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Technical guidance manual for performing waste load allocations book ll-streams
and rivers-chapter 3 toxic substances.
Publisher Info. [Washington, D.C.]: United States Environmental Protection Agency, Office of
Water, [1984]
Internet Access http://purl.access.gpo.gov/GPO/LPS67675
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:EP 2.8:440/4-84-022
0607-C (online)
424 p.: digital, PDF file.
Title from title screen (viewed oh Mar. 14, 2006).
"June 1984."
"EPA 440/4-84-022."
Includes bibliographical references.
Mode of access: Internet from the EPA web site. Address as of 3/14/2006:
http://www.epa.gov/waterscience/library/modeling/wlabook2chapter3.pdf; current
access available via PURL.
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UnrtM Slltn
vvEPA
QMici o* Mite R»guu»ani
•no S«rxJ»n3«
Monxormg fno Oitf Suooorr
Fin*
Technical Guidance
Manual for Performing
Waste Load Allocations
Book II Streams and Rivers
Chapter 3 Toxic Substances
-------
Click here for
DISCLAIMER
Document starts on next page
-------
(SB)
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON. D.C. 204«O
JUN 13 B84
MDCRANDGM
SUBJECT:
FROM:
TO:
Technical Guidance Manual for Performing Waste Load
Allocations Book II, Stream and Rivers, Cnapter 3,
Toxic substance Impacts
Steven Schatzow, Director
Office of Water Regulations and Standards (VH-551)
Regional water Division Directors
Regional Btvircrwental Services Division Directors
Regional Wasteload Allocation Coordinators
Attached, for national use, is the final version of the Technical
Guidance Manual for Performing waste Load Allocations Book II, streams and
Rivers, Chapter 3, Toxic Substance Impacts. We are sending extra copies
of this itenual to the Regional Wasteload Allocation Coordinators for
distribution to the States to use in conducting uasteload allocations.
If you have any questions or cements or desire additional information
please contact Tin S. Stuart, Chief, Monitoring Branch, Monitoring and
Data Support Division (W3-S53) on (FTS) 382-7074.
Attachment
REVISIONS:
10/85 - pages 21, 86, and 97.5
-------
TECHNICAL GUIDANCE MANUAL FOR
PERFORMING WASTE LOAD ALLOCATIONS.
BOOK II STREAMS AND RIVERS
CHAPTER 3 TOXIC SUBSTANCES
by:
Charles G. Oelos. EPA, OURS
win lam L. Richardson, EPA. Large Lakes Research Station
Joseph v. DePlnto, CTarksan University
Robert B. Ambrose. EPA. ERL - Athens
Paul W. Rodgers, Llmno-Tech. Inc.
Kenneth Rygwelskt. Cranbrook Institute of Science
John P. St. John, HydroOuat. Inc.
w.J. Shaughnessy. versar Inc.
T.A. Faha, versa'r Inc.
w.H. Christie, Versar Inc.
August 1984
Office of Water Regulations and Standards
Monitoring and Data Support Otvlston
Water Quality Analysis Branch
U.S. Environmental Protection Agency
401 M. Street, S.W.. Washington, D.C. 20460
-------
ACKNOWLEDGMENTS
The development of this document grew out of a research project,
modeling the behavior of metals in the Flint River, undertaken by the
Office of Research and Development. Large Lakes Research Station. Grosse
He. Michigan. In response to the needs of the waste Load Allocation
Section of OURS, the scope of this work was broadened to Include this
.volume of the Guidance Manual.
Project direction was provided by Nor&ert Jaworski and Ne.ison Thomas.
ERL-Ouluth. and Michael Sllmak. OURS. Victor 81erman was Instrumental
during the Initial phases of the research project. The field and
laboratory work for the Flint River case .study was oerfomed by the
Cranbrook Institute of Science, coordinated by V.E. Smith, and by the
U.S. Geological Survey, coordinated by T.fi. Cunnings and J.B. Miller.
Richard Hobrla. Stephen Buda. and others at the Michigan Department of
Natural Resources provided Invaluable guidance and helped keep tne
development work on a practical course. Robert wethlngton of Computer
Sciences Corp. contributed to the modeling effort.
The following individuals contributed to the improvement and
completion of this document through their most ne1pfu> review and comment
on the draft report: Robert V. Thomann, Raymond P. Canale. Donald j.
O'Connor. Thomas 0. Barbell, Bruce Zander, James S.,Kutzman. Gary
Williams, Gary Mllburn. £. Dale wismer, Patrick J. Harvey, tmory 3. tang.
James J. McXeown. Alexander NcBrlde, John Maxted, and James Bonner.
Although several of the coauthor? contributed broadly to the document
through their critical review, the primary responslbV'ty for eacn section
can be ascribed as follows:.
Section 2.1 - 2.5:
Section 2.6:
Section 3.1:
Section 3.2:
Section 3.3.1:
Section 3.3.2, 3.3.3:
Section 3.3.4:
Section 4.1 - 4.2:
Section 4.3:
Section 4.4:
Section 5:
Appendices A, B:
Appendix C:
Attachment 1 :
Attachment 2:
W.L. Richardson. J.V. OePlnto. and
R.B. Ambrose
C.G. Oelos and J.v. OePlnto
J.V. OePlnto
P.M. Rodgers
W.L. Richardson
C.G. Oelos. K. Rygwelskl. and W.L,
W.L. Richardson and C.G. Oelos
C.G. Oelos
W.L. Richardson
J.V. OePlnto
J.V. OePlnto and C.G. Oelos
K. RygwelSkl
J.P. St. John
w.1. Shaughnessy. T.A. Faha, and w.
C.G. Oelos
Richardson
N. Christie
W.L. Richardson coordinated the work of most of the coauthors and edited
the 1982 preliminary draft. C.G. Oelos edited the 1983 and 1984 draft
and final versions and addressed comments. The staff of versar Inc.,
coordinated by W.J. Shaughnessy, carried out the production of the 1983
and 1984 versions (Including the drafting of most figures); Oonna Barnard
typed the text and most tables.
-------
CONTENTS
Page
ACKNOWLEDGMENTS 1
t.O INTRODUCTION . . . 1
2.0 BASIC MODEL FRAMEWORKS AND FORMULATIONS 5
2.1 fieneral 5
2.2 Dilution Calculations - Point of Discharge 8
2.3 One Dimensional, Steady-State Model of
Conservative Total Pollutant 11
2.4 One Dimensional, Steady-State Mode! of
Nonconservatlve Total Pollutant 13
2.5 One Dimensional, Steady-State Models For
Separate -Dissolved and Suspended Phases,
Having Bed Interactions and Multiple Process Rates 16
2.5.1 Model Framework 16
2.5.2 Relationship with Other Approaches 23
2.6 Complex Models Having Multi-Dimensional, Dynamic, or
Spedation Capabilities. . 28
2.6.1 Transport and Bed/Water Exchange . 32
2.6.2 Sorptlon 3*
2.6.3 Spedation : 35
2.6.4 Transfor.-natlon 37
3.0 ESTIMATION AND USE OF MODEL PARAMETERS 40
3.1 Exchange oet^een Bed and Water 40
3.1.1 Particle Transport and Exchange 41
3.1.2 Diffusion of Dissolved Material 49
3.2 Partitioning Processes 51
3.2.1 Metals Partitioning 51
3.2.2 Organlcs Partitioning 61
3.3 Decay or Transformation Processes . . . 6?
3.3.1 Blodegradatlon 67
3.3.2 Photolysis 73
3.3.3 Hydrolysis 78
3.3,4 Volatilization 80
4.0 GUIDANCE FOR MODEL APPLICATION 89
4.1 Approach to Waste Load Allocation Problem 89
4.2 Data Needs 96
4.2.1 Obtaining Model Input Oata 97
4.2.2 Calibration and Ver1f1cat'r»n: Model Accuracy .... 101
4.2.3 Additional Oata 105
4.2.4 Quality Assurance 106
4.3 Forecasting 107
4.4 Resource Requirements 113
-------
5.0 .CASE STUDY: MCOttiNG HEAVY METALS TRANSPORT
IN THE FLINT RIVER 119
5.1 Introduction 119
5.2 Description of Flint River Study Site 119
5.3 Flint River August Survey 121
5.3.1 August Survey Data Summary 124
5.3.2 August Survey Node) Calibration 128
" 5.3.3 August Survey Sensitivity Analysis 136
5.4 Flint River December Survey 147
5.4.1 December Survey. Data Summary 151
5.4.2 December Survey Model Calibration 159
5.5 Flint River March 1982 Survey 170
S.S.I March Survey Data Summary ..... 180
5.5.2 March.Survey Model Calibration 180
6.0 REFERENCES (for Sections 1-5 and Appendices A - 0) 190
APPENDIX A. DEVELOPMENT OF MODEL EQUATIONS A-1
A.I Conservative Pollutant A-1
A.2 Nonconservatlve Pollutant A-2
A.3 Water-Sediment Model Having Separate PartUulate
and Dissolved Phases • A-iQ
APPENDIX 3. SE3INE.HT TRANSPORT CONSIDERATIONS 3-1
B.I Sediment Properties 8-1
8.2 Transport of Sediment Loads 8-9
8.3 Deposition and Erosion 3-U
8.3.1 Deposition 8-15
8.3.2 Bed Erosion 8-18
8.3.3 Particle Exchange: Continuous versus
v Discontinuous 8-20
8.4 Sediment Sources 8-22
APPENDIX C. FIELD AND LABORATORY METHODS FOR FLINT
RIVER SURVEYS c-i
APPENDIX 0. BEHAVIOR OF HALOGEN DISINFECTION RESIDUALS 0-1
ATTACHMENT I. WATER-SEDIMENT PARTITION COEFFICIENTS
FOR PRIORITY METALS l-l
ATTACHMENT II. CATALOGUE OF MODELS II-l
-------
SECTION 1.0
INTRODUCTION
This document addresses methods For predicting concentrations of
individual constituents resulting from pollutant loads to tne aauatic
environment, within the context of the waste load allocation (WIA)
process, tne methods predict the ambient concentrations expected to
result from existing or projected pollutant loadings. By relating the
predicted concentrations to ecosystem or human health effects levels, an
appropriate level of pollution abatement can be specified, tailored to
protection of the environment of a specific site.
As the focus of the material is the prediction of ambient
concentrations. It will not address all facets of the allowable load
determination. In order to use predictions effectively. It is also
necessary to establish (a) a target for allowable concentrations, and (5)
a target frequency for not exceeding the allowable concent.-attons. 3a:a
on the former are contained in the water Quality C.-iteMa documents: :a:a
on the latter are sparse. Neither subject Is within tne scope of this
volume.
The organization Intended for the first four volumes of the complete
manual is shown In Table 1.1. In order to reduce redundancy, mater'al
discussed 1n Book II, Chapter 1 (900. dissolved oxygen ana ammonia) is
not repeated here. In particular, U Is assumed that the reader is
familiar with the concepts of advectlon and dispersion, variations of
depth and velocity with flow, first order reaction rates, surface
transfer of oxygen, and steady-state versus time-variable analyses. This
document 1$ Intended for use In conjunction with chemical data references!
such as Nabey et al. (1982) and Callanan et al. (1979).
-------
Table 1.1 ORGANIZATION OF GUIDANCE MANUAL FOR PERFORMING OF
WASTE LOAO ALLOCATIONS
800* I GENERAL GUIDANCE
(Discussion of overall WLA process, procedure*. considerations)
BOOK It STREAMS AND RIVERS
Chapter 1 - BOO/01ssolved Oxygen Impacts and Ammonia To*1city
Chapter 2 - NuiMent/EutropMcatlon Impacts
Chapter 3 - Toxic Substances Impacts
BOOK UI ESTUARIES
Chapter 1 - BOO/01ssolved Oxygen Impacts
Chapter 2 - Nutrlent/EutropMcation impacts
Chapter 3 - Toxic Substances Impacts
BOOK IV LAKCS, RESERVOIRS. IMPOUNDMENTS
Chapter 1 • BOO/Qis solved Oxygen Impact
Chapter 2 - Nutrient/EutropMcatlon Impacts
Chapter 3 • Toxic Substances Impacts
-------
Because predictions are needed in a variety of different situation!.
there 1s no one set of technically acceptable procedures that can be put
forth as a standard method. The appropriate level of effort, and thus
the appropriate approach, depends on the difficulty with which pollutant
controls can be implemented, the complexity of the environmental
problems, the resources available, and the technical expectations of all
parties Involved. Consequently, the Intent of this document 1s to
describe a variety of different approaches, covering a wide range of
complexity, to help guide the analyst in choosing a cakulational
framework, or model, appropriate to the specific problem. Rather than
recommending particular levels of effort as appropriate for analyzing
particular wU situations, this document is intended to help guide the
WLA analyst toward the most effective use of whatever resources are
available.
The remainder of this document Is organUed Into the following
sections:
Section 2.0 describes mathematical frameworks for predicting toxicant
concentrations 1n rivers. The approaches span a range of complex'ty,
from dilution calculations to complex, multl-dimension*'. t'">e-*ary'ng
computer models. This section describes assumptions and limitations
associated with each approach.
Section 3.0 presents the mathematical formulation of important fate
and transport process and provides background information for specifying
the parameter values.
Section 4.0 presents technical guidance for conducting waste load
allocations for toxicants. It suggests that the analysis progress
through three phases from simple to complex and discusses the associated
needs for and management rf supporting data. Quality assurance and cost
estimates are covered for both field data and model parameters. Thi«
section also contains technical guidance in applying models and asse.smg
the adequacy of site-specific model predictions.
-------
Section 5.0 presents a case study of modeling metals transport In the
Flint River, Michigan. Emphasis is on the calibration of the toxicant
model with field data obtained under three very different flow regimes.
A sensitivity analysts of the model parameters relative to the flint
River calibration 1s also presented.
Finally, there are appendices containing (A) derivations of model
equations. (8) a discussion of sediment exchange and transport modeling,
(C) a summary of Flint River (case study) survey methods, and (0)
chlorine behavior. In addition, two other reports are attached. One
«• -i
contains metals partition coefficients derived from field data collected
nationwide. The other Is a catalogue of 14 models designed for toxicant
Studies. It briefly summarizes eacn model's theory, input and output,
strengths and limitations, and resource requirements.
-------
SECTION 2.0
BASIC MODEL FRAMEWORKS AND FORMULATIONS
2.T GENERAL
This section provides a summary of modeling frameworks, with
associated equations and assumptions, applicable to predicting
concentrations of discharged toxicants, as affected by stream hydrology
and morphology, reactions, and sediment Interactions. Because the Intent
of this document 1s to present a range of approaches, 1t Is useful to
consider a means of categorizing water quality models according to their
components and characteristics. In selecting an approach, a HIA analyst
1s likely to be Interested In environmental simulation capabilities,
which can be categorized as follows:
A. System components
- Water column
- Bed sediment
• Terrestrial watershed
B. Processes modeled
- Dilution
- Advectlon. dispersion
• Decay, transformation, speclatlon
• Transfer between water, sediment, and air
C. Spatial variability or resolution
• 0, 1, 2, 3-01mens1onal variability
- Near or far field
0. Time variability
- Steady state
- Time variable
-------
Tht analyst must also be concerned with the Input data and hardware
requirements associated w4th any approach. These tend to follow from the
capability characteristics listed above.
A general schematic framework for Illustrating many factor; that
determine the concentration of toxicants In a river 1s depicted in Figure
2.1. The conceptual elements Include: (a) mixing of effluent and
upstream waters, (b) partitioning of toxicant between dissolved and
participate phases 1n both the water column and the bed. (c> exchange
between the water column and the bed, (d) decay by irreversible chemical
transformations, (e) losses by burial and volatilization, and (M
downstream transport via stream flow and bed load. Simple analytical
frameworks may employ only a few of the elements shown; sophisticated
computer codes, on the other hand,-may articulate more complex
arrangements than shown In the figure.
the selection of -any approach requires a trade-off between system
realism and analytical efficiency. The simplest approacnes tend to v.nge
on a few critical assumptions (as will be described shortly); the
technical Issues and uncertainties that surface thus tend to be few *.n
number out could be Intractable In nature. Furthermore, restrictions in
the form of their results can constrain the formulation of the bas^c
<
questions they are Intended to answer. Complex analyses, on the otner
hand, with their numerous Input parameters, call for the support of
considerable laboratory and field data. The assumptions they rest on and
the uncertainties they surface may be greater 1n number but more subtle
in nature than those of the simpler approaches. The complex approaches
are applicable to a wider range of questions than the simpler approaches.
tt may not be necessary to choose a model at the outset of a
project. Rather, as discussed In Section 4.0, It may be efficient to
apply the analysis In stages, starting simply', and then moving to the
appropriate levtt of complexity, as the issues, costs, benefits, and
-------
LOAD
UCTMfAM
LOAD
i VOUTILIZAT10M
AIH
DJSSOtVEO
WATER
SCOIMC.1T
4 TRANSFO
ANSFORMAT1QN '•*—I
—-'H
CXCHAMCE
/ CHEMICAL ^.
4 TRANSFORMATION^-
Otlf
SCOIMCNT
f
IURIAL
FIGURE 2.1 IMPORTANT FATE AND TRANSPORT PROCESSES
FOB TOXICANTS IN RIVERS
-------
decision needs evolve. Nevertheless, because the collection of
data can be the most expensive project component, no major field survey;
should be done before the analytical Framework has been selected and tne
Input data requirements identified.
The approaches covered In'this document can. for purposes of
discussion, be placed in the following types of catagories:
• Point of discharge dilution calculations for total pollutant;
steady-state or dynamic.
* One-dimensional, steady-state models for conservative total
pollutant.
• One-dimensional, steady-state models for nonconservatlve tota'
pollutant.
• One-dimensional , steady-state models for separate dissolved and
partlculate phases; having bed interactions and multiple process
rates.
• *ult1 -dimensional . and/or dynamic mode's for separate d's;o'*ed
and partlculate pnases or multiple species; having aed
interactions and multiple process rates.
The approaches differ In discerning spatial and tempora- var*at'ons.
environmental media, and pollutant forms and behavior. The approaches
are described in the sections that follow. Mathematical derivat'sns °r~
fundamental equations are provided in Appendix A.
2.2 DILUTION CALCULATIONS - POINT OF DISCHARGE
The mixing of the effluent flow with the river flow is tne first
process normally evaluated In predicting ambient concentrations of
toxicants. At the point where mixing has been completed. Che
concentration of the total pollutant Is given by:
CT,O,
ou OT (2.D
-------
where, C_(0) - Concentration of total pollutant immediately after
complete mixing (ug/l)
CT * Efftuent concentration (ug/l)
CT(J « Upstream concentration (ug/l)
0T • Combined effluent Mow (Q^) plus upstream flow (0 ) (I/sec)
Vij. . Combined effluent plus upstream load (ug/sec)
This formulation assumes that:
\
1. Nixing 1s relatively rapid.
2. Decay or settling 1s slow compared to nixing.
The combination of these two assumptions Implies that little decay has
time to occur before mixing 1s complete. The formulation says nothing
about the size of or concentration within the mixing zone. Nor does t:
say anything about the concentrations further downstream of the
discharge. .
Used by themselves without regard for downstream fate. ci'ut'on
'calculations nave found considerable use In setting water aua*'ty sasec
effluent limitations for both conservative and nonconservaMve
pollutants. This 1s because Water Quality Standards are often
Implemented in such a way that the toxicant concentration 1s not
permitted to exceed the numerical criterion at any point (outside the
mixing rone), without regard for the length of the stream affected.
Consequently, for single dischargers In a regulatory situation that gives
no consideration to the number of stream miles affected, the analyst may
simply apply the'dllutlon formula (Equation 2.1) to determine the
concentration occurring Immediately below the discharge, before any
processes (except for upstream dilution) can act to reduce the
concentration.
This approach Is not suitable for situations where two or more
discharges, separated by a substantial distance, affect trv toxicant
cc «.enuat1on. In this case some consideration of the pollutant
-------
behavior in the reach between trie two discharges Is needed In order to
predict the concentration (Cj ) above the second discharger. The
approach 1$ nevertheless applicable in the numerous situations where only
one of the several dischargers is of Importance for a particular toxicant.
Because toilcants may rapidly partition between the dissolved and
suspended solids phases In the water column, or may rapidly Irtterconvert
between different species or complexes, the concentrations in Equation
2.1 are usually interpreted as being the total concentration of the
toxicant, when only one form of the pollutant 1s biologically active
(such as unionized ammonia), it is customary to determine the dilution
concentration as total, and then to separately determine what fraction
will be biologically active. For example, the fraction of-unionized
ammonia Is determined by pH and temperature.
Perhaps the chief disadvantage of the dilution calculation at the
point of discharge 1s that it says nothing about the spat'a'i extent of
the araolem, which in turn partiaV'y determines* the env'ronmenta'.
benefits of pollution control. The restricted vision of this approach
thus somewhat hampers the analyst's ability to respond to decision*
makers'•questions about environmental benefits.
The spatial restriction of this approach may 5e partially offset 3y
the comparative ease with which the temporal confines can be expanded.
It Is not unduly difficult to determine the frequency distribution of the
output concentration (C^CQ)) using the frequency distributions or rea'
time sequences of the four Input parameters (CJu. P/u. CTw, 0^).
Facile methods for determining the overall frequency of standards
violations at the point of discharge are being refined (DUoro and
MtzpatMck 1983) and appear to promise Substantial Improvements In the
evaluation of toxlclty problems.
10
-------
2.3 ONE,DIMENSIONAL. STEADY-STATE MODEL OF CONSERVATIVE TOTAL POLLUTANT
This approach goes beyond that of the previous section In that U
predicts the concentration profile throughout the downstream reach. This
requires an assumption about downstream behavior. In this case the
assumption Is conservative pollutant behavior because the discharged load
1s not reduced as It travels downstream. Consequently, since dilution 1s
the only process affecting the concentration, the model equation 1s the
previously described dilution formula (Equation 2.1).
This formulation assumes:
1. The pollutant Is essentially conservative (I.e.. does not decay or
settle from the water).
2. The system Is represented by average conditions over some
representative time period so that the model equations can t>
-------
a
IM^/StC)
MAS LOAD
Ow
OlSTANCf I
COHCINTflATlOM
OtSTANCI 3
FIGURE 2J STIAOY.STATE. CONSERVATIVE BEHAVIOfl.
-------
Nevertheless, where the spatial distance under study Is very small,
conservative assumptions nay hold up quite well for the total form of
most pollutants. Thus, It has been customary to consider behavior within
legal mixing zones to be conservative, since the time of passage Is so
small. Some other conditions under which conservative behavior may be
predicted are described In Section 2.5.2.
2.4' ONC DIMENSIONAL, STEADY-STATE MODEL OF NONCONSERVATIVE TOTAL
POLLUTANT
This approach predicts the concentration of the total form of a
nonconservatlve pollutant 1n the water column throughout a
one-dimensional stream reach under steady-state conditions. The model
formulation 1s:
/ • •
x "
CT(x) - CT(0) e T u {2.3}
wnere. CT(X) • Concentration at points downstream of effluent (ug/l)
Cy(0) * Concentration Immediately below effluent (from Equation
KT « Overall loss coefficient (I/day)
U • Stream velocity (m/day)
x • Distance downstream of effluent (in)
Several assumptions accompany this model:
1. KT« tn« overall first order decay coefficient, Includes net
settling and all other losses or transformations.
2. The average river and waste load conditions represent a
steady-state condition (dc/dt » 0) over some time period.
3. The pollutant discharge 1s mixed Instantaneously with the river
(I.e., no mixing zone or lateral and vertical concentration
gradients).
4. Dispersion Is negligible 1n the longitudinal direction (i.e.. only
advectlve transport Is cons lore significant: plug flow).
13
-------
5. Average Flow, average cross-section area, and average depth
sufficiently represent conditions within a single reacn.
Figure 2.3 depicts tnis model graphically. This model 1$ directly
analogous to BOO disappearance in a classical Streeter-Phelps 00 model.
The model equation 1s applied to each river reach with calculated
concentration from the end of an upstream reach becoming the upstream
boundary concentration for the next downstream reach. The selection of
reaches 1$ determined by significant changes in river geometry, flow, and
location of point source tributaries.
The overall decay coefficient 1s both site- and time-sped Me.
possibly varying with changes In controlling parameters such as flow.
cross-sectional geometry, solids concentrations, aquatic vegetation,
temperature, sunlight, and pH. usually this approach is applied to a
specific site where sufficient field data are available to calibrate K.
to the ooserved rate of disappearance. That 1s. KT Is adjusted until
the calculated concentrations reasonably match the measured
concentrations along the length of the river downstream of the effluent.
As In BOO modeling. U Is considered undesirable to spatially vary the
decay coefficient without a good underlying Justification. The.observed
data must be expected to exhibit scatter about the predicted curve, due
to time variations and measurement errors.
While this empirical approach Is somewhat data intensive, 1t Is
fairly straightforward, with few degrees of freedom to manage. A key
limitation 1$ that 1s sheds no light on the factors that control
-------
(U
HOW
a
a»
LflAO
«
(KG/SIC)
CfiMCfMTftArtOM
CT
(MG/U
OlSTAMCf X
FIGURE '3 SIMPLE FIRST ORDER DECAY ANALYSIS
i Oft TOTAL POLLUTANT
15
-------
Nevertheless, this general approach has been applied to phenols and
cyanide in the Mahonlng River (EPA 1977). Application of this approach
to the settling of metals in the Flint River 1s described in Appendix A.
2.5 ONE-OINEXSIQNAL. STEADY-STATE MODELS POR SEPARATE DISSOLVED AND
; SUSPENDED PHASES. HAVING BED INTERACTIONS AND MULTIPLE PROCESS SATtS
Unlike the approaches described previously, this approach
developed specifically for toxic pollutants which have important
Interactions with the bed sediments, and which may vary In biological
activity and other behavior, depending on form. Because this type of
model discerns multiple Individual processes. 1t provides a more complete
understanding of pollutant behavior. The trade-off is that there are
more parameters to specify, and It 1s more difficult to rigorously
validate using field data. On the other hand, because this type of model
relates some aspects of pollutant behavior to readily observable pnysica-'
properties of the site and to known chemical properties of .-"any
pollutants, some model predictions may be attempted without lav'ng
surveyed the pollutant's 'downstream profile at the site.
This level of analysis is sufficiently complex that a compute'
program is helpful (but not essential) For executing the computations.
The SlmpllMeo Lake and Stream Analysis (SLSA). which is available as
both a calculator algorithm and a computer program. n perhaps the
simplest version of this type of model. This program was developed by
Hydro-Qual and is available from the Chemical Manufacturers Association.
The computer program MICHRIV has a somewhat similar framework but is more
rigorous and flexible In Its handling of a partlculate bound pollutant.
This program was developed by the EPA Large Lakes Research Station.
Grosse He, specifically for WLA purposes.
2.5.1 Model Framework
*se framework for this type of model 1s Illu ^aMd tn Figure 2.4.
The iflodel discerns two media, water and bed sediment, and two forms of
pollutant within each media, dissolved and partlculate bound. Process
16
-------
AIR
LOAO (WT>
TOTAL SUISTAHCK - (
MftTICULATC
SUISTANCC!Cr1)
3U3HMOEO
SOLIDS M
ACTIVt
SIOIMfMT
OIW
U01MCMT
OIS5QLVEO
3UBSTANCZ
ICgt)
W
* *t Sf DIMCNTATION
FIGURE 2.4 MICHHIV FRAMEWORK
17
-------
rates are specific to the media and to the pollutant form: for example,
only the partlculate phase 1n water settles1 to the bed, and only the
dissolved phase In water volatilizes. Derivation of the model equation*
and listing of assumptions are presented in Appendix A.
In summary, the MICHRJV program predicts the dissolved and
partlculate concentrations In water and bed sediment, using tne following
types of input data: flows and loads, hydraulic geometry, water.bed
exchange parameters, partition coefficients, and decay coefficients.
Nomenclature for the following discussion Is presented in Table 2.1.
The first major step In HICHRlY's solution (after applying the
dilution formula, Equation 2.1) Is to predict the concentration profile
of suspended solids downstream of a point source. The downstream solids
concentration, rn^x). Is related to the Initial concentration. m^O).
and to the settling and resuspenston velocities, w and w by the
expression:
W W, X
\J_ J_ / - J \
.,(») . rn^O). Hl Ul . *" "* f 1 -* H1 Ul J (2-4)
"i V /
for which all parameters are defined in Table 2.1. It Vs assumed tnat
the bed solids concentration, m_. Is constant throughout the reacn.
(SLSA differs from MICHRIV In that It also treats RL, as a constant,
rather than a state variable, and thus omits Equation 2.4.)
The sediment exchange velocities are related by assuming that the
mass (or thickness) of the active bed layer does not change over time.
Balancing the solids fluxes results In:
18
-------
TABLE 2.1: NOMENCLATURE FOR WATER/SEDIMENT MODEL
i «
Parameters Water Column Sediment
Concentrations and Loads
Total toxicant (j»g/D* Cyi C?2
Dissolved toxicant Ug/l)» Cd] C^
Partlculate toxicant (ug/D* C , C
P 1 02 .
Partlculate toxicant («g toxicant/
mg solids) r-j r2
Total solids (mg/i}' "v, m2
Toxicant load (ug/sec) w_ ---
Partitioning
Dissolved fraction f' f ,
a'. C2
Partlculate fraction f , f
51 32
Partition coefficient (l/mg)
(. • r/C<} - Cp/mCd) ., ,2 ,
Channel Geometry
Downstream distance (m) x x
Cross-sectional area (m ) A ...
Depth (m) H. H.
Flow (m3/sec) QI
velocity (m/sec) (U . Q/A) ^
19
-------
TABLE 2.1: NOMENCLATURE FOR WATER-SCO WENT MODEL (Continued)
Water Column Sediment
Rate Parameters
Aggregate decay rate coefficient
- for dissolved (I/day)
- for partlculate (l/day)
- for total n/
-------
Revised
10/83
The sedimentation or burial velocity, w.. reflects the rate of change
1n elevation of the benthat surface at a particular point over time. A
positive value for w^ Indicates that the channel 1s gradually filling
In during the modeled condition; material is being lost to deep
(inactive) sediment, beneath the boundary of the modeled system. A
negative value Indicates downcuttlng of the channel, and brings material
Into the modeled system. . ,
This type of attention to solids behavior is necessary because the
movement of adsorbed toxicant fallows the movement of solids. The
fraction of total toxicant that Is adsorbed on partlculates (f.} In
water.
in bed) and" the fraction that Is dissolved (f
^.
depend on the partition coefficients applicable to the water and bed
(«.j and »2- respectively), and the solids concentrations (ia\
and « respectively):
(2.6)
(2.7)
where all parameters apply together either to the water column or to the
bed. Use of the partition coefficients assumes that the dissolved and
participate phases are In dynamic equilibrium within their respective
media. It also assumes that the equilibrium adsorption Isotherms are
linear. But It does not assume that any type of equilibrium exists
between the bed and the overlying water column. (Such an equilibrium can
be set up under certain conditions: w, • 0 and K- • 0 will cause the
fluxes of total pollutant between the.water column and bed to cancel each
other at steady-state; however, unless the additional condition »1 .
«2 were Imposed, a net movement of partlculate pollutant could occur,
for example, out of the water column, balanced by a net movement of
dissolved pollutant out of the bed.)
21
-------
The steady state solution for the total toxicant concentration in the
water column can be expressed in a familiar form:
. CTI(O, .
The overall removal rate coefficient, KT (I/day), can be expressed by
the function:
where Ktf1 and K^ are the toxicant decay coefficients in the water
and bed. respectively, and K{ 1s the sedimentation loss coefficient.
which 1s related to the burial velocity by the expression:
s • vH? {2JO)
The aggregate decay coefficients K-, and Kj are simply the sum of the
coefficients specific to each competing process, such as hydrolysis,
biolysis, photolysis, and volatilization.
The parameter group ^r./r^ controls the Importance of sediment
decay and loss processes in Equation 2.9. The parameter fl, called the
sediment capacity factor (DUoro et al. 1982). Is defined 1n terms of
solids masses in water and sediment (proportional to mH) and fractions
partlculate, f:
0
The ratio of toxicant concentrations on partlculates, r2/r-|
determined from the expression:
fl {wr» * Wd)fp2 * \ fd2•
-------
This ratio is controlled by sediment exchange velocities. w * w.
rs d
(or w through Equation 2.5); the diffusion coefficient, K , for
exchange between the water column and the InterstUual water of moderate
to high porosity sediments; the decay velocity 1n sediment, K2H2* and
the fractions dissolved and paniculate.
Finally, the total concentration of toxicant 1n the bed, CT,, Is
given by:
pl H2
cT1u)
{2. U)
It should be noted that Equation 2.9 and 2.11 utilise f^and f . as
01 pi
1f they were constant throughout the reach, when in fact they vary with
m^x). Consequently, in order to treat them as constants, the'MICHRlv
program divides each reach into small computational increments, within
which m^ Is virtually constant, and solves the equations for each
increment, moving downstream.
It can be seen that the model 1$ not entirely simple, *ost
first-time users may find some aspects of Its behavior not intuitively
obvious; some sensitivity runs coupled with examination of the
formulating equations may be helpful to obtain a good feeling for how tne
model responds to Its Input parameters. OlToro et al. (1982) discuss
many aspects of the behavior of this type of formulation. Appendix A of
this document provides a more complete derivation of model formulation.
Section 3.0 provides Information on selecting parameter values; Section
5.0 describes a case study using the model. Thomann (1964) suggests a
simplification of this type of model.
2.5.2 Relationship with Other Approaches
In predicting the total pollutant 1n the water column, U can be seen
that MICHRIV and SlSA use the same first order decay formula
i"qft1on 2.8) a tht. simple empirical approach (Equation 2.3). In
-------
MICHftIV, however, unlike SlSA and the empirical approach, the decay
coefficient, KTI. Is not necessarily constant within a reach. Rather,
U 1s a function of'the fractions dissolved and participate (per
equations 2.9), which 1n turn vary with any change In the suspended
solids concentration moving downstream (per Equations 2.6 and 2.?}. as
previously mentioned.
Consequently, 1f the suspended solids levels do not vary within the
reach (such as would happen If deposition and resuspension fluxes
balanced each other), a steady-state loss of toxicant would occur only as
a result of degradatlve processes or volatilization.
For metals and other nondegradable. nonvolatile substances the sole
mechanism for reduction of the water column load 1s burial beneath
depositing sediment (or possibly transport downstream as bed load).
Consequently, the behavior of such substances would be predicted to be
conservative under the conditions of (a) s'teady pollutant loading to ;a;
a graded stream with (c) Insignificant Ded Idad and ;d) steady ?*ow. A
graded stream is one where neither downcuttlng nor sedimentation 1$
significant; the bed elevation Is not significantly changing over t*ne.
Solids settling and resusoenslon fluxes would-thus balance under the
above conditions. When a pollutant loading first began, exchange
processes would cause a net transfer of pollutant out of the water
column Into the previously uncontamlnated sediments. After a period of
steady conditions, however, the bed concentrations would reach
equilibrium with (become saturated with respect to) the water column
concentrations. Then the pollutant flux out of bed would balance the
flux Into the bed and no reduction of the water column load would take
place.
24
-------
. In real .systems, however, time variable flows and loads would produce
unsteady concentrations and disequilibrium between the water column and
sediment bed. While the long term loading of the total form of a
pollutant may be conserved within the water column, short term loadings
(such as measured during Meld surveys) may not be conserved. During
periods of high concentrations 1n the water column or of net deposition
of sediment, the stream bed may-act as a sink. During periods of low
concentrations In water or of resuspenslon of the bed, It may act as a
source.
In evaluating metals or other nonvolatile nondegradable substances,
MICHRIV differs from simple first order decay models in that loss through
burial only takes place until the suspended solids attain their
equilibrium concentration or (If resuspenslon equals zero) until only the
dissolved fraction remains. In any case, the asymptote which the total
metal concentration approaches is not zero, as illustrated in figure 2.5.
Overall, the main advantages of using 'HICHRIV (or SlSA) are t.ia:'they
discern between dissolved and partlculate phases and they predict tne
degree of contamination of the bed. In addition, they better delineate
the factors affecting the overall loss rate, thereby allowing better
utilization of previous collective experience In determining parameter
values and producing a much better understanding of the controlling
factors. The number of degrees of freedom, however, complicates the
calibration procedure; 1n some situations more than one set of parameter
values may fit the field data.
Relative to some of the more complex models described in the next
section, the most Important limitations of this approach might be that It
1s one-dimensional, steady-state, and plug flow. In addition. NICHRlv
and SlSA lack complex kinetic routines for Internally deriving a chemical
Degradation rate from Input data.
-------
suvtaoio
MUDS
OBBOIVIQ
(A)
T9TJU.
FIGURE 15 TYPICAL BEHAVIOR PREDICTED BY MICHR1V FOR (A) METAL.
AND (I) DS6RADASLC ORGANIC,
-------
For studies of far field, Impacts (as opposed to mixing rone Impacts),
lateral and vertical variations In concentration are seldom sufficient 1n
rivers and streams to Justify modeling In more than one dimension.
Longitudinal dispersion Is likewise seldom sufficient 1n rivers and
streams to discourage use of a plug flow assumption (Oriscoll et al.
1983). The plug flow assumption does deter applying sucti models to
estuaries, however.
The steady-state assumption affects the rigor with which time
variability can be analyzed. When successive runs of steady-state model
are .used to simulate a time sequence of events, the output of each
successive run 1s Independent of the previous run. Unlike a dynamic
model, the steady-state model has no memory of the previous state of the
system: 1t assumes that the modeled conditions have persisted since time
Immemorial. To the extent that the real system can •remember• Us
previous condition, for example through longitudinal dispersion and a
long hydrau.llc retention time, a modeling error 1$ generated. In this
case the steady-state model would tend to overpredic: during ser'ods of
high or steadily rising concentrations and underpredlct during periods of
low or steadily decreasing concentrations.
. Hulkey et al. (1962} have compared the frequency distributions of
concentrations predicted by state-state and dynamic models. They applied
the steady-state model EXAMS and the dynamic model H$PF to a situation of
a constant effluent load discharging to a river with variable flow.
(Both models are described 1n Section 2.6 and in Attachment II.) With
the steady-state model, a frequency distribution of dissolved chemical
concentration In the water column was generated by making several runs.
each with a different flow having a known frequency of occurrence, with
the dynamic model, the frequency distribution was constructed from a
continuous, day-by-day simulation operated from the dally flow record.
They found the frequency distribution produced by the steady-state model
to be nearly Identical to that pr.iuc<»d by the dynamic, continuous
27
-------
simulation model, regardless of whether the chemical was assumed to be
strongly or weakly adsorbed by the sediments. It is essential to note,
however, that this equivalence between the frequency distributions
generated by the two approaches applies only to rivers and only to the
water column. It does not apply to waters having considerable
longitudinal dispersion and long hydraulic..retention times, such as
Impoundments and estuaries, and 1t does not apply to concentrations In
the bed sediment (which similarly has a long retention time). Also, U
may not necessarily apply to ^situations where the effluent load or other
key factors are rapidly varying over a wide range.
2.6 COMPLEX MODELS HAVING MULTIDIMENSIONAL. DYNAMIC. OR SPECIATJON
CAPABILITIES
The waste load allocation models described in the previous section
were one-dimensional, steady-state, water column and sediment models with
equilibrium partitioning and linear transformation kinetics. The models
described In this section employ less restrictive assumption? and contain
more degrees of freedom. They tend to 1nvolv.e more process-oriented
descriptions of chemical transport, sorptlon. spedatlon, and
transformation. Enhanced process descriptions can provide a more
r-
confident extrapolation of model results from the calibration conditions
to different conditions at the same site or to similar conditions at a
different site. These models can also be operated to provide more
detailed resolution 1n time or space.
Choice of a model will depend on characteristics and variability of
the waste load and the receiving environment, the level of certainty
required In model extrapolation, and the type of data available. For a
given level of predictability, the more complex models generally require
a greater variety of data, but with fewer constraints than simpler
models. For example, steady-state models require data averaged over
steady conditions, whereas dynamic models can use data taken during
i f
steady or unsteady periods. The use of more C-T.^U-. models require* no- .-
28
-------
technical competence and resources to obtain predictions, but not
necessarily more wisdom and experience to Interpret the predictions and
fain Insight Into the problem.
A variety of fairly complex models exist or will soon be available.
Those general purpose toxic chemical Riddel codes described In this
section Include: EXAMS. EXAMS 2, and TQXIHASP. developed by Athens'
Environmental Research Laboratory; HSPF, developed by Hydrocomp and
Anderson-Nichols for Athens ERL; SERATRA, TQOAM, and MEXAMS, developed by
Battelle Pacific Northwest Laboratory for Athens ERL; WASTOX. developed
by Manhattan College for Gulf Breeze ERL; UTM-TOX. developed by Oak Ridge
National Laboratory for the Office of Toxic Substances; TOXIC, developed
by University of Iowa for Athens ERL; and CTAP. developed by Hydro-Qual
for the Chemical Manufacturers Association. To assist comparison.
NICHRIV and SLSA. the slightly less complex models described In the
previous section, are tabulated here as well.
Table 2.2 categorizes these computer codes. General characteristics
of concern are the type of aquatic system that can be simulated (general
aquatic system or river), the chemical capabilities (generalized
pollutant that could be a metal or an organic compound, metal species,
• • n •=
and daughter product}, the sediment capabilities (descriptive input, one
size fraction simulated, or several size fractions simulated}, the
dimensionality (one-dimensional, two-dimensional, or box, which can be
arrayed as pseudo three-dimensional), the numerical solution technique
(finite difference, finite element, steady-state algorithms), the time
frame (steady-state, seasonal, dynamic), and their availability.
Attachment II of this document contains additional Information.
f ' ,
Clearly, a range of models 1s available with widely differing
capabilities. Table 2.3 summarizes what components of these models can
be corsldered more complex or more general than those of MICHRIV and
SLSA: their transport, sorptlon. speclatlon, or t an-formation
-------
Table 2.2 General Categorization of Computer Models
(listed in alphabetical order)
CTAP
EXAMS
EXAMS 2
HSPF
MEXAMS
MiCHRIV
SERATRA
TQOAM
TOXIC
TQXIUASP
UTM-TQX
WASTQX
ase
H- U*
9 V»
' 0^
3c v»
6
S
6
R
6
R
R
R
6
6
R
6
i/i
0
Uri
5-
0
0
0.0
o.o
M
0
Q
Q
Q
Q
O.N
0
*
9C vi
SS
UJ —
IS* «/»
s
0
D
3
D
1
3
3
1
1
4
3
^
_j
S
o
«/>
x-
£
o
8
8
8
1
8
1
2V
1
8
8
1
8
•4 ~
51
« •—
uj a
ic!
Z v^
SS
ss
FO
FO
SS
ss
FE
FE
FO
FO
FO
FO
>;
u.
UJ
SS
SS
S
D
SS
ss
o
0
0
0
0
0
£
•~
i
^J
»
c
A
A
A
A
31
A
A
A
6 - general Aquatic System; R - River
0 - generalized Pollutant; N - Metal, Specifically; 0 - Daughter Product
0 - Descriptive Input. Not Simulated
8 - Boi Approach, Pteudo 3-Olnwnslonal; 2V - Two Dimensions (x-z)
FO - Finite Difference; FC-F1n1te Element; SS - Steady State
S - Seasonal; 0 - Daily
A - Available from EPA Center for Water Quality Modeling, Athens. GA
C - Available from Chem. Manuf. Assuc.; fit - Available from EPA Grosse He Lab
See Attachment II for additional Information.
-------
Table 2.3° Model Components which could be considered somewhat more
complex or general than
'
CTAP
EXAMS
EXAMS2
HSPF
MEXAMS
WICHRIV.SLSA
SERATSA
700AM
TOXIC
70X1 WASP
UTN-TOX
WAS TO A
5
o
f^
^k
i/l
OE
•
*
*
f
• *
•
'
*
*
s
1
*
•
'
»
|
• £
•
•
t
1 ! J
0
1
Si
— **
i
^
V
•
•
•
•
' •
• -
•
•
„
|
t
\
• s
•
*
s
s
-------
algorithms, or their linkage to hydrologlc, flow, and/or effects models.
t
Those components not labeTed as more complex may be roughly equivalent
to, or even simpler and more restrictive than PUCHRIV and SLSA.
Generally It 1s sound practice to use the simplest approach that win
properly handle the problem. Nevertheless, to satisfactorily resolve
some ULA problems, 1t may be necessary to apply a very complex analysis
to some facets of the modeled system. To help discern the range of
analytical complexity available, the major model components are discussed
below.
2.6.1 Transport and Bed/water Exchange
Movement of both dissolved and partlculate phase contaminants may
occur within the water column, within the bed, and between the bed and
the water column. (Transfer between the water column and the air is
presented elsewhere In the guidance manual as "volatilization".)
In the less Intricate MICHRIV and SLSA, transport in the water column
follows the one-dimensional steady-state solution to the advectlve
transport equation for chemical and suspended sediment. Settling and
resuspenslon velocities are specified for partlculates (and thus sorbed
chemical). Less restrictive transport and bed/water exchange assumptions
can allow multiple sediment size fractions with different sorptlon and
settling properties, vertical or lateral resolution In the spatial grid,
and In many cases, unsteady flow. These properties are tabulated in
figure 2.4.
In place of a single mixed layer of bed, some models discern multiple
layers In the bed. Dissolved chemical may be transported through the bed
by pore water percolation, or exflltratlon, or diffusion. Sorbed
chemical may be transported by sedimentation, erosion, or physical mixing
of the sediment. Some models allow horizontal movement of the upper bed
layer (representing "fluid mud* or bed load). Other models can represent'
this process with benthlc water colurn se-.nents carrying a high suspended
solids load. These properties are tabulated In columns 4 and S of Table
2.4 for each model.
32
-------
Table 2.4 Transport and Bad/Water Exchange Properties
CTAP
CXAMS
EXAMSZ
HSPF
«IXA«S
HICHRIV.
SLSA
SERATRA
TODAM
TOXIC
TQXIWASP
UTN-TOX
MASTOX
^
a
ll
s £t
•
•
*
*
• •
*
*
•
»—
SS'
**
oS
UJ — •
wi vi
5
0
0
3
0
1
3
3
1
1
4
3
|
3?
i
3
8
8
8
. -\
B
1
2V
1
B
8
1
8
£
U4
«
O
LU
a
H
H
tt
1
H
1
o
o
o
UJ
OB
«i
u* ^
5.1S
3« ij*
o
at t/i
• a* •—.;—.
e*n ^ S
^*^ ^^ * i * i
« ° S S
P C
P.T
i
.
| S ' F
i
' P.T 1
i
P C
1
H
N
1
Pt
3
N
.W
W
•
•
S F
S
P
P.T
P
P .
F
C
F
C
• - Capable
0 - Descriptive Input. Not Simulated ,
B - Box Approach, Pseudo 3-01men$\onal; 2V - Two Dimensions (x-z)
« - Multiple Bed Layers '
w - Bed load Approximated with Lower water Layer
P - Pore Water Dispersion; T - Enhanced Diffusion From Bloturbatlon or Physical
Nixing; S - Direct Sorptlon between Bed and water Column
C - Calibrated (Cmpnlrlcal) Scour-Deposition Parameters
F - Functional Scour-Deposition Paramete s
33
-------
In HICHRIV and SISA. chemical transport between the bed and the water
column occurs through pore water diffusion and through steady scour and
deposition of sediment. Some of the models considered here omit pore
water diffusion and describe this exchange as direct, first order
sorptlon between bed and water column. Mathematically, the results are
the same, given equivalent parameter values. Other models add a
parameter to describe enhanced dispersive exchange due to blotarbation or
physical mixing. The value of this parameter can be specified in a
qualitative sense only. Finally, In place of the above calibration input
parameters, some models can Internally compute sediment exchange
parameters from functional relationships between flow, shear stress, and
scour. These properties are tabulated in columns 6 and 7 of Table 2.4.
2,6.2 Sorotlon
Sorptlon of a chemical onto sediment is generally considered to
proceed rapidly compared to-other transport or transformation processes.
MlCHRIV and SISA assume adsorption and desorption are completely
reversible, and proceed rapidly. Mathematically, these two assumet'qns
lead to the use of a partition or distribution coefficient for
sorptlon/desorptlon. This coefficient can be measured In the laboratory
and'adjus ted for conditions tn the environment.
Many of the other models also use partition coefficients adjusttfl 'or
organic carbon content of Che sediment. One model aUo automatically
adjusts the coefficient for sediment concentration based on higher
partitioning at lower sediment concentrations. Some models make use of
the langmulr or Freundllch Isotherms widely used >n soil science. These
empirical relationships predict progressively less additional sorptlon as
chemical concentrations become higher, reflecting the saturation of
binding sites on the sediment particles. At low chemical concentrations.
these Isotherms approximate a linear isotherm, or partition coefficient.
Use of these Isotherms, then, should be Important only when relatively
high chemical on >ntr tl "is are expected.
34
-------
Other mod* Is as SUM a linear Isotherm at equilibrium, out specify a
first-order rate at which equilibrium Is achieved. This may be Important
when transport or transformation processes proceed as rapidly as sorption
(say, on the order of nlnutes to an hour). It can also be Important
close to the point of discharge of an effluent high in solids entering a
river low In solids (as Illustrated 1n the Flint River case study), or
visa versa. . •
Three types of theoretically-based sorptlon algorithms have been used
1n these models: Ion exchange, constant capacitance double layer, and
triple layer site binding. The Ion exchange technique can be useful for
1on1c compounds where selectivity coefficients For exchange reactions are
available. The constant capacitance and triple layer models consider
charge-potential relationships at the surface and the changing properties
of the surface as. a result of changes In pH or Ionic strength (Felmy et
al. 1983). They require specific experimental work to obtain the
parameters, and are thus limited to applications where sorptlon-oH
dynamics are Important, and where experimental work 1s
Table 2.5 tabulates the sorptlon properties of the general puraose
models considered here. It Is Important to note that research is active
In this field, that other formulations have been described in research
models, and that these formulations will be tested and available In
general purpose models within a few years.
2.6.3 Soedatlon
Many chemicals or metals discharged Into an aquatic environment will
bt found 1n several species or complexes, A common speclatlon process is
lontzatlon. which 1s controlled by pH. Both chemical reactivity and
toxUHy can be significantly affected by the extent of loniration.
35
-------
T*blt 2.5 Sorptlon properties.
. oe uj
O
CTAP
EXAMS. EXAMS?
HSPF
HEXAHS
NICHRIV. SLSA
SESTRA '
TOOAM
TOXIC
TOXIWASP
UTM-TOX
WASTOX
*
36
-------
For metals, an important process *s,i*s-gan1c complexatlon. Taole
2.6 gives, for example, the possible d'sii-lved species of- lead In water
containing nitrate, chloride, sulfate. **^or1de, and carbonate (Felmy et
al. 1983). To calculate these species, cne needs experimental data on
the equilibrium constants ana env'.ronner:al data for pH, chloride.
sulfate, flouMde, nitrate-, 'an
-------
Tablt 2.6. Dissolved Species of Pb
P6C1
Pb
(AQ)
PbS04 (AQ)
"
From Ftlmy et al. 1983.
38
-------
Input data. The analyst may obtain this rate coefficient Oy theoretical
calculation or by calibration. First, order rate coefficients for
competing processes are combined by simple addition to obtain an overall
first order rate coefficient (with no loss of rigor). SLSA performs tnis
addition Internally.
The other ten (nodeIs allow decay to be formulated as a second-order
process: proportional to the toxicant concentration, and proportional
to some other concentration or environmental parameter, such as hydrogen
Ion concentration (In addle hydrolysis), or bacterial concentration (in
biolysis).
' Rate . KC^Cj
where. K • Second-order coefficient
C. • An environmental parameter
C. • Concentration of toxicant
With respect to the toxicant, the product KC& is sometimes called a
•pseudo' first-order decay coefficient: a first-order coefficient «n'cri
varies as a function of another parameter. To combine multiple
processes, the models Internally add together the 'pseudo' first-order
coefficients In order to obtain an overall first-order decay coefficient.
In four of the eight time-variable models considered nere, the
overall reaction rates can vary 1n response to the time variation of the
relevant environmental properties, such as temperature, OH. light, wind
or current velocity. These four models are EXAMS2, HSPF, TQxlWASP. and
UTM-TOX.
lastly. EXAMS 2 and HSPF are able to handle daughter products along
with parent compounds in a single simulation. Other models reauire two
separate Simulations, with Internal loadings from the parent compound
specified as external Input to the second simulation.
39
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SECTION 3.0
ESTIMATION AND USE OF MODEL PARAMETERS
Tht purpose of this section 1i to provide Information for estimating
parameter! for a model of Intermediate complexity, such as MtCHRlv.
described in Section 2. Some discussion of the basts for estimating
t *
process rates will be presented; however, this document will not
duplicate chemical-specific data and coefficients presented elsewhere:
Mabey et al. (1982) tabulate values for the kinetic coefficients required
by EXAMS (and similar models) for the organic priority pollutants;
Callahan et al. (1979) review the fate characteristics of the 129
priority pollutants; Mills et al. (1982) summarize fate data for selected
pollutants; lyman et al. (1982) present methods for estimating cnemica<
properties; and M111 et al. (1982) present laboratory protocols for
evaluating the fate of organic chemicals.
, r
The section will cover partitioning between aqueous and particular
phases, exchange between the water column and the bed. exchange between
the water column and the atmosphere, and transformation or degradation of
the chemical. In order to maintain focus on toxicant modeling, the
discussion will not cover transport of the bulk fluid (advection and
dispersion); such transport is adeduately covered in conventional
pollutant texts. Furthermore, downstream movement of the bed will not be
dealt with.here, as this process is ordinarily not expected to be
important for toxicant transport and is not Included 1n most models.
3.1 EXCHANGE BETWEEN BED AND WATER
In modeling the transport and fate of chemicals in aquatic
systems, It has. been Increasingly evident that knowledge of how a given
chemical is distributed among various phases -solution, suspension, air
and bottom sediment Interfaces, biota - Is essential. One of Che most
significant mechanisms for the movement of toxic chemicals through the
-------
aquatic environment 1s the adsorption or uptake of the chemical by both
nonvlable and viable participate matter, followed by the transport of the
Interacting participates. Association with suspended matter not only
alters the transport regime of a chemical - by Introducing additional
mechanisms such as deposition and entrapment - but the process can also
Indirectly affect the rate and extent of transformations and blotlc' *
accumulation. For example, partitioning of a portion of a chemical in
suspended solids will reduce the flux of that chemical Into the
atmosphere via volatilization of tne soluble phase. On the other hand,
such solid phase partitioning would be likely to Increase the chemical
flux Into the bottom sediments by deposition processes. Accordingly.
accurate determination of the transport and fate of a chemical requires
concurrent knowledge of the transport and fate of Interacting partlculate
matter.
Literature on sediment transport In riverine systems Is extensive:
however, accurate prediction of sediment dynamics from basic theory
appears tenuous. Rather than attempt an extensive development of the
theory Involved 1n river sediment transport, the focus of this subsection
will be to provide some methods for estimating sediment transport
parameters. A further development of concepts is appended (Appendix 3} .
3.1.1 Particle Transport and Exchange
The capacity for particles to Interact with aqueous toxicants 1s
related to the partUulate surface area. As small particles have greater
surface-to-volume ratios than large particles, it Is the smaller (silt
and clay sized) particles that tend to be more Important in determining
pollutant behavior. Smaller particles are also more readily carried by
the streamflow than large particles.
Within the MtCHRIV modeling framework, the variables or parameters
which control the exchange of particles between the bed and the water
column are: m^ and m_, <*..- .Me1* concentrations In the water and
41
-------
bed; and w V w , *nd wj- the'velocities (m/day) of settling.
resuspenslon, and burial, (sedimentation). The thickness of the active
bed is assumed constant. Specifying any Four of these five variables
allows the last one to 6e calculated from:
As HICHRIV and SLSA (as well as some more complex models) recognize
only one particle size or type, the parameters may represent average or
median values.
In several model frameworks, Including MICHftiv and SlSA, the bed
solids concentration, m., 1j a user specified constant. For a typical
river bottom with water content 60-90X by weight, and a typical solids
density of. 2.5 g/cm . m will vary in the range 50,000-500.000 mg/i
(in terms of bulk volume). Flint River bottom sediments were measured ai
200,300 .ng/l (Section 5).
In most models (Including NlCHRlv but excluding SlSA and EXAMS) the
solids concentration in the water column, n^. 1$ a state variable
predicted from the solids loadings and the settling and resuspenslon
velocities. In NICHRIV (as described 1n Section 2). m M given by:
W X
w
m^i) -m1(0)e H1U1 • "r^? M - e "lul 1 (3.2)
where all parameters are constant.
It 1s useful to Identify three basic conditions of particle
exchange, first, a condition may exist where m. remains constant
(moving downstream) and the sediment bed 1s neither accumulating nor
scou. tig way. Although solids settling and scour may be occurring, t'
42
-------
are in a state of equilibrium (i.e., they balance each other);
consequently, wfl » 0. . ., .
In the second condition, m. decreases moving downstream because the
settling flux exceeds the resuspenslon flux. As the bed cannot move
horizontally In MICHRIV, the resulting excess 1n settled solids Is "buried
at velocity w, > 0. In the third condlton. m^ increases because the
resuspenslon flux exceeds the settling flux; for the resulting net scour.
w, < 0. These three possibilities are depicted In Figure 3.1.
o
.It must be noted that 1n models which allow downstream movement of
the bed (bed load), w, could be given a different Interpretation. For
example, when settling exceeds resuspenslon (wtf > 0). an increase in
bed load (at the expense of the suspended load) could be permitted to
remove the excess sediment. In this case w^ could reflect the rate,of
Increase of the bed load. As noted 1n Appendix 8. however, bed load 1$
seldom expected to be an important transport mechanism for adsoraec
toxicants.
If the downstream profile of m. follows Equation 3.2, then it may
be possible to evaluate w and w (or wj Indirectly. The
procedure 1s analogous to determining a first order decay coefficient
from the disappearance profile, but has the complication of an additiona"
degree of freedom. In the following discussion 1t is assumed tnat'm .
U1 (velocity), HI (depth), and QI (flow) are known constants
throughout the, "reach, m.(x) has been measured at several points through
the reach, as Illustrated In figure 3.1. w$ and w^ are unknown but
constant, and w.(x) 1s unknown and variable (per Equation 3.1). It is
also assumed that steady state conditions prevail and that the bed is the
only source of solids below the head of the reach (for example, no
nonpolnt loads and no phytoplankton growth).
By evaluating Equation 3.2 for large values of -. wr can be related
to the m. asymptote:
-------
(«} STASH CONDITION
(k) NIT SITTLING
(ONCT SCOUR
MIVIR DISTANCE
FIGURE XI
-------
wrs.
(3.3)
where «.(•) 1s the asymptote (m^d) at x • •), estimated visually
from a prof lie.such as shown In Figure 3.1(6). If Equation 3.2 1s
normalized for the asymptote and put 1n logarithmic form, the derivative
(slope) of the resulting expression 1s directly related to «s:
-U1M1
(3.4)
AX
This in analogous to determining a decay coefficient from tne slope of a
semi.logarithmic plot of concentration versus Mn»e. Having thus determined
w and wrs< the value of w^x) 1$ given by Equation 3.1.
Alternatively, tne value of *d(x) at any particular point can a«
expressed In terms of the linear (rather trian semulogarunmic) s'ooe af
the profile:
(3.5)
The value of w would .then be given by combining equation 3.1 and
3.3, and the value of w by Equation 3.3.
The Indirect estimation of solids exchange velocities, as described
above, may be difficult In many practical situations. If hydraulic
conditions (such as U^ and H.,) vary along the length of the river.
then w and w may also vary. Normal scatter In the suspended
solids data (such as caused by time variability) may make identification
of slope and asymptote ambiguous. The analyst may thus need other means
4S
-------
for estimating w and w . Both may be Independently estimated, or
having Independently estimated one. the other can be more easily
calibrated, using the solids profile (as Illustrated in the Flint River
case study). In any.case, calibration Is assisted by recognizing that
the magnitude of w (or w.) controls the distance needed to approach
the asymptote, while the ratio w /w controls the value of the
asymptote.
A direct estimate of the settling velocity, w can be made using
Stokes' equation. Figure 3.2 Illustrates the solution of this formula.
$ - S|! (S, - SJ (3-6)
v
«
18.
where,
g
d
V
Sy
S
Stokes' settling velocity (cm/sec)
Gravitational acceleration (approi. 980 cm/sec2]
Particle diameter (cm)
Kinematic viscosity (cm2/sec) (Figure 3.29)
Specific gravity of particle -(dlmensionless ratio)
Specific gravity of water (1.0).
This calculation Is Intended to apply to noncoheslve spherical particles
1n a quiescent medium. Substantial differences may exist between tne
calculated Stokes1 velocity, v$, and the effective settling velocity,
w , of natural particles under both laboratory and field conditions.
Such differences may result from particle Interactions and fluid
turbulence and shear.
Coagulation or clumping together of suspended particles creates
larger diameter particles having higher Stokes1 settling velocity. The
inter-particle collisions necessary to bring this about may result from
(a) 8rown 1 an motion (diffusion), (b) shear (velocity gradients) internal
to the fluid, and (c) differential settling velocities, causing more
rapidly settling particles to Intercept slower settling particles beneath
them (O'Hella 1980, Hayter and Hehta 1982). The Inter.particle
coheslveness needed to produce aggregat'.* from colliding particles may
result from (a) van der Waals forces, \j) electric charges on particle
-------
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sill
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. • •
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522 2 £2 25
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-------
surfaces, (c) Interactions of aqueous Ions attracted to the* charged
surfaces (double layer), (d) chemical bonds, and (e) other mechanisms
(Parthenlades 1971). As. a result of coagulation Into larger particles
the observed settling velocity may ae orders of magnitude larger than the
Stokes' velocity of the disaggregated particles (uchrln and Weber 1980).
Nayter and Mehta (1983). in modeling fin* sediments in estuaries,
Indicate that the effective deposit'o.i velocity, ** , decrease; with
increasing shear stress, r. produced by the fluid passing over the
bed. Shear stress -In an open channel 1s determined as Follows (Graf
1971):
T . T«S (3.7)
wnere, T • Shear stress (Newton/m2)
T.- Specific weight of water (approximately 9807 N/m«)
R • Hydraulic radius, approximately equal to stream depth (m)
S • Slope of the energy g- 3,3.1 line, approximately equal to t.ie bed
slope (m/m)
If the stream velocity. U, 1s assumed to follow Manning's equation. men
the bed shear can be expressed as "il
T » T (n U/1.*86)2/R1/3 (3.8)
wnere n 1s Manning's roughness coe'-'l :'.ent. Under t.Vs condition, sed
shear 1s related to the square of velocity.
Parthenaldes (1971) notes that tnere ts sonw critical velocity or
shear above which no deposition of fine- particles will occur. As
velocities drop below thl; critical value a rapidly Increasing proportion
of the fine particles are capable of depositing.
In this vein, HydroQual (1982) recommends settling w equal to
25-50% of the Stokes' velocity in most rVvers, and to as little as 10% of
the Stokes' velocVtv in shailo-. st-ja.ns. They a'so point out that the
Stokes ".U.latlon shou " bi treated as a preliminary estimate, to be
modified by calibration to Me scs^erded solids data.
-------
For quiescent waters, Thomann (19S2b) and Richardson et al. (1983)
have calculated w in the range O.US m/day. tn the Hint River case
study, w was calibrated at 0.25 m/day during low Flow and 0.6 in/day
during a higher flaw period. (Hn Increase in settling velocity with an
increase In flow 1s the result of suspension of larger or denser
particles oy the higher flows). The manual SedJ men ta11 on Enc 1 ne_er ing
(ASCE 197S) presents a thorough discussion of the effects of sediment and
fluid properties on the settling velocity.
Direct Instream measurements with sediment traps can be used to
estimate w . HydroQual (1982) briefly discusses such measurements.
summarizing the findings of Bloraqulst and Hakansom (1981) on the accuracy
of various types of sediment traps.
The resuspenslon velocity, w. also depends on the shear stress.
T, as well -as on the strength of bed to res. 1st shear. The strength of
the lied is related to the nature the particles and the deposition history
(Including age and degree of consolidation). Below some critical
velocity or shear, Uttle or no resuspenslon may take place, while aoove
this critical value, resuspenslon may increase rapidly (Parthenaldes
1971. Hayter and Hehta 1983). The parameters determining resuspenslon
rates must generally be empirically measured.
Bonazountas and Mathlas (1984) discuss data and methods, and propose
algorithms for determining both deposition and resuspenslon. Their
computer model, SCOIN, Is Intended to be used for estimating the Input
parameters of commonly used toxicant models. It employs the formulations
of Einstein, Meyer-Peter and Mueller, and toffaletl. adapted to account
for the field data available to the analyst.
3.1.'2 Diffusion Between the water Column and Pore Water
Diffusion of dissolved pollutant between the *»ate- column and the ' •
sediment Interstitial water operates to move mater la. from the region of
49
-------
higher concentration to the region of lower concentration, in accordance
with Mck's law. If no dissolved ,concentration gradient exists then
diffusion 1s unimportant. Water column and pore water would be expected
to attain the same concentration under the following condition: .(a)
steady state with (b) partition coefficients equal 1n the bed and the
water column, and (c) no decay within the bed.
Unsteady conditions produce concentration gradients that can make
diffusion Important. In addition. 1f partition coefficients are lower in
the bed than In the water column due to the higher sol Ids concentration
In the bed (O'Connor and Connolly 1980). then deposited partUulate
pollutant may have the opportunity to desorb and diffuse back into the
water column 1n the dissolved phase. Decay in the bed. on the other
hand, would tend to depress pore water concentrations relative to water
column concentrations. Thomann (1984) Indicates that for metals copoer,
cadmium, and zinc, sediment diffusion.Is an Important process in waters
having suspended solids concentrations less than about 'Q ng/l.
The exchange coefficient describing dissolved exchange between tne
bed and water column has been termed K, In Section 2. HydroQual (1982)
notes that this parameter 1s difficult to measure directly. They believe
that X. In the range 10 - 100 cm/day may appear reasonable based on
field and microcosm calibration results.
Both molecular diffusion and physical stirring of the bed may
contribute to the magnitude of the exchange coefficient K . where only
molecular diffusion Is Important K, can be estimated from the
expression (HydroQual 1982):
(3.8.1)
where:
OL • molecular dlffuslvlty o* chemical In water (cm^/day)
• . ec'ment porosity (dime1 * iQi.. ess)
4 » length of vertical concentration gradient in sediment (cm)
n • a power
SO
-------
The sediment porosity * appears the numerator to account for the
reduction In diffusion caused Dy the tortuosity In sediments. It appears
1n the denominator to account for the conversion of concentration units
from InterstUual volume to bulk volume. The vertical concentration
gradient length 4 may be taken to be equivalent to the active sediment
thickness H. used in-Section 2.5. The value of n-ls a sediment
dependent property Indicating the relationship aetween porosity and
tortuosity. .HydroQual (1982) assigns the value n • 2; However Chapra and
Recknow (1983) indicate that other values may also apply.
Chapra and Reckhow (1983) present the molecular diffusion coefficient
data of U and Gregory (1974). Tor priority pollutant metals 0. is
e < £ t
frequently between 6 x 10 and 12 x 10 cnr/sec at 2S*C.
Qlffusivlty Is roughly linearly related to temperature; values at 0*C are
about half of those at 25'C.
Equation 3.8.1 does not Include the effect of ahys'cal st'.rring of
the bed sediment caused Dy currents and bent.i'.c an!maT$ (s'otwrsa:-an}.
Heathershaw (1976) and Fisher et al. (1980), among others have noted t.ie
importance of bloturbatlon In Increasing the effective rates of diffusion.
Additional explanation of sediment diffusion processes and
formulations are provided by Berner (1980), Chapra and Recfchow (1983),
and QIToro and Connolly (1980).
3.2 PARTITIONING PROCESSES
3.2.1 Metals Partitioning
The Interaction between dissolved metal spedes and riverine
partUulate matter, under normal physlcochemical conditions, generally
leads to a large fraction of the metal being associated with solids.
when a significant fraction of the total metal in a system 1s in the
solid phase, the fate, transport, and bloavailablHty of the metal are
51
-------
altered considerably. There Is ample evidence in the literature that
metal associates with participate natter; however, theoretical (as
opposed to empirical) approaches have not been widely applied to
quantifying this process In natural systems. The purpose of this section
"1s to present the theoretical considerations that have led to the simple
parameterization of metals partitioning commonly used, and to indicate
the factors which Influence partitioning.
The accumulation of heavy metals 1n aquatic solid substances can be
• *i
characterized by the following five major mechanisms (Globs 1973):
1) adsorptlve bonding on fine-grained substances. 2). precipitation of
discrete metal compounds. 3) copreclpUatlon of metals by hydrous ft and
Mn oxides and by metal carbonates, 4) association with organic molecules.
and S) Incorporation Into crystalline minerals. Inconsistent
Interpretations of metal solids interactions in natural waters can easily
arise in situations where different mechanisms are operating under
different environmental conditions. For example, even though adsorjt'on
1s a necessary first step for heterogeneous surface precipitation, Corey
{1981} distinguishes between the two as follows: 1} adsorption Is a
two-dimensional, surface layer process while precipitation involves
three-dimensional crystal buildup, and 2) in adsorption, solution
adsorbate concentration 1s controlled by surface site concentration
whereas the degree of precipitation Is controlled by solution
concentration, for heterogeneous precipitation to occur conditions
leading to a critical supersaturatlon of the adsorbate ion must exist.
In a system where the ultimate result 1s the formation of a precipitate.
however, the strict assumption of an adsorptlon-desorptlon equilibrium
may be Invalid.
In most cases encountered 1n river systems 1t would seem that
adsorption of metals to Inorganic surfaces Is the dominant binding
mechanism. However. In situations where there Is a 1arg» fraction of
biological solids, sorptlon Into blomass or binding by c.ganu surface
-------
functional groups can play an Important role. In fact, most of the
models describing the interaction of adsorbate ions and surfaces have an
Implicit definition of adsorption as a two-dimensional, surface
phenomenon.
• • • <
Adsorption models developed from a theoretical basis are generally a
compos He of surface complex formation theory (Schlndler et al. 1976;
Huang and Stumm 7973) and various electrostatic models
(Gouy-Chapman-Stern model In Shaw 1978; Grahame 1955; James and Healy
1972). More recent models, such as the one proposed ay Oavis, James and
Uckle {Oavis et al. 1978; Oavts and Leckle 1978; James et al. 1978).
combine surface complexion with electric double-layer theory and
Interpret adsorption phenomena In terms of a knowledge of the speclatlon
of the adsorbate and the adsorption site.
Current adsorption models have been reviewed by a number of authors
(Westall and nohl 1980; James and Parks 1981; Schlndler 1981; Morel 1981)
and have been shown to have a sound theoretical basts. The application
of these models to well-characterized laboratory metal-ligand-surface
systems have shown excellent agreement with experimental observations
(e.g., Oavis and leek1e I978a; James et al. 1981; Theiss and aichter
1980; Benjamin and Lecxle 1980). However, without further baste reseaVch
and experimentation with natural aquatic sediments, it will be difMcu't
to apply the theoretical models to adsorption In natural systems. The
application to natural systems 1s mainly hampered by the need for data-on
numerous Intrinsic parameters for each adsorbent phase in the system of
Interest.
Despite the lag that currently exists between the development of
theoretical metal adsorption models and their practical application to
natural systems, there has arisen (through model development and
experimental observation) general agreement on many of the characteristic
features o' idf^rptlon reactions. Metal adsorption is considered to be
53
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analogous to the formation of soluble complexes, with the only difference
being that the llgand 1n the reaction 1s a surface site (Stumm and Morgan
1981; Benjamin and Leckle 1981). Therefore, the same factors affecting
soluble complex formation also affect the Interactions at surfaces.
Of course, pH Is one of the most Influential parameters in governing
metal.adsorption, affecting both the type of surface sites and the
speclatlon of the metal ion In solution through hydrolysis reactions.
For example, surface hydroxyl groups can exist In three possible charge
states, with the relative distribution depending on the pH and acldiry
constants.
,S rS
At the same time the metal Ion will undergo hydrolysis as 0H increases. The
resultant surface association of metal ions with hydrous oxide surfaces
tends to demonstrate a rapidly Increasing metal ion adsorgtion as 3H
Increases over a very narrow range of 1-2 unl'ts (James and Mealy 19~2a: and
many others).
This 'pH adsorption edge,* as It 1s commonly called, often is
demonstrated by a plot of percent metal adsorbed versus DM. An example
of a typical metal adsorption edge Is shown in Figure 3.3. In most
cases, fractional adsorption decreases (the pH edge shifts to the rignt)
as total metal concentration (Me.) 1n the system Increases, other
conditions being constant (Benjamin and lecxle 198Q). This effect Is
most .often evident at low adsorption densities, when excess surface sites
are available.
In situations where complex?ng Ugands (either organic or Inorganic)
are present 1n an adsorbing, system, the above generalization for the
relationship between metal adsorption and pH ,s not always true. In
fact, depending upon the particular metal, llgand, adsorbent and pH
54
-------
100
to
•0
u
5 40
«!»
HIGHER Mt
to
FIGURE 34 TY-'CAI pH-ADSORATION EDGE FOR METAL ADSORPTION
r H 1ROUSOXIOE SURFACE
55
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range, fractional metal adsorption has been observed to decrease as pH
Increases (NacNaughton and James 1974). Benjamin and leckle (1961; 1982)
have proposed a conceptual model to explain this behavior, where the
possibility exists for free metal, a metal-Hgand complex, or free Ugand
to be associated with a surface. Then the percent adsorption of a
metal-Ugand complex will Increase with pH 1f It behaves as a free metal
1n Us surface Interaction ("metal-1Hte") and will decrease with pH 1f
Its adsorption reaction 1s similar to that of a free llgand ("llgand
like').
Another characteristic feature of metal adsorption Is competition
between adsorbate metals. Major cations, such as calcium and magnesium,
have been shown to Influence the adsorption of a given metal ion.
Predictability of the Influence of major cation 1$ difficult, and
observations range from Inhibition to no effect. At any rate the effect
certainly seems to be smaller than those due to variations Vn JH and
Ugand levels.
Competitive adsorption Inhibition among'adsorbing trace metals U
observed even at very low total adsorption densities (at most a few
percent of all surface sites occupied). Benjamin and Leckle (1981) nave
suggested that the explanation for this phenomenon 1s the presence of
several distinct groups of surface sites. Then tne possibility exists
for two metals to be competing for the same group of preferred binding
sites, which may represent only a small fraction of all surface sUes.
The existence of multlslte surfaces may also partially explain the
variation of pH-adsorpt1on edge among different metals adsorbed
Individually to the same adsorbent. In this case different binding sites
are preferred by each metal.
Although numerous experimental adsorption studies with "model"
adsorbents have been conducted, the number of laboratory studies
'n» stlgatlng the uptake of trace metals by natural aquatic sediments is
56 U.S. EPA Headquarters Library
Mai! Code 3404T
1200 Pennsylvania Avenue, NW
. >iibn DC 20460
ix... 366-0556
-------
relatively small. The results of these adsorption experiments of trace
metals partitioning measurements made 1n natural aquatic systems are
generally quantified in terms of relatively simple empirical expressions.
Including general exchange equilibrium expressions (Langmulr 1981} and
adsorption Isotherms (Oakley et. aj.. 1981) of the freunclic-i or Langmulr
type. . • - .
The data from a typical metal adsorption Isotherm run at specified
environmental conditions, when plotted as the adsorption density versus
equilibrium dissolved metal concentration, generally can be fit to a
Freundllch or Langmulr Isotherm (Figure 3.*}. The Freundllch isotherm 1s
an empirical equation having the general form
,1/n
r»n
(3.9)
where K^ and n are fitting constants. The Langmulr eouat'on has a more
theoretical base and-may be deduced from e1t..ier kinetic or thermodynamic
considerations (Weber 1972). The Langmulr equation assumes t.tac •nax'mufn
adsorption density corresponds to a saturated mono layer surface covering of
adsoroate. that the energy of adsorption is.constant regardless of adsorption
density, and that there is no migration of adsoroate In the surface
As shown In Figure 3.4 the Langmulr equation can be expressed
where r • Specific adsorption density of the metal [*ole* We/mg
Adsorbent] ('gamma').
ra • Maximum adsorption density In forming a complete mono layer
(Moles/mg] (i.e., number of usable surface sites per unit
mass.of solid), and
[W] • Equilibrium dissolved metal concentration [Moles/l].
The constant r A . which normally Is written Vb 1n the Langmulr
ma.' '
equation. 1s shown 1n this manner becaus/ .. .an be thought of as a
57
-------
r.
lm/2
I I
MAXIMUM SKC1FIC ADSORPTION
I I i I
CaullllRIIIM OI3SOLVJD Mi CONC. (MOLES Mt/l)
FIGURE 3.4 EXAMPLE Of PARAMETERIZATION OP LANGMUIH ADSORPTION
ISOTHERM FOR METAL ADSORPTION ON A SURFACE
58
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conditional adsorption equilibrium constant (l/mg) for a surface-metal
complexatlon reaction of the Form:
X Is termed a conditional equilibrium constant because Us value in
only, a constant For specified surface and bulk solution chemical
conditions.
tt Is convenient to express the Langmulr adsorption Isotherm equation
1n the above form because, due to low metal adsorption densities in
natural systems, adsorption Is often linear with respect to dissolved
metal concentration. Then, since r.A » [H], Equation 3.10
ma
reduces to
or
(3.11}
where [N-S] * Concentration of metal In metal-surface cample*
(mole/1) and
Sj • Total concentration of Interacting (adsorbing) solids
(mg/l).
Converting the Langmulr nomenclature to the toxicant modeling
nomenclature of Section 2.5 (Table 2.1). It can be recognized that K
Is the same as * (l/mg), [H] {mole/1) corresponds to C^ (ug/t).
(H-SJ (mole/1) corresponds to C (ug/l), ST is m (mg/t), and
r (mole/mg) corresponds to r (ug/mg). Thus. EQuatlon 3.11 can be
written:
' . c»
.. j- j- (]..?)
The distribution of metal between dissolved and solid phase can therefore
be determined by specifying the partition coefficient and the
concentration of Interacting solids.
59
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It should be emphasized that the value of the partition coefficient
for a given metal Is dependent on a number of environmental conditions
such as pH, PC. ionic strength, concentration of complexlng organic and
Inorganic Ugands, concentration of competing .surfaces, and concentration
of competing adsorbate species. The use of * ts, therefore, limited to
conditions very similar to those for which 1t was determined, tf a wide
range of environmental conditions are encountered, then » must be
Quantified (either experimentally .or theoretically} for the conditions of
Interest in order to accurately compute the soluble/solid phase metal
distribution. This point became apparent .during the model application to
the Flint River system.
The partition coefficient might be adjusted as a function of
environmental factors, as described below:
1} Metal adsorption 1s highly dependent on the type and relative amount
of each solid phase making ,up the solid* In an aquatic system.
Oakley et al. (1981) demonstrated this postulate using bentonite
clay, amorphous iron oxide, hydrous manganese oxide, and numic add.
Sediment organic content can be highly correlated with metal
partitioning for those metals, such as copper, that have a high
affinity for humlc adds (Oakley et al. 1981,; flamamoorth and Rust
1978; Suzuki et al. 1979).
2) Metal partition coefficients depend on the size distribution and
concentration of adsorbing aquatic sediments. It 1s obvious that
smaller particles would have a larger surface area-to-mass ratio.
thus having a higher capacity for the metal (Tada and Suzuki 1982).
It 1s not obvious why a higher solids concentration gives a lower
calculated partition coefficient, although It has been observed in a
number of studies on both metals and organic* (O'Connor and Connolly
1980; OIToro et al. 1982).
3) Of course, pH and other water chemistry parameters (particularly the
presences and concentration of metal-complexlng and adsorbing
Ugands) will affect partition coefficients. Many'of the same
observations made on •model1 systems In controlled laboratory studies
have been observed In studies of natural aquatic sediments. (Gardiner
197'; Huang et al. 1977; Vuceta and Morgan 1978; Tada and Suzuki
1982; Brown 1979).
60
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An Alternative to adjusting metals partitioning as a function of
environmental conditions based on theory and laboratory experimentation
would be to empirically derive correlations from extensive field data.
By measuring partition coefficients for a range of water Quality In a
given system or by reviewing partition coefficient data from many
different river systems, a multiple regression might be found for a given
metal partition coefficient versus such Important water quality
parameters as temperature, pH, hardness, alkalinity, suspended solids.
dissolved organic carbon, and chlorophyll a.. Attachment 1 contains a
summary of data retrieval of field measurements of metals partition
coefficients with pertinent water quality parameters. Figure 3.5
summarizes the regression results for the major metals for data obtained
from streams. These data are useful for estimating metal partition
coefficients for specific river systems where actual field data are not
available.
3.2.2 Oroanlcs Partitioning
Most organic contaminants of concern tend to be .relatively
hydrophoblc, non-polar compounds. Such organic compounds tend to have
strong affinity for-natural aquatic partlculate material, making
solution/sediment distribution of these chemicals as important in
predicting their fate and transport as It 1s for heavy metals.
The sorptlon of hydrophoblc organicJ Is considered by most
researchers to be a true equilibrium partitioning between the water and
sediments. The linearity of sorptlon Isotherms In dilute sediment/water
systems and the lack of competitive effects between two sorbates have led
to the proposition that partitioning to sediment organic matter is the
primary mechanism of sorptlon of nonlontc organic compounds (Chlou et al.
1962). This being the case, much emphasis on the characterization of
this sorptlon process has been focused on the properties of the chemicals
61
-------
•0'
10'
10s
..i.i
j_
iO . iOO
SUSPENDED SOLIDS (mg/i)
iOCO
METALS >
2 -CADMIUM
3 • CMflQMIUM
9- LC*0
r-
• - ZINC
FIGURE 3-5.
PARTITION COEFFICIENT AS FUNCTION ^F SUSPENDED SOLIDS
ALL METALS IN STREA.'tS
-------
related to their solubility and hydropnobidty and on the size and
organic content of the sorbents.
Data relating the chemical concentration in the aqueous phase to that
In the solid phase are frequently expressed in terms of the Freundlicn or
langmulr Isotherms previously described (Equations 3.9 and 3.10). For
typical environmental pollutant concentrations, sorption isotherms m the
sediment/water suspension are very close to linear and both Freundlicn
and langmulr equations can be reduced to:
r . «C - (3.13)
where r • chemical concentration In solid phase (ug/mg)
C. . chemical concentration in aqueous phase (ug/l)
a
» . partition coefficient (l/«g)
The prediction of « for a given chemical/Suspension system has
relied on correlations with chemical solubl-Mty. octanol/water partition
coefficients (KQWK
the organic carbon/water partition coefficient
(K }. coupled with the organic carbon content (weight fraction) of the
oc
sediments. In general, the more Insoluble and hydrophobic a chemical is
the more likely It 1s to have a larger ». Likewise, in comparing
sorption of a given chemical among various sediments, the sediment wit.i
the highest organic content 1$ likely to sorb the most chemical ana
produce the largest «.
In quantifying the above relationships the first useful correlation
Is between the octanol/water partition coefficient and the chemical's
aqueous solubility. This work was pioneered by pharmacological Interests
In the partitioning of drugs Into the aqueous and fatty phases of living
tissues (Hansch et al. 1968). More recently correlations have been
developed for organic chemicals of environmental Interest In aquatic
systems /Freed et al. 1977; Chlou et al. 1977; Banerjee et al. 1980;
Nackay t ai. 1980).
63
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Given either the adueous solubility (S) or the octanol/water
partition coefficient (KQU) of a compound, a correlation can be
developed between * and S or K • For a range of chemicals. More
often than not, however, recent experimental interpretations have
Included several sediments with a range of organic caroon content. Then.
by dividing the measured partition coefficient (K ) by the organic
carbon weight fraction of the particular sediment (O.C.), a sedlment-
•independent partition coefficient (K..) between the aqueous phase and
oc
the organic portion of the sediments may be obtained.
Table 3.2 contains a summary of empirical correlations for predicting
sediment partitioning and biological partitioning (bloconcentratlon
factors) of nonpolar organic compounds.
, Care must be taken in applying the above (or other)
correlations to unstudied systems. While these correlations will
probably give reasonable estimates for application to .a WIA problem.
there are a number of potential -pitfalls that should be cons'de*ed. Some
of these considerations are discussed below:
1. These relationships are all log-log correlations; therefore, *nat *»ay
seen like a small deviation 'from the regression line could produce a
rather large error In final fate and transport' determinations .
2. These relationships are useful only for nonpolar compounds; they
sould not be applied to semlpolar or polar organic compounds, where
electrostatic Interactions nay become significant (Pavlow, 1980;
leenheer. 1980) .
64
-------
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3. The water chemistry of the aquatic system (1n addition to the water
concentration of the compound of interest) can alter the empirically
predicted partition coefficient, for example, 1f the compound
Interacts Dy surface adsorption or Ion exchange, then solution
properties such as pH. Ionic strength, and temperature will affect
uptake on solids (Hollander et al. 1980). Even with nydrophoblc
compounds the presence of other dissolved organic matter has been
shown to reduce sorptlon by river and sewage participate matter
(Hassett and Anderson 1982).
4. Properties of tne sediments other than-their organic caroon content
m«y Influence sorptlon. HlraUural et al. (1979) found a good
correlation between partition coefficients of PCS and the specific
surface area of adsorbing marine partlculates'. Of .course, the
concentration of adsorbing sol ids has been observed to affect
partition coefficients for organlcs as well as metals (O'Connor and
Connolly 1980).
5. finally, the question of kinetics and hysteresis arises in all
adsorptlon-desorptlon problems. Karlckhoff (1980) has Found that the
kinetics of approach to equilibrium 1n sediment suspensions (either
during adsorption or desorption)'could be characterized Dy a rapid
component and a much slower component that may require days or weeks
to reach equilibrium. A possibly related problem has been observed
by OIToro and Horzempa (1982) and OIToro et al. (1982). 1n that the
lack of reversibility In PC8 adsorptlon-desorptlon reactions could be
described by Invoking a two-component formulation. Adsorption and
desorptlon are assumed completely reversible for the first component.
following Equation 3.13. Adsorption 1s assumed Irreversible for the
second component. OIToro et al. (1983) presents the mathematical '
formulation for this model and shows the results of further
laboratory ,,'sfing. The practical difference between modeling with
the classical reversible'expression and modeling with the
reversible-Irreversible expression Is that the former may predict
higher dissolved concentrations, particularly in dynamic models.
66
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3.3 TRANSFORMATION PROCESSES
transformation processes are those in which the toxicant is
essentially irreversibly destroyed, modified, or eliminated from the
system. In most cases these processes apply only to organic compounds.
First-order decay coefficients for Individual processes are additive;
together they form an aggregate degradation co fHdent:
Kd " *B * *H * S * *V (3>15)
where Kd • Aggregate degradation coefficient {'/day)
K. • Biolysis coefficient (I/day)
KH . Hydrolysis coefficient (I/day)
Kp . Photolysis coefficient (I/day)
Ry • Volatilization coefficient (I/day)
Some models also distinguish non-biological oxidation, KQ. separately.
although this process Is not Important for most organic*.
In models with simple first-order kinetic structures (such as
N1CHRIV). the analyst either enters the aggregate Ktf or enter: the
individual process coefficients K-. KH> JCp, and Ky. In models
with more elaborate kinetic routines (such as CXANS). each ind' dual
first-order coefficient (K.. etc.) Is Internally calculated as j
function of several other parameters wftlch the analyst must* enter.
3.3.1 aiodegradatlon
Biological transformations (biolysis) are enzyme mediate* reac.lons
usually performed during metabolic activity, primarily by bacteria and
fyngl. The catalyzed transformations Include oxidation, reduction, and
hydrolysis. The rates of biologically mediated transformations c. .1 be
very rapid 1n comparison to chemical transformations that lack'enzymatic
catalysts. It 1s precisely because oP these accelerated rat*; that the
blograf *.iou of organic contaminants Is often the most Important
transformation loss process in aquatic ecosystems.
67
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Conceptually. blodegradatlon should not be thought of as a single
step process. Rather, It,.is a multi-step process where intermediate
products may accumulate. The total conversion of organic substances to
inorganic products, including carbon dioxide, is termed mineralization.
The process termed blodegradatlon. however, often involves only the
partial metabolism of an organic. For Instance, detoxification of a
contaminant may involve only the transformation to an innocuous
Intermediate compound.
Process Oescr1pt1on
When heterotrophlc microbes degrade organic compounds, energy and
carbon are frequently obtained for growth, thereby accomplishing
metabolism. Occasionally a compound may be biologically transformed
without the responsible microbes acquiring growth requirements.
Typically, this process of cometabolism will proceed at relatively slower
rates and will not Impact the activity of the .decomposer community.
Frequently; when an organic contaminant is first introduced to an
aquatic community an acclimation period is observed when the mlcrooial
community must adapt Itself to the chemical. This acclimation per'od .'.*
most often termed a lag phase. The lag phase Is marked by enzyme
induction, selected population Increases, and a progressive increase \n
the rate of observed blodegradatlon. Once the microbes have become
acclimated to an organic pollutant, the rate of specific decay becomes of
Interest.
There are three primary factors that determine the extent and rate of
biological decay of an organic In a natural system. These are: 1) the
properties of the organic contaminant. Including Us structure.
concentration, and history within Us environment; 2) the characteristics
of the acting mlcroblal community, such as community diversity, size and
general health; and 3) th*e status of the -r--'ironment In terms oF
temperature, the presence of additional o.panics or supporting growth
66
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requirements and. especially, the dissolved oxygen status:
witnln the framework of a model designed to describe the fate of an
organic contaminant, U 1s necessary to formulate and define a kinetic
expression of blodegradatlon. This task Is made difficult because of the
great complexity of factors Inherent 1n a natural system. Contemporary
fate models have simplified this task by representing the loss or decay
of an organic by first-order kinetics or 1n some cases second-order
kinetics.
First-order kinetic representation In the WlA model 1s described as:
£ . -k,C (3.16)
where k? • First-order olodegradatlon rate constant (time' }
C • Concentration of an organic contaminant (mass/volume)
The concentration. C, susceptible to decay may be the dissolved
fraction. Therefore, if a contaminant partitions onto solids. trie
respective participate and dissolved fractions must be Quantified, r.its
expression describes the loss of an organic due to biological activity
and Is analogous to expressions commonly used for the decay of BOO.
Larson (1981). among others, has shown that first-order kinetics
represent the decay of organic* reasonably well at bacteria
concentrations, evident In many environmental situations.
In many respects representation of second-order kinetics 1s a
simplification of a modified Honod expression (Paris et al. 1981).
represented as: .
(3.17)
where B • Magnitude of bacteria (count or blomass /volume) and
kj • Second-order blodegradatlon" rate constant
(volume/org;* sfl.Mme).
-------
The decay of a contaminant is seen as not only a function of Us
concentration, as 1s the case In First-order kinetics, but also a function
of tne bacteria population. However, bacteria count has not always proven a
reliable indicator of bacteria activity, especially in regard to » specific
contaminant organic. This development In blodegradatlon process
representation has been offered by many models (e.g.. EXAMS) as a way of
increasing the application of a single, contaminant-specific decay rate to a
wide variety of environments. Since blodegradation ts recognUed as being
Influenced by ambient temperature, process representation can include a
function relating the blodegradatlon rate to the temperature regime. The
analyst nay derive a temperature specific rate by making use of an Arrhenius
function such as:
KT . K2Q e (3.18)
where K, • Temperature specific biolysis rate.
*?. * Cxpected rate at 20* centigrade,
T > Characteristic temperature, and
e * Temperature correction factor.
Theta (d) Is frequently between 1.04 and 1.095.
Sate Selection
In nearly every circumstance tne selection of an appropriate decay
rate is constrained by incomplete Information regarding the contaminant
or the system of Interest. However, there are a number of approaches and
relevant considerations to guide prudent selection of a representative
decay rate. Inherent In this selection process is the realization that
no rate is applicable to all conditions for a specific contaminant.
Instead a range of estimates will more likely emerge that will impart to
the analysis a range of output. The Importance of this range may be
established by a sensitivity analysis, whereby variability in model
output 1s compared to incremental changes In the decay rate over tt,«
range of expected values.
70
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A 11st of relevant considerations or approaches to estimating a decay
rate 1s offered below. Although a ranking of these considerations by
order of Importance could only be made on a problem specific basis.
awareness of an overall ranking is evident In the presentation. •
A. Properties of Contaminant
A thorough literature survey of the properties of the organic
contaminant of Interest should logically be an Initial step.
Previously reported data relating decay rates for the organic in the
laboratory or the field would be an Important step in defining a
probable range of decay rates, for each of the organic priority
pollutants flabey et al. (1982) have estimated the general
susceptibility to blodegradatlon. Definition of likely metabolic
pathways may also be helpful In several regards. Aerobic pathways
will generally be more rapid and complete than anaerobic pathways.
Also, the loss rate of one chemical may not be indicative of changes
In toiidty, If Intermediates form that are toxic and possess
different decay characteristics. Therefore, the analyst would -ant
Information regarding the toiidty and blodegradabllIty of probable
Intermediates. In cases where the knowledge of an.organic 1s very
sparse, 1t may be necessary to compare the structure and physical
characteristics of the organic of Interest to a host of better known
contaminants, when approached 1n great detail, this procedure ts
called structure activity analysis.
B. System Examination
Information regarding the trophic level and pollution level of the
target system may assist 1n defining the expected decay rate. More
highly Impacted waters have demonstrated shorter lag phases and
greater decay rates of organlcs {Spain et al.. 1981. Rodgers and
Salisbury 1981). Previously reported f'c'd <1ata may, to varying
degrees, yield Insight Into the sp't.al and time distribution of the
71
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organic, as well as Important environmental factors (temperature
regime, volume and flow). IF available In sufficient Quantity and
quality, this Information may allow the user.to 'calibrate' a
b1ode$radat1on decay rate by accounting for other components of a
mass balance and then solving for the magnitude.of the biolysis term.
C. Experimental Program
Laboratory measurement of decay rates may be necessary in evaluating
decay rates for organic; for which no Information is available.
Methods for measuring decay rates have been demonstrated for both
batch (Paris et al. 1981) and continuous cultures or by. use of
microcosms (Biddings et al. 1979). In site specific applications it
is the practice to use the natural waters as the test media.
Sterilization of, the water before Introduction of the test organic
serves as a control. The batch cultures yield a decay rate by
plotting the log concentration of organic vs. time, while continuous
cultures can yield a decay rate via a mass balance approach since
other sources and sinks can be controlled and thereby Quantified.
Should a second-order formulation be invoked, relating the rate to
both the pollutant concentration and the microblal population, then
the magnitude of the mlcroblal population must be assessed.
0. Field Program
The Waste Load Allocation process may Involve a field program. To
assist in the accurate evaluation of a blodegradation rate, the
measured parameters should Include both total and soluble
concentrations of the pollutant of Interest, solids concentration,
dissolved oxygen, C300, flow, temperature, and basic physical
dimensions. Basic Information regarding the biology of the system is
desirable, especially bacteria counts and Identification of toxic
conditions which mlg >i IrMuence bacterial activity. Th. sp'tial and
temporal scale of sampling will impact the calibration dtd accuracy
-------
of the blodegradatlon term, as well as aU other kinetic processes.
Data upstream and downstream of all major loads are important'. The
frequency of sampling should reflect the relative dynamic nature of
the system 1n terms of hydraulic residence {Mow regime) and the
major forcing functions (temperature, loading, light, etc.). Some
attempt to reflect the seasonal-variation in forcing functions may be
especially helpful.
3.3.? Photolysis
Some substances that absorb sunlight in the ultraviolet and visible
portion of the spectrum may gain sufficient energy to Initiate a chemical
reaction. Some of these photochemical reactions result in the
decomposition or transformation of the substance. This process.
photolysis, can determine the fate of certain pollutants 1n the aquatic
environment. Zepp (I960) provides a more complete discussion of tnts
process.
Process Theory
A sunwary- of the theory Involving the transformation of organics via
photolysis 1s outlined from mils et al. (1982). The basic
characteristics of photolysis are as follows:
» Photolysis Is an Irreversible decay process activated by the
energy of the sun.
• Molecules which absorb sunlight 1n the ultraviolet and visible
portions of the spec'trum gain sufficient energy to initiate
chemical reactions.
• Products of photochemical decomposition may remain toxic;
therefore, decomposition does not necessarily Imply detoxification
of the environment.
• The photolysis rate d'.-pends on several chemical and environmental
facto.s.
73
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Tht chemical and environmental factors controlling the rate are as
follows:
A. Absorption Spectrum of the Pollutant:
The probability of a photon being absorbed varies with wavelength of
light In a manner unique to every chemical species. To change a
molecule's structure, the absorbed photon must be sufficiently energetic;
generally, radiation with wavelengths in the visible or ultavlolet range.
or shorter, has sufficient energy. Consequently, the pollutant's
visible/ultraviolet absorption spectrum Is most Important.
B. Solar Radiation:
Radiant energy from the sun depends on the composition of the
atmosphere (cloud cover) and geographic location.
C. Light Attenuation:
Light Intensity reduces with depth In water column, due to reflection
(< 10% reduction plus slight change In the spectrum) and absorption and
scattering. Absorption Is determined by Lambert's Law:
XI (3.19)
where I > Irradlance
1C • Diffuse light attenuation coefficient, given by:
K - aO » Sb . (3.20)
where a • Absorption tern
0 • Radiance distribution function
» Backward scattering of light
74
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The value of K {diffuse light attenuation function) depends on
variations In amounts and types of partlculates and dissolved substances,
I.e., suspended solids, chlorophyll 4, dissolved carbon. The value of 0.
which represents variable light path lengths, U 1.2 where scattering 1$
Ignored. Average value for natural waters is 1.6 as reported by Miner
and Zepp (1979).
An empirical relationship developed by Burns et al. (1981) enao'es
the attenuation coefficient to be estimated based on system status:
(Aw * *ach1a * Adoc°OC* AssSS)
(3.21)
where R • Diffuse .light.attenuation coefficient
Y
Adoc
Ass
Ciila
DOC
SS
• Absorptivity of water, (m* }
« Absorptivity of chlorophyll a pigment, (mg/t]" (m" )
• Absorptivity Of dissolved organic carson, (mg/l)" (iT
-1 .\
• Absorptivity of suspended sediments, (fig/i) (u ,
. Concentration of Chtorophyll-a pigment, (mg/l)
. Concentration of dissolved organic carbon, (mg/l) and
M Concentration of suspended sol ids, (mg/l)
Mills et al. (1982) tabulate tne values of A^. A^. A^, and ASJ
for different wavelengtr'S.
0. Quantum yield:
Not every absorbed photon induces a chemical reaction. The fraction
of adsorbed photons resulting 1n the desired reaction is termed quantum
yield, +.
moles of given species formed or destroyed
moles of photon absorbed
75
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environmental factors affecting quantum yield Include:
a) molecular oxygtn -- as a quenching agent.
b) suspended solids — change reactivity of compounds adsorbed
(usually negligible). • • •
c) chemical spedatlon -- photolysis rates may vary with pH,
especially Important when pxa 1s 7 ^ 2.
d) temperature effect -- until further research 1s completed this is
assumed to be negligible.
Type of photochemical reaction affects quantum yield. Quantum yields
vary over several orders of magnitude depending on the nature of the
#• "
molecule which absorbs light and the nature of the reactions U
undergoes. Two major classes of photochemical reactions of interest In
the aquatic environment are 'direct' and "sensitized" photolysis.
Direct photolysis occurs when the reacting molecule directly
light, various reactions, can occur: fragmentation.' reduction,
oxidation, hydrolysis, acid-base reaction, addition, substitution,
IsomeM ration, polymerization. Quantum yield data obtained from
experimentation can assist the HLA analyst In determining whether or not
to Include direct photolysis in the analysis.
Sensitized (Indirect) photodegradatlon occurs when a Hgnt-aosootng
molecule transfers Its excess energy to an acceptor molecule causing t."»e
acceptor to react as 1f It had absorbed the radiant energy directly.
Natural humlc acids (and synthetic organic compounds) can mediate
reactions, for example.
Bate estimation
Photolysis follows a psuedo-Mrst-order reaction:
(3-22)
76
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•K. *'K
0 S
where X- • Rate constant
p
X, • Direct photolysis rate, and
y
s
>
Sensitized photolysis rate.
One .practical means of obtaining the appropriate photolysis rate is
to use experimental data from literature and extrapolate to the specific
site 1n question. There are two methods reviewed below for using
environmental data to calculate the expected photolysis rate.
One method Involves extrapolating near surface rate data to a
specific site (Mills et al. 1982):
!-• Z
do
J--2-
•o °o
(3.23)
where
.1.
do
a Direct photolysis rate .constant (day* ),
a Near surface rate constant (measured) (day'
« Total solar radiation (langTeys-day"1),
• Total solar radiation under conditions
at which K. was measured (langleys«day"
90
).
D • Radiance distribution function,
0. • Radiance distribution near surface (approximate
o
value .1.2),
K(X*) * Light attenuation coefficient calculated from Equation
3.20 for x«, the wavelength (nm) of maximum light
adsorption, and
Z * Depth of water 1n meters.
The second method Involves evaluating the rate constant Integrals.
If certain data are available for a substance (i.e.. absorption spectrum
«(x) or a (X). and the quantum yields, », or 0,), It is
» o $
possible to estimate the photolysis rate for a specific site from the
following (Hills et al. 1982):
2.3 • J
0 •.!« • K' 1 - e
-K Z
(3.23)
K • Z
77
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where 1 • wavelength interval Index,
U • Photon Irradiance near surface (photons cm" sec nm" ).
w a u • &\
J . Conversion factor . 1.43 x 10* (mole cm sec l"
day"1)
c • Base 10 molar extinction coefficient {lmol~ cm" of toxicant}.
• - Disappearance Quantum yield,
K • Diffuse light attenuation near surface (rn~ ).
Z • Nixed water depth (m) and,
• • Base e absorption coefficient of the sensltlcer (mg~ cm).
for toxicants for which photolysis may be significant, Nabey et al.
provides data on absorption spectrum and quantum yield.
3.3.3 Hydrolysis
Certain organic compounds may be chemically transformed by direct
reaction with water. This occurrence In an aquatic system is termed
hydrolysis. A hydrolysis reaction may either be acid, neutral or base
dependent. Essentially, this means that the concentration of hydrogen
and hydroxide Ions, and therefore pH, is often an Important factor in
assessing the rate of a hydrolysis reaction.
Products of hydrolysis may be either more or less toxic than the
original compound. For this reason one should be aware of the probable
products of transformation processes. In addition, transformation via
hydrolysis will Hkely alter other characteristics of the chemical
Including Us susceptabllUy to other transformation processes.
Process Representation
In a natural system hydrolysis may be either microbially mediated or
be abiotic and dependent only upon the status of the water. Mlcroblal
Influence 1s covered In Section 3.3.1; consequently. only direct, abiotic
hydrolysis wl * bt examined here.
78
-------
Abiotic hydrolysis Is normally represented by a first order reaction
which In Us most simplified form Is:
- XMC (3.25)
dt H
where C • Concentration of an organic (Mass/volume) and,
JCU • Specific first-order hydrolysis rate constant (Time"
n
In the scientific literature KH 1s typically represented as:
KH . kn » ka [H*] » kb [OH-] (3.26)
where k -Neutral hydrolysis rate constant (Time ),
11
k • The acid catalyzed hydrolysis rate constant (Molar" Time" ).
li"
k& • The base catalyzed hydrolysis rate constant (Molar Time ).-
[H*] « Molar concentration of hydrogen ions and.
[OH*] • Molar concentration of hydroxide Ions.
This representation conveys .the strong pH dependence often observed
1n hydrolysis reactions and 1s a convenient method of-representing
detailed laboratory results.
The adsorption of an organic onto sol Ids often removes t.ie
partlculate fraction from hydrolysis reactions. Therefore, the
hydrolysis rates In Equation 3.25 and 3.26 are only applied to the
soluble fraction of the toxicant. If the model being employed does not
discern between dissolved and participate phases, then the observed
partitioning should be used In adjusting the magnitude of the rate
constant.
Bate Selection '
A great deal of data has been reported 1n the chemical literature
regarding the observed hydrolysis of chtmlMls in distilled water.
Natural waters, however, contain organlcs and metals which may catalyze
and accelerate hydrolysis. Consequently, the querj which consistently
79
-------
emerges Is, how applicable art distilled water rates to
conditions? Research designed to answer this question has been reported
within the last several years (e.g., Zeop et al. 1975). The approach has
been to use field samples and to remove as many competing processes as
possible. For example, dark conditions were used to eliminate photolysis
and ultra-filtration to remove the biological community, thereby
eliminating biolysis.
Specific hydrolysis coefficients for many organic* or classes of
compounds are reported in the professional, governmental, and industrial
publications. Recent sources Include Wolfe (1980). Nabey and Mill
(1979), and Nabey et al. (1982). These coefficients should give the user
a range of values from which to calibrate the model or to guide a
sensitivity analysis. Wolfe (1980) also reviewed a technique based on
linear free energy relationships (LFER) for estimating hydrolysis rate
coefficients when experimental values are not available. When there 's a
paucity of reported values for a chemical of interest, other .measures may
be taken to estimate a rate. The general format would be similar to that
presented for the biolysis rate constant in Section 3.3.1.
Lastly. In translating literature values into computer model 'nout.
It should be noted that some values are reported as second-order
coefficients because they are a function of either tne hydrogen or
hydroxide 1on concentration {as represented In Equation 3.26). In using
first-order kinetic models the analyst must translate these second-order
values Into pseudo-first-order rate coefficients by multiplying by the
appropriate 1on concentration.
3.3.4 volatilization
Volatilization, loss of toxicants from the water column to the
atmosphere. Is customarily treated as an Irreversible decay process.
because of Us mathematical similarities to these processes. Actually.
80
-------
however. 1t Is- a reversible transfer or environmental partitioning .
process,, In which the concentrations in air and water shift .toward
equilibrium. The volatilization rate depends on the properties of tne
chemical as well as the characteristics of the water body and possibly
the atmosphere. The chemical properties favoring volatl 11 ration are high
vapor pressure, high dlffusWUy, and low solubility. The environmental
conditions favoring volatilization are high surf ace- to- volume ratio and
turbulence.
The partitioning of pollutant between water and air 1s deserlaed in
terms of an air/water partition coefficient. H :
(3.27)
where H • Henry's law constant (dimenslonless, mass/vol. basis)
C (eq) • Gas phase concentration .at equilibrium (mg/t), and
CJeq) • Dissolved aqueous concentration at equilibrium (mg/l)
The value of H can be determined by measuring C and C^ In an
. c • g d
equilibrated system. More commonly, however, it 1s calculated from the
toxicant vaoor pressure (equivalent to the gaseous concentration in
equilibrium with the pure.toxicant phase) and solubility (aqueous
concentration in equilibrium with the pure toxicant phase):
He . 16.04 PM/TS (3.28)
where P • Vapor pressure (torr).
M * Molecular weight (g/mole).
T * Temperature (K*). and
S - Solubility (mg/l).
SI
-------
It should be noted that H may be reported In an assortment of
units or nonequlvalent dimension less bases. One useful conversion is:
Hc (dlmenjlonless) - Hc (atm - m/moU)/RT (3.29)
where R • 8.206 x 10 atm - m /*K - mote. For the organic priority
pollutants the values for P. M, and S 1n Equation 3.28 are provided by
Callahan et al. (1979). and the values of. H_ provided directly by Mabey
et al. (1982). For other substances data may be available in Mills et
al. (1982), Perry and Chllton (1973), and Mackay et al. (1982}. if vapor
pressure data are not available, Nackay et al. (1982) suggests the
following equation for estimating P (torr) for hydrocarbon* or
ha togenated .hydrocarbons with boiling point greater than 100'C:
in (P/760) . - (4.4 » in T8). x (1.803 (T8/T - 1) - 0.803 tn (T8/T)
- 6.8 (TH/T - 1)
where:
T * Anotent temperature {*)
F| • floHtng point (K)
TN • Melting point (K)
If the melting point TM Is less than the ambient temperature 7. than
ff
the third term Is eliminated.
The net rate of transfer (mg/i • day) from water to air 1s governed
by the difference between (a) the gross transfer from water to air,
proportional to the actual dissolved concentration C.. and (b) the
gross transfer from air to water, proportional to the air concentration
V
Hate . KV
-------
The tern C /H Is the water concentration which would be in -
g c
equilibrium with (saturated with respect to) the local air
concentration. Unlike common gases like oxygen, the environmental
concentrations of toxicants, C^ and CVHC, typically vary over many
orders of magnitude. Consequently. 1t Is usually the case that either
(a) C0 « C-/HC, and the net Input from the atmosphere is a
constant load, essentially Independent of the modeled C.. or (b) C,
a a
» C-/H . and the volatilization rate 1s essentially independent of
the air concentration:
Rate . XyCd ,. (3.31)
Most computer models Incorporate Equation 3.31 rather than Equation 3.30.
The rate coefficient iCy (I/day) Is related to the mass transfer
coefficient {or velocity), ky (m/'day) by:
Ky . ky/H (3.32)
where H is the water depth (the Inverse of the surface to volume ratio).
The "two film* theory is generally applied to the calculation of the
mass transfer coefficient. This theory envisions diffusion resistances
in a liquid surface flln and a gas surface film as controlling the mass
transfer (Canale and Weber 1972; llss a/ifl Slater 1974; Mills et al.
1982). Reciprocals of mass transfer coefficients are used to represent
these resistances:
_L_ . 1 „ 1 (3.33)
Overall Liquid flln 6as flln
resistance resistance resistance
where k& • liquid film transfer coefficient (m/day), and
k • gas film transfer coefficient (m/day).
-------
It 1s useful to discern three basic cases. («) When k «
H k . then ky In Equation 3.33 Is essentially equal to kfc
(liquid phase controlled); (b) when ^ » Hck . then ky 1s
essentially equal to H k (gas phase controlled); and (c) when k&
and H k are of the same magnitude, then both contribute
c 9
significantly to k^,
As the chemical-to-chemical variability of H( tends to be greater
than the site-to-site variability of k& and k . the value of the
H tends to be more Important than the environmental conditions In
determining whether the liquid or gas phase resistance controls the
volatilization rate.
Sa. s Phase Resist a nc e
The movement of air causes a mixing of the air surface film which
results In an Increase in k . Because the evaporation of water 1$
controlled by k , and because this process has considerable engineering
Importance, data are available relating k (for water vapor) to the
ambient windspeed. Such data are presented by O'Connor (1980) and
HydroQual (1982). fly Including theoretical effects of dlffuslvlty and
viscosity, they arrive at an expression applicable to any substance:
kg - 0.001 (Dg/wg)*)-*.7 W (3.34)
where 0 • D1ffus1v1ty of substance In air (cm /sec).
9 2
v • Kinematic viscosity of air (.0.15 cm /sec), and
U • Wind speed U/T).
As the expression Is dlmtnslonaUy correct, consistent units will
result 1n k having the same units as W. Average windspeeds tend to be
1n the neighborhood of 5 ra/sec. Although transient periods of no wind
are common In many localities, such periods are not long. Consequently,
use of a steady state condition of little or no wind in Equation 3.34 (or
i.3S) may not produce a realistic result.
84
-------
mils et al. (1982), using a similar type of data and analysis as
O'Connor (1980) and HydroQual (1982), suggest the general relationship:
k, . 170 (18/*)1/4« • (3.35)
where W is 1n m/sec.
Molecular weight. «. enters the expression because of Us
relationship to
-------
Revised
10/85
Mills notes, however. that 1n field studies using radioactive tracers
(Rathbun and Tal 1981), such relationships were difficult to discern.
Rather, the volatilization rate could be adequately predicted by:
kt{ toxicant) • 0.655 kt(02) (3.39)
Nabey et al. (1982), using a more complicated procedure relating 0&
to molar volume, has calculated the toxicant/oxygen transfer rate ratios
for all volatile priority pollutants.
In any case, the difficult step in this approach is not to obtain the
above ratio, but rather to predict the oxygen transfer coefficient,
k^O,), correctly. This coefficient 1$ a function of water
turbulence, which may be generated either by water flow or by wind.
In free flowing rivers, water turbulence Ts generated by the flow,
and numerous formulas are available for calculating k^Oj) (I.e..
J4K (0,)) from hydraulic parameters such as velocity, depth, and
$Too«. Wilson and Madeod (1974) and Rathbun (1977) review many of the
reatratlon formulas which have been proposed over the last three
decades. One example of such a formula 1s that of O'Connor:
kt, • (Ot u/H)0-5 (3.405
where u 1s stream velocity and units for parameters on both sides of the
equation are chosen to be consistent. Ot for 02 Is 1.81 x 10'
(i2 /day. The equation can be used to directly calculate kt( toxicant) 1f
BI( toxicant) can be estimated.
In Impounded waters and other slow moving water bodies, water
turbulence may be gene-4ted by wind. O'Connor (1980) and HydroQual
(1982) summarize data /elating k% to windspeed. u. These dat
suggest a relationship:
kt • 0.17 C0 (Oi/n)0'6 U (3.41)
86
-------
where C. • Drag coefficient (unities;), and
• • Kinematic viscosity of water {•0.0100 cm /sec).
The units of all other parameters must be chosen to be computable.
C. also appears to vary with wlndspeed. W, but nay maintain a value
around 0.001 for u less than 10 m/day. As with using Equations 3.34 and
3.35. sustained periods of little or no wind are not common; *.(02)
values substantially less than about O.S m/day are not usually expected.
Table 3.3 Illustrates parameters needed 'to determine a wind controlled
volatilization rate for two toxicants.
Hydrosdence (1971} and EPA (1976) present data and a nomograph for
estimating k (Q_) for a variety of hydraulic conditions. Their
data suggest that k.(0j) would not be expected to be much less than
about 0.6 m/day nor much more than about 12 m/day, except under unusually
stagnant or turbulent conditions.
Identlrylnfl. thf__Jmeortant Parameters
Equation 3.33 can be examined In light of the observed relationships
of k. and it versus wlndspeed. and the reasonable range of k.
I 3 l
suggested by Hydrosdence (197:) and EPA (1976). Some simplifications of
the two film analysis are thereby indicated.
If H Is less than about 3 x 10"4. then the gas phase coefficient will
control the overall transfer coefficient k In all aquatic environments, even
standing waters. This is because H k will increase much more slowly than
k^ as a function of wlndspeed. [n this case, the analyst need not consider
the turbulence of the water body at all. Furthermore, surface transfer will be
slow for substances of this type, and the rate will decrease as HC decreases.
Benzo(a)pyrene, dleldrln, and pentachlorophenol are examples of compounds In
this das*.
87
-------
10/85
TABLE 3.3 VOLATILIZATION PARAMETER VALUES
Parameter
o
ot
y!
^
w
p
cs
M
T
H
Olffn$1vlty of PCS In Air
Olffuslvlty of .PCB In Water
Kinematic Viscosity of Air
Kinematic Viscosity of Water
Drag Coefficient
wind Speed
vapor Pressure
Saturation Concentration
Molecular weight
Temperature
Henry's Law Constant
c«2/sec
cm^/see
c»2/$ec
cnr/sec
-
N/sec
m Hg
«g/£
g/nol
K
.
Aroclor
0.
5.
0.
0.
0.
s.
4.
0.
261
289
0.
04652
387xlO*6
15
or
001
0
06*10-4
35
OU7S
Aroclor
0
4
0
0
0
5
4
0
372
289
0
.03673
.253x10
.15
.01
.001
.0
-6
.06ilO"5
.027
.0309
37.3
-------
If H 1s greater than about 3 x 10, then the liquid phase
coefficient k^ will, control, tht overall transfer coefficient k^
under nearly all commons, even when the water 1s very turbulent
(k1(0j) -12 m/day) and the air calm (W « 2 m/sec). For H( In
this range the analyst need not consider the air phase parameters. It 1s
also Important to note that among substances with a high H . the
volatilization rate 1s Independent of H ; rather, It Is dependent on
the substance's water*dlffuslvlty. As dlffuslvHIes vary relatively
little among most toxicants, the volatilization rates of all highly
volatile toxicants are nearly Identical. Examples of such compounds are
vinyl chloride and trl- and tetrachloroethylene.
If HC falls between about 3 x 10~4 and 3 x TO*1, there can be
some environmental conditions under which resistance in both the liquid
and the gas phase controls tht rate of volatilization. Nevertheless.
under other environmental conditions only one phase may still control the
overall rate. For example, under conditions of moderate turbulence
(k.(0.) - 2 m/day) and wind (U • S m/sec). the liquid phase solely
' 2
controls for any H greater .than about 2 x 10 .
80
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SECTION 4.0
GUIDANCE FOR MODEL APPLICATION
4.) APPROACH TO WASTE LOAD ALLOCATION PROBLEM
Within the pollutant-by-pollutant modeling context considered by this
document, the basic question confronting the waste load allocation
analyst 1s, 'How much of a specific substance can be a Mowed to be
discharged 1n areceiving water, vetnot violate the numerical water
quality, standard?' This section of the guidance document provides some
principles and direction to answering this question. The intent here is
not to provide a standard method to be followed verbatim. The various
models and example application are provided as guides to be used to gain
Insight Into the process. Modeling results, as depicted in Figure 4.1
should be used by decision makers In conjunction with water quality
standards to develop waste discharge permit limitations. This process
assumes that the decision makers (e.g.. state water quality boards or
administrators) have the the desire and legal means to allow use of trie
assimilative capacity of water systems. The models may assist 1n
i
choosing some optimal mix of treatment, production modification, standard
modification, and time schedule for Implementation. It .1s important tnat
the analyst be Involved In this process and communicate modeling results
Including estimates of accuracy and uncertainty to others involved.
furthermore, it is desirable to get the affected parties Involved in
the process early In order to Identify the most Important Issues. By
obtaining agreement on the approach for defining and evaluating the water
quality problem, the regulatory agency, the dischargers, and any
Interested citizen groups may be able to work with In a cooperative rather
than adversarial context. Considering the level of uncertainty inherent
1n estimating allowable ambient concentrations, allowable recurrence
Intervals, and allowable effluent loadings, such agreement may be helpful
for successfully completing the endeavor.
-------
WO STANOAROS OEVELflP^ENT
IOEMT1PY POTENTIAL USES ANO
PQSSIILE LEVELS OF PROTECTION.
OIVELOP ALTERNATIVE CRITERIA.
WASTCTATER TtCHMOLOSY gVAlUATIQM
lOEimrr A^itcASLE TECHNOLOGICAL
COMTROU ANO ASSOCIATED COSTS.
WOtHKB
PREDICT AMSICMT CONCENTRATIONS
RESULTING FROM ALTERNATIVE
EFFLUENT LOADINGS.
WEIGHING ALTERNATIVES
CON90ER ItNEFITS Of f ARTICULAR USES ANO LEVELS
Of PROTECTION. CONSIDER COSTS FOR ATTAINMENT.
DECISION MAKING
DESIGNATE USE. LEVEL OP PROTECTION.
ANO ASSOCIATED CRITERION. ASSIGN THE
CORRESPONDING PERMIT LIMITATION.
4.1 WASTE LOAD ALLOCATION PROCESS
90
-------
An Important Issue confronting ULA analysts and managers concerns tht
amount of efFort needed to make a sound, scientifically credible
analysis. The appropriate level of effort depends partially on the
complexity of the environmental problem. Single discharger, single
toxicant, and relatively uncomplicated river problems can be expected to
require less analysis effort than multiple discharge, multiple toxicant.
and hydrologlcally complex problems. Nevertheless, the appropriate level
of effort depends on other factors-as well: such as the expectations of
the decision makers, affected dischargers, and other parties. These
expectations may be related to their, previous ULA experiences, to the
anticipated costs and the potential benefits, and to the resources
available. The appropriate level of effort depends heavily on the
consequences of a wrong decision.
Thus. 1t 1s not desirable for this document to attempt to specify
from afar a particular level of effort as appropriate to a particular
environmental problem. Rather, Section 2 has suggested a range of
analytical approaches.- Furthermore, the discussion tnat Follows suggests
a phased procedure for efficiently approaching whatever type of analysis
Is finally selected.
Phase 1; Dilution Calculation
A dilution formula calculation (Section 2,2} determines the
concentration at the point of discharge, before any fate processes can
act to remove or destroy the pollutant. The Inputs required are the
effluent flow and concentration and the upstream flow and concentration.
The effluent data should be available from the permit application.
Upstream flow may be available from U565 or previous pollutant reports
for the area, or they may be estimated from the drainage area. Upstream
concentration may be available from STQRET or other water quality
records; In many cases, the upstream toxicant load may be nearly zero
compared to. the effluent load. .
91
-------
Stream concentration? can be provided for flows and loads associated
with a particular design event or for any number of events having various
return frequencies (as described by DlToro 1982). This analysis for the
point of discharge, however, provides no Information on the downstream
concentration, the area-of Impact, or the fate of the pollutant. These
factors may need consideration primarily If (a) the load must be
allocated among two or more dischargers spread out along the reach (in
order to assess the degree of depuration occurring between dischargers).
or If (b) the environmental benefits must be assessed (since they depend
on the size of the Impact area}. Less probable reasons for pursuing fate
modeling are 1f (c) sensitive downstream reaches require special
protections, or (d) the pollutant produces hazardous degradation products.
For many permits, there may be little reason to proceed beyond tne
dilution calculation.
Phase 2: DownstreamEstimates
The purpose of Phase 2 would usually be to estimate .the saat'a!
extent of the problem or. for multiple discharges, to estimate the
addltlvlty of loads. Beyond Phase I. this entails predicting the
downstream behavior of pollutants using a fate and transport model (suc.i
as described In Sections 2.5 and 2.6). The model's Input parameter:
would be estimated from whatever data are already available on the
hydraulic and water quality characteristics of the reach, together with
published Information on the chemical characteristics of the pollutants.
The model could then be used to estimate concentrations throughout the
reach, for various control alternatives under various environmental
conditions.
Computer data bases, such as STORET. 1FO, Reach File. CHEM FATE.
ISHOW, and Individual state Information systems, allow rapid retrieval of
some types of Information needed to apply the model. Table 4.1
the contents of these data bases and how to obtain access.
92
-------
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This phase, relying on existing information, does not undertake the
collection of new field data. While published Information on the
chemical characteristics of many toxicants Is. .often reasonably sound, the
site-specific environmental data are often sparse. In particular,
bed/water exchange parameters, partition coefficients, some pollutant
degradation parameters, and even the channel depth and velocity may be
uncertain. Depending*on potential environmental benefits and treatment
costs estimated (using the model) to hinge on the WLA, It may be
desirable to Implement a monitoring program designed specifically to
calibrate and verify the model, thus proceeding to Phase 3. A
sensitivity analysis of model parameters can be used to identify the key
uncertainties.
Phase 3; Monitoring and Model Validation
• When modeling results Indicate that the WlA decision Is sensitive to
poorly defined or understood parameters, then more Intensive data
collection may be warranted. Unlike the Phase 2 gathering of existing
Information, the Phase 3 monitoring program would be designed and
Implemented for the specific purpose of relating the receiving water
response to the pollutant Input, through calibrating and verifying the
model.
Such monitoring of rivers Is most effective If performed as Intensive
surveys. Their success requires careful design and substantial
resources. Survey programs can vary greatly 1n magnitude, from single
•plug flow" surveys, such as Illustrated by the December 1981 Flint River
survey (described In Section 5} ranging to large regional programs, such
as the Delaware Estuary study (Thomann 1972).
The resources needed for a single Intensive survey a^e determined
largely by the size and complexity of the system. As the normal
variability of the environment and effluent can be considerable, a
fragmentary survey fay often produce data that a:* lT"os$lble to
95
-------
reconcile satisfactorily with modeling results. Consequently, If
undertaken at all. an Intensive, survey should be tailored to the needs of
the Model, and designed to be Insensitive to temporary aberrations in the
system, as will be discussed further,
Having demonstrated accord between the node! predictions and field .
observations for on* or two or more conditions, the Phase 3 model can be
used'to forecast entirely new conditions with somewhat greater confidence
than-the Phase 2 model.
4.2 DATA NEEDS
Site-specific calibration of a toxic substance model for a Phase 3
analysis requires (a) waste load and boundary condition data,
(b) environmental and chemical data for process rate estimation, and
(c) calibration and verification data. The amount of data needed to be
collected In time and space depends on the particular site, the
variability of the system, the accuracy desired, and the resources
available. The desire here 1s to suggest a realistic, achievable data
collection plan. .
The reader Is referred to Book II. Stream* and givers. Chapter 1;
Biochemical Oxygen Demand/Dissolved Oxygen and Aimronla TotUUx. Section
4, for a thorough discussion of general problem definition and data
requirements for stream models. The toxic substance problem should be
considered as a special case of stream modeling, building upon a
historical base of conventional monitoring and research.
In the following discussion. It 1s assumed that the WLA analyst has
defined the problem, reviewed historical data, made preliminary modeling
calculations, presented the Initial findings to management, and developed
a consensus to proceed with the collection of additional field data. It
is also assumed that the ULA analyst can direct or at least recommend a
monitoring plan and that he or she has visited the site and obtained a
•feel" for the situation.
96
-------
4.2.1 Obtaining »odel Input Data
TtbIt 4.2 suinnarUes typical data needs for setting up and
calibrating a toxicant model. Not all Items are applicable to all
pollutants. Generally, channel data are needed for all types of
pollutants; 1n addition, velocity and depth ordinarily have significant
flow dependencies which must be ascertained. Effluent and boundary
concentrations and flows are likewise needed for all pollutants.
Sediment related data (partition coefficients, settling and ^suspension
velocities, and bed characteristics) are needed for pollutants which
readily adsorb to partlculates. Degradation rate data are needed for
organic pollutants, depending on which processes (hydrolysis, photolysis,
etc.) are applicable to the particular compound. References like
Catlahan et al. (1978). Habcy et al. (1982), and the CHEM FATE data base
can be consulted to determine what processes are Important for particular
chemicals and to provide selected non-site-speclflc coefficients or
data. Once the Initial estimates are made, adjustments may be necessary
during model calibration.
Site-specific environmental parameters can be obtained or Inferred
from direct measurements over the appropriate time period. The time
frame selected would be determined by considering:
1. Residence time of the pollutant In the system.
2. Time variability of the system.
3. Time and frequency qualification to the water quality standard or
criteria.
4. The expected critical time period -
a. low /low with little dilution.
b. high flow, with nonpolnt loadings and sediment resuspenslon.
c. periods critical to fish survival.
S. Production and treatment schedules and cycles.
97
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Whenever possible, .point source surveys should be scheduled for
seasons when the sy?t«n is likely to be most stable, unless specifically
designed to evaluate tine variability.
4t2.2 Calibration and Verification: Comparing Prediction with Observation
Calibration refers to the procedure of adjusting the Input parameters
until the output predictions (e.g., dissolved and total toxicant profile
and suspended solids profile) reasonably match the observed
concentrations. In multi-parameter models such as described in Sections
?.S and 2.6, numerous different combinations of Input values may allow a
fit between predictions and observations. -Consequently, before
attempting to fit the data. It Is customary to fix the values of as many
parameters as possible, based on direct measurements. It may then be
feasible to adjust the values of a small number of parameters, within the
range of uncertainty for those parameters. In order to match the
observations. '
Verification generally, refers.to comparing predictions with
observations for a second Independent survey or time period. In
practical WLA contexts (In contrast to some academic or research
contexts), the distinction between calibration and verification may
become'hazy; the Initial calibration may be modified or compromised such
that the model can reasonably match both surveys. It 1s considered best
If.a single set of decay, partition,'and sediment exchange coefficients
fit both (or all) surveys adequately; however, 1t may be the case Chat
some.coefficients may need to vary between surveys, as Illustrated In the
Flint River study. If this Is the case, then it 1s essential that the
values vary In consistent, reasonable, readily justifiable ways.
Ideally, then, the WLA monitoring program would include at least two
Independent surveys. One survey might be more Intensive because of the
requirements for calibration. This survey may cover a longer time
period, perhaps *»v *al days. It.may Include some master station to
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discern diurnal variations, particularly for thost organic compounds
wnlch photolyze readily,-and for sites where waste flows comprise a large
fraction of the river flow. Station locations depend an the- sources.
tributaries, and stream characteristics. At a minimum, there should*be
one station,to define boundary concentrations upstream from the first
point source, one station just downstream of the mixing zone, and at
least one some distance (travel time) downstream,.reflecting the effect
of the loss processes. The final plan would reflect the complexity of
the system and the resources available.
.. • • ^ , -
A second survey night be less Intensive, covering a shorter time
period or perhaps employing a 'plug flow* or 'slug sampling" survey
Strategy. -This strategy. Illustrated In the December 1981 Flint River
survey. Involves sampling the point sources and river according to the
passage of a plug of flow marked by a dye tracer. Although this method
entails considerable coordination 1n the field, fewer samples are
required to be analyzed and, as a result, it Is less costly. This method
also has the advantage of filtering out many variations, which Is ideal
for steady state models. Resource estimates for survey options are
discussed further In Section 4.4.
Many WLA studies have not used two or more surveys for support.
Obtaining complete.and unambiguous data is more Important than performing
a particular number of surveys. Faced with a situation where resources
are sufficient for only a single good comprehensive survey, the analyst
may be better off with Implementing the one survey than with splitting
the resources between two abbreviated or fragmented surveys.
V
While the supporting site-specific data are a key element of any HLA
analysis, the ability of the model to curve fit a verification data set
is hardly the only measure of adequacy. Its consonance with aggregate
modeling experience, the overall reasonableness of Us Input v^ues. and
the general understanding demonstrated by the analyst are at \east as
.mportant.
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Hodyl Accuracy
The question will undoubtedly arise concerning the accuracy of the
model. Without any calibration or verification data, the question for
any site-specific situation may never be answered satisfactorily, with
or without water quality data, however, the appropriateness of the model
Input values (and possibly the model formulation) may always be
questioned.
/ < V
Some Indication of predictive reliability can be obtained by
sensitivity analysis: varying, one at a time, the key model parameters,
such as partition and decay coefficients, over a reasonable range. Such
an analysts shows the sensitivity of the results to errors m estimating
model parameters. For a more thorough evaluation, all key model
parameters can be varied at the same time using either of two
approaches: (a) Monte Carlo simulation and (b) first-order variance
propagation. Both techniques require specifying a probability
distribute of values for each Input parameter of the model. In the Monte
Carlo simulation, parameter values are selected randomly from the
specified distributions, and the model run over and over again, each time
with a different set of parameter values. The model output at each
station can then be described .by a frequency distribution. In
first-order variance propagation, the variance In the output distribution
-v
Is calculated directly from variances of the input distributions. Surges
and Lettenmaler (1975) Illustrate application of both techniques to
500-00 models; Scavla et al. (1981) Illustrate their application to
eutrophlcatlon models. Chapra and Reckhow (1983) provide a more detailed
description of these techniques.
For comparing model predictions with field observations, several
measures of model accuracy have been suggested by Thomann (1982). These
include regression analysis of observed and predicted values, relative
error, t-test comparison of means, and root mean square error. An
analysis of observed and predicted value: ror the calibration/
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verification runs of IS dissolved oxygen models indicated an overall
median relative error of 10%. Median relative error of Individual models
ranged fron a few percent to about 60 percent. For a eutrophlcatlon
model of Lake Ontario with complex.kinetics and fine spatial scales,
median relative error over a 10-year simulation period for S variables
was 22 to 32%. Relative error Is defined as
where c Is the relative error, x Is the average observed concentration
at each station, and c 1s the computed average concentration. This
statistic. It should be noted, behaves poorly for small i. and tends to
weight overpredlctlon more heavily than underpredlctlon.
Typical accuracies of toxicant model applications have not been
evaluated. Because the aggregate experience with toxicant modeling is
less extensive than with dissolved oxygen modeling, and because typical
levels of almost any toxicant vary over a far wider range than do levels
of dissolved oxygen, toxicant models may not always attain the accuracy
of dissolved oxygen applications. However, as the effect levels for
toxicants are so much more uncertain than effect levels for oxygen
depression, the need for very high accuracy seems less pressing.
Nevertheless, In the Hint River case study, the calibration/verification
accuracy seemed quite satisfactory by conventional wiA yardsticks.
Predictive accuracy of either conventional or toxic pollutant models
can be expected to be less for a new survey for which the model has not
been calibrated. This Is particularly true for an event with conditions
outside the range of those for which the model was calibrated. Thus.
predictive accuracy for conventional design events (extreme drought flows
coupled with hypothetical Improvements 1n effluent quality) may be
somewhat less than the calibration/verification accuracy. In particular,
It may oe difficult to estimate to what degree lower stream flow and
Improved effluent '..-a" ty will affect-parameters such as the settling and
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resuspenslon velocities (flow and particle-size dependent) or partition
coefficient (also part1cU-slze dependent). Such model adjustments must
be based on analyst Judgment.
In concluding this section it must be noted that an adequate
discussion of approaches for evaluating model accuracy and uncertainty 1s
beyond the scope of this volume (apparently along with the other volumes
of tnls Manual, thus far). Chapra and Reckhow (1983). however, provide a
more thorough treatment of the subject.
In actual UUk practice, the analysis of model uncertainty is seldom
quantitative. It Is most common to compare observation and prediction
graphically, declare the model 'validated,* and proceed to apply the
model for determining the allowable waste load. Although a sensitivity
analysis may be performed on some of the Input parameters, the results
are unlikely to Influence the decision-making process. Where the WLA is
being done within an adversarial context. It is perhaps understandable
that the analyst may not consider It helpful to spotlight the
uncertainties. However, If the model verification is not treated as a
pass/fall proposition, then quantitative estimates of model uncertainties
can be more readily Incorporated into the decision-making process. Once
a Monte Carlo or first-order variance analysis has been set up for tfie
model, pollution control alternatives can be evaluated in terms of their
probability of bringing about particular water quality outcomes. Section
4.3 further discusses the use of Monte Carlo simulation For this purpose.
4.2.3 Additional Data
The data presented 1n Table 4.2 are directly applicable to setting up
the model. Some additional parameter measurements may be useful for
Interpreting results and substantiating the actual existence and cause of
the reach's use Impairment.. Incremental costs of this work would be
small, since the major expense for the survey would be for the field crew
and the chemical analyses of toxicants. The additional measurements
could Include:
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a) Hardness and alkalinity: to Interpret toxldty and determine
metals criteria.
b) Conductivity: to confirm transport.
c) Total organic carbon.
d) Dissolved oxygen, ammonia, and chlorine residual: to Interpret
toxldty and blotlc status.
e) Qualitative description of sediment bed: to support estimates of
bed/water exchange.
f) Concentration of pollutant In biota: to Indicate long terra
exposure.
Furthermore. H Is preferable to coordinate the chemical sampling
with a biological survey. As the numerical criteria of water quality
standards are mostly derived from single-species laboratory tests, an
observation that a criterion H violated for a certain time period may
provide no Indication of how the Integrity of the ecosystem 1s being
affected, tn addition to demonstrating the Impairment of use, a
biological survey, coordinated with a chemlcai survey, can help in
Identifying the culprit pollutants and 1n substantiating the criteria
values. The resulting data base may also provide Information
transferable to other sites. For multi-faceted surveys. It may be
advantageous 'to try to coordinate efforts with universities, research
Institutions, or Industries, especially If they can contribute their own
resources.
4.2.4 Quality As stance
The WLA analyst should refer to Book II, Chapter 1, Section 4.3, for
a general discussion of quality assurance requirements for waste load
allocation studies. This discussion will focus on the unique
requirements for toxic substances.
During the development of the monitoring plan, the UlA analyst shoulj
•*eet with the laboratory director and quality i'ju^ance officer to
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request a Quality issuranct proposal. The proposal should consider
sample collection, handling, preservation, preparation, and analysis. Of
particular concern to the UU analyst would be the detection
(quantltatlon) limit for each toxicant.
Some production laboratories, although very reputable, nay not report
concentrations at levels at or below criteria limits.because doing so
requires additional care and quality control, reduces the productivity in
term of numbers of analyses performed and may require alternate
analytical methods. water quality managers need to recognize this
possibility and make special concessions for lower productivity during
UU comprehensive surveys.
Samples to be used for toxic substance analyses require special
collection and handling procedures unlike those for conventional
parameters. Depending on the specific chemical, precautions should be
taken ta prevent sample contamination from collection devices and
containers. This is not a trivial concern.
Samples that will be filtered for partlculate and dissolved fractions
should be delivered to the field laboratory for filtration within the
shortest period possible (one or two hours maximum For metals samples) or
filtered and preserved on site. For unstable chemicals, samples should
be preserved using prescribed methods.
Key to the entire effort Is proper sample logging, recording of
results. Input of Information Into a computerized data based such as
STORET, and verification and correction of data 1n the data base.*
4.3 FORECASTING
The purpose for developing a site-specific model ts to forecast the
environmental consequences of pollution abatement alternatives.
Environmental goals for a stream reach are. of course, embodied in the
'*
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quality needed to protect the designated uses my bt sped fled as
numerical criteria,, which Indicate acceptable chemical concentrations (if
known). Criteria are generally derived fron laboratory tests in which
particular species are exposed continuously to a toxicant. As the tested
concentrations do not vary over time. 1t 1s not obvious precisely how
they should be related to ambient concentrations, which often vary
considerably over time. It Is not clear how often the criteria can be
violated without Impairing the use.
In actual practice, lacking a firm technical basis for specifying a
target frequency of attainment, WLA analyses have often Incorporated the
convention of designing for the criteria to be met during the 7-day, once
In 10-year (7Q10) low flow. This assumes that upstream dilution has a
dominant Influence on water quality, a premise which Is correct for many
water courses and pollutants, but not true for all. Indeed, several
other time-variable parameters may also affect the modeling results; for
example, temperature affects most degradatlve processes, pH affects add
and basic hydrolysis, wind velocity affects volatilization in sluggish
waters, solar radiation and turbidity affect photolysis, and suspended
solids affect partitioning. In BOO and ammonia UUs, the other key
parameters, usually temperature, upstream concentrations, and pH, have
been specified by various procedures; depending on the procedure used,
the values may either frequently or seldomly be expected to accompany th«
7Q10 low flow.
In Judging pollution control alternatives within such a framework,
the measure of effectiveness generally applied Is the change 1n
concentration during the single rare tvent. Other measures are not
easily applied because the conventional procedure generally obscures both
the expected frequency of violation and the overall toxicant exposure
level, due to:
a.. The use of a single rare event.
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b. The nature of the extreme value statistics used to generate the
flow recurrence Intervals.
c. The lack of consideration for the probability distributions' of
other environmental Input parameters.
As a consequence, neither the analyst nor the decision-maker may realize
what level of protection the design condition Is providing. Indeed, they
may not even realize that 7Q10 design conditions provide different levels
of protection In different streams. For example. In a large river the
upstream dilution flow may be less than or equal to the 7Q10 only 1% of
the time, but In many small streams It may be at a zero flow 7Q10 for a
substantial percentage of the time.
An alternative framewo'rk for model forecasting has been proposed by
Freedman and Canale (1983). They suggest a conceptually simple Monte
Carlo technique which can account for both the time-variability and the •
uncertainty 1n alt model parameters: (a) environmental conditions, (b)
effluent quality, (c) rate coefficients, and (d) water quality criteria
values. By generating a probability distribution of water quality
outcomes for each pollution control alternative, the framework can
provide a more realistic comparison of their likely effectiveness.
The analyst begins by describing the probability distribution for
each of the key model Input parameters. Statistical evaluation of the
historical data can define the variability of parameters such as flow.
upstream concentrations, effluent loads, PH. and temperature (using
dally,-weekly, monthly, or any other averaging periods). Published data
and analyst judgment can suggest the uncertainty of parameters such as
decay and partition coefficients. The distributions can be defined 1n
terms of standard statistical functions such as normal, log normal,
gamma, or uniform distributions, or they can be numerically defined in
terms of the probability of exhibiting discrete values. Correlations
between parameters may need to be taken Into account.
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A Monte Carlo simulation can then be performed by randomly selecting
model Input values from the assigned distributions. By tallying the
water qua-Hty predictions resulting from each set of randomly selected
Inputs, the overall distribution .of resulting water quality 1$
generated. A simple Illustration of applying this procedure to a few of
the Input parameters for a model of a stream with two dischargers 1$
shown 1n Figure 4.2.
Some other methods can also provide probability distributions of
water quality, accounting for time variability but not necessarily
parameter uncertainty. A computationally simple technique has been
suggested by OIToro (1982). Using log normal distributions for flow,
loading; and other environmental parameters. It generates a log normal
distribution of concentration Immediately below the outfall; The method
was Intended for dilution calculations, not downstream fate predictions.
Perhaps the most straight-forward means of addressing time
variability Is to apply a continuous simulation model such as HSPF or
SEBATRA. A several year sequence of flow, temperature, loading, and
other Input Is used to generate a time sequence of water quality, whlcn
nay be summarized Into a frequency plot or possibly evaluated in other
more toz1co1og1cally relevant ways. While dally records for flow are
usually readily available, time sequences for other model inputs nay be
more difficult to construct.
Compared with the deterministic analysts of a single rare event,
probabilistic and continuous simulation techniques provide a broader
perspective over the entire water quality response. In comparing
different control options, the measure of effectiveness can be the
probability of exceeding the criteria, or It can even be the frequency
coupled with the severity of violation (as Illustrated by the shaded area
exceeding the criteria in Figure 4.2).
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In situations where rules require that the WLA be designed for a
particular flow, suet) as the 7Q1Q, the Monte Carlo technlaue can be
applied to all Incut parameters except flow. Control alternatives can
then be evaluated In terns of probable outcomes for that particular flow
event, In situations where the analyst wishes to construct a single
event corresponding to a particular recurrence Interval. Book VI of the
guidance Manual (USEPA 1984) describes a method for selecting flow,
temperature, and pH. The method does not consider all variable inputs
and may be restricted to single discharger situations.
With the 7Q10 (or similar) design convention, a level of protection
decision Is made automatically, grounded more on past precedent than on
technical rationale. Its level of protection, however, nay vary from
site to site somewhat haphazardly, unrelated to use attainment. If such
a conventional design condition 1s not used,' the level of protection may
.become a technical Question; that 1s, it must be determined what
frequency (or other measure) of standards attainment will protect
particular uses.
For protection of human health, the decision can often be based on
readily available Information. Many health criteria are based on long
tern (possibly life-time) average exposures, tf the long term mean
concentration were appropriate for the criterion, and If probabilistic or
continuous simulation approaches were not used, specifying a design
condition that produces the mean concentration is still not necessarily a
trivial task. For example. It 1s the harmonic (not arithmetic) mean flow
that produces the arithmetic mean concentration below a single discharger
(because concentration Is proportional to the Inverse of dilution flow).
For the protection of aquatic life the allowable exeeedance frequency
1s a particularly difficult technical question. As the criteria are .
based on laboratory tests with constant rather than time-variable
concentrations, and because mobility for many sp'-les 1s less constrained
112
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in. the field than in the laboratory, relatively lUtle technical data can
bt brought to bear,an the question. In the past the question of
exceedance frequency has probably not received the attention 1t
deserved. It should be recognized that the uncertainty in .the entire
waste Uad allocation analysis 1s a combination of the uncertainty In the
target concentration, the uncertainty in.the target attainment frequency.
and the uncertainty in the model predictions.
4.4 RESOURCE REQUIREMENTS
In this section, estimates are presented for conducting a water
quality analysis for a hypothetical river.. The estimates are based on
the experience of the EPA Large Lakes Research Station at Grosse He.
Michigan. In developing and applying a toxic substance model to heavy
metals In the flint River and PCS surveys and model development for
Saglnaw Bay. Lake .Huron.
The estimates apply to setting up a model comparable to MICHRIV,
using two Intensive surveys for calibration/verification. The following
presents the assumptions for which the costs were estimated:
1 Two ma)or discharges.
2. 50-m11e river reach.
3. Three metals and three organic compounds.
4. Sampling points at bridges.
S. Organic subttances readily photolyze according to literature.
6. All capital equipment such as laboratory, field, and computer
equipment is installed and available.
The estimates apply to an experienced ULA analyst, office support, and
the laboratory and field personnel. The estimates exclude standards
promulgation, permit negotiation, management, and o-erhead.
113
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The resource estimates art summarized In Tables 4.3 and 4.4. rt is
obvious that the most costly component U for the chemical analyses for
surveys, particularly the synoptic-type approach for Survey No. 1. These
costs could vary widely depending on unit costs, analytical procedures.
quality assurance, etc. The cost for organic analyses assumes that high
resolution capillary column GC's are used. Metals are assumed to be
analyzed using graphite furnace atomic adsorption.
There may be Instances where the system Is extremely complex, with
nonpolnt sources, complicated hydraulics, multiple and Intermittent
discharges, and multiple pollutants that would warrant surveys over a
year's time frame Including event sampling covering a range of
conditions. In these cases. If the costs of the surveys are compared to
the potential cost of remedial controls, they should be minor. In many
situations, the regulatory agency may suggest or require that the
permittee assist with the collection of the necessary data.
In summary, a waste load allocation project may vary from very simple
to very complex. The resources estimates presented herein consider a
typical problem setting. In the final analysis, the use of surveys and
models depends on the site and chemical-specific problem.
114
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SECTION 5.0
CASE STUDY: MODELING HEAVY METALS TRANSPORT
IN THE FLINT RIVER
S.I INTRODUCTION
Tlit Flint R1v*r project, discussed 1n this section, was undertaken as
a demonstration study for the development of procedures that can be used
In regulating point source discharges of priority pollutants. The
results of the one year study have served as a technical basis for the
preparation of this document. Specifically, the field data aided
considerably In the development of the NICHRIV model. This section
contains the results of application of the MICHRlV model to the Flint
River survey of zinc, cadmium, and copper. The emphasis will be on the
calibration and, to a certain extent, the field testing of the model wUh
the Flint data set. The project also serves as an example of data
acquisition methods for the application of Che model to a HLA problem.
Section 5.2 describes the study reach of the Flint River. Sections
5.3, 5.4, and S.5 describe the application of the model to the August,
1981, the December, 1981. and the March, 1982, survey data.
5.2 DESCRIPTION OF FLINT RIVER STUDY SITE
The Flint River, located In Southeastern Michigan, 1s a major
tributary to the Saglnaw River, a major tributary to Saglnaw Bay. The
Saglnaw watershed had been Identified as one of several national priorUy
sites. The Flint River Is also considered a high priority site for
development of toxicant WLA procedure? by the Michigan Department of
Natural Resouroes (MONR).
The Flint River watershed occupies 3",500 square kilometers (Figure
5.2.1) and contains significant agricultural and uiban development. The
north and south branches of the river join in Lapeer Counts nd flow In, ,_
119
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southwesterly direction to the City of Flint, within this reach are two
Impoundments. Holloway Reservoir and Mott Lake, which are used for
recreation and occasionally for low flow augmentation 1n the summer
months. Downstream from the City of Flint, the river flows northwest
before Joining the Shlawassee River in Saglnaw County. Municipalities
downstream of Flint include Flushing, Montrose, arid Fosters.
Because the purpose of this project was to study a river system in
enough detail to develop a metals transport model, and because there were
Insufficient resources to quantify,all sources to the river in the dty
of Flint, the reach selected for the model application was the 60
kilometers from N111 Road (Km 71.9) to Cresswell Road (Km 11.0). This
reach, shown In Figure 5.2.2, contains two major point discharges of
metals - Flint wastewater treatment plant (Km 70.7) and Genesee Co. No. 2
(Ragnone) wastewater treatment plant (Km 41.1). Several tributaries.
also monitored. Join the river along the study reach.
5.3 FLINT RIVER AUGUST SURVEY
The Flint River August Survey, conducted during August 4.14, 1981.
was intended to develop a quantitative cause-effect relationship between
metals loadings and resulting concentrations during summer, low flow
conditions. Thirteen river stations, four tributary streams, and Mv«
point source discharges were sampled during the two week survey. A lut
of the stations, their distance from the river mouth 1n kilometers and
the sampling schedule for each station are presented in Table 5.3.1. The
August survey 1s an example of a routine monitoring schedule. Most river
stations were sampled dally; however, four 'master* stations were sampled
at more frequent Intervals as a check on diurnal variations.
Temperature, dissolved oxygen, pH, alkalinity, and conductivity were
measured In the field. Samples were also filtered and preserved 1n the
121
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U4 'LINT RiVM ITUQV MUCK - H.IHT TO
122
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TABLE 5.3.1 SAMPLING STATIONS FOR AUGUST, 1981 FLINT RIVER
HEAVY HETALS SURVEY
Station/Description
Km. Point , Sampling Schedule
FROOQl/Utah Street river station
FEQOQ1/GH Plant discharge
FR0002/Ha«11 ton Street river station
FR0003/Grand Traverse Street river station
FR0004/Swart* Creek tributary
FRGQOS/OievroItt Street river station
FR0006/N111 Road river station
FEOQ02/F11nt WWTP discharge
FROQQ7 /Linden Road river station
FEOOAI/Fllnt fly. ash pond discharge
FEQOA2 /Flint My ash pond discharge
FROOQS/Elms Road river station
FROOQ9/Na1n Street river station
FROOIO/Ht. Norris Road river station
FROOU /Vienna Road river station
FR0012/Brent Run tributary
F£OOQ3/Ragnone wwTP -discharge
FRQ013/East Surt Road river station
FR0017/P1ne Run tributary
FROOU/SDver Creek tributary ,
FRQ01S/M-13 river station
FROOl6/Cres swell Road river station
83.6
93.4
81.9
79.3
79.1
77.9
71.9
70.7
70.5
70.0
70.0
66.3
61.2
52.6
43. 5
41.6
41.1
32.1
29.7
25. 2
14.9
11.0
Grab - every 2« ^ours
24 hour composite
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
24 hour composite
Grab - every 24 hours
Single grab
Single grab
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
24 hour composite
Grab * every 24 hours
Grab - every 24 hours
Grab • every 24 hours
Grab - every 24 hours
Grab - every 24 hours
123
-------
field. Hardness, suspended solids and total and flltrable zinc, cadmium
and cooper were analyzed at the Grosse lie Laboratory. Field sampling
and analytical work was the responsibility of Cranbrook Institute of
Science. The USGS, Lansing Office participated In the field work and
provided flow and t1me-of-travel Information (Cummlngs and Miller 1981).
5.3.1 August SurveyOata Summary
During the survey a precipitation event interrupted the steady-state
conditions that existed for the first four days, of the survey. The hydro-
graphs from the USSS gaging stations near Flint and Fosters (figure
5.3.1} Illustrate the event. The water .quality 1n the river responded
predictably to the event, as Illustrated by the hydrograph and various
tine profiles at Station FRQ8 (Figure 5.3.2). Suspended solids and
partlculate metals (as reflected In the total metals peaks with no change
In dissolved phase concentrations) peaked In response to the Flow event.
As discussed later this phenomenon represented resuspenslon of sediments
from the river bottom caused by higher shear stress. Also, dissolved .
constituents not particularly associated with sediment material were
diluted by the Increased flow. This process Is Illustrated by the
conductivity and hardness profile.
Although the event phenomena are quite Interesting, the model applied
/
is steady-state. Consequently, the modeling described here Is restricted
to the first four days of the August survey. Observations at each
station will be reported as four-day means plus or minus one standard
deviation.
The necessary Input data for the model Include basic hydrologlcal and
morphological Information on the river and loads of suspended solids and
total metals to the system. Table 5.3.2 is a summary of the flows and
river geometry froa the H111 Road station (FR06) to the Cresswell Road
station (FR16) for the four day steady-state, low-flow period ir, August.
These values have been established primarily from measu'tments made by
US6S (Cummlngs and Miller 1981) during the August survey.
124
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TABU 5.3.2 SEGMENTATION. FLOWS. AND GEOMETRY FOR FLINT RIVER
DURING AUGUST 4-7, 1981
Segment
No.
Boundary
• Km.
Point
Segment
How
(m3/s)
Cross-
Sectional
Area
Mean
Depth
(m)
1
2
3
4
s
6
7
a
9
mil Road 71.9
Flint WUTP 70.7
Flint fly*ash oonds 70.0
Downstream of Elms Road 65.0
Brent Run 41.6
Ragnone WWTP 41.1
Upstream of E. Burt Road 36.0
Pine Run 29.7
Silver Creek 25.2
Cresswell Road 11.0
2.66
4.34
4.38
4.38
4.53
5.22
5.22
5.28
5.36
14.5
20.5
17.8
15.7
22. B
24.5
19.5
17.S
17.8
0.45
Q.M
0.47
0.34
0.47
0.64
0.56
0.56
0.70
127
-------
For this modal application the river reach from Mill Road to
Cresswe 11 has been divided Into 9 segments. The segmentation was
primarily governed by location of point sources and tributaries, although
changes In river geometry at so contributed to segment boundary
selections. The segmentation Is also presented 1n Table 5.3.2. where
flows and geometry are given by segment.
The upstream boundary conditions and the effluent and tributary loads
for the steady-state period are presented 1n Table 5.3.3. The two
fc t
municipal plants represent the major source of metals to the river. Only
total metal loads art reported, because equilibrium partitioning with
solids 1s assumed. It should be noted, however, that 1n reality the
metals discharged from the Flint plant were primarily 1n the dissolved
phase while those from the Ragnone plant were primarily 1n the
participate phase. This Information will be discussed further In the
model calibration section.
5.3.2 August Survey Model Calibration
The model calibration was performed In two stages. First, the
suspended solids simulation was calibrated to the existing data. This
could be done independently of the metals calibration sine* the solids
submodel does not depend on metals Interactions or transport. The second
stage consisted of calibrating the three metal predictions without
altering the suspended solids calibration.
ilven the Input data presented In the previous section, the only
parameters at one's disposal for calibrating the suspended solids
submodel are suspended solids settling rate (w 1. solids resuspenslon
rate
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The typical river bottom Ml 11 have i water content between 60 and 95X
by weight; therefore. 1f the sol Ids have a specific gravity of 2.5, the
solids concentration 1n the bed win vary from approximately 50.000 -
500,000 mg/l of bulk sediment. Based on some bottom sampling conducted
during the August survey, a value of «2 • 200.000 rag/l was selected
for the river reach.
Since the August survey was during a relatively low flow period, the
first solids calibration attempt was made assuming the resuspenslon rate
(*rs) was equal to zero. Furthermore, since there was no reason to
suspect that the settling rate would vary along the river, a single value
of w$ was used In all segments. It Is possible that the solids
settling rate would be a function of flow In the river; however, the flow
differences along the river were not considered to be significant enough
to Justify segment-to-segment variation of w$. The calibration with
w$ . 0.25 m/d 1s shown In Figure 5.3.3a.
The calibration In Figure 5.3.3a Is quite good until just downstream
of the ftagnone treatment plant (about Km pnt. 35). From this point
downstream It seems that the model underpredlcts suspended solids. One
possible explanation Is that resuspenslon was occurring 1n the lower
portion of the river. By applying a very small entrapment rate of 4.0
g/m -d In segment 7-9 (Xm 36.0 11.0) on top of the settling rate of
0.25 m/d throughout the reach, the calibration shown In Figure 5.3.3b was
obtained. The above entralnment rate corresponds to a resuspenslon
velocity of 2.0 x 10"* m/day.
Justification for applying a resuspenslon factor 1n segment 7-9 comes
from a review of the experimental work of Lick (Lee et al. 1981; Fukuda
and Lick 1980) and from a comparison of calculated bottom shear stresses
arnon? segments of the river. Lee et al. (1981) 11st five factors on
which resuspenslon depends: (1) turbulent shear stress at the
sediment-water Interface; (2) water content of bottom sedl'—nts;
130
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(3) composition (mineralogy, organic content, sire distribution) of
sediments; (4) activity of benthlc organisms; and (S) vertical
distribution of sediment properties. I.e.. manner of deposition.
The effects of the first two factors are qualitatively understood.
Lee et al. (1981) found that for the western basin of Lake Erie, bottom
sediment resuspenslon rates were directly proportional to shear stress
and water content. Sediments with a fine-grained (clay size) fraction
deposited at the surface were more easily erodable than vertically
well-mixed sediments with the same composition. These considerations
suggest that resuspenslon 1n a particular river may be predicted from an
empirical relationship between entrapment rate,and shear stress.
In the case of the Hint River the best Justification for Increasing
the resuspenslon velocity below Km 36 comes from comparing the bottom
shear stress among various river segments, using Equation 3.6 (Graf
1971). For the August steady-state conditions, bottom shear stress
values for several segments of the flint River are presented in Table
5.3.4. Although the absolute values of shear stress are only estimates.
the relative differences should be valid because of the consistent method
of calculation. Note that the three downstream segments have greater
shear values than the four upstream segments. There 1s typically a
threshold value of shear for a given sediment condition above which
entrapment rate Increases rapidly. It Is possible that for the Flint
system the threshold value Is In the neighborhood of 10 dynes/era2.
It should be noted, nevertheless, that resuspenslon Is not the only
possible explanation for Increasing suspended solids profiles In rivers.
Growth of phytoplankton blomasss can also produce this phenomenon, with
each additional S »g/l chlorophyll-a equivalent to 1 rag/t suspended
solids (Canale 1983). Unlike resuspenslon, phytoplankton growth should
Increase suspended solids concentrations without Increasing total metals
concentrations, while both Interpretations seem compatable with the
132
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TABU 5.3.4. BOTTOM SHEAR STRESS IN SEGMENTS Of FLINT RIVER
DURING AUGUST 1961 STEADY.STATE PERIOD
Segment
1
3
4
6
7
8
9
Segment Boundaries
(Kn. Points)
71.9 - 70.7
70.0 .65.9
65.0 - 41.6
41.1 - 36.0
36.0 - 29.7
29.7 - 25.2
25.2 - 11.0
Shear Stress
(dynes/cms)
3.91
6.92
9.87
4.70
10.54
13.4
12.5
133
-------
August survey data, resuspenslon would be a more viable explanation
during the winter surveys (described later). For the Flint River the
model's overall results are not parted tarty sensitive to the question,
however.
Once the suspended solids submodel was calibrated, only the metal
partition coefficients were used to calibrate the metals predictions.
Sediment-water diffusion of dissolved metals was considered to be
Insignificant.
Calibration of the metals system began with observed partition
coefficients and adjusted these values within reason in order to match
total and dissolved metal profiles. There are so many Factors that can
affect metals partitioning that Insufficient Information Is available In
this case for a. priori establishment of partition coefficients. Plots of
the observed partition coefficients for the three metals In question
during the modeling period are presented in Figure 5.3.4. These
data indicate that the partition coefficient for zinc should fa'l
0.1 and 0.3 l/mg. There 1s a great deal of variation In observed
cadmium partitioning; this variation, between about 0.05 and 0.4$ l/mg.
Is probably due to dissolved cadmium values being near the detectable
limit. Finally, copper demonstrated the lowest partitioning with a range
of approximately 0.02 - 0.10 l/mg.
It 1s worthy of note that for all three metals the Linden Road
samples, which are from a site Just downstream from the Flint STP
discharge, tended to have lower partition coefficients than the
downstream sites. One possible explanation is that the Flint discharge
contained metals primarily 1n a dissolved (filterable) state and that an
equilibrium partitioning had not been attained In the first few
kilometers downstream. The metals In the Flint discharge averaged 91X,
84%. and 75% dissolved phase for zinc, cadmium, and copper,
respectively. As the MICHRIV model does not consider adsorption
134
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kinetics, the way to hand It this phenomenon was to lower the
partition coefficient for approximately four kilometers downstream of the
Flint discharge.
" The results of the model calibration for the three metals art
presented 1n Figures 5.3.5-5.3.10. Table 5.3.5 summarizes the partition
coefficients used for calibration. Recall that once the solids model had
been calibrated, metals partitioning was .the only remaining calibration
parameter for the metals. For all three metals the lower partition
coefficients In segments 2 and 3 downstream of the Flint discharge*are
necessary to simulate the higher proportion of dissolved metals in this
region. Also, an Increased partition coefficient for copper downstream
of the Ragnone discharge was employed In the calibration. This was
justified by the observed data (Figure 5.3.4) as well as the Fact that an
average of 83X of the Ragnone copper discharge was 1n a participate phase.
It Is encouraging to not* that the relative magnitude of calibration
partition coefficients among the three metals for the August 1981 Flint
study Is the same as was found In a Saglnaw Bay modeling effort (Dolan
and Blerman 1982). Even the absolute calibration values were quite
similar. The calibration values for Saglnaw Say were 0.225, Q.US, and
6.OS l/mg for zinc, cadmium, and copper, respectively. The Flint River
ultimately flows Into Saglnaw Bay via the Shlawassee and Saglnaw Rivers,
5.3.3 August Survey Sensitivity Analysis
As Indicated above, the main calibration coefficients for metals In
tiie Flint River are the suspended solids settling (w ) and resuspenston
(wp$) rates and the partition coefficients for the respective .metals.
A sensitivity analysis on these model parameters would be Instructive in
determining the accuracy necessary In defining these parameters for a
given model prediction accuracy. It would also confirm the need for the
respective terms In the model framework.
136
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TAILE 5 3.5. CALIBRATION VAUJES OF PARTITION COEFFICIENTS IN FLINT RIVER
DURING AUGUST 1981 STEADY.STATE PERIOD
Segment
1
I
3
4
5
6
7
8
9
Zinc
0.2$
0.10
0.10
0.25
0.2S
0.25
0.25
0.25
0.25
Metal Partition Coefficient ft/ma)
Cadmium
0.20
0.03
0.03
0.20
0.20
0.20
0.20
0.20
0.20
Copoer
O.OS
0.03
0.03
O.OS
0.05
0.09
0.0$
0.09
0.09
143
-------
Results of varying the above model coefficients have been evaluated
la term of percent change of total and dissolved metal concentrations
(and suspended solids when applicable) \n the river for a given percent
change of each parameter Individually. Percent changes of both model
coefficients and model output are related to the final calibration run
presented In the previous subsection. Figure 5.3.11 presents the
predicted response of suspended solids, and total and dissolved zinc at
Km 45 to changes In the solids settling rate (w ). (One hundred
precent on the x-axis represents the calibration value of w For the
August survey.) Suspended sol Ids 1s the most sensitive state variable;
with a value of w • 0 overpredlctlng the suspended solids
concentration by a factor of 2. No solids settling would lead to an
overpredlctlon of total zinc by SO percent; the extent of this variation
depends on the partition coefficient. Dissolved zinc (and other
dissolved metals) are relatively Insensitive to vertical solids flux
rates.
An example of the model response to the water column partition
coefficient Is presented 1n Figure 5.3.12. In this case the dissolved
zinc Is very sensitive to the choice of partition coefficient, with the
sensitivity among metals depending upon the relative value of the
calibration partition coefficient. Total metal levels are relatively
Insensitive to changes tn water column partitioning, unless « Is
drastically underestimated or omitted altogether.
Since steady-state concentration profiles are not constant in the
longitudinal direction, the percent change of model output depends on the
distance along the x-ax1s over which the coefficient perturbation 1s
applied. To demonstrate this concept, the sensitivity analysis results
for the August survey are given at four different locations along the
river: (1) kilometer point 65. 5 km downstream from the Flint discharge;
(2) kilometer point 45. 25 km downstream of Flint; (3) kilometer point
3d. about 5 km downstream from the Ragnon discharge; and (4) kilometer
point 10. about 30 kilometers downstream uf Ragnone. For the settling
144
-------
Model Response to Change in Ws
1 I
8/4-7/81 at Km. 45
0 50 100 150 200
Parameter (%of Calibr.)
FIGURE S.3. 11
KS
U S EPA Headquarters Library
' Mail Code 3404T
1200 Pennsylvania Avenue^ NW
Washington DC 20460
202-566-0556
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Model Response to Change rn TT1
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50
100
150 200
Parameter (%of Calibr.)
FIGURE 5.3.12
146
-------
velocity and partition coefficient, the results art presented in Tables
5.3.6 and 5.3.7.
5.4 FLINT RIVER DECEMBER SURVEY
Another survey, conducted during the period December 1.4, 1981,
studied metal profiles In the Mver during a relatively high-flow
period. It was also felt that calibration of models for toxic substances
In rivers under different flow regimes was an essential step in
developing a model that could be applied to WlA problems with confidence.
Another benefit derived from the December survey was the
demonstration of data collection' for a steady-state system via the slug
sampling method. In this sampling method a finite slug of river water is
sampled periodically as It moves downstream. Any tributaries or point
sources contributing materials to the slug are also sampled as the slug
passes these points. This approach can provide an efficient (In terms of
number of samples required) way to obtain a steady-state longitudinal
profile of tne river by eliminating much of the confounding influence of
diurnal loading variations. Conducting the December survey in this •.
manner provided motel calibration data In a shorter period of time ar.d
with many fewer samples than the August data.
The parameters measured In the December survey were the same as those
In the August survey, with the exception that dissolved oxygen analysis
was omitted. The USGS once again participated In the field'work; This
time, 1ft addition to providing discharge measurements, they conducted the
dye dump and monitoring so as to coincide with the water quality
sampling. By following the dye slug downstream, the sampling crew was
assured of collecting water from the same slug as It passed the various
sampling locations along the study reach. A list of the sampling
Stations for the December survey Is presented In Table 5.4.1.
147
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S.4.1 December Survey Data Summary
The December survey actually consisted of two distinct slug
monitoring runs down the river. On December 1, 1981, at 7:00 a.m. the
dye was dumped at Grand Traverse Street, a point 7.4 kilometers upstream
of the Initial water Quality 'sampling station (Mill Road). This
permitted the dye slug to adequately mix over the river cross-section by
the time It reached Hill Road. At Mill Road and at all subsequent river
stations and point source locations, estimates were made (based on
average river velocity estimates) of the time of travel between sampMng
points along the river. These estimates were confirmed by following the
dye slug along the river and sampling on-s1te at each location via
Huorometric analysis when the leading edge and peak of the dye slug was
passing. Three water quality samples were collected at each location,
separated in time by about 1/2 hour, as the dye was passing. An attempt
was made, :m most cases successfully, to obtain one water quality sample
prior to passage of the peak of dye, one at the peak, and one after
passage of the peak. In this way a good representation of the water
• *£
quality 1n the dye slug could be obtained.
Hydrographs of the Flint River at the M-57 (Vienna Road) and M-13
sampling locations during the week of the December survey are presented
In Figure 5.4.1. These hydrographs Indicate two major things. First,
the discharge of the river during the December survey was an order of
magnitude larger than the August low-flow survey. Second, the
hydrographs are reasonably flat. Indicating that the river flow was dose
to steady-state during the survey. There was a small peak 1n each
hydrograph due to a brief rainfall late Tuesday afternoon; however, this
event occurred between two sampling runs, as Indicated In the figure.
A record of the dye slug tlme-of-travel and sampling times for both
sampling runs has been reconstructed In Table 5.4.2. A very fortunate
oceurrp«ce Is evident from this table. The river flow conditions were
151
-------
5.4.1 December Survey Data Summary
The December survey actually consisted of two distinct slug
monitoring runs down the river. On December 1. 1981. at 7:00 a.m. the
dye was dumped at Grand Traverse Street, a point 7.4 kilometers upstream
of the Initial water quality sampling station (Mill Road). This
permitted the dye slug to adequately mix over the river cross-section by
the time It reached Mill Road. At Mill Road and at all subsequent river
stations and point source locations, estimates were made (based on
average river velocity estimates) of the time of travel between sampling
points along the river. These estimates were confirmed by following the
dye slug along the river and sampling on.site at each location via
fluorometrlc analysis when the leading edge and peak of the dye slug was
passing. Three water quality samples were collected at each location.
separated 1n time by about 1/2 hour, as the dye was passing. An attempt
was made, in most cases successfully, to obtain one water quality sample
prior to passage of the peak of dye. one at the peak, and one after
passage of the peak. In this way a good representation of the water
quality In the dye slug could be obtained.
Hydrographs of the Flint River at the M-57 (Vienna Road) and N-13
sampling locations during the week of the December survey are presented
In Figure 5.4.1. These hydrographs Indicate two major things, first.
the discharge of the river during the December survey was an order of
magnitude larger than the August low-flow survey. Second, the
hydrographs are reasonably flat. Indicating that the river flow was close
to steady-state during the survey. There was a small peak in each
hydrograph due to a brief rainfall late Tuesday afternoon: however, this
event occurred between two sampling runt', as Indicated in the figure.
A record of the dye slug t1me-of-travel and sampling times for both
sampling runs has been reconstructed 1h Table 5.4,2. A very fortunate
oecurr^ce 1$ evident from this table. The river flow conditions were
151
-------
TABU 5.4.2..
COMPARISON OF DYE CLOUDS TIME-OF-TRAVEL WITH SAMPLING SCHEDULE
FOR DECEMBER. 1981 SURVEY OF FLINT RIVER
Site to. Point
till Road
' 300061
• int WWTP
fFEQ002^
'.'idtn Road
' 30007}
Eiras Road
TROQ08)
< \n Street
'.20009}
4t. Morris
» id fFRQOIOI
i »nna Road/-
«<-57 (FR0011)
Vjnone WWTP
-.00031
_ake Road
•c»noi8
Run
,,aa (FR00191
-<-l3.
00151
71.9
70.7
70.5
66.3
61.2
52.6
43.5
41.1
38.3
30. 5
14.9
Arrival of
Peak Dye
Date Concentration*
12/1/81
12/3/81
12/1/81
12/3 /fll
12/1/81
12/1/81
12/1/81
12/3/fll
12/1/81
12/3/81
12/1/81
12/3/81
12/1/81 .
12/3/81
12/1/81
12/3/81
12/1-2/81
12/4/81
12/2/81
12/4/81
12/2/81
12/4/81
1005
1010
1030
1035
1045
1050
1220
1220
142Q
1430
1745
1800
2120
2150
2200
2220
2345
0015
0330
03SO
1020
1130
Sampling Times
0945;1005;1030
0950:1010:1030
1005; 1035; 1105
1005:1025:1045
101S;104S;1U5
1020:1040:1100
1200:1230:1250
1200:1230:1300
1400;H25:1455
1400:1430:1500
1700:1730;1600
1730:1800:1830
2020:2050:2145
2115:2145:2215
2120:2220:2250
2215:2245:2315
2345:0020:0045
0000:0030:0100
0300:0330:0400
0315:0400:0445
0930:1000:1030
1015:1100:1145
Tlme-of-Tra
of Peak (hr
3.1
3.2
3.5
3.6
3.75
3.8
5.3
5.3
7.3
7.5
10.75
11.0
14.3
14.8
15.0
15.3
16.75
17.25
20.5
20.8
27.3
28.5
Dye dumped at Grand Traverse Street (K«. Pt. 79.3) at 7:00 a.m. on 12/1/81 and 12/3/81.
153
-------
vtry similar during the two sampling runs, effectively providing a
replicate experiment that permitted a certain degree of field testing of
the,model. The table Indicates the success attained in sampling river
stations near the peak of the dye slug. The t'1me-of-trave1 over the
study reach from Mill Road to H-13 was 24.25 hours for run 1 and 25.33
hours for run 2. These travel times corresponded to average velocities
through the study reach of 2.35 tcm/hr (0.653 m/s) and 2.25 kra/hr (0.625
n/s) for runs 1 and 2, respectively.
Based on discharge measurements, t1me-of-travel data, and cross-
sectional area, data provided by USGS. the river reach from Mill Road to
Cresswell Road was segmented and the hydrologlcal and morphological Input
data were compiled by segment. This Information is presented in Table
5.4.3. The same nine segments used In the August model application were
sufficient for the December survey; however, a flow balance (based on .
available flow and gaging station measurements) showed that there were
tributary or groundwater sources of water to some segments for which no
accounting-was available. The segments of concern are shown In Table
5.4.3 with two entries under the 'segment flow" column; the first entry
1s the flow at the upstream boundary, and the second entry 1s the flow at
the downstream boundary of the segment. The model was set up to handle
this situation by distributing the flow Increment of any given segment
uniformly along the length of the segment.
The Initial conditions at Mill Road and the point source loads at the
time the dye cloud passed each point are presented in Tables 5.4.4 and
5.4.5 for runs 1 and 2. respectively. Once again the two municipal
plants represented the major source of metals to the river. Both plants
had higher discharge flows In December than In August, with
correspondingly higher metals loads. It Is worthy of note at this time
that the suspended solids and metals loads from the Ragnone plant were
almost an order of magnitude greater during run 2 than during run 1.
This occurrence provided an excellent opportunity to determine how the
154
-------
TABLE 5.4.3. SESMENTATION. PLOWS. AND GEOMETRY FOR FLINT RIVER
DURING DECEMBER 1981 SURVEY
Segment
Number
•Boundary
Ka.
Point
Segment Flow Cross -Sectional Mean Oep*
(m»/s) Area (m*) (a)
Run l' - 12/1-2/81
1
2
3
4
5
6
7
a
9
H111 Road
flint UUTP
Flint Fly Ash Ponds
Downstream of Elms Road
Brent Run
Ragnone WUTP
Upstream of East Burt Rd.
Pine Run
Silver Creek
Cres swell Road
71.9
70.7
70.0
65. 0
41.6
41.1
36.0
29.7
25.2
11.0
26.3
28. 5
26.6-29.3
29.3-32.3
32.9
34.0-34.9
34.9-36.1
36. a
37.4
39.5
44.0
41.3
46.3
47.4
62.8
44.6
59.5
60.9
1.2
1.2
1.0
o.as
1.0
1.2
1.2
-
1.2
1.3
155
-------
TABLE 5.4.3. (Cont'd.)
Segment
Number
Boundary
-------
1
at
M
^
^
0
M4
u
OE — •
a «
X eJ
5-
si
< X
o a
mm
3 X
o ac
14
< to.
IM
at
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*
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t*l
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t_
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8
hta
JC
u
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^•F GP G» <• 3*
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model would perform under similar river Flow conditions with drastically
different loads - an exercise often required in performing waste load
allocations, finally. It sftould be noted that once again the metals
discharged from the Flint outfalls were primarily dissolved/while those
from the Ragnone discharge were largely participate.
S.4.? Dec ember Survey Model Calibration
. - > . i
The procedure taken In calibrating the model to,the December data was
to first calibrate the model using data from run 1 only; then the
calibrated model was applied to run 2 data as a field test of the model
performance under similar Flow conditions with very different loadings.
As with the August survey, calibration of the suspended solids transport
system was performed First by adjustment of w$ and wr?; this step was
followed by calibration of the metals-system us.lng the respective
partition coefficients. Degradation and sediment-water diffusion of .
dissolved metals have again been considered Insignificant. .
In calibrating the suspended solids system .one should not expect the
sediment transport regime to be the same in December as It was In
August. In the higher flow regime of the December survey, one might
expect the river to.have the capacity to carry larger participate matter.
which would have a larger intrinsic settling velocity (per Stokes
formula). On ,the other hand, higher flows lead to higher stream
velocities and depths, and thus result 1n greater bottom shear stress.
Assuming that the other factors governing entrapment are the same,
the December solids resuspenslon velocities (« ) should also be
greater than those determined In August. Depending, of course, on the
magnitude of change in w and w . it Is possible that the net flux
of solids between bottom sediments and overlying water many not be
significantly different From the August results. It Is likely therefore,
that because of the characteristically shorter detention time 1n the
hlghtr fl v river system, the longitudinal distribution of suspended
159
-------
solids In December will not exhibit as great a variation as was observed
1n .August.
The hypotheses presented 1n the above paragraph were largely
confirmed by the calibration of the model to the December run 1 data.
Calibration values for w and w for each of the nine segments are
presented in Table 5.4.6. Illustration of the suspected relative
flatness of the solids longitudinal distribution and the comparison of
model simulation with field data for run 1 are presented In Figure 5.4.2.
Once again the calibration was made without varying the sett!ing velo-
city, w$. among segments. Because of the greater uncertainty In ascer-
taining the factors governing sediment erosion, 1t was felt that1 there
would more likely be Intersegment variability In w than In w . A
settling rate of 0.6 m/d (as opposed to 0.25 m/d In August) does not seem
unusually nigh for a river flowing at about Five times the discharge
rate. Assuming the river suspended solids had a specific gravity of 2.5.
the effective Stokes diameter for 0.6 m/d settling velocity, would be 3.0
urn compared with 1.8 urn for a settling velocity rate of 0.2S m/d.
The bottom shear stress in the various river segments calculated in
the same manner as 1n the August survey ranged from 25 to 54
dynes/ca. Once again the lower segments (7-9) had slightly higher
values than the upper reaches. All these shear stress values are
considerably higher than the 4.13 dynes/en 'ange calculated for the
August flow conditions. In fact, both stream velocities and shear
stresses In December are roughly three times the August values. It seems
logical, therefore, that the calibrated resuspenslon values for December
are greater In each segment (see Table 5.4.6) and greatest 1n the
downstream segments again. There are other possible reasons for greater
downstream erosion rates, related to some of the other governing factors
mentioned by Lee f£ aj.. (1981). The downstream segments of the Flint
River pass through an almost *«lu$1ve1y agricultural area, perhaps
160
-------
TABU 5.4.6. CALIBRATION COEFFICIENT FOR SOLIDS TRANSPORT SYSTEM
USING FLINT RIVER, 0£CE*6£R 1-2, 1981 (RUN 1) DATA
Segment
1
2
3
4
5
6
7
1
9
Settling Velocity
(ra/d)
0.6
0.6
0.6
0.6.
0.6
0.6
0.6
0.6
0.6
Resujoenston Velocity
(m/d)
0.2xlQ-4
O.ZxlO-4
0.4xlO-4
0.4xlO-4
0.4x10-4
0.2xlO-4
\.2x10-4
l.OxlO-4
l.Oxlfl-4
-------
t I > I 1
i a / i
- s
III T
5s * t i i
as-"-
-------
resulting 1n different bottom sediment characteristics. Furthermore, the
downstream segments tend to have steeper, more loosely packed banks. It
1s also possible that the deposition*! pattern downstream of the Ragnone
treatment plant, which tends to discharge high solids concentrations.
might Favor high erosion rates. Temporal variations in recent
deposition*! history for any river reach may lead to variability In
bottom sediment resuspenslon rates for a given flow regime.
Once the solids transport submodel has been calibrated, calibration
for the metals Is performed by adjustment of partition coefficients. The
final calibration values for the three metals in each segment are
presented In Table 5.4.7. A comparison of the model simulations using
these coefficients with the run 1 field data 1s presented In Figures
S.4.3-S.4.7. The partition coefficients used to generate these
simulations are very similar to those obtained 1n the August
calibration. Where they do differ, such as for zinc and for copper
downstream.of the Ragnone discharge, they tend to be slightly lower for
the higher flow case. This result might be expected, since the solids
being transported In December are probably slightly larger, thus navlng a
smaller surface area to mass ratio.
Once again the high fraction of dissolved.solids in the Flint dis-
charge forced a calibration with lower partition coefficients for all
three metals in segments 2 and 3. Also, the copper In the high
partlculate metals load from Ragnone seemed to remain in a partlculate
phase through the end of the study reach. These necessary adjustments
froa segment to segment reflect a need to characterize the partitioning
of metals 1n the effluent stream as well as 1n the Mver.
As Indicated earlier, the second plug flow survey provided an
excellent opportunity to field test the model for Its ability to simulate
variation In river-solids and metals levels for different loading
conditions under the san.* flow regimes. This test was performed oy
163
-------
TABU 5.1.7. CALIBRATION VALUES OF PARTITION COEFFICIENTS
IN fllNT RIVER DURING DECEMBER T-2. .1981 SURVEY
Segment
1
2
3
4
5
6
7
a
9
Zinc
0.20
0.08
0.08
0.20
0.20
0.20
v 0.20
0.20
0.20
Ketal Partition Coefficient (l/mc5
.Cadmium
0.20
0.05
O.OS
0.20
0.20
0.20
0.20
0.20
0.20
Copper
0.05
0.03
0.03
0.05
0.05
- 0.07
0.07
0.07
0.07
-------
s I
- a
I2St*StSlS13
S85-**SS2-»-
-------
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J
i
I
c
f
9
i
i
r
(I •
' o
T
r
I
i
a
s
2 1
t» •
amznio
-s
— • a
to
\
*
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s
S
I I I
! s ;
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I
*
I
8
i
96
00
1
•A
(I/*") HfllNOVS 1UV4
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the model to the December 3-4, 1981 (run 2) loading oata (Table
5.4.5) and hydrologlc data (Table 5.4.3} without adjusting the
calibration coefficients (wj( wrj, «zn> *cfl, ,^) obtained using
run 1 data. The results of this model run are presented in Figures 5.4.8
- 5.4.12. The main difference In the two data sets, of course, was the
large Increase in solids and participate metals discharge from the
Ragnone WWTP when the second dye cloud (run 2} was passing. Based on the
comparison of model predictions with field data, the model performed
quite well, without a need for recallbratlon. The only significant
falling of the model was the over-prediction of dissolved copper
downstream from the Ragnone discharge. Obviously, with participate
copper from the Ragnone discharge comprising over half of the total load
of copper In segments 6-9, a higher partition coefficient would have
helped to simulate the dissolved copper profile. In fact. If one were
actually calibrating the model to the run 2 data, the only change from
the run 1 calibration would be to raise the copper partition coefficients
1n segment 6-9 from 0.07 1/mg to 0.12 t/mg.
5.5 FLINT RIVER MARCH 1982 SURVEY
The primary goal of the Mint River March Survey (March 23-26, 1982)
was to evaluate the Status of metals and solids transport in the river
during a period that would likely represent the highest discharge flow in
an annual cycle. It was fortunate that the survey was scheduled about
two weeks after a major snowmelt period In southeastern Michigan. At the
time of the survey the study reach was discharging water which had
previously collected In upstream reservoirs during the snowmelt. The
flow In the study reach during the survey was about 4000 cfs (113
• /s). roughly 4 tints the December flow and 20 times the August flow.
Studying the river under this range of flow conditions provided a good
Idea of the range that Is likely to exist 1n the values of those
•V.
parameters, such as sediment settling and resuspenslon velocities and
partition coefficients, that app -a- *•> be flow dependv.it.
170
-------
I I
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1
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t' >*
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t I I I t
amz uiv4
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I I I I ~ .
I « 1 • 1
S S, ". .*
((/•'H 9HIZSBO
-------
I I I
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31
3
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s
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• •« t«»
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((/•"I
-------
TABLE 5.5.1. SAMPLING STATION FOR MARCH 1981 FLINT RIVER
HEAVY METALS SURVEY
Station/Description Km. Point
FR0006/wm Road river station
FE0002/F1lnt wwTP discharge
FEOOAl/FUnt Fly Ash Pond discharge
F£OOA2/F1int Fly Ash Pond discharge
FROOOa/Elns. Road river station
FR0022/Mud Creek tributary
FRQ023/Co1e Creek tributary
FROQIQ/Mt. Morris Road river station
FR0024/Brent Creek tributary
FR0025/Armstrong Creek tributary
FRQOIl/Vlenna Road (N-57) river station
FR0012/Brent Run tributary
FE0003/Ragnone WWTP discharge
FR0020/B1rch Run Road river station
FRQ017/P1nc Run tributary
FROOU/snver Creek tributary
FROQ1S/N-13 river station
71.9
70.7
70.0
70.0
66.3
64.4
60.2
52.6
50.6
44.6
43.5
41.6
41.1
30.5
29.7
25.2
14.9
176
.
-------
i
g 100.000
5
11 a
40&OOQ I
J2 200.000
u
O
o
«. a.
o
§"
J I
HINT
1 I
Mjnw I I
3 tBJMO
S
I
i. a.
s
S S B
11. n. 10
RIVtH KM MIT
JM
8
4f
|
j/s-am
i
o
I i °
* « °
h Si
a .51
i H
SI. 71. 1Q
NfVf R KM MIT
s
s |
a •
I
s
i
5
s
S S S
« o 5
! ii
-------
TABU 5.5.2. HYOROL06IC AND HORPHOMETRIC INPUTS FOR FLINT RIVER
MARCH 1982 HEAVY METAL MODELING APPLICATION
Numter
Boundary
K«. Segment Floy
Point («»/s)
Cross-Sectional
Area (m»)
Mean Oep
(«)
1
2
3
4
5
6
7
8
9
H111 Road 71.9
Flint WHIP 70.7
flint Fly Asn Discharge 70.0
Cole Creek • 60.0
Armstrong Creek 44.6
Ragnone UUTP 41.1
Upstream of Cast Burt Rd. 36.0
Pine Run 29.7
Silver Creek 25.2
PM3 14.9
93.4
96.1
96.25-99.9
100.7-106.4
107.7
110.3-111.7
111.7-113.5
115.5
117.5
97.5
95.0
90.0
110.
96.0
109.
65.0
US.
120.
1.8
1.8
1.5
1.5
1.6
1.6
1.8
2.2
2.2
-------
o ^
**
§s
e a
§
-------
Because of the expected short travel time For the study reach in
March - it turned out to be about 16 hours - and because of the high
possibility of encountering rapid tine variations during the monitoring.
the survey strategy was to sample fewer river stations more frequently.
Also, more tributaries were added to the 11st In case surface runoff in
the study reach was a significant source of solids and associated
metals. All river stations were sampled every 4 hours, tributaries were
sampled every 12 hours, and 4-hour composites were collected from point
source effluents during the 72-hour survey. A list of the sampling
stations for the March survey 1s presented in Table S.S.I.
S.S.I March Survev Data Summary
During the 72-hour March survey the Flint River flow was receding
somewhat from the flood stage recorded during the rapid snowmelt. for
example, the discharge at the farthest downstream station (fl-13) dropped
from approximately 4500 cfs to 3800 Cfs during the survey. This rougnly
IS percent flow decrease, however, did not appear to negate the
possibility of applying the'steady-state model to the data, as evidenced
by the relatively narrow spread of conductivity and suspended solids
value* at each river station, as Illustrated In Figure 5.5.1-. Saved on
the hydrologlcal and morphological data collected during the survey,
therefore, the segmentation, river flow and geometry used as input for
the steady-state model are given In Table 5.5.2.
Initial conditions and loads obtained for the March survey are
presented In Table 5.5.3. It Is apparent that flow and loads at the
upstream boundary exceed the flow and loads of all point sources and
tributaries combined. .
5.S.2 March Survey Model Calibration 1
The results of calibration of the model to the M*rch survey data
are presented In Figures 5.5.2 - 5.S.6. The calibration was done using
180
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larger settling and resuspenslon velocities than used 1n previous
surveys. The more significant Increase, however, was In the resuspenslon
velocity, resulting 1n a net Flux of solids from sediment to overlying
water throughout the study reach.
The calibration coefficients for the solids transport submodel are
given In Table 5.5.4. A large gross settling velocity would be expected
1n a system transporting larger particles. The Increase in resuspenslon
rates in all segments Is Justified by the large Increase in calculated
bottom shear stress over the December values, as shown in Table 5.5.4.
In general, there Is a reasonably good correlation between bottom shear
stress and calibrated resuspenslon rate for a given river segment.
Although only three data points were available (one for each survey).
they did seem to follow a linear trend, as shown for three sample
segments shown 1n Figure 5.5.7.
Calibration of the metals data by adjusting water column partition
coefficients also produced reasonably predictable results, with
presumably larger solids being transported with much of the material
originating from the river bottom, one might expect to see slightly lower
water column partition coefficients than were observed at lower flow
conditions. This hypothesis was confirmed by comparing the final
calibration partition coefficients to average observed values for each
metal at each river station during the survey (Figure 5.5.8). The
calibration values tend to be slightly lower then the measured values;
however, this, observation Is often made In natural systems since measured
values of total partlculate metals tend to Include a certain portion that
does not readily exchange with the bulk solution.
1B6
-------
TABU 5.5.4. CALIBRATION COEFFICIENTS FOR SOLIDS TRANSPORT
DURING MARCH 1981 FLINT fllVCR SURVEY
Segment
1
2
3
4
S
6
7
a
9
Settling velocity
(m/d)
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
Resuspenslon Velocity
(m/dk.
1.0x10-*
1.0x10-*
2.0x10-*
2.0x10-*
2.0x10-*
2.0x10-*
4.0*10-*
3.0x10-*
3.0x10-*
Bottom Shear Stress
(dynes/cm )
92.5
92.5
127.
94.3
108.
108.
176.
115.
115.
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FIGURE 5.5.3
189
-------
6.0 REFERENCES (for Sections. 1-5 and Appendices A-0)
Alonso, C.V. 1981. Stochastic model? of suspended sediment dispersion.
Jourr». Hydraulics 01v., ASCE. 107(HY6): 733-757.
Antsyferov, S.M. and R.O. Kos'yan. 1980. Sediments suspended in stream
flow. Journ. Hydraulics 01v.. ASCE. 106(HY2): 313-330.
ASCE. 1975. Sedimentation Engineering. V.A. vanonl (ed.}. Amer. Soc.
Civil Engrs., N.Y.
Banerjee. S.. S.H. Yalkowsky, and S.C. Valvanl. 1980. water soluoility
and octanol/water partition coefficients of organlcs. Limitations of the
solubility-partition coefficient correlation. Environ. Sci. Techno 1..
14(1(1}: 1227-1229.
Bender, N.C., H.H. Roberts. R. Olaz and R.J. Huggett. 1975. Proceedings
technology and ecological effects of blofoullng. Maryland Power Plant
Siting Program. Baltimore. Maryland.
Benjamin. M.M., and J.O: leckle. 1981. Conceptual model for
metal-Mgand-surface Interactions during adsorption. Environ. Sci.
Technol.. 15(9): 1050-1057.
Benjamin. H.M.. and J.O. Leckle. 1980. Adsorption of metals at oxide
Interfaces: effects of the concentrations of adsorbate and competing
metals. In: Contaminants and Sediments, Vol. 2. R.A. Baker (ed.). Ann
Arbor Science. Inc. pp. 305-322.
Benjamin, M.M.. and J.O. leckle. 1982. Effects of complexatlon by
CljSO^ S^On on adsorption behavior of Cd on oxide surfaces.
Environ. Set. Technol., 16: 162-170.
Berner, R.A. 1980. Early Olagenesls. Princeton Univ. Press.
Princeton, NJ.
Blonqulst, S.. and L. Hakanson. 1981. A review on sediment traps In
aquatic environments. Arch. HydrobloK. 91(1): 101-132.
Bonazountas. H. and S. Hathlas. 1984. SEOIM - a model for estimating
sedimentation Input parameters of selected stream quality models. U.S.
EPA. Office of Water Regulations and Standards, Washington, O.C.
Contract No. 68-01-6160.
Brown. O.H. 1979. Adsorption of lead from solution on the quartz- and
feldspar-containing »11t fraction of a natural str ambed sediment. In:
Chemical Modeling V- .kqu.ous Systems. ACS Symposium Series 93. E.A.
Jenne (ed.). pp. 238-260.
190
-------
Surges, S.J., and O.P. Lettennialer. 1975. Probabilistic methods 1n
stream Quality management. Water Resources Bull. 11: 115-130.
Burns. 1.A.. D.H. CUne, and R.R. LassUer. 1981. Exposure analysis
model ing system (EXAMS): User manual and system documentation. Draft.
U.S. EPA, Environmental Research Laboratory. Athens. Georgia.
Callahan. N.A.. N.w. Slimaic, N.W. Gabel, I.P. Nay. C.F. fowler. J.R.
Freed, P. Jennings. R.L. Durfee, F.C. Whltmore, 9. Maestri, U.K. Nabey.
8.R. Holt, and C. Gould. 1979. water related fate of 129 priority
pollutants. U.S. EPA, Office of Water Planning and Standards.
Washington. O.C. EPA-440/4-79-029.
Canale, R.P., and W.J. Weber, Jr. 1972. Aeration and gas transfer.
In: Physlcochemlcal Processes For Water Quality Control. W.J. weber.
Jr., (ed.). WHey-Intersdence. New York.
Canale, ft.P. 1983. Unpublished communication.
Chapra. S.C. and K.H. Reckhow. 1983. Engineering Approaches for Lake
Management, Volume 2: Mechanistic Modeling. Butterworth Publishers.
Boston, MA. .
Chlou. C.T., V.H. Freed, O.W. Scheddlng. and R.L. Kohnert. 1977.
Partition coefficient and 'bloa'ccumulatlon of* selected organic chemicals
Environ'. Scl. Techno 1.. 11(5): 475-478.
Chlou. C.T.. P.E. Portor and I.J. Tlnsley. 1982. Resolution of soil
sorptlve mechanisms In aqueous and nonaqueous systems. Paper presented
at 3rd Annual SETAC meeting, Arlington. Virginia.
Connecticut OEP. 1981. Impact of a chlorinated sewage treatment plant
effluent on the aquatic habitat of Deep Brook, Newtown.
Corey. R.B. 1981. Adsorption vs. precipitation. In: Adsorption of
Inorganics at Solid-liquid Interfaces. M.A. Anderson and A.J. Rubin
(eds.), Ann Arbor Science Publishers, Inc.. Ann Arbor.
Culbertson. J. 1977. Influence of flow characteristics on sediment
transport with emphasis on grain size and mineralogy. In: The Fluvial
Transport of Sediment-Associated Nutrients and Contaminants.
International Joint Commission, pp. 117-133..
Cunnings. T.R.. and J.8. Miller. 1981. Time of travel of the Flint
River. Utah Cam to Highway N-13, Michigan: August 4-8, 1981. USSS
open-file. Lansing, M1ch.
191
-------
V
Davis, J.A., R.O. James, and J.O. Leckle. 1978. Surface lonlzatlon and
complexatlon at the oxide/water interface: 1. Computation of electrical
double layer properties in simple electrolytes. J. Colloid Interface
Sc1., 63: 480-499.
Davis. J.A., and J.O.,Leckle. 1978a. Effect of adsorbed complexlng
Ugands on trace metal uptake by hydrous oxides. Environ. 3d. Tecnnol..
12: 13Q9-1315.
Oavls. J.A., and J.O. Leckle.
at the oxide-water Interface:
Interface- Sc1., 67: 90.
1978. Surface 1on1zat1on and complexatlon
3. Adsorption of anlons. J. Colloid
Dietrich. W. 1982. Settling velocity of natural particles.
Resources Research. 18(6): 1615-1626.
Water
OUoro. O.N. and J.C. Connolly. 1980. Mathematical models of water
quality In large lakes. 2. Lake Erie. U.S. EPA. Environmental Research
Lab - Ouluth. EPA-600/3-80-065.
OUoro. O.M., and L~M. Horzempa. 1982. Reversible and resistant
components of PCS adsorptlon-desorpMon: Isotherms. Environ. Set.
Techno1., 16(9): 594.602.
OUoro. P.M., L.M. Horzempa. M.M. Casey. and-w.L. Richardson; 1982.
Reversible and resistance components of PCB adsorptlon-desorptlon:
adsorbent concentration effects. J. Great Lakes Research, 8(2): 336-349
OUoro. O.M.. Q.J, O'Conner. R.V. Thomann. and J.P. St. John. 1982.
Simplified model of the fate of partitioning chemicals in lakes and
streams. In: Modeling the Fate of Chemicals 1n the Aquatic
Environment. K.L. Olckson, A.M. Makt, and J. Cairns. Jr. (eds.) Ann
Arbor Science. Ann Arbor.
OUoro, O.M., and J.J Fltxpatrlek. 1983. Verification analysis of the
probabilistic dilution model. U.S. EPA, Office of Water Programs
Operations. Washington* O.C. Contract No. 68-01-6275.
OUoro, O.H., J.J. FltzpatMck, and R.V. Thomann. 1983. Documentation
for water quality analysis simulation program (WASP) and model
verification program (MVP). U.S. CPA. Urge Lakes flesearch Station.
Srosse lie. HI. EPA-600/3-81-044.
OUoro. O.N., L.H. Horzempa. and N.C. Casey. 1983. Adsorption and
desorptlon of hexachloroblphenyl. U.S. EPA, Large Lakes Research
Station. Srosse He. MI. EPA.600/S3-83-088.
IBZ
-------
Dolan, D.N., and V.J. Blermaiy.Jr. 1982. Mass balance modeling of heavy
metals in Saglnaw Bay, Lake Huron. J. Great Lakes Research. 8(4):
616-694.
Oonlglan, A.S., Jr. and N.H. Crawford. 1976. Simulation of aricultural
runoff. U.S. EPA. Washington. O.C. EPA-600-9-76-016.
Oontglan. A.5.. Jr. and H.H. D«y1s. Jr. 1978. User's manual for
Agricultural Runoff Management, tARM) Model. U.S. EPA. Washington, O.C.
EPA-600/3-78-080.
Oossls P., and L.J. Warren. 1WO. Distribution of heavy metals between
the minerals and organic debru 1n a contaminated marine sediment. In:
Contaminants and Sediments. Volume I. P.A. Baker (ed.). Ann Arbor
Science.
OMscoll, E.O., J.L. Nanclnl. and P.A. Mangarella. 1983. Technical
guidance manual for performing waste load allocations. Book II streams
and rivers. Chapter 1 biochemical oxygen demand, dissolved oxygen and
ammonia toxldty. U.S. EPA. Office of Water Regulations and Standards.
Washington, O.C.
Edzvald. J.K.. J.B. Upchurcn and C.R. O'Mella. 1974. Coagulation m
estuaries. Environmental Science Techno 1. B.
Einstein, H.A. 1950. The bed load function for sediment transportation
In open channel flows. Technical Bulletin No. 1026. U.S. Department of
Agriculture, Soil Conservatd Service. Washington, O.C.
Einstein. H.A. 1964. River hydraulics. In: Handbook of Applied
Hydrology. V.T. Chow (ed.)-.uyMcGraw-Hlll. N.Y.
Felmy. A.R.. S.N. Brown. Y. Qn1$h1. R.S. Argo and S.B. Yabusaki. 1982.
MEXAHS - The metals exposure analysis modeling system. 'U.S. EPA. Office
of Research and Development. Contract No. 68-03-3089. Athens. Georgia.
Fisher. J.B., W.J. Ucx, F.L. JlcCall. J.A. Robblns. 1980. Vertical
mixing of lake sediments by tuBHIdd ollgochaetes. J. Geophys. Res..
85: 3997-4006.
Forstner. U. 1977. Fonts and sediment associations of nutrients and
metals.- trace metals. In: Th« Fluvial Transport of Sediment-Associated
Nutrients and Contaminants, u Shear and A.E.P. Watson (eds.).
Internatlnal Joint Commission
Freed, V.H.. C.T. Chlou. and R. Hague. 1977. Chemodynamlcs: transport
and behavior of chemicals 1n the environment - a problem In environmental
"«.3lth. Environmental Health .Serspe* * ,vt.. ZO: 55-70.
Freedman. P.L.. and R.P. Canale. 1983. Modeling uncertainty and
variability for waste load allocations. U.S. EPA. office of water
Regulations and Standards. Washington. O.C.
193
-------
Fukuda. M.K.. and w. lick. 1980. The entrapment of cohesive sediments
In frcsn water. Jour. Geophysical Research. 85{C5): 2813-282*.
Garde. R.J. and K.G. Ranga Raju. 1977. Mechanics of Sediment
Transportation and Alluvial Stream Problems. John Wiley & Sons. N.r.
Gardiner, 0. 1974. The chemistry of cadmium in natural water - II. The
adsorption of cadmium on river muds and naturally occurring solids.
water Research. 8: 157-16*.
Gelger, W.F. and H.R. Dorset). 1980. Quantity-quality simulation (QQS):
a detailed continuous planning model for urban runoff control. U.S.
EPA. Washington. O.C. EPA-600/2-8Q-011.
Gessler. J. 1971. Aggradation and degradation. In: River Mechanics.
H.W. Shen (ed. and ouol.). Fort Collins, CO.
V . -
Glbbs. 8.J. 1980. Mechanisms of trace metals transport In rivers.
Science. 180: 71-73.
Glddlngs. J.M., B.T. Walton. G.K. Eddleman and K.G. Olson. 1979.
Transport and fate of antlacene m aquatic microcosms. In: MUroolal
Degradation of Pollutants in Marine Environments. EPA-&00/9-79-GU.
Graf. W.H. 1971. .Hydraulics of Sediment Transport. McGraw-Hill, New
York.
Grahame, O.C. 1955. On the specific adsorption of ions in the "•
electrical.double layer. J. Chem. Phys., 23: 1166.
Guy. H.P. 1970. Fluvial sediment concepts. In: Techniques of
water-Resources Investigations of the United States Geological Survey,
Book 3. Applications of Hydraulics. U.S. Geological Survey.
Haag. W.R.. and N.H. Uetzke. 1961. A kinetic model for predicting the
concentrations of active halogens species In chlorinated saline cooling
waters. Oak Ridge National Laboratory. ORNL/TM-7942.
Haas. C.N. 1981. Application of predator.prey models to disinfection.
Journal WPCF S3: 378-386.
Hansch. C., J.E. Oulnlan. and G.L. Lawrence. 1968. The linear
free-energy relationship between partition coefficients and the aqueous
solubility of organic liquids. The Journal of Organic Chemistry. 33(1):
347.350.
Hassett, J.P., and M.A. Anderson. 1981. Effect' if dissolved organic
matter on adsorption of hydrophoblc organic comp 4nd^ by river- and
sewage-borne particles. Water Research. 16: 681-686..
194
-------
Hayter, E.J., and A.J. Henta. 1983. Modeling fine sediment transport In
estuaries. U.S. EPA, Environmental Research Laboratory - Athens.
EPA-60Q/S3-83-045.
Heathershaw. A.O. 1976. Measurements of turbulence In the Irish Sea
bentnlc boundary layer. The Benthlc Boundary Layer. I.N. McCave (ed.j.
Plenum Publishing Corp., New York.
Helnemann, T.J., G.F. Lee, R.A. Jones, and 8.W. Newberg. .1981.
environmental chemistry - fate modeling of domestic wastewaeer treatment
plant effluent - derived chloramlnes In Colorado front range rivers.
Fourth conference on Water ChloMriatlon Environmental Imoact and Health
Effects. Abstracts of Oral Presentations.
Hlrafzuml. r., N. Takahashl. and H. Nlshlmura. 1979. Adsorption of PCS
onto sea bed sediment, marine plankton, and other adsorbing agents.
Environ. Scl. Technol. 13(5): 580-584.
Hollander, A.F.. P. Somasundaran, and C.C. firyte. 1980. Adsorption of
polyacrylanlde and sulfonated PAH on Na-Kao11n»te. In: Adsorption from
Aqueous Solutions. Plenum Press. N.Y., 143-162.
Huang. C.P.. and W. Stumm. 1973. Specific adsorption of cations on
hydrous T-AljOj. J. Colloid Interface Scl. 43: 409.
Huang. C.P., H.A. Elliott, and A.M. Ashmead. 1977. Interfaclal
reactions and the fate of heavy metals in so 1.1-water systems. J. Water
Pollution Control Fed... 49: 745-756.
HydroQual. Inc. 1982. Application guide for CMA-HydroQual chemical fate
models. Chemical Manufacturers Association. Washington. D.C.
Hydrosdence. Inc. 1971. Simplified mathematical modeling of water
quality. U.S.. EPA, Office of Water Program. Washington. D.C.
James, ft.O.. and, T.w. Htaly. 1972. Adsorption of hydrolyza&le metal
Ions at the oxide-water Interface - III. A thermodynamlc model of
adsorption. J. Colloid Interface Set,, 40: 65..
• t i • "•
James, R.O., and T.W. Healy. 1972a. Adsorption of hydrolyzable metal
Ions at the oxide-water Interface: I. Co(II) adsorption on 5102 and
TIOj as model system. J. Colloid Interface Scl.. 40(1): 42-52.
James, R.O., J.A. Oavls, and J.O. Leckle. 1978. Surface 1on1zat1on and
complsxatlon at tie oxide-water Interface: II. Surface properties of
amorphous Iron hydroxide and adsorption of metal Ions. J. Colloid
Interface Set.. 65: 331.
195
-------
James. R.O., and 6.A. Parks. 1981. .Characterization of aqueous colloids
by their electrical double layer and Intrinsic surface charge
properties. In: Surface and Colloid Scl. Vol. 12. t. Mat1Jev1c (ed).
Plenum Press. Mew York.
James. R.O., P.J. Stlgllch, and T.w, Healy. 1981. The T102/aqueouj
electrolyte system - applications of colloid models and model colloids.
In: Adsorption from Aqueous Solutions. P.M. Tewarl (ed.). Plenum
Press. M.t. pp. 19-40.
Johnson. O.J. 1978. Measurement and persistence of chlorine residuals
In natural waters. In: Water Chlor1nat1on Environmental Impact and
Health effects. Volume 1. R.L. Jolley (ed.) Ann Arbor Science
Publishers. Ann Arbor, HI.
Jolley. R.I. 1975. Chlorine-containing organic constituents In
chlorinated effluents. J. Water Pollut. Control Fed.. 47(3): 601-608.
KaMckhoff, S.W.. O.S. Brown, and T.A. Scott. 1979. Sorptlon of
hydroghoblc pollutants on natural sediments. Water Research 13(3):
241-248. '
KaMckhoff. S.w. • 1980. Sorptlon kinetics of hydrophobic pollutants in
natural sediments. In: Contaminants and Sediments, Vol. 2. R.A. Baker
(ed.). Ann Arbor Science, pp. 193-205.
Klemetson. S.I.., T.N. Keefer. and R.K. Simons. 7980. Movement and
effects of combined sewer overflow sediments in receiving waters. U.S.
EPA. Washington. O.C. EPA-600/2-80-126.
Krenkel. P.A; and V. Novotny. 1980. water Quality Management. Acedemtc
Press. M.Y.
Langmulr. D. 1^81. The power exchange function: a general model for
metal adsorption onto geological materials. In: Adsorption from Aqueous
Solutions. P.M. Tewarl (ed,). Plenum Press, pp. 1-18. .,
Larson. R.J.. C1lnckema11He and 1. van Belle. 1981. Effect of
temperature and dissolved oxygen on blodegradatlon of nltrllotrlacetate.
Water Research. 15: 615-620.
Lee. O.Y., W. L1ck. and S.W. Rang. 1981. The entralnment and deposition
of fine-grained sediments 1n lake Erie. Journal of Sreat Lakes Research,
7(3): 264-275. .
Leenheer, J.A. 1980. Study of sorptlon of complex organic solute
mixtures on sediment by dissolved organic ca Son fractlonatlon analysis.
In: Contaminants and Sediments, Vol. 2. ».A. Baker (ed.). Ann Arbor
Science, pp. 267-278.
196
-------
Leytham, K.M.. and ft.C. Johanson. 1979. Watershed erosion and sediment
transport model. U.S. EPA. Environmental Research Laboratory. Athens,
GA. EPA-60Q/3-79-028.
LI, Y.H. and S. Gregory. 1974. Diffusion of Ions In sea water and in
deep-sea sediments. Geochlm. CosraocMn. Acta. 38:703*714.
Lick, U. 1982. Entralnment, deposition, and transport of fine-grained
sediments In lakes. Hydroblologla 91: 31-40.
Llss, P.S., and P.G. Slater. 1974. Flux of gases across the air-sea
interface. Nature. 247: 181-184.
Lyman, W.J.. et al. 1982. Handbook of Chemical Property estimation
Methods. McGraw-Hill. Mew York.
Mabey. H.. and T. mil. 1978. • Critical review of nydro.lysls of organic
compounds In water under environmental conditions. J. Phys. Chem. Ref.
Data. 7(2): 383-41S.
Mabey. W.R.. J.H. Smith, R.T. Podoll. H.L. Johnson, T. mil. T.w. Chou.
J. Gates, I.W. Partridge, and 0. Vandenberg. 1982. Aquatic fate process
data for organic priority pollutants. U.S. EPA, Office of water
Regulations and Standards, Washington. O.C. EPA 440/4-81-014.
Nackay, D.. A. flobra, and U.Y. Sh1u. 1980. Relationships between
aqueous solubility and octanol/water partition coefficients.
Chemosphere, 9: 701-711.
Mackay. 0.. A Bobra. O.W. Chan, and w.Y.'Shln. 1982. vapor pressure
correlations for low-volatility environmental chemicals. Environ. Scl.
Technol. 16: 645-649. .
MacNaughton, M.S., and R.O. James. 1974. Adsorption of aqueous Hg(ll)
complexes at the oxide/water Interface. J. Colloid Interface Scl.. 47:
431-440.
NeElroy, A.O.. S.Y. Chin, J.H. Nebgen. A. Aletl. and F.u. Bennett.
1976. Loading functions for assessment of water pollution from nonpolnt
sources. U.S. EPA, Office of Research and Development. Washington,
O.C. EPA-600/2-76-151.
McNown, J.S. and J. Malalka. 1950. Effect of particle shape on settling
velocity at low Reynolds number. Transactions American Geophysical
Union, 31: 74-82.
Means. J.C.. S.S. Wood. J.J. HMsett. and W.I. .Banwart. 1982. Sorptlon
of amlno- and carbexy-substlti *.d ^Jlynuclear aromatic hydrocarbons by
sediments and soils. Environ. Sc1. Technol., 16(2): 93-98.
197
-------
Metcalf & Eddy. 1982. Impacts of wastewater disinfection on coldwater
fisheries. U.S. EPA, Region I. Boston, MA.
Miller, 6.C., and R.G. Zepp. 1979. Effects of suspended sediment on
photolysis rates of dissolved pollutants, water Research. 13: 453-459.
M111. T., W.R.Mabey, O.C. Bomberger, T.W. Chou, O.C. Hendrey, J.H.
Smith. 1982. Laboratory protocols for evaluating the fate of organic
chemicals In air and water. U.S. EPA, ERL-Athens. EPA-600/3-82-0220.
Hills, W.B.. J.O. Dean, O.B. PorceMa. S.A. Gherlnl, R.J.M. Hudson.
w.E. FMck, G.L. Rupp, and G.L. Bowie. 1982. Water quality assessment:
a screening procedure for toxic and conventional pollutants. U.S. EPA,
Office of Research and Development, Athens, Georgia. EPA-60Q/6-82-004.
Morel. F.M.H. 1981. Adsorption models: A mathematical analysis In the
framework of general equilibrium calculations. In: Adsorption of
Inorganics at Solid-liquid Interfaces, pp. 263-294.
Mulkey, 1.*.. R.S. Ambrose. Jr., and T.O. Barnwell. Jr. 1982. Aauatlc
fate and transport modeling techniques for predicting environmental
exposure to organic pesticides and other toxicants - a comparative
study. International Workshop on the Comparison of Applications of
Mathematical Models. UNESCO. laCoruna. Spain. (Available from authors
at EPA Environmental Research Lab. Athens. GA).
National Research Council.. 1979. The chemistry of disinfectants in
water: reactions and products. U.S. Environmental Protection Agency.
(ITIS: Pfl-292 776.
Neely. U.B.. O.R. Branson, and 6.E. Blaw. 1974. Partition coefficient
to measure bloconcentratlon potential of organic chemicals in Hsn.
Environ. Sc1. Techno!., 8(13); 1113-1115.
Nordln, C.F.. and R.S. McQulvey. 1971. Suspended load. In: River
Mechanics. H.w. Shen (ed. and publ.). Fort Collins, CO.
Novotny, V. 1980. Delivery of suspended sediments and pollutants from
nonpolnt sources during overland flow, water Resources Bulletin 16(6):
1057-1065.
Oakley, S.N., P.O. Nelson, and K.J. Williamson. 1981. Model of true
metal partitioning 1n marine sediments. Environ. Scl. Technol.. IS:
474-480.
O'Connor, O.J. 1980. Physical transfer processes. In:. Mode11 no of
Toxic Substances In Natural U«eer Systems. Manhattan College (Sun er
Institute).
198
-------
O'Connor,-O.J., and J.P. Connolly. 1980. The effect of concentrations
of adsorbing solids on the partition coefficient, water Research, H:
1511..
O'Mella. C.R. 1980. Aquasols: the behavior of small particles in
aquatic systems. Environ. Sd. Techno). 14(9): 1052-1060.
Omerntk. J.M. 1977. Monpotnt source stream nutrient level
relationships: a nationwide survey. U.S. EPA, Office of Research and
Development, EPA-600/3-77-105.
Paris, O.F., H.C. Steen, 6.L. Baugnman. and J.T. Barnett, Jr. 1981.
Second-order model to predict mlcroblal degradation of organic compounds
In natural waters. Applied and Environmental Microbiology. 41(3):
603-609. '
Parthenlades, E. 1971. Erosion and deposition of cohesive materials.
In: River Mechanics. H.W. Shen (ed. and publ.}. Fort Collins. CO.
Pavlow, S.P. 1980. Thermodynamlc aspects of equilibrium sorptlon of
persistent organic molecules at the sediment-seawater interface: a
framework for predicting distributions In the aquatic environment.' In: .
Contaminants and Sediments, Vol. 2. R.A. Baker (ed.). Ann Arbor
Science, pp. 323-332.
Perry. R.H. and C.H. CMHon. 1973. Chemical Engineers Handbook. 5th
Edition. McGraw-Hill. New York.
Porcella. D-B., and O.L. Sorensen. 1980. Characteristics of nonpoint
source urban runoff and. Its effects on stream ecosystems. U..S. EPA.
Office of Research and Development. EPA-600/3-80-032.
Ramamoorth, S., and B.R. Aust. 1978. Heavy metal exchange processes,in
sediment-water systems. Environ. Geol., 2: 165-172.
ftathbun, R.E. 1977. ReaeratIon coefficients of streams - state of the
art. Journal of the Hydraulics Division. ASCE. 1Q3(HY4); 409-424.
Rathbun, R.E.. and O.Y. Tal. 1981. Technique for determining the
volatilization coefficients of priority pollutants In streams. Water
Research. 15(2).
Richardson. E.V. 1971. Sediment properties. In: River Mechanics.
H.W. Shen (ed. and'publ.}. Fort Collins, CO.
Richardson. W.{.., V.E. Smith., and R. Wethlngton. 1983. Dynamic mass
Balance of PC8 and suspended sol Ids In Sa^lnaw Say - a case study. In:
•i.yi.cal Behavior of PCBs In the Sreat Lakes. »*ckay, Paterson.
EUenrelch, and Simmons (ads.). Ann Arbor Sdev».
199
-------
Roberts. M.H. Jr., and R.A. .Gleeson. 1978. Acute toxUHy of(
brombchlorlnated seawater to selected estuarlne species with a comparison
to chlorinated seawater toxlclty. Marine Environ. Res. 1: 19-30.
Rodgers, P.M., and 0. Salisbury. 1981. water quality modeling of Lake
Michigan and consideration of the anomalous 1ce cover of 1976-1977. j.
of Great Lakes Res.. 7(4): 467.480.
Scavla. 0., W.F. Powers, R.P. Canale. and J.L. Moody. Comparison of
first-order error analysis and Monte Carlo simulation In time-dependent
lake eutrophUatlon models, water Resources Res. 17: 1051-1059.
Schlndler, P.M., 8, Furst. R. Olck. and P.U. Wolf. 1976. Llgand
properties of surface sllanol groups - I. Surface complex formation with
Fe*5, Cu*2, Cd*2. Pb*2. J. Colloid Interface Sc1.. 55: 469.
Schlndler, P.w. 1981. Surface complexes at oxide-water, interfaces.
In: Adsorption of Inorganics at Solid-liquid Interfaces. Anderson and
Rubin (eds.). Ann Arbor Science, pp. 1-50.
v
Schwarzenback. R.P.. and J. Westall,. 1981. Transport of nonpolar
organic compounds from surface to groundwater: laboratory sorptlon
studies. Environ. Scl. Techno 1., 15(11): 1360-1367.
Shaw, D.J. 1978. Introduction of Colloid and Surface Chemistry, 2nd
edition, Butterwortfts. London.
Shen, H.W. 1971. Wash load and bed load. In: River Mechanics. H.M.
Shen (ed. and publ.). Fort Collins. CO.
Shen, H.W. 1971. Total sediment load. In: River Mechanics. H.W. Shen
(ed. and publ.). Fort Collins. CO.
Salomon. S.I.. and S.K. Gupta. 1977. Distributed numerical model for
estimating runoff and sediment discharge of ungaged rivers. 2. Model
development. 3. Comparison with other simple techniques, water
Resources Res. 13(3): 619-636.
Spain. J.C., P.M. Prltchard and A.w. Bourguln. 1980. Effects of
adaptation and blodegradatlon rates In sediment/water cores from
estuarlne and freshwater environments. Applied and Environmental
Microbiology. 40(4): 726-734. ,
Stum, W.. and J.J. Morgan. 1981. Aquatic Chemistry: An Introduction
emphasizing Chemical Equilibria 1n Natural Waters. 2nd edition. John
Wiley and Sons. New York.
.Suzuki. M.. T. Yamada. T. Mlyazakl. and X. Kawazoe. :*»79 Sorptlon and
accumulation of cadmium 1n the sediment of the Tama p>.
-------
Tada, f. and S. Suzuki. 1982. Adsorption and desorotion of heavy metal $
in bottom mud of urban rivers, water Research, 16: U89.
Tchobanoglous G.. A.O. Levlne, and J.R. Koltz. 1983. mtraO'e solids
as a design parameter for yastewater treatment processes, prepared for
presentation at the Stxty Symposium on Waste Treatment. Montreal Canada,
November 16-17. 1983.
Thelss, T.U, and R.O. Rlchter. 1980. Adsorption reactions of nickel
species at oxide surfaces. In: Participates m water. M.C. Kavanaugn
and J.O, Leckle (eds.). Advances 1n Chemistry Series #189. ACS.
Washington. DO. 73-96.
Thomann, R.V. 1972. Systems Analysis and water Quality Management.
Environmental Science Services 01v., New York.
Thomann. R.V. 1982. Verification of water Quality models. Journal of
the Environmental Engineering Division. ASCE, lOB(EES): 923-9*0.
Thomann, R.V. 19826. Physico-chemical model of toxic substances in the
Great lakes. U.S. EPA, Office of Research and Oevelooment.
Thomann, R.V. 1984. Simplified heavy metals mode! for rivers. Paper
presented at ACSE National Conference on Environmental Engineering. LOS
Angeles, 'CA.
Thomas. W.A. 1977. Sediment transport. In: Hydrologlc Engineering
Methods for Water Resources Development. Hydrologlc Engineering Center,
Corps of Engineers. Davis. CA.
Todd, O.K. 1970. The Water Encyclopedia. Water Information Center.
Port Washington, N.Y.
Tomllnson, R.O., et al. 1980. fate and effects of participates
discharged by combined sewers and storm drains. U.S. EPA, Office of
Research and Development. EPA-600/2-80-111.
Uchrln, C.J., and W.J. Weber. Jr. 1980. Modeling the transport
processes for suspended solids and associated pollutants in
river-harbor-lake systems. In: Contaminants and Sediments. Vol. 1. Ann
Arbor Science.
U.S. environmental Protection Agency. 1976. Areawlde assessment
procedures manual, Volume I. Municipal Environmental Research
Laboratory. Cincinnati, OH. EPA-600/9-76-014.
U.S. Environmental Protect'JP Agency. 1977. Mahonlng River waste load
allocation study. M1ch^>n-0n1o District Office, west Uke.
201
-------
U.S. Environmental Protection Agency. 1964. Technical guidance manual
for performing waste load allocations. Book VI, design conditions. •
Office of water Regulations and Standards, Washington. O.C.
valloulls. I.A., and E.J. List: 1984. Numerical simulation of a
sedimentation basin. 1. Model development. Environ. Sd. Technology
18:242-247.
Velth. G.O., O.L. DeFoe. and B.V. Bergstedt. 1979. Measuring and
estimating the bloconcentratlon factor of chemicals in fish. J. Fish
Res. Bd. Can.. 36(3): 1040.1048.
Vueeta, J., and J.J, Morgan. 1978. Chemical modeling of trace metals in
fresh waters: role of eomplexatlon and adsorption. Environ. Scl.
Techno!., 12(12): 1302-1319.
Weber, W.J., Jr. 1972. Physlcochemlcal Processes for Water Quality
Control. Wlley-Intersdence, New York.
Westall. J.. and K. Hohl. 1980. A comparison of electrostatic models
for the oxide/solution Interface. Adv. Coll. Interface ScL. 12:
265-294.
Williams, J.R. 1975. Sediment yield prediction with universal equation '
using runoff energy factors. In: Present and Prospective Technology for
Predicting Sediment Yields and Sources. U.S. Department of Agriculture.
AftS-S-40.
Williams. J.R. 1980. SPNM. a model for predicting sediment, phosphorus,
and nitrogen yields from agricultural basins, water Resources Res.
16(5): 843-848.
Wilson. G.T., and N. Macleod. 1974. A critical appraisal of empirical
equations and models for the prediction of the coefficient Of reaeratlon
of deoxygenated water. Water Research. 8: 341-366.
wvschmeler. W.H., and D.D. Smith. 1960. A universal soil-loss equation
to guide conservation farm planning. Seventh International Congress of
Soil Science. Madison, HI.
Wischmeler. W.H. 1976. Use and misuse of the universal soil loss
equation. J. Soil Hater Cons. 31(1): S-9.
Wolfe. N.t. 1980. Determining the role of hydrolysis In the fate of
organIcs In natural waters. In: Dynamics. Exposure and Hazard
Assessment of Toxic Chemicals. R. Haque (ed.). Ann Arbor Press.
Zepp. 9 ft. N.I. Wolfe. J.A. Gordon, -id 6.1. "Baughman. 1975. Dynamics
of 2.4-0 esters In surface waters. **..1r«,i. Sc1. Technol.. 9(13):
1144-1150.
202
-------
Zepp, R.6. 1980. Assessing the photochemistry of organic pollutants 1n
aquatic environments. In: Dynamics, Exposure and Hazard Assessment of
Toxic Chemicals, ft. Hague (»d.). Ann Arbor Press, pp. 69*110.
Zlson, S.W., K.F. Haven, and W.B. mils. 1977. water duality
assessment, a screening method for nondeslgnated 208 areas. U.S. EPA,
Environmental Research Laboratory. Athens, SA. EPA-600/9-77-023.
203
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APPENDIX A
DEVELOPMENT OF MODEL EQUATIONS
August 1984
-------
APPENDIX A
DEVELOPMENT OF MODEL EQUATIONS
A.I CONSERVATIVE POLLUTANT
The basic assumption In the conservative substance model is that .
there are no Internal source/sink reactions that significantly affect the
toxic substance concentration 1n the receiving water. Only external
sources of the contaminant, or Inflow of dilution water by advectlon or
dispersion can alter the contaminant conconcentratlon. according to this
conservative assumption.
Under the conservative substance assumption, and assuming that longi-
tudinal dispersion Is negligible relative to advectlve transport, the
general river transport equation reduces to
dC 1 d(OC)
Furthermore, application of Equation Al 1s almost always made under the
assumption that the river reach In question is at steady-state with a con-
staht, continuous point discharge of the contaminant In question. Also, flow
Is assumed to be constant over the reach. Under these assumptions, the
solution for Equation Al 1s simply
C(x) - CQ; x > 0
(A2)
C(x) - Cu; x < 0 .
where C * upstream river concentration of contaminant and C • river
u o
contaminant concentration at x • 0 after nixing upstream river water with point
discharge. The concentration, C . Is determined by performing a mass balance
for C at x • 0, assuming instantaneous mixing at that point. Therefore, C is
A-l
-------
calculated from C . the point source concentration (C^), the point source
flow (Qw). «nd upstream river flow (0 } as follows:
p [y CuHi-M
* " — — U3)
Given the above assumptions, CQ 1s Independent of x downstream of the
effluent unless there Is another downstream discharge of the substance or a
(
dilution of the substance by inflow of uncontamlnated diluting water.
Multiple point discharges can be handled reapplylng Equation A3 at each
successive discharge point, using the result of the previous discharge mass
balance as the upstream boundary conditions.
There Is a large body of literature which suggests that most priority
pollutants do not behave conservatively 1n water bodies. Recent results
from dynamic mass balance modeling studies of heavy metals and several
synthetic organics in the Great Lakes have indicated nonconservative
behavior (Oolan and Blerman, 1981; Richardson, et al.. 1983; and Rodger*,
1961}. Flint River data (presented later In this section) collected by
Michigan ONR In 1978 demonstrated that total zinc and cooper did not
behave conservatively 1n certain stretches of the river. Unless
advectlve transport In a given reach Is rapid relative to the transport
and transformation processes discussed In Section 2.2 or unless relevant
Internal source/sink fluxes just balance, the Instream concentration of a
pollutant 1s likely to vary with longitudinal distance.
A. 2 NONCONSERVAWE POLLUTANT - SIMPLE WATER COLUMN ANALYSIS
Often the net result of the combined effects of transport and transfor
mation forces acting on a chemical substance Is a first-order die-off of the
substance with distance (or t1me-of -travel) downstream from a discharge.
This type of concentration profile can be simulated by lumping several pro-
cesses Into a single first-order loss term applied to the general river
transport equation. Given this approach
A-2
-------
dC f2 d2C Q dC K . ....
ai ' e - • *C • (A4)
.r
where K. [time" ] 1s an aggregate first-order decay coefficient for tie
substance in question. ' .
Several further assumptions are often Involved in applying the above
equation to a specific site. They are as follows:
1} The river Is at steady-state with respect to flow and loads;
2) Concentration of the modeled substance is uniform over the.
cross-section of the river (I.e.. one dimensional system); thus,
any point discharge Instantaneously mixes with the river flow at
the point of discharge;
3} Dispersion 1s negligible in the longitudinal direction; that 1s.
only advectlon Is considered significant in the direction of flow;
thus, E « 0 In equation A4;
4) Flow, cross-sectional area, and mean depth are constant over the
reach In question. •'
Given the above assumptions. Equation A4 reduces to
0 .. - ' - KC . , (A5)
•The solution to Equation AS Is
* *T*
C(x) - C(0)exp(—i-J (A6)
where. U * Average river velocity In reach [length/time]
C(0) • Initial' concentration of the modeled substance at x • 0
[mass/length3].
TM$ approach limits Itself to only the water column and only oi.e
,fom of the • M* :tant.
* - t
There are ,two methods for applying Equation A6 to a problem of
multiple discharges In a river system. Since Equation AS 1s an ordinary.
A-3
-------
linear differential equation, the Independent solutions for Individual point
sources can be addltlvely superimposed to obtain a total concentration
profile along the river. Alternatively, the river reach in question can be
segmented according to significant changes In river geometry or flow, or at
locations of point sources. Then each, segment 1s modeled sequentially
moving downstream. The Initial (upstream) concentration of each segment 1s
determined by the concentration entering from the upstream segment,
augmented by any effluent load entering at the segment boundary.
Great care must be taken In applying this type of model to a specific
site without enough field data to confirm the validity of the aggregate
decay coefficient. Kf for a particular pollutant may vary from site to
site, or may vary over time due to changes 1n controlling parameters like
flow or river cross-sectional geometry.
Application of Model to Flint River August 1978 Oata--
As a brief example of analyzing a system with first-order decay model
of the water column, metals and suspended solids data obtained during a
preliminary survey of the Flint River will be compared to the model
presented above. In this application the aggregate first-order
coefficient, K^, Is assumed to be an apparent net settling velocity
from the water column; therefore. --
*7 • *t/* • (A7)
•,.
where. KT- First-order loss rate coefficient of total metal or suspended
solids [t1mv-1].
vj. Apparent net settling velocity [length/time].
H • Mean depth of river [length].
The study reach of the Flint River used 1n this Investigation was
from the Utah Street Oam 1n the city of Flint (Km 83.6) to :he bridge at
Crosswell Road (Km 11.0). Oata on river metals and solids concentrations
A-4
-------
and point source Inputs were obtained from a Michigan ONR.survey
conducted 1n August of 1978 {Roycraft and-Buda, 19^9). Table Al is a
summary of the point discharges considered 1n this study, and Table A2
contains the river hydrology and geometry at the time of sampling. As
Indicated In Table A?, the river reach has been divided Into four
segments.
Figures Al through A3 contain the survey data and model predictions
for total zinc, total copper, and suspended solids, respectively. In
attempting to simulate the data points In these figures, the only
parameter that was varied was the net apparent settling velocity. v$.
which determines the stream concentration through Equations A7 and A6.
Of course, when v 1$ set equal to zero. It Implies that the pollutant
1s transported conservatively down the river. All other parameters.
those In Tables Al and A2, and the Initial upstream conditions were held
constant.
It 1$ apparent that none of the three substances behaved
conservatively within the entire study reach; the conservative assumption
considerably over-predicts the downstream concentrations. This type of
error could be especially Important In situations where a waste load must
be allocated among multiple discharges along a river reach, since the
conservative pollutant assumption omits the effect of depuration
occurring between points of discharge.
In the segment between the Flint UUTP and the Ragnone plant {Km
70.7-41.1), total zinc appears to settle at an apparent rate of 1.0 ra/d,
while total copper Is lost at a rate close to fl.S m/d. The apparent
settling rate for zinc 1n this segment may be slightly less than 1.0 m/d,
or there may have been an unaccounted for source of zinc at about
kilometer 46. The available data base did not permit this distinction.
The suspended solids data and simulations (Figure A3) confirm the metal
findings. In the segment bet*, en Hint and ftagnone plants, solids are
settling at a rate between 1.0 and 1.2$ m/d. The larger net settling
rate observed for sol Ids Is consistent with the assumption that not all
A-5
-------
TABU Al, POINT DISCHARGES FOR AUGUST 1978 SURVEY
Source
G.«./Bu1ck
Flint WWTP
Ragnone WWTP
Km
83.4
70.7
41.1
Flow
fm /s)
0.09
0.86
0.84
Loadlnas (ka/d)
Total Zinc
0.77
29.. 0
11.0
Total Cu
0.48
3.6
4.0
Susoended Solid s
.
2.710
8,000
TABLE A2. FLINT RIVER HYDROLOGY AND GEOMETRY FOR AUGUST 1978 SURVEY
Seamen t
1
2
3
4
.Starting
Point
(Km)
83.4
81.9
70.7
41.1
Segment
Length
(Km)
1.S
11.2
29.6
30.1
Mean
Depth
fm)
3.0
0.66
0.66
1.0
Cross-Sectional
Area
140
30
30
30.6
Flow
6.2
6.2
7.06
7.9
A-6
-------
I I
i
*
§11
1
§
MM
MM
i
a
a
si
11
s3
i
U hU
X CB
o <
-------
TT:
i
•
L.
cs >•
5
I
s s
()/•") U1M09 1V1U
-------
- s
l
_ U
il
(«/••) SOU OS
-------
the metals 'in the river are In a participate Form; therefore, the
apparent settling rate For metals should be somewhat less than for
suspended solids. Furthermore, the finer-grained, slower-settling
participates probably have a higher metals content than the larger,
size participates.
Some difficulty was encountered In simulating the data downstream of
the Ragnone discharge For all three substances. IF the data set is in
fact representative of a steady-state condition In this segment (a fact
which cannot be established from such a small sampling}, then it appears
that the net loss of metals and solids in this reach was close to zero.
This could have been the result of sediment resuspenston 1n this segment
due to higher water velocities. This behavior 1s addressed further in
Section 5.0. which 1s a case study of the more extensive 1981-82 Flint
data.
Finally. 1t 1s quite apparent that the metals' behavior in the river
1s closely related to the suspended solids' behavior. This observation.
coupled with the need to know the exposure of aquatic biota to dissolved
contaminant concentrations, leads to the rationale for the somewhat, more
complicated approach described next.
A.3 WATER-SEDIMENT WOOEL HAVING SEPARATE PARTICIPATE AND DISSOLVED
CONTAMINANT PHASES
One of the most significant mechanisms for the movement of pollutants
through an aquatic environment 1s the adsorption or uptake of the
chemical by both nonvlable and viable partlculate matter, followed by the
transport of the interacting particulars. Association with suspended
matter thus significantly alters the transport regime of a chemical by
Introducing additional transport processes, such as settling and
resuspenslon. Furthermore, the association with suspended matter can
Indirectly affect the rate and extent of chemical transformations and
I'otic accumulations. For example, partitioning of a portion of a
chemical In suspended solids could reduce the flux of the chemical's
dissolved phase Into the biota, thus potentially reducing Us toxlclty.
Accordingly, determination of the fate and potential toxlclty of
A-10
-------
pollutants 1n aquatic systems requires knowledge of two Important
processes: 1) partitioning of metals between dissolved and particulate
phases In aquatic systems, and 2} transport of particulate matter (i.e.,
settling and resuspenslon) as affected by hydraulics and particulate
physical properties.
.A conceptual diagram of the NICHRIV model is presented in Figure A4;
nomenclature Is presented In Taolt A3. Note that the calculation scheme
permit; the estimation of the equilibrium partitioning of total chemical
between dissolved and solid phases 1n both the"water column and the
sediment bed. With this approach It Is necessary either (a) to specify
the (water column) suspended solids concentration as a parameter, or
(b) to model suspended solids as a state variable. The former approach
1s used In the SLSA model; the latter approach, described below. 1s used
1n the HICHRIV model.
Settling, resuspenslon. and burial apply only to the particulate
bound pollutant. Diffusion between the sediment pore water and water
column applies only to the dissolved phase. The first-order decay
coefficient represents the sum of a number of potential processes, most
of which are Insignificant for metals 1n streams. For organlcs, however,
the loss rate can Include volatilization, hydrolysis, photolysis,
chemical oxidation, and blodegradatlon (described in Section 3).
In the current version of the MICHRIV model the decay coefficient
applies only to the dissolved phase. Volatilization is a process that
clearly .applies only to the dissolved phase; While hydrolysis,
photolysis, oxidation, and blodegradatlon may often be far more rapid in
the dissolved than In th* adsorbed phase, there seems to be no consensus
that this Is true 1n all casts. Consequently, to maintain generality the
decay coefficient for total pollutant, I, has been formulated below as
the weighted sum of dissolved and particulate phase decay coefficients.
*dfd * *P'P ^w1th appropriate subscripts 1 or 2)
A-11
-------
Aii
IOAO (WT)
WATER
DECAY (K4t)
TRANSPORT
ACTIVE "2
SfOIMiNT
StOUUNT
TQTAi SUUTAMCX - 4Cj)
PAKTICULATE
SUBTAMCf C.|
SCTTIINC W,
H6URE A4. MICHRIV FRAMEWORK
-------
TABLE A3: NOMENCLATURE FOR WATER-SEDIMENT MODEL
Parameters Water Column Sediment
Concentrations
Total, toxicant (i.g/l)« CTI CT2
Dissolved toxicant {ug/D* C^ Cd2
Partlculate toxicant (»g/l)' C C .
Participate toxicant ( g toxicant/
mg solids) f] r2
Total solids (mg/l)» m^ • »2
Toxicant load (kg/day) «T
Sediment porosity — *
Part_1_t1_on_1nq
Dissolved fraction f., f ..
di az
Partlculate fraction f_ f .
Partition coefficient (l/mg) (» • r/Cd) .^ «2
Channel Geometry
Oownstream distance x x
Cross-sectional area (m ) A. ---
Oepth (m) H. H
Flow («3/sec) Q.J
Velocity (n/sec) (U . 0/A) u.
A-13
-------
TABLt A3: NOMENCLATURE FOR WATER-SEDWENT MODEL (Continued)
Parameters water Column Sediment
Hate Parameters
Aggregate decay rate coefficient (I/day)
- for dissolved Kai
. for participate Rpl
- for total (K • Kflffl » Kpfp) PC} Kg
Settling velocity (m/day) w ; ---
fiesusoenslon velocity (in/day) — u
Sedimentation (burial) velocity (m/day) — w
9
Sedimentation loss coefficient (I/day} --- K
Diffusive exchange coefficient (fli/day) K n
•In terms of bulk volume.
-------
HUMn the. conceptual framework of the model shown in Figure A*, the
following assumption's are used to develop mass balance eauatlons:
1. Constant hydrologlcal and morphological conditions for eacfi r'ver
segment;
dCT • am
2. Steady-state conditions exist: i . . 0;
3. Vertical and lateral uniformity 1n water column and sediments; no
mixing zones
4. Dispersion 1s negligible In. the longitudinal direction;
S. No longitudinal (downstream) movement of the bed: Oj • 0;
6. No spatial variation of the sol Ids content of the bed: mj Vs
constant (although vr\ Is not constant).
7. Partitioning between dissolved and solid phases 1s rapid relative
to transport and other transformation kinetics.
The solution for pollutant concentrations in such a one-dimens'onal .
steady-state system is developed below. The solution is based on four
coupled, differential equations representing mass balances for solids in the
water column and in the bed, and for the toxicant 1n the water column and -n
the bed.
using the subscript 1 for water column variables and the suoscript 2
for sediment variables, the mass balance for solids suspended In the
water column (ut) takes the form:
(advectlon) (settling) (resuspenslon)
°i dml ws wrs
ar - iq- *i • * • — mz
Assuming that m. 1s not a function of x. and that w and w are
constant, this equation has the solution:
(initial solids) ' (resuspended sallds)
- w x - w x,
n i. " ' w * m_ r .. M 1
w$ L J
A. is
-------
It can be seen that m. 1s a function of the travel time downstream
(x/U^). the settling velocity (w ) or Us associated death-dependent •
rate coefficient (w /H ), and the resuspenslon flux (* m ).. when
the resuspenslon velocity (w ) Is zero, the second term drops out of
Equation A9, and m. Is no longer dependent on m.. In comparing Equation
A9 predictions with.field data 1t* 1s Important to account for all external
and Internal sources of suspended solids. One potential .internal source is
phytoplanfcton growth: the concentration of phytoplahkton solids nay be 200
fold greater than the concentration of chlorophyll-a (Canale 1983).
A second mass balance equation, this one for solids in the bed. can .
be written:
(advectlon) (settling) (resuspenslon) (burial)
It 1s assumed that the bed does not move (Q. • 0) and that m^ 1s
constant (dm./dx • 0). Cither of these assumptions causes the advectlon
tern to drop out. Consequently. Equation AID reduces to an algeorak
equation:
(settling) (resuspenslon) (burial)
-s»7 • -n ' "«• •' (A11)
The sedimentation velocity, w.. represents the movement of material downward
and out of the active sediment layer, the thickness of which (H ) does not
change with tint. This velocity thus represents the rate of change 1n elevation
of the surface of the bed, Ignoring any effect of compression of the deep
sediment. If the resuspendlng flux exceeds the settling flux, then w. 1s
negative. Implying that channel downeuttlng Is occurring. If the downward flux
exceeds the upward flux. wtf is positive. Implying that the chanrel bed 1s
rising over time, for the conditions being modeled. Where chemical
-------
The third mass balance equation 1s for the toxicant in the bed
(advectlon) (settling) (diffusion in)
°2
-------
If a term fl. the 'sediment capacity factor- (OUoro et al. 1982). 1s defined
as:
a .
then
flV2
Combining Equations A13 and A17. and solving for r /r
p2
V rp2
The first term 1n the numerator 1s modified by noting the Equation'An
relationship between w , w , and wfl. The second term 1s modified by
noting that:
and f.
and f.
m«f.
Consequently.
o2
(A19
(A20)
The ratios CT2/CT1 and r./r1 thus depend on the water.sediment
P4rtlc1e exchange rate*, the water.sediment diffusion rate, and the decay
rate within sediment. They do not depend on the decay rate within the water
column.
A-IS
-------
The fourth mass balance equation Ms for tne toxicant in the water
column:
(advectlon) (decay) (settling) (diffusion out)
r -
-3x— * *1 CT1 *
(rtsuspenslon) (diffusion 1n}
Combining Equations A21 ana A13 results in:
dCT1 T wf K.f.,, flr. / \T C-,
_I1 . .r . -121 . -L51 . -J"(w f . K f ) -II
«! t Mi «i Vi ^ 'J yi
All terms on tne right side of the equation are constant for a particular
exceot for CTI, fd1 , and f ^ (and sofiseauently 0). The fractions f
and f . are functions of m. per Equation AH; m. Is a function of z per
Equation A9. However, If the Increments of x are small enough, then m, ,
f., , and f , are essentially constant. Consequently «1tnln small
01 pi
Increments of x. Equation A22 has .a simple solution:
K.
T
(A22)
(M35
where
(decay) (settling) (diffusion out) (resuspenslon) (diffusion in)
By stepping down the react) 1~ small increments of x, C-. can be computed
from the input parameters • . p^, K , »^, »j, HI, H_. m , KI,
A-19
-------
K., and the input solids and toxicant loads using Equations A3, Aig, A20,
A23, and A2«. Then CT2 can be computed from CTI using Equation A13 or
A1S. .For the solution to be valid, the increments of x must be short enough
that the relative change in m^ is small within each increment. That U.
the Increments Ax must be shortened until ftm./ra Is small.
i
While Equation A24 1s satisfactory as written, some simplification of
It Is helpful for better understanding the model. Using the relation-
ships shown in Equations A19 and AU. the "diffusion in" term of Equation
A24 can be put In terms of f,. and combined with the "diffusion out"
term. Using Equations All and A16, the "settling* term can oe expressed
In terms of resuspenjlon and burial. The resulting equation Is:
(decay) (settling) (resuspenslon) (net diffusion)
, Equation A20 can be solved in terms of K as follows:
Substituting this relationship Into the "net diffusion* term of Equation A25
causes several terms to cancel out; then, after defining the sedimentation
or* burial rate coefficient as K$ . ^/H^, the equation can be
expressed as:
6% '
This result expresses K_, the overall rate coefficient for disappearance of
the toxicant from the water column, In terms of the three avenues for
elimination of -the toxicant from the water-sediment system: deca* In water.
decay in *edlment, ai.J burial. The rates of sediment decay and burial are
A-20
-------
modified Dy 3r /r wMch 1s a function of the water-sediment mass ratio,
the partitioning parameters, the sediment-water exchange parameters, as well
as the sediment decay rate Itself. It might also De noted that for a
condition where *-, • «j, KI • K. » 0, and w • 0, this model
reduces to the simpler model expressed by Equation A7.
A-Zl
-------
APPENDIX 6
SEDIMENT TRANSPORT CONSIDERATIONS
-------
APPENDIX P
SEDIMENT TRANSPORT CONSIDERATIONS
The pollutant fraction associated with participate material is
determined (at equilibrium) by the partition coefficient and the solids
concentration. In many natural waters the participate phase on average
contains a small percentage of the alkali and alkali-earth metals such as
sodium and calcium. 20-30% of the strontium and boron. 30-70% of the
cadmium, fine, copper, and mercury, 70-85% of the chromium and lead, and
98% of the aluminum and Iron (Forstner 1977). .The bulk of many
pollutants Is thus carried on participate material.
Predicting the transport and fate of partlculate-assodated
pollutants requires an understanding of the behavior of particles.
Predicting particle Behavior 1s. however, one of the most'difficult and
uncertain aspects of water quality modeling. Much of the existing
knowledge pertains to the larger particles which control the
configuration of the streamoed rather than to the smaller particles
likely to adsorb many of the toxic pollutants. Consequently, future
findings In this area may significantly Improve predictive abilities.
8.1 SEDIMENT PROPERTIES
An Individual sedimentary particle may oe characterized by Us size,
shape, density, fall velocity, mineral composition, surface texture, and
other properties. Particle size can be described by a number of
different measures, Including but not limited to (a) nominal diameter -
the diameter of a sphere having the same volume as the particle, (b)
sieve diameter - size of sieve opening through which the particle will
pass, approximately equal to the nominal diameter, and (c) fall diameter
- diameter of a sphere with specific gravity 2.65 (quartz) that has the
s«me all velocity (Richardson 1971, Guy 1970). Table 81 and Figure 81
show the size ranges corresponding to particle classifications.
8-1
-------
TABLE 81. KINDS OF SEDIMENT MATERIALS ANQ SIZE CLASS TRANSPORTED
IN STREAM (from CulDertSon 1977)
Sediment
Boulders
Cobbles
Gravel
Sand
sin
Clay
Organic Detritus
Size Class
>2S6 am
64-256 nm
2-64 mo
0.062-2 am
4.62 nm
0.2-4 wfl
Mode of Transport
Bed Load
Bed Load
Bed Load
Bed Load or
Suspended
Suspended
Bed Load or
Suspended
Suspended
Including leaves,
trees biological
remains, etc.
Biota
Including floating
and bottom duelling
organ1sns
Bed Load or Suspended
B-2
-------
10- 1Q 10'*
10-4
10
-7
10-*
»0
'*
'*
COILOIOS
1
SUSFENOCO ^ARTICLES
•ACTIHIA
vmus
I
i
I MICRO '
_ _ » •i'-. {if v£S — — —* —• —
. I . SIEVES .
F1L 7T» TY*tS
MOL«CULA«.
MCMVNANI
1 SANO
OlArOMACEOUS
EANThS
| ACTIVATED
. CARBON 'CAAlNSi
MICRO- 'OHC OPENINGS
Figun 81. Size Rang* of Sediment Par-tides and Filter Pores
(from Stumm and Morgan 1981).
B-3
-------
Fill velocity is the average terminal velocity of a particle falling
alone in Quiescent distilled water. It is related to a number of
particle and fluid characteristics Including particle and fluid
densities, fluid viscosity, and particle diameter, shape, surface
texture, and tumbling frequency.
9
Mineral composition influences density, size, shape, and thus fall
velocity. Most mineral sediments carried by stream flow have a specific
gravity of around 2.65 (Culbertson 1977). Consequently, the fall
velocity of quartz spheres having specific gravity 2.65 1s used as
somewhat of a benchmark. Nevertheless, substantial variations in density
may be observed, with organic particles especially tending toward lower
density.
Tor sediment transport the most useful expression of particle snape
1s given by the Corey shape factor, c//ib". where a. b. and c are the
lengths of the longest. Intermediate, and Shortest mutually perpendicular
axes, respectively (Ncflown and NalaUa I960).
Sulk sediment 1s a complex mixture of differing individual
particles. Bulk properties are related to the above individual
properties and to the way they are distributed. Bulk properties of
particular Importance may be the size distribution, specific gravity,
porosity, and conesIveness.
Measured size distributions may be expressed In a number of different
ways, frequency distribution histogram show the prevalence of material
within given class Intervals. Cumulative distribution plots show the
total percentage of material with size smaller than particular values.
Cumulative distribution plots can be used to specify guartlle values.
d2s< djg, and d?$ (where dz Is the diameter greater than x
percent of the particles). Table 82 shows particle size distributions
^bserved in raw sewage, primary effluent, and secondary effluent of one
Municipall v (f nazountas and Kathlas 1964). Figure B2 shows the size
distributions observed In stream beds of 11 rivers (Guy 1970). mils et
al. (1982) also presents some sediment data for several rivers.
B-4
-------
o
t/t
«M
et
»• A
el
•it
w*
• a. js
tt *• «* w
•• a* >•
*' f S
<« w ..
w TI «*
«J «l W
c^ w> •» l_
v. * . 9
J6 ^» ? 5
-------
Silt
100
s
o
X
s
o
z
i
- »
<
TECHNIQUES OP WATER-RESOURCES INVESTIGATIONS
Sand Gravel Coboi* Boulder
0.01
SIZI. IMMIkLIMITtllS
1 - Mnamipp* Riwr at H«aeJ 9t
2 - MffMHBO* Riw atCiwo. III.
3 - Mtacwn Rnw n Omafca, N«cjr.
4 — RcpwMiean Ri««r at day Ctnrar.
S — South han» Rhwr at Sowdi Hatw. Colo.
• - PwnCMia RIMT at WtfhMU. N. Dak.
7 - S«n»q C/Mk .war Rock«ill«, Md.
I — SrandywiiM Ot*k it U«n«O«. ^a.
9 — Brantfywrma C/MM it Cornoq. PI.
10 - YrtlowttoiM Rnr«r at Billing. Mant.
11 - W. •eric Rock C/«*k n*ar R«J Loaq«. Mont.
Figur* B2. P«rtid«-*ia» Oinhbution of Stnambod Matwial Typical of
Indierad Strains in tira United Statn (from Guy 1970).
B-6
-------
The sUe distribution of natural sediment 1s ordinarily expected to
plot as a straight line on log probability paper. If this is the case,
then the median wltl equal the geometric mean, and the ratio dcn/d.,
9(J . 1 b
and d../de. will equal the geometric standard deviation or 'gradation
8* 50
coefficient.* The complete distribution can thus be described By the
median (or geometric mean) and the geometric standard deviation.
For a given shape, texture, and density, particle size Is Inversely
proportional to the particle surface-to-mass ratio. As discussed in
Section 3.2, the partition coefficient of organic contaminants can be
related to the quantity of organic sol ids (i.e., the product of solids
concentration and percentage organic material) without regard for the
solids surface-to-mass ratio. For metals, however, the partition
coefficient is likely to be related to the mineral composition and
surface-to-mass ratio of the solids. Tada and Suzuki (1982) and to some
extent Oossls and Warren (1980) observed higher participate metal
concentrations in smaller particles. Hayter and Mehta (1983) present
similar data, as shown In Figure 83. Thus, the smaller size fractions,
particularly the readily transported silt and clay fractions, are
expected to more strongly affect contaminant behavior.
The porosity of bed sediment Is a measure of the Interstitial volume
per unit of bulk volume 1n place. Porosity may vary between 0 and 1,
with 0 signifying 100* solid and 1 signifying 10QX water In the bed.
Porosity affects the shear strength of the bed. which 1n turn affects the
rate of resuspenslon under various shear stresses or current velocities.
Bed porosity must be distinguished from individual particle porosity.
Coheslveness describes the attraction the Individual particles have
for each other. NoneonesIve sediments are composed primarily of sand and
gravel. Cohesive sediments consist of silts and clays. The behavior of
cohesive sediments differs from that of noncoheslve sediments in some
Importar* ways.
8-7
-------
40 80
%< Hum
£ 2
I I I
v I I
0
(bl
«o so
80
OJ4
« 0.20
•
u
aot
0.04
0.0
a
(cl
40 80 80 IOC
F'igun 83. Vwittion of Meal Concentration with Scdimtnt Partieit Siz«
(tarn H«yar mtt Mctra Y983>.
B-8
-------
In suspensions of nonconesive material the baste setting. ynU is the
individual grain. Particle Interactions are strictly mechanical, sucn as
momentum transfer between colliding grains. Noncoheslve sediment beds
resist erosion by the submerged weight of the Individual grains, wnich
may.provide mutual support by Interlocking or by friction (°artneniades
1971).
Cohesive sediments consist of particles small enough, with
surface-to-mass ratio large enough, that their surface physico-chemical
forces may become much more important than their weight. These forces
may Include (a) van der Waals forces. {&) surface electric charges, (c)
cheaical bonds, and (d) Interactions of.tne double layer (counter.ions
attracted from the solution). These forces are only partially understood
and may vary with the water environment (Parthenlades 1971).
For day particles tn distilled water the net effect of these forces
i
may be repulsion, allowing enormous concentrations to be suspended at
small current velocities. However', even small amounts of dissolved salt
will or ing about particle attraction (through double layer compression).
resulting m the aggregation of colliding particles Into floes having
size and fall velocity much larger, than those of the Individual day
particles. The basic settling unit Is thus the floe, the size
distribution of which may depend on the flow conditions and on the
physico-chemical•properties of the water and sediment: Cohestveness
provides a sediment bed with additional shear strength to resist
erosion. Parthenlades (1971) notes that fresh waters ordinarily contain
enough salt to bring about day particle flocculatlon. Nevertheless,
Edzwatd et al. (1974) and Hayter and Kenta (1983) found estuarlne
salinity to measurably Increase fall velocity over that in ordinary fresh
water.
8.2 TRANSPORT OF SEDIMENT LOADS
The gravity-driven (4-vnh1l1 movement of stream flow 1s resisted by
the friction of the flu.d passing over the stream bed. This results in a
variation of velocity with depth: velocity decreases near the bottom of
8-9
-------
the Water column. Tor the stream How to keep particles m suspension
the flow turbulence must counter the tendency of the particles to
settle. Consequently, the stream flow tends to carry heavier particles
near tne bed. while 1t Is likely to carry fine particles more uniformly
throughout the water column, as illustrated 1n Figure B«.
The tgtal sediment load (mass/time) passing a river cross section can
be split Into two parts using any of three related but nonequivalent
schemes (Thomas 1977):
Based on Mode of Transport:
The suspended load consists of sediment particles that are
transported entirely within the body of fluid with very little contact
with the bed. The bed Toad consists of particles either rolling and
sliding along the bed as surface creep or Intermittently leaping into the
flow, settling to the bed. and resting on the bed (Shen 1971; ASCE
1975). Such intermlttant movement is called saltation.. As there is no
sharp distinction between saltation and suspension, there is likewise no
sharp boundary between suspended load and bed load. The bed load ts
usually a small fraction of the suspended load (Thomas 1977). The
suspended load plus the bed load equals the total sediment load.
Based on Sampling Capabilities:
The tern measured load refers to that portion of the sediment load
that can be measured with sampling equipment. The unmeasured load 1s
the portion that would escape detection. Current equipment can sample
over the entire range of depth to within Inches of the bed. All but a
small percentage of the total load is usually measurable (Thomas 1977).
Based on Availability 1n the Stream Bed:
This division 1s baseu on particle sizes. Wash load is that portion
of the total load comprised of grain sizes finer than those found 1 -
B-10
-------
CQNCINTftATIOM: 1
Figure B4. plow-weighted Concentrations of Different Particle Sizes
for the Missouri River at Kansas City (Guy 1970).
B-U
-------
significant quantities In the stream bed. The magnitude of the wash load
Is controlled by the rate of entry of these particles from the
terrestrial watershed. The bed material load consists of coarser
particles, readily found In the bed; the magnitude of this load is
determined by the ability of stream flow to move the bed particles. H.A.
Einstein (1964) describes this distinction as follows:
Either the availability of material in the watershed or the
transporting ability of the stream may limit the sediment load at a
cross section. In most streams the finer part of the load, i.e., the
part which the flow can easily carry In large quantities. Is United
by Its availability in the watershed. This part of the load is
designated as wash load. The coarser part of the load. i.e.. tne
part which Is more difficult to move by flowing water, is limited in
Its rate by the transporting ability of the flow between the source
and the section. This part of the load is designated as bed material
load.
wash load Is often considered to be silt and clay, while bed material
load would be sand, gravel, and larger material. However, no uniform
line of demarcation Is possible since It depends on flow conditions and
on sediment sources.
The bed material load Is of great Importance 1n determining the shape
and stability of stream channels. For this reason considerable
engineering research has been directed toward Its prediction. Einstein
(1950), using dyed particles, was able to demonstrate that a continuous
exchange of particles between the bed and the water column takes place in
a reach where the number of particles leaving the downstream end equals
the number of particles entering the upstream end. Gessler (1971) notes
that aggradation occurs when the upstream sediment supply exceeds the
capacity of the flow to transport sediment out of the reach. Given
sufficient time, the sediment depositing at the upstream end of the reach
causes the bed slop* to Increase, which In turn Increases the velocity or
bottom shear stress, thereby increasing resuspenslon until a new
equilibrium Is attained. Degradation, on the other hand, occurs when the
sediment carrying capacity of the flow exceeds the upstream sapp1* rate.
B-12
-------
The resulting net erosion reduces the slope, wMch \n turn reduces the
velocity or bottom shear stress, thereby reducing resuspenslon until a
new equilibrium 1s attained. Thomas (1977) notes that degrading reaches
may tend to become incised while aggrading reaches may tend to meander.
Despite the amount of study that has gone Into sediment transport.
accurate predictions remain difficult. As discussed briefly in the
Guidance Manual Book VIII, Screening Procedure (Mills et at. 1982). many
procedures require data on the suspended sol Ids concentration at some
reference depth. The Einstein (1950) procedure and its modifications do
not require such data but are rather complex. The Einstein procedures.
furthermore. Involve only bed material load; wash load 1s determined by
external sources and Is thus not predictable from the stream's sediment
carrying capacity (Nordln and McQulvey 1971). Nevertheless, it can be
noted that many sediment transport formulas can be put in the form
(Sessler 1971):
gs • »(T - TC)P (Bl)
wnere g 1s sediment load per unit width, T is shear stress, t
1s a crHlcal shear stress at which sediments start to move, a is some
coefficient, and g some power.
Shear stress. T (Newton/m ), 1s given by:
t - T« (82)
where T Is the specific weight of water (approximately 9807 N/ra ). R
1$ the hydraulic radius (m), and S is the slope of the energy grade line
(m/ra). To obtain T 1n dynes/cm Multiply N/mZ by 10. The
Importance of shear stress In controlling both settling and resuspenslon
will be further discussed later.
Shen and Hung (Shen 1971) have suggested t simple empirical
B-13
-------
regression formula for predicting the suspended bed material
concentration. Using flume and river data they obtained:
log C - a0 «• ai* •• a2x » *3x (83a)
x • v1 sJ w* (83b)
where C 1s bed material load concentration (mg/i), v 1s the average
flow-velocity "(ft/sec), S again the energy slope (ft/ft), and w the fall
velocity (OT/ sec). The regression values are:
a0 • -107404.459 1 • 0.007502
ai . 324214.747 ' J . 0.004288
12 • -326309.589 k . -0.002*00
a3 . 109501.872
The standard error of log C was 0.217 (68X of the data was within 0.21?
base 10 logarithmic cycles of the predicted value).
Equation 83 and all of the numerous other approaches for predicting
bed load and bed material load may be of limited value for toxicant
modeling. Much of the toxicant may be adsorbed to the finer particles
(with higher surf ace-to-mas s ratios) comprising the wash load. By
definition of the wash load, these finer particles are not found in the
stream bed 1n substantial quantities. By Implication, toxicant bound to
wash load particles would have limited Interaction with the bed.
S3. DEPOSITION ANO EROSION
The ntt particle flux (vg/cm /sec) across the bed-water Interface
can be expressed as the difference between the deposition (settling)
flux. Sg, and the erosion (entralnaent or resuspenslon) flux S.
(Fukuda and lick 1980). The deposition flux Is related to the settling
velocity. w$ (cm/sec) , and the water column solids concentration, m.
(mg/l). by:
S0 • ws «i (P4)
B-14
-------
Th« erosion flux 1s related to the resuspension velocity, w , and the
bed solids concentration, n>2. by:
The assumption here 1s that deposition and resuspenslon are independent
processes that can occur simultaneously.
S.3.1 Deposit Von
'""^^^•^••'••'••""•""^"''^^^•^^^ i^B* _ i
In evaluating the settling velocities :of sediment particles, three
physical processes can be considered (O'Mella 1980): (a) gravity,
(p) Brewnlan motion or molecular diffusion, and (c) turbulent or laming
fluid shear (velocity gradients). The degree to which each of these
processes governs particle behavior depends 'on the characteristics of
both the fluid and the particles.
The effect of gravity on .particle settling can 3e expresses in terms
of Stokes Law:
vs • (g/lBw) (PS - p)d2 (86)
where v, 1s the Stokes settling velocity (cm/sec), g 1s the
5 2
acceleration of gravity (980 era/sec ). t • p is the difference
in the densities of the particle and of water. «
8-15
-------
Aggregation Into floes occurs when particles having sufficient
physico-chemical attraction collide with each other.. Such coHsions may
result pMmlaMly from Brownlan motion for small particles, and fluid
shear and differential settling velocities for larger particles.
Valloulis and List (1984) nave modeled these processes in a
sedimentation basin. Olsaggregatlon of flocculant particles may a'so
occur through fluid shear and through collisions (Lick 1982). ucftrm and
Weber (1980) noted that laboratory measured settling velocities were
substantially more rapid than expected from the Stokes velocity of tne
individual particles, apparently due to particle aggregation.
vertical movement of particles may also be brought about by
dispersion, consisting of BrownIan motion and turbulent diffusion
(resulting from eddies produced by fluid shear). Away from the bed-water
boundary Brownlan diffusion 1s expected to be negligible compared with
turbulent diffusion. The Importance of turbulent diffusion relative to
settling can be directly compared (Lick 1982). A characteristic time for
settling to occur is t » H/v . where H 1s depth of water. A
7
characteristic time for turbulent diffusion Is ttf « H /2Q , where
0 Is the vertical eddy d1ffu$1v1ty. The dominant mechanism is that
with the shorter characteristic time. Increasing the particle sUe and
the depth favors settling as the dominant mechanism; Increasing the
turbulence favors diffusion (Lick 1982). Thus, the HydroQual (1982)
recommendation to reduce w to perhaps 10% of v in shallow streams
seems consistent with this reasoning.
In this vein Hayter and Nehta (1983). constructing a general model of
particle behavior In estuaries, applied the relationship:
w$.(1- J-)V (67)
where TC Is a critical shear stress above which little deposition of
the sediment wo Id occur (as measured In flume tests). They suggest a
minimum value of w being SX v v .
8-16
-------
Uck (1982) applies a different line of reasoning to a thin f»'m of
water near the bed-water interface, where turbulent diffusion 1s assumes
to decrease. The flux through this film can be written:
(0
dm,
__ * v m,
— s 1
where SQ - S- (ug/cm - sec) is the net downward flux (per
Equations B* and 85). Oy U vertical eddy diffusivity (cm2/sec).
-------
vd - 0.06 (T/*) (»/Otaii) (811)
Figure 85 Illustrates the solution of w , v . and v over a range
of particle sizes for a shear stress, T, of 10 dynes/cm . For large
particles w • v and tne effect of diffusion through trie boundary
film Is negligible. For small particles w « v and the effect of
s d
gravity settling Is negligible. The particle size at which control of
deposition shifts from diffusion to settling depends on shear stress.
This theoretical approach assumes that all particles that hit the bed
surface adhere to 1t. This limitation night be related to why increasing
T Increases w for small particles, a contrast to the previously
described empirical approach (Equation 87), where increasing T
decreases w$.
8.3.2 Bed Erosion
Erosion or entrapment Is the scour of sediments from any part of the
stream bed Into suspension In the water column. To remove material from
the bed the flow-generated forces must overcome the srtabUUIng forces.
which consist of the Immersed weight and (for jilt or clay beds) the
cohesive strength. Lee et al. (1981) and Lick (1982) 11st five factors
controlling entrapment: (a) turbulent shear stress .at the bed-water
Interface, (b) water content (porosity) of the bed. (c) sediment
composition, Including nlnerology, organic content, and size
distribution, (d) activity of benthlc organisms, (e) vertical
distribution of sediment properties, related to the manner of
deposition.
Lee et al. (1981) and Fukuda and tick (1980) found entrapment rates
to be directly proportional to shear stress and water content. Also.
sediments with a fine-grained (clay size) fraction deposited at the
surface were more easily erodable than vertically well-mixed sediments
with the same composition. For example, after a brief net deposUVonal
period, the fresnly deposited sediments will tend to have a smaller mean
B-18
-------
10-3
r4
0.1 1.0 10.0
PARTICLE SIZE
Ftgur* BS. Deposition velocity w} as a function of p*mel«
for a sh«w stren of 10 6yrm/an2 (from Lick 1982).
-------
, sUe and a Higher water content; therefore, these surface sediments will
be wore easily entrained when shear stress Increases.
The erosion rate may be formulated In terms of the shear stress on
the bed. T. and the erosion resistance of the bed. The erosion
resistance of the bed is generally empirically estimated; U cannot be
predicted solely from the basic properties of particle size distribution
and porostty.
Figure B6 Illustrates a typically measured relationship between
erosion flux and shear stress. Once beyond critical shear stress,
T . the erosion flux, S-. Increases rapidly. In modeling
consolidated estuaMne beds, Hayter and Mehta (1983) estimate
CB12)
where both a and T are empirically derived constants.
6.3.3Particle Exchange: Continuous yersuf Discontinuous
The conservation of sediment load through a stream reach may occur
under two conditions: (a) deposition and resuspenslon are occurring
continuously, but at equal rates (SQ • S^. or w{m^ • wrj m^),
or (b) deposition and resuspenslon rites are both zero. The former
situation can be considered an equilibrium state; the Utter cannot. For'
the equilibrium condition, the suspended solids concentration would be
given by m. » m, «../«.• p<>r *n* z*r(> r*te situation, whatever
concentration exists at the head of the reach Is carried downstream
unchanged.
In flume experiments wlttr noncoheslve sediments, Einstein (1950)
demonstrated (using dyed particles) that conservation of load was the
result of an equilibrium balance between deposition and resuspenslon.
B-20
-------
0.02
I
N
g
0.01
I I I I
I t I t
0.2 0.4 0.6 0.8 1.0 1.2
Figur* 86. Examplt of Relationship b«cw««n Erosion !Utt S£ and Bed Shear Stress
(afc«r Hayear and M«hc« 1983).
-------
Although tne concentration did not change, a continuous exchange of
particles was occurring through tne simultaneous processes of deposition
and resusoenslon.
whether cohesive sediments exhibit the same behavior 1s open to
question. For clay particles Parthenlades (1971) and Hayter and Menta
(1983) note evidence that the critical shear stress below which no
•erosion can occur Is greater than the critical shear stress above which
no deposition can occur. That 1s, there appeared to be a shear stress
range within which neither erosion nor deposition is s'gniflcant. *itn!n
this range the velocity was sufficient to prevent the suspended partic'es
from flocculating and adhering to the bed but Insufficient to break tne
cohesion of the consolidated bed particles. Above this range only
erosion occurs, while below this range only deposition occurs. .
In the experiments with lake sediments. wMch are likely to be ?'ne-
and more cohesive than river sediments. Lick (1982) observed' a complex
behavior seemingly Intermediate between the continuous and simultaneous
deposition and erosion observed for sand and the alternating deposition
or erosion observed with clay. He found that to a partial degree a
continuous exchange of particles was occurring through simultaneous
deposition and erosion. Some types of particles, however, tended to
remain only in the water column; others tended to remain only in tne bed.
Lick (1982) thus notes that erosion and deposition are not completely
reversible and that a hysteresis effect 1s often present. For a
particular shear stress, the steady state concentration will be mgner if
the shear stress (and suspended concentration) had .been decreasing over
time than If 1t had been Increasing over time.
8.4 SEDIMENT SOURCES
External sources of suspended sediments can or'gln.ce Front either
ptMnt or nonpolnt sourr-s. ?o1nt sources of sediments are generally
minimal, and in any event, are easlly quantifiable. Nonpolnt sources of
8-22
-------
concern ire governed by natural and culturally accelerated erosion
processes. Although urban runoff can have significant localized impacts
on streams (Porcella and Sorenson 1980; Tomllnson et al. 1980), the
preponderance of sediments delivered to U.S. streams by accelerated
erosion are derived by sheet erosion from agricultural lands (Qmernlk
i • • r
1977}. Sheet erosion Is the wearing away of a thin layer of land surface.
Sheet erosion rates depend on rainfall and flow properties.
geomorphology and topography, and land use (Including vegetative cover
and soil management practices). Although predicting soil loss is very
complicated, the Universal Soil Loss Equation, developed Dy wiscnmeler
and Smith (I960), has been extensively used to estimate average annual
soil loss in tons/acre. To predict sediment yield of a watershed, the
USLE 1s coupled with a 'sediment delivery ratio", the fraction of an
area's soil loss that actually reaches the stream. Taole 83 summarizes
the range of sediment yields expected 1n various regions of the country.
Details on use of the USLE and sediment delivery ratio are contained
in Volume VIII of the Guidance Manual (Mills et al. 1982) and. in several
other EPA publications. Including HcElroy et al. (1976). uiS. EPA (W6).
and 21 son et al. (1977). That material will not be repeated here.
However, it can be noted that for many water duality modeling purposes.
the utility of the USLE Is constrained by being limited to annual average
soil loss. It Is not Intended for event modeling (Wlschmeier 1976). To
predict sediment yield from single events, Mills et al. (1982) describes
the Williams (1975) modification of the USLE.
Several other approaches are available for predicting the sediment
and pollutant yield of events. For urban runoff these include U.S. EPA
(1975), Mills et al. (1982). Gelger and Oorsch (1980). and Klemetson et
al. (-1980). For agricultural runoff they Include Williams (1980).
Novotny (I960), and Oonlglan and Crawford (1976). Given sufficient
resources, the metbw'l c* choice might be the Agricultural RunofF
Management Model (ARM) (Oonlglan and Oavls 1978).
8-23
-------
TABLE 53. SEDIMENT YIELD FROM DRAINAGE AREAS OF 100 SQUARE
MILES OR LESS OF THE UNITED STATES (Todd 1970)
Region
i
North Atlantic
South Atlantic Gulf
Great Lakes
Ohio
Tennessee
Upper Mississippi
Lower Mississippi
Sourls-Red-Ra1ny.
Missouri
Arkansas wMte-fted
Texas Gulf
Rio Grande
Upper Colorado
Lower Colorado
Great Basin
Co lumo la-North Pacific
California
Estimated sediment velld
High
1,210
1,850
800
2,110
1,560
3.900
8.210
470
6.700
8.210
3.180
3.340
3.340
1,620
1,780
1,100
5.570
Low
tons/sq mi/yr
30
100
10
160
460
10
1.560
10
ID
260
90
150
150
150
100
30
00
Average
250
800
100
350
700
800
5.200
50
1.500
2.200
1.300
1.300
1.800
600
400
* 00
1.300
B-24
-------
APPENDIX C
fiUO AND LABORATORY METHODS
FOR
FlINT RIVER SURVEYS
Cranbroofc Institute of Science
9311 Groh Road
GrojJe He. Michigan. 48138
August 1984
-------
Metals in the ambient environment frequently occur at level* below the
detection limit of many of the analytical methods commonly employed by State
and Federal agencies. Consequently. 1n order to assure obtaining data
useful for model calibration, the WLA analyst needs to be able to discuss
the overall adequacy of the methods used by laboratory and field personnel.
The key Issues are (a) the sensitivity, and perhaps accuracy, of the
analytical methods and (b) the freedom from detectable contamination during
sample handling, a problem If very sensitive analytical methods are used.
This appendix describes the sampling and analytical methods found to be
useful during the Flint River surveys.
The sampling program began In August 1981 and ended in March 1982.
During this time, four sampling surveys were conducted on the Flint
River, water was analyzed for the total and dissolved forms of cadmium,
copper, and zinc. Chemical and physical parameters of the water, wnic.i
are believed to Influence metal spec 1 at 1 on or to interact with solids,
were also analyzed. The parameters included were suspended solids. on.
specific conductivity, hardness, dissolved oxygen, total alkalinity, and
temperature. River flow and velocity were also estimated.
All aspects of sample collection, filtration, and preservation were
evaluated so that the final analytical results reflected actual quality
of the river water sampled. Care was taken to choose equipment made of
materials that would minimise contamination.
River water was collected using a half-gallon linear polyethylene
wide mouth Malgene bottle fixed to a polypropylene rope with
stainless steel clamps. The bottle was weighted from below with lead,
and the bottle mouth was sheltered with a plastic awning or Hd suspended
from the rope just above It. The purpose of the 1ld was to keep out
debris as the sample was pulled up.
C-l
-------
Sampling was usually done from bridges at three market) positions that
are at 1/4 trie distance across the stream, at 1/2 the distance, and at
3/4 the distance. The sampling device was lowered quickly below the
surface of the water, .rinsed once, emptied, then filled again. Three
such samples from the various bridge positions were combined in a ten-
liter polyethylene carboy which was previously rinsed with some water
from the first sample. It 1s from this composite sample that an aliquot
for analysis was taken.
A sample processing scheme Is-presented In Figure Cl. All filtering
operations were conducted in the-mobile laboratory as well as PH, con-
ductivity, alkalinity, and metal preservations. Temperature and
dissolved oxygen were measured In-sltu. Total metal analysis, dissolved
metal analysis, and hardness were analyzed at the £PA large Lakes
Research Station.
Trace Metals
Trace metal samples were collected 1n new linear polyethylene bottles
washed with hot water 1n a dishwater, rinsed with delonijed water, with
30% v/v nitric acid, and with delonlzed water; then they were soaked in
2X v/v nitric add for two weeks, rinsed six times with delonUed water,
and dried In an oven with the caps ajar. Bottle blanks were analyzed to
Insure that contamination was kept to a minimum, and to provide a value
used to correct for low level background contamination. Ten of every 100
bottles were randomly selected and analyzed for background levels. A
blank test was performed by filling the bottles with a pre-analyzed .-
acidified batch of water (3m nitric add/liter). This batch was
generally below the detection limit for each metal. The solutions in the
bottles were then analyzed, and the resulting mean concentration is the
bottle blank. The stored bottle blank samples were analyzed with the
river samples.
C-2
-------
WHOLE »ATM
FILTH
OISSOLVCO METALS
WHATMAN tiff
RESIDUE
SUSPENDED SOLIDS
CONDUCTIVITY
TEM^CMATURE
DISSOLVED OXYGEN
MAROHESS
TOTAL ALKALimTY
TOTAL METALS
FIGURE Cl SAMPLE PROCESSING SCHEME FOR FLINT RIVER WATER
03
-------
Every tenth sample included a duplicate aliquot or spin of the
composite water which was processed the same way as any other sample
collected. The standard deviation calculated for samples and their
duplicates gives an estimate of the overall precision, including both
field and instrumental variations.
The following equation was used to calculate the standard deviation.
Standard deviation • , u - where d • difference between the sample
/ Td and its duplicate
* * k • number of duplicates
Since the matrix of the sample can affect the precision, river water from
each survey was handled separately. See Table CI for results. Detection
limns for the metals analyzed are reported In Table C2.
Certain samples'were analyzed once a day for a number of days as
"between run' replicates (Table Cj). The variability of these replicates
is assumed to be due to laboratory and Instrumental procedures only. The
field duplicates mentioned earlier have potentially higher standard
deviations since there is additional variability from field techniques.
I.e.. bottle blanks, filtering and possible non-homogenity of the water
in the 10 composite sample. Comparing the results of Tables Bi and 83'
suggests that the variability of .the results for ail the metals was
mainly' due to laboratory and instrumental procedures.
Total river water (unflltered) was collected in a 500 mi linear
polyethylene bottle ore-cleaned as above. A 100 ml portion of that
water was filtered through a .45 HA Sartorlus cellulose acetate
filter. The filtering apparatus was a Ml 111 pore polycarbonate
SterlMl filtration system. Before use. the system was soared in
4X v/v UNO,, then rinsed well with delonUed water. The filter was set
1n place, and SO ml of delonlzed water was filtered, then discarded.
Fifty mi of sa-,Me was then filtered and dVscarded. Sample water as
then filtered until the filter began to clog. Before filling the 175
mi bottle with filtrate, the first 50 mi of filtrate was used-to
rinse U out.
C-4
-------
TABLE Cl. RESULTS OF FIELD DUPLICATES (SPLITS)
Metal ' •
Dissolved Cd
Total Cd
Dissolved Cu
Total Cu
Dissolved Zn
Total Zn
August 1981 Survey -
Flint River Samples
Number of
Pairs
19 '
18
19
18
20
.- 18
Standard
Deviation
fuO/l)
.05
.07
1.1
.9.
4
4 .
December 1981 Survey -
Flint River Samples
Number of
Pairs
6
7
7
7
7
" 7
Standard
Deviation
^t«q/l)
T.02
.04
.4
.3
S
W2
March 1982 Survey
F1»nt River Samole-
Number of
Pairs
14
14
13
U
U
13
Standar-
Oevlatl-.
(.iiO/ll
.03
.04
.5
.7
2
2
NOTE: If 1 {Detection Limit] < [Measured Metal Concentration) < (Detection Limit],
then the result U recorded as "T" preceding the detection limit.
If (Measured Metal Concentration] < . (Detection Limit], then the result is
recorded as *W* preceding a value . the detection limit.
TABLE C2. DETECTION LIMITS
Metal
Cadmlua
Copper
Zinc
Detection Limit (uS/l)
.02
. -08 ,
4
-------
TABLE C3 RESULTS OF BETWEEN-RUN REPLICATES
Metal ' Number of Samoles
Dissolved Cd
Total Cd
OU solved Cu
Total Cu
Dissolved In
Total Zn
6
IS
4
18
5
18
Standara Deviation fyq/i)
.03
.07
.6
1.2
W2
2
-------
The models 603 and 460 (Perkln Elmer) atomic absorption Instruments
equipped with graphite furnaces were used to analyze the samples. The
drying, charring, and atomUatton program were optimized For river and ef-
fluents using optimization as described In 'Analytical Methods for Atomic
Absorption Spectrophotometry Using the HGA GrapMte Furnace.* Perkin
Elmer (1977). See Table C* for Information on analytical conditions.
All fTameless analyses were done In duplicate while the flame analysis
(zinc) was done in triplicate.
If chemical Interferences were present which enhanced or suppressed
the analytical atomlzatlon signal, then the standard method was used to
calculate the sample concentration. If no Interferences were present,
then samples were calculated directly from a linear regression of the
synthetically prepared metal standards. The latter case still involved a
standard addition determination on every fifth sample 1n order to monitor
recovery. Recovery here 1s defined as the slope of the standard addition
calculation on a sample, times 100. divided oy the mean slope of the
standard addition on standards.
No chemical Interferences were found when analyzing copper and zinc.
However, since Interferences were present which suppressed the analytical
signal for.cadmium, standard additions were used to determine the
concentration of this metal.
Standards were prepared fresh dally and acidified (3 mi
HNOj/l). Typically. 5 standards were digested along with every 20
samples and 3 blanks. The digestion procedure was a modified nitric acid
digestion for total metal determination from 'Methods for Chemical
Analysis of Water and wastes' (U.S. E.P.A.. 1974). Hydrochloric acid was
eliminated from the EPA procedure due to the Interference of chloride ion
with the analysis of zinc and cadmium (Analytical Methods for Furnace
C-7
-------
<* «i rw
§
§3
a »*
wt
e
f i-
iS, 2- 'I3
•/•a — -a M» ' ^
•— x o e e e
MM W X « » £
•o i a "5 e IS
e
a
e
-^ 8
in o*
j[
e
s
e e
*
I
is
i'-
o
3 «»
e —
m e
-------
Atomic Absorption Spectroscopy, Peru in Elmer. I960). The digested
consisted of del on(zed water plus the same amount of HNO added, to the
samples and standards. The median absorbence of the blanks was used to
correct the samples. Digested standards'were corrected by a standard
blank. Calculations of concentrations were tnen based on these corrected
absorbences. The filtered samples {dissolved) received no sample
pretreatment. :
In order to determine contamination Introduced in the fi'ter'.ng
process, two filter blanks were taken in each eight-hour shift in the
field. This Involved filtering an aliquot of deionUed water. An
unMHered sample of this delonlzed water was also taken at tne same time.
This unflltered sample is the batch blank in Table CS. Tne analytical
results of the two types of samples were compared; if they were equal.
then no filtering contamination was believed to occur. Equality nere U
confirmed by a T test. The results in Table CS snow that no correction
was required for the filtering process in the August, Decemoer, and *arc.i
surveys.
Since tne filter blank results from both a bottle blank and .a Blank
for the filtering process, It Is assumed that if the filter blank -ere
negligible, then the bottle blank would also be negligible. This was the
case for all the metals during the surveys except for copper in August
1981. Bottle blanks were therefore checked For copper in the August 1981
.set. The levels found in these bottles were below the detection limit
for all three metals. We therefore concluded that the .447 ug/t Cu
In the sample of batch water was due to the copper In the batch water
on 1 y.
Trace metal water samples were preserved by adding 3 mt of «NO
per liter of sample. Samples were refrigerated at 7»C.
C-9
-------
TABLE C5. FILTRATION BLANKS
(Note: 3.a. - Batch Blank and F.B. • Filter Blank)
Metal .
Cadmium
Cadmium
Cadni urn
Coooer
Cooper
Cooper
Zinc
Zinc
Zinc
Survey
Aug. 81
Dec. 81
Mar. 82
Aug. 81
Dec. 81
Mar. 82
Aug. 81
Dec. 81
Mar. 82
Number
(B.fl.. F.B.)
47. 47
15, 13
18, 18
47. 47
15. 13
17, 17
47. 47
16. 14
18. IB
Standard
Mean
Ug/D
(B.B.. F.B.i
0.011. 0.022
0.001. -0.001
0.018. 0.038
0.447. 0.562
-0.067. . 0.092
•0.026. -0.104
0.681. 0.745
2.938. 3.143
•0.487. .0.394
Deviation
(ug/D
(B.B.. F.B.I
0.022. 0.027
T-Ttst
Result
•Same
0 . 008 0 . 009 Same
0.009. 0.108 Same
0.440. Q.563
0.209. 0.263
0.494. 0.239
1.476. 2.027
1.769. 2.107
1.889. 1.83
Same
Same
Same
Same
Same
Same
•At the 95X confidence level the mean batch blanks and Filter blank* were
equal; therefore, no blank correction was needed for the filtering process.
C-1Q
-------
Results of several intercomparlson studies are presented in Table
CS. In both of tftese series, performance was considered 'good.' The
true values of the unknowns fell within our 9SX confidence interval.
This interval Is defined as our reported result plus or minus two
standard deviations.
*
for both digested and dissolved samples, five standards were run at
the beginning and end of each day's run. Half of the standards at the
beginning of the day were spiked with known standards (standard
additions). The remaining standards were spitted at the end of tne day.
The average slope of these standard additions to standards was used in
the denominator of the recovery formula.
Convent 1ona j^Rarame t ers
Methods used for non-metal parameters are described in Table C7 . Dis-
solved oxygen and temperature were In-sltu measurements. Specific con-
ductivity, pn, total alkalinity, and total non-MIterable residue
(suspended solids) were analyzed In the mobile laboratory. Hardness was
analyzed at the Grosse tie. Lab.
c-u
-------
TABU Co INTERCOHPARI50H WITH U.S. EPA ENVIRONMENTAL MONITORING AND
SUPPORT LABORATORY. CINCINNATI
(Concentrations In
Sample
Q.C.. Series
475
Sample 1
Sample 2
O.C. Series
575
Samp 1e 1
Cd
Cu
(Result. True value) I (ttesult^Truc valued (Result. True
11.3, V.5)
(-31. .46)
(7.8. 6.0)
(1.4. 1.4)
(65. 60)
(16.7, 12)
(29. 30)
C-12
-------
TABLE C7. ANALYTICAL METHODS - CONVENTIONAL PARAMETERS
Parameter
Temperature
Dissolved
Oxygen
Specific
Conductivity
OH
Alkalinity.
Total
Residue, Total
Non-FMterao1e
Hardness
Method
Thermometry
Dissolved
Oxygen Probe
Electrical
Conductance
pH Electrode
Tltratlon to
pH 4.5 with
.02* H SO
Gravimetric
Measurement
mrlmetrlc
Equipment/
Instrumentation
Thermometer
Yellow Springs
Instruments Co. , Inc.
Beckman Conductivity
Bridge (Model RC-19]
Fisher Accumet (Model
520 pH/IOtt meter)
Fisher Automatic
TUratlon Model 471
GfF Filters (Wha titan)
SartoMous 2003 MP1
Balance
Fisher Automatic
TUratlon Model 471
Used 1n the Manual
Node
Source of Method
Standard Methods (1975)
Yellow Springs Instruments
Co., Inc.
Beckman Manual (1973)
Fisher Instrument Manual
No. 26285
EPA, Methods for Chemical
Analysis of Water and
Wastes (19?9)
EPA, Methods for Chemical
Analysis of water and
wastes. (1974)
Standard Methods for the
Examination of water and
Wastewater, 14th Ed. (1975)
C-13
-------
APPENDIX 0
BEHAVIOR OF HALOGEN DISINFECTION RESIDUALS
-------
APPENDIX 0
BEHAVIOR OF HALOGEN DISINFECTION RESIDUALS
This appendix presents information on the aquatic fate of was tester
disinfection residuals. This discussion has been added at tne request of
the Office of water Programs Operations (Construction Grants).
recognizing that (a) chlorine residuals are commonly discharged in
quantities toxic to aquatic life, and (o) chlorine 1s not discussed in
the portion of the Guidance Manual covering SOD, DO. and ammonia
(OMscoll et al. 1983). and since U Is not a "priority pollutant', it is
not covered by MaBey et a). (1982) and Callahan et al. (1979).
As chlorlnatlon is by far the most common disinfection practice in
this country, the emphasis is on chlorine residuals; nevertheless, some
Information on bromine chloride Is also included. The discussion 1s
Intended to apply to fresh water; halogen chemistry In saltwater.
described by Haag and Lletzke (1981). Is not identical to that in fresh
water.
The discussion Is limited to the fate of halogen oxldants. It does
not deal with the formation of halogenated organic by-products; such
formation 1s of minor Importance In determining the half-life of the
disinfectant Itself. Although some of these by-products may be
carcinogenic, their production Is of greater public health significance
during potable water treatment than during wastewater disinfection
(Metcalf & Eddy 1982). Information on production of halogenated organIcs
Is provided by the National Research Council (1979) and Jolley (1975).
It Is worth noting here, however, that the formation of trlhalomethanes
(the by-products of greatest concern) appears to be depressed by the
presence of ammonia, a usual constituent of municipal wastewaters that
have not undergone complete nitrification (Metea 1C & Eddy 1982).
The follow,ng -"scirsslon has been edited from the PttcaVf & Eddy
(1982) report. Impacts of Wastewater Disinfection Prar'ices on Coldwater
0-1
-------
Additional details on disinfectant chemistry can be found in
Weber (1972).
0.1 AQUATIC fATE
Host wastewater treatment plants discharge effluent through an
outfall pipe or through a small ditch which then combines with the
receiving water. In such cases, Initial mixing of the effluent depends
upon the outfall or ditch characteristics, the river characteristics, and
the magnitude of flows of each, for a few large treatment plants, waste
1s discharged through submerged multi-port dlffusers. • >
i.
A common method of estimating the dilution of wastewater effluent U
to calcuate the ratio of river flow to effluent discharge flow. This
number may range several orders of magnitude.- Typical ratios may be 100
for small plants discharging ta average sired rivers, and 1 or 2 for
plants discharging to small tributaries.
j..
The pitfall of using the ratio of flows to estimate dilution 1s that
complete mixing (lateral and vertical) 1s Implicitly assumed. In cases
of small tributaries with low .dilutions (e.g.. 1 or 2) thVs may be a
reasonable assumption. However, for higher dilutions (e.g., 100 or
more), a long distance 1s often necessary to complete the lateral -mixing
process. In most cases, complete vertical mixing may be a reaonable
assumption.
Chlorine
The initial chemical reactions of chlorine In aqueous solution depend
on.the application form. Chlorine gas hydrolyzes 1n solution as shown
below:
C12 » H20—-HOC1 » H* » Cl"
This *eact1on 1s rapid and essentially complete If the pH 1s greater than
6. Application of sodium or calcium hypbchloMte will yield BypochloHte
0-2
-------
Ion (OC1~) initially, wnlch win rapidly establish equilibrium
nypochlorous acid (HOC): . ,
NaOCI
rHOC! • Na » OH
Chlorine present 1n wastewater or receiving waters 1s usually
measured as total residua! chlorine (TBC>. TRC 1s the sum of free
residual chlorine and combined residual chlorine. Tree residual chlorine
(FRC) 1s the free available oxldant In solution consisting of '
hypochlorous add (HOCT) •and hypochlorlte ion (OCl"). Combined
residual chlorine (CRC) generally refers to the chloramlnes formed wften
hypocnlorous acid reacts with ammonia. Free chlorine can also react «Un
other organic compounds containing aialno groups to form organic
chloraralnes. Bactericidal strength 1s 1n the.order: nypocnlorous acid >
hypochlorlte Ion > chloraralnes/ '
Chlorine demand occurs both' 1n wastewater and the receiving waters.
CMorlne demand is the difference Between the aopplted chlorine dose and
the free residual chlorine. It fs due to a variety of reactions Ine1u
-------
TABLE 01. PRINCIPAL REACTIONS OF CHLORINE IN SOLUTION
Reaction type
Hydrolysis
Ammonia
Substitution
Oxidation
Inorganic oxidation
Decomposition
(with sunlight)
Organic reactions
Oxidation
Substitution
C12 * H20— »HOC1 » HCL
HN3 » HOC1
I HNC1; * HjO
* H2°
HOC1 » 3 H*
HOCl * 2
» 3 H» » Cl-
2 HOCl
•2 H* » 2 C1- *
RCHO » HQC1 —-.RCOQH • H* * Cl~
RNH2 *' HOCl --^ RNHC1 »
0.4
-------
AAPtO
REACTIONS
SLOW
REACTIONS '
i
FREE CHLORINE
HOC1»-MDC!" * M*
JNH,
CH LOR AMINES
/
CHLORO-ORGANICS
O
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FISURE 01. DIAGRAM OF CHLORINE REACTION PATHS IN FRESHWATER
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of free chlorine Is an order of magnitude faster than of monochloramlne.
and (3) Htld decay rates are.normally an order of magnitude faster than
laboratory rates.
Bromine Chloride
Reactions of bromine chloride 1n Freshwater are more complicates
since two halogens are Involved. Reactions In Freshwater Include the
production of both hypobromous add and hypochlorous add:
BrCl * H2Q = HOBr » HCl '
Brj » HjO ——- H08r » H* » Br
Clj »• H20 r *" HOC1 » H* » Cl
As with chlorine, hypooromous acid will react with ammon'.a to Form
bromamlnes. Roberts and Gleason (1978) presented data on the decay of
bromine residual in seawater with ammonia, concentration; of 0.2 mg/1.
Decay was extremely rapid since bromine residuals were-not detected aFte-
. only two hours. NO other data on bromine -residual decay were available.,
0.2 CASE STUDY
The concepts discussed above are applied here For demonstration
purposes. The Connecticut Department of Environmental Protection
conducted physical, chemical, and biological measurements to assess the
Impact of the falrfleld Hills Sewage Treatment Plant on Deep Brook in
Mewtown (CT DEP 1981). The plant provides advanced treatment with the •
main treatment units being primary and secondary settling tanks,
trickling Mlttrs, and Intermittent gravity and sand filters. Current •
plant flow averages about 0.3 mgd. Plant effluent concentrations of TRC
typically range From 0.0 to 3.0 rag/1.
Deep Brook 1s a fast flowing, well oxygenated tributary of the
Pootatuck River with art average Flow of 0.27 m^/s and a 7-day 10-year
low flow of 0.014« /$. The plant effluent discharges into Deep Brook
about 610 meters above the Pootatuck River. Pootatuck River average flow
0-7
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1s 1.1 n3/s and 7-day 10-year low flow 1s 0.12 IB /s. Measurements of
tn-strea» and effluent TRC were conducted on August 29. 1980.
Mean values of TRC concentrations measured using the amperoraetric
tltration method are presented 1n Figure 02. On the date of these
measurements, river flow was 0.025 m /s and plant flow was 0.014
m3/s. Total mixing Is said to occur 15 meters downstream of the
discharge point, although the basis for this statement (i.e.. visual, dye
study, etc.) 1s not stated. It seems reasonable to expect that mixing
would be rapid with the plant flow nearly as large as the river flow
(dilution ratio of 1.8). As shown 1n Figure 02. at a point IS meters
downstream the effluent concentration of TRC had been reduced from 3.8 to
2.0 mg/1. or diluted 1.9 tines. This tends to support the 15-meter
complete mixing assumption.
The TRC concentrations decreased to 0.2 mg/1 (a factor of 10) at a
point Just before the confluence with the Pootatuck River. Since no
dilution water enters the brook In this reach, the loss of chlorine was
due to chemical reaction and decay. When in-stream chlorine
concentrations are plotted on semi-log paper, a straight line gives a
reasonable fit with the data. Indicating that the die-off of chlorine for
this case Is approximately first order. Using the formula for
first-order decay (Eauatlon 2.3 in Section 2.4 of the text), a rate
coefficient of about 100 per day 1s calculated. (To obtain this value, a
stream velocity of 0.3 a/s has been assumed, as the actual value was not
given.) This Indicates that the 1n-stream loss of chlorine is extremely
rapid. However, the value of 0.2 mg/1 1s still more than an order of
magnitude higher than published maximum In-stream criteria. In Pootatuck
River. TRC could not be detected after the Deep Brook confluence.
Biological measurements Indicated a highly stressed condition 1n Deep
Brook downstream of the discharge.
In suranry. a slnple dilution calculation followed by a first order
reaction coefficient was adequate 1n this case to estl -cte the ol
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concentrations 1n peep Brook. However, tftls method must be used with
caution for several reasons. If stream-flows are higher, complete ml
would not occur as aulckly, and this method does not apply in trie zone of
Incomplete mixing. Also, as discussed earlier. 1n-streara reactions are
extremely variable depending on environmental factors such as light.
temperature, and streamHow.
0-10
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ATTACHMENT I
WATER-SEDIMENT PARTITION COEFFICIENTS
FOR PRIORITY METALS
by
HydroQual, Inc.
1 letftbridge Plaza
Maftwah, New Jersey 07430
November 1982
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ACKNOWLEDGEMENTS
The technical analysis reported in she study was performed by
•lichael T. Kontaxis, Project Scientist. Dominic «. OiToro and
Donald J. O'Connor served as Project Consultants and provided
technical guidance. John P. St. John served *s Princ:pal-;n-
Charge and drafted the report.
William L. Richardson of the U.S. Environmental Protection
Agency, ERO-L at Gross* lie, served as Project Officer for -the
Government.
1-1
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ABSTRACT
The Office of Water of the U.S. Env i ronnental Protection
Agency is responsibly for inanaqinq waste load allocation (WLA)
activities throughout the nation. These orocedures generally
involve the application of mathematical model inq activities which
require specialized information Cor proper implementation. One
area which will receive increasing attention in this regard is
the fate and transport of toxic pollutants, particularly certain
priority heavy metals. An important characteristic of these
materials is an affinity to complex and/or be adsorbed
(partition) to partlculate materials in the natural environment.
As realistic modeling frameworks must properly traex mth
dissolved and particulate forms of substance in tfte receiving
water environment, it is important to determine partition
coefficients for the priority netals for use in these analyses.
It w4s the purpose of the investigation reoorted herein to
retrieve information and data by which to docunent and/or
calculate water-sediment partition coefficients for various
priority heavy metals. In addition, the available data was to be
examined to determine possible functional relacionshiss 5 -no no
partition coefficients and various environmental water quality
variables.
1-2
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TABLS OF CONTENTS
Section Paqe
No. • NJL?
1 INTRODUCTION 1-i
-1.1 BACKGROUND 1-1
1.2 OBJECTIVES OF THE. STUDY i-2
1.1 SCOPE OF THE REPORT i-3
2 CHEMICAL PARTITIONING 2-1
3 SUMMARY OF AVAILABLE DATA....;. 3-i
3.1 DATA REQUIREMENTS 3-1
3.2 SOURCES OF DATA 3-2
3.? AVAILABILITY OF DATA 3-3
3.4 -CLASSIFICATION AND DISTRIBUTION OF
DATA 3-5
4 METHODS OF ANALYSIS. '. 4-1
4.1 TECHNICAL OVERVIEW 4-i
4.2 9 IN ANALYSIS. *-l
4.3 STATISTICAL ANALYSIS 4-3
5 RESULTS OF ANALYSIS..., . 5-1
5.1 .PARTITION COEFFICIENTS 5-1
5.2 CORRELATION WITH ENVIRONMENTAL
VARIABLES 5-2
APPENDIX
1-3
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SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
The available technical literature contains very limited
useable data for determination of partition coefficients for
priority metals.
Of the various computerized data bases investigated in this
study, the water quality file of STORET contains the lariest
amount of pertinent information by a large margin,
approximately 29,090 useable records from various water body
types. The most applicable data were derived from water
column samples; bed sediment data, while available, did not
provide sufficient information for calculation of partition
coefficients.
Retrieved data were in the following order of abundance by
priority metal: tine, copper, 'lead, arsenic, nickel,
chromium, cadmium, mercury, and silver. Analysis was
confined to data collected in streams, and lakes. Insuffi-
cient data were available for analysis of arsenic in lakes
and silver in both types of water bodies.
4. Sufficient data are available for calculation of represent-
i
ative values of partition coefficients for the various
priority metals, with the exceptions noted above. Much less
information is available by which to assess relationships
among partition coefficients and various environmental
variables other than suspended solids.
5. Analysis oc data Indicated a pronounced aoparent relationship
between partition coefficients for the v^iujs priority
1-4
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metals and suspended solids concentration. However, for any
given solids concentration, calculated partition coefficients
varied over a wide range of values, perhaps multiple orders
of magnitude. No consistent correlation was found amonq
partitioning and other environmental measures such as pH,
alkalinity, temperature or 800. Partition coefficient values
for lakes were determined to be consistently greater than for
streams for all priority metals except mercury.
<. The partition coefficient values determined for the various
priority metals are satisfactory for aoplication analyses.
However, the values resulting from the regression analyses
developed in this study are order of magnitude estimates .only
and the wide range of calculated partition coefficients
should be considered in practical use.
It is recommended that a refined data base be accumulated for
the various priority heavy metals. Such a data base should
consist of controlled sampling of a variety of natural waterways
and include' simultaneous measurement of all ohysical, chemical
and biochemical factors which m*y have a bearing on heavy metal
partitioning. Laboratory studies may be appropriate to
supplement- the field investigations. These data .should be
evaluated to reassess the results of the present investigation.
1-5
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SECTION 1
INTRODUCTION
1.; BACKGROUND
The Office of Water of Che U.S. Environmental Protection
Agency is technically responsible for managing waste load
allocation (WLA) activities within the organization and far
providing technical assistance to the states. In addition, this
office also has technical responsibility to review various
advanced treatment (AT) projects proposed under the construction
grants program by regional offices and the states. The AT
projects often result from water quality studies and mathematical
modeling analyses which are used to establish WLAs indicating
that technology based effluent limitations are not sufficient to
achieve or maintain water quality standards. It is important
that WLAs be established in a proficient and technically correct
manner so that recommended facilities are properly developed and
cost-effectively designed*
In the performance of its mandate, the Office of water has
determined certain specific areas whereby assistance to the
States is advisable to help maintain and/or improve the technical
bases for WLAs and recommended AT facilities. One such are*
which will receive increasing attention is the fate and transport
of toxic materials, particularly certain priority pollutant
metals, as discharged from POTW's and other sources. Treatment
requirements for these substances will depend upon prooerly
determined WLAs, which in turn must be based on mechanistically
realistic assessments of the transport *nd f*te of these
materials in the aqueous environment. An inportAnt character-
istic of metals in this regard is the affinity to complex and/or
•
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o« adsorbed (partition) to natural particulate Tiateriils.
Realistic modeling frameworks must, have the caoacity to tr*c
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The specific priority metals of concern -wnich are considered
in the'study are:
Arsenic
Cadmium
Chrom ium
Copper
Lead
Mercury
Nickel . .
Silver
Zinc
1.3 SCOPE OF THE REPORT- ....
The report summarises various technical procedures wrier, were
implemented to obtain, cateqorize and evaluate data fsr sriority
neavy metal partition coefficients. Theoretical considerations
are presented to orovide a background for the analysis and to
indicate data requirements. Required data are summarized hy
s^urr* *nd availaoility and classification procedures are
descr;5ed. Methods of -snalysis are described for tne cateqor-
iration of data, calculation of partition coefficients, and
statistical evaluation of relationships between these values and
various ambient environmental variables. finally, the results of
the analysis *re presented and discussed.
1-8
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SECTION 2
CHEMICAL
of
One of the ma^or characteristics wnich s i f fer en tia tas -nan/
chemicals and heavy metals from classical water quality variables
is an affinity for adsorption to particulate material. Figure
2-1 schematically illustrates the principle. If a •nass
soluble chemical is placed in a laboratory bear aid stirred,
portion of dissolved chemical will be sorted s-ts tne sart
ulates and some of the chemical concentr 31 io*. w. 11 tner. ^e
particulate form, c
If this process is
i its red with t l-ie as
shown on the diagram, dissolved chemical will ae reduces a-.-i
particulate chemical will increase in a reversible reaction ur.;:l
an equilibrium is achieved at some point. The total chenir>:
concentration at any time is equal to the sjm -5 f the -iissolve-
and particulate concentrations:
in which - is total chemical concentration and all concentra-
tions are expressed on a bulk volume (liquid plus solid) basis.
The rat* with which this reacion taxes place and the relative
relationship between the dissolved and particulate chemical, that
is, the water-sediment partitioninq, ire both chemical specific.
In most cases, reaction between tht dissolved chemical and
particulates occurs very rapidly, minutes to hours, *r\A.
equilibrium is achieved quickly relative to the time character-
istics of the environmental s*ttinq. rr * tendency t? sorn is
highly chemical specific and will ranqe from very w«,»tc to st
in the case of -naterfals with Tow solubility.
1-9
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The affinity of a oarticular chemical or heavy metal to sorb
can be quantitatively expressed by a sediment-water partition.
coefficient, K . A series of experiments of the tyoe schemat-
ically indicated on Figure 2-1 may be conducted with differing
initial dissolved concentrations of a specific chemical. After
equilibrium is achieved, the particulars chemical concentration
to suspended solids ratio , c /s , expressed as microqrans of
chemical per gram of particulate material {ag/g>, may be clotted
as a function of the dissolved chemical remaining, c . , exoressec"
G
on a volumetric basis as nicrograjn per liter of water («g/l).
Figure 2-2 schematically illustrates the results of this
laboratory experimenc. A specific chemical will oroduce one of
the lines shown on the logarithmic diagram, the relative position
of which determines the partition coefficient. For a particular
dissolved concentration, greater particulate concentrations
result from larger partition coefficients as shown schematically
by the various distributions. Data from chemicals w>-.i;h. car. be
plotted and correlated as shown on Figure 2-2 behave according to
the Freundlich isotherm defined as:
Vss * Wl/n f2'2''
in which n is a constant characterizing the slope of the
relationship. If the slope is near 1 indicating a linear
relationship, the partition coefficient is defined as:
As indicated, a specific chemical or heavy *etal will yield
one of the relationships indicated schematically on Figure 2-^
( c * specific type of sorbing particulate -naterial. However,
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FIGURE 2-2
'EXAMPLE ISOTHERMS AND PARTITION COEFFICIENTS
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different re lationsnips, and therefora, different oareitis-
coefficients, may be observed for the sane chenical with various
types of sorbants. For examole, orqanic oarticulates or silt>
materials may attract a certain 'chemical more strongly than s*ndy
materials. Further, different size classes of pirticulate
material, as. they may reflect different classes of particulars
as sands, silts, clays, etc., may exhibit differing affinities,
and partitioning, for a specific cnemical. In principle, it is
most advantageous, therefore, to perform exser ime-.ts ar.s
determine a chemical's partitioning characteristics with the tyce
of particulate material (suspended and bed sediment' t» wh;c-. ::
will come in contact in the natural environment.
As described, the nature of the sorbant -nay have a -e^r;n= - r.
the magnitude of the partition, coefficient for a par tic-Jl a:
substance. It has also been observed .'O'Connor and Conns'. I/
that partition coefficients may vary in accordance with t~-.
concentration of the sorbant as well as its nature. Figure "?-;
presents some empirical relationsnips between 'partitic-
eoefflcients and sediment concentration for a variety -f
substances. For certain chemicals, it is observed trat, part::io~.
coefficients may be expected to vary by an order af m aq r. i t ud e sr
more depending upon the solids concentration. In the case sf
heavy metals, other factors such as pH, alkalinity or hardr.ess,
temperature, *nd conductivity may have an effect on tne partition
coefficient due to the complex chemical reactions which OCCJT-
with these substances in the natural environment.
Much valuable information with which to define partition
efficients and relationships with various environmental
riables can be determined from carefully •levelope'i and
ntrolled . lar.:-ra^ory experiments. It is noted, however, that
coe
va
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«.<*•.*»«*•»— •*«.... i.a-.«ry experiments. It is noted, however, that
such values
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estimate what occurs in the field, this is, in tne natural
environment. Althouqh the laboratory data *r* quite useful, it
is appropriate to utilize field data wherever .wailaole in orier
to calculate partition coeffient values from those settings -is
which they will be subsequently appl'ied. These natural
conditions are likely to be far more physically, chemically, a-*
biologically complex than the laboratory sett;-.q. but t.-.e
information derived, therefore, is realistic in the natural sens*
and can serve to indicate how well or poorl-- sirtitio-
so«fficients can be defined for the natural setting-.
1-15
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SECTION 3
SUMMARY OF AVAILABLE DATA
3.1 DATA REQUIREMENTS
The minimum basic data requirements for calculation o £•
partition coefficients for the various priority metals are
dissolved and particulate concentrations and a measure of the
associated particulates, suspended solids. This information
allows determination of partition coefficients in accordance with
Equation (2-3} and also provides for an examination of any soli-is
dependent partitioning relationships such as illustrated on
figure 2-3. Additional information is required to assess other
potential correlations between partitioning and ambient
environmental variables.
In the case of heavy metals, other pertinent related data
include pH, alkalinity, hardness and temperature, variables which
may influence the chemical reactions which metallic substances
undergo in the natural environment. Further, some information on
the nature of the sorb ing solids in terms of organic and
inorganic fractions and size distribution is appropriate.
Measures which may provide some information on the organic nature
of the sorbing material are volatile susoended solids,
biochemical oxygen demand (BOO), chemical oxygen demand (COD),
and chlorophyll-*. Flow data, whether low, average, or high in a
particular stream, nay provide some indication of the tyoe of
particulate material (sand, silt, clay) likely to comprise
suspended sediment in the water column in the absence of other
Information.
1-16
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I
The foregoing type of information was souaftt fro*
laboratory and field investigations. In the case of field data,
data were sought for both water column and sed sedi-ieit and for
different types of water body, particularly streams and lakes. A
distinction between partition coefficients calculated far each of
these types of water bodies is appropriate as the nature of
corresponding suspended sediment may be different, 'that is, more
organic in the case of lakes.
3.2 SOURCES OF DATA
A large amount of field data resulting frnn various ty?es 5?
surveys resides on computer* zed data bases. The following data
bases were examined for data ava ilaoil i ty : ST3R5T "JS£?v ,
NASQAN {'JSCS^ , the data base maintained by NOAA, arsrf 57^*
(Canadian Centre for Inland Waters}.. In addition, -..-«
computerized reference service DIALOG was utilized to identify
and secure additional reference material including bo'th field -ar.rl
laboratory investigations. These data bases were reviewed t=
avoid potential multiple countirtq of samples. The fol!sw;nq is a
brief description of .these data hases in relation t? tr.is
project: ?
1) STORST
The water quality file of 'this data base contains water
quality information obtained *t numerous .stations located in
all states and operated by various agencies. It is the
largest data base for water quality information ^y fir.
2)
Water quality 3ats of this file *.e contained i- STOSET.
NASQAN data updates «re transferred to STORET on i "jiw-ekly
basis. Data can be retrieved in the USGS format.
1-17
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3} NOAA
This data base contains data Cor ocean, near-shore ar
estuarine samples. The estuarine samples cover the areas of
Puqet Sound and New Yor* Bight. A search of these areas WAS
requested. The search Cor Puget Sound revealed no metals
data. The search request for New York -Siqht did not result
In information in time for processinq.
4) STAR
other
This data base contains two subsystems. One s
contains data exclusively for the Great La
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Enviroline
Compendex
Ocean Abstracts
Comprehensive Index (Dissertation Abstracts)
A small number of usable publications were identified and
obtained'.
5)- Miscellaneous
A search of all pertinen-t in-house pu^l ica iisr.s -as
conducted.
3.3 AVAILABILITY OF DATA
The snail number of references obtained from OIALCC Toncair.ed
no significant amount of useful information. Only a modest
amount of pertinent data was obtained froi in-house sources 3:
reference material. On this basis, and in accordance with tr.e
evaluation of the various computerized data ftases as descr:5«d
'above, it was concluded that "the water quality file of STS3S?
would provide the vast majority of usable sa-ipl-s, and almost all
effort should be directed to this source. A retrieval strateay
was chen 'devised to search the data records of all stations in
all states, and identify a-nbient, remar*-code-f ree saiales w. lei-.
contain, at minimum, the concentration of total *nd dissolved
species of tietal and suspended solids. For such samol-s, data
retrieval also included temperature, pH, «l»cal inity, h«r-iness,
flow, COD, 800, volatile non-filterable residue 'volatile
suspended solids), chloro phyl i-a corrected, *-ri -jnco r r fz ted
chlorophyll. No data were available on the size d i str i* ;t i-an if
suspended solids in ' th water column. A1, thoui* l3t-^
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sufficiently comolete (dissolved chemical •nissinq) for
calculation of partition coefficients. Data retrieval was
therefore restricted to water column data. The results of these
retrievals were obtained on magnetic tapes for subsequent
analysis.
3.4 CLASSIFICATION AND DISTRIBUTION OF DATA
It was determined from initial STORCT retrievals that the
useful data base for some of the priority metals was quite larqe
with samples of Interest numbering in the thousands. Data
handling and subsequent analysis was therefore performed by
computer. All of the basic minimum riata which were retrieved for
each priority metal permitted calculation of a partition
coefficient in accordance with Equation (7-3). However, a
purpose of the study was to assess any relationshiss =«:w««n
partitioning and various environmental variables as disc-jssed.
In order for these determinations to be valid* the basic r*at*
must be measured simultaneously on the same sample or *t the same
time. Hence, the data base for each priority metal was sorted
into various types of records, each of which was characterLred by
the simultaneous determination of various parameters. In this
manner, a large data base could be examined for sanplir-3
information most useful for cross-correlation purposes, and the
appropriate data records accessed for analysis.
The definitions established for the various data records are
as follows:
I -20
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Data Content of Record '
Total and ^issolve-1 -»etal and
suspended solids concentrations
data of type 3 and oH
data of type 1 and alkalinity
data of tyoe 2 and temperature
data of type 3 and ?C2D or 9CD)
data of type 4 and 'vol-stil*
suspended solids, or cnl-a corrected
or chl-a uncorrectedi
data of type 4 and 5
Hardness was judged to be redundant with alkalinity fcr
correlation purposes and thus not included in the data records.
Flow information was judqed to be too meager far nean;r.3fj'.
correlation and also excluded from evaluation.
Type
8
1
2
3
4
5
For each priority metal, the total nunaer of records and the
distribution o£ these records Per cyae was determined for each
station in each state. At the end of. the first staqe of data
processing, a summary table was developed for each uetal wric-.
contains an aggregation of records and stations per state, *
distribution of records per type for each state and the totil
number of all records.
Table 3-1 is an example data summary for the priority -netal ,
zinc. The table shows the total number of records and stations
and the distribution of record types within each state reporting
data. The total record count Co. zinc is 5397, These records
include data from streams, lakes, est"-rUs, coastal zones,
manholes, and other miscellaneous origins. Figure 1-1 is *
[-21
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TABLE 3-1
ZINC DATA SUMMARY
STATE TOTAL 8T «CCO«0 TTPC
3CCOAOS 0 1 2 3 ** 3
AX
A2
AR
CA
CO
TL
SA
:p
*«*
IN
:A
KS
KY
LA
MS
MA
MI
MS
MC
MT
NE
HV
NJ
NM
KY
NC
NO
OH
OK
OR
PA
RX
SC
SO
TN
TX
trr
VA
WA
w
MT
WY
A
S
19
20
21
22
23
26
2*
29
30
31
32
33
36
37
if
«0
«*^
W9
31
33
3*
37
• «
2
97
3
UA
79
7
a
126
ti
8
100
1036
12
ia
393
10
220
16
120
»6
27
it 33
i 7
20
1
13
43
139
12** t
in:
11
1
0
0
1 **
?3
7**
0
26
7
15
1
9
78
0
7
231
0
97
30
0
0
0
12
j
7
1
0
1
3
2
12
12**3
j
0
b
0
0
21
a
tt
u 6
y
0
ll
8
ll
1
6
1
11
2
0
1
1
3
1
1
2
^
9
Q
2
(4
0
1
1
0
^
1
0
1 1 0
c
32
1
U
3
C
y
1
0
0
0
0
0
0
0
7
0
1
0
1
6?2
0
0
2
2
5
1
0
1
1
0
0
a
i
<4
0
0
0
0
0
0
1ft
0
1
n-
3
1
0
1
0
0
1
0
0
0
210
9
3
7
6
12
1
1
3
20
8
0
313
27
0
0
6
a
9
73
j
8
S
2
7
21
1
2
1
57
)
0
2
a
«*
3
0
H
C
n
5
10
i*
1
0
0
71
3? I
0
2
10
2
it
0
?3
0
1 **
10B
22
1
n
u
1
0
1
0
0
f)
f)
3
1 ^
17
0
14
2
•
A
r.
0
0
0
12
o-
0
0
0
0
0
71
0
0
1
0
0
2
l n 9
0
?
1 ? S
0
0
Q
0
••4
0
0
322
1
0
7
0
0
3
1 ^
0
0
0
3
3
3
3
i
2
0
0
0
1 A
u
3
U
0
0
0
20
a
0
a
0
33
0
0
1
a
0
3
0
0
0
3
1
0
0
C
a
0
0
n
u
rj
u
72
j
a
w
IJ
I.
II
•*
•
STATIONS
1
7
2!
19
1
1
sl
2u
4
It
1
la
to
• 6
13
1
&
29
22
c.
T3TAL
•U.S. Territories & Possessions
T-22
-------
-------
summary of the geographical distribution of the available records'
by state. Similar information is presented for each of th
V
priority metals on Flqures A-l throuqh A-9 in th- Aopendix.
In the basis of this analysis, the total number of records
available for each metal is as follows:
Metal Total Number of Records
zinc 5397
copper 4557
lead 31fA
arsenic 2335
nickel 1998
chromium 020
cadmium 799
mercury *31
silver 50
These records formed the basis of she subsequent technical
analysis.
1-24
-------
SECTION 4 .
. METHODS OF ANALYSIS
4.1 TECHNICAL OVERVIEW
A substantial amount of data for the priority -netals is
available for analysis. Approximately 20,030 data records were
developed for the priority metals in accordance with the data
classifications described previously. This amount of information
required the application of computer processing -and the
development of a technical strateqy for data handling anc
analysis. As a result, specialised software was developed to
process ST3RET information for each ariority metal wnicn was
contained on a series of magnetic tapes. This software was used
to select appropriate data, compartmentalize it as approoriate
into a series of "bins* and calculate partition coefficients.
The values were then subsequently processed by various
statistical techniques to search for relationships amonq
partitioning and various environmental variables.
4.2 3IN ANALYSIS
An objective of the study Is to determine correlations, i*
any, which exist among calculated oartition coefficients *nd
ambient environmental conditions as represented by various
physical, chemical 4nd biochemical measurements simultaneously
performed or observed. The data records for each priority «*etV.
reflect a wide range of conditions for analysis. In order to
search for correlations, the procedure ' selected consisted of
-segmenting the data progressively into a series of eomoar tien.i,
or bins, «*ch of which would be specifically defined by a
o~ru.eular ranqe of environmental variables. For exv..p\e, a bin
1-25
-------
.-nay be defined as that portion of the data base which .-.as limits
of suspended solids of 13 to 39 mg/1, a pH range of *.
-------
4.3 STATISTICAL ANALYSIS
At Che conclusion of the bin analysis, * statistical analysis
was performed to assess any empirical functional relationships
between the calculated partition coefficients and the er,v;ron-
mental variables selected to define the bins. A multiple
correlation routine was used for this purpose. The following
generalized regression equation was applied.
KP ' Kpo <'«1»" {X2>"
in which:
K » partition coefficient fl/'
-------
The regression analysis includes appropriate statistical measures
such as correlation coefficient, r, and standard deviation a, c*
the exponential constants.
1-28
-------
SECTION 5
RESULTS OF ANALYSIS
5.1 PARTITION COEFFICIENTS
The bin analysis was used to calculate partition coefficients
for each priority »etal as described. The data base was
segregated by origin of sample and separate analyses were
performed on data reported from streams and lakes. In order to
facilitate the identification of possible interrelationships by
direct observation as well as by statistical means, ^he bin
analyses were restricted to three dimensional arrays. T^e
initial analysis was focused on variables which could exert a
pronounced affect on partitioning for heavy metals, suspended
solids, pH, and alkalinity. Thus, all records identified as Type
2 and higher order in Section 3 were selected from the data base
for initial analysis. The bin intervals for stream data were
specified as follows:
Susoended solids (mc/1)
10 to 30; 30 to 50; 50 to 100; 100 to 200; 200 to 500
SSL
5.8 to 4.0; fi.0 to 7.0; 7.0 to 8.0; 8.0 to 9.0
Alkalinity
0 to
to
50 to 130; 100 to 2&0; 20« to ?c
-------
Thus, the initial analysis of stream data consisted of 10n
compart-nents in each of which a series of partition coefficients
were calculated depending upon data availability. The bin
intervals were selected to represent reasonable ranges of the
indicated variables while also maintaining a sufficient
population of data in a large number of bins for statistical
reliability. Analysis of lake data also included a suspended
solids interval of 0 to 10 mg/1.
Table 5-1 illustrates the results of this analysis for zinc
data reported for streams. The table presents the bin mean of
the partition coefficients calculated within each bin in
liters/kilogram, the coefficient of variation, and the number of
bin records. For zinc, a total of 1782 records were used in the
calculations.
Similar analyses were performed for all priority metals
except silver (streams and lakes) and arsenic (lakes) for which
sufficient data do not exist. The analysis was also repeated
with other environmental variables as subsequently discussed.
5,2 CORRELATION. WITH ENVIRONMENTAL VARIABLES
Observation of the calculated partition coefficients in Table
5-1 indicates an apparent inverse variation with suspended
solids, but a less clear relationshio, if any, with pH and
alkalinity. Similar results were obtained for the other priority
metals. Simple and multiple regression analyses were then
performed to better define any functional relationships.
Table 5-2 presents a summary of the statistic*! parameters
obtained for -*nc in streams. The table indicates an overall
geometric "tea.. i^rL.tion coefficient of *oprox inately 55, 00^ l/kg
1-30
-------
TAS1E S-l
BIN ANALYSIS
ZINC IN STREAMS
.go to ».te
, »*.»3 SO. »«.f«> irj. lOO.r:
S.M*16 84 *.a«OOC 00 J.300CC SJ J.UV»C "V l.
*T«| 1 I.fla
.
4 B,0»fl • 3.008 I O.t
4.00 TO Lot
»«. JJ.'o «. »
o* «.i«oic i«
Jl l.tji »» i.kr* » o.40« 1 ».aoe
'0 O.Ot
«.«»«oe •* o.*i»oi o* ».!«»»• :v ».?.»tr •* '.
»;»•• i) 7.7i» »o /.»». it* ;.'»» t-t i.;
: "0" *.oo ft
.T9 10. 70. >0
o.ooooj »o . . . .
R.OOO I J.4T01' » I.IfO II >.«»^ »f* J.«?«
r»p» «.«• rj ».«•
o.oosat oo
o.ooe 3
a.aafoi oo J.s»o:: •: :.r---r :: i.—.t
a.ooa o j.-«i : j.ier 9 ',:ne
ji« '3 '•«•
» O.'O >«. :o>*j ^0.*
«.oo
*» -,-•»
»» *!»««
o.ri
f.r**
j.oo*
-------
4.:" 'a
,. .
*t**»»Il 'r
1*1.
A. TO J8.
on
ft
.'-J 1*1.
: si t.utir
d i.n-
rt •»
».TO Jo. ja.Tj «o. 10. re iJ?. na.ij ?««. ??-.t; «•-.
e.n*n B» i.»9«ir~ «; s.j^^^i «« a.uasic :a :.;::se v
1.11: i o.oos a B.IAJ i ;.ooa a e.jeg ;
.«: '3
! "9» ».:: '3
**s **" !"''1
o; s.
o.aeo
r* -.seasr
. < 0.:t5»' ^« ».i «*r -•
7 1.1*1 »» !.'*> UJ l.Hfc «» ».'«• '•
ta
o
o*
»• »«•.*.»: «•
-------
SUMMARY CF STATISTICAL PARAMETERS "OR IIS
DEGRESSION WITH SOLIDS, PH, AL
0 1.2 3 u. 5 6 Total
Secoras Sy Type 1988 3?9 686 861 6*1] 67U !46 5357
Sort &y Type >^2 & Sy Water Body Stream: Records Analyzed *~32
FIRST BIS ANALYSE
So. of Acseptaole Slns^O 5 Records) 50: Bin Avg < (l/k?; a *a.9'S
CORRELATION CC£?TICI£NTS (partial)
riK, ss; s -:.333; rex., ?H) a o.i63: r(K., Ai) s O.:<;T
? p p
REGRESS 12 f«
i: K va as:
Kp s 1.05 « 1CS x OS)-0'685; r s fl.833. . 3.065
2: X va as i CH];
.A . «.& . i '••Q * oo3 ».. i<"»Q «OooT MB»£ —
s 0.32 » 10 * (33) I [H] ; r-it1 . • O.B-»6. -t^
= 0.036C
p mi <* ™* 3
3: K vs ss & [H'j & Al;
K , 0.32 * 106 x (33)"°-683 x [HT0'0687 i tUl'0'003; r * 0.3U6, *
p it ui * espi
*eip2
_ s 2.372
eip3
r.-n
-------
?ABL£ 5-2 'continued)
SSCONC 3IN ANALYSIS ( ror *' 8 K SS~' !
P
•
No. of Acceptable Sins J> 5 Records) is; Bin Avg x » 1.3
CORRELATION COEF'ICIEMT (partial): r(K*. pH) a 0.0«<5, r'.K*. Al) r 0.-36
P P
3 EGRESSIONS
*
3* . 0.6ia » (A1)°'1T9; r . „ • 0.«86. a » 0.362
r wui t ex p
, 0.52T. .
2: K * v» U A
P
1*34
-------
from all dat,a. Simple correlation coefficients *re presented far
partition coefficient variation with suspended solids, oH, and
alkalinity, resaectively . These values indicate a very stronc
dependency of partitioning with suspended solids and wea<
relationships with the other variables on a si-iple Das s.
Statistical parameters are also shown for the multiple step-w se
regression. Exponential constants, multiple cerrelat on
coefficients, and the standard deviation of the exponent al
constants are presented for the sequential recressian of
partition coefficients with suspended solids, suspended solids
and pH, and- finally suspended solids, oH and alkalinity.
It is o.bserved fron Table 5-2 that the exoonential cs-.star.ts
for pK, as represented by the hydrogen ion concentration, ar.z
alkalinity are relatively small. Further, the m u 1 1 i s 1 e
correlation coefficient is not markedly improved oy the inclusion
of these variables in the regression. It is possible that the
strong dependency of partitioning with solids is mas*;. no; a
correlation with these variables. Hence, a second bin analysis
was perfor-aed where the solids dependency of partitioning was
removed by rearranging the regression equation:
K • Kp'
-------
Graphical presentation of the results supports the observation.
Figure 5-1 shows the partitioning parameter Kp-ss~0 for zin-
plotted as a function of alkalinity fo.r various pH levels in bot.
streams and lakes. Similarly, Fiqure 5-2 oresents the same data
with the partitioning parameter plotted as a function of pH for
variqus ranges of alkalinity. No consistent trends are observed
from these diagrams. The multiple regression analysis was
per famed on stream and some lake data for all priority metals
with similar results.
A similar type of regression analysis was performed to
determine any relationship between the partitioning 'parameter and
other environmental variables: temperature, and 303 as a
surrogate parameter possibly representative of organic material.
As with alkalinity and pH, no consistent relationships could be
determined among the variables. Table 5-3, for zinc data in
streams, is an illustration of the results of these analyses.
Sufficient opportunity was not available within the t:n<
constraints of the investigation to 'assess other potential
interrelationships.
From th« foregoing analyses, i.t was concluded that the only
clear and consistent relationship, observed between partition
coefficients and the environmental variables tested was with
suspended solids. For almost all priority metals, a stronq
correlation was indicated between the mean partition coefficient
valu* within the various bins and the average bin suspended
solids value. In view of this consistent dependency, the final
functional relationships developed for these variables w*s based
on analysis of ill available data records which contained the
basic information necessary for calculation of the partitio-
coefficlent. The only data needed to determine these relatior.
1-36
-------
V)
trt
O.i
PM
7-9 9-9
-a 6* « o a
S
..... I . ....... I
10 100
ALKALINITY {mq/l CoC03)
lOOO
FIGURE 5-1
K* AS FUNCTION OF ALKALINITY
P
-------
iQC
50
1 a
*
0 i
O i
; ALK -0-20
-
fe
mm
»
o o o
*
_.!.!_ ! J^
2O -30
.
O
O
1 1 T 1 1
SO-iOO
t
0 ° °
1 ! 1 1 1
iQO-200
O
O
o
1111!
200-3OC
o
o
I.I I !
36?«9967«9)«7ft9567l9*6?t
-------
TA3L2 5-3
SUMMARY CF STATISTICAL PARAMETERS FOR ZINC
0 1 2 3 «* 5 6 Total ?e<
Records sy Type 1988 379 636 861 663 67« i«6 529?
Sort Sy Type 3 4 Sy Water 9ody streams: Records Analyzed 225?
FIRST SIM ANALYSIS
So. of Acceptaole Sins .(> 5 Records) 7: 3in Avg K ( I/kg) r a5.300
:CRREUTI:N cctrriciENTS (partial)
r^<,. SS) s -0.995:
iC? vs S3;
K ! 1.25 i 10* i (S3)"0'7035; • „ « Q.03'7
P ei p
StCCKD 3IN A.'fALTSIS (For K* * K SS"*)
P P
So. of Aceeptaol* 31ns C> 5 Seccrds) '5; 3ln Avg K s 9. 7
P
CORRELATION :C£5?ICIEK7 (partial): r(K*. 900) s -O.U50, r(K , T) s -0.22«
3 £33 ESS 1C *S
': .< vs BOD;
K/ , 30.2 i OODr0'4113: r , O.«50. « , 0.293
2: K vs SOD *> T;
32.2 * (SOD)--.* lO-; r . 0.«65. ..0.316
-------
snips are Dissolved and particulate chemical , and susoenrled
solids, The lowest order of data records, Tyoe 9, and above
could be used, thus substantially expanding the data b^se for
final analysis. Hence, the final correlations between
partitioning and suspended sol\ds concentration in streams and
lafces were based on all available data records in ST3RET for
these types of water bodies from which a partition coefficient
value could be determined. This represents a relatively large
fraction of the total available data base with only data Iron
estuarine, scaan, miscellaneous sources, etr., not included in
the analysis.
An illustration of the results of this analysis is presented
an Figures S-3A and S-3B, for the priority metal sine in streams
and la*es, respectively. As before, the data records were sorted
into a number of bins, each of which was characterized by a
specified range of suspended solids. Partition coef fie ients. were
calculated from the data records falling within each comoartm*nt,
and the din means and other statistical parameters were
determined. The bin means were then regressed with bin mean
suspended solids concentrations. The logarith-sic diagram's
present the bin mean partition coefficients plotted with hin
average suspended solids concentration ar.d the regression line
which correlates the data. Also shown on the diagrams are the
standard deviations of the bin values for the log normally
distributed partition coefficients. The strong correlation
between the calculated partition coefficients and suspended
solids is evident from the diagrams. The analysis also indicates
a wide variation in the partition coefficients calculated within
the various solids intervals as indicated by the large standard
deviations. Values can vary by four orders of magnitude.
1-40
-------
•0
(0 100
SUSPENDED SOLIDS
«oco
FIGURE 5-3A
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLID*
ZINC IN STREAMS
-------
lO tOO
SUSPENDED SOLIDS (mq/l)
'000
FIGURE 5*38
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
ZINC IN LAKES
-------
The analysis indicated above was performed far each ariar::-/
metal far toth streams and lakes. Tn* excestions were s:lv«r
{streams and lanes) and arsenic (lakes for which sufficient
inf-jr-iation was not available. Figures 5-4A and 5-43 sres*rst a
summary of trie reqressiJn lines for cartition cjefficients ar.d
suspended solids for the various priority metals for streams ar.c
lakes, respectively. These diagrams can 5e used for tr.e sest
escimate af a partition coefficient value far a sartic-'.ar
priority metal based on suspended' solids concentration. "iz-res
A-10 through A-i? in the Appendix ores^nt tr.e iata ar.c tr.e
reqression lines for each priority wetal in streams ar.d la-ces.
These diagrams should be consulted in the -selection -f 3
partition coefficient value in order to indicate the dearee sf
variasil:ty which may exist around a particular value or. • t*.e
basis of the.analysis of available data.
Table 5-4 is a summary of the statistical properties s: z-.*
regression equations for partition coef f ic ier.ts ar.d sussended
solids as developed for the priority metals. The number of ia:a
records in each evaluation is indicated alons -;t". f«
exponential constant, correlation coefficient, *-*•*. stan-Jar-!
deviation of the slope. It is evident from trse tar-.'.e tr.at tr.e
bin mean partition coefficients are very highly correlated *i:h
suspended solids in all cases but lead.
It is noted that the calculated partition coefficients from
lakes are consisteotly greater than values in streams for tr.e
same priority metal, in all cases except for mercury. Tw.is -My
be due, in general, to a more organic nature o'f suspended
materials in lakes Chan in streams.
1-43
-------
•o4
•01
METALS:
2 -
3 -
«-
9-
• -
r-
•o
SUSPENDED SOUOS ( mg / i )
-------
>0 iOO
SUSPENDED SOLiOS (mq/ I )
>OOO
METALS'
i -A*SEWiC (NO DATA
Z -CADMIUM
3 -
5- I.EAD
8- ZINC
LAKES 1
FIGURE 5-4B
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIOS
ALL METALS IN LAKES
-------
t~ — e f f~ < «
e — — c
i i c e c e c 10
o
1
i i i
e
•o
e o e «- e 10
r i i i i i
<
to
i' a
«* **
a. a
o
u
Ol
e o o c c i e
(M
f
i ,
o c «*< m in —
°-7'i'i?-i'? lc?
=
o- *o *c -o *e -e
S §
X
S s
(•• m
«v ^
» t *^
9> in
»« fti
» CM
?
to
5
&
a e i
•• 3 -.
e «•• •
1*1
to
•
I
e
a
-------
APPENDIX
1-47
-------
Alaoama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
district of Colum&ia
Florida
Georgia
Ha wa i i
Idaho
Illinois
Indiana
Iowa
Xar.sas
Ken;ac*y
Lou: s: ana
Wain*
Maryland
Massachusetts
Michigan
.Minnesota
Mississippi
TABLE A-l
STATS cooes
ai
02
84
35
0<<
08
39
13
11
12
13
IS
18
19
20
21
22
23
23
25
2<5
27
28
Mi ssour i
Montana
Nebraska
Nevada
New Hampsnire
New Jersey
New Mexiro
New YorK
North Ca r a 1 : n a
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvan;a
Rhode Island
Souch Carolina
South 3a
-------
••» f "^ * «
- ^ ^'. / f : /—
"r/.^...',-)
***•». • •*
\
-------
»•*
-* *
. -.
•*
r-
-------
• *» * • -s • •. «• -. t ' • * ;•• —* , • • r v **;•«».; . •- -
i <
-------
•sszn
2
• •
CM
I
<
!~
<
\
-------
-. ;
i . ,.,.,..._.-j- ., a...-.,,......, s -i =
.. ......
"--- —4
-------
r
»«
e
<
>•
\
-------
-------
I
' * * s * — •. ;
»0 •«*._>•••««£
• *
<
7 <
-------
•* •• —• —— €
< <
>
5-
-------
• C'
10 OOO
FIGURE A-iOA
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
ARSENIC IN STREAMS
-------
SUSPENDED SOLIDS (m
-------
•0*
-------
•cV
•c
I
I
1
rO '00
SUSPENDED SOLIDS
-------
• 0'
iQ '00
SUSPENDED SOLIDS ( m q / ' )
iCCO
FIGURE A-12B
PARTITION COEFFICIENT AS f'JNCTION OF SUSPENDED SOLIDS
CHROMIUM IN LAKES
-------
• iO '00
SUSPENDED SOL'CS
FIGURF: A-13A
PARTITION COEFFICirN'T AS rtT;CTION OF SCSPE.'.'DED SOLIDS
COPPER IN STREAMS
-------
10 'OO
SUSPENDED SOL!CS (mq/
.FIGURE A-13D
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
COPPER IN LAKES
1-64
I
-------
•o1
OMlTTCO OUt TO v£RY MIQM
COEf »ICIENT. 0' VARIATION.
'OO
SUSPENDED SOtlOS (m
1\
•ceo
• " < FIGURE A-.14A
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
LEAD IN STREAMS
-------
OCO
SUSPENDED SOLiOS -(mq/i)
FIGURE A-148
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
LEAD IN LAKES
i
-------
lO '00
SUSPENDED SCL'OS !mq/
OCC
FIGURE A-!5A
PARTITION COEFFICIENT AS FUNCTION OF SL'S?EN'OE3 SOLIDS
MERCURY IN STREAMS
-------
1 00
•oco
SUSPENDED SOLtOS
FIGURE A-i5B
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
VERCL'RY IN LAKES
-------
0*
• 0'
<0 >OO
SUSPENDED soucs img/
ccc
FI-L'RE A-16A
PARTITION COEFFICIENT AS FUNCTION' OF SUSPENDED
NICKEL IN STREAMS
-------
• 0*
iO '00
SUSPENDED souos (m
-------
«0
•o
>c
iO 'OO
SUSPENDED SOLIDS («
-------
•O'l
:«3«3t
'OCO
SUSPENDED SOLiOS
FIGURE A-I7B
PARTITIOK COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
2I.VC IN LAKES
1-72
-------
Catalogue of waste Load Allocation
models for Toxic Compounds
EPA Contract *o. 63-01-6160
Work Order *o. 9
Prepared by:
Versar Inc.
SflSO Versar Center
Springfield, Virginia 22151
Under Subcontract to:
Arthur 0. LHtle. Inc.
CaraeMdge, Massachusetts 02UO
Prepared for:
U.S. Environmental Protection Agency
Monitoring and Data Support Division
Washington. O.C. 20460
April 1984
-------
TABLE OF CONTENTS
list of *odei Summaries
?aoe
Introduction
s*
water Oua''ty Assessment "etJiodology (*GA*; . \:-l
Steady State »odels
lane/Stream Ara'ysis (SLSA) ... . . .:
'.v«r «*ocei (MlCwfilv) .............
ransDort anfl Ana'yses Program !CTA?: ..... •'«
exposure Ana'iys'.s "oae'lnq System (£*A*S; ...... -18
"etaU £xoosure Analyses ^cdeUng System i-tAAMS', . . -2«
-var.-aD'* "oce^s
•stuary and water Cual'ty "odel (v«AS70X) . . . I -28
ChemUa* Transport anc Fate ^oflel (TOxi'^AS?) ...... ; -3'
Tox'c Organ U Suostance Transport and aioaccjrnu'af.on
Model (TOXIC) ....................... I -:S
Channel Transport "ode1. (CHNTRA) .............. 1-29
f^nUe t'ement Transsort Podel (T£TRA) ........... : -«3
Sediment-Contaminant Transport Model (StSATRA,. . . I -48
Transient Qne-Oimens'.onal Degradation and
Migration Model (TQOAH) ........... ..... I -53
HydroloqU Simulation Progranr-FORTRAH (HS?f) ....... I -57
-------
:2r Me:ncc
-------
Introduction
water quality based effluent limitations, as envisioned by Section
303 of the Clean water Act. call for an analysis of the capaoiiities of
water bodies to accept pollutant loadings without impairment of their
beneficial uses. Ambient water quality standards indicate the pollutant
concentrations allowable for attaining the use. Predicting the effluent
loading restrictions needed to prevent violation of the ambient standards
can ae accomplished on a site-specific basis using -nat.nematica' models.
The desirability of controlling toxic pollutant discharges has >ad
to the recent development of a number of algorithms and computer codes
which articulate the environmental transport and transformation processes
relevant to toxicants. The purpose of this catalogue is to s^nroar'je t.ie
' ^ ) C * Q I I • to W %WA*^4llfe£i I I Hf ^ *p * ^ V * % V' k i « " J *M*M'W^jw
-------
water QuaV.ty Assessment methodology
The water Quality Assessment
Methodology (WQAM) (Mills et al. 1982)
was developed by Tetra Tech Inc. of
Lafayette, California; Monitoring and
Data Support Division. OW8S; and the
Center for Environmental Research
Information (CER1). The. methodology
•as designed to perform preliminary
(screening) assessments of surface
'reshwaters. ' The original
methodc'cgy (ZUon et al, 1977) addr
associated with sediment, nutrients.
Debutants m streams.- lakes, and estua
Capsule Suraary w0A/f
» far-field, steady seace.
dljnen^ionaj jnodel
• Procedures for Assessing
JaJte, and escuaxy «accr
• Plrsc -order decay tuned
L Acquires only a desk ccp
i for caiculaclons -
essed the ident"icat'on
dissolved oxygen, ana
rles. The updated vers'on
I-
of srca'eiis
s;me u-sar
iow '^c'-jces
by a
for the assessment of toxic chemicals in the environment. *
'd methodology in that all of the calculations are MtsncJes
desk calculator.
The methodology was designed as a screening- procedure that -naites jse s>*
avai'aa'e gener'.c data. The analyses reaulre little external ^nout s'^ce -nu-
•of the neeoea information is provided oy taaUs and Mgures -%tn'.n t:.
•nanua',. I: arecicts far field, average steady-state, ao« 'utant concentrat'cn?
'.i rivers, lakes, and estuaries as a function of 'ong term .average xax*mum anc
non-solnt source and point source loads.
Calculations performed by WQAM are divided Into four sections. The f. rst
set Is for waste load estimations of toilc and conventional pollutant loadings
from both, joint and non-oolnt sources. Procedures Include load estimations
for single event and annual loads from agricultural, forested, and yrsan
areas. The Universal Soil LOSS Equation (USlE) 1s used for agMcu'tura:
areas; the URS Urban water Quality Management procedure (Amy et al. l^M anc
the Stormwater Management Model (S*HH) level One Screening procedure are used
for uroan areas. The estimations may then Be used to assess the water quality
Impacts in rivers, streams, lakes, and estuaries.
The response of rivers and streams to the release of pollutants is
predicted oy the second set of calculations. variations In longitudinal
pollutant concentrations are estimated. The calculations are r* s< on
steady-state, plug flow1 solutions to the conservation of 'mass equation.
Conventional pollutant interactions presented include BOO. 00, temperature.
conforms, nutrients, and sediment transport. Procedures for toxic
include methods for point and non point sources as well as for large
event spills. The fate and transport of toxic chemicals are assessed uslr.
volatilization, sorptlon. and first order degradation.
II-2
-------
Methods for assessing water quality and physical conditions in lakes are
addressed In the third section. They are oased on empirical stratificat'on
relationships and mass balances. In addition to toxic materials, sediment
accumulation, thermal stratification, 900-00 interactions, and eut-opnUatlon
are covered. The fate of toxic pollutants is estimated with respect to
biological uptake and Bloconcentratlon in addition' to the pnysico-cnem'ca'
properties of tne water and the chemical.
The last section provides methods for estuar'-ne -ate-- uaal'ty assessment
prediction. The procedures Include means for estuary classi^'cat'on
The last section provides methods for estuar'-ne -ate-- aaal
and prediction. The procedures Include means for estuary
(vertically straMMed or. vertical 1y well mtxed). turo'.dify.
thermal pollution, transport of conservative and non-csnservat*
thermal pollution, transport of conseryat
and flushing •<«»• «^.*«^*^/»i« «f ««v.
pollutant di
approach and tne near rie'd ai
the buoyancy and momentum ef
apprsach ignores them
classi":a:'on
sed'meritat'cn.
ve po'-'jtants.
iiiutlon, transport of conservative and non-csnservat* ve po'-'jtants.
Ing time prediction of pbl'utants. Analysis of 1'ongitjd'na'
distribution is estimated by two different methods. :r,e 'ar f'e'c
md the near field approach. The near field acproac*^ a'ccsu-'.s esr
ncy and momentum effects of tne pollutant «n''e t!"e ''A' *'e'5
gnores them.
is designed to operate w
aata 'available, the more accurate
Information, most of It Is general In nature.
1th minimum
the analys1.
ata.
.o'«
arovldes most of the data required for trie, ca'cu'atlons. '.?.
the only data not provided are c'imatic and hycrologic. :''ma:'c data
•nclude sreclp.1 ta'tlon, cloud cover, and humidity. The tyoe of .lydralsg^c
information required Includes runoff quantities, statistical f'aws sjcri 4;
7010, stagnant regions, stratification, and eVtuarlne tidal prism. Qtr.er
basic information is also needed such as land use, stream lengths, U*e c
and volumes, and estuary salinity distributions.
Pursue
output, includes predicted concentrations of a pollutant
(conservative and/or non-conservative) over distance from the input source
In addition to predicting pollutant concentrations, wQAM predicts:
• stream concentrations of BOO. 00. total N, total P. and temperature.
• lake nutrient concentrations, eutrophlc status, and hypolimnion DC
concentrations.
• estuaMne concentrations of BOO, 00, and *lta> N and P.
rr-3
-------
and H
The major advantage 'of wQAN lies in its simplistic approach to waste loaf
assessments. All the eauations in the methodology are algebraic. and tney c
be solved using a desk calculator. TM$ is a major advantage over othei
models in that tne user does not need any programing experience. *QAH
provides "typical* data which can be used .in Ue'u of actual data for
predicting chemical concentrations. Another advantage of wQAH is that l t can
be used for waste load assessments of estuaries as -ell as '.axes and rivers.
Because of Us simplistic approach, *QAH cannot 'nc'ude a*' the
physico-chemical processes acting upon a pollutant. It 's des'gnec far
long-term pollutant loading and- average -steady-state conditions anc joes not
address tne snort-term effects that may be associated *ith tsx'cs 'oaangs.
The methodology does cover the assumptions under which tne a'gcr'trms are
developed and provides the user the limitations of some of the tso's presented.
«CAi* clase'y relates the loading of conventional anc tax^c ao'-'utant? tc
rece'^'ng -aters to the loss of soils and sediments anc tne amount af "
"he non-ooint source loading section utVHce-s the un1. versa 1 So'.' ^355
for. agricultural areas. It ^as develooed primary far croo'ancs ar.^ coes -c:
erosion from streamoanfes, ditches oes'.de 'oacs. 3r ;u''*es. "he
and river section is based on steaoy:state, s^yq '•=* sc'ut'sns. It
assumes s'saerslon to Se small compared to advectlon. The ca'.cj"dt';n$ ass-me
the '.otic system to be vertically and laterally mixed and that ary 2ecay a-
the so'iutant to be first order.
In estimating the fate and transport of pollutants' in a laice. tne
methodology accounts for biodegradatlon, volatl Hiation. ana sec'.Tientat' :n .
However, the model neglects several Important physUo-che-mca' srocesses
(e.g.. gnotolysU. oxidation, hydrolysis, coagulation, f 'occupation. and
precipitation) .
The transport of pollutants in estuaries assumes cont'ni.ous. steady-state
discharges of pollutants. The distribution of pollutants '.s bases an the
fraction of freshwater and/or modified tidal prism, methods for calculating
flushing times. The fraction of freshwater method assumes uniform salinity
and uniform mixing of freshwater. The modified tidal prism method models for
the entire estuary, regardless of where the pollutant source is located.
11-4
-------
Aooli sa • e I ana
The original wfjAH *as applied and tested on the Sandusky RUe- Sasin and
four Chesapeake Say sub-pasins: tne Patuxent, Chester, ware, and 3c:ocuan.
The model -as-, used for simulating sedimentation, strati? icat'on, eutra-
phlcatlon. and 00 depletion.
ne modeling of toxic pollutants nas ieen done using aata frjm Csra'
Iowa. The insecticide dleldrln had accamu'atsc 'r tne "ese
from its use on- agrVcu' tural fields. The data frcm Scinoor-* ;i3gi; 'eso
tne reservlor yas used to test tne accuracy of wCAM. The resu'ts '^.cv.es
to 5e in1 agreement «itn Scrmoor ( 198' ) 'or die'iflrin concen:-at' :r 'i
•ater and fisn tissue.
: or.
Aii 3f the calculations in wCAH are algecra'c soress'ens *ecy"'^"; en'/ *
land ca'c-jlator *or solving. A programas'e calculator s^n: 3e jse'u' 'zr t-e
•sumerous advecfve-OUpersion eauations in tr>e estuary sect'on
Cao^es of <»CAM (£?A-600/6-S2-OC«a-0) a'e-ava'.iao-e
'nclnr.at* . Ohio, ( 534-634-7562) .
tne :i=
User assistance xay Qe. obtained 5y, contacting:
t Amorose
UStPA
[PA Athens Environmental Research Laboratory
Center for water Quality Modeling
Athens. Georgia 30613
FT5 250-3535 CDH 404-546-3535
«efercneea
Amy S, Pitt R. Singh R. Bradford- WL. LaGraff MB. 1974. ^ater sua'ity
management planning for urDan runoff. U.S. Environmental Protection Agency.
Washington, D.C. EPA440/9-75-OQ4; f>B 241 689/AS.
Mills we, Oean jo, Porcella OS, et al. 1932. Tetra Tech, Inc. water quality
assessment: a screening procedure for toxic and conventional pollutart
Part .. Athens, Georgia: Environmental Research laboratory. QffU< of
Reset, ch and Development, U.S. Environmental Protection Agency.
£PA-600/&-32-Q04a.
11-5
1
-------
•MUs i*a. Oean JO, Porcel'a OB, et al. 1982. Tetra Teen, Inc. 'water
assessment: a screening procedure for toxic and conventional pollutants •
Part 2. Athens, Georgia: Environmental Research laboratory. OfMce of
Besearcn and Oevelooment. U.S. Environmental Protection Agency.
£PA-600/6-82-00
-------
State
-------
Simplified Lake/Stream Analyst (SLSA)
The
Summary
I -dimension*! . ccmptrynenz .-node:
Simplified/lake Stream
(SLSA {HydroQual 198?) is a
simplified waste load assessment model > steady scat* *nd ci.-ne *aryi;
developed by HydroOual Inc., Mahwah,
New Jersey. for the Chemical
manufacturers Association, nasnington.
O.C. It analyzes, organic and
Anorganic chemicals In simplified lake
and stream settings. SLSA calculates
the dissolved and sor&ed steady state
concentrations of a pollutant in the
water column and bed sediment using
provides a less rigorous approach to pollutant
compute- programs. It 1$ most applicable to single
loadings. The intent of this model rs to rca«e
po'lutant *.n a freshwater system unders"tandd5:e to an
I
systems.
Simple Slsac-oedee
Suitable foe hard caJcuJacicr or
Simple rCRTRAtf program-
zo sec j? and use
an analytical so'ut'on.
s'mu'at'cn •
(or ounc.ied;
tr»e
•»cae
pc'nt
•S ana vs'
SLSA models streams and rivers as oetng we'! mixed id cross-sect 'en arc a:
having a relatively constant flow and geometry. An analytical solution is
given for pollutant concentration as a continuous 'unction of d'sta.ice
downstream f^om the loading source. The model also estimates so''jtant
concentrations 1n the water column and bee sediment of unstraf'ed
Impoundments or lakes. SLSA simplifies the hydrodynamics of t.ie systems;
aqueous transport is a function of the mean infjow rate of water, t^e depth
and volume of the segment modeled, and the stream velocity or ?ake nydrau'^c
retention time. Sedimentation and .exchanging oed conditions are accounted
for, however4, the bed 1s assumed to be completely uniform.
SLSA only considers advectlon 1n the transport of a pollutant. Pollutant
losses due to degradation processes are represented by simple first-order rate
constants supplied by the user. The constants are then summed to yield an
aggregate decay value. Practical metnods- for evaluating the interactions
between the water column and bed sediment particulate sedimentation and
resuspenslon and diffusive exchange are provided.
SLSA 1s es>enMally a l-d1menr:inal , steady-state model; however, it Is
capable of th.ee quasi-time varyln, analyses of lakes in which the pollutant
discharge rate 1$ at steady state. The first time-variable evaluation
pertains to the water column and bed sediment pollutant responses to an
11-7
-------
instantaneous chemical load. The other two evaluations deal witfi the water
column and bed sediment responses to either an initiation or cessation of long
term pollutant
SLSA is amenable to desk calculations, though a computer program is
available for convenience. The program Is written in FORTRAN Iv level G and
Is convertible to most standard computer systems with FORTRAN compilers. The
relatively snail core requirements of the program and the speed of execution
make the program very compatible with microcomputers.
rout 0
-------
use can
models.
3e accomplished In relatively snort time as compared to ot.ier
SlSA has some time varying capabilities and can account for some
Interactions Between the water column and bed sediment.
Because of SLSA's simplistic approach U has several limitations.
OUpersive f'ow 1s not accounted for as in other -models sucn as CTAP. t^us
limiting its use to relatively Simplistic systems. The bed sedi-nent 's
assumed to De completely mixed and undergoes no movement. Decay tiec"an'sns
are all first-order. Suspended solid concentration 's »ep: constant at tne
input value and only a single particle s1?e is considered!. Only one -
with a single point source loading and no acd'tiona' inflows are per--nittso.
ftodel Applications
SLSA nas Oeen applied to a 90 km reacfl of Sao'd Cr»e< 'n Rao^ C'ty. Sogtn
Oaitota. 'he study area -as located downstream of a munidDa' «aitewa:e'
treat.-nen: p'ant. 'he pollutant considered -as :">e sjr'actan:. '^ea»
a'
-------
'JS9t
i Xc t 1 v I e I ea
Copies of tne SLSA user manual, as well as documentation, can ae ootalne*
from:
will 1am Gull edge
Chemical manufacturers Association
2581 M Street. N.H.
Washington, O.C. 70037
202-887-1183
Otner ass'stance can se obtained by contacting:
John St. John
Domenlc OiToro
MyCroOual Inc.
l Letfioridge Plara
xanwan. ^ew Jersey 07430
201-529-5'51
SoneraI Pefergncgj
-lydrcCua' , Inc. 1981. Analysis of Fate of c^eflUaU '•"
3*iase I. '"soared"for: Cnemical manufacturers Assoc'at'on. «as?«'^gton. D.;,
', Inc. 1982. Application guide for c*A - HydroQual ciemica' ?4te
for; Cnemlcal Manufacturers Association, wasVngtort. DC
11-10
-------
Michigan River *odei
trat'ons
Capsule
The Michigan River Model ... .
jDePinto et a)., n.d.) was developed at the
£?A's Environmental Research UOoratory -
3u-uth. Large Unes Research Station. ,
Srasse I'e, "icnlgan, specifically for -jse ,
in tse «a$:e load allocation program. ,
simulates steady-state concen- j
of pollutants from loadings into [
or t.ie -ater column and oed
secinent. I: nas tne aoi'ity to model successive
one) using an analytical solution. It Is falr'.y
sMiplif'ec and more flexible.
'caches
.
«ICHRIV 1* C3n?paraD> to
SvSA 5-
excest *
i; MICHR:V predicts participate concentrat isro -n
vartaoie); SlSA treats it as an input data csns
ware
?; "iC-iaiv can mode- successive reac.ies; SlSA can .larc'e
3) "(ICHRlv is not intended for lakes wnereas SLSA is.
••ICHRlv simulates tS* advective transport of d'ssoivec anc ac:sr;e:
pol'ytants. *he node* employs first-order decay mechanisms far s'ec'ct'"?
ponutanc distrioutions. An aggregate first-order loss rate C3e*"c'ei:.
representing tne sum of a numoer of processes. Including vo'ati "zat'an,
hydrolysis, pnotoylsis, oxidation, and eiodegradation. is used in tne -ncce'.
Bed-water interaction* include settling, resuspens ion. ouriai of partica'ates.
and dl'^asian of dissolved constituents.
The model Is written In FORTRAN. Is user oriented, and prjvides gu
for Input data preparation and model option selection. NICHRlv has f*«i?a*e
batch input routines suitable for multiple reacnes.
KICHRIV requires oa$lc Information for modeling:
• Loading rates of pollutants and solids to the receiving river.
• flow rates, length of reach, water depth, and cross-sectional area.
-------
• Partition and first-order decay coefficients For &otn tne water column
and bed sediment.
• Sediment/water exchange parameters; sediment solids concentration.
OucsuC OeaerlPCion
KICHRIV predicts pollutant concentrations as a function of distance from
the loading source. Total and dissolved pollutant concentrations for act* trie
water column and bed sediment are resorted. Suspended sediment concentrations
are predicted as well.
4dy«neaqea trd Llait
was developed saedficaUy for riverine waste .load
Its level of complexity was Intended to be suUaote for
application. It requires less than two dozen input sarameters
tnere'ore. model set up time 1s relatively rapid.
eac*.
NtCHRI-V Is designed for slnqte river systems and 's not
river net^orits. lakes, or estuaries without modifications. O
of «!:H»:V include:
'a:e
he
steady*sXate wltn resoect to flow ams
• Decay processes are first-order; H has no specialized
organic decay routines.
• Dispersion is assumed to be negligible.
• Sorptlon/desoratlon are assumed to be instantaneous.
• Bed load is not permitted.
MICHRIV was tested and applied to a 60 Km reach of the Hint River,
Geres ee County, Michigan. The application of the model dealt wVtn tne
distribution of {Inc. cadmium, and copper from point sources. The main
purpose of the study was for calibration and field testing of the model.
Calibrations were made on solids transport and water column partition
coefficients to yield reasonable predictable total and dissolved metal
concentrations. The results were .-asonable enough to demonstrate MlCHRZV's
ability to accurately simulate sediment and water column concentrations of a
ooUutant.
11-12
<
-------
Pesource
remen es
XICHRIV is written 1n FORTRAN. The user manual and documentation are
contained within an E?A draft report on technical guidance for waste load
allocation studies.
User Support
KICHR1V 1s currently under review and should aecome ava^'aole m the
future. Technical assistance for MTCHRIV can Sei ootainea ay contacting:
Bill L. Richardson
US CPA
Environmental Research Laboratory - Ouluth
large Lakes Research Station
Grosse He, Picf>lg«n *8138
or
Joseoh V. OePinto
Clarkson Col'ege of Technology
Potsdam. New ror* 1367S
De.Pinto JV. Richardson WL, Rygwelsitt JC, et
manual for oerforfltng waste load .al local -ons .
U.S. Environmental Protection Agency.
al. n.d
Draft
"ecnn'ca* g
. nasritngton. 3C:
11-13
-------
Chemical Transport ana Analysis Program (CTAP)
The Chemical Transport and Analysis
Program (CTAP) (HydroQual 1981) was
developed ay HydroQual Inc.. Mahwah,
New jersey for the Chemical
manufacturers Association, Washington.
D.C. CTAP 1$ ah extension of the
Simpltf'ed Lake/Stream Analyses
(SLSA). also developed Sy HydroQual
and was designed for more complex
Capsuie
CTAP
i
r
•
r
Steady-state. J-dimensional
comparonent model,
screams, stratified rivers,
estuaries, and eoastaj «m6aya»nts
Multiple waste inputs.
Simple first-order kinetics.
CTAP. lUe SLSA. is designed to account for trie dissolved anc
steady-state concentrations of organic and Inorganic pollutants *n notn trie
•ater column and oed sediment. However; its greater complex'ty aV.ows '. t ts
mode' sfat'.fied lakes. r'.vers. tidal- rivers, estuaries, and csasta".
emoa^fneits. CTAP Is essentially 1 Ue SLSA In tnat it ^s a comoartment ^ooe'
•n «'n';n eacn csmcartment is equivalent to one SLSA ">a*e'. Mo^ever. CTAP •«
more comolex in tf^at these comoartments (up to <25) may be arranged in any !.
2. or 3-d'.menslonal configuration (spatial concentration variations may ex".-
in one, two. or three dimensions). whatsmore. the ccmoartments at
interactive with eacn other via advectlve and dispersive transport. *ass
sa'ance ec^ations are written for each compartment of aoth tne -ater ca-^nn
and Ded,sed'ment and are interconnected to adjacent compartments. The -esu't
is a matrix of equations wnich are solved By digital computation.
CTAP accepts
aquatic system.
well.
multiple chemical load Inputs from different locales to the
It can also account for tributary inflows and withdra^a's as
CTAP can be us«d to simulate aiultl -dimensional bed sediment conditions
In addition. U allows for a moving bed. where the upper-most layers are
subject to movement in the direction of water flow.
CTAP utilizes the same first-order reaction kinetics as SLSA. The
coefficients for photolysis, hydrolysis, oxidation, and blodegradatlon are
supplied by the user and then summed for an aggregate etc ; constant "he
sorptlon-desorptlon mechanisms are assumed to occur Instantaneously; a. It 1s
assumed that soluble and partlculate chemicals within each compartment are in
a state of local equilibrium. Interactions between the water column anc bed
sediment include settling, resuspenslon, burial of partlculates , and diffusive
exchange of dissolved constituents.
<
ll-l*
-------
I
CTAP data requirements are more Intensive than those of SlSA. ".n-add't'cn
to the standard physico-chemical parameters of the aquatic -system. :~A?
requires:
• Sources and amounts of pollutant loading.
* Segment volumes and lengths.
• Segment Mows per phase and dispersion rates.
• Solids types, distributions, loadings and concentration.
• Partition coefficients by phase and segment'.
• First-order coefficients for - water column pnoto'ys's/- *o'at' '-' :at* zf ,
hydrolysis. oxidation, and biodegradation; sec'me«>t ^ycra'/s's.
oxidation, anc blodegradatlon.
Data r-cuireaents are described by the model along «itn a s'scass'on s* -c- ". :
prepare sata far input.
Output Descriptions
The CTAP outsut presents less diagnostic information man ScSA. au: pr'nt;
out *ore computed chemical cancehtratlons in tne dissolves anc ;a":'c-"at?
pnases. The concentrations are presented in tabuiar farm for potn tne -«4t«-
column and sediments. The output is also ar'ranged so that tne concert- afsns
for each segment^ compartment are reported.
and Limieacions
CTAP Is a compartmental model, very flexible in configuration (up to tir««
dimensions in both water column and bed sed'ment). and applicable, to -ncit
types of water bodies. It can account for multiple point source waste inputs.
but no non-point sources. Spatial variable flows can be handled, tnqugn tne
user must specify them since they are not predicted. :
The model has no specialized organic decay, routines of the type used in
EXAMS; the user must specify first-order decay rates. A single decay rate,
which Is the sum of first-order coefficients of nnotolysls. hydrolysis,
oxidation, biodegradation, and volatilization, is used to predict chemical
fate. B> l-water Interactions are articulated with an Intermediate level of
CPmptexUy. CTAP allows for up to five different partUle sU«s; U also
allows for bed load.
11-15
-------
*odel Applications
CTAP was applied to the data collected by
of Rapid Creek. Rapid City. South Dakota.
downstream of a municipal wastewater treatment
WAS the surfactant linear alkylbenzene
was the only
SISA model.
Games (1981) from a 9Q-*m. -ear-
The study area was 'oca:
plant. The chemica', canside-es
sulfonate (LAS). The treatment s'ant
known source of IAS. This Is the same scenario used to app'y tne
The results of the CTAP modeling
of long. term loading are shown In
figures 1 and 2 for concentrations in
the -ater column and sediments.
*espect1vejy. The circled joints are
the actual concentrations. The
predicted concentrations in the water
column were In close agreement with
the actual; however, the predicted
sediment values were slightly higher.
•hen diurnal load variability was
ac-3M« r a •«•
«. • &.*•«•
accounted *or
values wepe 'n
the predicted sediment
Setter agreement.
Although not all the capabilities
o? CTAP were used in this application.
the incut -as sufficient to accurately
predict , LAS concentrations.
(.valuation of the Inputs showed the
accuracy and validity of these
estimates to be good.
.- •• -
Aesource
CTAP is written in Fortran IV and Is suitable For operation. «ith s'*^:
modification/ to the IBM 360/370. Unlvac HC8. COC 6600 mainframe computer:
and to minicomputer systems such as the POP 11/70, VAX 750/730. 13* 1'30. and
DSC Neta/4. The minicomputer version of CTAP requires 32K bytes of storage
with subroutine overlay and disk scratch files for temporary storage.
Support Acclv 1C lea
Copies of the CTAP user manual, as *ell as documentation, can be obtained
for a fee from:
11-16
-------
William Gull edge
Chemical manufacturers Association
2511 M Street, M.W.
Washington, O.C. 20037
201-887-1183
Technical assistance can be obtained by contacting:
Joftn St. John or
Doi>en1c OlToro
HydroOual Inc.
1 lethOMdge Plaza
*an*an, New Jersey 07*30
201-529-5151
Genera 2
HydroCua! Inc.. 1981. CTAP documentation - cnemlca' transsort ar.
program. Prtjared For tne Chemical Manufacturers Assoc'a.t ion. Washington
Inc. 1.982. Application guide for CJ*A-Hy
-------
Exposure Analysis KodeMng System
C4psuJ« Summary:
EXAMS
Steady
ecmparswnc model
systems.
Coop «•« ens jve second-order *!
for oryanic ertemjcaJ decay
The Exposure Analysis Modeling
System (EXAMS) (Burns et al.. 1982)
1s a steady-state water quality model
designed by the U.S. Environmental
Protection Agency's Environmental
Research Laboratory m Athens.
Seorgla. The model was designed to
allow for the rapid screening and
evaluation of the behavior of
synthet'c organic chemicals In
freshwater aquatic ecosystems. HUh a description of :ne pnys<
chemical properties of the compound of Interest, and trie re'evant t
and physical/chemical characteristics of the aquatic system, £*AMS
the exposure (steady-state environmental concentration).
pollutant removal system haU.-Mfeh and fate {d1 str iaut '-on
fraction consumed by each removal process) of
calculations are based on tne assumptions that
time averaged.
ca. an:
ranspo-"1
^
eacn compound -noce'ea
tne loadings are ';f>g
Th« £HA«S program is an Interactive modeling system that a'-'ows
to speedy and store the pnys leal/chemical properties of 3otn tie
compounds and the aquatic environment.
tie •j-'.e
:r.emi:a
The aquatic system Is user specified and is represented by a set of
segments or distinct compartments (water and sediment) tn tne system. As :nany
as 100 compartments can be handled by EXAMS.
The program 1s based on a series of mass balances that give rise to a
single differential equation for each compartment. Mass balances accounting
for all compound mass entering and leaving are calculated by EXAMS as the
algeoralc sum of (1) external loadings, (?) transport processes that export
the compound, and (3) transformation processes within the system that convert
the chemical to daughter products. working from the transport and
transformation process equations. EXAMS compiles an equation for the net rate
of change of chemical concentration in each compartment.
004 3 P
IMS
-------
tXANS computes the kinetics of transformations attrlbutaJ^e to dl*ect
photolys's. hydrolysis, biolysis, and oxidation reactions. The input criemlca*
data for hydrolytic, blolytlc, and oxidative reactions can be entered either
as single vaiyed second-order rate constants or as pairs of values defining
the rate constant as a function of the environmental temperature specified for
each segment. CXAMS includes two algorithms for computing the rate of
pnotalytlc transformation. The first requires an average pseudo-f1r$t-oraer
rate constant applicable to near-surface waters; and the second computes trie
photolysis rate directly from the absorption spectra of the ccmcounc ana ^ts
ions, measured values of the reaction quantum yields, and the environment•
concentrations of competing light aosorbence (chloropny'5. seaimeits, etc.).
Internal transport and export occur via advect've and a'spe-s^e -nov-me":
of dissolved, sedlment-sorsed. and piosorsed materials, anc Sy vo'at'*':aticn
losses at the air-water interface. EXAfS provides a set of vectors, that
aT'ows the user to specify the location and strength o? sot", acvect've s^c
d'spers've transport pathways. SXA^S can csmpute transsc-t 3* a c"e^':a' ^'j
e-sedraent Dedloads. suspended ses'.me^t -asn'oacs. 5-:gr2«a*.e-
'tration, transport through the thermoc!'ne of a 'a"f*^e"t
streams, etc.
External 1cadlngs of a chemical can ente- trie eccsystefi v'a rc"t $3yr:»^.
ion-po'?t sources, dry fallout or aer ".a l •' -r' f t, afncssner': -a:^-:ut. i".
grouncwater seepage entering the system.
CXAPS is available m both a batch and interactive
* ca ffifgu 1 re.ign ta
requires an extensive amount of environmental data, however, tne
program can be run with a much reduced data set when the chemistry of a
compound of Interest precludes the existence of some of the transfsr-nat'ci
processes, "or example, pH and pOH data can be omitted in the case of ieut'3'
organics that are not subject to acid or alkaline hydrolysis. Si* 'canor'ca'"
environments are Included wHh most model versions and can 3e used for
non-specific screening Investigations.
Input parameters Include:
• A set of chemical loadings on each sector of the ecosystem.
• Molecular weight, solubility, and lonlzatton constants of the
compound.
0043?
11-19
-------
• Sedlment-sorption . and biosorptlon parameters; Kp. KOC or Row.
biomasses. aentnic water contents and .bulk densities, suspended
sediment concentrations, sediment organic carbon, and ion exchange
capacities.
• Volatilization parameters: Henry's law constant or vapor pressure
data, wlndspeeds, and reaeratlon rates.
e Photolysis parameters: reaction quantum yields, absorption
Spectra, surface scalar Irradlance, cloudiness, scattering
parameters, suspended sediments, cnloropnyl. and dissolved organic
carbon.
• Hydrolysis: 2nd-order rate constants or Arrhen'.us functions far
the relevant molecular species: 0H, pOH. and temperatures.
• Oxidation: rate
concentrations.
constants. temperatures,
and
oxidant
• Biotransfor-natVon: rate constants, temperatures, tata' and active
bacterial population densities.
• Parameters defining strengtn
dispersive transport pathways.
and d'rectlon o? advect^e
• System.geometry and hydrology: volumes, areas, depths, rainfall.
evaporation rates, entering stream and non-point source Hows and
sediment loads, and groundwater flows.
OutSUt
EXAMS' 17 output tables Include an echo of the input data, and tabulations
giving tne concentration. Fate, and persistence of the chemical. Printer
plots of longitudinal and vertical concentration profiles can be invoiced Sy
the Interactive user.
The major technical strengtn of the EXAMS program lies In Its ability to
utilize well defined, chemically based fate process information in
second-order rate expressions for the hydrolysis, photolysis, and oxidation
processes. Volatilization 1s modeled in 4 way that is consistent with
accepted mass transfer processes. Thus the model's strengtn 1s In evaluating
the chemical's kinetics.
C043P
11-20
I
-------
From the user's standpoint, the model can be.-un In an interactive node
for rapid evaluation of scenarios reflecting varying system physica' and
cnemlcal conditions. Furthermore, the model contains a built-in, on-'^e
'help-file1 to eiplaln tne command options and required Input data.
EXAMS is a steady-state model and as such was not designed to evaluate t*e
snort-term variations of an aquatic system.
EXAMS does not account for sediment and. contaminant loss 5y bur'a'
bentMc layer. Furthermore, it has only a single exchange :oe"*c%er.t
the process of water-sediment particle exchange and the process o* ..at
water diffusion.
tne
EXAMS has the capability to model ponds, -'ve^s. anc
the capaoillty of modeling estuarine aquat'c environments
The model does not simulate sediment-po1. 'utant loass e'3in :c'-:t
non-point sources. Solid concentrations uost ae der
-------
S'mu'ations of LAS steady state
concentrations in water and sediment
were compared with observed
concentrations. In a qualitative
sense, agreement was good (see Figure
l). However, tms agreement was only
obtained by assigning an arbitrary
«alue to the dispersion coefficient
at tne sediment/water interface. The
value chosen was in the expected
range. but little or no rationale for
tne value could be provided. Since
this term is fairly Important {as
determined by a sensitivity analysis)
and is seldom measured, it acquires
tne characteristics of a calibration
parameter.
• •• It .1 <•>«!• 1 tt
sensitivity analyses with
"espect to errors In measurement of
cre«* few -ate. oiodegradation rate
constants, a.ic adsorot'on coefficient
we's a'so conducted. Results
indicated tnat model calculations are
most sensitive to the least
understood parameters, that is. the
uen-t/water exchange coefficient
the seciment biodegradation rate
constant. However, this pnenomena
may De inherent in chemical and aquatic systems and nay iot se a arob'em
jnique to EXAMS.
«n am i
and
''0»o»
In other applications. EXAMS has been successfully used to mode'
volatilization of organics in specific field situations, and for a genera!
assessment of the behavior of phthalate esters m acuatic systems.
been Implemented by a number of manufacturing firms for *nv
evaluations of newly synthesUed materials and has been uses 'n an academ-c
setting for both teaching and research.
Reacutef
EXAMS is available from the EPA Athens Environmental Research Laboratory
in eltn-r a batch or an interactive version. The batch version reauires 6*ic
bytes (overlaid) of memory (for aquatic systems of up to 17 segments); this
version doc.- fit reaulre mass storage capaol 1 It -';. The interactive version
also requ* es &•* bytes (overlaid) of memory, plus an additional mass storage
capability. The interactive version of EXAMS requires lOQK bytes of mass
storage for utility files. 2K bytes for each chemical in the active Mies, and
2.Sic bytes for each active defined environment. An overlay capability is
0043P
11-22
-------
requi'ed to implement EXAMS on small computers such as PDP-11 or HP 3QCQ
systems. Execution times range from a few seconds 49 several minutes
depending on the problem 'to be solved. The software is aistr'auted on
magnetic tape; the source code consists of about 16.000 card images.
It has been estimated that approximately one to two man-months of effort
are required to setup the model (not Including the effort nequir.ed to evaluate
the results). This estimate is based on the following assumptions: (i) a'l
data necessary to meet the input requirements of the model are avai'ao'e and
(2) qualified personnel are available to implement tne model.
Suooorr *cri»
Free copies of the user's manual and system documentat'on are »va°aa'e
from 080 Publications. Center for Environmental Resear;n :n(3r^a:'or.. U*£=A.
Cincinnati. Ohio 45268 (Telephone: 513/684-7562; ask *or 3uD''cat'3n No
EPA-&CO/3-32-C23) . The computer tape of the program ;?r2v«cea 'or tre
requestor to copy and return) Is available from Center for «ate' :ua*':/
g. Environmental Research Laboratory, USEPA, College Station 3cac.
-Gecrg'.a 30613 (Telephone:- 404/5*6-3123).
J:e' a:s'stance can 5e ootalned 5y contacting:
',awrence A. Burns
Environmental Systems Branch
U.S. Environmental Protection Agency
Environmental Research Laooratory
College Station Road
Athens. Georgia 30613
FTS 250-3123 COM' 4Q4/546-3J23
David «. Cline
Automatic Data Processing
U.S. Environmental Protection Agency
Environmental Research Laboratory
College Station Road
Athens, Georgia 30613
FTS. 250-3123 COM 404/546-3123
Burns L'. Cllne OM, Lasslter RR. 1982. Expo"ire analysis modeling system
'(EXAMS) «>ei manual and system documentation. ' .S Environmental Protection
Agency. Athens. Georgia. Publication No. EPA-600/3-82-023:
Games L M. 1982. Field validation of exposure analysis modeling system
(EXAMS) in a flowing stream. En: Modeling the fate of chemicals in the
aouatlc environment. OUkson Kl, Make AW. and Cairns J. Jr. eds. Ann Arbor
Science Publishers.
OO.JP "-»
-------
"etaIs Exposure Analysis Modeling System
1982) 1s
computer
chemical
systems. This 1s
Unking the
MINTEQ. with the
Modeling System
al. 1982). an
acuat'c
Combines,
of metal
ssec'a
The Metals Exposure Analysis
Modeling System (MEXAMS) (Felmy et al.
a synthesis of two existing
models that accounts for the
and pnyslcal processes
affecting the fate and transport of
metals in aquatic
accomplished by
geochemical model.
Exposure Analysis
(Sums et
exposure assessment model.
tnese models provide the capability to {1}
line'/ to oe in solution and (?) consider
SUBIJIATy:
aodel.
*pec:*e.ion,
idsocbed *rtd
cazed .i»caJ esnceneracions
constants *nd
for < jeve
c.*>* models daca
'on on adsorption or'precipitation -of metals,
tne amount of metal in solution.
estimate tne
tne effect of
50th of .n':?! :
crienr
an act
a*
to
The cnemical Interactions are nan^led ay *IHT£Q, us^ng tn
eguiliorlum relationships and water quality data to calculate spec'at 'on
dusolved. adsorjeo. and precipitated metal concentrations.
Spec'.ation 1s calculated using an equilibrium constant approach «nerein a
series of mass action expressions are solved subject to mass balance
constraints on each chemical component. An estimate of aqueous spedat'.cn is
necessary to predict the Quantity of metal that will be taken out of solution
by precipitation and adsorption, and to evaluate environmental Impacts. In
the case of the latter, toxldty and bioavaUabiHty of individual metal
species can, vary ay several orders of magnitude; therefore, estimates of metal
$peda"on are required to predict aquatic Impacts.
Adsorption is treated as being analogous to aqueous spedatlon. There-
fore. mass action expressions can be formulated, for adsorption • reactions .
MINTCQ contains six algorithms for calculating adsorption. It computes the
mass of metal transferred Into or out of solution as a result of the
dissolution or precipitation of solid phases.
The migration and fate of the metal are handled by the aquatic exposure
assessment model EXAMS, a steady-state transport model developed primarily for
use with organic compounds (see EXAMS).
11-24
^
-------
The coupling of MINTED and EXAMS was accomplished in such a way as to (1)
retain all of the original EXAMS options and capabilities and (2) aypass
unnecessary calculations or calculations either not applicable to metals or
duplicated by MINTED. For Instance, there is no need for EXAMS to compute
adsorption since MINTEQ will provide the quantity of meta-1 soraed to sediments
and biota. Another example Is chemical degradation which is applicable to
organic* but not to metals. Through the proper specification of EXAMS inputs.
most of these calculations can be bypassed. Thus, the user Mill not be
required to maintain two versions of EXAMS-, one for organics-anq one for
metals.
MEXAMS was designed primarily to be used in performing sc-«*t'ng 'eve'
assessments using generally available wate- sua'^ty data. It ca.~ a's; se us«2
to interpret data collected during bioassays and as a framewjrK *zr gu'a'ng
research' related to the aquatic Impacts of- pollutant metals
The model of :ne
of reaction, and otner
species or solid phase.
information required to predict the formation of each species or solid p
The water quality data are physical and chemical properties of trie water
being analyzed (e.g.. pH, pOH, temperature).
The user need only to generate trie water quality data in oraer to
implement NINTEQ. The thermodynamlc data (for the specific metals currently
handled by the model) are contained In a data base that accompanies the model.
Ou CPU c D«3crl pti on j
The model output 1s divided '.et.;en the EXAMS and MlN'-t* imponents.
EXAMS provides tabulations presenting estimates of the expo>jre. fate, and
persistence of the metal. The MIMTEQ outputs give details on the chemical
interactions occurring In each compartment of the simulated aquatic system.
.11-25
-------
And Llxl raco
represents an improvement in metals modeling in that U accounts
for the complex chemistry affecting the behavior of metals as well as the
transport processes that affect their migration and fate. Specifically.
••EXAMS considers the effect of chemical spedation on adsorption or
precipitation of metaU. ..
The modeling system is user oriented. It contains an interactive program
that helps the user prepare water aual'ty data for input to MINT?Q. :t also.
queries the user to ootaln user run information wnich 1s then used to central
tne ooe'-at'on of «INT£Q and EXAMS and the transfer of simulation results
ser-een the models.
The themodynamic data base associated with "I.NTEC conta* is ' ecu* ••':<• 'urn
onstants and anci'*ary data for only a limited numoer of po'-utant meta's
'.e.. As. Cd. Cu. ?5. Hi, Ag. and Zu).
ic csmp'eiation can nave a significant impact on tne
Although N-NT-fQ is capable of handMng organic cim
Sara aase does not contain the necessary ecui ' 'S
anc'"a-y iata to evaluate tni.s pnenomena.
of
t ion,
df-s precipitation/dissolution. oxidat*on/r«suct'on. and
as equlllBr'um processes -hen In fact they ^ay *ot 3e in
EXAMS does not describe vertical changes in 0H. and ox'dation-reductlon
-eactions in the bed sediment. The latter can be very significant' in
t'nq the fate of metals In lakes and polluted rivers.
The "EXAMS inetftodology 1$ currently under development and has not been
applied in the field.
f*odfl Applications
Although MINTEQ and EXAMS have been applied Independently, as they are
currently linked In the MEXAMS program, they have not been applied ir an
environmental analysis. :
11-26
-------
«1T' require a system wUn 32* memory. An overlay caaaoi ". ty '$
required to implement M£XAMS on small computers sucn as a POP !i/?0 or HP3GCO
system.
t/3ft Supper ; Ac g j v i ti es -.
Copies of tne user manual and system oocamentat'on wi *' 3e
sometime during trte summer of ^983. At tnat time, it '.s ant^c'satea :*a
support «m 5e providea &y tne Center for water Qua1':-/ "oae''"?. £=?•. ,
support «m 5e providea &y
Atnens, Georgia.
Additional information concerning the model can oe oDta'riea ay contac
Cnlshl
Sattel'e. Pacific Hort^est laooratories
R'.cnlanc. -asMngton 99352
References
Felffiy AS. 8rswn SM. Qnlsni t. Argo ?S. YaOusaK^ S3. '982. «£.
-------
,
"
-------
Estuary and Stream Qual'.ty *ode! (WASTQ.X)
UASTQX (Connolly 1982} was
designed as a -time-variable
compartment model .for simulating tne
t-ansoort anc transformation of
organ'c cnemica's .m tne water column
a«C tne sefl'ment
estuaries; a'tnougn
genera' \y app'iicao'te
-ater scales.
CapsuJe Summary.
tes
of streams and
tne model is
to all types of
eornpdranvnc.
Streams *nd e5cuac
*nd a* lint oft tec
Comprehensive second-order
-AS7CX ae'onqs to the WAS? model (OiToro et al. 198!) «am'. ly anc :ie*e-
•>as capat» titles and features slml'.ar to TOXIWAS? (Am&rose et a' -9935
maior differences between WASTQX and TOXIWASP are: (11 wASTQx can account
and TOXIWASP are: (1) wASTOx can account
size fractions; TOXIWASP accounts for one. (2;
sart't'on coefficients are expressed as a function of
T3x:»iAS? assumes a constant parV'
"ft« nwjor deferences between
*cr tnree sedlfflent
isncentrav.on; T3X'.'nAS? assumes a constant part/: '.on ing coe"'c'*nt.
•ASTCx assumes certain system orooerties oeCM>iiicn».. \ i ) MMJIUA uoi UCC" ina i n . f urii
-------
Segment volumes ana flows
velocities In compartments.
including time of f'ow duration, and
• environmental and pollutant parameters such as geometry of t.ie system,
sedimentation transport/dynamics parameters, PH. teape-atyre.
concentration of compound degrading bacteria In water, sec ore -or:e»
Siodegradatvon constants for dissolved and adsorbed toxicant, f'rst-
and .second-order alkaline hydrolysis ratio, otner first-order decay
rates, Henry's constant, molecular weight of tox'cants.
correcting parameters, sol'ds dependent partitioning coe* *'c'
• Initial conditions, boundary conditions, and waste 'oads.
CutsuC
A finalized output Fornat does .tot ex's:, s'.ice
developmental stage. The output is expected to be similar t
consisting of a listing of input data, and tabu'at'ois givn
and aers'stence of tne' cneffllcal In aH water and sea*iter.t r
•ater aody.
AS"Ox '$ ,
tne «A$= cyt
t-anuc-t . *"
<e requirements are diff'ca't to -neet
from routinely available data.
ffeaoureg
WASTOX Is available 1n a batch/tape version, is written in FORTRAN [V, and
uses up to a 32K-byte user area on a POP 11/70 machine. Execution t'mes range
from a few seconds to several minutes depending on the temporal a .id spatial
r*c i. two man months of effort are required to have an operational model with
a rough understanding of Its overall behavior or performance.
!
-------
User Suoporr AczivjcifS
To obtain the WASTQX documentation along with sample data sets and supper'
software, write or contact:
Or. Parmely H. PMchard
Environmental Research Laboratory
Sulf .Breeze, Florida 32561
(904) 932-5311
Or. John P. Connolly
Environmental Engineering and Science
"annattan College
Sronx, NY 10471
(212) 920-0276
Ambrose a. H111 S,
and fate model TOxiwASf
Research and development.
L. 1983. User's manual for tne cnera'ca' fa
version I. Draft document, U.S. E?4, 3ff
Athens Research Laboratory. Athens. Georgia.
ce o-
nASTQX.
Florida.
TP. 1982. Preliminary estuary and stream version documents:'on 3.
£'* Cooperative Agreement NO. *8C7 927-02. Ea&, ^c'* S!"??:?
Kannattan College, Bronx. New York.
3*. ntzpatrlck JJ,
Simulation program (WASP)
documentation. • Mydroscience.
P-otectlon Agency. Duluth. NN
Thomann 3V. 1982. water cua'*t
and model verification program
Inc.. westwood. NY. for . u.S. En
Contract No. 68-01-3872.
ana /s
11-30
-------
Chemical Transport and fate *ooe1 (TOxIwASP)
The foxier water Analysis Simu-
lation Program (TQXIWASP) (Ambrose et
al. 1983) was designed as a time-
variable compartment model for
simulating tie transport and
transformation of organic toiic
cftemtcais In the water column and tfle
sediment of stratified lares and
reserves, large rivers, estuaries.
and coastal waters.
Capsule Summary; TS
Rivers. .IdJces. estuaries
ve second-order
"OXIWAS? *as
model (Sums et
program (OlTorg
algorithms along,
created "3y first adapting tne k'netic structure ^
al. 198?) to trie transport frameworic sroviced 2y
et al. 1981), and by trier, acting slir.c'e
witfi special input and output softwa-e.
t-ie
the
Since TQXIWAS? uses tne compartment modeling approach, .nereoy
cart ae arranged 1n a 0-. 1-, ?-. or 3-dlmens lonal conf igarat'cn. "Cx:«A:3 \\
water/sediment quality program only, and as sucJi, it requires tne -ate- aoc
and tne sedimentation dynamics (e.g.. flow, velocity, aed sed'ment ve'odty
as user Inputs. TOXIHAS? can 'oe employed for analyses
transport and loading capabilities than EXAMS,
mecnanlstic sediment predictions than S£RATRA (Onlsnl
ve
requiring -nore dynamic
but less aeta'. '.e
-------
tne chemical transport an
-------
Chemical Transport and Fate ^odel {T
SUPUHLfM
The foxier w«iter Analysis Simu-
lation Program (TOXIWASP) (Ambrose et
al. 1983) -as designed as a tlme-
vaMable compartment model for
simulating the transport • and
transformation of organic toxic
chemica's m the water column and trie
sediment of stratified lakes and
reservlors, large rivers, estuaries,
and coastal waters.
CapsuJe
TCx;v*£?
cceparsnenc node2
Rivers. JdJtes, *sclaries
Comprehensive secsr.d-G:dez
was
mode' ;3urns et
program CM'oro
a'ong
created 3y first adapting :.ie «'net'c strjct-'e 3'
al. 198?} to tne transport frameworx. jrov'cec sy
et a'. 1981). and by t.ien acld'ng s^.^c'e cec'^e"
«'tn saec'-al input and output software.
tie :.t4"C
tie
S'nce TOXIWAS? uses tne compart.-nent nodeting aoproacn, -fte-esy 5«^.e".t:
can J>e arranged In a 0-, 1-. 2-, or 3-d1mensional canf'gurat'on. "CxlfiA^? -s a
water/iedifnent quality program only, and as sucft. it requires tne .a:*- DCC<
and t.ie sedimentation dynamics (e.g.. flow, velocity, 3e<3 sec'sefit
process, and the kinetic time derivative is calculated from this rate.
yielding a time varying chemical concentration for a user.specif led spatial
network. EXAMS uses a Kinetic structure that allows tne study of five
different ionic forms of a chemical, several ways to calculate photolysis, and
other capaollUles. In TOXIWASP, all tnose features, nave been aggregated in
one formulation, but with an expanded library of kinetic subroutines. In that
respect, TOXIWASP allows simulations of toxic organic chemical behavior m the
aquatic environment resulting from loading pulses that cannot be modeled via
steady-state cotfe..
1
11-31
-------
Se'garding t.ie cnem'ca' transport and fate processes cons'ce^es. T:X:WA$?
can account for vo^at1 l 1 ;aHon. pnotolysls. hydrolysis, ox'dat'on. 3'olys's,
sorptlon on botn sediment and biomass. advectlon, and diffusion. . Sorptlon on
sediments and aiomass 1$ calculated assuming local equrt'Sr'um us'ng a
constant partition coefficient and'" soatVally varying envlronnenta' organic
carbon fractions. for eacn compartment, one differentia' equation for the
pollutant dissolved pnase and one differential'ecuation for trie adsorbed phase
are formulated and solved. AS ' contrasted to wASTQx (Connol'y "9825. tne
effective first-order decay rate can vary with time.
Exchange between tne water column and tne bed can occur ay set:'*ig or
-esussension of participates, diffusion (of dissolved goilytarst- sef-een tne
•ater co'umn and tne pore water, by direct adsoration/desorst'c.i ner-eei t.ie
•ater ca'amn and tne Sed sur*ace. and 3y percolation or infilt*at*on. ^'tn-i
tte, 3ed, tne sol^utant can move .vertically 5y 3"*'js'on, turnover
;;'spersion). serco'ation. and Durlal. Also wit.iin tne aed. :.-e 3o''jtant
cannot move horizontally (i.e., no bed load), in contrast witn «ASTCX.
'.TPU ; . Ja £a Pegu 1 re.'nen ;j
t reculremeits for TQXIWAS? include:
cseff'c'ents between ::mcar:.?er,t: -sjet as s'::€-:';-- :e:
segments, -ater column and sediment, sediment arc -at?- '^ 3ed
material. .
Segment volumes and flows.
Soundary conditions.
Envlronmenta' and pollutant characteristics sucn as number of
constituents, temoerature, cloudiness, bacterial pcDuUt'on.. a''amass,
hydroxide ion activity, molar concentration of ox'dants. organic
carbon, pH, decay coefficients', Arrhenlus constants* second-orae«-
rate constants for biolysis in tne benthic environment, octanol -a:er
partition coefficient. Henry's law constant, vapor pressure, and
solubility.
'•Model output consists of a listing of input data and tabulations giving
transport, fate, and persistence of the chemical in all water and sediment
compartments of tne water body.
11-32
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Advantages dS not Oeen acoMed to a real situation; ftc-eve-. :.-e i^--;
.-node' nai seen acplied 'n numerous, situations (see £XA?<$ aescr'sr's.-. a-c :•*
program, available since 1970. nas been app''ed *r» -nore :,".ar. ;;
:; (3lToro et al. 1981).
1s not an Interactive modeling package; ratner. 't «s a srar.cari
software package in. FORTRAN, operational via. a standard CRT unVt or a can
deck. TOXIWASP requires an IBM 370 (OS/MVS Operating System).-or a "QP tl/70
(IAS Operating System), programmed. In FORTRAN [V» or FORTRAN IV. The firs:
version can accommodate 100 compartments. the second 50 compartments. The 'Q3
11/70 computer utllUes an IAS operating system and allocates a 32* «ord '. M<
Dyte) user area for execution of a program. TQXIWAS? occupies at least 22<
words of memory in either machine, execution times range from a few secancs
to several minutes, depending on the temporal and spatial resolution of the
environment analyzed and the machine used. At this stage, it 'is estimated
that one to two man-months of effort are required to have an operational
model, with a rough understanding of Us overall oenavior/performance.
11-33
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support Activities
TOX-WASP '1s
Laboratory In a
along with sample
available from the EPA Athens Environmental Research
batch/tape version. To obtain the TOXIWASP docymentatio'
data sets and support software. write or contact:
Mr. Robert Ambrose
Center for Mater Quality Modeling
environmental Research Laboratory
U.S. EPA. College Station Road
Athens, Georgia 30613
(404) 546-3546
Amorose R, H111 S. MuUey L. 1983. Uier's manual for the chemica' t-ansport
and fate model TOXlWAS?. Version 1. Draft document, U.S. E?A. Qf«*ce of
Research and-Development. .Athens Research Laboratory. Athens. Georg'a.
3ona?ountas
£nvi f onmental
eds. '982. Arthur-, 0. LU.tle. :nc.
•oathematica' modeling . handbook/cata^oQue.
Of*^ce of 'ol-Uy of Resource .Management . U.S. Environmental ?rot*ct'on
LA. CUne OH. Lasslter RR.
(EXAMS}, user manual and system
Environmental Protection Agency.
1982. Exposure
documentation.
anal ys 1 s
Atnens.
•node'
.NVISO:
D.C.:
\ t em
J S.
Connolly TP. '982. Preliminary estuary and stream version aocjDe".:a:'3n o
WAS7QX. E?A Cooperative Agreement lo. R807' 827-02. £PA Gylf Sreere. f'or'ca.
"anhattan Col'ege, Bronx, New York.
OUoro DM, FVtrpatrick JJ. Thomann Rv.,' 1981. Hydrosc lence. Inc. wter
quality analysis simulation program (WASP) and model verification program
(MVP) - documentation. Oulutn. MN: U.S. Environmental Protection Agency.
Contract No. 68-01-3872.
Onlsnl Y, Wise SE.. 1982. User's manual for the Instream sediment contaminant
transport- model, SERATRA. Athens, Georgia: U.S. Envlronmenta' Protection
Agency. EPA-600/3-82-005 (\n press).
U-34
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Toxic Organic Substance Transport and Bioaccumulat'on *odel ;*3*IC;
Capsule Summary.-
The Toxic Organic Substance |
Transport and Bloaccumulation *odel • Quasi-dynamic.
(TOXIC) (Scnnoor and WcAvoy 1981) is a
quasi-dynamic water quality model
designed to simulate tne behavior of
pesticides in } reservoir and bio-
concentration of pesticides in aquatic
11 f e.
iapouno
foe
nc
reservoir
c sys:es>s .
biological
The quasi-dynamic approach ut^itUes: ( '• ) 'steady, anr>ua
(from da'ly averages) for long-term simulations; (2)-st*3Cy,
flow-«e'gnted
time-varlaole
sol ids
toxicant
(from da 11y.
loac'nqs
suspended so1! ids -neasure-ne'
av«-ace
and
TOx.'C includes a routine «n)cn calculates a niass Sa^ance an sea-men:$ ar.:
tne adsorbed cnemical. Sediment deposition and scaur are inc'uces. as ':
diffusion of toxics from sediment oore *ater to tne overlying ..a:?1-. "-••
model also computes contaminant uptake and depuration Qy fls.i.
TOXIC considers t^e aouatlc system being simulated as
numoer of compartments {m one application of tne model
nave been utilized). Each compartment 1s considered
system.
ing divided 'nti a
up to 100 compartment:
to be a completely m'.iec
The concentration of the contaminant tnrougn time is described by a- set a*
ordinary differential equations, one for each compartment. The basic equat'on
Is written to include tne sum of the first-order or pseudo-f Sr$t order
reactions (hydrolysis, biological degradation, biological uptake, photolysis.
and volatttlration) as well as adsorption and desorptlon kinetics as a
function of particle size distribution. The coupled equations are tnen solves
via a variable step Sl:e fourth order Runge-Kutta numerical technique.
The Inputs to tne model can be classified ..Jet the following categories:
• Geometric properties, such as volumes of compartments, distances
between them, surfa'ce ateas, and locations with respect to other
compartments.
11-35
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• flows set ween compartments and between eacri compartment and the
outside of the system.
9 Reaction rates, settling rate constants, and partition coefficients
• Sol Ms concentrations In each compartment .
• Bulk dispersion coefficients between compartments.
•'• Simulation parameters such as step size and time of simulation.
Oueguc Descriptions
Output from TOXIC Includes:
• Solids balance description listing the concentration of tne soMds in
the water column and In the sediment., and the net £'u* .-e'.-e«". •?,*
sediment and the water column over time
• Dissolved. particuUte. and. total concentration of tne contam'-.
addition to chemical reaction pathways .< f )sn uptake and depuration ;eicr»t'on
and metabolism) are Included in the model. Previous models have not comomec
fate and transport 'modeling with the biological effect (aioconcentration)
TOXIC Includes a routine which calculates a mass aalance en
contamlnant-sorbed and unsorbed sediments. Sediment deposition and scour are
also Included, as Is the diffusion of toxics from sediment pore water to tne
overlying water.
Coefficients and rate constants must be supplied by the user thus
requiring a working knowledge of kinetic processes, sedtm.-nt transport
mechanisms, and the ability to adjust the model's comput"r code.
11-36
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The -node^'s sMiu'at'. on caoaoilities .as designed to De aoo-'-eo :o
reservo'r or impoundment aquatic ecosystems anfl may therefore 3e unattract've
for use in situations -here multiple aquatic systems (e.g., rivers, streams.
and Impoundments) exist.
user support for the model 1s rather limited. A user's manual is
l '.aole at this time ana the model is not current'/ . supported 5y trie
Center for rfater Quality Modeling. CSi, USE?*, Athens. Georgia.
k
xcdel Application
TOxlC has Seen applied to lo*a reservoir aata to simulate 't*.e :«-a<';' a*
the 'fisect'ciae d'eldrin and the heraic'.des a'acn-ar and atrar'^.e ::»4Cy-
state analyses and' quasi-dynamic simulations with tlme-^ar'aD'e •":-? 310
-ere undertaken.
measurements for-alach'or we'e used 'n the -nose' : "ru/at' :n;
*!tn scod agreement 5et*een nodel " predict'ons and measwroc canceit-at'cn
.atoratory measurements we'e a'so used in the a:-a:'i« e ootalnefl 5y contacting:
J. L. Schnoor
CW11 and environmental Engineering
tnergy £ng1neerlng Division
University of Iowa
Iowa City. Iowa 52248
(319) 353-7262
11-37
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Scnnoor JL. McAvby OC . -1981. A pesUldde transport and
model. Journal Environmental Engineering Division. ASCE. Volume 17. NO. EEC
Scnnoor JL. 1981. Fate and transport of dleldrln in Coralville Reservoir:
Residues in Msn and water following a pesticide ban. Science. 21', 50.
840-842.
Scnnoor JL. 1982. neld validation of water cjual'ty criteria far *ysr3C
pollutants. In: Proceedings of the 5tn symoosium. aauat'-c 'ax^c'ty. AST
Scnnoor JS. 1982. fate and transport modeling for tax'.c sjBstances.
XodeMng tse fate of cnemlcals in tne aquatic envi-onment . 3«'
Conference Proceedings, Ann Araor Science
Scnnoor Ju. et al . 1983. verification of a toxic suastancs t* »'*<;c-t a^
aioaccursulation model. t?A 600/2-83-C07 . Environmental ^c$ea':" .sDs-itsr/
3A. 3G&13.
11-38
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Channel Transport Model (CH.NTSN)
Capsule SuBinary CHHTM
Tin*- varying, l-tl
eomparunenc ruodei
flyers.
-order
The Channel Transport Model
(CHNTRN) (Yeh 198?) *as developed .by
tne Environmental Science Division of
the Oak Ridge National Laboratory, Oak
Ridge, Tennessee, for the EPA's Office
of Pesticides and Toxic Substances.
~he purpose of CHNTSN' is to s*mu'ate
time varying distributions of
sediments and chemicals .in receiving
*aters. CHNTSN can mode. tne |
transport and fate of a pollutant In a
*'.ae variety of aquatic systems, mat include: tida1 a.ic icr.-
*a*es. and reservtors, streams, estuaries, and csasta" seas. A :
'eature of CHNTSN is its capability to deal witn a oetwor* s/stem
consist of any numeer of Joined and aranc.ied streams/"'vers 3? ;
s';e. CHNTSN. comotneti «.stn tne Channel Hydrodynamic
Comprehensive
for or9an^c5
^ and da
ia'
•f.:u ar
i tfte .lycrodynamlc computations or r sws ar>s
constitutes a software package for predicting tne transport.
transformations of organic pollutants in a stream/river system.
transfer, arc
CHNTRN can model complex problem settings t^at can
!-dimensional segments. Codification of tne model
3-dimensional proolems is relatively easy because
compartment approach. The spatial scale of segments can
kilometers, and the temporal scale can vary from seconds to hours
se ac;-"3x'^ates -'t.i
:o treat 2- ano
of t.ie 'ntsgrated
vary from neter$
to
CHNTRN uses tne chemical kinetics of EXAHS (Exposure Analysis *oce'1ng
System) to account for hydrolysis, oxidation, photolysis, volatilizat'on.
blodegradatlon. and adsorption by biota. Consequently, other pnys ico-cnemica1-
factors (e.g.. temperature. 00. pH) are also reouired. Sediment transport,
deposition, and scouring are simulated for three particle tyoes, cane. silt.
and clay. Provisions for adsorption/ desorptlon and. pollutant accumulation In
the bed sediment are Included.
The model code Is In basic FORTRAN language. It consists of a main
program and IS subroutines. The equations that govern tne system's kinetics
are derived from 3-dlmens1onal mass balance equations. An integrated
compartment method (Yen 1981) Is used to solve the differential equations. In
this method, the link matrlce? ir» derived based on the fluxes of mass a Ion-
each of the links that Intertwine the compartments of the river system. Th>
global matrix associated with spatial derivatives is assembled From these link
matrices.
11-39
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The resy't is a system of ordinary differential eouat'ons with respect to time
that govern the dynamic evolution of suspended sediment, aea sediment,
dissolved chemical concentration, participate chemical concentration, and oed
sediment chemUal concentration. Chemical concentrations for ooth the wat
column and oed sediment are solved By the time split scheme. Two options f-
solut'on are provided; one is tne explicit scheme for fast, computat'on; the
second is the implicit scheme which generates stable solutions for large time
steps.
CHNTSN user manual and documentation are currently in draft fom and it
yet to ae field verified.
CHNT3N is a sophisticated model recuir'.ng extensive sata input. 3e*3r-
can se executed, hydrodynamU variables such as flow rates, .ate-
iecth. crass-sect'.onal area, width, and wet perimeter must 5e osta'riec *-c^
actual aata if availaBle; if not avaiUole, tn^s infor-nat'on can se •st'^ate-:
using CHNHYO. Otner data input includes:
• Cnvi-onmental parameters - air temperature, solar rac'at'on. .'^fi
speed, vapor pressure. »ate- te^pe-ature. e*t'«ct'sp esc" ': '«^t . SH.
sCM. oi'.aation radicals.
• S'o'oqica'i information . sacteriai population aens'ty. s'siem
for activation energy, and the bacterial portion ^rwo'v«s
degradation.
• Coefficients for pnotolysfs. hydrolys's. oiidation, a^oce
• Sediment types and distributions.
• Transport Information - solids in water column anc
sedimentation and resuspenslon velocities, partition coe'f '.c'ents ,
dispersive coefficients between phases, and volatil i:ation rates.
• System geometry - areas, depths, volume.
• Sources and amounts of pollutant.
I! -40
-------
Oucgue Jgserlpelona • •
CHNTRN calculates and presents tne following for individual
1n tabular form:
• Dissolved cnemlcal concentration 1n tne water column as a funct'on a?
distance from tne source.
• ^articulate concentration in suspended and bed sediment.
• Suspended sediment concentration anc amount of 3ed sec'^ei
in a unit bed association.
~he major advantage of CHN'SN is, its casac'ty '.z s"nu'a:? :'-
distributions in a*' types . of water Socles. CHNT?«» accounts
advective and dispersive flows and tne total fiux sf ar acuaf: sys
cnemical Kinetics are tne second-order rate eipression? of £
cs' ct
tem. ~*»
SN is a complex .node' and as suc.i 's very rata '
Cons 'ceras'e time .uijnt :e neesea far .me accui s ' t* 3-"> 3' data ic:
availap'e. Computations *ar comp'ex systems *'' also -ec-j'-e la-2e ar
t'me 'or so'ution, as will tne simu'at'on execut'on ti.-ne.
If nydrodyna.-nic infor-r^t'on is not availaDle. it can ae estimates
us'.ng CHNHY3 anc t^en s-sstylng tne -"Suits ts :.-H*5,N. Alt.-cuc.i Tr
not ae defined as 'user-friendly*, it 3oes tune jrsv's'.sns :z al'sw
to -nase some modifications.
CHNTRN Has yet to De field validated.
sy f'-:t
^N «cu':
:re jse*
Model Appllci Ciena
CHNTRN has been applied to two river network sample profclefns *«'
demonstration purposes. The first sample 1s a single river system, anc :n-»
second is a network of five rivers. Typical data were used for tne
simulations. In each example, the rivers are divided up into compartments.
11-41
«
-------
The first scenario produced seemingly unrealistic results. Closer
analyses of the input data revealed U to be erroneous and illustrated the
•garbage in, garbage out* results. The second scenario showed reason?
results for day. silt, sand, dissolved chemical, clay-adsorbed c.iemic
silt-adsorbed chemical, and sand-adsorbed chemical concentrations. Because no
analytical solutions were available. H is not possible to assess the accuracy
of the results by comparing them with analytical, results. However, tne
results intuitively indicate that the model can realistically simulate the
behavior of the sediment and chemical variations In a stream/rive' network.
CHNTRN Is written In FORTRAN IV and nas been Implemented =" an
computer. Simulation execution time may ae extensive.
2923
Copies of CHNTSN's draft user manual and documentation as -e"
assistance -nay be obtained from:
3'.
G. T.
environmental Sciences Divisi
Ca* aidge National Laoo-atory
P.O. Box X
Oak Ridge. T* 33830
(615) 574-7295
Yeh GT. 1982. CHNTRN: A channel transport model for simulating sediment and
ch««i1cal distribution in a stream/river J network. Oak R^dge Hat'ona!
Laboratory, Oak Ridge, TH. ORNL-5882.
feh 6T. 1982. CHNHYO: 'A channel hydrodynamlc model for simulating flows ar.c
water surface el'evatlons 1n a stream/river network. Oaic Ridge lat'ona'
Laboratory, Oak Ridge. TN. ORNL-5701.
Yeh QT. 1981. ICM: An Integrated compartment method for numerically solving
partial differential equations. Oak Ridge National Laboratory. Oak Ridge.
TN. ORNL-5701.
11-42
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Finite Element Transport Model
SianarM
The Finite Element Transport
(FET8A) 1s a time-varying, J-dimenslonal
(longitudinal and lateral) transport
model developed by Sattelle ?adfic
Northwest laboratories. F£TW utilizes
a finite element solution technique and
consists of three submodels coupled to
simulate the transport of sediments and
contaminants in rivers and estuaries
tnrougn the
o? advectlon.
F£*RA
ia«c systems
C4p*uie Summary FSTKA
Complex sediment :ra/?spor:
capabilities.
Csmprehenai*e second-order
9 River, estuary. And
degrafiat 'on/decay .
jnsat.-a
can be appMed
svsce«. ;
to
ion. afi
rivers.
estuaries. ;sasta • , a--£
Tne sediment transport submodel simulates sediment --ncve^e": fsf :.--•?-
sed'ment sire fractions or sediment types. This suomcde- "sc'jdes f-?
mechanisms of: (1) advectlon and dispersion of sediments. !2) fa'.- *-'3c*t* a-:
cones iveness . and (3) deposition or erosion frsm tne oed . It a'so ca'cuiatsi
cnanges in bed conditions, including bed elevation c.nanges due ta scour'nq ;r
decositlon. and gives a 3-dimef»$ional distribution of sediment s':?s .'ts'-". t."e
bed.
The dissolved contaminant transport submodel simulates t.ie i
contaminant interaction with sediments In motion and witn stationary ;ed
sediments. The submodel Includes the mechanisms of: (1) advectlon and
diffusion/dispersion of dissolved contaminants; (2) adsorption of dissolved
contaminants by both moving and stationary sediments or desorpt'.on f^sm :»*
sediments Into water; and (3) chemical and biological degradation ;r
radlonuclide decay of contaminants.
The participate contaminant transport submodel simulates the transport of
sediment-attached contaminants for each sediment size fraction. It includes the
mechanisms of: (1) advectlon and dispersion of particulate contaminants; (?)
adsorptlon/desorptlon of dissolved contaminants with sediment; (3) chemical and
biological degradation or radlonuclide decay of contaminants; and (4) deposition
of particulate contaminants on the bed or erosion from the. bed.
11-43
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~ie temporal scale of FPTRA u on the order- af minutes to hours.
Hydrodynamic data are supplied by exterior models such as CAFE-l (ocean
currents) and 1330 (wave refractions) for coastal Caters applications. and
EXPLORE-! (velocities and Mo* depths) for estuarine and riverine application
£xPLQRE-I -s a comprehensive mathematical water quality model to be used
in river oasin planning and water resource studies. This generalized river
basin water Quality model can' predict the hydrodynamics and water Quality
dynamics far rivers and well mixed estuaries. The EXPLORE-! mode' 's an
extended and modified version of the Storm water management "ccei. receives
-ater component, -nlch was developed for studies of DO/900 Syramics. ~*e
model is capable of simulating a number of hydraulic regimes in e'tne' a
dynamic or steady-state mode, and U has been set up. ca'4bratea. and ve^'^ed
on a jort^on of the yi 1 1 '.amette 3iver iasin. conslst'nq Df -na^or *.r*iuta''es .
tX?'.3R£-I -as developed by Sattel'e^Northwest laboratories for the EPA.
•Trpyg SJ:a Pe-ru-i rgr
input data reoui Cements for r£TPA a»"e au".e e*tens'-ve.
' iaia
• Csmnon 2ata resui rements *ar all the sucmcdel s •.
- Channel geometry.
- Discharges during the simulation
- Discharges of tributaries, overland r^noM. anc otne- so'r.: ars
non-point sources.
- Lateral and longitudinal dispersion coefficients.
• . Additional Requirements for .sediment transport submodel:
- Sediment size fraction.
- Sediment density and fall velocities for sand. sVlt. and day.
- Critical shear stresses for erosion, ana deposition of cones'.ve
sediment (sll't ana clay).
- Credibility coefficient of cohesive sediment.
II-44
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- Sediment concentration for eacn secernent si;e fract'on.
- 3ottofn sediment sUe fraction. .
- Sediment concentration at the upstream end of trie study
- Contributions . of sediments from over land, tributaries, and
other point and non-point sources.
• Additional requirements for the Dissolved cantamtran:
partlcglate contaminant transport
Distribution coefficients and transfer rates of contan-iar.t
with sediment in each sediment s'ze f-action ;'..«.. sane. s''t,
and day). If values of .d'st.-itjut'on coef *'c *e"ts ar» not
available, it is necessary to know c'ay minera- ano organic
sediment content to estimate t*es* ^a
Second-order iecay rates of ccntaninarts
Boundary conditions.
Csnf'Syt'ons of dissolved and sa-ticj'ate ::".t5n<
concentrations f*cm trioutaries, cve-'ana. and otie' so'^t
non.po"'n: sources.
m'.m tne input data descMoed aDove, r£'a^ simulates t.N.e fo'« 'o*'ic:
• Sediment simulation ard longltudlna !/ latera' d'.strtsut'sns a?
total sediment and size fractions and changes in oed elevat'on.
• Contaminant simulation and 1ongi tudinal/'atera l distributions "of
dissolved contaminants, contaminants adsoroed Dy sediment and in
tne DOttom sediment for each sediment sUe.
And Llmltaciona
FSTSA Is designed, for tlme-varlaole analyses of I- or 2-dlmenslonal
(nori'ontal) water bodies. Its sediment transport rogt'nes are
sopni sticated and will predict tne resusoenslon velocities and aed "oad
given the sediment* and hydraulic characteristics. The model can &e
coupled with a hydrodynamlc model 1n order to generate Hows and
'velocities.
11-45
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Input data -eau'rements for FETSA are eitenstve. ana computational
time for long term continuous simulations may, ae high. Sesource
Acquirements for set up and execution are eipected to De substantial.
cannot discern.water body stratification.
••>!••£• ••••<..•
Applications
FETRA has Been applied to the
James Slver estuary In Virginia
(Onis.v, 1.981) and to the Irish, Sea
(Cn'shi et al. 1902). The purpose of
tne James River, application «as to
Simulate sediment movement and the
transport of the pesticide Kepone
•f^cn .as d'scharged to tne rUer In
suostantia* suantities during the
ear'y '9'Cs. The purpose of the Ir'.sn
Sea app'^cat'on «as to evaluate
exposure 'evels of radionuc1 ides.
•neta^s. and otner toxic
j's in coastal -aters. *esu>ts
a* the ;-*sn Sea application nave not
*e: sesf sup'ished. A discussion of
"the James 3'wer application follows.
James 9'ver application -as a
;*on and verification study o'
rt*»lA. SeC'ment transport was modeled
for three sediment types: (1) cohesive
(silt and clay); (2} - noncpftes!ve
(sand); and, (3) organic matter. ...•..•»«—••«
These results {see Figure 1) were
compared to field data wnicn Indicated that a considerate amount o*
partlcuUte Kepone was transported Oy organic mater^als mov'nq 'r\ceaendeit' y
-1th other sediments. Predicted part'culate Depone concentrat'ons
witn each type of sediment and weighted average partlculate
together with measured field data of average partlculate
in Figure 2. The computed results and tne field data
Figures.
Kepone are shown
(epone concentrations
closely agree in
-------
ftgsoufge
The computer. program Per FETRA 1$ written m FORTRAN [v language "'rSA
can be used on IBM, VAX, or COC-7&OQ computers. Execution times anc run costs
vary, depending on the characteristics of the system to be modeled. For the
James River application; computer time required to calculate a"1.: seven
substances per computational mode per time step was 0.0023 cp second on *ne
COC-7&00 computer.
t/aer Support
The user's manual and system documentation are st''i unae-go'-n; ' -«v'«w a
and are ngt yet available for puol.'.cation. >e "£*5A
operational, has Seen Implemented in selected applications, ana '*
to the puolu. .
Node! information can De cPta'ned &y contact'ng:
Yasuo Onishl
Sattelle - Pacific Northwest 'Laboratories ,
aicfi'and. Washington 99352
FTS 4*4-8202 CCM 509-376-8202
Onlshl Y. 1981. Sediment-contaminant transport -nodel. Jour^a" 3? t-.e
Hydraulics Division, ASCE, Vol. 107, No. MY9. Proc . !>aper 16505.
pp. 1089-1107.
On1sh1 Y, Mayer. OW. Argo. RS. 1982. Sediment and toxic contaminant transscr:
modeling in coastal waters. In: Finite Clement Flow Analysis, '-ac.
pp 733-740.
On1sh1 Y, et al. 1981. Critical review: Radibnuclide Transport, S
Transport, and Water Quality Mathematical Modeling; anc Sad'ongcl 'c?
Adsorption/desorptlon Mechanisms. R*. en Una, Wash'.ngtorc. 'acific
Laboratory, 3atte11e Memorial Institute. NURES/CR-1322', PNi. 2901 .
USE PA. 1982. EPA Environmental Modeling Catalogue. Abstracts of
Environmental Models, pp. 363-366.
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Sediment-Contaminant Transport Model (SERATRA)
Capsule Suwiwry;
The Sediment-Contaminant Transport
Model (StRATRA) (Onlshi and wise I982a) is
a time-varying 2-dlmenslonal (longitudinal
and vertical resolution in tne water
co'umn and bed) sediment and contaminant
transport -nodel developed by 9atte11e-
3ac'Mc Northwest Laooratorles. The model
predicts distributions of' sediments- and
tax'c contaminants in rivers and some
'^counanefits. The model consists of 'the
) advection and dispersion of dissolved and" ^articulate contaminants;
chemical " resulting from hydrolysis, oxidation, pno'tolysis. aio'.og
activities, and radionucUde decay where applicable; (3) volatilization;
adsorption/desorptlon; and (5) deposition and scouring of particu
contaminants, SERATRA also computes changes in Mver&ed ;canditiorts
sediment and contaminant distributions.
'.2]
cal
(<)
ate
*or
Required input includes channel and sediment characteristics and
adsorptlon/desorptlon properties of the contaminants. In addition, SERATRA
"equires discharge and depth distributions wnlcn can be obtained by a
nydrodynamic model sucn as EXPlORE-I. EXPLORE applications for use with
SERAT8A do not require reprogrammlng; however. some reformatting and
recalculation of the Input parameters may be required. EXPLORE is discussed
in further detail 1n the FETRA summary.
IMS
-------
SERATRA 1s similar to FETRA in that'Both consist of the same three coupled
submodels; and provide time-varying. 2-d1men$lonal transport simulation using
comprehensive second-order decay kinetics. Both provide longitudinal
resolution, whereas the other dimension for FETRA is lateral rather than
vertical (as in SERATRA).
f/ipa
SERAT8A consists of the same three coupled submodels that csmoMses >"
and therefore- requires identical Input data. Sefer to the FETRA summary
discussion of SERATRA's input data requirements.
A provides output identical to the Ft'SA outsut e*cest
long4tucMna' and vertical, rather than long', tudina' ano late»a', c'sf's
anc -esolut^on are provided.
lUe ^t'^A, SESA'RA provides the capaoil.ity of simulating the :smc'e«
mechan'. ins '.nvolved In contaminant migration ay cogp^ng contam'nart
transport and degradation with sediment transport. SE3AT3A aiso' lane'es
tlme-var'.aole analysis of stratified (2-dlmenslona1) water iodes.
Ad.sorption/desorptlon mechanisms are expressed by a distribution
coefficient and a transfer rate wnich descrioes the rate at -*nicn
dissolved and particulate contaminant' concentrations reach their
equilibrium condition. Unlike most Other models, SERATRA 'uses different
distribution coefficients for adsorption and desorptlon and treats
adsorptlon/desorptlon mechanisms as not being fully reversible.
SERATSA requires extensive Input data, which may l^mlt Us
applicability. It also requires rather extensive computer time, In which
long-tern, continuous simulations can be expensive.
11-49
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The model cannot be applied to estuary systems because longitudinal
diffusion 1s neglected and lateral sediment concentrations are assumed to
be uniform. However, the model does handle vertical variations of
longitudinal velocity to cause some longitudinal dispersal of sediment.
SF.RATRA requires an exterior hydrodynamlc model to supply required
hydrodynamic data. EXPLORE applications require adjustment of several
parameters for use with SERATRA.
node 1
SERATRA has been applied under both steady and unsteady flow
conditions and has also undergone field applications with calibration and
verification data (Qnlshl et al. 1982). Also, SERATRA has been field
tested as an Integral component of the Chemical Migration Disk Assessment
(C."RA) »ethodology (Onlshl et al. 1981).
under steady .flow conditions, SCRATRA was applied to t.ie ColumoU
River 1n Washington and the Clinch River In Tennessee (Onlsnl et al.
1982). The Columbia River application simulated trie transport of
sediments, radioactive 65Zn. and a heavy metal. The CUncn River
application simulated instantaneous and. continuous releases of
radioactive 13?Cs and 90Sr. Reasonably good agreement between predicted
and measured results was obtained 1n both applications.
Under unsteady flow conditions, SERATRA was applied to two
streams with rapidly changing flows (Onlshl et al. 1982). This
application simulated migration and fate of a pesticide and stream
sediments. No measured field data are available for comparison of the
model's predicted results.-
The calibration and verification application of SCRATRA simulated the
transport of sediment and four radlonuclldes in the Cattaraugus Creek
watershed In New York (Onlshl et al. 1982). Although there were some
discrepancies between predicted and measured values, considering the
complexity of the mode Ling system and field data accuracy, agreement
between predicted and measured results were judged to be reasonable.
SERATRA. as part of CMRA, was applied to the Four Nile Creek
watershed in Iowa for a three-year field study (Onlshl and wise 1V82&).
Migration and fate of a herbicide were simulated In this application.
"lir nation r«;»lt. revealed a strong seasonal pattern of herbicide
transport.
11-50
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Resource
The computer program for SERATRA is written 1n FORTRAN preprocessor
language, FiECS. A standard FORTRAN IV version of SERATRA ts also
available. SERATRA can be Implemented in a batch mode on VAX or POP 11/70
computers.. Execution time and run costs vary, depending on the
characteristics of the system to be modeled. One cost estimate is
JO. 0088 per time step per segment. As part of the. CMRA Methodology, four
man-months were estimated to be required For the SERATRA component to be
Implemented at a cost of approximately J100 to 1200 per run per one year
simulation (for all four of the CNRA components}. This time estimate 's
based on the following assumptions: (i) all data necessary te ueet tie
input requirements are available; and (2) qualified personnel are
available to Implement the model.
User Suppor;
Copies of the user's manual are available *pom 0*0 Pyb' 'cat -on$ .
Center for Environmental Research Information. USEPA. Cine '.^nat ' . Cn'o
45268 (telepnone 513/684/7562; ask for publication EPA-&OC/3-3Z-CS5 »
User assistance can oe obtained by contactlnq;
Robert Ambrose
USEPA
EPA Athens Environmental Research Laboratory
Center for Hater Quality Modeling
Athens. Georgia 30613
(404) 546-3546
Model Information can be obtained by contacting:
rasuo Onlshl
Sattelle Pacific Northwest Laboratories
P.O. Box 999
Rlchland. Washington 99352
(509) 376-8302 .
The SERATRA model Is operational, has been implemented In selected
applications, and is available to the public.
11-51
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flgference*
r, wise SE. 1982a. User's Manual for tne Instream
Sediment-Contaminant Transport Model SERATRA. EPA 600/3-82-055.
Environmental Researcn Laboratory. Office of Research ana Development.
USEPA, Athens, Georgia.
Onishl *. Brown SK, Olsen A«. Parknurst MA. 1981. "Chemical
and 8Uk Assessment Methodology." Proceedings of the Conference
Environmental Engineering. Proc. Paper, ap. 'bS-HJ.
OnUni Y. rafiusakt SB, tOncaiC CT. 1982. 'Performance Testing of tne
Sediment-Contaminant Transport *odel, SESAT!?A.* P^oceedin^t s^ t^e
> to H
PO. 623-U2.
wise SE. 19825. mathematical "ode'. StaATaA. for
ontaminant Transport in 9^ve"$ ane : :s _AppM;at ^sn _;s
"i rpur "t^e anc no 1 f Creecs ^ !ova. £3ft sCC-3 32-0*5.
U-52
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Transient' One-Dimensional Degradation and Migration Model
The Transient One-D1mens1onal
Degradation and Migration Model
(TOQAM) 1s a time-varying, 1-d1men-
slonal (longitudinal) transport model
developed by BatteHe Pacific
Northwest Laboratories . 1n Rlchland,
Washington. TODAM Includes the long-
itudinal dispersion term and can
handle river/stream systems
estuaries, and dry bed conditions. The model
where vertical stratification is not a concern.
CapsuJe Summary: TCOA/1
l-dlater.slar.dj.
snt cra/isport
Tifl»-vary 1.19.
Complex sedi.n
apt* 111 ties.
vf second-order kl.ieties
a/Jd escu-ary ,ay3terns
Is suitable for many
TOOAM 1$ a modified and simplified version of tne Z-dimens^ona'
model, Sediment Contaminant Transport Model (S£SA'RA). a'sc '
Sattelle. 700AM is composed of the following three sucmooe's camo
descrioe sediment-contaminant interaction anc migration:
o Sediment transport
o Dissolved contaminant transport
o Soroed contaminant (contaminants adsoraed Dy sed'ment) transport
nec
These submodels solve an advectlon-dlf fusion equation using a finite
solution technique with decay and sink/source terms with appropriate
and Boundary conditions.
"he sediment transport submodel simulates transport, deposition, and
erosion of three sediment size fractions (or sediment types) of cohesive ana
none ones Ive sediments. The dissolved contaminant transport submodel includes
mechanisms of contaminant adsorptlon/desorption. as well as radlonucllde decay
and contaminant degradation resulting from hydrolysis, oxidation, photolysis.
volatilization, and biological activity. The particulate contaminant
transport submodel simulates transport, deposition, and erosion of
contaminants associated with each sediment size fraction.
TO DAM includes the mechanisms of advectlon and dlffuslon/dispersiun
sorbed contaminants; adsorption (uptake) of dissolved contaminants by
of
11-53
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sediments or desorption from sediments; radlonucllde decay; deposition of
sorped contaminants to the river bed or resuspension from fe river bed; and
contributions of sorped contaminants from point and non-point sources into tne
system. TCOAM also computes changes In river bed conditions, including bed
elevation, sediment size distribution, and sorbed contaminant. distribution
within the bed.
An exterior hydrodynamlc model, such as EXPLORE, or tne Distributed
K'nematic wave Model for Channel Flows (OKWAV), is required to provide channel
f'ow. cross-sectional area, depth, jhear stress, and wetted perimeter for y$e
sy TQOA«. deprogramming ts not required if either EXPLORE or OKWAV is used.
£X?'.3RE appHcations require some reformatting and recalculation of input
parameters; «nereas. OKWAV applications can be directly integrated far
OKWAV. also developed by Battelle. 1s an unsteady, l-dimensiona*.
secono-or3er. explicit. finite-difference 'model wnich simulates the
•wC'odynamics '•" dendritic river systems to obtain time varying dlstnsgt'ons
or' 3es:.i and ve'odty in a channel. The model, which can be easily combined
«?tn overland f'ow models, routes flows through arbitrarily shaped channels *n
•men the channel reach is divided into sections bounded by points ca-ed
"nodes", ~'ow routing is performed frsm node to node by a marcntng solution.
**e ecuations of motion with the kinematic wave approximation are numerically
via a modified version of the cax-wendroff. second-order,
erence scheme. Numerical stability 1s based upon the Courant
'oint inflow or continuous (or both) lateral inflow is
'i -n^.i so'nt inflow occurs at nodes or continuous lateral inflow
set'-eei icdes. associated with each channel section is Us own seepage
*e'oc*ty. A cross-sectional area versus discharge relationship exists for
eacn segment between nodes. Based on this relationship, other characteristics
parameters (flow depth, wetted perimeter, and so forth) of each section can
also be obtained. Each channel section retains Its- own individual
characteristics, which include a roughness parameter, lateral or point inflow
rates, slope, seepage velocity, and natural cross-sectional shape.
"n'te-d***
The EXPLORE model Is discussed in further detail in the FETRA summary.
The following Items are Input data requirements of TOO AM:
t Channel geometry
11-54
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• Flow characteristic*
- Depth and velocity distributions
t Sediment characteristics
- Sediment size distribution
- Density
- Critical shear stresses and credibility cae'^'c'ent
sediment
• Contaminant characteristics
- Distribution coefficients
- Transfer rates
- Decay and degradation' rates or associated o
- Initial conditions
- Boundary candU'ons
ccnes've
CugpuC
.with the input data described above, TODAM provides tne 'o'.'s^'^q autsut.
1. Sediment, simulation and distributions of total sed'.ment. sec'ment s':e
fractions, and changes in bed elevation.
2. Contaminant simulation and distributions of dissolved contaminants.
and concentrations adsorbed by eacn sediment size and witmn trie oed,
Lioii saclona
The major strength of the TODAH model is that H*e FtTRA and SE9ATRA, 't
has very sophisticated sediment resuspenslon and bed load predictive
capabilities. Its 1 -dimensional framework makes TODAM more tailored to river
applications. TODAM '$ kinetics are comprehensive second-order.
Also, TOOAM can handle reversible flow and dry bed conditions. TODAM, as a
simplified v,.-rsl-»n of S£RATRA. can be substituted for SERATRA in estuarlne
applications.
11-55
(
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requires extensive input data
used only in 1-dlmen$1onal applications,
and computer time.
model can
»odfl
TOOAM was applied to *ortandad and South Mortandad Canyons in New «e«ica
to estimate 1n-stream flow, sediment transport, and radionuclide. transport in
intermittent streams. Transport of seven substances -as simulated: sand.
silt. day. d.tssolve-d *^9 pu> an. at JS£3»
are *>ot yet avai'Uo'le for puolication. The TGDAM mode' is in ope'at'on.
2een imo'emented in selected -appi icat ions. and «s ava''aD'e to tr^e 5-3''c
information on this model can oe obtained oy contacting:
irasuo Onisni
Battelle Pacific .Northwest Laboratories
P.O. Box 999
Ricnland." Washington 99352
(509) 376-8302
References
Onishi Y. Whelan G, and Skaggs RL. 1982. OeveVooment of a ••ui
^ad'ongclide exposure Assessment Methodology for low-Level waste management.
PML-3370. BatteUe-Padfic Northwest Laboratory. JMcnland. Washington.
11-56
202-566-0556
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i
Hydrologual Simulation Program-"OR'SAN -;HS?n
The Hydrological Simulation
Program-fQRTSAN (HSPF) (Jonanson et
al. 1980) is a series of fully
integrated computer codes capable of
simulating -atershed hycrology and the |
benaV.or of convent'onal and organic :
poi'utants in land surface runoff and i
receiving .aters. Simulations are '
per'arraeC; on a time-varying, 1-
dmens'or.a" sas's and can ae performed
far streams and non-tidal rivers and
*or «e'l .fl'xed. non-stratl.fled reser-
voirs.
capsule Summary. HSPF '
l-di.inensisr.Al ncdcl-
oe
.•ncdul
-------
/*'
Sediment-contaminant runoff contributions from rural ana urban land
surfaces can be simulated through the execution of the appropriate non point
source modules {user-sped fled) contained in the model code. These modules
predict sediment-contaminant loadings, associated with pervious and impe-vious
•an riourly
The PLTGEN module creates a spec'ally fo"nattec
takes a
An. interactive editor to prepare
development and will be available from
Athens, Georgia.
Input sequences
the environmental
for HS?f 'S under
Research Laboratory.
Saea Seoulremenea
^^^^hM^MI^^H^^^^^^^B^^M^—^^^^^^^ ^
If fully implemented, the MSPF methodology requires an extensive amount of
input data. However, If not all modules are selected for use In the
simulation by the user, the amount of input data will be reduced accordingly.
Furthermore, many parameters may be defaulted, but default values are not
provided fo- the more sensitive, site-specific parameters. The time series.
constant parameters, and water Quality 1-nput requirements include:
•
• 71m* series Inputs which Include: air temperature, predpl-
. tatlon, evapotransplratlon, channel Inflow. surface and
.groundwater inflow, and wind movement.
11-58
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I Constant parameter inputs nn'cfi include: channe' geometry.,
vegetative cover index, surface detention storage, grauncwater
storage volume, soil moisture content, overland f'o* s^ape.
snow-pacic data, infiltration indei. and interflo* index.
• Land sediment factors: soil detacnment coefficients, sediment
influx, surface cover, sediment yashoff coefficient.
• Soil temperature data: air temperature time series, s'ope ana
Intercept of land temperature to air temperature ecaat'cn.
• Dissolved gas in land water: ground e:evatian. interf's* anc
groundwater 00 and COj concentrations. ,
• Quality constituents associated
factor, scaur potency factor.
segment. •as^c
:cte":y
QuaMty constituents concentrations -n interf -ow and S'^yrc-at
a' 3ua'*ty constituent!: salute ieacft'-g "acta-s, ic'1
layer depths; soil densities, ana pest'c'ie a^C lut-'er: $3'Dt'3r.
parameters. soluOlllty factors, degradation rates
• Imoerv'cuS land quality factors: surface runoff -eticva : -ate?.
so'.'cs -as^cf c;ef f '.dent. '3ie of scl'cs s'dcewe^t and
on surface, and overland f'ow Some sollutant acc-jmu'at 'or.
storage rates.
• Reacn and reservoir water quality characteristics . cce* ( ':' e
and rates.
Oueguc Pscrlpeio/i
output consists of multiple printouts Including system state
variables, pollutant concentrations at a point versus time, and yearly
summaries Describing pollutant duration and flux. The node1 a'so
includes a frequency analyses which provides a statistical summary of
tlfite-varylng contaminant' concentrations and provides the Unit aet-een
simulated vnstream toxicant concentrations and risk assessment.
11-59
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^
HSPF 1j designed for year. around simulation of river bas'.n hydrology.
pollutant runoff or discharge, and receiving water quality. Its modular
structure allows it to be readily used in more restrictive ways, using
streamfldw and effluent time series inputs, without the complications of
applying the rainfall/ runoff simulation module. HSPF provides a
frequency distribution summary of the output, thereby providing a
year-around perspective.
The sediment-contaminant kinetics routines nave trie 'same general
characteristics as other complei water quality models, however, v.ke
EXAMS it has the added capability of simulating the production anc
interactions of contaminant daughter products.
HS?F contains . a code to calculate the frequency of occurrence anc,
durat'on of contaminant concentrations in the receiving Caters.
Because of Its 1 -dimensional approach to pollutant simulation,
does not discern stratification in the water column and bed sed'merts
The mode''* cade has been optimized for both mini, and nw'n
computers. On minicomputers, usage of direct access flies Is max'.rm:ed
On mainframes, maximum use is made of fast memory and di-ect access I/C
is miniml:*d. versions of HSPF are available for botn types of systems
Data requirements to implement HSPF are potentially ex
(depending on the application modules invoked) and may, therefore, result
in high data production costs and significant manpower requirements.
Model Applications
HSPF has been applied on numerous occasions where an evaluation of
best management practices (BMP) for controlling non-point source
pollution from surface land runoff was needed. In this context, the
model was applied to the Occoquan River Basin In Virginia to project
long-term receiving water quality Impacts' from existing and -future land
use patterns; the Clinton River Basin in Michigan to evaluate a proposed
floodway, estimate the Impact of developing wetlands, and investigate
various lake operating procedures; and various EPA studies to evaluate
Us application and use as a planning tool in determining agricultural
BHPj.
It-60
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•H-'
Resource ffegu ir ttments
• HSPF requires a FORTRAN compiler that supports direct access I/O
Twelve external files are reaulred. The system requires 129* bytes of
instruction and data storage on virtual memory machines. or about 250K
Bytes with extensive overlaying on overlay-type machines, The system wai
developed on a Hewlett-Packard 3000 minicomputer and has been ysec on ISM
370 series computers. It has been installed on trie following systems:
IBM. DEC VAX and System 10/20. Prime 350 and above. Data Genera: «v<200.
C3C Cyber, HP3000 and HP1000, Burroughs and Harris. Instal 'at'cn notes
are available for sped.flc machines. .
's 'n the puOl'.c domain and can ae oota'.nec from tne Center far
water Cuallty "cdellng. Environmental aesearcn Lasoratorv. ySE9«. C:''e-3e
Stat'.cn ^cad. Atnens. Georgia 3C613 {telephone *C4 546-2533).
User assistance can be obtained by contact'r»g:
U.S. Environmental Protection Agency
EnvircMienta* 5esear:n laboratory
College Station aoad
Atnens. Georgia 30613
250-3175 C2H 40<-5<6-3l75
References
Oonlg^an AS. et al. 1983. Guide to the Application of the Hyd.-oVog'ca'
Simulation Program - FORTRAN (HSPf). Draft report. Envl ronmenta"
Researcn Laboratory. Athens. GA. 30613.
Jonanson UC. Imfioff GC. Davis HH. 1980. User's manual for Hyd-oloqica":
Slmulat'on Program- -FORTRAN (HSPF). EPA. 600/9-9-80-015. Environmental
Sesearcn Laboratory. Athens, GA. 30613.-
Johanson RC. and Kittle JL. 1983. Design, Programming, and Maintenance
of HSP?, Journal of Technical Topics In CW11 Engineering, vol. 109. NO.
1. PP. 41-57.,
Imhoff JC, et. al. 1981. User's Manual for Hydrologlcal Simulation
Program - roftTRAN (HSPF). Release 7.0 Draft Report.
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