
       Kds J   =      soil/water partition coefficient for component "i" (L/kg)
       BD    =      soil bulk density (g/cm3)
       CsLi   =      total soil concentration of component "i" (mg/L,)
       Cds j   =      concentration of "i" dissolved in pore water (mg/L)
       Cps i   =      concentration of "i" in paniculate phase (mg/kg)

A derivation of the soil equilibrium equations is given in  Section 3.3.2.

       D.3.1.3        Loads to Watershed Soils

       The total pollutant load term Lw in the mass balance equation is the sum of the loadings for
each component "i."  Component loadings include wetfall and dryfall fluxes, atmospheric diffusion
fluxes and internal transformation loads:


                             Lw.  =  Dydw, + Dywwi  + Z^ , + LDIFJ                         (48)

where:
       DydWj =      yearly average  dry depositional flux of component "i" (g/m2-yr)
       Dywwj =      yearly average  wet depositional flux of component "i" (g/m2-yr)
       LIs i   =      internal transformation load of component "i" per areal basis (g/m2-yr)
       LDIF i  =      atmospheric diffusion flux of component "i" to soil (g/nr-yr)

Internal transformation loads  are set to 0 in the equilibrium component approach. Wet and dry
depositional fluxes were determined by measurement or by air modeling and were specified as input to
this model. The load due to vapor diffusion is given as the following:


                                  LDIF.  = 0.31536^.0^,

where:
       LDiFi  =      atmospheric diffusion flux of component "i" to soil (g/m2-yr)

June 1996                                     D-42                         SAB REVIEW  DRAFT
-------
        Kti    =      gas phase mass transfer coefficient for component "i" (cm/s: see Eq [4-6],
                      IED)
        ^atmi  =      §as phase atmospheric concentration for component "i"  (ug/m  )

        D.3.1.4        Loss Processes in Watershed Soils

        The total chemical loss rate constant ks in the soil mass balance equation is the weighted sum
of the chemical loss rate constants for each component "i":
where:
        kS
total soil loss constant for component "i" (yr  )
fraction of soil concentration that is component "i" (i.e., elemental, divalent,
and methylmercury)
The total chemical loss rate constant for component "i" is due to several physical and chemical
processes:


                               ksf = kslj +  kset + ksri  + ksgt +  ksv.
where:
       kSi
       ksl
       kSV:
soil loss constant for component "i" due to all processes (yr"1)
soil loss constant due to leaching (yr"1)
soil loss constant due to erosion (yr"1)
soil loss constant due to runoff (yr"1)
soil loss constant due to chemical transformation/ degradation (yr"1)
soil loss constant due to volatilization (yr  )
The degradation constant, ksgi; is set to 0 in the equilibrium component approach.  The other four
constants are given by these equations:
                                                                            CAR RPVTPW PIT? APT
-------
                                       - Ro  - EV (	1	'

                                       0f Z        [1.0  + Kd. BD/6
                                        S          \         S,t   '   S t
                                  _ 0.1 Xe SD ER  (   Kdrf BD
                                        BD Z      10,  + Kdv BD
                                         ksv. =
where:
       P       =      average annual precipitation (cra/yr)
       I       =      average annual irrigation (cm/yr)
       Ro     =      average annual runoff (cm/yr)
       Ev     =      average annual evapotranspiration (cm/yr)
       9S      =      volumetric water content (dimensionless; cm3/cm3)
       Z       =      watershed mixing zone depth (cm)
       BD     =      soil bulk density (g/cm3)
       SD     =      sediment delivery ratio
       ER     =      contaminant enrichment ratio
       Kdsi   =      soil-water partition coefficient for component "i" (cnvVg)
       Xe '    =      unit soil loss (kg/m2-yr; see Eq [9-3], ED; Wischmeier and Smith,  1978)
       Kej     =      equilibrium coefficient for component "i" (s/cm-yr); see Eq [4-5], IED; Travis,
                      et al., 1983)
       Ktj     =      gas phase mass transfer coefficient for component "i" (cm/s; see Eq [4-6], IED;
                      Travis, et al., 1983)
       0.1     =      units conversion  factor

       Sc is the concentration resulting  from contaminated particles depositing on and mixing with
surface soils. For mercury components,  where Kds j values are large, Scj was essentially equal to the
sorbed concentration, Cpsi, and the dissolved phase concentration, Cds •, was small.  Mercury
components depositing as particles were  assumed to reequilibrate in the soil/soil water system (see the
state equations above). In the listing of state equations, the reequilibrated sorbed phase concentration,
CpSji, and the dissolved phase concentration, CdSii, were used to estimate loads to the water  body due
to soil erosion and surface runoff, respectively.

D.3.2  The Aquatic Equations

       The following framework for estimating surface water impacts  from stack emissions estimates
water column as well  as bed sediment concentrations.  Water column concentrations included
dissolved, sorbed to suspended sediments and total (sorbed plus dissolved, or total contaminant divided
by total water volume).  This framework also provides three concentrations for the bed sediments:
dissolved in pore water, sorbed to bed sediments, and total. The model accounts for five routes of
contaminant entry into the water body:  erosion of chemical sorbed to soil particles; runoff  of

June 1996                                     D-44                          SAB REVIEW DRAFT
-------
dissolved chemical in runoff water: deposition of particle-bound contaminant through wetfall and
dryfall: and diffusion of vapor phase contaminants into the water body. The model also accounts for
four dissipation processes that remove contaminants from the water column and/or bed sediment
reservoirs:  decay of total contaminants (sorbed  + dissolved) within the water column; decay of total
contaminants (sorbed + dissolved) within the bed sediment; volatilization of dissolved phase out of the
water column: and removal of total contaminant via "burial" from the surficial bed sediment layer.
This burial rate constant is a function of the deposition of sediments from the water column .to the
bed; it accounts for the fact that much of the soil eroding into a water body annually  becomes bottom
sediment rather than suspended sediment.  The impact to the water body was assumed to be uniform.
This tends to be more realistic for smaller water bodies as compared to large river systems.  Key
assumptions in the surface water impact algorithm are the following.

       •       The partitioning of the contaminant within the sediment/water matrices - suspended
               solids in the water column, and  bed sediments in the benthos of the water body - can
               be described by partition coefficients.

       «       One route of entry into the surface water body is direct deposition.  A second route of
               entry is contaminant dissolved in annual surface runoff. This is estimated as a
               function of the contaminant dissolved in soil water and annual water runoff.  A third
               route of entry is via soil erosion. A sorbed concentration of contaminant in soil,
               together with an annual soil erosion estimate, a sediment delivery ratio and an
               enrichment ratio, can be used to describe the delivery of contaminant to the water
               body via soil  erosion. A sediment delivery ratio serves to reduce the total potential
               amount of soil erosion (where the total potential equals a unit erosion rate in kg/m
               multiplied  by the watershed area, in m2) reaching the water body recognizing that most
               of the erosion within a watershed during a year deposits prior to reaching the water
               body. The enrichment ratio accounts for the fact that eroding soils tend to  be lighter
               in texture, be more abundant in  surface area, and have higher organic carbon.  All
               these characteristics lead to concentrations in eroded soils that tend to be higher in
               concentration as compared to in situ soil$. A fourth and final route of entry is via
               diffusion in the gaseous phase.  The dissolved concentration in  a water body is driven
               toward equilibrium with the vapor phase concentration above the  water body.  At
               equilibrium, gaseous  diffusion into the water body is matched by  volatilization out of
               the water body. Gaseous diffusion  is estimated with a transfer rate (determined
               internally given user inputs) and a vapor phase air concentration.  This air
               concentration is specified by the user and is an output of the atmospheric transport
               model.

       •       For the surface water solution algorithm, it is assumed that equilibrium is maintained
               between contaminants within the water column and contaminants  in surficial bed
               sediments.  Equilibrium is established when the dissolved phase concentration in the
               water column is equal to the dissolved phase concentration within the surficial bed
               sediments.  This condition is imposed by the water body equations.

       •       A rate of contaminant "burial" in bed sediments  is estimated as a  function of the rate
               at which sediments deposit from the water column onto the surficial sediment layer.
               This burial represents a permanent sink, recognizing that a portion of the eroded soil
               and sorbed contaminant becomes bottom sediment while the remainder becomes
               suspended sediment.  This solution  assumes that there will be a net depositional loss,
               even though resuspension and redeposition of sediments is ongoing, particularly with
               moving water bodies. For cases where the net deposition rate is zero, there will be no
               burial loss  calculated.
-------
       •       Separate water column and benthic decay rate constants allow for the consideration of
               decay mechanisms that remove contaminants from the water body, optionally linking
               them through internal loading to a reaction product.  For the equilibrium component
               approach to mercury, decay constants are set to 0.
                                                                                      »
       Figure D-3 displays the framework for this analysis, with a listing of the ten concentrations
that were part of the solution algorithm.  In the following sections, the mass balance equations and the
equilibrium state equations that link the concentrations are developed.
       D.3.2.1
Chemical Mass Balance in the Water Body
       Taking Figure D-4 as a control volume for the water body, it can be seen that a steady-state
mass balance equation can be written that balances chemical loadings with outflow and loss:
                                  wtot
where:
        'wtot
       VL
        xwt
        water
total water body concentration, including water column and benthic sediment
(mg/L)
total chemical load into water body, including deposition, runoff, erosion,
atmospheric diffusion, and internal chemical transformation (g/yr)
average volumetric flow rate through water body (m3/yr)
total volume of water body or water body segment being considered, including
water column and benthic sediment (m3)
total first order dissipation rate constant, including water column and benthic
degradation, volatilization, and burial (yr'1)
fraction of total water body contaminant concentration that occurs in the water
column
depth of the water column (m)
total depth of water body, dw+db (m)
The first term in the denominator accounts for the advective flow of chemical from the water column,
while the second term accounts for loss processes from the bed and water column.  This mass balance
equation is derived in Section D.3.3.3.  The terms in this equation are discussed in sections below.
       D.3.2.2
Sediment Mass Balance in the Water Body
       Before calculating chemical fate, a mass balance equation for sediments in the water body
must be solved.  Taking Figure D-4 as a control volume for the water body, it can be seen that a
steady-state mass balance equation can be written that balances sediment loadings with outflow and
loss:
June 1996
                        D-46
SAB REVIEW DRAFT
-------
                                              Figure D-3
                            Overview of the IEM2 Water Body Processes
Ct D, D
^atm ^yds ^yws


c
Pv
>-'c|r 1 >
/"" Runoff
1
n ,

	 r\
w 1 — IX'
Erosion








c

Transformation,,//


Transfc

f\\



c
rrftaiion,//
P1
^bt
^


^
A
Volatilization



V


\s
c
A/t j
1


Bt
C


\




Dry Fall
7 \



^dsw -v. O
dw "^"•"



'^•'sw


Wet Fall
7
J


— TSS
n

Exchange ^ i,
|>
/ V
k



V

"^dbs x^ r1 DC
^u ^x..

^



x^* cK


Burial

n
Advection





Water
Column

Benthic
Sediment

v
Burial
    yds
    yws
   "dw
   -bt
    sb
                             Definitions for Figure D-3

concentration dissolved in soil water
concentration sorbed to soil
yearly dry deposition to surface water
yearly wet deposition to surface water
vapor phase atmospheric concentration
total concentration in water column
total water concentration in surface
water system, including water column
plus benthic sediment (not shown in figure)
dissolved phase water concentration
sorbed phase water concentration                  mg/kg
total concentration in bottom sediment
concentration dissolved in bed sediment pore water  mg/L
concentration sorbed to bottom sediments          mg/kg
 mg/L
mg/kg
mg/yr
mg/yr
ug/m3
 mg/L
 mg/L
 mg/L

 mg/L
Tnnf>
                                                 D-47
                                                                                  SAR RFVTFW DRAFT
-------
                                          Figure D-4
                     IEM2 Steady State Sediment Balance in Water Bodies
          X
             w
                     erosion
                                                      advection
                                                     TSS
                         ^
                         c/ep
                                              x
                                              A
                              W
                                  b
                                          X
                                                             DC
                                                             DO
                                              b
                                                                        X
                                                                             a
   Xw
   xa
   xd
   xb
   TSS
   BS
    W,
    Wu
dep
                  Definitions for Figure D-4

soil erosion input from depositional area
advective loss from water body
deposition onto bottom sediment
burial below bottom sediment layer
suspended solids concentration
bottom sediments concentration
rate of deposition onto bed sediment
rate of burial
g/yr
g/yr
g/yr
g/yr
mg/L
g/L
m/yr
m/yr
June 1996
                                       D-48
                                                         SAB REVIEW DRAFT
-------
                                            X  WA, SD 103
                                     TSS =  — '- - - -
where:
        TSS    -      suspended solids concentration (mg/L)
        Xe     =      unit soil erosion flux, calculated in the soils section from the USLE equation
                      (kg/m2-yr)
        WAL   =      watershed surface area (m )
        SD     =      watershed sediment delivery ratio (unitless)
        Vfx    =      average volumetric flow rate through water
        wdep   =      suspended solids deposition rate (m/yr)
        WA^,   =      water body surface area (m )
        103    =      units conversion factor
The first term in the denominator accounts for the advective flow of sediment from the water column,
while the second term accounts for depositional loss from the water column.  This mass balance
equation is derived in Section 3.3.3.  The terms in this equation are discussed in sections below.

        In the second part of the solids balance, the mass deposited to the bed, Xd, was set equal to
the mass buried, Xb.  Solving for the burial rate gives the following:
where:
       Wb     =      burial rate (m/yr)
       ^dep   =      deposition rate (m/yr)
       TSS    =      suspended solids concentration (mg/L)
       BS     =      benthic solids concentration (kg/L)
       10~6    =      conversion factor (kg/mg)

       Finally, the benthic porosity, the volume of water per volume of benthic space, was calculated
from the benthic solids concentration and sediment density:


                                        ete = i  - BS/PS

where:
       0bs     =      benthic porosity (L/L)
       BS     =      benthic solids concentration (kg/L)
       ps      =      solids density, 2.65 kg/L

For input benthic solids concentrations between 0.5  and 1.5 kg/L, benthic porosity ranged between 0.8
and 0.4.

       The suspended solids, benthic solids, and benthic porosity were used in the chemical
equilibrium speciation equations.  The burial rate was used in the chemical burial equation.  These
equations are developed below.

June 1996                                     D-49                           SAB REVIEW DRAFT
-------
       D.3.2.3       Equilibrium Speciation in Water Body

       Total mercury in the water body is assumed to be distributed among three components — Hg  ,
Hg(II), and methylmercury.  The steady-state fraction of the total in each component in the water
column is specified by the user using the following relationship:
                                              = C   • f
                                                ^wt   Jiv2


                                              = C   • f
                                       w        ^~wt   Jw3

where:
       Cwt           =      water column concentration of total mercury (ug/L)
       Cwt,HgO       =      water column concentration of elemental mercury (ug/L)
       ^wt,Hg(H)      =      water column concentration of divalent mercury (ug/L)
       Cwt>MeHg      =      water column concentration of methylmercury (ug/L)
       fwl           =      fraction of water column  concentration that is elemental mercury
       fw2           =      fraction of water column  concentration that is divalent mercury
       fw3           =      fraction of water column  concentration that is methylmercury

       The total concentration of each mercury component in the water body, Cwtot -r was assumed to
reach equilibrium between the benthic and water column  compartments and between its paniculate and
aqueous phases within each compartment. Cwtot j gives the mass per volume of the entire water body,
including both water column and benthic sediment. The  water column concentration, Cwti, was based
on the water column volume; the benthic concentration, Cbti, was based on the benthic volume.  The
equilibrium relationships are given by the following equations, which are derived in Section 3.3.4.

Surface Water System --

                                <^M = C

                                        = see  mass balance equation
                 f
                  f
       Water Column —

                                   Cwtj  ~ fwaterj '^wtotj '
june 1996                                     D-50       •                  SAB REVIEW DRAFT
-------
                          C    = C   -f   = C
                          ^~dw    ^wt Jdwj     wtj
                                                          1
                                                 i + Kd  .-rss-icr6
                                        swj      swj   dwj
       Bed Sediment --
                                      t  ~ fbenthj '
Note that by substituting the relationship between CwU and CwtoU into the expression for Cbu,
can obtain benthic concentrations as a function of water column concentrations:
                                                                                       one
where:
       ubs
       Kd
          'sw,i
       Kd
          bs,i
       TSS
       BS
        water,!
        benth.i
       1dw,i
                      bed sediment porosity (Lwater/L)
                      suspended sediment/surface water partition coefficient for component "i"
                      (L/kg)
                      bottom sediment/pore water partition coefficient for component "i"  (L/kg)
                      total suspended solids (mg/L)
                      bed sediment concentration (g/cm3)
                      depth of the water column (m)
                      depth of the upper benthic layer (m)
                      total depth of water body, dw+db (m)
                      fraction of total water body component "i" concentration that occurs in the
                      water column
                      fraction of total water body component "i" concentration that occurs in the
                      sediment
                      fraction of water column component "i" concentration that is dissolved
                      fraction of bed sediment component "i" concentration that is dissolved
-------
       D.3.2.4        Loads To The Water Body

       The total chemical load term Lj in the mass balance equation is the sum of the loadings for
each component "i."  Component loadings included wet and dry deposition, impervious and pervious
runoff, erosion, atmospheric diffusion, and internal transformation:


                          LTj = LDtp,i + LRI,i + LK  + LE,i  + LDifj + LU

where:
       LTi    =       total component "i" load to the water body (g/yr)
               =       deposition of particle bound component "i" (g/yr)
               =       runoff load from impervious surfaces (g/yr)
       LR J    =       runoff load from pervious surfaces (g/yr)
       LEii    =       soil erosion load (g/yr)
       LDifi   =     .  diffusion of vapor phase component "i" (g/yr)
       LJJ     =       internal transformation load, equal to 0 for equilibrium mercury chemistry
                      (g/yr)

       The runoff and erosion loads required estimation of average contaminant concentration in
watershed soils that comprise the depositional area.  These  concentrations were developed in terrestrial
sections above.

Load due to direct deposition - The load to surface waters via direct deposition is solved as follows:
where:
           p i         direct component "i" deposition load (g/yr)
       DydSj   =      yearly dry deposition rate of component "i" onto surface water body (g
                      pollutant/m -yr)
       Dywsj  =      yearly wet deposition rate of component "i" onto surface water body (g
                      pollutant/nr-vr)
       WAW   =      water body area (m2)

Load due to impervious surface runoff -- A fraction of the wet and dry chemical deposition in the
watershed will be to impervious surfaces.  Dry deposition may accumulate and be washed off during
rain events.  If the impervious surface includes gutters, the pollutant load will be transported to surface
waters, bypassing the watershed soils. The average load from such impervious  surfaces  is given by
this equation:
where:
       LRIi    -      impervious surface runoff load for component "i" (g/yr)
               =      impervious watershed area receiving pollutant deposition (m )
               =      yearly wet deposition flux of component "i" onto the watershed (g/m2-yr)
               =      yearly dry deposition flux of component "i" onto the watershed (g/m2-yr)
June 1996                                     D-52                         SAB REVIEW DRAFT
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Load due to pervious surface runoff -- Most of the chemical deposition to a watershed will be to
pervious soil surfaces.  These loads are accounted for in the soil mass balance equation.  During
periodic runoff events, dissolved chemical concentrations in the soil are transported to surface waters
as given by  this equation:
                          L   = Ro (WAL -
                                                         BD
                                                        Kdsl BD
                                                          10
                                                                    -2
where:
Ro

BD


WA'L  =
WAT   =
io-2   =
                      pervious surface runoff load for component "i" (g/yr)
                      average annual runoff (cm/yr)
                      component "i" concentration in watershed soils (ug/g)
                      soil bmk density (g/cm3)
                      volumetric soil .water content (cm3/cm3)
                      soil-water partition coefficient  for component "i" (L/kg or cm /g)
                      total watershed area receiving pollutant deposition (mA)
                      impervious watershed area receiving pollutant deposition (m2)
                      units conversion factor (g2/kg-ug)
Load due to soil erosion -- During periodic erosion events, paniculate chemical concentrations in the
soil are transported to surface waters as described by this relationship:
                       LE. = Xe (WAL - WA{) SD ER
                                                       Sc. Kds4 BD
                                                           Kds. BD
                                                              10
                                                                       -3
where:
       BD
       WAL   =
       WAj   =
       SD
       ER
       io-3    =
               soil erosion load for component "i" (g/yr)
               unit soil loss (kg/m2-yr)
               component "i" concentration in watershed soils (ug/g)
               soil bulk density (g/cm3)
                                              •7    o
               volumetric soil water content (cm /cm )
               soil-water partition coefficient for component "i" (L/kg or cm /g)
               total watershed area receiving pollutant deposition (m )
               impervious watershed area receiving pollutant deposition (m2)
               watershed  sediment delivery ratio (unitless)
               soil enrichment ratio  (unitless)
               units conversion factor (g-cm2/ug-m2)
Load due to gaseous diffusion — The change in the total water concentration over time due to
volatilization is given by this:
                                        D
                                            f    -f* C
                                            J water;H dwti  i
                                                      wtotj
where:
        "wtotj
                      total water body component "i" concentration (mg/L)
-------
       Kv j    =      overall component "i" transfer rate (rn/yr)
       D      =      depth of water body (m)
               =      fraction of total water body component "i" concentration that occurs in the
                      water column
               =      fraction of water column component "i" concentration that is dissolved
               ='      component "i" vapor phase air concentration over water body (|ig/m3)
               =      component "i" Henry's Constant (atm-mj/mole)
       R      =      universal gas constant, 8.206*10~ atm-m/mole-K
       Tk      =      water body temperature (°K)
       10"6    =      units conversion factor

       This treatment of volatilization is based on the well-known two-film theory (Whitman, 1923),
as implemented in standard chemical fate models (Burns, et al., 1982, Ambrose, et al., 1988).  The
right side of the volatilization equation contains two terms.  The first term constitutes a first order loss
rate of aqueous Contaminant, which is covered in more detail below. The second term in the
volatilization equation describes diffusion of gas-phase contaminant from the atmosphere into the water
body.  Because this term is independent of water body contaminant concentration, it can be treated as
an external load.  As formulated above, this term has units of mg/L-yr.  It must be convened to
loading units by multiplying by the water column volume, V. " Noting that V/D is equal to  the surface
water area WA^,,  we see that the atmospheric diffusion load is given as this:
                                                HJRTk
where:
               =      diffusion of vapor phase component "i" (g/yr)
               =      the overall component "i" transfer rate (m/yr)
               =      surface water body area (m2)
               =      component "i" vapor phase air concentration over water body (ug/m3)
       Hj      =      component "i" Henry's Constant (atm-m3/mole)
       R      =      universal gas constant (8.206 x 1CT5  atm-m3/mole-°K)
       Tk      =      water body temperature (°K)
       10"6    =      units conversion factor

       D. 3.2.5        Advective Flow From The Water Body

       The first term in the denominator of the chemical mass balance equation accounts for
advective flow from the water body.  It is the product of the average annual volumetric flow rate, Vfx;
the fraction of the chemical in the water body that is present in the water column,  fwater; and the
adjustment  factor dz/dw, which normalizes the outflowing chemical concentration to a water column
volume basis.  An impacted  water body derives its annual flow from its watershed or effective
drainage  area.  Flow  and watershed area, then, are related, and compatible values should be specified
by the user. Given the area  of drainage, one way to estimate annual flow volume  is to multiply total
drainage  area (in length squared units) by a unit surface water runoff (in length per time).  The Wafer
Atlas of the United States (Geraghty et al., 1973) provides maps with isolines of annual average
surface water runoff, which is defined as all flow contributions to surface water bodies, including
direct runoff, shallow interflow, and groundwater recharge. The values ranged from 5 to 40 in/yr in
various parts of the United States.
Tnnp. 1Q96                                     D-54                          SAB REVIEW DRAFT
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        D.3.2.6        Chemical Dissipation Within The Water Body

        The second term in the denominator of the chemical mass balance equation accounts for
dissipation within the water body.  It is the product of trie water body volume, Vt,  and the total first
order dissipation rate constant, k^.  The water body volume, in units of m  , together with the  annual
flow rate, in m3/yr, determines the average residence time of a pollutant traveling through the water
body.  The residence time for Lake Erie is about 10 years, for example, while for the larger Lake
Superior it is estimated to be 200 years.  For a  swiftly moving river, on the other hand, the residence
time can be on the order of hours (1 hour = 0.00011 yr).  Larger volumes and residence times  allow
the internal dissipation processes to have a larger effect  on pollutant concentration, while smaller
volumes and residence times  lessen the effect.  It is necessary to specify reasonable volumes for the
type of surface water body being represented.  In addition, compatible values for related water body
parameters, such as surface area, WAW must be used. The water body volume divided by the  surface
area gives the average depth, which can vary from a fraction of a meter for small streams to a  few
meters for  shallow reservoirs to tens of meters for deep  lakes.

        The total dissipation rate constant, kwt,  applies to the total water body concentration, Cwtot, and
is the weighted sum of the chemical loss rate constants for each component "i":

                                        £  = ^ k   •  f
                                         wt    2-j  wtj  Jvi
                                               i

where:
        ksj     =      total soil loss constant for component "i" (yr"1)
        fsi      =      fraction of soil concentration that is component "i"  (i.e., elemental, divalent,
                      and methylmercury)

The total chemical loss rate constant for component "i" includes processes affecting any  of the
chemical phases — dissolved  or sorbed in the water column or benthic sediments.  Volatilization, water
column and benthic degradation, and burial are  considered in this  relationship:


                    k    = f     k    +   f     k     +  f    k    +  f    k
                    wt,i   J waterj  gwj     Jbenthj gbj     J waterj  vj    Jixnthj  b,i

where:
        kwtji    =      overall total water body dissipation rate constant for component "i" (yr"1)
               =      water column degradation or transformation rate constant for component "i"

        kgbi    =      benthic degradation or transformation rate constant  for component "i" (yr"1)
        ky i    =      water column volatilization rate  constant (yr"1)
        kb;i    =      benthic burial rate constant for component "i" (yr"1)
        f\vater,i  ~      fraction of total water body  component "i" concentration that occurs in the
                      water column
        *benth,i  =      fraction of total water body  component "i" concentration that occurs in the
                      benthic sediment

These processes are described below.

Chemical/Biological Degradation -- Contaminants  can  be degraded and transformed by a number of
processes in the water column or in the benthic sediment.  Mercury components  are subject to
oxidation, reduction, and methylation.  In the equilibrium approach taken here, the  transformation
rates were set to 0 and the fraction of total chemical in each component was specified directly.
-------
Volatilization — Volatile chemicals can move between the water column and the overlying air. as
described by Equation (3-41).  The right side of this equation contains two terms.  The second term
describes diffusion into the water from the atmosphere and was treated as an external load.  The first
term, (K^, jfwater jf^w l^wcoU^' constitutes a  first order loss rate of aqueous contaminant.  This term
includes the quantity fwater /dwa^-wtot i> wmch *s equal to the  water column dissolved phase
concentration Cdwi and which is subject to volatilization loss.  The rate constant for volatilization from
the water column, k^  ( is given  as this:
                                                  D

where:
       ky j     =      water column volatilization loss rate constant for component "i" (yr"1)
       Ky J    =      overall transfer rate, or conductivity for component "i"  (m/yr)
       fdw i    =      fraction of component "i" in the water column that is dissolved
       D      =      water body depth (m)

       The overall transfer rate, Ky j or conductivity, was determined by the two-layer resistance
model (Whitman, 1923; or see Burns, et al., 1982 or Ambrose, et al., 1988).  The two-resistance
method assumes that two "stagnant films"  are bounded on either  side by well mixed compartments.
Concentration differences  serve as the driving force for the water layer diffusion.  Pressure differences
drive the diffusion for the air layer.  From mass balance considerations, it is obvious that the same
mass must pass through both films; thus, the two resistances combine in series, so that the
conductivity is the reciprocal of the total resistance:
where:
       RL J    =      liquid phase resistance (year/m)
       KL i    =      liquid phase transfer coefficient (m/year)
       RG i    =      gas phase resistance (year/m)
       KQ j    =      gas phase transfer coefficient (m/year)
       R      =      universal gas constant (atm-m 3/moIe-°K)
       E{     =      Henry's law constant for component "i" (atm-m 3/mole)
       Tk     =      water body temperature (°K)

       The value of K^, the conductivity, depends on the intensity of turbulence in a water body and
in the overlying atmosphere.  As the Henry's Law coefficient increases, the conductivity tends to be
increasingly influenced by the intensity of turbulence in water.  As the Henry's Law coefficient
decreases, the value of the conductivity tends to be increasingly influenced by  the intensity of
atmospheric turbulence.

       Because Henry's Law  coefficient generally increases with increasing vapor pressure of a
compound and generally decreases with increasing solubility of a compound, highly volatile low
solubility compounds are most likely to  exhibit mass transfer limitations in water, and relatively
nonvolatile high solubility compounds are more likely to exhibit mass transfer  limitations in the air.
Volatilization is usually of relatively less magnitude in lakes and reservoirs than in rivers and streams.
-------
        The estimated volatilization rate constant was for a nominal temperature of 20 "C.  It is
adjusted for the actual water temperature using the equation:
where:
                                            _ v-    a(T-20)
                                        ,i,r  - Av,i,20 °
                      temperature correction factor, set to 1.026.
                      water body temperature (°C)
     There have been a variety of methods proposed to compute the liquid (KL4) and gas phase
transfer coefficients.  The particular method that was used in the exposure assessment is the O'Connor-
Dobbins (1958) method.

    The liquid and gas film transfer coefficients computed under this option vary with the type of
water body. The type of water body was specified as one of the surface water constants and can either
be a flowing stream,  river or estuary, or a stagnant pond or lake. The primary difference is that in a
flowing water body, the turbulence is primarily a function of the stream velocity, while for stagnant
water bodies, wind shear may dominate. The formulations used to compute the transfer coefficients
vary with the water body type, as shown below.

Flowing Stream or River - For a flowing system, the transfer coefficients are controlled by flow-
induced turbulence. For these systems, the liquid film transfer coefficient (KL) was computed using
the O'Connor-Dobbins (1958) formula:
                                            D   «
                                                     (3.15xl07)
where:
       KU    =
       u       =
       Dw,i    =

       10-4   =
3.15x10
liquid phase transfer coefficient for component "i" (m/year)
current velocity (m/s)
diffusivity of the component "i" in water (cm2/s)
water depth (m)
units conversion factor
units conversion factor
The gas transfer coefficient (KQ) was assumed constant at 36500 m/yr for flowing systems.

Quiescent Lake or Pond — For a stagnant system, the transfer coefficients are controlled by wind-
induced turbulence. For stagnant systems, the liquid film transfer coefficient (KL) was computed using
the O'Connor (1983) equations:
where:
                                                                                        r>r> A c-r
-------
                                     .  k
                                         0.33^
                                              Sc:f67(3.15xl07)
u *  = C/-5 W
                           0,1
                 1.9
               MW-
                   .2/3
       vo =  (1.32 + 0.009 Ta) x 10
                                   -i
               Pw Dwi
       n   =
            ~
              22x10
                    '5
               - 8.8 xlO'5 T
                                      1301
                  998.333  + 8.1855(7  -20) + 0.00585(7 -20)2
                                                                - 3.0233
and:
        *
       u

       W
       Pa
       Pw
       k

       Scai

June 1996
                    shear velocity (m/s)
                    drag coefficient (= 0.0011)
                    wind velocity, 10 m above water surface (m/s)
                    density of air corresponding to the water temperature (g/cm 3)
                    density of water corresponding to the water temperature (g/cm 3)
                    von Karman's constant (= 0.4)
                    dimensionless viscous sublayer thickness (= 4)
                    air Schmidt number for component "i" (dimensionless)
                                            D-58
SAB REVIEW DRAFT
-------
        ScWI   =      water Schmidt number for component "i" (dimensionless)
        Da ;    =      diffusivity of component "i" in air (cm /sec)
        Dwi    =      diffusivity of component "i" in water (cm "/sec)
        ua      =      viscosity of air corresponding to the air temperature (g/cm-s)
        u^v     =      viscosity of water corresponding to the water temperature (g/cm-s)
        va      =      dynamic viscosity of air (cnr/sec)
        MWj   =      molecular weight of component "i"
        Ta     =      air temperature (°C)
        Tw     =      water temperature (°C)
     3.15xl07  =      units conversion factor

Deposition and Burial - The benthic burial rate,  Wb, was determined as a function of user input
variables as part of the sediment balance. This burial rate is used to determine the mass loss of
contaminant from the benthic sediment layer.  As seen in Figure D.3, the burial loss rate was applied
to the total^benthic contaminant concentration, Cbt. The water body contaminant burial loss rate was
solved by equating the mass loss rate of total  water body chemical with mass loss rate of benthic
chemical:
                                        v  t   -  c   v    °
                                        vt  Kbj     *~btj Vb ~T
                                                           ab

where:
       C\vtot,i  =      tota' water Docty component "i"  concentration, including water column and
                      benthic sediment (mg/L)
       Vt      =      total volume of water  body or water body segment being considered, including
                      water column and benthic  sediment (m3)
       kb i     =      first order burial rate constant for total component "i" (yr"1)
       Cbti    =      total benthic component "i" concentration (mg/L)
       Vb     =      volume of upper benthic sediment layer (m3)
       db      =      depth of the upper benthic sediment layer (m)
       Wb     =      benthic burial rate (m/yr)

From the equilibrium state equations, it is seen that the  total benthic contaminant concentration, Cbt A,
can be expressed as a function of the total water body concentration, Cwtot -r  Solving for the total
chemical burial rate gives this:
                                                                           CAD DC-urn™/ rvo A TTT
-------
                                        k   -f     -?*
          '                               W   Jbenthj   j
                                                    db

where:
       %enth i  =      fraction of total water body component "i" concentration that occurs in the bed
                      sediment
       db      =      depth of the upper benthic sediment layer (m)
       Wb     =      burial rate (rn/yr)

D.3.3  Derivation of Select Equations

       In this section, some of the equations in Sections 3.1 and 3.2 are derived.  A summary of
notation is given in Section 3.4.

       0,3.3.1        Terrestrial Mass Balance Relationships

       Taking Figure D-2 as a control volume for the watershed soil, a mass balance equation can be
written. Contaminant concentration is expressed per unit mass of soil.  The change in concentration
per unit time is equal to the loading rate per unit soil mass minus the total loss rate:

                                     dSc    Lw'A
                                             WS
                                     dt     BD-VS
                                             100 L
                                                  w
                                             BD-Z
                                                    - ks-Sc
This is a first order, ordinary differential equation with this general solution:

                                          100LW
                                   Sc =
                                         ks-BD-Z
                                                                       a

where Cx is an unknown constant. Applying the condition Sc=0 at t=0 gives the following:
                                         ks-BD-Z
       If a stable natural background concentration exists in the soil that is independent of the loading
and loss processes, then this Csb can be added to the above solution to obtain the overall
concentration.

       D. 3.3.2        Terrestrial Equilibrium Relationships

       Within the soil, the dissolved chemical concentration equilibrium with the sorbed concentration
is defined by a partition coefficient:
Assuming that the soil gas phase is negligible on a mass basis, the total soil chemical concentration is
composed of dissolved chemical plus sorbed chemical:
                                                                            CAR DPVTTT\X7 riT? APT
-------
Substituting the partitioning relationship between dissolved and sorbed chemical, one gets the total soil
chemical concentration:
The fraction of chemical dissolved in the soil water is the following:
                                 f     £V?£         e«
The fraction of chemical sorbed in the soil is l-fdsi:

              .                -                Kd ,-BD
The concentration of chemical dissolved in the soil water is derived as follows:
                                                    6  f Kd -BD
and the concentration of chemical sorbed to the soil is this:
                                                  Sct-Kdsi-BD
                                             i   Q
                                                         s,t
        D.3.3.3        Surface Water Mass Balance Relationships

        Taking Figure D-3 as a control volume for the water body, at steady-state total chemical^
loading equals the sum of chemical outflow and chemical loss. The  chemical outflow is the product
of the volumetric flow rate and the water column concentration:

                                       Outflow =  Vfx-CM

The fraction of the total water body chemical concentration that is in the water column is defined in
this way:
                                       Jwattr       f~,


The outflow can, thus, be given in terms of the total water body concentration:

                                 Outflow = Vfx-fwater-Cwtot-dJdw


The chemical loss can also be given in terms of the total water body concentration:
Equating loading to outflow plus loss, and solving for the total water body concentration gives this:

Time. 1Q96                                     D-61                          SAB REVIEW DRAFT
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                                                  Lr
       In a similar manner, the mass balance equation for sediment in the water body can be derived,
Taking Figure D-9 as a control volume, the soil eroding into the water body, Xw, equals the sum of
the amount depositing into the upper bed, Xd, and the advective loss from the water column, Xa. Xw
is the product of the areal soil erosion flux, the watershed surface area, and the watershed sediment
delivery ratio, with a factor converting kg to mg:

                                     Xw = Xe-WAL-SD-ltf

Xd is the product of the suspended solids concentration and the deposition rate:
Xa is the product of the suspended solids concentration and the volumetric flow rate:

                                         Xa =  VfrTSS


Substituting these relationships into the solids mass balance and solving for the suspended solids
concentration in the water column gives this equation:
                                    TSS  =
                                           X  WA, SD 103
                                         = -^ - - --
       D.3.3.4        Surface Water Equilibrium Relationships

       Within the water column, the dissolved chemical concentration equilibrium with the sorbed
concentration is defined by a partition coefficient:

                                       swj    dw,i   ^^swj

The total chemical concentration in the water column is composed of dissolved chemical plus sorbed
chemical:

                                  C^-^  +  C^.JSS-lO-*                            (120)

Substituting the partitioning relationship between dissolved and sorbed chemical, the total chemical
concentration is calculated as this:
The fraction of chemical dissolved in the water column becomes the following:
June 1996                                     D-62                          SAB REVIEW DRAFT
-------
                                f   =  Cdw'i =
                                *  ~       "
        Within the bed sediment, the dissolved chemical concentration equilibrium with the sorbed
concentration is defined by a partition coefficient:

                                        Cdbj = Csbj ' Kdbs,i

The total benthic chemical concentration is composed of dissolved chemical plus sorbed chemical:
                                                                                    «
                                     Cbt,i = CdW6ta *  C*j'BS

Substituting the partitioning relationship between dissolved and sorbed chemical, one calculates the
total benthic chemical concentration:
The fraction of chemical dissolved in the benthic pore water is the following:

                                        *-* J*. ; \J I	          Ot_
                                 f   =  "'*•'
                                •/,j
        The total chemical concentration on a total water body basis is the sum of the water column
and the benthic concentrations, weighted by the respective volumes is given by these relationships:

                         r     ~ c  •  w +  c   •— -  r   •— +  c  •—
                         ^WtOtJ   ^Wtf  y  +  <~btj  y     ^Wtj   ,     ^bt4   ,
                                        z         *z         uz         az

Substitutrng in the relationships for Cwti and Cbti, and noting that at equilibrium Cdwi = Cdbi, one
obtains the total water body concentration as a function of the  dissolved water column  concentration:
                         C^. [(1 +

The fraction of the chemical that is in the water column is defined as this:

                                       L
                                       J -water j
                                                   t> j. . •
Substituting in the relationships for Cwt]i and Cwtot i( one obtains expressions for fwater i and fbenth •1 as
functions of environmental and chemical properties:
                   'waterJ
-------
                  Jb
                   benthj
                           (1
D.3.4  Summary of Notation
       BD
       BS
         wt,HgO
       Cwt,Hg(II)
D

Da
D,,,;
       Da,i
       DywSj

       Dywwi
       ER
       Ev
       f,
        benth.i
        dw,i
        ps.i
 surface area of watershed soil element (m )

 representative watershed soil bulk density (g/cm3)
 benthic solids concentration (kg/L)

 component "i" vapor phase air concentration over .watershed (ug/m )
 total benthic  component "i"  concentration (mg/L)
 drag coefficient ( = 0.0011)
 concentration of "i" dissolved in pore water (mg/L)
 concentration of "i" in paniculate phase (mg/kg)
 background "natural"  soil concentration (ug pollutant/g soil)
 total soil concentration of component "i" (mg/L)
 water column concentration of total mercury (ug/L)
 =      water column concentration of elemental mercury (ug/L)
 =      water column concentration of divalent mercury (ug/L)
 =      water column concentration of methylmercury ((Jg/L)
 total water body component "i" concentration, including water column and
 benthic sediment (mg/L)
 total water body concentration,  including water column and benthic sediment
.(mg/L)

 depth of the upper benthic sediment layer (m)
 depth of the water column (m)
 total depth of water body, d^,+db (m)
 depth of water body (m)
 diffusivity of component "i" in  air  (cm 2/sec)
 diffusivity of component "i" in  water (cm -/sec)
 yearl-y dry deposition rate of component "i" onto surface water body (g
 pollutant/m2-yr)
 yearly average dry depositional flux  of component "i" onto watershed (g/m2-
 yr)
 yearly wet deposition rate of component "i" onto surface water body (g
 pollutant/m2-yr)
 yearly average wet depositional flux of component "i" onto  watershed (sJm2-
 yr)

 soil enrichment ratio (unitless)
 average annual evapotranspiration (cm/yr)

 fraction of total water body component "i" concentration that occurs in the
 benthic sediment
 fraction of bed sediment component  "i"  concentration that is dissolved
 fraction of soil component  "i" concentration that is dissolved
 fraction of water column component "i" concentration that is dissolved
 fraction of soil component  "i" concentration that is sorbed
June 1996
                        D-64
                                                                    SAB REVIEW DRAFT
-------
f^      =      fraction of soil concentration that is component "i" (i.e., elemental, divalent.
               and methylmercury)
fsl      =      fraction of soil concentration that is elemental mercury'
fs2      =      fraction of soil concentration that is divalent mercury
fs3      =      fraction of soil concentration that is methylmercury
fwater i  ~      fraction of total water body component "i" concentration that occurs in the
               water column
fwl     =      fraction of water column concentration that is elemental mercury
fw2     =      fraction of water column concentration that is divalent mercury
fw3     =      fraction of water column concentration that is methylmercury

H!      =      Henry's law constant for component "i" (atm-m  /mole)

I       =      average annual irrigation (cm/yr)

kbi     =      bentK: burial rate constant for component "i" (yr"1)
kobi    =      benthic degradation or transformation rate constant for component "i"  (yr"1)
kowi    =  .    water column degradation or transformation rate constant for component "i"
               (yr-1)
kv i     =      water column volatilization loss  rate constant for component "i" (yr" )
kwt     =      total first order dissipation rate constant, including water column and benthic
               degradation, volatilization, and burial (yr"1)

k-wta    =      overall total water body dissipation rate constant for  component "i" (yr"1)
ks      =      total chemical loss rate constant  from soil (yr"1)
ksj     =      soil loss constant for component "i" due to all processes (yr"1)
kslj     =      soil loss constant due to leaching (yr"1)
ksej    =      soil loss constant due to erosion (yr"1)
ksrj     =      soil loss constant due to runoff (yr"1)
ksgj    =      soil loss constant due to chemical transformation/ degradation (yr" )
ksVj    =      soil loss constant due to volatilization (yr"1)
KG J    =      gas phase transfer coefficient (m/year)
KL i    =      liquid phase transfer coefficient for component "i" (m/year)
Kti     =      gas phase mass transfer coefficient for component "i" (cm/s; see Eq [4-6],
               IED)
K^    =      overall transfer rate, or conductivity for component "i"- (m/yr)

Kdbsi  =      bottom sediment/pore water partition coefficient  for component "i" (L/kg)
Kds>i    =      soil-water partition coefficient for component "i" (L/kg or cm3/g)
Kdsw J  =      suspended sediment/surface  water partition coefficient for component "i"
               (L/kg)
Ked     =      equilibrium coefficient for component "i" (s/cm/yr); see Eq [4-5],  IED)
Ktj     =      gas phase mass transfer coefficient for component "i" (cm/s; see Eq [4-6],
               IED)

        =      deposition of particle bound component "i" (g/yr)
        =      atmospheric diffusion flux of component  "i" to soil (g/m2-yr)
        =      diffusion of vapor phase component "i" (g/yr)
LE>i     =      soil erosion load for component  "i"  (g/yr)
Lj j     -      internal transformation load, equal to 0 for equilibrium mercury chemistry
               (g/yr)
LIs i    =      internal transformation load  of component "i" per areal basis (g/m2-yr)
-------
         e
       LR j    =      pervious surface ranoff load for component "i" (g/yr)
       LRI i   =      runoff load from impervious surfaces (g/yr)
       LT    =      total chemical load into water body, including deposition, runoff, erosion.
                      atmospheric diffusion,  and internal chemical transformation (g/yr)
       LTi    =      total component "i" load to the water body (g/yr)
       Lw    =      yearly average load of pollutant to watershed on. an areal basis(g pollutant/m2-
                      yr)

       MWj   =      molecular weight of component "i"

       P      =      average annual precipitation (cm/yr)

       R      =      universal gas constant  (8.206 x 10~5  atm-m3/mole-°K)
       RG j    =      gas phase resistance (year/m)
       RL i    -      liquid phase resistance (year/m)
       Ro    =      average annual runoff (cm/yr)

       Sc     =      average watershed soil concentration after time period of deposition (ug
                      pollutant/g soil)
       Scai   =      air Schmidt number for component "i" (dimensionless)
       Scj    =      total component "i" concentration in watershed soils (ug/g)
       ScH Q  =      soil concentration of elemental mercury (ug pollutant/g soil)
       ScHg(II)        =      s°il concentration of divalent mercury (ug pollutant/g soil)
       ScMeHg        =      s°i* concentration of methylmercury (ug pollutant /g soil)
       Scw J   =      water Schmidt number for component "i" (dimensionless)
       SD    =      watershed sediment delivery ratio (unitless)

       Ta     =      air temperature (°C)
       Tk     =      water body temperature (°K)
       Tw    =      water temperature (°C)
       Tc     =      total time period over which deposition has occurred (yr)
       TSS    =      suspended solids concentration (mg/L)

       u      =      current velocity (m/s)
       u      =      shear velocity (m/s)

       Vb    =      volume of upper benthic sediment layer (m3)
       Vs     =      volume of watershed soil layer (m3)
       Vw    =      volume of water column (m3)
       Vz     =      total volume of water body or water body segment being considered, including
                      water column and benthic sediment (m3)
       Vfx    =      average volumetric flow rate through water body (nvVyr)

       W     =      wind velocity,  10 m above water  surface  (m/s)
       Wb    =      benthic burial rate (m/yr)
       Wd    =      suspended solids deposition rate (m/yr)
       WAj   =      impervious watershed area receiving pollutant deposition (m2)
       WAL  =      total watershed area receiving pollutant deposition (m2)
              =      water body surface area (m2)
       X     =      unit soil erosion flux, calculated in the soils section from the Universal Soil
                      Loss Equation (USLE); see Eq [9-3] , IED (kg/m2-yr)

June 1996                                     D-66                          SAB REVIEW DRAFT
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       Z      =      representative watershed mixing depth to which deposited pollutant is
                      incorporated (cm)

       k      =      von Karman's constant ( = 0.4)
       X-,     =      dimensionless viscous sublayer thickness ( = 4)
       ua     =      viscosity of air corresponding to the air temperature (g/cm-s)
       (^     =      viscosity of water corresponding to the water temperature (g/cm-s)
       pa     =      density of air corresponding to the water temperature  (g/cm  )
       ps     =      solids density, 2.65 kg/L
       pw     =      density of water corresponding to  the water temperature (g/cm  )
       va     =      dynamic viscosity of air (cm2/sec)
       6      =      temperature correction factor, set to  1.026.
       9S     =      volumetric soil water content (Lwatei/L)
       0bs    =      bed sediment porosity (Lwater/L)
D.4    References

Ambrose, R.B. et al. 1988. WASP4, A Hydrodynamic and Water Quality Model-Model Theory,
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-------
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Iverfeldt, A., 1991. Occupance and turnover of atmospheric mercury over the nordic countries.
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Iverfeldt, A., and  0. Lindqvist, 1986.  Atmospheric oxidation of elemental mercury by ozone in the
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Johnson, W. B., 1983.  Interregional exchanges of air pollution:  Model types and applications.
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Johnson, W. B., D. E. Wolf, and R. L. Mancuso, 1978. Long-term regional patterns and transfrontier
       exchanges of airborne sulfur pollution in Europe.  Atmospheric Environment 12: 511-527.

Keeler, G. J., S. M. Japar, W. W. Brachaczek, R. A. Gorse, Jr., J. M.  Norbeck and W. R. Pierson,
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 Keeler, G., G. Glinsorn and N. Pirrone (1995).  Paniculate mercury in the atmosphere:  Its
        significance, transport, transformation and sources. Accepted for publication in Water, Air and
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 Lamborg, C. H., W. F. Fitzgerald, G. M. Vandal and K.  R. Rolfhus (1995).  Atmospheric mercury in
        northern Wisconsin:  Sources and species.  Accepted for publication in Water,  Air and Soil
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 Lindberg, S. E., R. R.  Turner, T. P. Meyers, G. E. Taylor, Jr. and W. H. Schroeder, 1991.
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        Watershed, Tennessee, USA.  Water, Air and Soil Pollution 56:577-594.

 Lindberg, S. E., T. P.  Meyers, G. E. Taylor, Jr.,  R. R. Turner and W. H. Schroeder, 1992.
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 Lindqvist, 0., K. Johansson, M.  Aastrup, A. Andersson, L. Bringmark, G. Hovsenius, L. Hakanson, A.
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 Munthe, J., Z. F. Xiao and O. Lindqvist, 1991.  The aqueous reduction of divalent mercury by sulfite.
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 Munthe, J, and W. McElroy, 1992. S.ome aqueous reactions  of potential importance in the
        atmospheric chemistry of mercury. Atmospheric Environment 26A: 553-557.

 Munthe, J., 1992.  The aqueous  oxidation of elemental mercury by ozone. Atmospheric Environment
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 O'Connor, D.J.  1983. Wind Effects on Gas-Liquid Transfer Coefficients.  Journal of Environmental
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 Pasquill, F. (1961).  The Estimation of Dispersion of Windborne  Material, Meteorol.Mag., 90:33-49.

 Pasquill, F. (1974). Atmospheric Diffusion, the Dispersion of Windborne Material from Industrial and
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 Pasquill, F. (1976). Atmospheric Dispersion Parameters  in Gaussian Plume Modeling: Part II.
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Penner, J. E., H. Eddleman and T. Novakov. 1993. Towards the development of a global inventory
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           APPENDIX E




CHEMICAL PROPERTIES OF MERCURY
-------
                             TABLE OF CONTENTS


                                                                                    Page

E.I    Chemical Properties of Mercury	,.,,....,..,	  E-l

E.2    Analytic Measurement Methods	  E-3
       E.2.1   Methods of N. S. Bloom and W. F. Fitzgerald  	E-5
       E.2.2   Swedish Sampling Program 	E-6
       E.2.3   Other Methods  	E-6
       E.2.4   Detection Limits	!	E-l

E.3    References	E-l
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E.I    Chemical Properties of Mercury

       Elemental mercury metal is a heavy, silvery-white liquid at typical ambient temperatures and
pressures. The vapor pressure of mercury metal is strongly dependent upon temperature, and it
vaporizes readily under ambient conditions: its saturation vapor pressure of 14 mg/m  greatly exceeds
the average permissible concentrations for occupational (0.05 mg/m3) or continuous environmental
(0.015 mg/m3) exposure (Nriagu, 1979; WHO, 1976). Elemental mercury partitions strongly to air in
the environment and is not found as the pure, confined liquid. Most of the mercury encountered in the
atmosphere is elemental  mercury vapor.

       Mercury can exist in three oxidation states, Hg°  (metallic), Hg2 +, (mercurous) and Hg +
(mercuric). The properties and behavior of mercury strongly depend on the oxidation state.
Mercurous and mercuric mercury can form numerous inorganic and organic chemical compounds;
however, mercurous mercury is rarely stable under ordinary environmental conditions.  Mercury is
unusual among metals because it tends to form covalent  rather than ionic bonds. Most of the mercury
encountered in water/soil/sediments/biota (all environmental media except the atmosphere) is in the
form of inorganic mercuric salts and organomercurics. Organomercurics  are defined by the presence
of a covalent C-Hg bond. This is thought to differ from the common behavior of inorganic mercury
compounds associating with organic material in the environment. The compounds most likely to be
found under environmental conditions are these:  the mercuric salts HgCl2, Hg(OH)2 and HgS; the
methylmercury  compounds CH3HgCl and CH3HgOH; and, in small fractions, other organomercurics
(i.e., dimethylmercury and phenylmercury).

       In the aqueous phase, mercury compounds often remain  as undisassociated molecules, and the
solubility values reported below reflect this.  Solubility values for mercury compounds which do not
disassociate 'are not based on the ionic product.  Most organomercurics are not soluble  and do not
react with weak acids or bases, due to the low affinity of the mercury for oxygen bonded to carbon.
CH3HgOH, however, is highly soluble due to the strong  hydrogen bonding capability of the hydroxide
group. The mercuric salts vary widely in solubility.  For example HgCl2 is readily soluble in water,
and HgS is as unreactive as the organomercurics, due to  the high affinity of mercury for sulfur. A
detailed discussion of mercury chemistry can be found in Nriagu (1979) and Fitzgerald (1994).
                                       -   Table E-l
                          Chemical Properties of Elemental Mercury
Property
Chemical Name
Chemical formula
CAS Registry Number
Molecular Weight
Melting Point (at 760 torr)
Boiling Point (at 760 torr)
Density as a Dry Gas at 25°C
Specific Gravity at 25°C
Vapor Pressure at 25°C
Value
Elemental Mercury
H^O
HS
7439-97-6
200.59
-39
357
7.0
13.59
2.0 X 10' 3
Units



g/mole
. °C
°C
Density of Air
g/cm3
mm mercury
References




MMES (1994)
MMES (1994)
MMES (1994)
MMES (1994)
MMES (1994)
                                                                        CAR
-------
                                Table E-l (continued)
Property
Physical Form at 25°C
Solubility in Water at 25 °C
Henry's Law Constant
at 25°C
Value
Silver-white liquid
6 X Iff5
7.1 X lO'3
Units

g/L
atm-m /mole
References

Schuster (19911
Lmdqvist and
Rodhe (1985)
                                     Table E-2
                       Chemical Properties of Mercuric Mercury
Property
Chemical Name/Form
Chemical formula
CAS Registry Number
Molecular Weight
Melting Point (at 760 tore)
Boiling Point (at 760 torr)
Density as a Dry Gas at 25 °C
«
Specific Gravity at 25 'C
Vapor Pressure at 25°C
Physical Form at 25 °C
Solubility in Water at 20°C
Henry's Law Constant at 25 °C
Log of Octanol'.Water coefficient
Value
Mercuric
Sulphide
(Cinnabar)
HgS
1344-48-5
232.68
583.5a
NA
8.0
Red: 8.10
Black: 7.73

Red or
Black
powder
2 X 10-9


Value
Mercuric
Chloride
HgCl2
7487-94-7
271.52
277
302
9.44
5.44
1.2 X 10'4
White
crystal/
powder
69
7.1 X 10'10
-0.215
Value
Mercuric
Hydroxide
Hg(OH)2

234.60





White
powder
0.126
7.8 X 10"8

Units



g/mole
°C
°C
Density of
Ail
g/cnr
mm
mercury

g/L
at'Ti-
m /mole

References




MMES, MMES
(1994)
MMES
MMES. Gmelins,
1967
MMES (1994)
U.S. EPA, 1993

Schuster, 1991
U.S EPA, 1993
Schuster, 1991
Lindqvist and
Rodhe, 1985
Lindqvist and
Rodhe, 1985
Halbach, 1985
June 1996
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                                           Table E-3
                             Chemical Properties of Methylmercury
Property
Chemical Name
Chemical formula
CAS Registry Number
Molecular Weight
Melting Point (at 760 torr)
Vapor Pressure at 25°C
Physical Form at 25 °C
Solubility in Water at 20°C
Henry's Law Constant at
^ 25 °C
^Log of Octanol:Water
1 coefficient
Value
Methylmercury Chloride
CH3HgCl
115-09-3
251.08
170
8.5 X 10'3
White powder
Hg° < Sol < HgCl,
(WHO, 1990)
4.7 X 10'7
0.405
Value
Methylmercury hydroxide
CH3HgOH

232.64



>232 (due to existence of
1M stock solution, but at
higher pH)
6.1 X 10'9

Units



g/mole

mm
mercury
°C
°C

Density
of Air
References




Chapman and Hall (1984)
WHO (1990)

WHO (1990),Iverfeldt and
Persson (1985)
Iverfeldt and Persson
(1985),Lindqvist and Rodhe
(1985)
Halbach (1985)
E.2    Analytic Measurement Methods

       Mercury contamination of samples has been shown to be a serious problem in past studies.
The development of ultra-clean techniques has been critical for the more precise measurements
required for detection of low levels of mercury.A number of methods can be employed to determine
mercury concentrations in environmental media.  Data on the concentrations of total mercury,
elemental mercury, organic  mercury compounds  (especially methylmercury) and information on
various Hg(II) compounds  can be collected, although exact speciation among Hg(II) compounds is not
usually attempted.  Methods that accurately and reliably measure the total mercury concentration in
environmental media have been established for some time.  Recently significant
improvements/standardizations in analytical methodologies enable reliable data on the concentration of
methylmercury, elemental mercury and the Hg(II) fraction to be readily separated from the total
mercury in environmental media.  It is possible further to speciate the Hg(II) fraction into reactive,
non-reactive and particle-bound components.  It is generally not possible to determine which
Hg(II)species is present (e.g.,  HgS or HgCl2).  For example, it has been suggested that much of the
mercury in sediments from East Fork Poplar Creek in Oak Ridge, Tennessee is HgS (Revis et al.,
1990).

       Summarized below are the analytical methods used to determine the speciation of mercury
outlined in the above table,  principally methods employed by N.S. Bloom and W.F. Fitzgerald in the
U.S.  and methods used in the  Swedish sampling  program.  This information is included in this report
to support the assertions made in Table E-4 and to illustrate the difficulties and limitations of species-
selective mercury determinations.
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                                                  Table E-4
  Comparison of U.S. and Swedish Methods for Obtaining Concentrations of Different Species/
Types of Mercury from Environmental Samples (an X  indicates this reaction of mercurj is often
                                     obtained in the recent literature)
Fractions of total mercury that
are determined in various
media
U.S.
Sweden
Air Samples
Total gaseous Hg°
methylmercury
dimethylmercury
particle bound mercury
X
X
X
X
X


X
Water/sediment/biota/precipitation
Total mercury
methylmercury
Hg°
dimethylmercury
"reactive" or "labile"
mercury
(HgR) a
Total Hg(II), reactive +
non-reactive
Hg(II) non-reactive b
Hg(II) inert c
X
X
X
X
X



X



X
X
X
X
a In the U.S. this is considered mainly if not exclusively Hg(II) species, and can more specifically be thought of as
environmentally active Hg(Il) (Fitzgerald et al., 1991).  It includes inorganic Hg(II) (Hg2+, Hg(OH)2, HgCL,...), simple
organic Hg(II) associations, and labile (acid reactive) Hg(II) particulate associations. It is suggested that the determination of
the amount of HgR is particularly important, since it represents the fraction of mercury which is susceptible to various
biological and chemical reactions, including reduction to Hg  and production of methylmercuiy.  The source of HgR in rain
(at least in rain distant from anthropogenic emissions) is most probably due to atmospheric or gas/solution oxidation process
which depends on the stable concentration of Hg  in the atmosphere (Fitzgerald et al., 1991: Lindqvist et al.,  1991. chapter 6;
Petersen et al., 1995).
         The fraction of mercury not accounted for in U.S. speciation (that is not methylated mercury, Hg  , or HgR) is
mercury that is bound to pardculates, some of which will be bound strongly to organic substances.  This fraction is also
made up of Hg(II) but is far less reactive that HgR.  In  Sweden this fraction is considered to be mainly inorganic Hg(II')
compounds, such as Hg2+, HgX0, HgX3-, HgX4—, with X as C1-, OH-, etc; also HgO on aerosol particles, and Hg"+
complexes with organic acids.  It is denoted as Hg-IIa.
b This  fraction, denoted Hg-IIb, includes methylmercury and other organomercuric compounds, Hg(CN)9 and HgS, and Hg2+
bound  to humic mater.
c Denoted Hg-IIc, this fraction is mainly mercury  strongly bound to particulates.
  ,^,^ ion*;
                                                                                       CAR PPVTPW
                                                                                                            AT=T
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        These are some of the latest, highest yield methods used to determine the amounts and
 speciation of mercury in the environment.  These methods show how much mercury information can
 be determined from environmental media with the latest methods that have been proven over several
 experiments and are commonly available to researchers:  experimental or non-established analytical
 routines are not included.  See Mitra (1986) for a review of methods for total mercury analysis
 including a number of out-dated routines.

 E.2.1   Methods of N. S. Bloom and W. F. Fitzgerald

        Water,  Sediment or Soil. Seston and Biota (Bloom and Watras, 1989V   A water sample is first
 buffered to pH 4.9, and then ethylated with sodium tetraethylborate to convert the mercury species into
 compounds which can be readily volatilized.  Sediment,  seston and biological samples are first
 digested with 25% KOH in methanol and then diluted and buffered to a pH range of 4 to 6 prior to
 ethylation.  The ethylation results in the formation of CH3CH2HgCH3 from reactive
 monomethylmercury and (CH3CH2)2Hg from reactive inorganic mercury.  (CH3)2Hg and Hg°
 contained in the sample are unaffected and may be purged and analyzed with the diakyl reaction
 products (in practice they are purged from unethylated samples for better precision). The volatile ethyl
 analogs are then purged onto  a graphitic carbon column (CarbotrapR). The carbon column is purged
 with N2 to remove all water,  and the organomercurials on the carbon trap are thermally desorbed into
 a gas chromatograph.  The mercury species elute at different times from the column, where the gas is
 pyrolized at 750 C to release  the mercury as Hg .  The amount of each eluted species is then
 determined by cold vapor atomic fluorescence spectroscopy.

        To determine the total amount of methylmercury in a sample, it is necessary to  account for
 organocomplexed and colloidal methylmercury, not just reactive CH3Hg.  A water sample treated with
 acidic saturated KC1 and extracted with CH2C12 will convert these unreactive methylmercury species to
 reactive CH3HgCl, which is then back-extracted into high purity water by solvent evaporation.  The
 aqueous phase is then analyzed for methylmercury as above, starting with the ethylation step.
 Recently (Hovart et al.,  1993b), it was found that distilling the water sample after pretreatment with
 KC1 (to bring the Cl- concentration to 0.08%) and acidification H2S04 at a rate of 6-8 ml/hr resulted
 in a sample (when ethylated as above) that resulted in higher and more consistent recoveries, lower
 detection limits and greater ease of use  (no organic solvents needed).  Distillation provided similar
 benefits for analyzing sediment samples  (Hovart et al., 1993a).

        This procedure can determine the total concentration of CH3Hg, the fraction of CH3Hg that is
 particle bound,  the reactive CH3Hg fraction, total mercury content and the fraction of total mercury
 which is particulate bound.

        Two other procedures are used to determine mercury  speciation further.  First, to determine the
 amount of "reactive" mercury (HgR) in a sample it is  acidified to pH 1, then reduce with SnCl?.  This
 reduces the reactive portion of the mercury present to Hg°. The sample is aerated, and  the Hg13 is
 collected on gold.

        To determine the total mercury present, the sample is first oxidized by BrCl, followed by
reduction of the BrCl with NH2OH-HC1.  This destroys many  strong organo-metal associations and
decomposes  methylmercury, making all this mercury available for SnCl2 reduction as above.

        Air.  The amount of Hg° in air is readily measured by pumping ambient air though  a gold
trap, since gold and mercury readily form amalgams.  Bloom and Fitzgerald (1988) uses gold coated
quartz sand as a trap.  The gold trap is then heated, and the amount of Hg° vaporized is  determined by-
atomic fluorescence spectroscopy.
June 1996                                     E-5                        SAB REVIEW DRAFT
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       Air samples are also collected by drawing air though a CarbotrapR, which is then subjected to
the protocol described earlier for CarbotrapsR derived from purged ethylated water samples (water
purge, followed by gas chromatography and atomic fluorescence detection).  This  allows determination
of the fraction of mono and dimethylmercury in air.

       Particulate mercury is collected by  drawing an  atmospheric sample though a guartz wool plug.
The plug is pryrolized, and the resulting mercury vapor is trapped on gold.  The gold trap is ±en
analyzed as above.

E.2.2   Swedish Sampling Program

       The Swedish government has undertaken a large, multidisciplinary research program to study
mercury in its environment, and much of the recent mercury literature is from this program. The
concentration/speciation protocol most commonly employed in this program is; outlined below  (see
work  by Brosset and Lord, (1991); Lindqvist et al, (1991); and Westling, (1991)).  Air sampling was
not reviewed, since the Bloom methods can speciate more directly  (Lindqvist et al.,1991, Chapter 1).

       Water/Precipitation.  Water samples are speciated using differing oxidation/reduction
procedures which determine reactivity of the extracted  mercury fraction.  The following notation is
used to divide up the mercury found in the samples.

       Hg-tot (total mercury):  The sample is oxidized with BrCl, followed by reduction of the BrCl
       with NH2OH-HC1, and then SnCl2  reduction. This is very similar to the Bloom method
       described above.  The volatilized Hg° was purged from solution and concentrated on a gold
       trap.  Upon desorption from the trap the amount of mercury is analyzed with a direct current
       plasma-atomic emission spectrometer.

       Hg-II (reactive + non-reactive):  The sample is treated with NaBH4, followed by the same
       collection procedure as for Hg-tot.

       Hg-IIa (reactive, acid labile, inorganic):  Reduction with SnCl9 in HC1 or H2SO4.

       Hg-IIb (non-reactive) = Hg-II - Hg-IIa

       Hg-IIc (inert) = Hg-tot - Hg-II

E.2.3   Other Methods

       These are some of the methods employed to pretreat biological samples, making them
applicable to the same mercury  analysis protocols  as the U.S. and Swedish methods  for water  samples.

       Plants/Soils.  Typically, these samples are digested in Nitric acid or HNO3-H2O2 followed by
procedures similar to the above for water samples (Siegel et al.,  1987).

       Fish. The sample is digested in nitric  acid with microwave heating under pressure, followed
by treatment with potassium permanganate. After standing overnight, the excess potassium
permanganate is reduced with sodium chloride-hydroxylamine hydrochloride (New Jersey, 1994). The
sample can then be analyzed as in water/precipitation methods described above.
June 1996                                     E-6                        SAB REVIEW DRAFT
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E.2.4  Detection Limits

       One of the significant advances in mercury analytical methods over the past decade or so has
been the ability to measure mercury concentrations accurately at much lower levels than were
previously reported.  Over the past two decades mercury determinations have progressed from
detection of ug levels of total mercury to pg levels of particular mercury species  (based on
information in Mitra, 1986 and Hovart et al., 1993a and  1993b). Typical detection limits for data used
or presented in this study are on the order of 1 ng/L for water samples  (Sorensen et al., 1994), 0.1 ng/g
for biota (Cappon, 1987; Bloom,  1992) and 0.1  ng/m3 for atmospheric samples (Lindberg et al.,  1992).

E.3    References

Bloom, N. and W. F. Fitzgerald (1988).  Determination of Volatile Mercury Species at the Picogram
level by Low-Temperature Gas Chromatography with Cold-Vapor Atomic  Fluorescence Detection.
Analytica Chimica Acta, 208:151-161.

Bloom, N.  S. (1992).  On the Chemical Form of Mercury in Edible Fish and Marine Invertebrate
Tissue.  Can. J. Fisher. Aq. Sci. 49:1010-1017.

Brosset, C. (1981).  The Mercury Cycle.  Water, Air and Soil Poll.  16:253-255.

Brosset, C. and E. Lord (1991). Mercury in Precipitation and Ambient Air: A new Scenario. Water,
Air and Soil Poll. 56:493-506.

Cappon, C. J.  (1987). Uptake and Speciation of Mercury and Selenium in Vegetable Crops Grown on
Compost-Treated Soil.  Water,  Air and Soil Poll. 34:353-361.

Chapman and  Hall (1984). Dictionary of Organometallic Compounds. Vol. 1, p.  1030.

Fitzgerald, W. F., R. P. Mason and G. M.  Vandal  (1991). Atmospheric Cycling  and Air-Water
Exchange of Mercury over Mid-Continental Lacustrine Regions. Water, Air and Soil Poll. 56:745-
767.

Fitzgerald, W. F. (1994). Global Biogeochemical Cycling of Mercury. Presented  at the DOE/FDA/EPA
Workshop on Methylmercury and Human Health,  Bethesda, MD March 22-23 1994.

Gmelins Handbuch, Der Anorganischen Chemie (34), Queck Silber, 1967 Verlag Chemie.  GMBH
Weinheim/Burger.

Halbach, S. (1985).  The OctanalAVater distribution of Mercury Compounds. Arch. Toxicol.  57:139-
141.

Hovart, M., L. Liang, N. S. Bloom (1993a).  Comparison of distillation  with other current isolation
methods for the determination of methylmercury compounds in low level environmental samples. Part
I:  Sediments.  Analytica Chimica Acta, 281:135-152.

Hovart, M., L. Liang, N. S.  Bloom (1993b).  Comparison of distillation  with other current isolation
methods for the determination of methylmercury compounds in low level environmental samples. Part
II: Water.  Analytica Chimica Acta, 282:153-168.
June 1996                                    E-7                        SAB REVIEW DRAFT
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Iverfeldt, A. and I. Persson (1985).  The Solvation Thermodynamics of Methylmercury(II) Species
Derived from Measurements of the Heat of Solution and the Henry's Law Constant. Inorganica
ChimicaActa, 103:113-119.

Lindberg, S. E., T. P. Meyers, G. E. Taylor, R. R. Turner, and W. H. Schroeder (1992). Atmosphere-
Surface Exchange of Mercury to a Forest:  Results of Modeling  and Gradient Approaches. J. of
Geophy. Res. 97(D2):2519-2528.

Lindqvist, 0. and H, Rodhe (1985). Atmospheric Mercury-a review. Tellus.  376:136-159.

Lindqvist, O., K. Johansson, M. Aastrup, A. Andersson, L. Bringmark,  G. Hovsenius, L. Hakanson, A.
Iverfeldt, M. Meili, and B. Timm (1991). Mercury in the Swedish Environment - Recent Research on
Causes, Consequences and Corrective Methods.  Water, Air and Soil Poll. 55:(all chapters)

Mitra, S. (1986). Mercury in the Ecosystem. Trans Tech Publications Ltd. Switzerland.

Martin Marietta Energy Systems (MMES),  1994. Material Safety Data Sheets for methylmercuric
chloride, mercuric sulfide, metallic metrcury, and mercuric chloride.  MMES, Inc. P.O. Box 2008,
M.S. 6291, Oak Ridge, TN 37831.

New Jersey Department of Environmental  Protection and Energy Division of Science and Research
(1994). Preliminary Assessment of Total Mercury Concentrations in Fishes from Rivers, Lakes and
Reservoirs of New Jersey. 93-15F.

Nriagu, J; O. (1979). The Biogeochemistry of Mercury in the Environment.  Elsevier/North Holland.
Biomedical Press: New York.

Westling, 0. (1991). Mercury  in Runoff from drained and undrained Peatlands in Sweden.  Water, Air
and Soil Poll. 56:419-426.

Petersen, G., A. Iverfeldt and J. Munthe, 1995.  Atmospheric mercury species over Central and
Northern Europe.  Model calculations and  comparison with observations from the Nordic Air and
Precipitation Network for 1987 and  1988.  Atmospheric Environment 29:47-68.

Revis, N. W., T. R. Osborne, G. Holdsworth,  and C. Hadderi (1990). Mercury in Soil: A Method for
Assessing Acceptable Limits. Arch.  Environ. Contain. Toxicol,  19:221-226.

Schuster, E (1991).  The Behavior of Mercury in the Soil with special emphasis on Complexation and
Adsorption processes - A Review of the Literature. Water, Air and Soil Poll. 56:667-680.

Sorensen, J.A., G.E. Glass and K.W. Schmidt. (1994).  Regional Patterns of Mercury Wet Deposition.
Environ. Sci.  Tech. 28:2025-2032.

U.S.  EPA (1993). Summary Review of Health Effects Associated with Mercuric Chloride.
EPA/600/R-92/199

Westling, O. (1991).  Mercury in Runoff from drained and undrained Peatlands in Sweden.  Water, Air
and Soil Poll. 56:419-426.

World  Health Organization (1990).  Environmental Health Criterir*  101:  Methylmercury.  Geneva.
June 1996                                    E-8                        SAB REVIEW DRAFT
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         APPENDIX F




DESCRIPTION OF MODEL PLANTS
-------
-------
                                   TABLE OF CONTENTS
F.I     Introduction	  F-l

F.2     Relationship  of Plant Process Conditions to Emissions  	  F-l
        F.2.1   Municipal Waste Combustors	  F-4
               F.2.1.1 Description of Source Category	  F-4
               F.2.1.2 Summary of Available Data on Emissions and Controls	  F-5
               F.2.1.3 Selection of MWC Model Plant Parameters  	  F-7
        F.2.2   Medical Waste Incinerators 	'	  F-8
               F.2.2.1 Description of Source Category	  F-8
               F.2.2.2 Summary of Available Data on Emissions and Controls	  F-8
               F.2.2.3 Selection of MWI Model Plant Parameters  	  F-8
        F.2.3   Utility Boilers	  F-9
               F.2.3.1 Description of Source Category	". .  F-9
               F.2.3.2 Summary of Available Data on Emissions and Controls	  F-9
               F.2.3.3 Selection of Model Plant Parameters  	  F-l 1
        F.2.4   Chlor-Alkali Production	  F-ll
               F.2.4.1 Description of Source Category	  F-l 1
               F.2.4.2 Summary of Available Data on Emissions and Control	  F-12
               F.2.4.3 Selection of Model Plant Parameters  	  F-12
        F.2.5   Primary Copper Smelters	  F-12
               F.2.5.1 Description of Source Category	  F-12
               F.2.5.2 Summary of Available Data on Emissions and Control	  F-l3
               F.2.5.3 Selection of Model Plant Parameters  	  F-l3
        F.2.6   Primary Lead  Smelters	  F-l3
               F.2.6.1 Description of Source  Category	  F-l3
               F.2.6.2 Summary of Available Data on Emissions and Controls	  F-14
               F.2.6.3 Selection of Model Plant Parameters  	  F-14
F.3     References	  F-15
June 1996                                     F-i                        SAB  REVIEW DRAFT
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                                  LIST OF TABLES
Table F-l     Process Parameters for Model Plants	 F-2
Table F-2     Mercury Removal Efficiencies	.....,......,..,,  F-10
June 1996                                F-ii                      SAB REVIEW DRAFT
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                                    LIST OF FIGURES
Figure F-l     Distribution of Mercury in EPA Method 29 Sampling Train, Camden County
              and Stanislaus County Carbon Injection Projects	,	 F-6
Tlinp 1QQfi                                   F-iii                       SAB REVIEW DRAFT
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F.I     Introduction

        Model plants representing six source categories were developed to represent a range of
mercury emission parameters.  The source categories were selected for the local impact analysis based
on their estimated annual mercury emissions or their potential to be localized point sources of concern.
The categories were municipal waste combustors (MWCs), medical waste incinerators (MWIs). utility
boilers, chlor-alkali plants, primary copper smelters, and primary lead smelters.

        Descriptive characteristics, or parameters for each model plant were selected after evaluating
the characteristics of the entire source class.  Important variables for mercury risk assessments include
the following: mercury emission rates, mercury speciation, and mercury "transport/deposition rates.
Important model plant parameters included stack height, stack diameter, stack volumetric flow rate,
stack gas temperature, plant capacity factor (relative average  operating hours per year), stack
concentration, and mercury speciation.

        Table F-l summarizes the model plant parameters modeled in this analysis. These parameters
represent operating  conditions associated with each source type's current mercury control. That is.
mercury emissions reductions being achieved by  the air pollution control devices presently in place
were considered for each source category.  These parameters are not meant to represent a "worst-case"
emissions scenario; they are believed to be representative of the full range of sources (of a given
category) across the United States. The amount of uncertainty surrounding the emission rates  varies
for each model plant. This uncertainty is reflected in Chapter fcL-- Research Needs -- of this report"
F.2     Relationship of Plant Process Conditions to Emissions

        Mercury speciation estimates for elemental mercury (Hg°) and divalent mercury (Hg2+) were
made using literature results of thermal-chemical modeling of mercury compounds in flue gas,
interpretation of bench and pilot scale combustor experiments and interpretation of available field test
results.

        The amount  and speciation of mercury emitted from high temperature process depends on the
composition of the feed material, amount of mercury in the process feed material, process operating
conditions, and process flue gas  cleaning  techniques (Lindqvist and Schager,  1990).

        "The inorganic mercury compounds that are considered important in high temperature
processes are HgS(s), HgO(s,g),  HgCl2(s,g), Hg2Cl2(s) and HgS04(s)."  Some organic compounds,
such as methylmercury, CH3HgCH3, and  CH3HgCl may also occur (Hall et al., 1991).
Thermochemical calculations indicate that at combustion temperatures above  700°C nearly all mercury
is vaporized to form gaseous Hg°.  As the flue gas cools, changing equilibrium conditions favor
oxidized forms of mercury.  When there are significant levels of HC1, C12, O2 and SO2 in the flue gas,
all of the above oxidized inorganic forms of mercury will tend to occur.  In flue gas from coal and
peat combustion at temperatures  below 200°C, the dominant equilibrium species are HgO and Hg°.
For combustion wastes containing relatively high levels of chlorine, HgCl2 will be the dominant
mercury compound (Hall et al., 1990; Hall 1991, Lindqvist and Schager, 1990).
June 1996                                     F-l                        SAB REVIEW DRAFT
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       Experimental evidence shows that the speciation of mercury is more complicated than
indicated by thermochemical equilibrium calculations. For example SO2, soot, activated carbon. CaO
and iron may promote low temperature reactions that reduce oxidized forms of mercury to Hg° (Hall
et al., 1990; Hall 1991). The presence of trace gases and particulate in flue gas promote mercury
reactions and provide surfaces for physical and chemical adsorption.  Reaction kinetics can also be
expected to play an important role under the changing thermodynamics conditions that exist in high
temperature flue gas streams.  Also, some gases such as HCl may not always be or reaction with
mercury because of mixing limitations.

       While thermochemical chemical calculations provide, information on likely mercury
compounds in flue gas, their presence and relative magnitude have not been confirmed with
experimental  data.  This shortcoming is primarily the result of difficulties in the sampling and analysis
for mercury compounds.  One study of mercury speciation on a pilot combustor and a full scale
municipal waste incinerator found that the flue  gas contained mainly mercury  chlorides, and that Hg°
mercury was  present in insignificant amounts (Metzger and Braun, 1987).  Another experimental study
of mercury sampling methods concluded that total mercury and  Hg° can be adequately measured, that
the results of the different sampling methods tested  for ionic mercury differed significantly  and that
additional efforts must be devoted the development  of mercury speciation methodologies (Lindqvist
and Schager,  1990).

       In a third study, conducted in the U.S., measurements with a sampling  train designed to
provide information on mercury speciation tentatively indicated  the presence of methylmercury in the
exhaust of coal and municipal waste combustors (Bloom, 1993). It was later reported that the
methylmercury reported in that study were the result of artifacts associ ated with the laboratory
analytical procedures.  Based on these studies it is concluded that the results of tests providing
information on total mercury, Hg  and Hg   are probably valid, but results of tests for methylmercury
and other compounds must be considered suspect until sampling and analysis protocols for those
compounds are validated.

       The capture of mercury in flue gas cleaning devices depends on the mercury form [e.g,
speciation and phase (gas, liquid or solid)] and  the control devices employed.  Most metals condense
to form solid particles as the flue gases  are cooled so that  the metals can be collected as particulate
matter (PM).   However, mercury specie such as Hg° and HgCl2 are vapors at  flue gas temperatures
and are difficult to control. Some mercury compounds such as  HgCl2 are soluble in water and can be
controlled by wet scrubbers.   Some specie such as HgCl2 can be adsorbed onto activated carbon and
fly ash carbon for subsequent collection as PM. Reagents can be used to produce mercury compounds
that condense for collection as PM.  Reagents can also be  used  to produce soluble mercury compounds
for scrubber collection.

F.2.1   Municipal Waste Combustors

       F.2.1.1 Description of Source Category

       There are three major types of municipal  waste combustors (MWC's):  mass burn combustors,
refuse-derived fuel (RDF) combustors and modular  combustors.  There are number of sub-categories
of these three major types, plus some other types  of MWCs, such as fluidized bed combustors. These
other types of MWCs constitute a minor fraction  of the total MWC population.

       As of December 1991, there were over 160 MWC plants in the U.S. with aggregate capacities
ranging from greater than 40  tons/day to 3000 tons/day (Kiser, 1993). Most large facilities contain


June 1996                                    F-4                        SAB REVIEW DRAFT
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from two to four mass burn or refuse-derived fuel (RDF) combustors. Approximately 50 percent of
the MWC capacity in the U.S. now employ spray dryers and fabric filters (SD/FFs) or SD and
electrostatic precipitators (SD/ESPs) for emission control.  The remaining large facilities do not have
acid gas control equipment and generally use good combustion practice and electrostatic precipitators
(ESPS) for emissions control.  Few U.S. facilities use wet scrubbers.  A number of facilities are
planning to use activated carbon to control mercury emissions as mandated by siting permits. At least
one MWC using activated carbon is in commercial operation.

       F.2.1.2  Summary of Available Data on Emissions and Controls

       Uncontrolled mercury emissions from MWC's range from less than 200 ug/dscm to more than
1500 ug/dscm depending on the mercury content of wastes being burned. Average uncontrolled flue
gas concentrations in mass bum combustors are estimated to be in the range of 600 to 700 ug/dscm
(U.S. EPA, 1993; White et al., 1992; Nebel et al., 1992; White et al., 1993). Uncontrolled flue gas
concentrations in RDF combustors are somewhat lower since some mercury contained in batteries and
other items is removed in the process that produces RDF.

       For combustion  sources, the degree of mercury control depends on the flue gas  composition,
the amount of fly ash carbon and the flue gas cleaning techniques employed. Welldesigned and
operated mass burn  combustors have little carbon in their fly ash; even when equipped with SD/FFs or
SD/ESPs they exhibit mercury control levels that typically range from 0 to 50 percent (Nebel et al.,
1992; White et  al.,1992). When powdered activated carbon is injected into  the flue gas upstream of
the spray dryer  in mass burn combustors, control levels exceeding 90 percent can be achieved for both
SD/FF and SD/ESP systems (Brown and Felsvang, 1992; White et al., 1992; Nebel et al.,  1992;  White
et al., 1993). However,  SD/ESP  systems require from 2 to 3 times more carbon than SD/FF systems
(Kilgroe et al., 1993).

       The RDF combustors contain a relatively high amounts  of carbon in the fly ash and exhibit
control efficiencies of approximately 80 percent when equipped with  SD/ESPs and above 90 percent
when equipped  with SD/FFs (White et al.,  1992). Injection of powdered activated carbon can also be
used to augment mercury control in  RDF combustion  facilities.

       Little information is  available concerning the performance of flue gas cleaning techniques for
controlling mercury in modular MWCs.  It is expected that the performance of flue gas cleaning
devices on modular units will be similar to the  performance of comparable equipment installed on
conventional mass burn combustors.

       Electrostatic precipitators and wet scrubbers are commonly used to control emissions  from
European MWCs.  Some European plants have installed activated carbon beds downstream of the
primary air pollution control devices to act as polishing filters for control of metals, dioxins and acid
gases.  The use of activated carbon filter beds in combination with conventional control equipment
have demonstrated mercury reductions exceeding 99 percent and mercury outlet concentrations of less
than 1  ug/dscm (Hartenstein, 1993).

       In conducting risk assessments, it is important to estimate the form and speciation of mercury
emitted in the flue gas.  Several studies estimate the speciation of mercury in MWC flue gases.
Metzger and Braun  (1987) estimate that nearly  all mercury emitted from MWCs at flue gas cleaning
temperatures is  in the form of mercury chlorides. Lindqvist and Scahger (1990) estimate that the
speciation of mercury emissions from European waste incinerators consist of 10 percent Hg°, 85
percent Hg2+, and 5 percent mercury associated with PM (Hg (PM)).  Pacyna (1991) estimates that
                                                                         C A
-------
 mercury emissions from European waste incinerators consist of 10 percent He . 85 percent Hg-
 5 percent Hg(PM).
                                          and
        There is currently no validated U. S. EPA method for determining the speciation of mercury in
 stack gas.  Information on the chemical behavior of mercury and the distribution of mercury in EPA's
 multi-metal sampling train (Method 29) can be used to estimate the form and speciation of mercury in
 MWC stack gas.  Mercury found in the probe and filter can be assumed to have been vapor-phase
 mercury adsorbed onto PM or be a solid-phase compound.  Both phases are associated with PM.
 Mercuric chloride is soluble in water and mercury found in the KMnO4/H2S04 impingers is probably
 Hg°. The distribution of multi-metal train samples collected during the activated carbon injection tests
 at the Camden County MWC and Stanislaus County tests is shown in Figure F-l (Nebel et al., 1992;
 White et al.,  1993).
                                         Figure F-l
                  Distribution of Mercury in EPA Method 29 Sampling Train,
               Camden County and Stanislaus County Carbon Injection Projects
e
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c
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n
t
uw.u —
70 0 "
RO 0 "
50.0 -i
40 0 "
30 0
20 0 "
100"
n ru
,

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I
i
/:
1 1
i
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ip9-y» i






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                                                                      1   Elemental Hg

                                                                     m  Solid Phase He
            0    100  200  300  400  500  600  700  800

                 Stack Concentration of Hg, ug/dscm
 June 1996
F-6
SAB REVIEW DRAFT
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        Tests showing mercury stack concentrations of greater tllan 100 iag/dscm represent either low
carbon injection feed rates or no carbon injection.  For these tests, Hg° ranged from 2 to 26 percent of
total mercury.  As carbon injection rates and mercury capture increased, the percentage of Hg  as a
fraction of total mercury increased.  This implies that Hg2+ is more easily captured by activated carbon
than Hg°.  For mercury  stack concentrations less than 50 ug/dscm, the fraction of Hg  ranged from
approximately 14 to 72 percent.  The fraction of Hg(PM) was generally below detection limits for
most tests.  It exceeded  10 percent for only one test and was below 10 percent for all other tests where
it was detected.

        At low levels of control, the stack concentration of mercury is probably 15 to 30 percent
Hg°(v) and the rest is Hg2+(v). At high levels of control, Hg2+(v) is selectively removed,  increasing
the relative concentration of Hg°(v), and the relative concentration of Hg°(v) may be 50 percent or
higher.                                      »                                               ^

        For this analysis the speciation profiles for  these course types were derived from Petersen ei
al., 1995.  The profiles are shown below for each model plant.

        F.2.1.3 Selection of MWC Model  Plant Parameters

        In this analysis,  the range of MWC plant conditions are represented by a large 2250 tons/day
model plant and a small 200 tons/day model plant.  The large model plant consists of 3 conventional
750 tons/day mass burn combustors each equipped  with a SD/FF.  The small MWC model plant was
assumed to consist of two conventional 100 tons/day mass burn combustors each equipped with a
system for dry sorbent injection followed by an electrostatic precipitator (DSI/ESP).

       Large MWC Model Plant

       Average uncontrolled mercury emissions for mass burn combustors are typically in the range
of 600 to 700 ug/dscm.  (U.S. EPA, 1993; White et  al., 1992; Nebel et al., 1992: White'et al.,  1993).
Control efficiencies of units  equipped with SD/FF may range from 0 to 50 percent with typical control
levels near 30 percent.  It was assumed for the baseline scenario that 30 percent control is  achieved by
the flue gas cleaning equipment (SD/FF) and that the uncontrolled emissions average 490 ug/dscm.
Stack emissions were assumed to consist of mercury consisting of 60 percent Hg^"4" 20 percent Hg  and
20 percent particulate mercury.

        Small MWC Model Plant

       The small MWC model plant was  assumed  to consist of two state-of-the-art 100 tons/day mass
burn combustors equipped with DSI/ESPs.  The waste composition and behavior of mercury in the
combustor was assumed to be  similar to that observed in large mass burn combustors. As a result,  for
the small MWC model plant it was assumed that no control  is achieved by  the flue gas cleaning
equipment and that the uncontrolled emissions average 700 ug/dscm.  Stack emissions were assumed
to consist of mercury consisting of 60 percent Hg2+ and 20 percent Hg°, and 20 percent particulate
mercury.
June 1996                                     F-7                        SAB REVIEW DRAFT
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F.2.2  Medical Waste Incinerators

       F.2.2.1  Description of Source Category

       Medical waste incinerators (MWIs) are small incinerators that burn from 1 toil/day (0.9
Mg/day) to 60 tons/day (55 Mg/day) of infectious and noninfectious wastes.  These wastes are
generated  by various facilities including hospitals, clinics, medical and dental offices, veterinary
clinics, nursing homes, medical  laboratories, medical and veterinary schools and research laboratories
and funeral homes.

       Approximately 3,700 MWIs currently operate throughout the country; geographic distribution
is relatively  even.  Of these 3,700 units, about 3,000 are hospital incinerators, about 150 are
commercial  units, and the remaining units are distributed among veterinary facilities ,\nursing homes,
laboratories, and other miscellaneous facilities (U.S. EPA, 1994).

       The  primary function of MWIs is to render the waste biologically innocuous and to reduce the
volume and  mass of solids that  must be landfilled (by combusting the organic material contained
within the waste).  Currently, three major types of MWI operate in the United States: continuous,
intermittent, and batch.  All three have two chambers that operate on a similar principle.  Waste is fed
to a primary chamber, where it  is heated and volatilized. The volatiles and combustion gases are then
sent to a secondary chamber, where combustion of the volatiles is completed by adding air and heat.
All mercury in the waste is assumed to be volatilized during the combustion process and emitted with
the combustion stack gases.

       F.2.2.2 Summary of Available Data on Emissions arid Controls

       A number of air pollution control systems are used to  control PM and acid gas emissions from
MWI stacks. Most of these systems fall into the general classes of either wet or dry systems.  Wet
systems typically comprise a wet scrubber, designed for  PM control (venturi scrubber or rotary
atomizing scrubber), in series with a packed-bed  scrubber for acid gas removal  and a high efficiency
mist elimination system.  Most  dry systems use a fabric  filter for PM removal,  but ESP's have been
used on some of the larger MWIs.  All of these systems have  limited success, in controlling  mercury
emissions.  Recent EPA studies indicate that sorbent injection/fabric filtration systems can achieve
improved  mercury control by adding activated carbon to the sorbent material (U.S. EPA, 1994).

       Mercury speciation data for  MWI facilities were derived from Petersen et al., 1995.  Stack
emissions  were assumed  to  consist of vapor-phase mercury consisting of 60 percent Hg + and  20
percent Hg°, and 20 percent Hg(PM).

       F.2.2.3 Selection of MWI Model  Plant Parameters

       To represent the MWI source category, two model plants were devised: one continuous
facility and one facility that is operated intermittently.

       Continuous MWI Facility

       The continuous MWI model plant was assumed to consist of one starved  air modular
incinerator with a design capacity of 1500 Ib/hr.  The unit was assumed to operate at an average
capacity of 1000 Ib/hr for 7,889 hours/yr.  The incinerator was assumed to be equipped with a
DSL/ESP with no control of mercury assumed to  occur across  this system (Lerner, 1992).  Mercury in


June 1996                                     F-8                        SAB REVIEW DRAFT
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 the Waste feed was assumed to result in flue gas emissions of 3000 ug/dscm (U.S. EPA. 1991: U.S.
 EPA,  1992b).  Stack emissions were assumed to consist of vapor-phase mercury consisting of 60
 percent Hg2+ and 20 percent Hg°, and 20 percent Hg(PM).

        Intermittent MWI

        The intermittent MWI model plant was  assumed to consist of one state-of-the-art starved air
 modular incinerator with a design capacity of 200 Ib/hr. The unit was assumed to operate at an
 average capacity of 133 Ib/hr, 5 days per week.  An annual capacity factor of 20 percent (1753
 hr/year) was assumed.  No flue gas  cleaning equipment was  assumed.  Mercury in the waste feed was
 assumed to result in uncontrolled flue gas emissions of 3000 ug/dscm.  Stack emissions were assumed
 to consist of vapor-phase mercury consisting of 60 percent Hg2+ and 20 percent Hg  , and 20 percent
 Hg(PM).

 F.2.3   Utility Boilers

        F.2.3.1  Description of Source Category

        Utility, boilers are large boilers used by  public and private utilities to generate electricity.
 There are approximately 1800 utility boilers in the U.S. which  burn coal, oil and natural gas.  In 1990,
.utility boilers consumed fossil fuel at an annual level of 21 x 1015 British thermal units (Btu).  About
 80 percent of this total energy consumption resulted from coal  combustion,  6 percent from oil and
 petroleum fuels and 14 percent from natural gas consumption.  Ninety-five percent of the coal burned
 is bituminous and subbituminous;  lignite accounts for 4 percent.  Mercury emission estimates were not
 calculated for natural gas combustion because reliable test data necessary to calculate an emission
 factor do  not exist.  Given these factors, the indirect exposure analysis focused only  on coal-fired units
 burning bituminous coal and residual oil-fired units.

        F.2.3.2  Summary of Available Data on  Emissions and  Controls

        About 80 percent of coal-fired utility boilers use ESPs  for PM control. Scrubbers (or flue gas
 desulfurization units (FGDs)) are the most commonly used device for sulfur dioxide (S07) control.
 Spray dryer absorption (SDA), or  dry  scrubbing, followed by a PM control  device may also be used.
 Mechanical collectors are used infrequently.  Coal washing, which separates coal and impurities from
 crushed and screened coal by differences in specific gravity,  is done routinely to meet customer
 specifications for heating value, ash and sulfur content.  Advanced coal cleaning techniques may
 reduce the concentration of mercury contained in the mineral and organic phases  of the coal, but the
 reliability and feasibility of these emerging techniques are unknown at this time.

        Carbon filter beds are being used successfully in Europe for control of heavy metals, organic
 compounds and acid gases (Hartenstein, 1993).  Five full-scale applications  of carbon beds are
 currently in use for utilities, with future applications planned for hazardous waste incinerators and
 MWCs. Activated carbon injection  has been used successfully in the U.S. for mercury removal from
 the stack gas of MWCs and MWIs.  This technology has been  tested on a pilot-scale basis in the U.S..
 Table  F-2 summarizes the control  efficiencies for various control technologies for utility boilers, based
 on pilot-scale test data.
                                                                           C A G
-------
                                          Table F-2
                                 Mercury Removal Efficiencies
Control Technique
Carbon bed
Fabric Filter + AC
(Low temp. + Low C injection rate)
Fabric Filter + AC
(High temp. + Low C injection rate)
Fabric Filter + AC
(Low temp. + High C injection rate)
Fabric Filter + AC
High temp. + High C injection rate)
SD A/ESP + AC
SDA/FF + AC
Fabric filter
Scrubber (FGd)
Dry scubber (SDa)
ESP
Mechanical collector
Coal washing
Advanced coal washing
Range of
Removal
Efficiency
(percent)
Unknown
76-99
14-47
95-99
69-91
75-91
50- >99
0- 51
18- 84
23 - 83
0-22
0
-200 - 64
Unknown
Median Removal
Efficiency
(percent)
99
98
29
98
73
86
NA
29
85
67
10
0
21 (average
removal)
-
Reference
Hartenstein (1993)
Volume VII, App. A.
Volume VE[. App. A
Volume VII, App. A
Volume VII, App. A
Volume VII, App. A
Volume VII, App. A
Volume II
Volume II
Volume II
Volume n
Volume II
Volume II
Volume VII
       Mercury emissions of mercury from utility boilers can vary depending on the mercury content
of the fuel and the control technique used.  Based on emissions  test data (as described in the mercury
emissions inventory, documented in a separate report), a mercury emission rate of 10 ug/dscm was
chosen to represent emissions from a coal-fired utility with PM  control.  Two ug/dscm was chosen as
the emission rate for mercury emissions from an uncontrolled residual oil-fired utility. This emission
rate is a worse-case estimate for an oil-fired plant. This high estimate was selected for the modeling
because the impacts from oil-fired boiler were expected to be very small even using the worst-case.

       As discussed above, the chemical specie of mercury being emitted affects both the removal
efficiency of the control device and the deposition of mercury from the atmosphere. Based on
Petersen  et al., 1995, it was assumed for the local impact analysis that the mercury emitted from the
utility model plants (both coal- and oil-fired) consisted of 50 percent  HgO and  30 percent Hg2+, and
20 percent Hg(PM).
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        F.2.3.3 Selection of Model Plant Parameters

        The source of data for selecting the model plant sizes was the Utility Data Institute
(UDI)/Edison Electric Institute (EEI) Power Statistics Database (1991 edition).  This database provided
information on fuel use, boiler sizes and stack parameters.  The database had information on 1708
units, of which 795 were bituminous coal-fired and about 225 were  oil-fired.  The remainder were
primarily fired with natural gas although there were some boilers burning lignite  and  anthracite coals..
Given the predominance of bituminous coal-fired units, those were the units chosen for the indirect
exposure analysis as well  as one residual oil-fired unit.

        The 795 coal-fired units were divided into 3 size classifications (by megawatt (MW))
according to 33rd percentiles.  The size classes had approximately the same number of units in each.
The "large" group which consisted of units greater than or equal to 575 MW had 262 units.  The
"medium" group which consisted of units between 199 MW and 575 MW had  256 units.  The "small"
group consisting of units greater than  25 MW but less than 200 MW had 277 units.  The model plant
parameters were chosen by evaluating each group separately and taking the average value for each
parameter from each group (e.g., for representative MW, stack height and stack diameter).

        Based on this analysis, three boiler sizes were chosen as the basis for the coal-fired model
plants: 975 MW, 375 MW, and  100 MW. The same type of analysis for the oil-fired units led to the
selection of a 285  MW residual-oil fired unit as a representative model plant size for  the oilfired units.

        Coal-Fired Utility Model Plants

        All of the coal-fired utility model plants had a capacity factor of 65 percent, and were
equipped with a cold-side ESP.  The inlet mercury level (i.e., the amount of mercury  entering the
emission control device) was assumed to be  10 u/dscm (4.4 gr/million dscf). For emissions, it was
assumed that no mercury control across the ESP was achieved and that the mercury emissions were 10
ug/dscm (4.4 gr/million dscf).

        Oil-Fired Utility Model Plant

        The oil-fired utility model plant was a 285 MW  boiler firing No. 6 fuel oil containing 1
percent sulfur and 300 ppm chlorine.  It was  assumed to have a capacity factor of 65  percent, and was
not equipped with any partictilate matter control device.  The inlet mercury level  associated with this
model plant was assumed  to be 2 u/dscm (1 gr/million dscf)-  It was assumed that no mercury control
was achieved and that the mercury emissions were 2 ug/dscm (1 gr/million dscf),

F.2.4   Chlor-Alkali Production

        F.2.4.1 Description of Source Category

        Chlor-alkali production using the mercury cell process, (which is the only chlor-alkali process
that uses mercury),  accounted for 17 percent of all U.S. chlorine production in  1988  (U.S.  EPA, 1993).
The three primary sources of mercury air emissions from chlor-alkali plants are the byproduct
hydrogen stream, the end box ventilation air and the cell room ventilation air.  The byproduct
hydrogen stream from the  decomposer is saturated with mercury vapor and may also contain fine
droplets of liquid mercury. The  quantity of mercury emitted in the end box ventilation air depends on
the degree of mercury saturation and the volumetric flow rate of the air. The amount of mercury in
the cell room ventilation air is variable and comes from many sources, including  end  box sampling,


June 1996                                     F-ll                        SAB REVIEW DRAFT
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removal of mercury butter from end boxes, maintenance operations, mercury spills, equipment leaks
and cell failure (U.S. EPA. 1984).

       F.2.4.2 Summary of Available Data on Emissions and Control

       The most recent source of mercury emission data is from Clean Air Act section 114 survey
questionnaires of the chlor-alkali industry (as referenced in section 4.2.1 of Volume II Qf this report).
The industry survey data were the basis for the nationwide estimate for mercury emissions of 5.9 Mg
(6.5 tons).  A previous report by the U.S. EPA was used to develop plant- specific parameters (U.S.
EPA,  1973).

       The control techniques that are typically used to reduce the level of mercury in the hydrogen
streams and in the ventilation stream from the end boxes are the following: gas stream cooling, mist
eliminators, scrubbers and adsorption on activated carbon or molecular sieves.  Mercury emissions
from- the cell room air circulation are not subject to specific, emission  control measures.
Concentrations are maintained at acceptable worker exposure levels through good housekeeping
practices and equipment maintenance procedures (U.S. EPA, 1984).

       Speciated emissions data for chlor-alkali plants  are extremely  limited.  For this analysis was
assumed that the emitted mercury was in the vapor phase and consisted of 70 percent  Hg° and 30
percent Hg2+ (Peterson et al., 1995).

       F.2.4.3 Selection of Model Plant Parameters

       For the indirect exposure analysis, one chlor-alkali  model plant, which produces 273 Mg  (300
tons) of chlorine per day, was devised.  This model plant represented  the mid-range size of chlor-alkali
plants in operation (U.S. EPA, 1984).  The model plant had individual flow rates from the hydrogen
and end-box streams of 4,080 dscm/hr (144,000 dscf/hr) each at 21 percent 02 (combined to equal
8,160 dscm/hr [288,000 dscf/hr]) (U.S. EPA, 1973).  A 90 percent capacity factor (operation for 7889
hr/yr) was assumed.

       The typical emissions control scenario for both  the hydrogen and end-box streams was
assumed to consist of a heat exchanger to coot the effluent gas, followed by a  knockout drum to
separate the condensed mercury from the hydrogen and end-box streams. A mercury level of 1,000
g/day (2.2 Ib/day) was assumed for the purpose of indirect  exposure analysis to be consistent with the
federally-mandated mercury standard for the hydrogen and end-box streams at  all  chlor-alkali plants
(U.S. EPA, 1984).

F.2.5   Primary Copper Smelters

       F.2.5.1  Description of Source Category

       Copper is recovered from a sulfide ore principally by pyrometallurgical smelting methods.
The ore contains significant quantities of arsenic, cadmium, lead, antimony and mercury. A
conventional copper smelting process sequentially involves roasting ore concentrates to produce
calcine, smelting of roasted or unroasted ore concentrates to produce matte, converting matte to
produce blister copper and fire refining  the blister copper in an anode furnace.  After fire refining, the
99.5 percent pure copper is cast into "anodes" and sent  to an electrolytic refinery for further impurity
removal (Buonicore and Davis, 1992).
June 1996                                    F-12                       SAB REVIEW DRAFT
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       F.2.5.2 Summary of Available Data "on Emissions and Control

       Mercury emissions  data for primary copper smelting facilities are very limited.  Only one
emission test report for the Copper Range Company located in White Pine, Michigan, containing
metals analysis results was  available (TRC Environmental,  1992). This facility operates a
reverberatory furnace with  an ESP to control PM.  The exhaust stream from the convenor (which is
uncontrolled) is mixed with the exhaust from the ESP outlet and is routed through the main stack and
discharged into the atmosphere.

       Because the feed mix varies from facility to facility and because the Copper Range Company
is the only facility in the United States that operates a reverberatory, the emissions data from the
Copper Range Company may not be representative of industry practice.  Because data were available
for this plant, the operating*parameters for the White Pine,  Michigan facility were selected for the
indirect exposure analysis.

       Speciated emissions data for primary copper smelters are extremely limited.  For this analysis
it was assumed that the  emitted mercury consisted  of 85 percent Hg°, 10  percent Hg2+ and 5 percent
Hg(PM)  (Peterson et al., 1995).

       F.2.5.3 Selection of Model Plant Parameters

       Copper smelters use high efficiency air pollution control options to control PM and S07
emissions from roasters, smelting furnaces, and converters. Electrostatic precipitators are the most
common PM control device at copper smelters. Control of S02 emissions is achieved by absorption to
sulfuric acid in the sulfuric acid plants, which  are common to all copper smelters. For the emissions
scenario, it was assumed that no mercury emissions control occurred with these emission control
devices.  Using the test  data for the Copper Range facility described above, a mercury emission factor
of 0.068  kg/hour (0.15 Ib/hour) was calculated (U.S. EPA,  1993). A 90 percent capacity factor  was
assumed.  This corresponds to a 236 Mg/day (260  tons/day) copper  production capacity.

       The mercury emissions for both of the primary lead smelter off-gas streams were  assumed to
be uncontrolled resulting in an outlet mercury  concentration of 1,000 ug/scm (436 gr/million  scf at 19
percent O2 for each stream.

F.2.6  Primary Lead Smelters

       F.2.6.1  Description of Source Category

       Lead is recovered from a sulfide ore, primarily galena (lead sulfide-PbS), which also contains
small amounts of copper, iron, zinc and other trace elements such as mercury.  Recovery of lead from
the lead ore in primary lead smelters consists of three main steps: sintering, reduction, and refining.
Sintering is carried out in a sintering machine, which is a continuous steel pallet conveyor belt.  Each
pallet consists of perforated grates, beneath which are wind boxes connected to fans to provide a draft
through the moving sinter charge.  The sintering reactions take place at about 1000°C (1832°F)  during
which lead sulfide is converted to lead oxide.  Since mercury and its compounds vaporize  below this
temperature, most of the mercury present in the ore can be expected to be emitted during  sintering
either as  Hg° or as  HgO.

       Reduction  of the sintered lead is carried out in a blast furnace at a temperature of 1600°C
(2920°F). In the blast furnace, the sinter is reduced to lead. The heat for the reaction is supplied by
                                                                               RFVTFW r>R AFT
-------
the combustion of coke.  Slag, consisting of impurities, flows from the furnace and is either land
deposited or is further processed to recover zinc.  The impurities include arsenic, antimony, copper and
other metal sulfides, iron and silicates.  Lead bullion, which is the primary product, undergoes a
preliminary treatment to remove impurities, such  as copper, sulfur, arsenic, antimony and nickel.
Residual mercury can be expected to be emitted during the reduction step.  Further refining of the lead
bullion is carried out in cast iron kettles.  Refined lead, which is 99.99 to 99.999 percent pure, is cast
into pigs for shipment (U.S. EPA, 1988a).

       F.2.6.2  Summary of Available Data on Emissions and Controls

       Primary  lead smelters use high-efficiency emission control systems to reduce the levels of PM
and S02 from the blast  furnace and sintering machines. Centrifugal collectors  (cyclones) are used in
conjunction with FFs or ESPs for PM control.  Control of S02 emissions is achieved  by absorption to
form sulfuric acid in the suliuric acid plants, which are commonly part of lead smelting plants. The
blast furnace and the sintering machine operate at very high temperatures (in excess of 1000°C      ^
[1832°F]), and as a result, mercury would be emitted from these sources in vapor form. Therefore,
particulate control devices would have little effect on mercury emissions from  the sintering machine
and blast furnace.
       Speciated emissions data for primary lead smelters are extremely limited.  For this analysis it
was assumed that the emitted mercury consisted of 85 percent Hg  , 10 percent Hg~+ and 5 percent
Hg(PM) (Peterson et al., 1995).

       F.2.6.3  Selection of Model Plant Parameters

       The lead smelter model plant consisted of a sintering machine, which feeds  to a blast furnace
and a dross furnace.  The sintering machine is an updraft machine, which does not employ
recirculation of the weak gas.  Consequently, there  are two off-gas streams, one strong SO7 stream and
a weak SO2 stream.  The strong stream is fed to  a single-stage acid plant at a flow rate of 31,600
scm/hr (1,120,000 scf/hr);1  the weak stream, with a flow rate of 138,000 scm/hr (4,860.000 scf/hr)2  is
sent to a FF for PM control and then emitted to the atmosphere. The off-gases from the dross furnace
are fed to  a fabric filter, at  a flow rate of 72,200  scm/hr  (2.550,000 scf/hr)3 for PM  control and then
emitted to the atmosphere.  The lead production capacity for this facility was assumed to be 90,900
Mg/yr (100,000 tons/yr) from concentrate containing 55 percent  lead and 16 percent sulfur.  The plant
is assumed to have a 90 percent capacity factor.  This corresponds to  a daily capacity of 276 Mg/yr
(304 tpd) (U.S.  EPA, 1974).

       The mercury emissions for both of the primary lead smelter off-gas streams were assumed to
be uncontrolled resulting in an outlet mercury concentration of 1,000 ug/scm (436 gr/million set) at 19
percent O2 for each stream.
June 1996                                    F-14                       SAB REVIEW DRAFT
-------
F.3    References

Bloom, Nicolas S., Eric M. Prestbo, Vesna L. Miklavcic, "Flue Gas Mercury Emissions and Speciation
from Fossil Fuel Combustion," Second International Conference on Managing Hazardous  Air
Pollutants, Washington, D.C. July 1993.

Brna, T. G. Toxic Metal Emissions from MWCs and their Control, In Proceedings: 1991  International
Conference on Municipal Waste Combustion, Volume 3, EPA-600/R-92-209c (NTTS  PB93-124196).
pp 23-39, November  1992.

Brna, T.G., J.D. Kilgroe, and C.A. Miller, Reducing Mercury Emission from Municipal Waste
Combustion with Carbon Injection into Flue Gas, ECO World '92 Conference, Washington, DC, June
1992.

Brown, B. and K.S. Felsvang, Control of Mercury and Dioxin Emissions from United States and
European Municipal Solid Waste Incinerators by Spray Dryer Absorption Systems, In Proceedings,
1991 International Conference on Municipal Waste Combustion, Volume 3, EPA600/R-92-209c (NTIS
PB93-124196), pp 287-317, November 1992.

Buonicore,  A.J., and W.T. Davis, , 1992, eds. Air Pollution Engineering Manual.  Van Nostrand
Reinhold, New York. 1992.

Edlund. H., 1993, Boliden Contech, telefax to K.  Nebel, Radian Corporation. August 17, 1993.

Hall, B. Reactions of Mercury with Flue Gas Components, Statens Energiverk, National Energy
Administration, Sweden, (STEV-FBT-91-18), 1991.

Hall, B., 0. Lindqvist, and D. Ljungstrom, Mercury Chemistry in Simulated Flue Gases Related to
Waste Incineration Conditions, Environ. Sci. and Tech., 24 (1990), 108-111.

Hartenstein, H.U., 1993. Activated Carbon Filters for Flue Gas Polishing of MWI's.   Presented at the
International Conference on Municipal Waste Combustion. Williamsburg, Virginia, March 1993.

Hartenstein, H. U. Activated Carbon Filters for Flue Gas Polishing of MWI'S, In Proceedings 1993
Conference on Municipal Waste Combustion, Air & Waste Management Association  (VIP32),
Pittsburgh, PA,  1993, pp 87-105.

Kilgroe, J. D. et al., Camden County MWC Carbon Injection Test Results,  Presented at the 1993
International Conference on Municipal Waste Combustion, Williamsburg, VA, March 30 to April 2,
1993.

Kiser, J. V. L. The IWSA Municipal Waste Combustion Directory: 1993 Update of U. S.  Plants,
Integrated Waste Services Association, Washington, DC, 1993.

Lerner, B.J., Beco Engineering Company, 1992.  Mercury Emissions Control in Medical Waste
Incineration.  Presented at the 86th Annual Meeting, Air and Waste Management Association, Denver,
Colorado.  June 1992.

Lindqvist, O. and P. Schager, Continuous Measurements of Mercury in Flue Gases from Waste
Incinerators and Combustion Plants, VDI Berichte, (NR.  838), 1990, pp. 401-421.


     1996                                   F-15                       SAB  REVIEW  DRAFT
-------
Metzger, M. and H. Braun. In-situ Mercury Speciation in Flue Gas by Liquid and Solid Sorption
Systems, Chemosphere, 16 (1987), 821-832.

Midwest Research Institute, 1992.  Medical Waste Incinerators -Background Information for Proposed
Standards and Guidelines:  Control Technology Performance Report for New and
Existing Facilities.  Draft Report.  July 1992.

Nebel, K.L. et al., Emission Test Report: OMSS Field Test on Carbon Injection for Mercury Control,
EPA-600/R-92-192 (NTIS PB93-10551S), Air and Energy Engineering Research Laboratory, Research
Triangle Park, NC, September 1992.

Pacyna, J. M. Anthropogenic Mercury Emission in Europe, Water, Air and Soil Pollution, 56 (1991),
51-61.

Peterson, G., A. Iverfeldt, J. Munthe, 1995. Atmospheric Mercury Species Over Central and Northern
Europe, Model Calculations and Comparison with Observations from the Nordic Air and Precipitation
Network for 1987 and 1988.  GKSS Research Centre, Institue of Physics, Max-Planck-Str. 1, D-21502,
Geesthacht. Germany.

TRC Environmental Corporation,  1992. Emission Characterization Program.  Prepared for Copper
Range Company, White Pine, Michigan. October 15, 1992.

U.S. Environmental Protection Agency, 1994. Medical Waste Incinerators - Background Information
for Proposed Standards and Guidelines: Industry Profile Report for New and Existing Facilities,  EPA-
453/R-94-042a.  Office of Air Quality Planning and Standards, Research Triangle Park, NC.  July
1994.

U.S. Environmental Protection Agency, 1993. Locating and Estimating Air Emissions form Sources of
Mercury  and Mercury  Compounds.  EPA 454/R-93-023.  Office of Air Quality Planning and
Standards, Research Triangle Park, NC. September 1993.

U.S. Environmental Protection Agency, 1992a. Medical Waste Incinerators - Background Paper for
New and Existing Facilities, Draft. U.S., Environmental Protection Agency, Research Triangle Park,
NC.  June 1992.

U.S. Environmental Protection Agency, 1992b. Medical Waste Incineration Emission Test Report —
Borgess Medical Center, Kalamazoo, Michigan.  September 1992.

U.S. Environmental Protection Agency, 1991. Medical Waste Incineration Emission Test Report --
Morristown Memorial  Hospital, Morristown, New Jersey.  EMB Report 91-MWI-8. December 1991.

U.S. Environmental Protection Agency, 1989. Municipal Waste Combustors, Background Information
for Proposed Standards, Post-Combustion Technology Performance, Volume 3, EPA450/-3-89-27c
(NTIS PB90-154865),  Research Triangle Park, NC, August 1989.

U.S. Environmental Protection Agency, 1988a. Compilation of Air Pollution Emission Factors,  A.P-
42, Fourth Edition, Supplement B. Office of Air Quality Planning and Standards. Research Triangle
Park, NC.
June 1996                                   F-16                       SAB REVIEW DRAFT
-------
U.S. Environmental Protection Agency, 1984.  Review of National Emissions Standards for Mercury.
EPA 450/-3-84-014. Office of Air Quality Planning and Standards, Research Triangle Park, NC.
December 1984.

U.S. Environmental Protection Agency. 1974.  Background Information for New Source Performance
Standards:  Primary Copper, Zinc and Lead Smelters.  EPA-450/2-74-002a.  Office of Air Quality-
Planning and Standards, Research Triangle Park, NC.  October 1974.

U.S. Environmental Protection Agency, 1973.  Control Techniques for Mercury Emissions from
Extraction and Chlor-Alkali Plants.  Office of Air Quality Planning and Standards, Research Triangle
Park, NC. February 1973.

White, D. M. et al., Field Test of Carbon Injection for Mercury Control, Camden County Municipal
Waste Combustor, EPA-600/R-93-181 (NTTS PB94-101540), September 1993.

White, D.M., K.L. Nebel. and M.G. Johnston, Municipal Waste Combustors:  A Survey of Mercury
Emissions and Applicable Control Technologies ,  In Proceedings, 1991  International Conference on
Municipal Waste Combustion, Volume 3, EPA-600/R-92-209c (NTIS  PB93124196), pp 247-257,
November 1992.

White, D.M., et al., Parametric Evaluation of Activated Carbon Injection for Control of Mercury
Emissions from a Municipal Waste Combustor, Paper  No. 92-40.06, 1992 Annual Meeting, Air &
Waste Management Association, Kansas City, MO, June 1992.
                                                                       
-------
-------
                       APPENDIX G




SUMMARY OF PREDICTED CONCENTRATIONS FOR ALL FACILITIES
-------
        Tables G-l and G-2 provide summary results for precited concentrations in various media for
all facilities.
June 1996
G-l
SAB REVIEW DRAFT
-------
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                    APPENDIX H

ESTIMATION OF HUMAN METHYLMERCURY EXPOSURE TO THE
       GENERAL UNITED STATES POPULATION AND
             IDENTIFIED SUBPOPULATIONS
          THROUGH THE CONSUMPTION OF FISH
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                             TABLE OF CONTENTS

                                                                                 Page


 1      FISH CONSUMPTION AMONG THE GENERAL UNITED STATES POPULATION    H-l
       1.1    Patterns Of Fish Consumption  	H-l
             1.1.1   1973 and  1974 National Purchase Diary Data 	H-4
             1.1.2   Nationwide Food Consumption Survey of 1977-78  	   H-7
             1.1.3   CSFII/1989-1991 	H-8
       1.2    Frequency of Consumption of Fish Based on Surveys of Individuals	   H-9
       1.3    Consumption Rates Among Subpopulations with Potentially Higher Rates of
             Fish Consumption than the General Population	H-12
             1.3.1   Introduction	  H-12
             1.3.2   U.S. Angling Population Size Estimate and Behaviors	 .  H-16
             1.3.3   U.S. Angler Surveys	  H-17
             1.3.4   Indigenous Populations of the United States 	  H-21
       1.4    Summary of Alaska and Hawaiian Fish Consumption Dat	H-27
       1.5    Mercury Concentrations In Fish 	H-30
             1.5.1   Mercury Concentrations In Marine Fish  	  H-30
             1.5.2   Mercury Concentrations In Fresh-water Fish	H-40

2.     CALCULATION OF MERCURY CONCENTRATIONS IN FISH DISHES	  H-43
       2.1    Additional Application Of Mean Mercury Concentrations In Fresh-water Fish  . .  H-44

3.     INTAKE OF METHYLMERCURY FROM FISH/FISH DISHES  	H-44
       3.1    Intakes "per User" and "per Capita"	H-44
       3.2    Fish Intake by Age and/or Gender Grouping of Subpopulations 	H-44
       3.3    Types of Fish Consumed	H-45
       3.4    Methylmercury Consumption  	H-45

4.     CONCLUSIONS ON METHYLMERCURY INTAKE FROM FISH  	H-64

5.     ACKNOWLEDGMENTS   	  H-67

6.     REFERENCES	  .	......  H-67

7.     ADDENDUM	 .  H.A
June 1996                                 H-i                      SAB REVIEW DRAFT
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                                 LIST  OF TABLES
H-l    Average Serving Size igmsi tor Seafood from USD A Handbook. # 1 1  Used to
       Calculate Fish Intake by FDA (1978) .............       ..   .    .    H-5
H-2    Fish Species and Number ot Persons Using the Species of Fish. (Adapted from Rupp
       et al..  1980) .......... . . .......... ... ...... .  ....................... H-6
H-3    Fish Consumption from the NPD 1973/1974 Survey  (modified from Rupp et al.. 1980) .  . H-6
H-4    Distribution of Fish Consumption for Females by Age* Consumption Category
       (grams/day) (from SRI (1980)) ............................... " ........... H-7
H-5    Number of Individuals in the 1989-1991 CSFII With Three-Days of Dietary Records  ... H-8
H-6    Respondents Reporting Consumption of AJ1 Fish and Shellfish in the 1989-1991 CSFTI
       Survey Based on 3-Days Diet Records  ....... ..... ........ . ............... H-8
H-7    Consumption of Fish and Shellfish (gms/day), and Self-Reported Body Weight (Kg) in"
       Respondents of the  1989-1991 CSFII Survey. "Per Capita" Data for AJ1 Survey
       Respondents ...... .............. . . ............ . . ..................  H- 1 1
H-8    Consumption of Fish and Shellfish (gms/day), and Self-Reported Body Weight (Kg) in
       Respondents of the  1989-1991 CSFII Survey .....  ..................... ". ....  H-l 1
H-9    Compilation of the 11 Angler Consumption Studies  ..........................  H-14
H-1'0   Fish Consumption by Native  United States Populations ........................  H-l 5
H- 1 1   The Median Recreationally Caught Fish Consumption Rate Estimates by Ethnic Group
       (Puffer,  1981)  ......... . . .........................................  H-17
H-l 2   The Freshwater Fish Consumption Estimates of Turcotte (1983) .................  H-l 8
H- 1 3   The Daily Intake of Sponfish and Total Fish for the  Fish-consuming Portion of the
       Population Studied by Fiore et al., (1989) .................................  H-18
H-14   Fish Consumption Rate Data for Groups Identified in Hovinga et al., 1992, as Eaters
       and Controls  [[[  H-20
H-15   Fish Consumption Rates for Maine Anglers ................................  H-20
H-l 6   Fish Consumption Rates of Florida Anglers Who Receive  Foodstamps  ....... . .....  H-21
H-17   Fish Consumption by Columbia River Tribes, Columbia River Inter-Tribal
       Commission, 1994  ....... .......  . ........  . ......... .... ..... . ......  H-24
H-18   Fish Consumption by Columbia River Tribes Columbia River Inter-Tribal Commission,
       1994 Daily Fish Consumption Rates Among Adults  ...........................  H-24
H-19   Fish Consumption (g/day) by the Tulalip and Squaxin Island Tribes (Toy et al. 1995)  . .  H-25
H-20   Local  Fish Meals Consumed  By Time Period for the Mohawk
       and Comparison Nursing Mothers (Source: Fitzgerald et al., 1995) ................  H-26
H-21   Species Composition of Hawaii's Retail
       Seafood Trade, 1981 (purchases) as described by Higuchi  and Pooley,  1985 .........  H-29
H-22   Mercury Concentrations in Marine  Finfish .................... . ............  H-31
H-23   Mercury Concentrations in Marine  Shellfish .........  .......................  H-33
H-24   Mercury Concentrations in Marine  Molluscan Cephalopods .................. ....  H-33
H-25   Summary of Mercury Concentrations in Fish Species
       Micrograms Mercury per Gram Fresh Weight (ug Hg/g) ......................  H-34
H-26   Fresh-water Fish Mercury Concentrations from Lowe et al., (1985) ...............  H-41
H-27   U.S. EPA (1992) and Bahnick et al. (1994) .................................  H-43
H-28   Consumption of Freshwater Fish (gms/day) and Methylmercury per Kg body weight
       from Fish among Respondents of the 1989-1991 CSFII Survey. Data for "Users"
       Only.  Fish methylmercury concentrations based on Bahnick et al., (1994)a ..........  H-46
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                           LIST  OF TABLES  (continued)

 H-29  Consumption of .-Ml Fish A: Shellfish (gms/da\) and Methvlmereury per Kg body
       weight from Fish among Respondents ot the 19X9-1991 CSFII Survey.  Data tor
       "Users" Only.  Bahmck et al estimates  for tresh-water fish Meihylmercury
       Concentrations  ...             .                                       H~T
 H-30  Consumption ot Fish &. Shellfish t gms/day) and \teth\lmercury per Kg body weight
       from Fish among Respondents of the 1989-1991 CSFII Survey.  Data for "Users"
       Only. Lowe et al. estimates for fresh-water fish Methylmercury Concentrations	H-48
 H-31   Respondents Reporting Consumption of Marine Finfish (Excluding tuna, swordfish.
       barracuda, and shark) in the 1989-1991 CSFII Survey Based on 3-Days Diet Records .  . H-49
 H-32  Consumption (gms/day) of Marine Finfish (Excluding Tuna, Shark, Barracuda, and
       Swordfish) and Self-Reported Body  Weight (Kg) among Respondents of the 1989-
       1991 CSFII Survey. Data for "Users" Only	 H-49
 H-33  Respondents Reporting Consumption of Tuna in the 1989-1991 CSFII-Survey Based
       on 3-Days Diet Records	 H-49
 H-34  Consumption (gms/day) of Tuna Fish and Self-Reported Body Weight (Kg) among
       Respondents in the 1989-1991 CSFII Survey.  Data for "Users" Only  	H-50
 H-35   Respondents Reporting Consumption of Marine Shellfish in the 1989-1991 CSFII
       Survey Based on 3-Days Diet Records   	H-50
 H-36   Consumption (gms/day) of Shellfish and Self-Reported Body Weights (Kg) of
       Respondents in the 1989-1991 CSFII Survey.  Data for "Users" Only	H-50
 H-37   Respondents Reporting Consumption of Shark, Barracuda, and/or Swordfish in the
       1989-1991 CSFII Survey Based on 3-Days Dietary Records 	 H-51
 H-38   Consumption (gms/day) of Swordfish, Barracuda, and Shark and Self-Reported  Body
       Weight (Kg) among Respondents in the 1989-1991 CSFII Survey. Data for "Users"
       Only	H-51
 H-39   Respondents Reporting Consumption of Fresh-water Fish in the 1989-1991 CSFII
       Survey Based on 3-Days Dietary Records	H-51
 H-40   Consumption (gms/day) of Fresh-water Fish and Self-Reported Body Weight (Kg)
       among Respondents of the  1989-1991 CSFII Survey.  Data for "Users" Only  	H-52
 H-41   Consumption of Freshwater Fish (gms/day) and Methylmercury per Kg body weight
       from Fish among Respondents of the 1989-1991 CSFII Survey.  Data for "Users"
       Only. Fish methylmercury concentrations based on Lowe et al., (1994)	H-53
June 1996                                 H-iii                      SAB REVIEW DRAFT
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                             LIST OF FIGURES

                                                                           Paeo

H-l        	       .       	   H-13
H-2                     . .	        H-54
H-3	,	  ... H-55
H-4	 .  ... H-56
H-5	H-57
H-6     	H-58
H-7	 H-59
H-8     	H-60
H-9     	H-61
H-10    	H-62
H-ll    	:	'	H-63
June 1996                              H-iv                    SAB REVIEW DRAFT
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 1.      FISH CONSUMPTION AMONG THE GENERAL UNITED STATES  POPULATION

        Consumption of fish is highly variable across the United States  population  unlike consumption
 or other dietary components, such as bread or starch dishes, that are almost ubiquitously consumed.
 Both  marine and freshwater fish bioaccumulate methylmercury in their muscle tissues.  Ingestion of
 methv I mercury -contaminated fish tissue by humans  results in exposure to this pollutant.  This appendix
 presents an estimate ot the magnitude of these exposures in the both the general fish-consuming U.S.
 population and in specific fish-consuming subpopulauons (e.g.. children and women of child-bearing
 age).  This is not an estimate of background exposure to methylmercury for the general population but
 rather an estimate for only that part of the U.S. population which consumes fish. Use of" a national
 data base  differentiates data in this Appendix from a site-specific assessment.  In this appendix
 estimates  of methylmercury concentrations in freshwater fish are based on nation-wide survey results.
 Data  presented in this Appendix differ from site-specific assessments in which consumption of
 contaminated local freshwater fish are included.  Concentrations in fish  are variable among water
 bodies and regions; thus, to do an assessment for a particular site or population,  local measurements
 are required.

        Inclusion of fish in the diet varies with geographic location, season of the year, ethnicity, and
 personal food  preferences. Analysis of dietary survey data (Crochetti and Guthrie, 1982) showed fish
 to be among the most infrequently consumed food groups.  Data on fish consumption have been
 calculated typically as either "per capita" or "per user".  The former term is obtained by dividing the
 supply of  fish across an entire population to establish a  "per capita" consumption rate.  The latter term
 divides  the supply  of fish across only the portion of the  population that  consumes fish, providing "per
 user" rates of consumption.

        Identifying differences in fish consumption rates for population  groups can be achieved
 through analysis of dietary survey data for the general United States population and specified
 subpopulauons: e.g., some Native  American tribes, recreational anglers,  women of child-bearing age.
 and children.  The United States Department of Agriculture (USDA) has conducted a series of
 nationally based dietary surveys including the  1977-1978 Nationwide Food Consumption Survey and
 the Continuing Surveys of Food Intake by Individuals over the period 1989 through 1991 (CSFII/89-
 91). Analyses of fish consumption patterns among the general United States population and selected
 age/gender groupings are described below.  Fish consumption rate data from specific Native  American
 tribes and angling populations are  identified and used to corroborate the nation-wide fish-consumption
 data.

        Fish bioaccumulate methylmercury through  the freshwater aquatic and marine food-chains.
 Mercury-contaminated phytoplankton and zooplankton are consumed by planktivorous fish (referred to
 in other parts of this Volume as trophic level 3 fish).  Methylmercury is thought to bioaccumulate in
 this group as well as in the piscivorous fish.  As a result high fish concentrations of methylmercury
 indicative  of bioaccumulation have been  well documented.  Consumption of these methylmercury-
 contaminated fish results in exposure to piscivorous human populations.

 LI     Patterns Of Fish Consumption

        Despite the lower consumption frequency compared with staple  foods such as grain products,
dietary intake of fish can be estimated from dietary  survey data.  An initial question in  how to
estimate fish consumption is choice of dietary assessment methods. Available techniques  include long-
term dietary histories and questionnaires  to identify  typical food intake or short-term dietary  recall
techniques. The first consideration is to  obtain dietary information that  reflects typical  fish


June 1996                                     H-l                        SAB REVIEW DRAFT
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consumption.  A true estimate of methv I mercury intake  from fish is complicated by chanses in fish
intake over time, differences  in species ot fish consumed and variation in the methylmercury
concentration in a species of fish.  Temporal  variation in dietary patterns is an issue to consider in
evaluation  of short-term recall/record data.  For epidemiological studies that seek to understand the
relationship of long-term dietary patterns to chronic disease. t\pical food intake is the relevant
parameter to evaluate (Willett. 1990).  Because methvlmercury  is a developmental  toxin that mav
produce adverse effects following a comparatively brief exposure period (i.e.. a few months rather than
decades), comparatively short-term dietary patterns can have importance. Consequently estimation of
recent patterns of methylmercury consumption from fish is the relevant exposure for the health
endpoint of concern.

        In this Appendix the  purpose is to describe the distribution of fish intakes for either the
general population or for subpopulations  defined by age or gender; e.g., women  of child-bearing age.
In the analysis of fish consumption data to estimate methylmercury intakes, the purpose is not to
estimate fish consumption by an individual and relate it to an individual outcome.  Dietary
questionnaires or dietary histories may identify broad patterns of fish consumption, but these
techniques  provide less specific recollection of foods consumed such as the species of fish eaten.
Likewise estimates of the quantity of fish consumed become less  precise as the eating event becomes
more remote in time. Selection of dietary survey methods to describe fish intakes by the
subpopulation of interest requires a balancing of specificity  of  information collected with the
generalizability of short-term dietary patterns to longer-term food intakes.

        After selection of the appropriate period of fish  intake to  evaluate, a second major area of
concern is  variation in methylmercury concentration of the fish  consumed.  A central  feature of food
intakes among subjects with free-choice of foods is day-to-day variability superimposed on an
underlying  pattern of food intake (Willett, 1990, pg. 35).  In epidemiology studies an individual's true
intake of a food such as fish  could be considered as the mean intake for a large  number of days.
Collectively the  true intakes of these individuals define a frequency distribution for the study
population  as a whole (Willett, 1990, pg. 35). It is rarely possible to measure a large number of days
of dietary intake for individual subjects; consequently a sample  of one or several days is used to
represent the true intake (Willett, 1990, pg. 35).  The effect of this sampling is to increase the standard
deviation artificially or, stated another way, broaden the tails of the distribution (Willett, 1990. pg. 35).
This results in estimates of intake that are both larger and smaller than the true long-term averages for
any subject. Overall, authorities in nutritional epidemiology (among other see Willett, 1990: pg. 50),
conclude that "measurements of dietary intake based on a single or small number of 24-hour recalls
per subject may  provide a reasonable (unbiased) estimate of the mean of a group, but the standard
deviation will be greatly overestimated."

        Assessment of recent dietary intakes can be achieved through dietary records for various
periods (typically 7-day records or 3-day records)  or dietary recall (typically 24-hour recalls or 3-day
recalls) (among other see Witschi, 1990).  Research is currently in progress to estimate usual intake
distributions that take into account properties exhibited by intake  data for foods  that are not consumed
on a daily  basis  (among pther see Nusser and Guenther,  1995).  This remains, however, a data gap to
be filled by additional research.

        Sources  of error in short-term recalls and records affect all dietary survey methodologies.
These include errors made by the respondent or recorder of dietary information; the interviewer or
reviewer.   Information used to calculate the intake of the chemical of interest is another source of
error.  The chemical may be  either nutritionally required (nutrients) or of interest because it is both
June  1996                                     H-2                         SAB REVIEW DRAFT
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potentially toxic and not nutritionally required (toxicants). The third source of error in dietary
assessments is trie data base used to calculate intakes or the chemical from the food consumed.

        The ability of the subject to remember the food consumed and tn what quantities it was
consumed is central to these methods (among many other see Witschi. 1990).  In an analysis ot data
from the Health and  Nutrition E%aluation Survey, the largest source ot error was uncertainty ot
subjects about roods  consumed on the recall day (Youland and Engle.  1976).  Fish consumption
appears to be more accurately remembered than most other food groups.  Karvetti and Knuts  (1985)
observed the actual intake of 140 subjects and later interviewed them by 24-hour recall.  They round
that fish was omitted from the dietary recall less than 5% of the  time and erroneously recalled
approximately 1% of the time.  The validity of 24-hour recalls for fish consumption was greater than
all other food groups.  Interviewer and reviewer errors  can be reasonably predicted to be consistent for
a given survey  and unlikely  to affect reporting of fish consumption selectively.

        Estimates of Fish Intake for Populations

        Data on fish  consumption have been calculated typically as either "per capita" or "per user". '
The former term is obtained by dividing  the supply of  fish across an entire population to establish a
"per capita" consumption rate.  The  latter term divides  the supply of fish across only the portion of the
population that consumes fish: i.e.,  "per user" rates of consumption.

        Survey methods can broadly be classified into  longitudinal  methods or cross-sectional surveys.
Typically long-term or longitudinal estimates of intake  can be used to reflect patterns for individuals
(e.g., dietary histories); or longitudinal estimates of moderate duration (e.g., month-long periods) for
individuals or groups.  Cross-sectional data are used to give a "snap shot" in time and are typically
used to provide information  on the distribution of intakes for groups within the population of interest.
Cross-sectional data typically are for 24-hour or 3-day  sampling periods  and may rely on recall of
foods consumed following questioning by a trained interviewer, or  may rely on written records of
foods consumed.

        During the past decade reviewers of dietary survey methodology (for example, the Food and
Nutrition Board of the  National Research Council/National Academy of Sciences: the Life Sciences
Research Office of the Federation of American Societies of Experimental Biology) have evaluated
various dietary  survey techniques with regard to their suitability for estimating exposure to
contaminants and intake of nutrients. The Food and Nutrition  Board of the National Research
Council/National Academy of Sciences in their 1986 publication on Nutrient Adequacy Assessment
Using Food Consumption Surveys noted that dietary intake of  an individual is not constant from day
to day, but varies both in amount and in type of foods  eaten (intraindividual variation).  Variations
between persons in their usual food intake averaged over time  is referred to as imerindividual
variation.  Among North American populations, the intraindividual  (within person day-to-day)
variation is usually regarded to be as large as or greater than the interindividual (person to person)
variations.  Having evaluated a number of data sets the Academy's Subcommittee concluded that 3
days of observation may be more than is required for the derivation of the distribution of usual
intakes.

        Major sources of data on dietary intake of fish  used in preparing this $port to Congress are
the cross-sectional data from the  USDA Continuing Surveys of Food Intake by Individuals conducted
in the years 1989 through  1991 (CSFII 89/91) and the  longer-term  data on fish consumption based on
recorded fish consumption for variable numbers of periods of one-month duration during the years
June 1996                                     H-3                        SAB REVIEW DRAFT
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 1973/1974 from the National Purchase Diary (NPD 75/74) conducted by the Market Research
Corporation.

       Currently  a fish consumption rate of 6.5 grams per day is the default value used by some pan
nt the L'.S  EPA in the calculation of human health criteria.  This value is based on data from the
National Purchase Diary Survey conducted in  the United States during the period 19"?3 and 19^4  The
overall fish consumption rate from this survey for fish-eaters was 14.3 g/day (U.S. EPA. 1989). This
value was used in setting Ambient Water Quality Criteria (U.S. EPA 1980).  This rate is a per capita
rate averaged over trie entire U.S. population including fish-eaters and nonfish-eaters.  According to
the NPD tabulation, fish consumers represented 94.0% of the entire U.S. population (SRI,  1980).

       Identifying differences in fish  consumption rates for population groups can be achieved
through analysis of dietary survey data for the general United States population and specified
subpopulations: e.g., some  tribes of Native Americans, recreational anglers.  The United States
Department of Agriculture  (USDA) has conducted a series of nationally based dietary surveys
including the 1977-1978 Nationwide Food Consumption Survey and the Continuing Surveys of Food
Intake by Individuals over  the period 1989 through 1991 (CSFII  89-91). Analyses of fish
consumption patterns among the general United States population are described below.

1.1.1   1973 and  1974 National Purchase Diary Data

       The National Purchase Diary 1973/74 (NPD 73/74) data  are based on a sample of 7.662
families (25,165 individuals) out of 9,590 families sampled between September 1973 and August 1974
(SRI International Contract Report to U.S. EPA, 1980; Rupp et al., 1980).  The fish consumption was
based on questionnaires completed by the female head of the household in which she  recorded the date
of any meal containing fish, the type of fish (species), the packaging of the  fish (canned, frozen, fresh.
dried, or smoked,  or eaten  out), whether fresh fish was recreationally caught or commercially
purchased, the amount of fish prepared for the meal, the number of servings consumed by each family
member and any guests,  and the amount of fish not consumed during the meal. Meals eaten both at
home and away from home were recorded. Ninety-four percent of the respondents reported consuming
seafood during the sample  period

       Use of these data to estimate intake of fish or mercury on a body weight basis are limited by
the following data gaps.

       1.      This survey did not include data on the quantity of fish represented by a serving and
               information to calculate actual fish consumption  from entries described as breaded fish
               or fish mixed with other ingredients. Portion size was estimated by using average
               portion size for seafood from  USDA Handbook #11, Table 10, page 40-41.  The
               average serving sizes from this USDA source are shown in Table H-l.
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                                          Table H-l
                          Average Serving Size (gmsi for Seafood from
                            US DA  Handbook # 11 Used to Calculate
                                   Fish Intake by FDA (1978)
Age Group
(years)
0-1
1-5
6-11
12-17
18-54
55-75
Over 75
Male
Subjects
(gms)
20
66
95
131
158
159
180
Female
Subjects
(gms)
20
66
95
100
125
130
139
       4.
       5.
There may have been systematic under-recording of fish intake as Crispin-Smith et al.
noted that typical intakes declined 30% between the first survey period and the last
survey period among persons who completed four survey diaries (Crispin-Smith et al.,
1985).

There have been changes in the quantities and types of fish consumed between
1973/1974 and present.  The United States Department of Agriculture indicated
(Putnam, 1991) that on average fish consumption increased 27% between 1970 to 1974
and 1990. This increase is also noted by the National Academy of Sciences in
Seafood Safety (1991).   Whether or not this increase applies to the highest percentiles
of fish consumption (e.g., 95th or 99th percentile) was  not described in the publication
by USDA.

Changes  in the types of fish  consumed have been noted.  For example, Heuter et al.
(1995) note that currently there is a much greater U.S. consumption of shark when
compared to past decades.

Although an analysis of these data using the sample weights to project these data for
the general United States population was prepared by SRI  International under U.S.
EPA Contract 68-01-3887 in 1980,  U.S. EPA was subsequently informed that the
sample weights  were not longer available.  Consequently additional analyses with these
data in a manner than can be projected, to  the general population appears to no longer
be possible.

Body weights of the individuals surveyed  do1 not appear in published  materials. If
body weights of the individuals participating in this survey were recorded these data
do not appear to have been used in subsequent analyses.
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       Data on fish consumption from the NPD  73/74 survey have been published b>  Rupp et al .
 1980 and analvzed by U.S. EPA's contractor SRI International (1980). These data indicate that when
a month-long survey period is used 94 T- ot the surveyed population consumed fish.  The species ot
tish most commonly consumed are  shown in Table H-2.
                                         Table H-2
                Fish Species and Number of Persons Using the Species of Fish.
                              (Adapted from Rupp et al., 1980)
                   Category
      Number of Individuals Consuming Fish
            Based on 24,652 Replies*
  Tuna, light
  Shrimp
  Flounders
  Not reported (or identified)
  Perch (Marine)
  Salmon
  Clams
  Cod
  Pollock
                     16,817
                      5,808
                      3,327
                      3,117
                      2,519
                      2,454
                      2,242
                      1,492
                      1,466
* More than one species ot" fish may be eaten by an individual.
       Rupp et al. also estimated quantities of fish and shellfish consumed! by 12-18 year-old
teenagers and by adults 18 to 98  years of age.  These data are shown in Table H-3. The distribution
of fish consumption for age groups that included women of child-bearing ages are shown in Table H-
4.
                                         Table H-3
                      Fish Consumption from the NPD 1973/1974 Survey
                              (modified from Rupp et al., 1980)
Age Group
Teenagers aged
12-18 Years
Adults aged 18
to 98 Years
50th Percentile
1.88 kg/year
2.66 kg/year
90th
Percentile
8.66 kg/year
14.53 kg/year
(1
99th
Percentile
25.03 kg/year
or
69 grams/day
40.93 kg/year
or
112 grams/day
Maximum
62.12 kg/year
167.20 kg/year
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                                           Table H-4
                     Distribution of Fish Consumption for Females by Age*
                     Consumption Category (grams/day) (from SRI (1980))
A«e (years)
10-19
20-29
30-39
40-49
47.6-60.0
0.2
0.9
1.9
3.4
60.1-122.5
0.4
0.9
1.7
2.1
over 122.5
0 0
0.0
0.1
•0.2
* The percentage of females in an age bracket who consume, on average, a specified amount  (grams) of fish per day  The
calculations in this table were based upon the respondents to the NPD survey who consumed fish in the month of the survey
The NPD Research estimates that these respondents represent, on a weighted basis. 94.0% of the population of U S residents
(from Table 6. SRI Report. 1980).
 1.1.2    Nationwide Food Consumption Survey of 1977-78

        Fish consumption is not evenly divided across the United States population.  Analysis of
patterns of fish consumption have been performed on data obtained from dietary surveys of nationally
representative populations. For example, Crochetti and Guthrie (1982) analyzed the food consumption
patterns of persons who participated in the Nationwide Food Consumption Survey of 1977/78.
Populations specifically excluded from this analysis were children under four years of age, pregnant
and nursing women, vegetarians, individuals categorized by race as "other" (i.e., not "white" and not
"black"), individuals not related to  other members of the household in which they lived, and
individuals with incomplete records.  After these exclusions, the study population consisted on 24,085
individual dietary records for a three-day period.

        Persons reporting consumption of fish, shellfish, and seafood at least once in their 3-day
dietary record were categorized as  fish consumers.  Combinations of fish, shellfish, or seafood with
vegetables  and/or starches (e.g., rice, pasta) or fish sandwiches were categorized as consumers  of fish
"combinations".  Among the overall population, 25.0% of respondents reported consumption of fish
with an additional 9.6% reporting consumption of fish "combinations" in the 3-day period for a total
of 34.6% reporting consumption of fish and/or fish combinations.  Frequency of consumption was
comparable for male and female respondents with 24.1%  of men and 25.7% of women reporting
consumption  of fish in their 3-day  dietary records.  Fish "combinations" were reported as dietary items
by 11.2% of women and 9.9% of men.  Both these food categories were consumed typically as mid-
day and evening meals, rather than as breakfast or as snacks.  For persons who listed fish in their 3-
day dietary records, 89.7% listed fish in one meal only with 10.1% of respondents consuming  fish  in
two meals  and 0.1% consuming fish in three meals. For  dishes that combined fish and  other foods
(i.e., fish "combinations"), among persons who reported eating fish combinations, 93.4% reported this
food in one meal only  with 6.5% of individuals consuming two meals containing fish "combinations."

        There appears to be  little difference between men and women in their likelihood of consuming
fish based on patterns observed in this national survey (Crochetti and Guthrie, 1982).  Based on this
analysis, allocation of fish consumption on a "per capita" basis does not adequately reflect the  fish
consumption  patterns of the  general population of the United States. While "per capita" estimates
June 1996
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resulted in an o.verestimate of fish consumption for the approximately 651 of the United States
population who did not report consuming fish, these types of estimates by their nature substantial!)
underestimated fish consumption rates by persons who consume fish.  This pattern or underestimation
is important  in an assessment of impact of infrequently consumed foods such as fish.

I  1 3   CSFFL'1989-1991

       The second set of nation-wide data (CSFII/89-91) are presented in Table H-5.  Based on
analysis of 11,706 respondents who supplied 3-days of dietary record in the USD A CSFII of 1989-
1991. the frequency of fish consumption within the 3-day period was determined.  Analyses of these
dietary records indicate that 30.9% of respondents consumed fish, either alone or as part of a dish that
contained fish. See Table H-6 for age/gender analyses of the fish-consuming population.  Most
respondents eating fish consumed one fish meal within the 3-day period. Two percent (2%) of
respondents reported consuming fish two or more times during  the 3-day period, and 0.5% of these
fish-eating respondents reported fish consumption three or more times during the 3-day  study period.
Among persons who reported eating fish within the 3-day period of the survey, 44.1% reported eating
marine finfish (other than or in addition to tuna, shark, barracuda, and swordfish).  Marine finfish were
more frequently consumed than fresh-water fish. Of the 1593 people who reported eating finfish, 492
(30.9%) identified these as fresh-water fish.
                                         Table H-5
                     Number of Individuals in the 1989-1991  CSFII With
                               Three-Days of Dietary Records
Gender
Males
Females
Total
Aged 14 Years
or Younger
1497(51.7%)
1396 (48.3%)
2893 (24.7%)
Aged 15 through
44 Years
2131 (42.9%)
2837(57.1%)
4968 (42.4%)
Aged 45 Years
or Older
1537 (40.0%)
2308 (60.0%)
3845 (32.8%)
Total
for All Age Groups
5,165 (44.1%)
6,541 (55.9%)
11,706
                                         Table H-6
                 Respondents Reporting Consumption of AH Fish and Shellfish
                 in the 1989-1991 CSFII Survey Based on 3-Days Diet Records
                   (Data weighted to be representative of the U.S. population.)
Gender
Males
Females
Total
Aged 14 Years
or Younger
380 (52.8%)
340 (47.2%)
720 (19.9%)
Aged 15
through 44
Years
646 (42.8%)
864 (57.2%)
1510(41.8%)
Aged 45 Years
or Older
556 (39.3%)
828 (58.5%)
1415 (39.2%)
Total
1582 (43.8%)
2032 (56.2%)
3614
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 1.2     Frequency of Consumption of Fish  Based on Surveys of Individuals

        [n the L'SDA 1989.  199(). and  1991 Continuing Surveys ot Food Intake by Individuals (CSFII
 K9-911. rood consumption data were obtained from nationally representative samples of individuals.
 These survey included women of child-bearing age: tfiat is. 15 through 44 \ears of age.  Data from
 the CSFII  tor the period including 1989 and 1991 were used to calculate fish intake by the  general
 population and women ot  child-bearing age.  This subpopulation  included pregnant women  which are
 a subpopulauon ot interest in the Mercury Study:  Report to Congress because of the potential
 developmental toxicity to  the fetus accompanies ingestion of methylmercury. Analysis of Vital and
 Health Statistics data from 1990 indicated that 9.5% of women in this age  group can be predicted to
 be pregnant in a given year.  The size of this population has been estimated using the methodology
 described in the Addendum to this appendix,  entitled "Estimated National and Regional Populations of
 United States Women of Child-Bearing Age."

        The data described in this section were obtained from nationally representative samples of
 individuals and were weighted to reflect the U.S. population using the sampling weights provided by
 USDA.  The basic survey was designed to provide a multistage stratified area probability sample   -
 representative of the 48 conterminous states.  Weighting for the  1989, 1990 and 1991 data sets was
 done in two stages.  In the first phase a fundamental sampling weight (the  inverse of the  probability of
 selection) was computed and the responding weight (the inverse of the probability of selection) was
 computed for each responding household. This fundamental sampling weight was then adjusted to
 account for non-response at the area segment level.  The second phase of computations used the
 weights produced in the first  phase as the starting point of a reweighting process that used regression
 techniques to calibrate the sample to  match characteristics thought to  be correlated with eating
 behavior.

        The weights used  in this analysis reflect CSFII individuals providing intakes for three days.
 Weights for the  3-day individual intake sample were constructed separately for each of the three
 gender-age groups:  males ages 20 and over, females ages 20 and over and persons  aged  less than 20
 years.  Characteristics used in weight construction included day of the week, month of the year.
 region, urbanization, income as a percent of poverty, food stamp use, home ownership, household
 composition, race, ethnicity and age of the individual. The individual's employment status  for the
 previous week was used for persons ages 20 and older, and the employment status of the female  head
 of household was used for individuals less than 20 years of age.  The end result of this dual weighting
 process was to provide consumption estimates which are representative of the U.S. population.

        Respondents were drawn from  stratified area probability samples of noninstitutionalized United
 States households.  Survey respondents were surveyed across all four seasons of the year, and data
 were obtained across all seven days of the week.  The dietary  assessment methodology consisted of
 assessment of three consecutive days of food  intake, measured through one 24-hour-recall and two 1-
 day food records. For this analysis, the sample was limited to those individuals who provided records
 or recalls of three days of dietary intake.

       For purposes of interpretability, it should be noted that assessment of fish consumption
 patterns by recall/record assessment methods will probably differ from assessments based on food
 frequency methods.   In order to be designated a consumer or "user" of fish for purposes of the present
 analysis, an individual would need to have reported consumption of one or more fish/shellfish products
 at some time during the three days in which dietary intake was assessed. Since fish is not a frequently
consumed food for the majority of individuals, this dietary assessment method will likely
 underestimate the extent of fish consumption, since some individuals  will be missed who normally


June 1996                                     H-9                        SAB REVIEW DRAFT
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consume fish, but who did not consume fish during the three davs of assessment. In contrast, such
users would he picked up by a food frequency questionnaire.  The recall/record dietary assessment
method does  have the advantage, however, of providing more precise estimates of the quantities or fish
consumed that would  be obtained wiu\a food frequency record.

        The information that follows comes from the CSFII/89-91 and was provided under contract tu
U.S. EPA bv  Dr. Pamela Haines of the Department of Nutrition of the University of North Carolina
School of Public Health.  Data are presented for following groups of individuals surveyed by USDA  in
the CSFII:  data for the total population,  data grouped by gender, and for data grouped by age-gender
categories for the age  groups 14 years or younger, 15 through 44 years, arid 45 years and older (Table
H-2).

        Fish consumption was defined to reflect consumption of approximately 250 individual "Fish
only" food codes and  approximately 165 "Mixed dish-fish" food codes present in the  1994 version of
the USDA food composition tables.  The USDA maintains a data base (called the "Recipe File") that
describes all food ingredients that are pan of a  particular food Through consultation with Dr. Betty
Perloff, an USDA expert in the USDA recipe file, and Dr. Jacob Exler, an USDA expert in food
composition,  the USDA recipe file was searched for food codes containing fish or shellfish.  The
recipe was then scanned to determine fish codes that were present in the recipe reported as consumed
by the survey respondent.  The percent of the recipe that was fish by weight was determined by
dividing the weight of the fish/shellfish in the dish by the total weight of the dish.

        As with most  dietary assessment studies, multiple days of intake were averaged to reflect  usual
dietary intake better.  Intakes reported over the  three-day period were summed and then divided by
three to provide consumption estimates on a per person, per day basis.

        Fish consumption was defined within the following categories.

        1.      Fish and Shellfish, all types reflected consumption of any fish food  code.
        2.      Marine Finfish, included fish not further specified (e.g., tuna) and processed fish
               sticks, as well as anchovy, cod, croaker, eel,  flounder, haddock, hake, herring,
               mackerel, mullet, ocean perch, pompano, porgy, ray,  salmon, sardines, sea bass, skate,
               smelt, sturgeon, whiting.
        3.      Marine Shellfish included abalone, clams, crab, crayfish, lobster, mussels, oysters,
               scallops, shrimp and snails.
        4.      Tuna,  contained only tuna.
        5.      Shark, Barracuda, and Swordfish contained just these three species  of fish.
        6.      Fresh-water Fish contained carp, catfish, perch, pike, trout and bass.
       The analysis was stratified to reflect "per capita" (Table H-7), as well as "per user" (Table H-
8), consumption patterns.  A "consumer" of Fish and Shellfish, all types was one who consumed any
of the included fish only or mixed-fish dish foods.  A Marine Finfish consumer was one who
consumed any of the species of fish included within the marine finfish category, and so on for each
category.  The percent of the population or subpopulation consuming fish was listed for the entire
population, as well as gender specific values, and age-gender category specific values.
June 1996                                    H-10                       SAB REVIEW DRAFT
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                                          Table H-7
       Consumption of Fish and Shellfish (gms/day). and Self-Reported Body Weight (Kg)
                        in  Respondents of the 1989-1991 CSFII Survey.
                        "Per Capita" Data for AH Survey Respondents
         •        (Data are weighted to be representative ot the U.S. population )
Gender
Males
Females
Aged 14 Years or
Younger
Mean
8.6
7.9
SD
19.8
18.0
kghw
26
24
Aged 15 through
44 Years
Mean
18.6
14.0
SD
34.5
28.4
kgbw
73
63
Aged 45 Years or
Older
Mean
20.2
17.5
SD
36.4
30.1
KgwJ
90
67
Total
Mean
16.7
13.8
SD
32.5
27.4
k?hw
68
58
                                          Table H-8
                       Consumption of Fish and Shellfish (gms/day), and
        Self-Reported Body Weight (Kg) in Respondents of the 1989-1991 CSFII Survey
        (Data for "Users" Only. Data are weighted to be representative of the U.S. population.)
Gender
Males
Females
Aged 14 Years or
Younger
Mean
31.8
29.2
SD
26.6
24.0
k&,w
27.7
23.7
Aged 15 through
44 Years
Mean
53.7
41.4
SD
39.3
35.4
k8bw
80.2
63.0
Aged 45 Years or
Older
Mean
51.4
42.4
SD
42.0
33.7
K&w
82.9
68.1
Total
Mean
48.8
39.7
SD
39.1
33.4
kgnw
58.6
53.9
       Consumption of fish-only and mixed-fish-dishes was summed across the three available days
of dietary intake data. This sum was then divided by three to  create average per day fish consumption
figures. In the tables that describe fish intake, information is presented on sample size, percent of the
population who consumed any product within the specified fish category, the mean grams consumed
per day and the mean grams consumed per kilogram body weight (based on self-reported body
weights), standard deviation, minimum, maximum, and the population intake levels at the 5th, 25th,
50th (median), 75th, and 95th percentiles of the intake distribution for each age-gender category. The
means and standard deviations were determined using a SAS program. Survey sample weights  were
applied.  Analysis with SAS does not take design effects into account, so the estimates of variance
may differ from those obtained if SUDAAN or such packages had been used.  It should be noted,
however, that the point estimates of consumption (grams per consumer per day, grams per consumer
per kilogram of body weight) will be exactly the same between the two statistical analysis packages.
Thus, the point estimates reported are accurate and appropriate for interpretation on a national level.

       Data were obtained  for 11,706 individuals reporting 3-days of diet in the 1989-1991 CSFII
survey.  Analyses were based on data weighted through statistical procedures (as described previously)
to be representative of the United States population.  The total group of respondents reporting
consumption of finfish and/or shellfish during the 3-day period were grouped as a subpopulation who
consumed fish, as can be observed in Table H-6. Fish and shellfish (total fish  consumption) were
reported to be eaten by 3614 persons (30.9%) of the 11,706 of the survey respondents (see Tables H-5
June 1996
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and H-6t  The subpopulation considered to be of greatest interest in this Mercury Study  Report to
Congress were women of child-bearing age (15 through -14 year-old females).  Among this group of
women ages  15 through 44 years. 864 women of the 2837 surveyed (30.5^ i reported consuming  fish
(see Tables H-5 and H-6).  Within this group,  334  women reported consumption of finrish during the
3-day survey  period.

       Consumption ot fish and shellfish varied by species of fish.  Overall, marine finfish (not
including tuna, swordfish, barracuda, and shark) and tuna were consumed by more individuals and in
greater quantity than were shellfish.  Tuna fish was the most frequently consumed fish product, and
separate tables are provided that identify quantity of tuna fish consumed. Two other categories of
finfish were identified:  fresh-water fish and a category comprised of swordfish. barracuda., and shark.
Fresh-water fish were of interest because U.S.  EPA's analysis of the fate and transport of ambient,
anthropogenic mercury emissions from sources of concern in this report indicates that fish may
bioaccumulate emitted mercury. Swordfish, barracuda, and shark were also  identified as a separate
category.  These are predatory, highly migratory species that spend much of their lives at the high end
of marine food web.  These fish are large and  accumulate higher concentrations of mercury than do
lower trophic level, smaller fish.

1.3    Consumption Rates Among Subpopulations with Potentially Higher Rates of Fish
       Consumption than the General Population

1.3.1   Introduction

       The purpose of this section is to document  fish consumption rates among U.S. subpopulations
thought to have higher rates of fish consumption.  These subpopulations include residents of the States
of Alaska and Hawaii and Native American Tribes; these groups were selected for analysis because of
potentially elevated fish consumption rates rather than because they were thought to have a high innate
sensitivity to  methylmercury.  The presented estimates are the results of fish consumption surveys
conducted on the specific populations.  The surveys use several  different techniques and illustrate a
broad range of consumption rates among these subpopulations.  In several studies the fish consumption
rates of the subpopulations corroborate the high-end (90th percentile and above) fish consumption
estimates of the NPD survey (Rupp et al. 1980), the Nationwide Food Consumption Survey of 1977/78
and the Continuing Surveys of Food Intake by Individuals over  the period 1989 through 1991
(CSFII/89-91). The consumption rates of fish-consumers are highlighted in  the surveyed populations
rather than estimates for the entire population,  which would include non-consumers. Summary tables
(Tables H-9 and H-10)  listing daily fish consumption estimates are provided at the end of this section:
selected values are shown graphically in Figure H-l.

       Fish consumption data for Alaskans and Hawaiian islanders are included because these
populations are not included in the nationwide surveys of the  contiguous U.S. Members of these
populations may exhibit elevated fish consumption rates as part  of their ethnic heritage.

       Analytic and survey methods to estimate the fish consumption rates  of the respondents are
described for each population.  This Appendix does not constitute an exhaustive  review of the  methods
employed.  An attempt  was made to characterize the population surveyed.  Additionally, to
characterize the entire range of fish consumption rates in the surveyed populations, the
June 1996                                    H-12                        SAB REVIEW DRAFT
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CD

1
g
o.

CO
o
O

SO
co
CO

2
O)
             390-
               -
                                            Figure H-l
                                  Distribution of Fish Consumption
                                  Rates Among Various Populations
                          Wolfe 4 Walker 87 Highest Response Group Mean m AK
 CRITFC 94 99th %ile Adult
                                                           LEGEND - POPULATIONS
                GENERAL U.S. POPULATION

                NPD   73/74  •
                CSFII         •
                RECREATIONAL ANGLERS
                PUFFER
                FIORE
                CONNELY
SUBSISTENCE FISHERS

WOLFE & WALKER   A



NATIVE AMERICANS

CRITFC       '  ^
TOY, TULALIP    W
NOBMAN
EPA '92 Wl TRIBES
                                             Toy '95 Tulalip Tnbe 90th %ile
                                                                      Fiore 89 95th %ile
                                                                         Wl Anglers
NPD 73/74 Adult 99th %ile
                    j  Nobmann '92 AK Tribes Mean  |
                                                          CRITFC '94 Adult Mean |
                                              Toy '95 Tulalip Tribe Median
                                                       -j Hore '89 75th %ile Wl Anglers
                                                                         NPD 73/74 Adult 90th %ile
                         Fiore 89 Wl AnglersliT^—[Connoly '90 NY Anglers Mean|+
                        NPD 73/74 Adult 50th %ile
                                                                    CSFII Age 15-44 Mean ? andc?
      June 1996
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                                    Table H-9
                   Compilation of the 11 Angler Consumption Studies
Source
Soldat. lv~0
Puffer 1981
as cited in U.S.
EPA. 1990
Pierce et al..
1981 as cited in
EPA. 1990
Fiore et al.. 1989
West et al.. 1989
West et al.. 1993
Turcotte, 1983
Hovinga et al.,
1992 and 1993
Ebert et al., 1993
Sekerke et al,
1994
Population
Columbia
Rner
Anglers
Los Angeles
area coastal
anglers
Commencem
ent Bay in
Tacoma. WA
Licensed WI
Anglers
Licensed MI
Anglers
Licensed MI
Anglers
GA anglers
Caucasians
living along
Lake
Michigan
ME anglers
licensed to
fish inland
waters
FL residents
receiving
foods tamps
Percentile
Mean
Median
90th Percentile
Ethnic Subpopulation
Medians
African-American
Caucasian
Mexican- American
Onental/Samoan
50th Percentile
90th Percenule
Maximum Reported
Mean
75th Percentile
95th Percenule
Mean
75th Percenule
95th Percenule
Mean
Mean for Minorities
Maximum Reported
Mean
Mean for Minority
Populations
Child
Teenager
Average Angler
Maximum Angler
Maximum Reported
Mean
50th Percentile
75th Percenule
90th Percenule
95th Percentile
Male Mean
Female Mean
Daily Fish
Consumption
g/day
1 8
37
224.8
24.2
46
33
70.6
23
54
381
12.3
15.5
37.3
26.1
34.2
63.4
19.2
21.7
>200
14.5
43.1
10
23
31
58
132
6.4
2.0
5.8
13
26
60
40
Notes
Estimate ot a\erage nntish
consumption trom nver
Estimates for anglers and
family members \vho consume
their catch. Consumpuon rate
includes ingestion ot both
finfish and shellfish.
Finfish only
Fish-Eaters. Daily Sportfish
Intake
Fish-Eaters, Total Fish Intake
Daily Sportfish Intake
Daily Sponfish intake
Estimates of Freshwater Fish
Intake from the Savannah
River
Re -examination of Previously
Identified High-End Fish
Consuming Population
Sponfish Intake
Total Home Fish Consumpuon
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                                      Table H-10
                   Fish Consumption by Native United States Populations
Source




Nobmann et
al.. 1992

U S. EPA,
1992b

Peterson et
al.











U.S. EPA.
1992

Toy et al..
1995









Fitzgerald
et al.. 1995






Population




351 Alaska Native
Adults (Eskimos.
Indians. Aleutsl
Wisconsin Tnbes. 1 1
Native American
Indian Tnbes
323 Chippewa Adults
> 18 years of age.











Wisconsin Tnbes, 1 1
Native American
Tnbes
Tulalip and Squaxin
Island Tnbes. 263
adult subjects.








97 Nursing Mohawk
women






Percentile




Mean


Mean


Mean = 1.7 fish meals/week.
( 1 9 and 1.5 fish meals/week for male
and for female respondents.
respectively).
0.26% of males and 0 15% of females
reported eating 3 or more fish-meals per
week.
50% of respondents ate one or less fish
meals per week.
21% of respondents ate three or more
fish meals per week.
2% of respondents ate fish-meals each
day.
Mean


50th percentile:

Finfish, 22.4 grams/day

Total fish consumed, 42.6 grams/day.

90th Percentile:

Finfish. 87.5 grams/day

Total fish. 155.9 grams/day.
24.7% ate 1-9 local fish meals/year
during pregnancy
10.3% ate >9 local fish meals/year
during pregnancy
41.2% ate 1-9 local fish meals/year one
year pnor to pregnancy
15.4% ate >9 local fish meals/year one
year prior to pregnancy
Fish-Meals
Consumed or
Fish
Consumption
in Grams
109 grams of
fish and shell
fish per day.
31.5 grams of
fish per day














31.5 grams





















Notes
























IB

Report contains
data for
anadromous
fish, pelagic.
bottom and
shell fish.
Data are based
on an average
body weight of
70 kg/day.

Study
conducted from
1986-1992 in
area where fish
are
contaminated
with PCB

June 1996
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consumption rates ot both average and high-end consumers as well as other specific angler
subpopulauons (e.g.. fish consumption b\ angler race or age) are presented.

        The sources of consumed fish are also identified in the summaries.  Fish consumed by humans
can be derived from many sources, these include self-caught, gift, as  well as srocery and restaurant
purchases.  Some studies describe onK the consumption rates tor self-caught fish or freshwater fish.
others estimate total fish consumption, and some delineate each source of fish.  Humans also consume
fish from many different types of water bodies  (U.S. FWS, 1988).  When described by the reporting
authors, these are also identified.

        Assumptions concerning fish consumption  made by the study authors are also identified.
Humans generally do not eat the entire fish: however, the species and body parts of fish which are
consumed may be highly variable among angler populations (for example, see Toy et al.  1995).
Anglers do not eat their entire catch, and. some species of fish are typically not eaten by specific
angling subpopulations.  For example, Eben et  al., (1993)  noted that some types and parts of harvested
fish are used as bait, fed to pets or simply discarded.  Study authors account for the differences
between catch weight and  number in a variety of different ways. Typically,  a consumption factor was
applied. These  assumptions impact the author's consumption rate estimates.

        Data from angler and indigenous populations are useful  in that they corroborate the ranges
identified in the 3-day fish consumption data.  The data are not  utilized in this  Report as the basis of a
site-specific assessment.  In a site-specific assessment the fish consumption rates among a surveyed
population  would be combined with specific measurements of methylmercury concentrations in the
local fish actually consumed to estimate the human contact rate.  Ideally, some follow-up analysis such
as concentrations in human blood or hair would ensue.

1.3.2  U.S.  Angling Population Size Estimate and Behaviors

        Many citizens catch and consume fish from U.S. waters. The U.S. Fish and Wildlife Service
(1988) reported that in  1985 26% of the  U.S. population fished: over 46 million people in the U.S.
spent time  fishing during 1985.  Within the U.S. population fishing rates ranged from a low of 17%
for the population in the Middle Atlantic states up to 36% in the West North Central States.  These
angling subpopulations included both licensed and non-licensed fishers, hook and line anglers as well
as those who utilized special angling techniques (e.g., bow and arrows, spears or ice-fishing).

        U.S. Fish and Wildlife Service (1988) also noted the harvest and consumption of fish from
water bodies where fishing is prohibited.  This  disregard or ignorance of fish advisories is corroborated
in other U.S. angler surveys.  For example. Fiore et al., (1989) noted that 72%  of the respondents in a
Wisconsin  angler survey were familiar with the State of Wisconsin Fish Consumption Health
Advisory, and 57% of the respondents reported changing their fishing or fish consumption habits based
on the advisory.  West  et al.,  1989 noted that 87.3% of respondents were "aware or generally aware"
of Michigan State's fish consumption advisories. Finally,  Connelly et al., 1990 reported that 82% of
respondents knew about the New York State fish health advisories. They  also  noted a specific
example in which angler consumption exceeded an advisory. The State of New York State
recommends the consumption of no more than  12 fish meals/year of contaminated Lake Ontario fish
species; yet, 15% of the anglers, who fished this Lake, reported eating more than 12 fish meals of the
contaminated species from the Lake in that year.
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  3 3   L' S Angler Surveys
       Anglers of the Columbia River,
       Soldat (1970) measured fishing activity along the Columbia River during the daylight hours or
one calendar >ear i 1967-A8)  The average angler in the sampled population made 4.7 fishing trips per
>ear and  caught an average or 1  fish per trip. Assuming 200 g or fish consumed per meal. Soldat
estimated an average or' 0.7 fish meals were  harvested per trip: this results in an average of 3.3
Columbia River fish meals/year.   The product of 3.3 meals/year and 200 g/meal is 660 g/year: an
estimate of 1.8 g/day results.  While not reporting the high-end harvesting or consumption rates.  Soldat
reported that approximately 15% of the 1400 anglers interviewed caught 90% of the fish.

       Los Angeles, California Anglers

       The results of studies from Puffer (1981) and Pierce et al. (1981) are described in  U.S. EPA's
1989.  Puffer (1981) conducted 1.059 interviews with anglers in the coastal Los Angeles area for an
entire year. Consumption rates were estimated for anglers who ate their catch.   These estimates were
based on angling frequency and the assumption of equal fish consumption among all fish-eating family
members. The median consumption rate for fish and shellfish was 37 g/day. The 90th percentile was
224.8 g/day.  Table H-l 1 notes the higher consumption rate estimates among Orientals and Samoans.
                                         Table H-ll
             The Median Recreationally Caught Fish Consumption Rate Estimates
                                by Ethnic Group (Puffer, 1981)
Ethnic Group
African- American
Caucasian
Mexican- American
Oriental/Samoan
Total
Median Consumption Rate
(g/day)
24.2
•
46
33
70.6
37
       Anglers of the Commencement Bay Area in Tacoma, Washington

       Pierce et al., (1981), as reported in the U.S. EPA's 1990 Exposure Factors Handbook,
conducted a total of 509 interviews in the summer and fall around Commencement Bay in Tacoma,
WA.  They assumed that 49% of the live fish weight was edible and that 98% of the total catch was
eaten.  The estimated 50th percentile consumption rate was 23 g/day and the estimated 90th percentile
consumption rate 54 g/day.  The maximum estimated consumption rate was 381 g/day based on daily
angling.
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       Anglers of the Sa\annuh Rner in Georgia

       Turcorte (1983) estimated fish consumption from the Savannah River based on total harvest.
population studies and a Georgia fishery survey (Table H-12).  The angler survey data, which included
the number of fishing trips per year as well as the number  and weights ot fish  harvested per trip, were
used to estimate  the average consumption rate in the angler population.  Se\eral techniques including
the use ot the angler survey data were used to estimate the maximum fish consumption m  the angler
population.  Estimates ot average fish consumption for children and teens was  also provided.
                                         Table H-12
                The Freshwater Fish Consumption Estimates of Turcotte (1983)
Georgia
Subpopulation
Child
Teen-ager
Average Angler
Maximum Angler
Estimated Freshwater Fish
Consumption Rate (g/day)
10
23
31
58
       Wisconsin Anglers

       Fiore et al., (1989) surveyed the fishing and fish consumption habits of 801 Licensed
Wisconsin anglers. The respondents were divided into 2 groups: fish eaters and non-eaters. The fish
eaters group was further subdivided into 4 groups: those who consumed 0 - 1.8 Kg fish/yr,  1.9 - 4.5
Kg fish/yr, 4.6 -  10.9 Kg fish/yr and 10.9 <.Kg fish/yr.  Using an assumption of 8 oz. (227 grams)
fish consumed/meal, the authors estimated that the mean number of sport fish meals/year for all
respondents (including non-eaters) was 18.  The mean number of other fish meals/year including non-
eaters was 24. The total number of fish meals/year  was 41  for fish eaters ajid non-eaters combined
and 42 for fish eaters only.  Recreational anglers were  found to consume both commercial fish as
well as sport fish. The estimated daily consumption rates of the eaters-only are presented in Table H-
13.

                                         Table  H-13
          The Daily Intake of Sportfish and Total Fish for  the Fish-consuming Portion
                        of the Population Studied by Fiore et al., (1989)
Percentile
Mean
75th
95th
Daily Sport-Fish
Intake
12.3 g/day
15.5 g/day
37.3 g/day
Daily Total Fish
Intake
2(5.1 g/day
34.2 g/day
63.4 g/day
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        Michigan Anglers

        West et al.. 1989 used a mail survey to conduct a 7-day fish consumption recall study tor
 licensed Michigan anglers.  The respondents numbered 11()4. and the response rate was 47.3'?  The
 mean fish consumption rate for anglers and other fish-eating members ot their households was 18 3
 g/dav.  and the standard deviation was 26 8 g   Because the study was conducted from January through
 June, an ott-season tor .some forms of angling in Michigan, higher rates ot fish consumption would be
 expected during the summer and fall months.  A full-year's mean fish consumption rate of 19.2 g/day
 was  estimated from seasonal data.  The mean fish consumption rate  for minorities was estimated to be
 21.7 'g/day.  The highest consumption rates reported were over 200 g/day: this occurred m 0.1 ac of the
 population surveyed.   Overall,  fish  consumption rates  increased with angler age and lower education
 levels.   Lower income and education level groups were found to be the only group which consumed
 bottom-feeders.

        New  York State Anglers

        Connelly et al., (1990) reported the results of  a statewide survey of New York anglers.  The
 10,314 respondents (62.4%  response rate) reported a mean of 20.5 days spent fishing/year.  Of the
 respondents. 84% fished the inland waters of New York State,  and 42% reported fishing in the Great
 Lakes.   An overall mean of 45.2 fish meals per year was determined for New York anglers.  The
 authors assumed an average meal size of 8 oz. (227 g) of fish and estimated a yearly consumption rate
 of 10.1  Kg fish (27.7  g fish/day).  Unlike the Michigan angler  study (West et al., 1989), the overall
 mean number of fish meals consumed increased with education level of the angler.  Fish consumption
 also  increased with increasing income; respondents earning more than $50,000/year consumed a mean
 of 54.3  meals per year, and those with some post-graduate education consumed a mean of 56.2 meals
 per year.  The highest reported regional mean consumption rates (58.8 meals/year)  occurred in the
 Suffolk and Nassau Regions of New York State.

        Anglers of Lake Michigan

        As part of a larger effort, Hovinga et al. (1992 and  1993) re-examined 115 eaters of Great
 Lakes Fish and 127 controls, who consumed smaller quantities of fish, originally identified in a 1982
 effort.  Both current (1989) as  well as 1982 consumption rates  of Great Lakes sportfish were
 estimated. All of the  participants in the study were Caucasian  and resided in 11 communities along
 Lake Michigan.  The population was divided into eaters (defined as  individuals consuming 10.9 kg (30
 g/day)  or greater) and controls (defined as individuals consuming no more than 2.72 kg/yr).  The
 consumption  rates  for the groups are reported in Table H-14.
June 1996                                    H-20                        SAB REVIEW DRAFT
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                                         Table H-14
                     Fish Consumption Rate Data for Groups Identified in
                          Hovinga et al.. 1992, as Eaters and Controls
Groups
Eaters
Controls
1982 MealsA'r
Mean (Range)
53.5 (24-132)
-
1982 Consumption
Rates (Kg/Yr)
Mean (Range)
17.64 Kg/Yr
(10.9-52.6)
-
1989 MealsA'r
Mean (Range)
38 (0-108)
4. 1 (0-52)
l
1989 Consumption
Rates ( KgA'r)
Mean (Range)
9.8 (0-48)
0.73 (0-8.8)
       Anglers of Inland Waters in the State of Maine

       Ebert et al., (1993) examined freshwater fish consumption rates of 1,612 anglers licensed to
fish the inland (fresh) waters of Maine.  They only analyzed fish caught and eaten by the anglers.
Anglers were asked to recall the number, species and average length of fish eaten in the previous year:
the actual fish consumption rates  were estimated based on an estimate of edible portion of the fish.
The 78% of respondents who fished in the previous year and 7% who did not fish but did consume
freshwater fish were combined for the analysis.  Anglers who practiced ice-fishing as well as fish
caught in both standing and flowing waters were included.  Twenty-three percent of the anglers
consumed no freshwater fish.  If the authors assumed that the fish were shared evenly among all fish
consumers in the angler's family, a mean consumption rate of 3.7 g/day was estimated for each
consumer. Table  H-15 provides the fish consumption rates for Maine anglers.

                                         Table H-15
                          Fish Consumption Rates for Maine Anglers
Percentile
Mean
50th (median)
75th
90th
95th
All Anglers
5.0
1.1
4.2
11
21
Fish-consuming
anglers
6.4
2.0
5.8
13
26
       Florida Anglers Who Receive Foodstamps

       As part of a larger effort the Florida Department of Environmental Regulation attempted to
identify fish consumption rates of anglers who were thought to consume higher rates of fish.  Face-to-
face interviews were conducted at 5 Florida Foodstamp Distribution Centers.  The selected Foodstamp
Distribution Centers were located in counties either thought to have a high likelihood of subsistence
anglers or where pollutant concentrations in fish were known.  Interviews with twenty-five household's
June 1996
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 primary seafood preparer were conducted at each center per quarter for an entire >ear   A total of fun
 interviews was collected.  The interviewed  were asked to recall fish consumption within the last 7
 davs.  Specifically, the respondents were asked to recall the species, sources  and quantities ot fish
 consumed.  Note that the respondents were only asked to recall fish meals prepared at home iactual
 consumption rates may have been  higher if the respondents consumed seafood elsewhere) and that the
 sources ot nsh were from both salt and  treshwaters.  The results ot the survey conducted by Sekerke
 et al..  1994 are in Table H-16.
                                           Table H-16
              Fish Consumption Rates of Florida Anglers Who Receive Foodstamps
Respondent
Adult Males
Adult Females
No.
366
596
«
Average Finfish
Consumption
60 g/day
40 g/day
Average Shellfish
Consumption
50 g/day
30 g/day
        Summary of Angler Surveys

       .The results of the 10 fish consumption surveys are compiled in Table H-9.  These results
 illustrate the range of fish consumption rates identified in angler consumption surveys.  There is a
 broad range of fish consumption rates reported for angling populations. The range extends from 2
 g/day to greater than 200 g/day.  The variability is the result of differences in the study designs and
 purposes as well as differences in the populations surveyed.

 1.3.4 Indigenous Populations of the  United States

        The tribes and ethnic groups  who comprise the indigenous populations of the United States
 show wide variability in fish consumption patterns.  Although some tribes, such as the Navajo,
 consume minimal amounts of fish as pan of their traditional culture, other native groups,  (such as the
 Eskimos, Indians, and Aleuts of Alaska or the tribes of Puget Sound) traditionally consume  high
 quantities of fish and fish products.  The United States' indigenous populations are widely distributed
 geographically.  For example, a U.S. EPA Report (1992b) identified 281  Federal Indian reservations
 that cover 54 million acres in the United States. Treaty rights to graze livestock, hunt, and  fish are
 held by native peoples for an additional 100 to 125 million  acres.  There are an estimated two million
 American Indians in the United States (U.S. EPA, 1992b).  Forty-five percent of these two million
 native people live on or near reservations and trust lands. High-end fish consuming groups include
 Alaska natives who number  between 85,000 and 86,000 people (Nobmann et al., 1992).

       Fish products consumed by indigenous populations may rely on preparation methods that differ
from ones typically encountered in the diet of the general United States population.  By way of
illustration, food intake data obtained from Alaskan natives  were used to calculate nutrient intakes
using a computer and software program.  These computerized databases had been developed by the
United States Veterans Administration for patients in the national Veteran's Administration hospital
system.  Nobmann et al. (1992) found they needed to add data for 210 dietary items consumed by
Alaskan Natives to the 2400 food items in the VA files.
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        In the mid-1990s data on fish consumption by indigenous populations of the United States
were reported for Alaska Natives (Nobmann et al.. 1992). Wisconsin Tribes (U.S. EPA. 1992). the
Columbia River Tribes (Columbia River Inter-Tribal Fish Commission. 1994) and selected Puszet
Sound Tribes (Tov et al.  1995).  Findings  from these studies can be used to assess differences in  fish
consumption  between these indigenous groups and the general United States populauon.

       Alaskan \anies

       Dietary analyses on seasonal food intakes of 351 .Alaska Native adults from 11 communities
were performed during 1987-1988 (Nobmann et al., 1992).  Alaska Natives include Eskimos. Indians
and Aleuts.  There is no main agricultural crop in Alaska which, combined with a short growing
season, results in limited availability of edible plants. Alaska Natives have traditionally relied on  a diet
of fish, sea mammals, game and a few native plants (seaweed, willow leaves, and sourdock) and
bemes (such as, blueberries and salmonberries).  Although consumption of significant amounts of
commercially produced foods occurs, use of subsistence foods continues. .

       The survey sample of 351 adults, aged 21 - 60 years, was drawn from 11 communities.
Information was obtained using 24-hour dietary recalls during five seasons over an 18-month period.
Fish were consumed much more frequently by Alaska Natives than by the general United States
population. Fish ranked as the fourth most frequently consumed food by Alaska Natives compared
with the 39th most frequently consumed food by participants in the nationally representative Second
National Health and Nutrition Assessment Survey (NHANES II). The mean daily intake of fish and
shellfish for Alaska Natives was 109 grams/day contrasted with an intake of 17 grams per day for the
general United States population described in NHANES II.  Among Alaska Natives fish was consumed
more frequently in the summer and fall months.

        Despite a degree of acculturation in the area of foods, native foods were still eaten frequently
by Alaskan Native peoples based on results of the  1987-1988 survey. Diets  that include major
quantities of fish (especially salmon) and sea mammals retain a major place in the lives of Alaskan
Native peoples. The consumption of traditional preparations of salmon and other fish continues: this
includes fermented foods such as salmon heads and eggs, other fish and their eggs, seal, beaver.
caribou and whale.

       Alaskans from Subsistence Economies

       Wolfe and Walker (1987) described the productivity and geographic distribution of subsistence
economies in Alaska during the 1980s.  Based on a sample of 98 communities, the economic
contributions of harvests of fish, land mammals, marine mammals and other wild resources were
analyzed.  Noncommercial fishing and hunting play a  major role in the economic and social lives of
persons living in these communities.  Harvest sizes in these communities were established by detailed
retrospective interviews with harvesters from a sample of households within each community.
Harvests were estimated for a 12  month period.  Data were collected in pounds of dressed weight per
capita per year. Although it varies by community  and wildlife species, generally "dressed weight" is
approximately 70 to 75% of the round weight for fish and 20 to 60% of round  weight for marine
animals.  Dressed weight is the portion of the kill brought into the kitchen for use, including bones for
particular species.  The category "fish" contains species including salmon, whitefish, herring, char,
halibut, and pike.  "Land mammals" included species such as moose, caribou, deer, black bear,
snowshoe and tundra hare, beaver and porcupines.  "Marine mammals" consisted of seal,  walrus  and
whale.  "Other" contained birds, marine invertebrates,  and certain plant products such as berries.
June 1996                                    H-23                       SAB REVIEW DRAFT
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        Substantial community-to-community vanability in the harvesting of fish, land mammals.
 marine  mammals and other wild resources were noted (Wolfe and Walker. 1985).  Units are pounds
 'dressed weight" per capita per >ear  The median harvest was 252 pounds with the highest value
 approximate^ 1500 pounds  Wild harvests (quantities of fish.land mammals and marine mammals) in
 46'r or rhe sampled Alaskan communities exceeded the western United States consumption or meat.
 tish. and poultr>  These communities have been grouped by general ecological  zones which
 correspond to ru.itonc./cultural areas. .Arctic-Subarctic Coast. Aleutian-Pacific Coast.  Subarctic Interior.
 Northwest Coast and contemporary urban population centers. The Arctic-Subarctic Coast displa\ed
 the greatest subsistence harvests of the five ecological zones (610 pounds per capita), due primarily to
 the relatively greater harvests of fish and marine animals.  For all regions the fishing output is greater
 than the hunting: fishing comprises 57 - 68% of total subsistence output. Above 60° north latitude
 fishing  predominates other wildlife harvests, except  for the extreme Arctic coastal sea mammal-caribou
 hunting communities.  Resource harvests of fish ("dressed weight" on a per capita basis) by ecological
 zone (and cultural area) were these:  Arctic-Subarctic Coast (Inupiaq-Yup'ik), 363 pounds/year or 452
 grams/day: Aleutian-Pacific Coast (Aleut-Sugpiaq), 251  pounds/year or 312 grams/day: Subarctic
 Interior (Athapaskan), 256 pounds/year or 318 grams/day: Northwest Coast (Tingit-Haida). 122
 pounds/year or  152 grams/day, and Other (Anchorage, Fairbanks, Juneau, Matanuska-Susitna Borough,
 and Southern Cook Inlet), 28 pounds/year or 35 grams/day.

        Wisconsin Tribes

        The U.S. EPA's 1992 document entitled Tribes at Risk (The Wisconsin  Tribes Comparative
 Risk Project) reported an average total daily fish intake for Native Americans living in  Wisconsin of
 35 grams/day. The average daily intake of locally harvested fish was 31.5 grams.

        Peterson et al. (1995) surveyed 323 Chippewa adults over 18 years of age living on the
 Chippewa reservation in Wisconsin.  The survey was conducted by interview and included questions
 about season, species and source of fish consumed.  The survey was carried out in May.  Fish
 consumption was found to be seasonal with the highest fish consumption occurring in April  and  May.
 Fish species typically consumed were walleye and northern pike, muskellunge and bass. During the
 months  in which the Chippewa ate the most fish, 50% of respondents reported eating one or fewer fish
 meals per week. 21% reported eating three or more  fish meals per week, and 2% reported daily fish
 consumption. The mean number of fish meals per week during the peak consumption period was  1.7
 meals: this  is approximately 42% higher than the  1.2 fish meals per  week that respondents reported as
 their usual fish consumption.  Higher levels of fish consumption were reported by males (1.9 meals
 per week) than by females (1.5 meals per week).  Among male respondents 0.26%  ate 3 or more fish
 meals per week, whereas 0.15% of female respondents ate 3 or more meals of fish  per week.
 Unemployed persons typically had higher fish consumption rates.

        Columbia River Tribes

        The Columbia River Inter-Tribal Fish Commission (1994) estimated fish consumption rates for
 members of four tribes inhabiting the Columbia River Basin. The estimated fish consumption rates
 were based on interviews with 513 adult tribe members who lived on or near the reservation. The
participants had been.selected from patient registration lists provided by the Indian Health Service.
Adults interviewed provided information on fish consumption for themselves and for 204 children
under 5  years of age.

        Fish consumption rates are shown in Tables H-17 and H-18. The values reflect an annual
 average, but monthly variations were also reported.  Fish were consumed by over 90%  of the


June 1996                                    H-24                       SAB  REVIEW DRAFT
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population with, only 9T of the respondents reporting no fish consumption. The average daily
consumption rate during the two highest intake months was  107 8 grams/day, and the daily
consumption rate during the two lowest consumption months was 30 7 grams/day.  Members  who
were aged 60 years and older had  an average  daily consumption  rate of 74.4 grams/day  During the
past two decades, a decrease in fish consumption was generally noted among respondents in this
survey  The maximum daily consumption rate for fish reported for this group was  9^2 ^rams/day
                                        Table H-17
                        Fish Consumption by Columbia River Tribes,
                       Columbia River Inter-Tribal Commission, 1994
Subpopulation
Total Adult Population, aged 18 years and older
Children, aged 5 years and younger
Adult Females
Adult Males
Mean Daily Fish
Consumption (g/day)
59
20
56
63
                                        Table H-18
                       ' Fish Consumption by Columbia River Tribes
                       Columbia River Inter-Tribal Commission, 1994
                        Daily Fish Consumption Rates Among Adults;
Percentile
50th
90th
95th
99th
Amount (g/day)
29-32
97-130
170
389
       Tribes of Puget Sound

       A study of fish consumption among the Tulalip and Squaxin Island Tribes of Puget Sound was
completed in November of 1994 (Toy et al. 1995). The Tulalip and Squaxin Island Tribes live
predominantly on reservations near Puget Sound, Washington.  Both tribes rely on commercial fishing
as an important part of tribal income. Subsistence fishing and shell-fishing are significant parts of
tribal members economies and diets.

       The study was conducted between February and April in 1994. Fish consumption practices
were assessed by questionnaire and interview using dietary recall methods, food models and a food
frequency questionnaire.  The food frequency questionnaire was aimed as identifying seasonal
variability.  Questions in the interview included food preparation methods and obtained information on
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 the parts of the fish consumed. Fish consumed were categorized into anadromous fish (King salmon.
 socke\e salmon coho salmon, chum salmon, pwik salmon, steelhead salmon, salmon unidentified and
 smelt), pelagic fish icod. pollock, sable fish, spmv dogfish, rockfish. greenhng. herring and perch i.
 bottom tish i halibut, sole/flounder and sturgeon), and shell fish (marula clams, little clams, horse
 clams, butter clams, cockles, ovsters. mussels, shrimp, dungeness crab, red rock crab, scallops, squid.
 sea urchin, sea cucumbers and moon snails i

       Among consumers of anadromous fish, local waters (i.e., Puget Sound) supplied a mean ot
 80% of the fish consumed.  Respondents  from the Tulalip Tribes purchased a mean of approximately
 two-thirds of fish from grocery stores or restaurants, while among the Squaxin Island Tribe, the source
 of fish was about 50% self-caught and 50% purchased from grocery stores or restaurants. For bottom
 fish, members  of both tribes caught about half of the fish they consumed.  Anadromous  fish were
 much more likely to be consumed with the skin attached. Most other fish were consumed minus the
 skin.  Approximately 10% of the respondents consumed parts of the fish other than muscle: i.e.. head.
 bones, eggs.                "                                             ^

       Data on fish consumption were obtained for 263  members from the Tulalip and Squaxin Island
 tribes. The mean consumption rate  for women of both tnbes  was between 10-and-12-times higher
 than the default rate of 6.5 grams/day used by some parts of the United States government to estimate
 fish intake. Among male members  of both  tribes, the consumption rate was approximately 14-umes
 higher than the default rate.   The 50th percentile consumption rate for finiish for both tribes combined
 was 32 grams/kg body weight/day.  Male members of the Tulalip and Squaxin Island tribes had
 average body weights of 189 pounds and 204 pounds, respectively. Female members of the Tulalip
 and Squaxin Island tribes weighed on average 166 pounds and 150 pounds, respectively.  If an average
 body  weight is assumed to be 70 kg, the daily fish consumption rate for both tnbes for adults was 73
 grams per day with a 90th percentile value of 156 grams per day for total fish.  Fish consumption data
 for selected categories of fish are shown in Table H-19.
                                         Table H-19
              Fish Consumption (g/day) by the Tulalip and Squaxin Island Tribes
                                      (Toy et al. 1995)
Type of
Fish
Anadromous
Pelagic
Bottom
Shell
Fish
Other
Fish
Total
Finiish
Total
All Fish
5th
Percentile
.0087
.0000
.0000
.0000

.0000

.0200

.0495

50th
Percentile
.2281
.0068
.0152
.1795

.0000

.3200

.6081

90th
Percentile
1.2026
1026
1095
1.0743

.0489

.1350

2.2267

95th
Percentile
1.9127
2248
2408
1.4475

.1488

2.1800

3.2292

Mean
.4600
.0390
0482
.3701

.0210

.5745

1.0151

SE
0345
.0046
0060
.0343

.0029

.0458

.0865

95th
Percent CI
3925. 0.5275
0300. 0 0480
0364. 0 4375
.3027. 0.4375

.0152.00268

.4847. 0.6643

.8456. 1.1846

June 1996
H-26
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       During the survey period. 21 of the 263 tribal members surveyed reported fish consumption
rates greater than three standard-deviations from the mean consumption rate.  For example, six subjects
reported consumptions of 5 85.  6 26. 9.85. 11 o. 22.6 and  11.2 grams of tinfish and shell fish/kg body
weight/dav  If a 70-kg body weight is assumed these consumption rates correspond to 410. 438. 690.
770 and 1582 grams per  day.

       Molia^k Tribe

       A study of fish consumption among 97 nursing  Mohawk women in rural New York State was
conducted from 1986 to  1992 (Fitzgerald et al.r  1995). Fish consumption advisories had been issued m
the area due to PCB contamination of the local water body. Using food frequency history and a long-
term dietary history, the women were asked about their  consumption of locally caught fish during
three specific periods of time: during pregnancy, the year prior to pregnancy, and more than a year
before pregnancy.  For comparison, the study also surveyed fish consumption rates among 154 nursing
(primarily Caucasian) women from neighboring counties. The socioeconomic status of the women of
the control group were similar to that of the Mohawk women. The  fish in these counties had
background  PCB  concentrations.

       The results (See Table H-20) showed that the Mohawk women had a higher prevalence of
consuming locally caught fish than the comparison group in the two intervals assessed prior to the
pregnancy; the prevalence of local fish consumption during pregnancy for the two groups was
comparable.  A decrease  in local fish consumption  rates was also noted over time; these  may be
related to the issuance of advisories.
                                        Table H-20
                     Local Fish Meals Consumed By Time Period for the
          Mohawk and Comparison Nursing Mothers (Source: Fitzgerald et al., 1995)
Fish Meals/
Year
0
1-9
10-19
>19
During Pregnancy
Mohawk
64.9%
24.7%
5.2%
5.1%
Control
70.8%
15.6%
4.5%
9.1%
1 Year Before Pregnancy
Mohawk
43.3%
41.2%
4.1%
11.3%
Control
64.3%
20.1%
3.9%
11.7%
>1 Year Before
Pregnancy
Mohawk
20.6%
43.3%
6.2%
29.9%
Control
60.4%
22.7%
5.2%
11.7%
       Summary of Native American Angler Surveys

       Table H-10 summarizes the reported consumption rates of Native Ajnericans detailed here.
Table H-10 presents the range of fish consumption rates observed in the U.S. subpopulauon.
June 1996
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 1.4     Summary of Alaska and Hawaiian Fish Consumption Data

 Alaska

        The CSFH analyses of food intake by the U S Department of Agriculture include the 48
 contiguous states but have not included Alaska or Hawaii.  A number of investigators have published
 data on fish consumption in Alaska by members  ot native populations (e.g.. Intuits. Eskimos) and
 persons living in isolated surroundings.  These reports focus on nutritional/health benefits of high
 levels of fish consumption, food habits of native  populations,  and/or effects of bioaccumulation of
 chemicals in the aquatic food web.

        General Population

        After contacting professionals from the Alaskan health departments and representatives of the
 United States Centers for Disease CoSitrol in Anchorage, the authors of this report have not identified
 general population data on fish consumption among Alaskan  residents who are not pan of native
 population groups, subsistence fishers/hunters, or persons living in remote sites.  Patterns of fish
 consumption among urban residents (e.g., Juneau. Nome. Anchorage) appear not to be documented in
 the published literature.

        Non-urban Alaskan Populations

        Native people living in the Arctic rely on traditional or "country" foods for cultural and
 economic reasons. The purpose  of the current discussion is not to assess the comparative risks and
 benefits of these foods.  The risks and benefits of these food consumption habits have been compared
 by many investigators and health professionals (among others see Wormworth, 1995;  Kinloch et  al..
 1992: Bjerregaard, 1995).

        Diets of Native Alaskans differ from the  general population and rely more extensively on fish
 and marine mammals.  These are population groups that are characterized by patterns of food
 consumption that reflect availability of locally available foods and include food preparation techniques
 that differ from those usually identified in nutrient data bases. For example, Nobmann et al, (1992)
 surveyed a population of Alaska Natives  that included Eskimos (53%), Indians (34%), and Aleuts
 (13%).  The distribution of study participants was proportional to the distribution of Alaska Natives
 reported in the  1980 Census.  The 1990 Census identified an overall population of 85,698 persons as
 Alaska Natives.

        Quantitative information on dietary  intakes of Native Alaskan populations are few.  Estimates
 can be derived from harvest survey data,  but these have limitations because not all harvested animals
 are consumed nor are all edible portions consumed.  Other edible portions may be fed to domestic
 animals (e.g., sled dogs). Substantial variability in intake of foods including ringed seal, bearded seal,
 muktuk (beluga skin with an underlying  thin layer of fat) and walrus has been reported (Ayotte et al.,
 1995).

       Nobmann et al. (1992) indicated that Alaska Natives  have traditionally subsisted on  fish; sea
 mammals; game; a few plants such as seaweed, willow leaves, and sourdock, and berries such as
 blueberries and salmonberries rather than on a plant-based diet. In preparing a nutrient analysis of the
 food consumed in eleven communities that represented different ethnic and socioeconomic regions of
 Alaska, these investigators added nutrient values  for 210 foods consumed by Alaska Natives in
 addition to the 2400 foods present in the  Veteran's Administration's nutrient data base.  Nobmann et


June 1996                                     H-28                       SAB REVIEW DRAFT
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al (1992, round fish were an important part of the diet.  The mean daily intake of fish and shellfish ot
Alaska Natives was K>9 grams/day   Fish consumption was more frequent in the summer and fall and
game meat was eaten more often in the winter

       Traditional Native foods were still consumed in 1987-1988.  Among participants in this study.
42'?  reported consuming traditional fermented roods at some ume in their lives. Twenty-three percent
of the surveyed population ate them in the fall of 1988: the season when these foods were most
frequently eaten.  Fermented foods of aquatic  or marine origin that were reported to be consumed
included salmon heads and eggs, other fish and their eggs, seal, beaver, and whale.

       Consumption of marine mammals was reported among Yupik Eskimos living in either  a
coastal or river village of southwest Alaska (Parkinson et al., 1994). Concentrations of plasma
omega-3  fatty acids were elevated (between 6.8 and 13 times) among the Yupic-speaking Eskimos
living in two separate^ villages compared with  non-Native control subjects (Parkinson et al., 1994).
Concentrations of omega-3 fatty acids in plasma phospholipid has been shown to be a valid surrogate
of fish consumption (Silverman et al., 1990).  Among coastal-village participants the concentrations of
eicosapentaonoic and docosahexaenoic acids reflected higher consumption of marine fish and marine
mammals  and the use of seal oil in food preparation.  Among nver village Natives, the increase
reflected higher consumption of salmon.

Hawaiian  Islands

       As indicated above, the CSFII 89/91 did not include the Hawaiian Islands. To the knowledge
of the authors of the Mercury Study Report to Congress, data describing fish consumption by the
general Hawaiian population that estimate Island-wide levels of consumption have not been reported.
However,  reports on commercial utilization of seafoods (Hawaii Seafood. 198X; Higuchi and Pooley,
1985: Hudgins, 1980) and analysis of epidemiology data (Wilkens and Hankin, personal
communication, 1996) provide a basis to describe general patterns of consumption.  Overall,  seafood
consumption in Hawaii is much higher than in the contiguous United States. On a per capita basis  the
United States as a whole consumed 5.45 kg and 5.91 (12 and 13 pounds) of seafood in 1973 and
1977, respectively (Hudgins, 1980).   By contrast Hawaiian per capita consumption for all fish products"
was 11.14 kg (24.5 pounds) in  1972 and 8.77  kg (19.3 pounds) in 1974.

       The most popular species of fish and shellfish consumed were  moderately comparable between
Hawaii and the contiguous 48 states.  The methods of food preparation differed, however,  with raw
fish being far more commonly consumed in Hawaii (Hawaii Seafood, 198X).  Sampled at the retail
trade level the most commonly purchased fish were:  tuna, mahimahi, and shellfish [see also Table  H-
21 based on data in Higuchi and Pooley (1985)]. A survey of seafood consumption by families was
identified.  In 1987 the Department of Business and Economic Development (State of Hawaii,  1987)
conducted a survey of 400 residents selected on a random digit dialing basis of a population
representing 80% of total state seafood consumption.  All data were collected in July and August,
1987 and would not reflect any seasonal differences in fish/shellfish consumption.  The respondents
were asked to describe seafood consumption by their families. Shrimp  was the most popular seafood
with mahimahi or dolphin fish as the second most popular (Hawaii Seafood!, 1988). Reports on fish
consumption in Hawaii separate various species of tuna: ahi (Hawaiian  Yellowfin tuna, Bigeye tuna &
Albacore tuna), aku (Hawaiian Skipjack tuna), and tuna.  In 1987 nearly 66% of the 400 families
surveyed had seafood at least once a week and 30% twice a week.  Only 4% did not report consuming
seafood during the previous week based on a  telephone survey.
June 1996                                   H-29                       SAB REVIEW DRAFT
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 Data on
        Wilkens and Hankin (personal communication. 28 February' 1996) analyzed fish intake trom
 1S56 control subjects trom Oahu who participated in research studies  conducted by the Epidemiology
 Program of the Cancer Research Center ot Hawai'i. University of Hawai'i at Manoa.  These subjects
 uere asked about consumption over a one-year period prior to the interview  Within  this group the
 most commonly consumed fish was tuna [canned with tuna species undesignated (70.8 c"r ot sublets
 reporting consumption)): shrimp (47.7^ of subjects): tuna (yellowfin  fresh designated aku. ahi with
 42. 2°^  of subjects reporting consumption): mahimahi [(or dolphin) with 32.5^ of respondents
 reporting consumption]: and canned sardines (with 29. \7c of subjects  reporting consumption).
                                         Table H-21
                  Species Composition of Hawaii's Retail Seafood Trade, 1981
                     (purchases) as described by Higuchi and Pooley, 1985
Fish/Shellfish
Tuna
Ahi (Hawaiian
Yellowfin, Bigeye &
Albacore Tuna)
Billfish (including Swordfish)
and Shark
Mahimahi and ono (wahoo)
Akule (Hawaiian Big Eye
Scad) and opelu
Bottom fish
Reef fish
Shellfish
Shrimp
Lobster
Other species
Salmon/trout
Snapper
Frozen filets
Frozen Sticks/blocks
Total
Pounds Purchased
11,600,000
(5,400,000)
5,900,000
9.900.000
4,00,000
2,600.000
3.500.000
8,200,000
(4,200,000)
(900,000)
8,300,000
(1,500,000)
(1,800,000)
(2,300,000)
(1,400,000)
54,000.000
Percent of Total Purchases
20.9
11.3
17.7
6.9
7.0
5.3
15.5
15.4
100.0
June 1996
H-30
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 1.5     Mercury Concentrations In Fish

 1 5 1    Mcrcurv Concentrations In Marine Fish

        Human mercury intake from fish was estimated by combining data on mercury concentration
in fish species \Mth the reported quantities and types of fish species reported as consumed by 'users'
in CSFII/.X9-V 1.  The mercury concentrations  in the consumed tish reported by the CSFTI/89-91 were
estimated using data on mercury concentration in fish expressed as micrograms of mercury per gram
fresh-weight of fish tissue.

       The CSFTI/89-91 is one of the United States Department of Agriculture's (USDA's) food
consumption surveys.  The food items reported by individuals interviewed are idenufied by 7-diait
food codes.  The USDA has developed a recipe file identifying the primary components that make up
the food or dish reported "as eaten" by a survey respondent.  The total weight of a fish-containing
food is typically not 100% fish. The food code specifies a preparation method and gives additional
ingredients used in preparation of the dish.  For example, in the Recipe File "Fish, floured or breaded.
fried" contains 84%  fish, by weight.  Fish dishes contained a wide range of fish:  from approximately
5% for a frozen "shrimp chow mein dinner with egg roll and peppers" to 100% for fish consumed
raw, such as raw shark.

        Data describing methylmercury concentrations in marine fish were predominantly based on the
National Marine Fisheries' Service (NMFS) data base, the largest publicly available data base on
mercury concentrations in marine fish.  In the early 1970s, the NMFS conducted  testing for total
mercury on over 200 seafood  species of commercial and recreational interest (Hall et al.. 1978).  The
determination of mercury in fish was based on flameless (cold vapor) atomic absorption
spectrophotometry following chemical digestion of the fish sample.  These methods are described in
Hall et al. (1978). Data supplied by NMFS give the mercury concentration in fresh weight of fish
muscle of numerous marine fish, shellfish, and other molluscan species shown in Tables H-22. H-23
and H-24.

       Although the NMFS data were initially compiled beginning in the 1970s, comparisons of the
mercury concentrations identified in the National Marine Fisheries Service's data base with compliance
samples obtained by the United States Food and Drug Administration indicate that the NMFS data are
appropriate to use in estimating intake of mercury from fish at the national level  of data aggregation.
Cramer (1994) of the Office of Seafood of the Center for Food Safety and Applied Nutrition of the US
Food and Drug Administration reported on  "Exposure of U.S. Consumers to Methylmercury from
Fish".  He noted that recent information from National Marine Fisheries Service (NMFS) indicated that
the fish mercury concentrations reported in  the 1978 report do not appear to have changed
significantly. The US FDA continues to monitor methylmercury concentration in  seafood.  Cramer
(1994) observed that results of recent US FDA surveys indicate results parallel to FDA's and NMFS's
earlier findings.  To illustrate, Cramer estimated the mean methylmercury content of the  1973 samples
of canned tuna at 0.21 ppm mercury, whereas a recently completed survey of 245 samples  of canned
tuna was 0.17 ppm mercury.  These data are considered to be comparable, although the small decrease
reported between these two studies may reflect increased use in canned tuna of tuna species with
slightly lower average methylmercury concentrations.  The National Academy of Sciences' National
Research Council's Subcommittee on Seafood Safety  (1991) also assessed the applicability of the
NMFS' 1970's  data base to current estimates of mercury concentrations in fish.  This subcommittee
also concluded that the 1978 data base differed little in mercury concentration from FDA compliance
samples estimating mercury concentrations in fish.
June 1996                                    H-31                       SAB REVIEW DRAFT
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                                     Table H-22  '
                       Mercurv Concentrations in Marine Finfish
Fish
Am.ho<.\ '
Barracuda. Pacific"
Cod3
Croaker. Atlantic
Eel. American
Flounder4
Haddock
Hake5
Hahbut6
Hemng7
FOngtish3
Mackerel9
Mullet10
Ocean Perch"
Pollack
Pompano
Porgy
Ray
Salmon
Sardines13
Sea Bass
Shark14
Skate15
Smelt. Rainbow
Snapper16
Sturgeon
Swordfish
Tuna18
Whiting (silver hake)
Mercury Concentration
u§'g. wet weight)
0.047
0 177
0.121
0 125
0.213
0.092
0.089
0.145
025
0013
0.10
0081
0.009
0.116
0.15
0.104
0.522
0.176
0.035
0 1
0.135
1.327
0.176
0.1
0.25
0.235
095
0.206
0.041
Source of Data
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
NMFS
FDA Compliance Testing
NMFS
NMFS
June 1996
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                                               Table H-22  Footnotes

  This is the  average ot N'MFS mean mercury ..on^entrationi for both -.tnped ancho\\ 0 OX_ ui'sii and northern ancho\\
i 0010 ug'gi
• USD A data ha>e •specified the consumption of the Pacific Barracuda and not the Atlantic Barracuda.
' The merman Content ror cod ^ the average or the mean concentrations in Atlantic Cod iC 114 un/a and the Pacific Cod
 oiri^j.
  The mer^ur> ..ontent tor tlounder is the average ot the mean concentrations measured m  9 t\pes ot tlounderGult iO 147
.ug/g). ^mmer '() 127 ug;gi. southern (0.078 |jg/g). four-spot (0090 ug/g). wmdowpane >0  i51  .ug/g). arrowtooth (0.020
ug/g). witch (0083 ug/g). >ellowtail (0.067 ug/g). and winter (0.066 ug/g).
  The mercury content tor Hake is the average of the mean concentrations measured in 6 types of Hake,  silver (0041  ug/g).
Pacific (0 091 ,ug/g).  spotted (0042 ug/g).  red (0.076 ug/g). white (0.112 ug/g). and blue (0.405 ug/g).
6 The mercury content tor Halibut is the average ot" the mean concentrations measured in 3 types of Halibut: Greenland.
Atlantic, and Pacific.
7 The mercury content for Hemng is the average ot" the mean concentrations measured in 4 types ot" Hemng: blueback ((70
ug/g). Atlantic (0.012 ug/g). Pacific  (0.030 ug/g). and round (0.008  ug/g).
8 The mercury content for Kingfish is the average of the mean concentrations measured in 3 types of Kingfish: Southern.
Gulf, and Northern.
  The mercury content for Mackerel is the average ot" the mean concentrations measured in 3 types of  Mackerel, jack lO 138
ug/g). chub (0.081 ug/g). and Atlantic (0.025 ug/g)
10 The mercury content for Mullet is the average of the mean concentrations measured in 2 types of Mullet: stnped (0011
ug/g) and silver (0007 ug/g).
11 The mercury content for Ocean Perch is the average of the mean concentrations measured in 2 types of Ocean Perch:
Pacific (0 083 ug/g) and Redfi'sh (0 149 ug/g)
   The mercury content for Salmon is the average of the mean concentrations measured in  5 types of Salmon: pink (0 019
ug/g). chum (0.030 ug/g). coho (0.038 ug/g). sockeye (0.027  ug/g).  and chinook (0.063  ug/g)-
'" Sardines were estimated  from mercury concentrations in small Atlantic Herring.
14 The mercury content for Shark is the average of the mean concentrations measured m 9 types of Shark: spiny dogfish
(0.607 ug/g). (unclassified)  dogfish (0.477 ug/g), smooth dogfish (0.991  ug/g), scalloped hammerhead (2.088 ug/g), smooth
hammerhead (2.663 ug/g). shorttin mako (2.539 ug/g). blackup shark (0.703 ug/g). sandbar shark (1.397 ug/g). and
thresher shark (0 481 ug/g).
15 The mercury content for skate is the average of the mean concentrations measured in 3  types of skate: thorny skate (0.200
ug/g). little skate 0.135 ug/g) and the winter skate (0.193 ug/g).
16 The mercury content for snapper is the average of the mean concentrations measured in types ot" snapper:
   The mercury content for sturgeon is the average of the mean concentrations measured in 2 types of  sturgeon.green
sturgeon (0.218 ug/g) and white sturgeon (0.251 ug/g).
18 The mercury content for tuna is the average of the mean concentrations measured in 3 types of tuna: albacore tuna (0.264
Mg/g). skipjack tuna (0.136  ug/g) and yellowfin tuna (0.218 ug/g)
June  1996                                             H-33                             SAB REVIEW  DRAFT
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                                                  Table H-23
                               Mercurv  Concentrations in Marine Shellfish
Shellfish
Ahalone1
Clam"
Crab3
Lobster4
Oysters0
Scallop6
Shrimp
Mercury Concentration
 al red shnmp
 (0074  ug/g), white shnmp (0.054 ug/g), brown shrimp (0.048 ug/g). ocean shnmp (0.053 ug/g),  pink shnmp (0.031 ug/g).
 pink northern shrimp (0.024 ug/g) and Alaska (sidestnpe) shnmp (0.042  ug/g).
                                                 Table H-24
                      Mercury Concentrations  in Marine Molluscan Cephalopods
Cephalopod
Octopus
Squid1
Mercury Concentration
(ug/g wet wt.)
0.029
0.026
Source of Data
NMFS
NMFS
1 The mercury content for squid is the average of the mean concentrations measured in 3 types of squid:  Atlantic
longfinned squid (0.025 |ag/g), short-finned squid (0.034 ng/g), and Pacific squid (0.018
June 1996
H-34
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                                     Table H-25
                   Summary of Mercury Concentrations in Fish Species
                  Micrograms Mercury per Gram Fresh Weight (ug Hg/g)
Data Used by US EPA
Mercufv Studv Report to
Congress
In Review
Fish Species
Abalone
Anchovies
Bass.
Freshwater
Bass. Sea
Bluefish
Bluegills
Borneo
Bonuo
Butterfish
Carp.
Common
Catfish
(channel. large
mouth, rock.
striped, white)
Catfish
(Marine)
Clams
Cod
Average
(Hg Hg/g)
0.016
0047
Avgs.= 0.157
(Lowe et al.,
1985) and
0.38 (Bahnick
et al.. 1994)
Not Reported
Not Reported
0.033
Not Reported
Not Reported
Not Reported
0.093
0.088
Not Reported
0.023
0.121
Data Used by US FDA
Report on the Chance of U.S.
Seafood Consumers Exceeding "The Current
Daily Intake for Mercury and Recommended
Regulatory Controls"
1978
Fish
Species
Abalone
Anchovies
Bass.
Striped
Bass. Sea
Bluefish
Bluegills
Bonito
(below
3197)
Bonito
(above
3197)
Butterfish
Carp
Catfish
(freshwater)
Catfish
(Marine)
Clams
Cod
Average
(ug Hg/g)
0.018
0.039
0.752
0.157
0.370
0.259
0.302
0.382
0.021
0.181
0.146
0.475
0.049
0.125
Maximum
(jig Hg/g)
0.120
0.210
2.000
0575
1.255
1.010
0.470
0.740
0.190
0.540
0.380
1.200
0.260
0.590
Data Used by Stern et al.
1996
Fish
Species
Not
Reported
(NR)
NR
Bass.
freshwater
Sea Bass
Bluefish
NR
NR
NR
Butterfish
Catfish.
freshwater
Clams
Cod/Scrod
See crab.
Crab
Average
-------
                                Table H-25 (continued)
                    Summary of Mercury Concentrations in Fish Species
                  Micrograms Mercury per Gram Fresh Weight (ug Hg/g)
Data Ised by LSEPA
Mercurv Studv Report to
Congress
In Review
Fish Species
Crab. King
Crab
Crappie
(black, white)
Croaker
Dolphin
Drums.
Freshwater
Flounders
Groupers
Haddock
Hake
Halibut
Halibut
Halibut
Halibut
Hemng
Kingfish
Lobster
Lobster
Average
(ug Hg/g)
0.070:
Calculations
based on 5
species of
crab
combined at
0 117
0117
0.114
0.125
Not Reported
0.117
0.092

0.089
0.145
0.250
0.250
0.250
0.250
0.013
0.100
0.232
0.232
Data Lsed by US FDA
Report on the Chance of U.S.
Seafood Consumers Exceeding "The Current
Daily Intake for Mercury and Recommended
Regulatory Controls"
1978
Fish
Species
Crab. King
Crab, other
than HI
Crappie
Croaker
Dolphin
Drums
Flounders
Groupers
Haddock
Hake
Halibut 4
Halibut 3
Halibut 2H
Halibut 25
Herring
Kingfish
Lobster.
Northern 11
Lobster
Northern 10
Average
-------
                                 Table H-25 (continued)
                   Summary of Mercury Concentrations in Fish Species
                  Micrograms Mercury per Gram Fresh Weight (ug Hg/g)
Data Lsed by L'SEPX
Mercury Study Report to
Congress
In Review
Fish Species
Lobster
Spiny
Mackerel
Mackerel
Mackerel
• Mackerel
Mackerel
Mackerel
Mullet
Oysters
Perch.
White and
Yellow
Perch.
Ocean
Pike.
Northern
Pollock
Pompano
Rockfish
Average
(ug Hg/g)
0.232: ,
Includes spiny
(Pacific)
lobster=0.210
0.081:
Averaged
Chub = 0.081.
Atlantic^
0.025:
Jack=0.138
0.081
0.081
0081.
0.081
0.081
0.009
0.023
0.110
0.116
0.310
0.127
0.150
0.104
Not Reported
Data Lsed by LS FDA
Report on the Chance of U.S.
Seafood Consumers Exceeding "The Current
Daily Intake for Mercury and Recommended
Regulatory Controls"
1978
Fish
Species
Lobster.Spin
y
Mackerel.
Atlantic
Mackerel.
Jack
Mackerel.
King (Gulf)
Mackerel.
King (other)
Mackerel.
Spanish 16
Mackerel.
Spanish 10
Mullet
Oysters
Perch.
Freshwater
Perch.
Marine
Pike
Pollock
Pompano
Rockfish
Average
(ug Hg/g)
0.113
0.048
0.267
0.823
1.128
0.542
0.825
0.016
0.027
0.290
0.133
0.810
0.141
0.104
0.340
Maximum
(ug Hg/g)
0.370
0 190
0.510
2.730
2.900
2.470
1.605
0.280
0.460
0.880
0.590
1.710
0.960
8.420
0.930
Data Lsed by Stern et al.
1996
Fish
Species
Lobster
Mackerel
Mackerel
Mackerel
Mackerel
Mackerel
Mackerel
Mullet
NR
Perch
NR
NR
NR
NR
NR
Average
lug Hg/g)
025
028
0.28
0.28
0.28
0.28
028
0.05

0.18





June 1996
H-37
SAB REVIEW DRAFT
-------
                                 Table H-25 (continued)
                    Summary of Mercury Concentrations in Fish Species
                  Micrograms Mercury per Gram Fresh Weight (ug Hg/g)
Data Lsed by L'SEPA
Mercurv Studv Report to
Congress
In Review
Fish Species
Sdblefish
Salmon
Scallops
Scup
Sharks
Shnmp
Smelt
Snapper
Snapper
Snook
Spot
Squid
Octopi
Suntlsh
Sword fish
Tillefish
Trout.
Trout
Average
-------
                                Table H-25 (continued)
                   Summary of Mercury Concentrations in Fish Species
                  Micrograms Mercury per Gram Fresh Weight 
-------
                                Table H-25 (continued)
                   Summary of Mercury Concentrations in Fish Species
                  Micrograms Mercury per Gram Fresh Weight lug Hg/g)
Data Lsed by LSEPV
Mercury Mudy Report to
Congress
In Review
Fish Species
Walleve




Trout (brown.
lake, rainbow)




Average
(ug Hg/g)
0.100 (Lowe
et al.. 1985)
and 0.52
(Bahnick et
al.. 1994).
0.149 (Lowe
et al.. 19851
and 0.14
(Bahnick et
al.. 1994 for
brown trout).
Data Lsed by L'S FDA
Report on the Chance of U.S.
Seafood Consumers Exceeding "The Current
Dailv Intake for Mercurv and Recommended
Regulatory Controls"
1978
Fish
Species











Average
(ug Hg/g)











Maximum
(ug Hg/g)











Data L'sed by Stern et al.
1996

Fish
Species











Average
(fig Hg/g)











Fish Species Reported by the State of New Jersey
and Not Reported by EPA or FDA
Blowfish
Orange roughy
Sole
Weakfish
Porgy
Blackfish
Whiting
Turbo t
Sardines
Tilapia


















































0.05
05
012
0.15
0.55
025
0.05
0.10
0.05
0.05
June 1996
H-40
SAB REVIEW DRAFT
-------
        Central tendency estimate^ of seafood mercury- concentrations were utilized in the report  Thi.s
seems appropriate since commercial seafood is widely distributed across the L" S  (Seafood Safety .
1991)   The source of a particular fish purchase is generally not noted by the consumer te a . canned
tunai   As a result, a randomness and averaging may be achieved.  Additionally, only common names
of commercial seafood were utilized, specific species which could be considered to be that type ot ri>h
were included in the central tendency estimate.  Again, typical Lonsumers  v\ere assumed to generally
not be aware of the species ot fish they were consuming, rather just the type.

        As noted above, there are other estimates ot" mercury concentrations in seafood. After the
analysis ot mercury exposure from seafood  was completed for this Report, two other databases were
obtained:  U.S. FDA and Stern et al., 1996.  These data are presented in Table H-22  for comparison
with those data used for this analysis.

1.5.2    Mercurv Concentrations In Fresh-water Fish

        Estimation of average mercury concentrations in fresh-water finfish. from across the U.S.
required a compilation  of measurements of fish mercury concentrations from randomly selected U.S.
water bodies.  A large  number of sources of mercury concentrations in fish were not used in this part
of the assessment.  Mercury concentrations  in fish have been analyzed for a number of years in many
local or regional water bodies in the U.S.; several of these studies are detailed  in this Report, and the
results of these as well as other studies are listed in Tables 2-2 through 2-9 in  Chapter 2 of Volume
III. Data described in this body of literature are a collection of individual studies which characterize
mercury concentrations in fish from specific geographic regions such as individual water bodies or in
individual states.  Many of the studies were initiated because of a problem, perceived or otherwise,
with mercury concentrations in the fish or the water body.  Thus, the sample presented  by a
compilation of these data may be biased toward the high-end of the distribution of mercury
concentrations in fresh-water fish.  Additionally, the methods varied from study to study, and there is
no way of determining the consistency of the reported data from study to study.

        Two studies, more national in scope, are thought to provide a more complete picture of
mercury concentrations in U.S. fresh-water finfish populations:  "National Contaminant Biomonitoring
Program:  Concentrations of Seven Elements in Fresh-water Fish, 1978-1981"  by Lowe et al. (1985)
and "A  National Study of Chemical Residues in Fish" conducted by  U.S. EPA (1992) and also
reported in Bahnick et  al. (1994).

        Lowe et al.  (1985) reported mercury concentrations in fish from the National Contaminant
Biomonitoring Program. The fresh-water fish data were collected between  1978-1981 at 112 stations
located  across the United States.  Mercury was measured by a flameless cold vapor technique,  and the
detection limit was 0.01 ng/g wet weight.  Most of the sampled fish were taken from rivers (93 of the
112 sample  sites were rivers); the other 19 sites included larger lakes, canals,  and streams.  Fish
weights and lengths were consistently recorded. A wide  variety of types of fishes were sampled:
most commonly carp, large mouth bass and white sucker. The geometric mean mercury concentration
of all sampled fish was 0.11 |ig/g wet weight; the minimum and maximum concentrations reported
were 0.01  and 0.77  ug/g wet weight, respectively.  The highest reported mercury concentrations (0.77
ug/g wet weight) occurred in the northern squawfish of the Columbia River. See Table H-26 for
mean mercury concentrations by fish species.
June 1996                                    H-41                       SAB REVIEW DRAFT
-------
                                          Table  H-26
               Fresh-water Fish Mercury Concentrations from Lowe et al.. (1985)
Species
Bass
B loater
Bluegill
Smallmouth Buffalo
Carp. Common
Catfish (channel, largemouth. rock, stnped. white)
Crappie (black, white)
Fresh-water Drum
Northern Squawfish
Northern Pike
Perch (white and yellow)
Sauger
Sucker (bridgelip. carpsucker, klamath, largescale, longnose,
rivercarpsucker. tahoe)
Trout (brown, lake, rainbow)
Walleye
Mean of all measured fish
Mean Mercury Concentration ^ig/g
(fresh weight)
0.157
0.093
0.033
0.096
0.093
0088
0 114
0 117
0.33
OJ27
0.11
0.23
0.114
0.149
0.100
0.11
        "A National Study of Chemical Residues in Fish" was conducted by U.S. EPA (1992) and also
reported by Bahnick et al. (1994).  In this study mercury concentrations in fish tissue were analyzed.
Five bottom feeders (e.g., carp) and five game fish (e.g., bass) were sampled at each of the 314
sampling sites in the U.S. The sites were selected based on proximity to either point or non-point
pollution sources.  Thirty-five "remote" sites among the 314 were included to provide  background
pollutant concentrations.  The study primarily targeted sites that were expected to be impacted  by
increased dioxin levels.  The point sources proximate to sites of fish collection included these:  pulp
and paper mills, Superfund sites, publicly owned treatment works and other  industrial  sites.  Data
describing  fish age, weight, and sex were not consistently collected.  Whole body mercury
concentrations were determined for bottom feeders  and mercury concentrations in fillets were analyzed
for the game  fish.  Total  mercury levels were analyzed using flameless atomic absorption; the reported
detection limits were 0.05 ug/g early in the  study and 0.0013 ug/g as analytical technique improved
later in  the analysis.  Mercury was detected in fish  at 92% of the  sample sites. The maximum
mercury level detected was 1.8 ug/g, and the mean across all fish and all sites was 0.26 ug/g.  The
highest  measurements occurred in walleye, large mouth bass  and carp. The  mercury concentrations in
fish around publicly owned treatment works were highest of all point source data; the  median value
measured were 0.61 ug/g. Paper mills were located near many of the sites where mercury-laden fish
June 1996
H-42
SAB REVIEW DRAFT
-------
     detected.  Table H-27 contains the mean mercury concentrations of the species collected b>
Bahruck et al. ( 1994).

        Both the studies reported by Lowe et al. (1985) and by Bahruck et al. (1994) appear to be
svstematic. national collections of fish pollutant concentration data.  Clearlv. higher mercurv
concentrations in  fish have been detected in other analyses, and the \alues obtained m these studies
should be interpreted as a rough approximation or' the mean concentrations in fresh-water finfishes.
As indicated in the range of data presented in Tables H-26 and H-27. as well as  the aforementioned
Tables in Chapter 2. wide variations are expected in data on  mercury concentrations in freshwater fish.

        The mean mercury concentrations in all fish sampled vary by a factor of two between the
studies. The mean mercury  concentration reported  by Lowe et al. was 0.11 ug/g, whereas the mean
mercury concentration reported by Bahnick et al. was 0.26 ug/g.  This difference can be extended to
the highest reported mean concentrations in fish species. Note that the average mercury concentrations
in bass  and walleye reported by Bahnick's data are higher than the  northern squawfish, which is the
species  with the highest mean concentration of mercury identified by Lowe et al. (1985).

        The bases for these differences in methylmercury concentrations are not immediately obvious.
The  trophic positions of the species sampled, the sizes of the fish, or ages of fish sampled could
significantly increase or decrease the  reported mean mercury concentration. Older and larger fish,
which occupy higher trophic positions in the aquatic food chain, would, all other factors being  equal,
be expected to have higher mercury concentrations.  The sources  of the fish also influence fish
mercury concentrations.  Most of the fish obtained  by Lowe et al. (1985) were from rivers. The fate
and transport of mercury in river systems is less well characterized  than in small lakes. 'Most of the
data  collected by  Bahnick et al. (1994) were collected with a bias toward more
contaminated/industrialized sites, although not sites specifically contaminated with mercury.  It could
be that there is more mercury available to the aquatic food chains at the sites reported by Bahruck et
al. (1994).  Finally, the increase in the more recent data as reported in Bahnick et al.,  1994 could be
the result of temporal increases in mercury concentrations.

        There is a degree of uncertainty in the  mercury concentrations selected for this assessment.
This uncertainty reflects both the adequacy of the sampling protocol for this application and the known
variability in fish body burden.  The variability in these data is as broad as the range of reported
concentrations, which extends from non-detect (below 0.01 ug/g wet weight) up to 9 ug/g wet weight.
Where possible, when specific fresh-water fish species are described in the USDA 3-day consumption
studies, the  mean methylmercury concentration for  that particular species was derived in two separate
calculations based on the data on methylmercury concentration in the fish reported by Lowe et al.
(1985) and by Bahnick et al. (1994).

        Data for mean mercury concentration in fresh-water fish from  Bahnick et al. (1994) were
combined with the U.S. consumption rates for  fresh-water fish from CSFII/89-91 to estimate
methylmercury intakes for the population.  The concentrations in the fish utilized are shown in Table
H-27.  The exposure estimates for fresh-water  fin fish consumption are found in Table H-28.  Bahnick
et al. (1994) fresh-water fish concentration data were utilized, along with data on mercury
concentrations in  marine fish and shellfish (Tables  H-22, H-23, H-24)  to calculate total exposure,  for
general  U.S. population, to  mercury through consumption of fish and shellfish (shown in Table H-28).
     f 1
        Some species of fresh-water fishes were not sampled by Bahnick et al. (1994), and some
respondents in the USDA CSFII/89-91  survey  did not identify the type of fresh-water fish consumed.
In these situations, it was assumed that the fish consumed contained 0.26 ug methylmercury/g, which


June 1996                                     H-43                       SAB REVIEW DRAFT
-------
                                           Table H-27
                            U.S. EPA (1992) and Bahnick et al. (1994)
Freshwater Fish
Carp
Sucker1
Catfish. Channel and Flathead
Bass2
Walleye
Northern Pike
Crappie
Brown Trout
Mean All Fish Sampled
Average Mercury Concentration lfie/g. wet weight)
0 11
0.167
0.16
0.38
0.52
0.31
0.22
0.14
0.26
        ' The value presented is the mean of the average concentrations found in 3 types of Sucker fish (White. Redhorse
        and Spotter).

        • The value presented is the mean of the average concentrations found in 3 types of Bass (White. Largemouth and
        Smallmouth).
is the average of all sampled fish Bahnick et al. (1994). It is important to note that the freshwater fish
data are for wild populations not farm-raised fish.

2.     CALCULATION OF MERCURY CONCENTRATIONS IN FISH DISHES

       To estimate the mercury intake from fish and fish  dishes reported as consumed by respondents
in the CSFII 1989/1991 survey several steps were taken.  Using the Recipe File available from USDA,
the  fish species for a particular reported food was identified.  The average mercury concentration in
fish tissue on a fresh (or wet) weight basis  was identified using the NMFS data or the data reported by
Bahnick et al. (1994).  The food intake of the U.S. population includes a large number of components
of aquatic origin.  A few of these appear not to have been analyzed for mercury concentrations.
Methylmercury concentration data were not available for some infrequently consumed food items: e.g..
turtle, roe or jelly fish.  Data on the quantity of fish present in commercially prepared soups were also
not available and were excluded from the analysis.

       Physical changes occur to a food when it is processed and/or cooked.  The NMFS and
Bahnick et al. (1994) data bases were used to estimate  mercury intake report mercury concentrations
on a ug mercury per gram of fresh tissue basis. Earlier research (Bloom, 1992) indicated that over
90% of mercury present in fish and shellfish is chemically speciated as methylmercury which  is bound
to protein in fish tissue.  Morgan  et al.,  (1994) indicated that over 90% of mercury present in  fish and
shellfish is chemically  speciated as methylmercury.  Consequently the quantity of methylmercury
present in the fish  tissue in the raw state will remain in the cooked or processed fish.  In cooking or
processing raw fish, there is typically  a reduction in the percent moisture in the food.  Thus, mercury
concentration data  were recalculated to reflect the loss  of moisture during food processing,  as  well as


June 1996                                    H-44                        SAB REVIEW DRAFT
-------
retention or methvlmercury in the remaining lowered-moisture content fish tissues.  Standard estimates
of cooking/processmg-related weight reductions were provided by Dr. Betty Perloff and Dr Jacob
Exler. experts in the L'SDA recipe file and in USDA's food composition.  F'ercent moisture lost tor
baked or broiled fish was 25°c.  Fried fish products lose weight through loss of moisture but add
weight trom tat added during frying for a total weight loss of minus  12*3-. The percent moisture in
tish that were dried, pickled or smoked was identified tor individual  tish species te.g.. hernna. i_od.
trout, etc.) from US DA handbooks of food composition. Information on the percent moisture in the
raw, and in the dried, smoked or pickled fish was obtained. The methylmercury concentration in the
fish was recalculated to reflect the increased methylmercury concentration of the fish as the percent
moisture decreased in the drying, pickling or smoking process.

2.1    Additional Application Of Mean Mercury Concentrations In Fresh-water Fish

       The mean mercury concentrations for all fish from Lowe et aJ. (1985) and Bahnick et al.
(1994)  were combined with the freshwater fish consumption data to estimate a range of exposure  from
total fish consumption.  Given the human fish consumption rates and the differences between the
mercury concentrations in the two data sets, it is important to use data from,  both studies of mercury
exposures to assess mean concentrations in fish.  For purposes of comparison both sets of data were
utilized to illustrate the predicted methylmercury exposure.  For this  comparison, the average mercury
concentrations for fish in the Lowe and the Bahnick data were analyzed separately by combining  the
freshwater fish data with the data in Tables H-22 through H-24.  The bodyweight data were obtained
from Table H-36 (Consumption of Fresh-water fish and Self-Reported Bodyweight among
Respondents of the 1989-1991 CSFII 89-91) and the fresh-water fish consumption rates were obtained
from Table H-36. Exposure to methylmercury based on an assumption of 0.11 ug methylmercury/g
fish tissue (wet weight) (Lowe et al., 1985). These values are estimated on a body weight basis.
Tables  H-26 and H-27 were developed using the mean data on mercury concentrations for all fishes
sampled for these two studies.

3.     INTAKE OF METHYLMERCURY FROM FISH/FISH DISHES

3.1    Intakes "per User" and "per Capita"

       Estimated intake of methylmercury from fish can be made by summing the grams of fish
consumed and dividing by the number of individuals surveyed to obtain a "per capita" intake of fish.
"Per capita" consumption of total fish is 13.5  grams per day with an overall  freshwater fish mean
intake of 2.2 grams per day (standard deviation of 13.7 grams per day) for ail 11,706 individuals
surveyed.  Overall, 30.9% of the surveyed population reported eating fish at least once during the
three-day survey period. The extreme variability of this estimate (standard deviation approximately
six-times the mean fish intake) indicates the high degree of heterogeneity of the population.  A
fundamental determinant of this  heterogeneity is the subset of the population who consumes fish on a
sufficiently regular basis that fish appears in the individuals diets at least once within the three-day
period surveyed.  In order to predict the impact of consumption of a chemical contaminant almost
exclusively contained in fish, the choice was made to report data on  a "per user" basis.

3.2    Fish Intake by Age and/or Gender Grouping of Subpopulations

       The age and gender distribution of respondents to the CSFII/89-91 survey are shown in Table
H-5. Tests of statistical significance comparing distribution of fish consumption by  age and gender
have not been conducted.  Generally the percent of the population ages birth through 14 years who eat
fish is smaller (19.0% of persons ages  14 years or younger) than the representation of this group in the


June 1996                                    H-45                       SAB REVIEW  DRAFT
-------
 overall survey population (24 7T >  The percent of adults ages 45 and older who reported
 consumption (59.2) is higher than their proportion in the overall surveyed population (32.8C>>

        The percent of males  and females who report eating fish is comparable to their distribution in
 the surveyed population.  Males overall and within each age category consumed more fish expressed
 either  as total grams per day or per kilogram self-reported body weight.  Average self reported hod\
 weights are shown in Tables H-7. H-8. H-32.  H-34. H-36. H-38 and H-40. and the fish consumption
 data for all age and gender distribution are shown in Tables H-5. H-6. H-31, H-33, H-35. H-37  and H-
 39.  The data specific to this survey rather than default values of 70 kilograms have been used in
 reporting fish and methylmercury intake data for CSFII/89-91.

        Although children ages birth through 14 years consumed smaller total amounts of fish than did
 older survey respondents, children were exposed to just over twice as much methylmercury relative to
 body size when exposure is expressed on  a per kilogram body weight basis.  These data are presented
 in Table H-7. Fish consumption among adult women of child-bearing age are shown in Table H-7.

        Estimates of methylmercury intake from fish have been calculated for children ages birth
 through 14 years.  These data are presented in Tables H-28, H-29.  H-30 and H-41.  Subpopulations
 within the general population  may be of concern because of their vulnerability to the toxicity of
 methylmercury or because of  increased likelihood of elevated exposure to methylmercury.  Particular
 emphasis has been given to the frequency of fish consumed by women of child-beanng age as
 methylmercury is a developmental toxicant. Children ages birth through 14 years have methylmercury
 exposures between two and three times adult levels  when methyimercury intake is expressed on a per
 kilogram body weight basis.

 3.3     Types of Fish Consumed

        To interpret the data presented in  Tables H-28 through H-41, it is essential to recognize that
•the data reported for a particular category of fish (e.g., marine finfish,  fresh-water fintish, tuna fish,
 etc.) are average intakes per day for persons who report consumption of a particular type of fish.
 Because an individual may consume more than one  type of fish during the three-day survey period.
 this individual may appear in  more than one category of fish consumer.  For this reason the number of
 consumers of individual categories of fish (e.g., tuna, shellfish) sum to more than the number of
 consumers of fish and shellfish reported in Table H-6.
             £
        In CSyil/89-91, marine finfish were consumed to a far greater extent than fresh-water fish.
 For example, marine finfish (excluding tuna, swordfish, barracuda, and shark) were consumed by
 1595 respondents, tuna by 1444 respondent, swordfish/barracuda/shark by 21 respondents for a total of
 2060 reported consumptions; this is in contrast to 492 respondents  reporting consumption of fresh-
 water finfish. For individual species of fish, tuna was the most commonly consumed fish.  Shellfish
 were consumed more often than fresh-water finfish,  but to a far smaller extent than marine finfish.

3.4    Methylmercury Consumption

       Quantities of methylmercury consumed in fish depend upon both the quantity of fish
consumed and the methylmercury concentration of the fish.  Although they are infrequently consumed.
swordfish, barracuda and shark have a much higher  methylmercury concentration than other marine
finfish, fresh-water finfish or shellfish.  By contrast  most shellfish contain low concentrations of
methylmercury resulting in far lower methylmercury exposures than would occur if finfish species
were chosen.
June 1996                                    H-46                        SAB REVIEW DRAFT
-------
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                                       Table H-31
  Respondents Reporting Consumption of Marine Finfish (Excluding tuna, swordfish, barracuda.
           and shark) in the 1989-1991 CSFII Survev  Based on 3-Davs Diet Records
Gtnder
Miles
Females
Total
Vged 14 Years or
Younger
164 (53 4%)
U3 (46 6%)
307 (192%)
Aged 15 through 44
Years
268 (445%)
334 (55 5%)
602 (377%)
Aged 45 Years or
Older
271 (396%)
413 (604%->
684 (42 9%)
Total
"03 ;44 l%»
390(559%)
1593
                                       Table H-32
 Consumption (gms/day) of Marine Finfish (Excluding Tuna, Shark. Barracuda, and Swordfish)
 and Self-Reported Body Weight (Kg) among Respondents of the 1989-1991 CSFII Survey. Data
                                    for "Users" Only
Gender



Males
Females
Aged 14 Years or
Younger
Mean

34.4
268
SD

22.3
22.1
kgbw

28
23
Aged 15 through 44
Years
Mean

46-.0
379
SD

28.3
276
*gbw

80
63
Aged 45 Years or Older

Mean

46.7
40.4
SD

31.8
28.0
kgbw

82.9
672
Total

Mean

44 1
36 8
SD

290
273
kgb
W
836
51 1
  Data weighted to reflect U S. population.
                                       Table H-33
 Respondents Reporting Consumption of Tuna in the 1989-1991 CSFII Survey Based on 3-Days
                                      Diet Records
Gender
Males
Females
Total
Aged 14 Years of
Younger
155(500%)
155 (50.0%)
310(21.5%)
Aged 15 through 44
Years
255 (39.6%)
389 (60.4%)
644 (44 6%)
Aged 45 Years or
Older
184 (37.6%)
306 (62.4%)
490 (33 9%)
Total
594(41 1%)
850 (58.9%)
1444
June 1996
H-50
SAB REVIEW DRAFT
-------
                                      Table H-34
 Consumption (gms/day) of Tuna Fish and Self-Reported Body Weight (Kg) among Respondents
                   in the 1989-1991 CSFII Survey. Data for "Users" Onlv
(render
Males
Females
\ged 14 \ears or
Younger
Mean
24.1
24.4
SD
28.3
21.3
k8bw
26.7
23.7
Aged 15 through 44
Years
Mean
37.9
28.3
SD
29.6
20.8
k«b
w
88
57
Aged 45 Years or
Older
Mean
31.3
24.7
SD
21.1
16.3
kgbw
85
59
t
Total
Mean
332
26.4
SD
27.5
195
kgh
604
48.6
 Data weighted to reflect U.S. population.
                                      Table H-35
 Respondents Reporting Consumption of Marine Shellfish in the 1989-1991 CSFII Survey Based
                                 on 3-Days Diet Records
Gender
Males
Females
Total
Aged 14 Years
or Younger
,41 (47.1%)
46 (52.9%)
87(13.6%)
Aged 15
through 44
Years
138 (44.8%)
170 (55.2%)
308 (48.0%)
Aged 45 Years
or Older
116(47.2%)
130 (52,8%)
246 (38,4%)
Total
295 (46.0%)
346 (54.0%)
641
  Data weighted for U.S. population.
                                       Table H-36
Consumption (gms/day) of Shellfish and Self-Reported Body Weights (Kg) of Respondents in the
                     1989-1991 CSFII Survey. Data for "Users" Only *
Gender
Males
Females
Aged 14 Years or
Younger
Mean
20.7
18.4
SD
23.9
19.6
kib
w
32
22
Aged 15 through 44
Years
Mean
38.7
38.1
SD
38.6
39.6
kgbw
87
68
Aged 45 Years or
Older
Mean
40.8
38.7
SD
33.6
33.7
k«bw
80
63
Total
Mean
42.7
35.9
SD
36.4
36.1
fcgbw
72
47
a Data weighted to reflect U.S. population.
June 1996
H-51
SAB REVIEW DRAFT
-------
                                       Table H-37
  Respondents Reporting Consumption of Shark. Barracuda. anoVor Swordfish in the 1989-1991
                      CSFII Survey Based on 3-Days Dietary Records
Gender
Males
Females
Total
Aged 14 Years
or Younger
0
0
0
Aged 15
through 44
Years
3
7
10
Aged 45 Years
or older
7
4
11
Total
10
11
21
                                      Table H-38
  Consumption (gms/day) of Swordfish, Barracuda, and Shark and Self-Reported Body Weight
       (Kg) among Respondents in the 1989-1991 CSFII Survey.  Data for "Users" Only
Gender
Males
FemaJes
Aged 15 through 44
Years
Mean
44.5
76,3
SD
21.7
39.7
kghw
87.8
57.2
Aged 45 Years or Older
Mean
56.2
51.9
SD
22.1
22.4
kgbw
84.5
59.1
Total
Mean
53.5
61.5
SD
22.6
23.7
kghw
85.6
57.8
  Data weighted to reflect U.S. population.
                                      Table H-39
 Respondents Reporting Consumption of Fresh-water Fish in the 1989-1991 CSFII Survey Based
                               on 3-Days Dietary Records
Gender
Males
Females
Total
Aged 14 Years
or Younger
60 (56.6%)
46 (43.4%)
106 (21.5%)
Aged 15
through 44
Years
80 (42.3%)
109 (57.7%)
189 (38.4%)
Aged 45 Years
or Older
82 (41.6%)
115 (58.4%)
197 (40.0%) "
Total
222(45.1%)
270 (54.9%)
492
  Data weighted to reflect U.S. population.
June 1996
H-52
SAB REVIEW DRAFT
-------
                                       Table H-40
    Consumption (gms/day) of Fresh-water Fish and Self-Reported Body Weight (Kg) among
             Respondents of the 1989-1991 CSFII Survey.  Data for 'Users" Only
Gender
Males
Females
Aged 14 Years or
Younger
Mean
304
22.2
SD
172
14.7
L k«b*
290
212
Aged 15 through 44
Years
Mean
704
496
SD
450
40.4
ta!bw
774
642
Aged 45 Years or
Older
Mean
587
48 1
SD
545
363
kEb*
832
709
Total
Mean
556
438
SD
45 4
?66
k8ha
645
54.6
1  Data weighted to reflect U.S. population.
June 1996
H-53
SAB REVIEW DRAFT
-------
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4.      CONCLUSIONS ON METHYLMERCURY INTAKE FROM FISH

        Methvlmercury intakes calculated in Appendix H to Volume III have been developed tor a
nationally based rather than site-specific estimates.  The CSFII/89-91 from L'SDA was designed to
represent the L'ruted States population.  The concentrations of methvlmercury in marine fish and
shellfish xv ere trom a data base that is national in scope.  Data on fresh-water finfish were taken  from
two large  studies that sampled fish at a number of sues throughout the United States.  The extent of
applicability or these data to site-specific assessments must rest with the professional judgments of the
assessor.  Because of the magnitude of anthropogenic, ambient mercury contamination, the estimates
of methvlmercury from fish do not provide a value that reflects methylmercury from nomndustrial
sources.  "Background" values imply an exposure against which the increments of anthropogenic-
activity could be added.  This is  not the situation due to release of substantial quantities into the
environment.

        Consumption of fish is much higher when expressed "per user" rather than "per capita."  A
U.S. fish consumption rate of 6.5 grams per day is the default value that has been used in the
calculation of human health-related criteria for mercury in water; for example, the Ambient Water
Quality Criteria (U.S. EPA 1980).  This value is based on data from the National Purchase Diary
Survey conducted in the United States during  the period 1973 and 1974.  This survey estimated a non-
marine fish consumption rate for the United States population. The overall fish consumption rate from
this survey was 14.3 g/day (U.S. EPA 1990, Exposure Factor Handbook).  This rate is a per capita rate
averaged over the entire population including  fish-eaters and nonfish-eaters.

        Conclusions on methylmercury consumption from fish must include consideration of variability
and uncertainty in these estimates.  Because the vast majority of methylmercury intake occurs via fish,
the first consideration is how accurately fish consumption patterns over a three-day period mirror long-
term fish consumption patterns.  Data from CSFII 89/91 yield "per capita" fish consumption data that
are comparable to those reported in other surveys. (Data indicate that 30.9% of respondents reported
consuming at least one fish dish  during the three-day period.)  This does not mean that all of the
remaining 69.1% of people avoid fish consistently, rather that fish did not appear as a dietary item
during the three-day survey period.  If the  survey were continued for a longer period, a higher  percent
of individuals would report consuming  fish. It is uncertain how much this percent would increase.
Because the CSFII/89-91 is conducted over all seasons of the year seasonality is not expected to  be an
issue in generalizability of the three-day fish consumption data.

        An additional consideration in evaluating the usefulness of three-day exposure data to the
question of methylmercury intake from fish concerns the ability of a respondent to specify the  species
of fish consumed.  Longer survey periods could have variable influences on species specificity
questions.  If the longer survey period is achieved by food history questionnaire, it is likely that the
ability to specify species of fish consumed would be reduced. If the longer survey period is achieved
by written dietary record, typically the diet is  simplified to reduce recording burden on the respondent.
Either of these  outcomes (difficulty in remembering species of fish  consumed, or diet simplification  in
response to recording burden) would reduce the accuracy of species identification and increase the
uncertainty in estimating methylmercury intake from fish.

        A second area of variability and uncertainty includes differences in rates of food consumption
based on age group of the respondent.  If data on methylmercury intake are expressed on a "per
kilogram body  weight" basis, the higher energy and protein requirements of children are associated
with a higher intake of fish (with accompanying methylmercury) relative to body weight. This
difference is largely a reflection of the  higher  caloric requirements of growing children.  For example,


June 1996                                   H-65                       SAB REVIEW DRAFT
-------
the recommended caloric intake for a child is approximately  as high as that of an adult female  The
average body weight reported for children in CSFII 89/91 was 25  kilograms in contrast with the adult
tor women or 63 kg.  It should be noted that self-reported body weights were utilized in these
calculations rather than default values.

        When methvlmercury intake is  expressed on a per kilogram self-reported body weight basis
children's exposure is approximately two-to-three times that  of the adult.  Because of limitations in the
methylmercury toxicology data base, the relevance of these differences in exposure to maintaining
children's health is uncertain.  There are no data on which to judge whether or not children differ from
adults in susceptibility to methylmercury toxicity. The extent of the effects on young children of
postnatal exposure to  methylmercury has not been evaluated.

        A third major source of variability and uncertainty in estimating methylmercury exposure from
fish includes the type of fish consumed. The data from National Marine Fisheries Service  indicate
substantial variation in the mercury concentrations in fish both within an across species.  In these data
analyses the mean value was used in the calculations.  Although the data were gathered across
approximately two decades,  comparison of these values with FDA compliance data has yielded the
broad general assessment that the concentrations in the NMFS data bases are not different from current
mercury concentration in fish.  This may reflect the large reservoir of mercury that is present is
present  in environmental  media, especially sediments, that continues to supply mercury to the aquatic
food web.

        For species such as tuna a large body of data on mercury  concentrations exists resulting in a
broadly based estimate of central  tendency. For other species of fish, for example ray, only a small
number of samples have been analyzed for mercury concentrations.  Consequently confidence in the
variability of methylmercury intake from some fish species (e.g., ray) is much lower than for other
species  (e.g., tuna). For fresh-water fish, in general, two  distinctly different data sets were  identified:
Lowe et al. and Bahnick et al. Consequently calculations were carried out using both of these data
sets. Many factors may contribute to the divergence among  these data. It is uncertain which or
whether either data set best represents methylmercury intake  from fresh-water fish for the general
United States population.

        The last area of uncertainty and variability to be addressed concerns the  highest consumers of
fish. Among CSFII 89/91 respondents, 97% of fish consumers reported eating only one fish  meal
during the three-day period.  The variability in fish intake reflected different portion  sizes of fish
consumed. Differences in body weight and species of fish selected would then determine the range of
intakes  of meihylmercury on a per kilogram body weight basis.  The remaining 3% of the fish-
consuming population ingested more than one fish meal in the three-day period.   Whether or not this
group of respondents represent a subpopulation  who are the very highest consumers of fish is
uncertain.  Based on reported high levels of fish consumption in studies of anglers and Native
American populations, it is certain that such high consumers  of fish are part of specialized
subpopulations.  In addition  to being part of groups defined by their cultural or geographic
membership in fish-catching subpopulation, individuals may  also consume high levels of fish due to
health considerations (e.g., efforts to reduce likelihood of cardiovascular disease) or reduce caloric
intake (e.g., weight watchers).  Whatever the reason, there is a portion of the United States general
population and some subpopulations who frequently consume high quantities of  fish.

        Issues dealing with confidence in data on the methylmercury concentration of fish consumed
include  these:
June 1996                                     H-66                        SAB REVIEW DRAFT
-------
        •       In a number of situations individuals cannot identify with accuracy the species ot fish
               consumed.  The L'SDA Recipe File Data Base has  'default" fish species specified it
               the respondent does not identify the fish species consumed.  There is no way. however
               to estimate the magnitude of uncertainty encountered by misidenufication of fish
               species by the survey respondents.

        •       The data base used to estimate methyimercury concentrations in marine fish and
               shellfish was provided by the National Marine Fisheries Service.  This data base has
               been gathered over approximately the past 20 years and covers a wide number of
               species of marine fish and shellfish.  The number of fish samples for each species
               varies  but typically exceeds  20 fish per species.

        •       The analytical quality of the data base has been evaluated by comparison of these data
               with compliance samples run for the Food and Drug Administration.  The National
               Academy of Sciences'  Report  on "Seafood Safety"  and the Food and Drug
               Administration have found this data base from NMFS to be consistent with 1990s data
               on methyimercury concentrations in fish.

        •       The methyimercury concentrations in fresh-water fish come from two publications,
               each giving data that represent fresh-water fish from a number of locations.  These
               data were gathered between  the early 1980s and early  1990s. These surveys by U.S.
               EPA (1992)/Bahnick et al. (1994) and Lowe et al. (1985) report different mean
               mercury concentrations; 0.260 ppm mercury (wet weight) and 0.114 ppm mercury (wet
               weight), respectively. The extent to  which either of these data sets represents
               nationally based data on fresh-water fish methyimercury concentrations remains
               uncertain.  Utilizing either the Bahnick et al. (1994) or Lowe et al. (1985) data set.
               exposures above the methyimercury reference dose (R/D) of 0.1  ug/Kg body
               weight/day occur among the subpopulation relevant to developmental effects of
               methyimercury (i.e., women of child-bearing age), when these concentration data are
               combined with the fish consumption data. Using the higher estimate of fish
               methyimercury concentration based on the Bahnick et al. (1994) data results in the
               prediction of a larger number in the vulnerable population being exposed above this
               level.  Even when the Lowe, et al. (1985) data are used, the 75th percentile of this
               group  is exposed to levels over 0.1 ug/kg body weight/day.  It should be noted that
               this  is not a site-specific assessment.

        Because methyimercury is a developmental toxin, a subpopulation of interest is women of
child-bearing age.  In  this analysis of methyimercury intake, dietary intakes of women aged 15 through
44 years were used to approximate the diet of the pregnant woman. From data on Vital and Health
Statistics,  it has been determined that 9.5% of women of reproductive age can be anticipated to be
pregnant within a given year. Generally food  intake increases during pregnancy (Naismith,  1980).
Information on dietary patterns of pregnant women has been assessed (among other see Bowen, 1992;
Greeley et al., 1992).  Most of these analyses  have focussed on intake of nutrients rather than
contaminants.  It is uncertain whether or not pregnancy  would modify quantities and frequency of fish
consumed beyond any increase that may result from increased energy (i.e., caloric) intake that
typically accompanies pregnancy.
June 1996                                    H-67                       SAB REVIEW DRAFT
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5.     ACKNOWLEDGMENTS

       L S. EPA acknowledges die assistance of Dr  Malcomb Meaburn from the National Manne
Fisheries Laboratory in Charleston. South Carolina  for advice on use of the NMFS data base.  The
assistance of Dr  Betty Perloff and Dr  Jacob Exler of the United States Department  of Agnc'ulture in
interpreting the USDA Recipe File and USDA's information on fish composition is  acknowledged

       The assistance of David Reisman. David Oberlin. and Dr. Bruce Peirano of"  the National
Center tor Environmental Assessment - Cincinnati in  compiling these data is also acknowledged.

6.     REFERENCES

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health: what effects should be expected? Science of the Total Environment 160/161:529-537.

Bahnick, D.. C. Sauer, B. Butterworth  and D. Kuehl.  1994.  A National Study of Mercury
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Bloom, N. S. (1992).  On the Chemical Form of Mercury in Edible Fish and Marine Invertebrate
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Bowen, D.J. (1992).  Taste and food preference changes across the course of pregnancy.  Appetite 19:
233-242.

Bjerregaard, P. (1995).  Health and environment in Greenland and other circumpolar areas. The
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Columbia River Inter-Tribal Fish Commission (1994)  A Fish Consumption Survey  of the Umtilla.
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October,  1994.

Connelly, N.A., T.L. Brown and B.A.  Knuth. New  York Statewide Angler Survey 1988. 1990.  New
York State Department of Environmental Conservation. NYDEC, Albany.

Cramer GM (1994) Exposure of U.S. consumers to methylmercury from fish. pps. 103-118. In:
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Bolger M, Cicmanec J, and Durkee J. Conference Number 9403156. Published through:  Biomedical
and Environmental Assessment Group, Brookhaven National Laboratory, Upton, New York.

Crispin-Smith J, Turner MD,  Marsh DO. Project III.  Hair methylmercury levels in women of
childbearing age.

Crochetti AF, Gutnrie HA (1982) Eating  Behavior and Associated Nutrient Quality  of Diets.  Final
Report for US Department of Agriculture Contract 53-22U4-9-192, The Human Nutrition Center,
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Eben. E.S . N W Harrington. K.J Bovle. J W  Krught and RE. Keenan.  1993. Estimating
Consumption ot Freshwater Fish among Maine Anglers.  North American Journal ot Fishenes
Management. 13.737-745.

FDA Compliance Testing as described in the N'MFS data base.

Fiore. B.J..  H.A. Anderson. L.P. Hanrahan, L.J. Olson and W.C. Sonzoghi.   1989.  Sport Fish
Consumpuon and Body Burden Levels of Chlorinated Hydrocarbons: A Study of Wisconsin Anglers.
.Arch. Environ.  Health.  44(2):82-88.

Fitzgerald E.F., S. Hwang, K. Brix. B. Bush, K. Cook, and P. Worsick. 1995. Fish  PCB
Concentrations and Consumption Patterns Among Mohawk Women at Akwesanse.  J. Exposure
Analysis  and Environmental Epidemiology: 5(1): 1-19.

Greeley S, Storbakken L, and Magel R (1992) Use of a modified food frequency questionnaire during
pregnancy.  J. Amer. Col. Nutr. 11: 728-734.

Hall RA.  Zook EG, and Meaburn GM (1978) National Marine Fisheries Service Survey of Trace
Elements in the Fishery Resource.  NOAA Technical Report NMFS  SSRF-721, National Technical
Information Service No. PB 283 851, March.  U.S. Government Printing Office, Washington, DC
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and Suggestions for Improvement", pp. 187 - 281.  National  Academy of Sciences Press, Washington,
DC, 1991.   .

Higuchi,  W.K.  and Pooley, S.G.  1985.  Hawaii's Retail Seafood Volume.  Administrative Report H-
85-6.  Southwest Fisheries Center.  Honolulu Laboratory, national Marine Fisheries Service.  P.O. Box
3830, Honolulu, HI 96812.

Hudgins,  L.L.  1980.  Per capita annual utilization and consumption  of fish and shellfish in Hawaii,
1970-77.  Marine Fisheries Review [February: No Volume Cited]; pgs. 16-20.

Hovinga,  M.E., M. Sowers and H.B. Humphrey.  1993. Environmental Exposure and Lifestyle
Predictors of Lead, Cadmium, PCB and DDT Levels in Great Lakes Fish Eaters. Archives of
Environmental  Health 48(2):98-104.

Hovinga,  M.E., M. Sowers and H.B. Humphrey.  1992. Historical Changes in Serum PCB and DDT
Levels  in an Environmentally-Exposed Cohort. Arch. Environ. Contam.  Toxicol.  22: 362-366.

Karvetti,  R. and Knuts, L. 1985. Validity of the 24-hour recall.  J.  Amer,  Dietet. Assoc. 85: 1437-
1442.

Kinloch,  D., Kuhnlein, H., and Muir, D.C.  1992. Inuit foods  and diet:  a preliminary assessment of
benefits and risks. The Science of the total Environment 122:  247-278.

Lowe TP, May TW, Brumbaugh WG, and Kane DA (1985)  National Contaminant Biomonitoring
Program:  Concentrations of seven elements in fresh-water fish, 1978-1981. Arch. Environ. Contamin.
Toxicol.  14: 363-388.
June 1996                                   H-69                      SAB REVIEW DRAFT
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 Morgan. J N . M R.  Berry  Jr. and R L. Graves.  1994  Effects of Native American cooking practices
 on total mercury concentrations in \\aileve  Presented at (SEE/TSEA Joint Conference September
 18-21. 1994

 Naisrmth DJ (198(1)  Maternal nutrition and the outcome ot pregnancy - a critical appraisal. Proc.
 Nutrition Society 39. 1-11.

 National Research Council/National Academy of Sciences (Committee on Evaluation of the Safety of
 Fishery Products). 1991. Seafood Safety.  Ed: Ahmed FE.  National Academy Press. Washington. DC.

 NMFS (National Marine Fisheries Service). The current publically-available National Marine Fisheries
 Service Data base was supplied to U.S. EPA via fax from Malcolm Meaburn (Charleston
 Laboratory/Southeast Fisheries Science Center/National Marine Fisheries Service/National Oceanic and
 Atmospheric Administration/US.  Dept. Of Commerce) to Kathryn Mahaffey (Environmental Criteria
 and Assessment Office-Cincinnati.OH/Office-of Health and Environmental Assessment/Office of
 Research and Development/U.S. Environmental Protection Agency). February 23, 1995.

 NOAA (1978) as described in the NMFS data base.

 Nobmann ED, Byers T, Lanier AP, Hankin JH, and Jackson MY (1992)  The diet of Alaska Native
 adults:  1987-1988.  American Journal of Clinical Nutrition 55: 1024-1032:

 Nusser SM, and Guenther PM (1995) Estimating usual food intake distributions. Paper presented  at
 the Second International Dietary Assessment Conference, January 22-24, 1995, Boston, Mass.

 Parkinson, A.J., Cruz. A.L., Heyward, W.L., Bulkow, L.R., Hall,  D., Barstaed, L., and Connor. W E.
 1994.  Elevated concentrations of plasma omega-3 polyunsaturated fatty acids among Alaskan
 Eskimos.  Amer. J. Clin. Nutr. 59:383-388.

 Peterson DE.  Kanarek MS, Keykendall MA, Diedrich JM,  Anderson HA, Remington PL. and Sheffy
 TB (1995)  Fish consumption patterns and blood mercury levels in Wisconsin Chippewa Indians.
 Archives of Environmental Health 49(11): 53-58.

 Pierce. R., D. Noviello and S.  Rogers.  1981.  Commencement Bay Seafood Consumption Report.
 Preliminary Report. Tacoma, WA: Tacoma-Pierce County Health Department.  As cited in U.S. EPA
 (1989).  Exposure Factors Handbook.  EPA/600/8-89/043.

 Puffer, H. 1981.  Consumption rates of potentially hazardous marine fish caught in the metropolitan
 Los Angeles area. EPA Grant #R807 120010-As cited in U.S.  EPA (1989). Exposure Factors
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 Putnam, JJ.  1991.  Food Consumption,  1970-1990.  Food Review 14(3):2-12. July-September.

 Richardson, M., M. Mitchell, S.Coad and R. Raphael.  1995. Exposure to Mercury in Canada:  A
Multimedia Analysis. Water, Air  and Soil Pol. 80:21-30.

Sekerke, H.J., R.L. Denger, C.M.  Adams  and S.D. Moss. 1994.  Seafood Consumption Survey in
Florida. Abstract presented at the  Society of Toxicology 33rd Annual Meeting  (Vol. 14 No.  1. March,
 1994.) Abstract No. 563.
June 1996                                   H-70                       SAB REVIEW DRAFT
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Silverman. D L.. Reis. G J .  Sacks. F M.. Boucher. T.M.. and Pasternak. R.C.  1990.  Usefulness oi
plasma phospholipid N-3 fatty acid levels in predicting dietary fish intake in patients with coronary
artery disease.  Amer.  J Cordiol 66. 800-862

Soldat. J K.  1970. A statistical study  or the habits or fishermen utilizing the Columbia River below
Hanrbrd.  Pages 302-508 m  W C. Rierug. editor  Environmental Surveillance in the uciruty or nuclear
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Consumption.  A Survey of  Seafood  Consumption in Hawaii.  Department of Business and Economic
Development.  Honolulu, Hawaii.

Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on Evaluation of Food
Consumption Surveys, Food and Nutrition Board, Commission on Life Sciences, 'National Research
Council,  National Academy  Press, Washington, DC.  1986.

Toy, K.A., Gawne-Mittelstaedt G.D., Polissar N.L.. and Liao S., 1995.  A fish consumption survey of
the Tulalip and Squaxin Island tnbes of Puget Sound.  Seattle, Washington.

Turcotte, S.  1983.  Georgia  Fishery Study: Implications for. Dose Calculations.  Technical Division
Savannah River Laboratory.  DPST--83--319-Rev.l.  Memorandum to H.P. Olson.

U.S. EPA. 1980. Ambient Water Quality Criteria Documents (various). U.S. Environmental
Protection Agency, Office of Water Regulations and Standards.  EPA 440/5-80 Series.

U.S. EPA. (1992).  A National Study of Chemical Residues in Fish.  (EPA 823-R-92-008a and b.)
Office of Water Regulations and Standards, U.S. EPA. Washington DC Vols.  1 and 2. September
1992.

U.S. EPA. (1992). Tribes at Risk. The Wisconsin Tribes Comparative  Risk Project.  Office of Policy
Planning and Evaluation and Region 5. EPA 230-R-92-017. October,  1992.

U.S. EPA. (1992b).  Tribes  at Risk.  The Wisconsin Tribes Comparative Risk Project. Office of
Policy Planning and Evaluation and Region 5.  EPA 230-R-92-017.  October, 1992.

U.S. EPA. (1992b).  Environmental Risk in Indian Country. U.S. EPA  Washington, DC.  Report No.
EPA 171-R-92-014. PB 92-182393.

Welch, H.E., Bergmann, M.A., Siferd, T.D., Martin, K.A., Curtis, M.F., Crawford, R.E., Conover. R.J.,
and Hop, H.  1992.  Energy flow through the marine ecosystem of the  Lancaster Sound Region, Arctic
Canada.  Arctic 45: 343-357.

West, P.C., J.M. Fly, R. Marans and F. Larkin. 1989.  Michigan Sport Anglers Fish Consumption
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Arbor, MI.

Wilkins,  L. and Hankin, J.   Personal  Communication.  28 February 1996.  University of Hawaii at
Manoa.  Cancer Research Center of Hawai'i, Epidemiology Program. 1236 Lauhain Street,  Honolulu,
Hawaii.
June 1996                                   H-71                       SAB REVIEW DRAFT
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 Willett \V 199(1. "Nature of Variation in Diet"  pp. 54-51   In.  Nutritional Epidemiolog>  Ed
 Willett. W -Nutritional Epidemiology  Monographs in Epidemiology and Biostausucs. Vol  15
 Oxford University Press. New York/Oxford.

 Witscru JC  1990. "Short-Term Dietary Recall and Recording Methods", pp. 52-68. In  Nutritional
 Epidemiology  Monographs in Epidemiology and Biostatisucs. Volume 15  Oxford University Press
 New York/Oxford.

 Wolfe. R. and R. Walker.  1987. Subsistence Economies in Alaska: Productivity, geography and
 developmental impacts.  Arctic Anthropol. 24:56-81.

 Wormworth, J.  1995.  Toxins and tradition:  The impact of food-chain contamination on the  Inuit ot
 Northern Quebec. Canadian Medical Association Journal 152(8): 1237-1240.
                                              «
- Youland DM, and Engle A (1976) Practices and problems in HANES:  Dietary data methodology. J.
 Amer. Dietet. Assoc. 68: 22-25.
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            ADDENDUM TO APPENDIX H

ESTIMATED NATIONAL AND REGIONAL POPULATIONS OF
 WOMEN OF CHILD-BEARING AGE: UNITED STATES, 1990
-------
-------
                        Estimated National and Regional Populations of
                       \Vi)men of Child-Bearing Age:  United States. 1990
        Because meih\lmercury is a developmental toxin, the subpopulation judged of particular
 Concern in this Mercury Study  Report to Congress was women of child-bearing age   Estimates n
 the size ot the population or women ot reproductive age. number of live births, number ot fetal deaths.
 and number of legal abortions can be used to predict the percent of the population and number of
 women of reproductive age who are pregnant in a given year.  This  methodology has  been previously
 used in the Agency for Toxic Substances and Disease Registry's (ATSDR's) Report to Congress on
 The Nature and Extent of Lead Poisoning in Children in the United  States (Mushak and Crocetu.
 1990).

        The estimates of number of women of child-bearing age calculated for this Mercury Studv:
 Report to Congress were prepared by Dr. A.M. Crocetti under purchase order from  OAQPS. The
 techniques used by Dr. Crocetu parallel those used to prepared the 1984 estimates for ATSDR.  To
 estimate the size of this population on a national basis Vital and Health Statistics data for number of
 live births (National Center for Health Statistics of the United  States, 1990;  Volume I, Natality, Table
 1-60. pages 134-140), and fetal deaths (National Center for Health Statistics of the United States.
 1990: Volume II, Mortality; Table 3-10, pages  16, 18, and 20). Fetal wastage, that is, spontaneous
 abortions  prior to 20 weeks of gestation were not considered since no systematically collected.
 nationally based data exist.

        The estimate of number of women of child-bearing age includes some proportion of women
 who will never experience pregnancy. However,  substitution of the  number of pregnancies  in a given
 year provides some measure of assessing the size of the surrogate population at nsk.  Estimates of the
 size of the population were based on "Estimates of Resident Population of the United  States Regions
 and Divisions  by  Age and Sex"  (Byerly, 1993).  The Census data for 1990 were grouped by age  and
 gender. The sizes of these populations are shown in Table H-l.

        Women ages 15 through 44 are the age group of greatest interest in identifying a
 subpopulation  of concern for the effects of a developmental toxin such as methylmercury.  This
 population consisted of 58,222,000 women living within the contiguous United States.  This
 population was chosen rather than for the total  United States (population 58,620,000 women ages  15
 through 44 years) because the dietary survey information from CSFII/89-91  did not include  Hawaii
 and Alaska. Based on estimates of fish consumption data for Alaska by Nobmann et al. (1992) the
 quantities of fish eaten by Alaskans exceeds those of the contiguous US population. It is also
 estimated that  residents of the Hawaiian Islands also have fish consumption patterns that differ from
 those of the contiguous United States.

        The number of pregnancies per year was estimated by combining the number of live births,
 number of fetal deaths (past 20 weeks of gestation) and the number of legal abortions.  The legal
 abortion data were based on information published by Koonin  et al. (1993) in Morbidity and Mortality
 Weekly Report.  These totals are presented in Table H-2. As noted in this table, the total of legal
 abortions includes those with unknown age which were not included in the body of each table entry.
There were 2,929 such cases for the United States in  1990 or 0.2% of all legal abortions. Another
complication in the legal abortion data was for the age group 45 and older.  The available data provide
abortion data for 40 years and older only.  To estimate the size of the population older than 45 years,
the number of legal abortions for women age 40 years and older were allocated by using the
proportions of Live Births and Fetal Deaths for the two age groups 40-44 and 45 and  older.


June 1996                                   H.A-1                      SAB REVIEW DRAFT
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        It was estimated that within the contiguous United States 95^ of women aaes 15 to 44 \ears
were pregnant in a given year.  The total number of live births reported in 1990 for this age group
4.112.579 with 30.974 reported fetal deaths and 1.407.830 reported legal abortions.  The  estimated
number of total pregnancies for women ages 15 to 44 \ears  was 5.551.383 in a population of
58.222.000 women.
REFERENCES

Byerly, E.R. (1993) State Population Estimates by Age and Sex:  1980-1992, U.S. Bureau of the
Census, Current Population Reports P25-1106, U.S. Government Printing Office, Washington. DC.

Koonin, L.M., Smith. J.C., and Ramick, M. (1993) Division of ReproducUve Health. National Center
for Chronic Disease Prevention and Health Promotion: Abortion Surveillance -  United States.  1990:
Morbidity Mortality Weekly Report, Vol 42/No. SS-6, pps. 29-57, December 17.

Mushak,  P., and Crocetti, A.M. (1988).  The Nature and Extent of Lead Poisoning in Children in  the
United States: A Report to Congress.  Agency for Toxic Substances and Disease Registry, United
States Public  Health Service, United States Department of Health and Human Services.

National  Center for Health Statistics of the United States (1990) Volume I.  Natality: Table 1-60:
pages 134-140.

National  Center for Health Statistics of the United States (1990) Volume II. Mortality; Table 3-10,
pages 16, 18, and 20.

Nobmann, E.D., Byers, T., Lanier, A.P., Hankin, J.H., and Jackson,  M.Y. (1992) The diet of Alaska
Native adults: 1987-1988. Amer. J. Clin. Nutr. 55: 1024-1032.
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                                       Table H-l

             Resident Population of the United  States and Divisions, April I, 1990
     Census by Gender and Age; in Thousands,  including Armed Forces Residing in Region
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
United States
Male
Female
ac Female
Total
248.710
121.239
127.471
51.3
< 15 Years
of Age
53.853
27.570
26.284
48.8
15-44 Years
of Age
117.610
58.989
58.620
498
> 44 Years
of Age
77.248
34.680
42.567
55.1
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
Contiguous
United States
Male
Female
^c Female
Total
247.052
120.385
126.667
51.3
< 15 Years
of Age
53,462
27.369
26.094
48.8
15-44 Years
of Age
116,772
58.548
58.222
49.9
> 44 Years
of Age
76.817
34.467
42.348
55.1
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
New England
Male
Female
% Female
Total
13,207
6.380
6.827
51.7
< 15 Years
of Age
2.590
1.327
1.264
48.8
15-44 Years
of Age
6.379
3.174
3.202
50.2
> 44 Years
of Age
4.239
1.878
2.361
55.7
June 1996
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                                Table H-l (continued)
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age: in Thousands, including Armed Forces Residing in Region.
Division/
Gender
Middle
Atlantic
States
Male
Female
% Female
Total
37.602
18.056
19.547
52
< 15 Years
of Age
7.471
3.824
3.645
49
15-44 Years
of Age
17.495
8.676
8.818
50
> 45 Years
of Age
12.638
5.554
7.083
56
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
E North Central
Male
Female
% Female
Total
42.009
20.373
21.636
51.5
< 15 Years
of Age
9.233
4.728
4.505
48.8
15-44 Years
of Age
19.596
9.744
9.851
50.3
> 44 Years
of Age
13.180
5.899
7.279
55.2
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
West North
Central
Male
Female
% Female
Total
17,660
8,599
9.061
51.3
< 15 Years
of Age
3,967
2.032
1.935
48.8
15-44 Years
of Age
8,017
4.020
3.997
49.9
> 44 Years
of Age
5.676
2.546
3.129
55.1
June 1996
H.A-4
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                                Table H-l  (continued)
Resident Population of the L'nited States and Divisions. April 1. 1990 Census by Gender and
Age: in Thousands, including Armed Forces Residing in Region.
Dhision/
(iender
South
Atlantic
Male
Female
% Female
Total
43.567
21.129
22.438
51.5
< 15 Years
of Age
8.864
4.531
4.333
48.9
15-44 Years
of Age
20.579
10.279
10.301
50.1
> 44 Years
of Age
14.122
6.321
7.H04
55.3
Resident Population of the United States and Divisions. April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
East South
Central
Male
Female
?c Female
Total
15.176
7.301
7.875
51.9
< 15 Years
of Age
3.316
1.698
1.618
488
15-44 Years
of Age
7.037
3.472
3.565
50.7
> 44 Years
of Age
4.823
2.132
2.692
55.8
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
West South
Central
Male
Female
% Female
Total
26,703
13.061
13.641
51.1
< 15 Years
of Age
6.366
3.256
3.110
48.9
15-44 Years
of Age
12.687
6.359
6.328
49.9
> 44 Years
of Age
7,651
3.445
4.204
54.9
June 1996
H.A-5
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                               Table H-l (continued)
Resident Population of the United States and Divisions. April I. 1990 Census by Gender and
Age: in Thousands, including Armed Forces Residing in Region.
Division/
Gender
Mountain
States
Male
Female
% Female
Total
13.659
6.779
6.880
50.4
< 15 Years
of Age
3.313
1.696
1.616
48.8
15-44 Years
of Age
6.435
3.259
3.176
49.4
> 44 Years
of Age
3.910
1.825
2.087
53.4
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
West North
Central
Male
Female
% Female
Total
17.660
8.599
9.061
51.3
< 15 Years
of Age
3,967
2.032
1.935
48.8
15-44 Years
of Age
8.017
4,020
3,997
49.9
> 44 Years
of Age
5.676
2.546
3.129
55.1
Resident Population of the United States and Divisions, April 1, 1990 Census by Gender and
Age; in Thousands, including Armed Forces Residing in Region.
Division/
Gender
Pacific (5 States
including Alaska
and Hawaii)
Male
Female
% Female
Total
39,127
19,562
19.565
50.0
< 15 Years
of Age
8.734
4.476
4.258
48.8
15-44 Years
of Age
19,394
10,004
9.379
48.4
> 44 Years
of Age
11,011
5,083
5.929
53.8
                                                                                  f
June 1996
H.A-6
SAB REVIEW DRAFT
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