United States
Environmental Protection
Agency
Environmental Research
Laboratory
Athens GA 30613
Research and Development
EPA/600/S3-86/045 May 1987
SEPA Project Summary
Case Studies and Model Testing
of the Metals Exposure Analysis
Modeling System (MEXAMS)
A. J. Medine and B. R. Bicknell
In .the EPA's wasteload allocation/
total maximum daily load (WLA/TMDL)
program, the agency must establish
more stringent effluent limitations and
guidelines for toxic chemicals (including
metals) if previous limitations are not
adequate to attain or maintain accept-
able water quality levels. The Metals
Exposure Analysis Modeling System
(MEXAMS) was recently developed to
assist in this effort. This model, linking
a complex speciation model with an
aquatic transport/fate model, should
help discriminate between the fraction
of metal that is dissolved and in bio-
available form, and the fraction that is
complexed and rendered relatively
nontoxic.
The MEXAMS model has been tested
with data from three rivers to determine
its ability to simulate fate, transport,
and speciation of heavy metals in river
systems. The rivers selected for study
were the Naugatuck River in Con-
necticut and the Ten Mile River in
Massachusetts, both of which receive
electroplating and metal finishing
wastes; and the White River in Utah,
which receives metal loadings from
natural sources. This report documents
the tests as case studies to guide future
users in application of the model. In
addition, the program has been en-
hanced by addition of sediment settling/
resuspension to the aquatic transport
submodel EXAMS.
This Protect Summary was developed
by EPA's Environmental Research Lab-
oratory, Athens, GA, to announce key
findings of the research project that Is
fully documented In a separate report of
the same title (see Project Report order-
Ing Information at back).
Introduction
Transport processes, environmental
distributions, and biological effects of
heavy metals in our aquatic environments
have been the focus of increasing concern.
Direct toxicity to aquatic organisms and
indirect toxicity to humans and other
higher organisms are at the center of this
concern. Although heavy metals are
natural constituents of aquatic environ-
ments, additional quantities introduced
by man's presence may result in an al-
tered chemical composition of the
aqueous and sediment phases, often with
detrimental impacts on the environment.
In an effort to reduce these impacts,
environmental transport/fate models of
heavy metals in aquatic ecosystems are
being developed.
One of these models is the Metals
Exposure Analysis Modeling System
(MEXAMS). This program links MINTEQ,
a geochemical model, with EXAMS, an
aquatic exposure assessment model.
MEXAMS was developed to allow assess-
ment of the impacts of "priority pollutant"
metals (As, Cd, Cu, Pb, Ni, Ag and Zn).
This report presents a case study and
tutorial for MEXAMS.
The first evaluation consisted of cali-
brating, testing, and assessing the model
response for an actual site application on
the Naugatuck River, located in the
Housatonic River Basin in western
Connecticut.
The second evaluation addressed the
use of the MINTEQ geochemical model
for the White River (Utah), a dynamic
lotic system. Water quality parameter
input, laboratory adsorption experiments,
chemical analysis, MINTEQ utilization,
and prediction of laboratory results are
described.
-------
The third evaluation addressed applica-
tion of MEXAMS to the Ten Mile River, a
small stream located in southeastern
Massachusetts, that receives inorganic
metal waste from numerous industries
along its banks. The evaluation involved
data analysis, preparation of detailed
MEXAMS input, and description of the
model simulation for a wasteload alloca-
tion study of the river.
A second purpose of this work was to
incorporate simple sediment settling and
resuspension into MEXAMS. Considera-
tion of metal losses from the water column
due to steady-state sediment settling will
add an important process to MEXAMS,
allowing more accurate calibration and
simulation of many systems.
Model Descriptions
The Exposure Analysis Model System
(EXAMS) describes the fate, transport
and impacts of organic contaminants in
aquatic systems. The program is a deter-
ministic simulation model, based on a
core of mechanistic process equations
derived from fundamental theoretical
concepts.
The model estimates exposure, fate,
and persistence of organic pollutant dis-
charges, using conservation of mass to
balance loadings, transport, and trans-
formation of the compound. EXAMS
simulates three transport processes:
advection, dispersion, and volatilization.
Advective and dispersive transport are
possible for dissolved species, sediment
sorbed material and bio-sorbed materials.
Transport through the ecosystem com-
partments may be represented by whole
sediment bed loads, suspended sediment
washloads, exchanges with fixed volume
sediment beds, and ground water
infiltration.
EXAMS is capable of simulating both
lotic and lentic aquatic systems. These
systems are initially compartmentalized
and classified as littoral, epilimnion,
hypolimnion, or benthic. Each compart-
ment is assumed to be completely mixed.
The computer then develops single dif-
ferential equations for each compartment.
MINTEQ, a computer program for cal-
culating aqueous geochemical equilibria,
was developed for incorporation into
MEXAMS. MINTEQ is used to predict
metal speciation including sorption, pre-
cipitation and ion exchange of "priority
pollutant" metals in aquatic environ-
ments. This chemical equilibrium problem
is described as a set of mass balance
equations, one for each component, and
a set of mass action equations, one for
each species. The equilibrium constant
approach is utilized to solve the equilibrium
problem, solving nonlinear mass action
expressions using linear mass balance
equations. The equilibrium composition
of an aquatic environment is determined
by minimization of Gibbs free energy of
the system within the mass balance con-
straints. This chemical equilibrium will
determine aqueous metal speciation in
addition to the effects of precipitation/
dissolution and sorption, and is used in
EXAMS to determine fate and migration
of a metal.
Solid phases are dealt with using the
"transformation of basis" method. This
method reduces the number of indepen-
dent variables to be determined and
allows the solution of a wider range of
chemical equilibrium problems.
MINTEQ also is capable of modeling
adsorption in a number of ways. Six
different algorithms are accessible
through the use bf MINTEQ for describing
sorption phenomena - "activity" Kd,
"activity" Langmuir equation, "activity"
Freundlich equation, ion exchange, con-
stant capacitance surface complexation,
and triple layer surface complexation.
EXAMS and MINTEQ are the two in-
dividual models linked to form MEXAMS.
The MEXAMS program provides three
different modes of operation. The first
mode is the MINTEQ only, which allows
the operator to determine how changes
in water chemistry will affect metal
speciation and solid phase interactions
without regard to transport processes.
The second mode is EXAMS only, which
deals with the ionization, sorption, trans-
port, and transformation of a given pol-
lutant. Finally, the third mode links
EXAMS and MINTEQ, allowing the user
to determine the effect of transport
processes and chemical interactions on
priority pollutant concentration. To use
the coupled mode, the first step is to
create an EXAMS input file that describes
the characteristics of the aquatic environ-
ment being assessed. The user then en-
ters the MEXAMS Interactive Software
Package (MISP). MISP will call for a
MINTEQ file for each compartment that
contains different water quality data. The
user will also input run-specific informa-
tion that controls the number of times
MINTEQ updates metal concentrations.
MEXAMS is now set to simulate metal
behavior, migration, and fate.
Naugatuck River Case Study
The Naugatuck River has a long history
of industrialization along its length. Cur-
rently approximately 30 to 300 cubic
meters/day of treated electroplating
waste is being discharged into the
Naugatuck River. Most industrial dis-
charges on the river currently adhere to
Best Available Technology (BAT) guide-
lines. Both long-term biological monitor-
ing and recent toxicity testing, however,
have shown toxic impacts of metals. The
data set available was used to illustrate
how to apply MEXAMS to field conditions
such as in the Naugatuck River.
The main issue surrounding model
calibration and verification is obtaining
estimates for model parameters and
comparing predicted concentrations with
actual observed data. Ideally, several data
sets are available for independent calibra-
tion and verification. In the case of the
Naugatuck River data, limited data per-
mitted only the calibration of the model.
Subjective variation of parameter
values and a qualitative comparison of
model solution and observation is the
most common approach to model calibra-
tion. Through this procedure, the calibra-
tion process attempts to account for (1)
spatial variations not represented by the
model formulation; (2) functional depen-
dencies of parameters that are either
non-quantifiable, unknown and/or not
included in the model algorithms; or (3)
extrapolation of laboratory measurements
of parameters to natural field conditions.
During calibration, the analyst may
choose to adjust some of the parameters
to improve model predictions or to alter
the structure of the relationships (or
system physical representation) between
the variables in the model.
The first step in the calibration was to
determine which model inputs have the
least reliability. These are the parameters
that will be adjusted to perform a sen-
sitivity analysis. Any sensitivity analysis
is system dependent because certain
input parameters may be more or less
sensitive depending on the system being
modeled. The first decision to be made in
the utilization of MEXAMS was to select
a system configuration. There are many
possible configurations due to the range
of advective and dispersive pathways.
Two initial configurations, containing
three water column and three benthic
compartments, were tested. Originally,
both of these configurations had the en-
tire groundflow routed through the
benthic compartments. This caused a
considerable problem because the sedi-
ment residence time within the benthic
compartments was so low that the metal
was being flushed out of the system. This
problem was corrected by routing a por-
tion of the groundwater into the water
-------
column compartments, increasing the
sediment residence time and thus in-
creasing the concentration of adsorbed
pollutant within the bed sediments.
The parameters that were manipulated
during the calibration process included
the advective pathway for groundwater
accrual, the benthic Kd's, and the benthic
dispersion coefficient. Varying the percent
groundwater flow into the benthic com-
partments between 0.01% and 1% al-
lowed adjustment of the benthic par-
ticulate metal concentration. Next, varying
the benthic-water column dispersion co-
efficient from 5.00*E-05 to 5.00*E-08
(mVhr) gave another relationship be-
tween dispersion coefficient and adsorbed
benthic metal concentrations. Finally, the
benthic partition coefficient was increased
from field-determined values, which were
considered inaccurate. It is very difficult
to obtain a benthic sediment sample
without entrainment of water from the
water column. This water dilutes sedi-
ment interstitial concentrations, resulting
in low Kd values.
To obtain greater spatial accuracy in
water column concentrations, a ten-com-
partment configuration was developed
(Figure 1). The final configuration was
calibrated by adjusting the percent ground
water flow into the benthic compart-
ments, the advective flow between com-
p-rtments, and the Kd value for the
benthic compartments.
The calibration results obtained from
the final configuration are presented in
Table 1. As stated previously, three pa-
rameters were adjusted to achieve a
calibrated model. Table 2 displays the
original log Kd's and the final log Kd's
used to obtain the ten compartment
calibration. The advective flow paths for
the new compartmentalizations were
presented in Figure 1, and the percent
ground water routed into benthic com-
partments eight and ten were changed
from 0.01% to 0.1% and 0.13%, respec-
tively. The final calibration obtained for
the ten-compartment model was reason-
able, and the concentrations predicted by
the model were well within the error bars
of the field data.
The most important aspect of this model
calibration was the compartmentalization
of the river reach. Initially calibrated for a
six-compartment configuration, the model
did not predict adequately the total and
dissolved concentrations in the down-
stream water column compartment due
to its length of 6.7 miles. As a result, a
ten-compartment configuration was used
to obtain the final calibration. This final
configuration accurately calculated total,
/
.90
2
1.0
.10
3
.90
4
1.0
.10
5
1.50
6
1.0
.10
7
.24
8
1.0
.76
9
.38
10
1.0
.62
Figure 1. Final calibration configuration, compartments 1 through 10. Values are advective
proportions.
Table 1. MEXAMS Model Results, Naugatuck River
Model Total Copper o/0
Location Compartment Field Model Diff.
Dissolved Copper %
Field Model Diff.
Water Column lug/1)
Palmer Br. Rd.
Bogue Rd.
Rte. 118
Campville Rd.
Br. Abutment
1
3
5
7
9
12.
A//» <
24.
16.
14.
11.5
Segment L
24.6
16.6
12.6
-4.1
tat a
+2.5
+3.8
-10.0
9.5
Kin
17.
14.
11.
9.1
Segment D
16.8
14.
10.6
-4.7
-1.2
0.0
3.6
Benthic Region (mg/l)
Palmer Br. Rd.
Bogue Rd.
Rte. 118
Campville Rd.
Br. Abutment and
Campville Rd.
Paniculate Copper
2
4
6
8
10
140.
62.
73.
177.
154.*
138.
59.
74.
179.
150.
-1.5
-4.8
+ 1.4
+ 1.1
-2.6
140.
62.
72.
176.
754.*
138.
59.
73.
178.
150.
-1.5
-5.0
+ 1.4
+0.6
-2.6
The average copper concentration measured at Campville Rd. and the Bridge Abutment was
used for comparison (based on model compartmentalization).
Table 2. Comparative Kd Data,
Naugatuck River
Compartment #
7
2
3
4
5
6
7
8
9
10
Original
logKd
0.30
3.72
-0.25
2.95
-0.05
2.67
-O.O5
2.67
-0.05
2.67
Calibrated
logKd
0.30
5.02
-0.25
4.20
-0.05
4.42
-0.05
5.30
-0.05
5.17
dissolved, and paniculate concentrations
for all water column and benthic compart-
ments. The calibration of this model is
only the initial phase of accurately
modeling this river reach, however, and
model validation to another data set is
required.
White River Case Study
Natural loadings of heavy metals into
the White River system associated with
tributary discharge from periodic storm
events can result in dissolved metal con-
centrations that exceed the toxicity thres-
holds. Dissolved copper concentrations
increased from 4 ng/\ to 340 HQ/\ as-
sociated with an increase of river flow
from 500 ftVsec to 1100 ftVsec due to a
storm event in March 1975. Elevated
dissolved concentrations for Zn, Pb, and
Cr also have been observed.
The White River is not extensively
developed, although deposits of oil shale
are presently being mined. The target
metals chosen to exercise the MINTED
adsorption model in this study (Zn, Pb, Ni,
Cu) are associated with potential con-
tamination to the lotic system from
retorted waste shale leaching into
groundwater and surface waters, retort
processing waters, and mine dewatering
activities. If leachate through ground-
water or accidental discharge into the
White River occurred, water quality stan-
dards established by the State of Utah
could be exceeded, thus resulting in vary-
ing degrees of metal stress to the aquatic
communities.
The upper basin of the White River
originates in the Flat Tops Wilderness
Area in northwestern Colorado, which
contributes the majority of the flow. The
lower basin drainage area extends from
-------
northwestern Colorado to the confluence
of the Greeen River in northeastern Utah.
The lower basin area is characterized by
a semiarid climate. The watershed con-
tains surficial deposits of lacustrine
sediments containing calcite and dolomite
deposited by Lake Unita (Palocene Epoch).
The physical-chemical processes associ-
ated with the geologic weathering and
erosional transport causes drastic
changes to water quality and gives the
White River a strong carbonate system.
The area of study on the White River is
located in the lower drainage basin area
in eastern Utah, near federal oil shale
lease tracts.
Because of the land morphology, the
study area has various perennial washes
and one ephemerial stream, Evacuation
Creek. For several months, the flow of
this creek is low and contains very high
levels of total dissolved solids. The
watershed contains anthropogenic metal
sources that are eroded and transported
during storm events. This overland runoff
can cause significant water quality
changes to the White River by heavy
metal and solids loading.
The flows of the White River are cor-
related to three flow regimes — upper
basin runoff, lower basin runoff, and
baseline. Concentrations of major anions
and cations found in the White River
occur in the order of:
HCOj > S042 > Cl > C032 > Ca+2 >
Na+ > Mg+2 > K+
Upper basin runoff provides the best
water quality conditions, with low dis-
solved constituent concentrations but high
total suspended solids concentrations.
Lower basin runoff shows intermediate
dissolved concentrations, where the
baseline regime shows the highest con-
centration of dissolved substances and
lowest total suspended solids values.
The chemical matrix (major anions/
cations) of the water system had to be
determined for subsequent input into
MINTED as Type I components. This
matrix can be determined by a full water
quality analysis on field samples, from
historical data, or from correlation graphs
that show the relationship of the major
anions and cations to a physical param-
eter (TDS). At least two years of constant
monitoring of the system should be used
to formulate the correlations, to encom-
pass possible seasonal fluctuations.
Redox chemistries were not considered
in the water quality matrix because of the
high dissolved oxygen concentrations, the
relative concentrations of ammonia,
nitrate and nitrite, and the low levels of
ferrous ion observed. It was assumed
that these conditions would reflect a high
pe with sulfide being rapidly converted to
sulfate. High sulfide levels were reported
in alluvial groundwater of Evacuation
Creek and Asphalt Wash.
To best utilize the capabilities of
MINTEQ and MEXAMS for describing
metal fate and transport, data are required
to describe the solid phase partitioning of
any potential metal loadings. In most
cases, limited data are available for suit-
able descriptions of sorption/desorption
and chemical precipitation. A certain
degree of caution must be exercised in
using literature data to estimate metal
partitioning to suspended solids and/or
bed sediments, because natural sedi-
ments are a mixed population of solid
types (oxides, clays, detritus, hydroxides,
silicates, etc.). Site-specific data may often
be necessary for successful model
application.
To illustrate how basic data may be
acquired for site-specific application,
adsorption experiments have been per-
formed and the details reported. These
laboratory reactor studies quantify equi-
librium concentrations of dissolved and
particulate metals under controlled condi-
tions. These equilibrium concentrations
can be determined for variable conditions
of pH, total metal concentrations, TSS
concentrations, ionic strengths and matrix
concentrations. By varying parameters,
adsorption relationships can be analyzed
and formulated for MINTEQ input.
Two reactor techniques were used for
determining adsorption behavior for tar-
get metals — small volume reactors and
large volume. Both of these techniques
used ambient solids concentrations and
ionic strengths. The parameters that were
varied were total metal and pH.
In implementing MINTEQ, the user
must first determine chemical activity by
developing an input file without consid-
ering adsorption and chemical precipita-
tion. Execution of this file creates output
characterizing, among other things, the
dissolved metal ion activity, the charge
balance, and the saturation indices for
solids. For the charge balance computa-
tions, two values should be noted, un-
speciated and speciated. An unspeciated
charge imbalance of greater than 30%
can indicate that one or more ionic con-
stituent may be missing. The more reliable
indicator, however, is the charge balance
after speciation. If this charge balance is
greater than 20%, then it is very likely
that an ionic component is absent. If
environmental data are lacking, an ion
concentration adjustment may be required
on a conservative Type I component.
An output section giving saturation
indices (SI) for all minerals and solids can
indicate what solids will precipitate foi
the full MINTEQ model run. The SI values
for diaspore, argonite, calcite and fluorite
were all positive, indicating the solid is
oversaturated in the system. Because ol
kinetic factors, however, the solids may
not actually obtain equilibrium in the
time frame considered, or may nol
actually form due to the constraints placed
on the system, such as no fixed partial
pressure (open to atmosphere). Some ol
these solids form under high partial
pressures of C02 (groundwater environ-
ment) and if used in a system with nc
fixed partial pressure, a Gibbs Phase Rule
Violation will occur. General guidelines
for selecting solid phases are given in the
MINTEQ Technical Manual.
To determine which MINTEQ adsorptior
model can be implemented for the White
River, data obtained from the metal addi
tion reactor studies and MINTEQ were
plotted. The first approach was to plo
particulate metal concentration (moles/I
versus dissolved metal ion activity con
centration (moles/1) obtained frorr
MINTEQ for various total metal concen
trations for a specific pH. A linear regres
sion was performed and the slope of the
line (activity Kd) was determined. The loc
value of the Kd was then utilized as ar
input parameter when implementing the
MINTEQ adsorption model.
In utilizing the activity Kd approach tc
model metal-sediment adsorption, a new
MINTEQ input file had to be developed
The component for surface sites (ID 990
was added as a Type I component and it!
activity fixed as a Type III species. Fou
Type V species (calcite, dolomite, diaspore
tenorite) were inserted. These specie;
are allowed to precipitate in the system i
their saturation indices are exceedec
during chemical speciation. The last addi
tion involved a Type II modification (inser
tion of species not in data base) tc
incorporate the adsorption "reaction'
between Zn and the surface sites (SON).
These new MINTEQ files were devel
oped and executed for all Zn reactoi
experiments involving four total meta
levels (0.124 mg/l, 0.224 mg/l, 0.52^
mg/l, and 1.024 mg/l) at two specific
pH conditions that conformed to the
activity Kd approach (pH 7.50 and 8.39)
In order to evaluate how well MINTEC
predicted the laboratory adsorption re
suits, the predicted molar values of the
sorbed mass (particulate) and aqueous
-------
(dissolved) masses were compared with
the actual experimental results from the
pH = 8.39 data set that conformed to the
linear Kd approach. Figures 2 and 3 show
the comparison plots of the MINTED pre-
diction versus actual laboratory results
for both dissolved and paniculate Zn.
If the system being modeled has a
relatively constant pH and the metal
variation is not too great (< 2x), the
activity Kd approach would give satisfac-
tory results. If pH, metal level, SS and
other parameters change dramatically,
however, then one of the other adsorption
routines should be selected. For example,
zinc adsorption in White River samples
exhibited a curvilinear relationship more
characteristic of Langmuir behavior. In
most situations, however, the metal level
is low enough that the linear portion
could be used to calculate a Kd. Lead
adsorption was linear in all pH ranges
tested (6.40, 6.80, 7.30, 8.12, and 8.90)
and nickel adsorption also appeared to
show a linear relation between 1 /(Ni+2)
and particulate Ni. A summary of the log
Kd's as a function of pH for the White
River are shown in Figure 4.
The Langmuir model can often be used
to predict metal sorption in natural sys-
tems. At low (Cu+2), the equation is linear;
whereas at very high (Cu+2), the equation
represents a saturation of surface sites
(CuS = ST). The equation could be used
over a wide range of (Cu+2)'s and could
account for different site availability by
variation of ST (maximum surface cover-
age). This equation would be very useful
on the White River where large fluctua-
tions in suspended solids and ambient
dissolved copper are observed. Metal
partitioning to solids is more significant
in the water column, as opposed to bed
sediments and thus, dissolved metal
should be related to suspended sediment
(and responsive to ST in the Langmuir
equation). As metal partitioning becomes
more dominated by bed sediment inter-
action, the usefulness of the Langmuir
decreases and the activity Kd might be
entirely appropriate.
An experiment using the White River
with pH = 7.5 and SS = 200 mg/l
illustrated the lower section of a Lang-
muir type fit. Attempts to increase the
range of the plot by Cu addition only
resulted in malachite precipitation. A plot
of the linearized equation showed a
curvilinear relation when a straight line
would be anticipated if data fit a Langmuir
model.
At this point, it is emphasized that the
MINTED input files were only considering
inorganic speciation of the metal. No
I I I I I I Ml I I I I I I I II I I I I I 111!
Actual Dissolved Zinc, m
Figure 2. Actual dissolvedzinc versus MINTEQ prediction.
organics were used even though the
White River contains 5 to 7 mg/l of
dissolved organic carbon (DOC, see Table
3). The curvilinear relation could result
from omission of organic complexation,
an effect that would be most pronounced
at low (Cu+2). Addition of EDTA simulates
the effect of natural organic complexation
on the (CU"2) calculation by MINTEQ.
The optimum fit (linear) was obtained
with 55 /ig/l of EDTA (10'666M). Using
this organic level for two other experi-
ments at pH = 6.7 and 8.3 gave linear
plots indicating that this artificial specifi-
cation of an organic complexing agent
(EDTA) might be a reasonable substitute
for actual complexing organics in the test
water. The curvilinear nature observed in
these experiments (without EDTA) is not
due to lack of consideration of desorbed
ions in the equilibrium solution. Under
the conditions of the experiment (addition
of >1 mg/l of metal), background con-
centrations of Ca+2, Mg*2, Na+, etc. would
appear essentially unchanged in the
White River matrix by release due to an
ion exchange phenomenon.
The slope of the lines for pH = 6.7, 7.5
and 8.3 and estimates for ST from the
experiments allowed a determination of
the Langmuir Constant, KL. The results
(Table 4) indicate that the metal adsorption
phenomena in the White River can be
adequately described using kinetics based
on the mass of solids, the dissolved (free)
metal, the available sites and pH, and,
that equilibrium metal experiments can
be fit to an "activity" Langmuir Isotherm.
The experiments at pH = 6.7 and 8.3 did
not have sufficient data to permit precise
estimates of ST and KL and the values in
the table should be regarded as estimates.
It is essential that continued development
of methods for determining dissolved
metal species be encouraged and that
organic speciation of metals be considered
in modeling systems highly transitory in
flow and water quality, such as that
observed in the White River.
Ten Mile River Case Study
This case study of MEXAMS involves
application to a wasteload allocation
program on the Ten Mile River in
Massachusetts. This river receives metal
wastes, treated municipal wastewater,
and urban runoff at a number of points
-------
10- T-
1
10-'
A I 1 I I I I
I I I I I I
10'* 10~s
Actual Paniculate Zinc, m
Figure 3. Actual paniculate zinc versus MINTEQ prediction.
White River
SS = 1560 mg/l
Lead
Nickel
Zinc
7 a
pH
10
Figure 4. Log Kd,s versus pH for Zn, Ni,
andPb.
along its 22-mile length, and estimates
of metal speciation within the river would
be useful for determination of acceptable
effluent limits to the basin.
In recent years, the question has been
raised as to whether current inorganic
industrial discharge limitations on Ten
Mile River are adequate to attain or
maintain acceptable water quality levels
with regard to heavy metals. Also, the
long history of metal waste discharges
has resulted in extremely high concen-
trations of heavy metals in the sediments
of impoundments along the river. These
sediments now act as additional sources
of metals to the river during high flow
periods.
The basic data required to execute
MEXAMS for a system such as Ten Mile
River are 1) detailed descriptions of the
hydraulics, including flows and channel
geometry; 2) dissolved and adsorbed
concentrations of all major chemical
species; and 3) loading rates of metals.
The 1984 data that are described are
preliminary; the data had not been com-
pletely analyzed and verified at the time
this project was completed.
River and tributary flow measurements
at eight stations were available for several
dates in the summer of 1984. Because
heavy rainfall had occurred prior to the
surveys, all flows were significantly higher
than base flows for the basin. In addition,
the flows were steadily decreasing during
the surveys; consequently, they do not
represent steady-state conditions. Chan-
nel depths and widths were measured at
the flow stations. Additional data to
characterize the channel geometry also
were collected at several cross sections
in the 1984 surveys. Impoundment
dimensions (depth and surface area) were
obtained from previous model estimates.
Chemical concentrations and water qual-
ity parameters were measured at river
monitoring stations and impoundments
on several dates during the 1984 surveys.
Table 5 lists the concentrations at various
monitoring stations used in this applica-
tion. Metal loadings data (concentrations
and discharge rates) from industrial and
municipal point sources represent aver-
ages of the data collected during separate
surveys in July 1984.
Available data for this study contained
problems that limited the scope of the
application and the reliability of the model
results. Primary among them is the lack
of steady-state hydraulic conditions — a
crucial requirement of MEXANS —which
existed during the data collection surveys.
Metal concentrations were imprecise due
to the analytical methods. The flame
atomic adsorption method provided an
accuracy of 0.01 mg/l for most metals,
which generally resulted in reported
concentrations of one significant figure
and many dissolved concentrations of 0.
Calibration of MEXAMS in Ten Mile River
requires metal concentration measure-
ments accurate to 0.001 mg/l. A related
problem was the lack of metal adsorption
data for Ten Mile River. MINTEQ adsorp-
tion models require precise measure-
ments of dissolved and adsorbed con-
centrations using native water and
sediments, as well as detailed chemical
analyses of the samples. Major cationic
species such as Ca2+, Mg2+, Na+, and K+,
were not determined individually; con-
sequently, ionic strength computations in
MINTEQ were inaccurate. Conductivity
measurements, from which ionic strength
can be estimated, were available.
Copper speciation and adsorption was
simulated in a 15-mile stretch of the river
from the Fuller Pond outlet downstream
to Ten Mile Reservation Pond. The EXAMS
input was based on flow data of July 24,
1984. Segmentation of the river generally
followed the gross channel morphology.
-------
Table 3. Water Quality Data, White River
Selected Parameters
White River Surface Water Quality (11/1974-9/1976)
Baseflow
Total Alkalinity (mgCaCo3/l)
Dissolved Solids (mg/l)
Total Hardness (mg/l)
pH (units)
Conductance (^.ohms/cm)
Calcium (mg/l)
Magnesium (mg/l)
Sodium (mg/l)
Potassium (mg/l)
Chloride (mg/l)
Sulfate (mg/l)
Sulfide (mg/l)
DOC (mg/l)
Orthophosphate (mgP/l)
Ammonia (mgN/l)
Nitrate (mgN/l)
Cu fag/ 1)
2N fag/I)
Cd (ng/l)
Cr fag/I)
Pb (ng/l)
Ni (ng/l)
Fe fag//)
Ba fag/I)
Alfag/t)
Mn fag/I)
Mean ±
199 ±
526 ±
289 ±
81 ±
813 ±
70 ±
27 ±
71 ±
23 ±
40 ±
176 +
0.2 ±
6.8 +
.02 +
.03 +
12 +
6.0 +
21 +
0
2
2
5
28 +
48 +
17 +
4
S.E.
1.7
5.7
3.3
0.05
12.7
1.1
0.4
19
0.09
1.3
2.2
1 18
01
004
02
308
507
54
158
22
Max
242
717
460
8.8
1650
120
39
180
6 1
120
230
2.5
140
.87
19
1.10
160
180
1
20
7
16
270
140
80
20
Min
147
449
240
70
625
61
22
34
.9
24
140
0
25
00
00
.00
0
0
0
0
0
0
0
0
0
0
Lower Basin Runoff
Mean ±
177 ±
588 +
301 ±
8.2 ±
874 +
70 ±
30 ±
82 +
23 ±
41 ±
209 ±
0.2 ±
5.8 +
.01 ±
.05 +
.14 ±
41 +
12 ±
1
2
4
2
33 +
38 +
30 +
9
S.E
11.2
8.8
4.8
0.12
137
096
06
1.9
0.28
1.2
38
1 39
002
013
.029
188
37
4.8
102
57
Max
221
676
340
94
1010
76
35
no
4.0
58
260
1 3
12
04
.29
52
340
60
2
10
37
4
80
100
90
30
Mm
144
513
220
6.8
700
56
20
65
20
25
180
0
30
.00
00
00
2
0
0
0
0
0
10
0
0
0
Upper Basin Runoff
Mean ±
134 ±
293 ±
184 ±
7.8 +
473 ±
46 +
16 ±
32 ±
1.7 ±
15.5 ±
88 ±
0 1 ±
58 ±
02 ±
02 ±
16 ±
4 ±
4 ±
0
0
1
3
38 +
27 +
30 ±
2
S.E.
39
137
69
0.11
23.3
1 6
0.9
25
0.07
1.5
69
0.53
004
004
02
1 2
1.5
5.6
145
44
Max
204
536
280
8.8
810
67
28
75
3.0
42
190
2
8.6
.06
04
.38
60
20
1
0
4
6
90
130
80
10
Min
107
212
140
6.5
320
35
11
17
13
3
69
0
44
.00
00
01
1
0
0
0
0
0
0
0
0
0
Table 4. Summary of Adsorption Modeling In The White River-Langmuir Constants and
Activity Kd For Copper Partitioning
Activity Langmuir Model
Activity
SS, mg/l
220
220
220
1590
1590
1590
1590
pH
670
750
830
680
7 46
831
895
KLST
143
243
6090
186
800
19300
136000
ST.
M/L
3 3 £-05
KL, L/M Kd
7 36 E +06
829
575
17000
933 000
Table 5. Water Quality Data, Ten Mile River
River Station
TM01
TM02
TM03
TM04
TM05
TM06
TM07
TM07A
TM08
TM08A
TM09
TM10
TM11
TM12
TM13
TM14
pH
7.2
6.8
6.9
7 1
7.1
7.2
72
70
7 1
72
7.1
7 1
72
7.1
6.9
73
Alkalinity
39.
27
26.
28
23
27.
34.
28.
29.
32
30
30
30.
29
25.
33.
Hardness
100
71
55.
52
42.
44
50.
39
39
42
42.
38.
38
36
38.
46
C1_
17
21
22
26.
22
21.
34.
31
67
35.
35.
32
29.
27.
28.
30
SO/
6.0
3.9
33
3.4
24
2.2
3.0
2.2
2.2
24
2.7
24
2.7
1.8
2.2
2 1
NO3-N
22
2.6
1.3
1.8
0.0
09
4.4
2.2
2.7
27
3 1
27
22
22
2.2
7.1
NH3-N
006
0.05
007
0.29
006
007
0.12
0 16
0.11
0 14
0 14
012
0.08
005
008
0.10
PO4-P
0.03
0.03
0.24
0.61
0.34
0.34
12
0.61
0.43
0.61
0.55
0.61
049
0.37
040
0.89
SS
15
6.5
12.
50
5.5
20
45
55
90
6.0
7.5
6.0
10
70
3.5
4.0
with impoundments and intervening
channel sections forming the segments.
Fifteen segments or 30 EXAMS compart-
ments were delineated and divided into
three sets of ten compartments, because
MEXAMS is limited to ten compartments
per run. A complete Ten Mile River
simulation required three successive
model runs, with simulated metal outflow
from the downstream end of one run
providing the input loading to the up-
stream end of the next run.
MINTEQ input was developed based on
the water chemical survey data of July 2,
1984, and mean copper loadings from
three July 1984 dates. Each EXAMS
compartment requires an associated
MINTEQ input set. Because many of the
chemical constituent concentrations ex-
hibited little spatial variation, all channel
water column and benthic compartments
in each ten-compartment section were
assigned the same MINTEQ input file.
Also, several ionic species were omitted
from the final MINTEQ input, because
they are unlikely to form complexes with
metals or to affect the equilibrium cal-
culation significantly The species omitted
were Cl, N03, PO43, Na*, 10, and NH^.
The adsorption model selected was
activity Kd. Extensive data requirements
of other MINTEQ adsorption models
-------
precluded their use in this study. The
available data were inadequate for reliable
application of the activity Kd model. Pre-
liminary MINTED runs were performed to
determine the activity Kd coefficients for
copper under different conditions of water
chemistry and sediment concentration.
These Kd's were subsequently used ii.
the MEXAMS simulation.
The activity Kd model is defined in
terms of the activity of uncomplexed metal
as opposed to the total dissolved metal
concentration. This assumes that only
free (uncomplexed) metal adsorbs. Thus,
in order to obtain an activity Kd coefficient
applicable to a specific water sample,
one must have "measurements" of this
activity. Analytical techniques measure
the dissolved metal concentration, which
includes uncomplexed metal plus aqueous
complexes. To obtain the activity of the
metal, preliminary MINTED runs were
performed on the water analyses used in
this application. The activity of uncom-
plexed metal computed by MINTEQ in
these runs was combined with the cor-
responding measured concentration of
adsorbed metal to obtain the activity Kd
for each water sample. The activity of the
uncomplexed metal is affected by the
total ionic composition of the water as
well as competition with complexes;
consequently, a complete water analysis
is necessary to develop the MINTEQ input.
MEXAMS was executed using the
available input data. Calibration of
MEXAMS was not performed in this study
due largely to the non-steady-state condi-
tions exhibited in the 1984 survey data
and lack of critical adsorption and metal
concentration data. It is instructive, how-
ever, to examine the results of the simu-
lation to suggest further data collection
and modeling efforts. In Table 6, simulated
copper concentrations in all water column
compartments and impoundment benthic
compartments of the first ten-compart-
ment section of Ten Mile River are com-
pared with observed concentrations. As
was noted previously, the MINTEQ input
for channel benthic compartments was
not differentiated from the overlying
water column because channel sediments
were not sampled, and impoundment
sediments were judged to be more
significant in transport/fate of metals.
The results show a definite over-pre-
diction of both dissolved and total copper
in the water column while the benthic
results are slightly low. Since the river
flow rates used in this study were higher
than average, the likely cause of the
discrepancies is either non-representative
metal loading rates (i.e., higher than the
Table 6. Comparison of Predicted and Observed Copper Concentrations
Observed Mexams
Station
TMOJ
Wetherells
Pond
TM03.TM04
Falls Pond
TM06
we
B
we
B
Dissolved
(mg/l)
0.
0.
—
0.02
0.02
—
0.02
Total
fmg/ir
0.47
0.07
1000."
0.04
0.06
850.*
0.02
Compart-
ment
1
3
4
5
7
8
9
Dissolved
(mg/l)
0.15
0.15
0.15
0.11
0.088
0.088
0.075
Total
(mg/ir
0.47
0.44
684.*
0.34
0.27
418.*
0.23
WC - Water column
B - benthos
* Benthic total concentration units are mg/kg
historical average) or an incorrect model
configuration such as not considering all
transport processes from the water
column to the benthic sediment. It is
likely that this system could be calibrated
by varying one or more processes so that
more metal is present in the sediment
(particularly the impoundments), and less
in the water column. This would be done
by further increasing benthic adsorption
relative to the water column and/or in-
creasing the dispersive transfer rates.
The resulting calibration would be difficult
to defend, however, given the uncer-
tainties in hydraulics, metal loading, and
observed metal concentrations.
The detailed MINTEQ speciation for the
metal of interest (Cu) is shown in Table 7
for water column and benthic environ-
ments. As expected for a natural system
of this type, sorption dominates copper
fate, and uncomplexed copper probably
represents a small fraction of the metal.
The factors that most affect metal fate
in Ten Mile River are adsorption and
transport within and through the im-
poundments, including settling of ad-
sorbed metal during high flow. Insuf-
ficient knowledge of these factors as v /ell
as uncertainty regarding typical flow
conditions, impoundment hydraulics, and
metal concentrations limited the scope of
this study.
Based on these conclusions, several
recommendations would facilitate a
Table 7. Typical Speciation of Copper
In Ten Mile River
Species
Cu2 (aq)
Cu-SOH
CuC03(aq)
CufOHMaq)
CuHCtf, (aq)
Water
Column
6
67
12
13
2
Benthic
100
quantitative application. Data collector
should be performed during low flow
steady-steady conditions, and all dati
should be collected concurrently when
ever possible. Because of their impor
tance, impoundments should be wel
characterized with regard to hydraulics
including dimensions, flows, and esti
mated sediment trapping efficiencies
Metal concentration data should be ac
curate to at least 1 ng/\, and routine
chemical analyses should include th(
following: H+, Na+, K", Cat2, Mg+2, SO42
Cr, P04* NO3, NhU, alkalinity Eh, anc
suspended sediment.
A thorough equilibrium adsorptior
analysis should be performed for each
metal, including precise metal deter
minations under varying conditions o
sediment concentration and total meta
to allow application of the Langmuii
isotherm adsorption model. Composite
sampling of metal discharges should be
performed to limit the effect of variable
flow and concentration. MEXAMS shouk
be applied in a manner similar to tha
described here with a refined compart
mental configuration and consideratior
of sediment settling.
Calibration of the system could be per
formed by varying settling rates, water
sediment dispersion, and adsorptior
coefficients to vary the water column
benthic interaction. Alternately, one coulc
use a dynamic transport model thai
includes adsorption and sediment trans-
port to characterize dissolved and par-
ticulate metal transport. Subsequenl
MINTEQ simulations would be performec
to obtain more detailed information o1
metal speciation in selected critica
reaches of Ten Mile River, such as near a
large point source or in an impoundment
Sediment Settling and
Resuspension In MEXAMS
Sediment transport is likely to be im-
portant in the transport of highly adsorbed
8
-------
and precipitated chemicals in rivers. The
significant transport processes include
advection and dispersion within the water
column, deposition (settling), and scour
(resuspension). In many rivers, net depo-
sition over time can lead to effective
burial of chemicals within the benthic
sediments. EXAMS' steady-state hydrau-
lic simulation does not explicitly consider
the processes of sediment settling and
resuspension. Currently, river sediment
washloads are represented in EXAMS by
advective and dispersive sediment trans-
port between water column compart-
ments. Also, bedload transport is repre-
sented by flows between benthic
compartments which are horizontally
adjacent. However, net sedimentation and
resulting burial of chemicals must be
represented by an artificial first-order
degradation rate of the chemical in the
benthic sediments.
A simple settling/resuspension al-
gorithm was added to MEXAMS to im-
prove the model's steady-state sediment
transport capabilities. This algorithm al-
lows the user to specify steady-state,
vertical settling from the water column
to benthic compartments and correspond-
ing resuspension. Net deposition of
sediment to the benthic compartment is
considered; however, net erosion is not
permitted. This section in the report
documents the incorporation of these new
capabilities in M EXAMS including com-
putations added to the code, input, output,
and limitations.
Allen J. Medina is with Ecosystem Research Institute. Logan, UT 84321; and
Brian R. Bicknell is with AQUA TERRA Consultants, Mountain View, CA
94043.
Lee A. Mulkey is the EPA Project Officer (see below}.
The complete report, entitled "Case Studies and Model Testing of the Metals
Exposure Analysis Modeling System fMEXAMS)," (Order No. PB 87-141 081 /
AS; Cost: $24.95, subject to change) will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Environmental Research Laboratory
U.S. Environmental Protection Agency
Athens, GA 30613
-------
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-------
United States
Environmental Protection
Agency
Environmental Research
Laboratory
Athens GA 30613
Research and Development
EPA/600/S3-86/044 May 1987
Project Summary
Modeling the Benthos-Water
Column Exchange of
Hydrophobic Chemicals
P. M. Gschwend, S-C. Wu, 0. S. Madsen, J. L Wilkin, R. B. Ambrose, Jr.,
and S. C. McCutcheon
An analysis and modeling framework
was developed to simulate and predict
the transfer of hydrophobic organic
chemicals between bed sediments and
overlying waters. This approach entails
coupling a description of the micro-
scopic scale process of sorption kinetics
with models of the exposure of bed
particles to adjacent waters of varying
composition (i.e., due to diffusion of
solutes in interstitial fluids or pore water
advection, due to biological mixing of
surficial sediments, due to suspension
of bed solids for a period into the over-
lying water column.) Numerical simula-
tion routines are developed both for
sorption kinetics and to demonstrate
coupling of this particle-water exchange
to particle movements in the case of a
biologically mixed bed. These routines
were used to assess the sensitivity of
sorption kinetics and the overall trans-
port to chemical and sediment pro-
perties. Similar computer programs can
be used as subroutines in global chemical
fate models. Also a formulation of bed-
load transport and of sediment resus-
pension was developed which yields
the contact time of bed particles with
the overlying water column. This model
result is then combined with the sorption
kinetics subroutine to estimate bed-
water exchange in instances where
these processes greatly facilitate bed
particle-water column contact.
This Project Summary was developed
by EPA's Environmental Research
Laboratory, Athens, GA, to announce
key findings of the research project that
Is fully documented In a separate report
of the same title (see Project Report
ordering Information at back).
Introduction
Several models have been recently
devised to describe the fate and transport
of pollutants in bodies of water. However,
these models are based on incomplete
descriptions of the processes that control
the exchange of chemicals between the
bed and water column. In the current
project, the authors describe the im-
portant processes and develop mathe-
matical descriptions that should be useful
in updating existing models and devising
new models. In addition, the final project
report will be a useful reference in
describing the conceptual framework and
relationships between direct sorption or
desorption, diffusion, advection; biotur-
bation and sediment transport.
Figure 1 gives the conceptual frame-
work for describing the benthic exchange
processes. For the purpose of this study,
the aquatic environment was envisioned
as consisting of a water column/n an
active, moving bed load transport layer
and an immobile bed where sediment is
stored. The definition of the active bed
layer is taken to be two grain diameters
in thickness for sediment transport, and
about 5 to 20 cm thick for bioturbation;
however these definitions are arbitrary
because thickness is difficult to forecast.
The depth of the immobile layer is to be
governed by burial, compaction, and
erosion processes. The water column may
be described with more than one layer if
significant chemical gradients exist and
are necessary to describe benthic
exchange.
Figure 1 also ranks the processes in
terms of process energy requirements
and expected contact time between bed
particles and the dissolved phase of a
-------
Geo-
Morphological
State
Hydraulic
State
Ci,w
Deposition, Burial, Compaction
Scour
Water
Column
\
Coastal Areas, Estuaries, Streams
Lakes, Coastal Areas, Reservoirs, Estuaries
Laminar or
Quiescent Flow
/-A
Ejection
Cone.
Ca.w
O O
ojo o
o
o
ef
o
o oo
o
p
b
oo
o
Exchange of
Sediment and
Surrounding
Fluid
Immobile
Bed
Direct
Sorption
Exchange
(D
Pore Water
Diffusion of
Dissolved
Species
(ID
Advective-
Dispersive
Flow
(III)
Biotur-
bation
(IV)
Bed Load
Transport
(V)
Suspended
Transport
(VI)
Increasing Energy, Velocity, and Sediment Movement
Generally Increasing Contact Time
Figure 1. Processes involved in bed-water column exchange
chemical in the water column. Direct
sorption is expected to be the least
energetic and slowest exchange process,
whereas sediment transport is expected
to be the most energetic and, to involve
some of the largest fluxes of material.
However, the limiting process may involve
the slowest, least energetic process.
Although this work significantly im-
proves our understanding and modeling
capability for bed-water pollutant ex-
change, several other important issues
remain incompletely developed. For
example, the inclusion of colloids and
their impact on transport. We do not
understand the sources and sinks of these
nonsettling sorbents, particularly in sedi-
ment beds, and our knowledge of their
mobility in porous media and ability to
bind pollutants is limited. Additionally,
the importance of bioturbation and other
sediment modifying activities of benthic
organisms to bed load transport and
resuspension is uncertain. Finally, sus-
pension of sediment particles from cohe-
sive beds remains poorly understood, and
therefore modeling of bed-water column
exchange for pollutants where cohesive
sediment is involved is limited to diffusion
and bioturbation-controlled situations.
Sorption Kinetics
The formulation for sorption kinetics is
a physically based description of the
microscale processes encompassing dif-
fusion of nonpolar hydrophobic chemicals
into the pore space of natural aggregate
particles coupled with local partition
equilibrium as illustrated in Figure 2. The
research conducted during this study
indicates that many natural particles of
importance to the sorption process can
be described as porous spheres having
an intraparticle porosity of about 0.13.
Based on this conceptual model the
sorption kinetics can be described as
-=D.
'eft
32Csw(r) 2 aCsw(r)
3r J
dr
(D
where csw(r) = total concentration of
sorbate (chemical) at a radial distance r
from the center of a particle and
Dmn'+1
(1-n)PsKp+n
(2)
in which D,,, = molecular diffusivity that
can be determined by the method of
Hayduk and Laudie, n = intraparticle
porosity of about 0.13, ps = specific gravity
of the particles, and Kp = partition coef-
ficient that can be predicted from the
normalized octanol-water partition coef-
ficient, Kow, and the fraction of organic
carbon contained in the natural particles.
Thus equation 2 provides a physically
based method for predicting sorption and
desorption.
The flux of material from a layer of
particles on the surface of the bed can be
determined from the description of the
fraction that is sorbed or desorbed at the
end of the residence time, tr. The fraction
-------
Turbulent
Flowing Exterior
Molecular Diffision
in Pore Fluid
Impenetrable
Mineral Grain
Stagnant, Nonflowing
Interior forewater
Figure 2. Physical picture of processes controlling sorption kinetics.
Bioturbation
In the case of bioturbation, mixing by
benthic organisms is described using an
eddy viscosity scheme. This results in a
flux expression of the form
3C z
Flux = - Eb = wbC = / f(z) C dz (5)
dz 0
where Eb = mixing coefficient, C = total
concentration of chemical in the dis-
solved, colloidal-bound, and sediment-
sorbed phases, wb = vertical sediment
velocity induced by biological mixing, and
f(z) = feeding activity due to ingestion of
particles.
For plow-like bioturbation involving
mixing at the surface. Equation 5 can be
applied by noting that wb and f(z) are zero.
Table 1 gives the known values of Eb and
the depth of the mixed layer for several
species of benthic animals. The wide-
spread application of the method will
require determination of Eb and mixing
depth for all species of interest. Alter-
natively, the rate of benthic mixing is
related to individual reworking rates, r'
depth of mixed layer, L, population density,
and bulk density of the sediments, pb via
Eb = L r'(population)/pb
(6)
sorbed or desorbed from or to an infinite
volume of water is given by:
(3)
1-(6/7r) J {(1/m2)
m=1
exp(-Deffm2(^)2tr/R2)}
where M, = mass sorbed to the layer of
surficial bed particles over time tr, Mre =
mass attached to surficial bed particles
after infinite time, m = the number of
particle sizes the sediment is arbitrarily
divided into, Def( = effective intraparticle
diffusivity that is essentially molecular
diffusivity retarded by sorption, and R =
particle radius. The residence time, tr, for
sediment particles can be determined
from descriptions of bioturbation and
sediment transport. Equation 3 for infinite
water bodies is expected to be accurate
for many streams, lakes, and estuaries
where water volumes are large compared
to the volume of surficial sediments. In
cases where this may not be true, a
numerical solution is derived to compute
M,/M«, and this solution is incorporated
in a basic program included in the final
report.
Diffusion and Advection
The diffusive and advective flux of dis-
solved and colloidal material is described
by
Flux =
(4)
dz
where n2 = porosity of the bed, i = an
empirical factor dependent upon n2 and
determined by the formation factor that
describes the effect of tortuosity on
molecular diffusion, Dm = molecular dif-
fusivity, Dc - diffusivity of colloidal
material, Sc = concentration of colloidal
and nonsettling material, and w2 = pore
water velocity in the bed. Here it is
assumed that the size of the pores is
large compared to the colloidal material.
Table 2 gives estimates of individual re-
working rates and mixing depths for
benthic ploughers and conveyor-type
species. Figures 3, 4, and 5 show the
sensitivity of the computed flux to particle
diameter, particle porosity, and phase
partitioning. The values on which these
calculations are based are given in Section
3.3.2.3 of the final report.
Conveyor-belt bioturbation involves
worms that ingest sediment at some depth
z into the bed and egest the reworked
sediment at the bed surface. The worms
ingest the sediment for the organic carbon
contained in the sediments and in the
process rework the sediment into pellets
or trails of inorganic sediment bound by
mucous. The reworking rate is
w'b = (
population
(7)
Figure 6 shows the sensitivity of the flux
to pellet diameter and the partitioning
coefficient. See Section 3.3.2.4 for more
details.
Sediment Transport
The description of sediment transport
is based on a physical framework for
cohensionless particles where the resis-
tance to movement derives from the
-------
Table 1. Biogenic Mixing Coefficients. (Source: review by Lee and Swartz)
Location
Species
L(cm)
Eb(cm2/sec)
8 Calculated from data.
b Calculated by Guinasso and Schink (1975).
c Calculated by Alter (1978).
Vertical diffusion coefficient.
"Horizontaldiffusion coefficient.
Method
Deep Sea, various sites
Mid-Atlantic Ridge
Long Island Sound
Chesapeake Bay
New York Bight
Rhode Island
0-1 cm
2-1 Ocm
La Jo/la, California
Barnstable Harbor,
Long Island Sound
Long Island Sound
Laboratory
Laboratory
Laboratory
Laboratory
?
?
Yoldia, Nucula
?
?
Leptosynapta, Scoloplos
Euzonous mucronata
f=Thoracophelia)
Pectinaria gouldii
Yoldia limatula
Yoldia limatula
Yoldia limatula
Clymenella torquata
Clymenella torquata
Molpadia oolitica
10-48
8
4
10-15
?
1
8
30
6
2
3
3
11
11
7-9
3.6 x Iff" -3.16 x Iff8
6x1ff9
1. 2-3.5 x10'6
1 x 10~6
5 x 10'7
2.9 x Iff6 -1.6 x Iff5
8.3 x 1 ff7 -4.3x1 ffe
1.5 x Iff5
7.6x1ffa
3.2 x 10'7
2 x Iff6
1 x Iff5
2-3 x 10~4
4.5 x Iff5
5.7-9.4 x Iff5
Dimensional analysis
210 Pb pattern
234 Th pattern
Dimensional analysis?
234 Th pattern
Dimensional analysis8
Dimensional analysis8
Dimensional analysis1'
Dimensional analysis'
Dimensional analysis'
Dimensional analysis0
Pore water profiles
Pore water profiles'1
Pore water profiles"
Depth of oxidized layer8
Table 2. Individual Particle Reworking Rates, Annual Reworking Rates, and Depth of Reworking. (Source: review by Lee and SwartsJ
Species
Annelids
Abarenicola claparedi
Abarenicola pacifies
Abarenicola pacifies
Amphitrite ornata
Amphitrite ornata
Arenicola marina
Clymenella torquata
Clymenella torquata
Guild
FUN
FUN
FUN
SISDF
SISDF
FUN
CB
CB
Individual
Reworking
Rate
(mg/ind/day)
3.600
10.900
0-4.500
0-15.000
—
5.100
2,600-5.200
4.700
900
1,650
Total
Reworking
Rate
(g/m2/yrj
—
—
—
—
310 kg
—
—
—
54,000
73,000
Depth of
Reworking
—
—
—
—
-------
Table 2. (continued)
Species
Callianassa major
6 species
Paraphoxus spinosus
Uca pugillator
Uca pugnax
Echinoderms
Caudina chilenses
Echinocadrium cordatum
Holothuria spp.
7 species
Leptosynapta tenuis
Leptosynapta tenuis
Scotoplanes sp.
Stichopus ntoebil
Stichopus variegatus
Enteropneust
Balanoglossus gigas
Guild
MISSDF-E
MISDF-V
MISDF-E
MISDF-E
CB
MSSDF-V
MESDF
FUN
FUN
MESDF
MESDF
MESDF
FUN
"o = original data C = calculated from data
Individual
Fteworking
Rate
(mg/md/dayl
3.500
8.910
96
75
160.000
3.000
Total
Reworking
Rate
(g/m2/yr)
12.6-630 kg
54-2,200 kg
230
820
—
—
Depth of
Reworking
to<10cm
0-1 cm
—
—
—
—
Comments
Amount deposited per entrance, just faces
Burrowing
Just feces. recalculated from data
Just feces. recalculated from data
25.000-220,000 — —
J0.400-J8.4OO
34,000
100.000
38.000
49,000
—
590-3,000 kg
—
—
—
0.5- 10 cm
1.15cm
1 iMni
—
—
Feces and below surface reworking
Feces and below surface reworking
Feces
200,000-250,000 — —
fP = calculated by Power (J977)
Source
0
C
C
C
P
P
P
0
C
0
P
P
P
dH = calculated by Hargrave (19721
NOTE: Guilds CB. FUN, SISDF, MISDF-V, MISSDF-E are primarily tube, funnel, or deep burrow forming species whereas MISSDF-V. MESDF and MIFF are primarily
surface ploughing or mixing species.
weight of the individual particles rather
than through interparticle bonds. Thus
this component of the description is
limited to silty sediment and coarser sizes.
Furthermore the formulation is limited to
particles of a uniform size, and following
the work of Einstein, assumes that several
discrete size classes can be separately
described. This ignores the effect of large
sizes on the critical shear stress of the
small particles and vice versa. Finally, the
conceptualization assumes that the
transport system is instantaneously in
equilibrium between the suspended, bed,
and immobile-bed loads illustrated in
Figure 7.
Based on this conceptual model, the
distribution of sediment mass at equili-
brium between the suspended, bed, and
immobile compartments is given by
P23
m, =
m, = •
M (8)
M (9)
P23 + P32 + P21 P32/P12
P32
P23 + P32 + P21 P32/P12
P21 P32/P12
P23 + P32 + P21 P32/P12
where M is the total mass in the three
compartments and pnm are exchange
coefficients for sediment between layers
n and m.
The mean downstream velocity for the
sediment mass is given as
MU = m, U, + m, U2 (11)
where U1 is the velocity of the suspended
sediment mass and U2 is the velocity of
the sediment mass in the bed-load layer.
From the average velocity of the sediment
mass, it is possible to compute the ex-
posure time of the sediment particles to
the water column over reaches of given
length as tr = length/U.
The time of exposure or residence time
is coupled with the sorption kinetics
model given in Equation 3 to describe the
transfer of a contaminant to or from the
sediment moving in the stream. The
solution of Equations 8 through 10 in the
downstream direction describes distribu-
tion of contaminated sediment. The final
report illustrates the solution of these
equations in examples for a river and
deep river or reservoir.
Summary and Recommendations
for Future Research
To estimate bed-water exchange of
hydrophobic organic pollutants, a two-
step modeling approach or description is
recommended. First, particle-water ex-
change on the microscopic scale must be
quantified; this can be done using the
retarded radial diffusion model, which
treats each case as a function of com-
pound solution diffusivity and hydrop-
hobicity and sediment particle size and
organic content. Section 2 of the final
report describes a numerical simulation
routine to handle such solid-water ex-
change of chemicals even in cases where
there is a spectrum of particle sizes in-
volved and the solution concentrations
vary in time. Second, this particle-water
exchange kinetics description must be
coupled with descriptions of the relative
translations of sediment particles and the
adjacent fluids (i.e., due to porewater
advection, bioturbation, bed-load trans-
port, or particle resuspension). This pro-
duces a prediction of the overall exchange
of chemicals between the bed and the
water column. Section 3 of the final
report demonstrates the coupling of
particle-water pollutant exchange in
biologically mixed beds. Section 4 devel-
ops a quantitative description of the expo-
sure of a moving bed particle to the
overlying water column and then couples
this transport to sorption kinetics. In any
case of interest, decisions concerning the
intensity of various processes facilitating
bed particle-water column contact are
necessary before good predictions of pol-
lutant transfer can be expected.
Several areas of future research are
suggested to improve and extend these
analytical methods:
(1) The sources and fates of colloidal
materials in sediments needs to be
examined. Additionally, the sorbent pro-
perties of these macromolecules or
microparticles should be assessed. These
sorbents may be particularly important in
transporting very hydrophobic pollutants
from beds that are not biologically mixed.
(2) The nature of bed particle and pore
water movements under the influence of
benthic infauna should be explored fur-
ther. Pore water pumping (or irrigation)
was neglected here for want of a general
-------
I
I
0.08 1
0.06
0.04
0.02
0.00
Diameter (cm)
0.005
Diameter (cm)
100
200
Time (days)
I
s
1
s
0.06
0.04
0.02
0.00
diffusion length scales and intra-aggre-
gate porosity for solids as they exist in a
bed should be researched further. Also,
to extend this approach to other con-
taminants such as trace metals and polar
organic compounds, the mechanisms
controlling their sorption kinetics inter-
actions with sediment particles should
be examined.
(6) Finally, efforts should be made to
test the accuracy of model predictions
against real world situations. Currently,
there is a dearth of field data for com-
parison with model predictions. Thus,
bed-water fluxes must be measured at
times and places where the prevailing
bed mixing processes are known and
ancillary data are obtained to estimate
their intensity.
Figure 3. Sensitivity of the plow-like bioturbation mediated pollutant flux to 5 different
sediment particle sizes. The values of other parameters are same as those in
the example problem in Section 3.3.2.3 of final report.
quantitative description of this process as
a function of organisms involved. Also,
approaches for estimating parameters and
better quantifying the mixing activities of
benthic infauna from field measurements
are needed.
(3) The development of a basic under-
standing for the factors and processes
governing cohesive sediment resuspen-
sion and transport is also necessary.
These cohesive organic-rich muds are
the predominant sites for collection of
many pollutants discharged to natural
waters, yet our ability to quantitatively
describe the movements of particles in
these beds remains poor.
(4) In the sediment transport models
formulated here, steady flow conditions
were assumed. The impact of unsteady
(e.g., tides in estuaries), and even
catastrophic (e.g., storms) phenomena to
the modeling of sediment transport still
remains an important area to be
examined.
(5) Further assessment of the con-
ceptualization of the microscopic scale
particle-water exchange of chemicals
from particles in beds to the surrounding
pore waters should be done. The retarded
radial diffusion model has been tested
primarily for aggregate particles in sus-
pension. Issues such as the appropriate
-------
I
CM
5
I
0.30 n
0.20
o.ro
o.oo
o.w
0.08-
0.06
0.04-
0.02
0.00
Microporosity
100
200
Time (days)
300
103
Figure 4. Sensitivity of the plow-like bioturbation mediated pollutant to 5 different sediment
intraparticle porosities. The values of other parameters are same as those in
the example problem in Section 3.3.2.3 of final report.
-------
0.10
0.00
0.80 n
0.60
0.40
0.20
0.00
/Cow
700 200 300
Time (days)
W3
Time (days)
Figure 5. Sensitivity of the plo w-like bioturbation mediated pollutant to chemical partitioning.
The values of other parameters are same as those in the example problem in
Section 3.3.2.3 of final report.
-------
Initial Concentration = 1 fjg/g
Reworking Rate - 0.052 cm/day
0.03 T Bulk Density of Sediments = 0.5 g/cm3
Microporosity = 0.13
0.02
0.0 1-
000
10*
10s
Kow
10e 107
10"
10*
Figure 6. Sensitivity of conveyor-belt type bioturbation mediated flux to Kovl and pellet size.
The values of other parameters are same as those in the example given in Section
3.3.2 4 of final report.
' U,
Layer 1 Suspended Load
ia^er 2.- Bed /.oarf
bed-water interface
Layer 3' Immobile Bed
H-^
I T >>»
Figure 7. Definition sketch: three-layer transport model.
P. M. Gschwend, S-C. Wu. 0. S. Madsen, and J. L Wilken are with the
Massachusetts Institute of Technology, Cambridge, MA 02139; the EPA
authors R. B. Ambrose, Jr., and S. C. McCutcheon (also the EPA Project
Officer, see below) are with the Environmental Research Laboratory, Athens,
GA 30613.
The complete report, entitled "Modeling the Benthos- Water Column Exchange
of Hydrophobic Chemicals," (Order No. PB 87-145 389/AS; Cost: $24.95,
subject to change] will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Environmental Research Laboratory
U.S. Environmental Protection Agency
Athens, GA 30613
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