TITLE: Technical Guidance Manual for Performing Wasteload Allocations, 

Book II: Stream and Rivers – 

Chapter 3: Toxic Substances

EPA DOCUMENT NUMBER: EPA-440/4-84-022 DATE: June 1984

ABSTRACT: 

As part of ongoing efforts to keep EPA’s technical guidance readily accessible to 

water quality practitioners, selected publications on Water Quality Modeling and 

TMDL Guidance available at http://www.epa.gov/waterscience/pc/watqual.html have 

been enhanced for easier access. 

This document is part of a series of manuals that provides technical information 

related to the preparation of technically sound wasteload allocations (WLAs) that 

ensure that acceptable water quality conditions are achieved to support designated 

beneficial uses. The document addresses methods for predicting concentration of 

individual constituents resulting from pollutant loads for use in the WLA process. 

Appropriate levels of pollution abatement can be determined to protect the 

environment of at a specific site by relating the predicted concentration to ecosystem 

or human health effects levels. Targets for allowable concentrations and frequency 

for exceedance of those concentrations must also be established for effective use of 

these predictions. 

Because of the wide range of different situations encountered in the real world, there 

is no canonical set of technically acceptable procedures that can be put forth as a 

standard method. Therefore, the document describes a variety of different 

approaches covering a wide range of complexity. This may help guide the analyst 

toward selection of a calculational framework or model that is appropriate to the 

problem at hand and makes effective use of available resources. 

A number of mathematical frameworks for predicting toxicant concentrations in rivers 

are presented, ranging from simple dilution calculations to complex computer 

models. Assumptions and limitations associated with these approaches are also 

described. The document also presents the mathematical formulation for important 

fate and transport processes along with background information of specification of 

related parameter values. Technical guidance is provided for conducting toxicant 

WLAs and gathering supporting data. A detailed case study is included for metals in 

the Flint River (MI) that emphasizes calibration and sensitivity analysis. In addition to 

appendices, reports on metals partition coefficients and on models designed for 

toxicant studies are attached. 

KEYWORDS: Waste Load Allocation; Model; Time-variable analysis; Steady-state 

analysis; Fate and transport; and Partition coefficients

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Environmental Protection 

United States 

Agency

Office of Water Regulations and 

Standards 

Monitoring and Data Support 

Division 

EPA-440/4-84-022 

June 1984 

Final 

Water

Technical Guidance 

Manual for Performing 

Waste

Book II Streams and Rivers 

Chapter 3 Toxic Substances 
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON, D.C. 20460 

MEMORANDUM 

SUBJECT: Technical Guidance Manual for Performing Waste Load 

Allocations Book II, Streams and Rivers, Chapter 3, 

Toxic Substance Impacts 

FROM: Steven Schatzow, Director 

Office of Water Regulations and Standards (WH-551) 

TO: Regional Water Division Directors 

Regional Environmental 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 manual 

to the Regional Wasteload Allocation Coordinators for 

distribution to the States to use in conducting wasteload 

allocations. 

If you have any questions or comments or desire additional 

information please contact Tim S. Stuart, Chief, Monitoring

Branch, Monitoring and Data Support Division (WH-553) 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. Delos, EPA, OWRS 

William L. Richardson, EPA, Large Lakes Research Station 

Joseph V. DePinto, Clarkson University 

Robert B. Ambrose, EPA, ERL - Athens 

Paul W. Rodgers, Limno-Tech, Inc. 

Kenneth Rygwelski, Cranbrook Institute of Science 

John P. St. John, HydroQual, Inc. 

W.J. Shaughnessy, Versar Inc. 

T.A. Fana, Versar Inc. 

W.N. Christie, Versar Inc. 

August 1984 

Office of Water Regulations and Standards 

Monitoring and Data Support Division 

Water Quality Analysis Branch 

U.S. Environmental Protection Agency 

401 M. Street, S.W., Washington, D.C. 20460 
ACKNOWLEDGMENTS 

The contents of this section have been removed to comply with 

current EPA practice.

1
CONTENTS 

ACKNOWLEDGMENTS............................................................... 1 

SECTION 1.0 INTRODUCTION...................................................... 1 

SECTION 2.0 BASIC MODEL FRAMEWORKS AND FORMULATIONS........................... 5 

2.1 GENERAL................................................................. 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 MODEL OF NONCONSERVATIVE 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 MULTIDIMENSIONAL, DYNAMIC, OR SPECIATION 

CAPABILITIES........................................................... 28 

2.6.1 Transport and Bed/Water Exchange................................. 32 

2.6.2 Sorption......................................................... 34 

2.6.3 Speciation....................................................... 35 

2.6.4 Transformation................................................... 37 

SECTION 3.0 ESTIMATION AND USE OF MODEL PARAMETERS........................... 40 

3.1 EXCHANGE BETWEEN BED AND WATER......................................... 40 

3.1.1 Particle Transport and Exchange.................................. 41 

3.1.2 Diffusion Between the Water Column and Pore Water................ 49 

3.2 PARTITIONING PROCESSES................................................. 51 

3.2.1 Metals Partitioning.............................................. 51 

3.2.2 Organics Partitioning............................................ 61 

3.3 TRANSFORMATION PROCESSES............................................... 67 

3.3.1 Biodegradation................................................... 67 

3.3.2 Photolysis....................................................... 73 

3.3.3 Hydrolysis....................................................... 78 

3.3.4 Volatilization................................................... 80 

SECTION 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 Data....................................... 97 

4.2.2 Calibration and Verification: Comparing Prediction.............. 101 

with Observation................................................ 101 

4.2.3 Additional Data................................................. 105 

4.2.4 Quality Assurance............................................... 106 

4.3 FORECASTING........................................................... 107 

4.4 RESOURCE REQUIREMENTS................................................. 113 

SECTION 5.0 CASE STUDY: MODELING 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 Model Calibration................................. 128 

5.3.3 August Survey Sensitivity Analysis.............................. 137 

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 

5.5.1 March Survey Data Summary...................................... 180 

5.5.2 March Survey Model Calibration................................. 180 

6.0 REFERENCES (FOR SECTIONS 1-5 AND APPENDICES A-D)........................ 190 

APPENDIX A DEVELOPMENT OF MODEL EQUATIONS................................... A-1 

A.1 CONSERVATIVE POLLUTANT................................................ A-1 

A.2 NONCONSERVATIVE POLLUTANT - SIMPLE WATER COLUMN ANALYSIS.............. A-2 

A.3 WATER-SEDIMENT MODEL HAVING SEPARATE PARTICULATE AND DISSOLVED 

CONTAMINANT PHASES................................................... A-10 

APPENDIX B SEDIMENT TRANSPORT CONSIDERATIONS................................ B-1 

B.1 SEDIMENT PROPERTIES................................................... B-1 

B.2 TRANSPORT OF SEDIMENT LOADS........................................... B-9 

B.3 DEPOSITION AND EROSION............................................... B-14 

B.3.1 Deposition..................................................... B-15 

B.3.2 Bed Erosion................................................... B-18 

B.3.3 Particle Exchange: Continuous Versus Discontinuous............ B-20 

B.4 SEDIMENT SOURCES..................................................... B-22 

APPENDIX C FIELD AND LABORATORY METHODS FOR FLINT RIVER SURVEYS............. C-1 

APPENDIX D BEHAVIOR OF HALOGEN DISINFECTIONS RESIDUALS..................... D-14 

ATTACHMENT I WATER-SEDIMENT PARTITION COEFFICIENTS FOR PRIORITY METALS...... I-1

ATTACHMENT II CATALOGUE OF WASTE LOAD ALLOCATION MODELS FOR TOXIC 

COMPOUNDS.................................................... II-1 
SECTION 1.0 

INTRODUCTION 

This document addresses methods for predicting 

concentrations of Individual constituents resulting from 

pollutant loads to the aquatic environment. Within the context 

of the waste load allocation (WLA) process, the 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, 1t is also necessary to establish (a) a target 

for allowable concentrations, and (b) a target frequency for 

not exceeding the allowable concentrations. Data on the former 

are contained 1n the Water Quality Criteria documents; data on 

the latter are sparse. Neither subject is within the 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, material discussed in Book II, Chapter 1 (800, 

dissolved oxygen and ammonia) is not repeated here. In 

particular, it is assumed that the reader is familiar with the 

concepts of advection 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 is intended for use in conjunction 

with chemical data references, such as Mabey et al. (1982) and 

Callahan et al. (1979). 

1
Table 1.1 ORGANIZATION OF GUIDANCE MANUAL FOR PERFORMING OF 

WASTE LOAD ALLOCATIONS 

BOOK I GENERAL GUIDANCE 

(Discussion of overall WLA process, procedures, 

considerations) 

BOOK II STREAMS AND RIVERS 

Chapter 1 - 800/Dissolved Oxygen Impacts and Ammonia 

Toxicity 

Chapter 2 - Nutrient/Eutrophication Impacts 

Chapter 3 - Toxic Substances Impacts 

BOOK III ESTUARIES 

Chapter 1 - 800/Dissolved Oxygen Impacts 

Chapter 2 - Nutrient/Eutrophication Impacts 

Chapter 3 - Toxic Substances Impacts 

BOOK IV LAKES, RESERVOIRS, IMPOUNDMENTS 

Chapter 1 - 800/Dissolved Oxygen Impact 

Chapter 2 - Nutrient/Eutrophication Impacts

Chapter 3 - Toxic Substances Impacts 

2
Because predictions are needed in a variety of different 

situations, there is 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 is to describe a variety of 

different approaches, covering a wide range of complexity, to 

help guide the analyst in choosing a calculational framework, 

or model, appropriate to the specific problem. Rather than 

recommending particular levels of effort as appropriate for 

analyzing particular WLA 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 organized into the 

following sections: 

Section 2.0 describes mathematical frameworks for 

predicting toxicant concentration's in rivers. The approaches 

span a range of complexity, from dilution calculations to 

complex, multi-dimensional, time-varying 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 of 

supporting data. Quality assurance and cost estimates are 

covered for both field data and model parameters. This section 

also contains technical guidance 1n applying models and 

assessing the adequacy of site-specific model predictions. 

3
Section 5.0 presents a case study of modeling metals 

transport 1n 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 

analysis of the model parameters relative to the Flint River 

calibration is also presented. 

Finally, there are appendices containing (A) derivations 

of model equations, (B) a discussion of sediment exchange and 

transport modeling, (C) a summary of Flint River (case study) 

survey methods, and (D) chlorine behavior. In addition, two 

other reports are attached. One 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 each model's theory. Input and 

output, strengths and limitations, and resource requirements. 

4
SECTION 2.0 

BASIC MODEL FRAMEWORKS AND FORMULATIONS 

2.1 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 is to 

present a range of approaches, it is useful to consider a 

means of categorizing water quality models according to their 

components and characteristics. In selecting an approach, a 

WLA analyst is 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 

- Advection, dispersion 

- Decay, transformation, speciation 

- Transfer between water, sediment, and air 

C. Spatial variability or resolution 

- 0, 1, 2, 3-Dimensional variability 

- Near or far field 

D. Time variability 

- Steady state 

- Time variable 

5
The analyst must also be concerned with the input data and 

hardware requirements associated with any approach. These tend 

to follow from the capability characteristics listed above. 

A general schematic framework for illustrating many 

factors that determine the concentration of toxicants in a 

river is depicted in Figure 2.1. The conceptual elements 

include: (a) mixing of effluent and upstream waters, (b) 

partitioning of toxicant between dissolved and particulate 

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 (f) 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 

approaches tend to hinge on a few critical assumptions (as 

will be described shortly): the technical issues and 

uncertainties that surface thus tend to be few in number but 

could be intractable in nature. Furthermore, restrictions in 

the form of their results can constrain the formulation of the 

basic questions they are intended to answer. Complex analyses, 

on the other 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 in 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. 

It 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 level of complexity, as the 

issues, costs, benefits, and 

6
FIGURE 2.1 IMPORTANT FATE AND TRANSPORT PROCESSES 

 FOR TOXICANTS IN RIVERS

7
decision needs evolve. Nevertheless, because the collection of 

field data can be the most expensive project component, no 

major field surveys should be done before the analytical 

framework has been selected and the input data requirements 

identified. 

The approaches covered in this document can, for purposes 

of discussion, be placed 1n the following types of categories: 

• 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 nonconservative 

total pollutant. 

 

• One-dimensional, steady-state models for separate 

dissolved and particulate phases; having bed interactions 

and multiple process rates. 

• Multi-dimensional, and/or dynamic models for separate 

dissolved and particulate phases or multiple species; 

having bed interactions and multiple process rates. 

The approaches differ in discerning spatial and temporal 

variations, environmental media, and pollutant forms and 

behavior. The approaches are described in the sections that 

follow. Mathematical derivations of 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 the 

first process normally evaluated in predicting ambient 

concentrations of toxicants. At the point where mixing has 

been completed, the concentration of the total pollutant is 

given by: 

 

(2.1)

8
where, CT(0) = Concentration of total pollutant immediately 

after complete mixing (ug/l) 

CTW = Effluent concentration (ug/l) 

CTU = Upstream concentration (ug/l) 

QT = Combined effluent flow (Qw) plus upstream flow 

(Qu) (l/sec) 

WT = Combined effluent plus upstream load (ug/sec) 

This formulation assumes that: 

1. Mixing is relatively rapid. 

2. Decay or settling is slow compared to mixing. 

The combination of these two assumptions implies that little 

decay has time to occur before mixing is complete. The 

formulation says nothing about the size of or concentration 

within the mixing zone. Nor does it say anything about the 

concentrations further downstream of the discharge. 

Used by themselves without regard for downstream fate, 

dilution calculations have found considerable use in setting 

water quality based effluent limitations for both conservative 

and nonconservative pollutants. This is because Water Quality 

Standards are often implemented in such a way that the 

toxicant concentration is not permitted to exceed the 

numerical criterion at any point (outside the mixing zone), 

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 dilution 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 

the toxicant concentration. In this case some consideration of 

the pollutant 

9
behavior in the reach between the two discharges is needed in 

order to predict the concentration (CTu) above the second 

discharger. The approach is nevertheless applicable in the 

numerous situations where only one of the several dischargers 

is of importance for a particular toxicant. 

Because toxicants may rapidly partition between the 

dissolved and suspended solids phases in the water column, or 

may rapidly interconvert 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 is 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 is that it says nothing about the 

spatial extent of the problem, which in turn partially 

determines the environmental 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 be partially 

offset by 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 (CT(0)) using the frequency distributions or real 

time sequences of the four input parameters (CTu, Ou, CTw, Ow). 

Facile methods for determining the overall frequency of 

standards violations at the point of discharge are being 

refined (DiToro and Fitzpatrick 1983) and appear to promise 

substantial improvements in the evaluation of toxicity 

problems. 

10
2.3 ONE-DIMENSIONAL, STEADY-STATE MODEL OF CONSERVATIVE TOTAL 

POLLUTANT 

This approach goes beyond that of the previous section in 

that it 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 is not reduced 

as it travels downstream. Consequently, since dilution is the 

only process affecting the concentration, the model equation 

is 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 

be solved for dc/dt = 0 (i.e., no. variations in time - 

steady-state). 

3. The pollutant is mixed essentially Instantaneously with the 

river (i.e., the model does not discern concentration 

variations within a mixing zone). 

Figure 2.2 depicts this model. If multiple discharges were 

present, concentrations along the length of the river would 

increase in steps. 

Chloride in dilute concentrations is an example of a nondegradable substance with little tendency to precipitate or 

absorb to sediment. It can be expected to remain in the water 

column with its steady state stream concentration determined 

only by dilution. 

Priority pollutants, however, may undergo a wider range of 

processes than chloride. The organics undergo degradation and 

transfer to air and sediment. Metals (as total metal), 

although not degradable, also generally have an affinity for 

bed sediments. Individual metal ions and complexes can undergo 

transformation to other species. Thus, an a priori assumption 

of conservative behavior will often be unsound. Such an 

assumption is general least appropriate when allied to the 

dissolved fraction of a pollutant, since adsorption can be a 

rapid process. 

11
12

FIGURE 2.2 STEADY-STATE CONSERVATIVE BEHAVIOR 
Nevertheless, where the spatial distance under study is 

very small, conservative assumptions may 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 ONE DIMENSIONAL, STEADY-STATE MODEL OF NONCONSERVATIVE 

TOTAL POLLUTANT 

This approach predicts the concentration of the total form 

of a nonconservative pollutant in the water column throughout 

a one-dimensional stream reach under steady-state conditions. 

The model formulation is: 

 (2.3) 

where, CT(x) = Concentration at points downstream of 

effluent (ug/l) 

CT(0) = Concentration immediately below effluent 

(from Equation 2.1 (ug/l) 

KT = Overall loss coefficient (l/day) 

U = Stream velocity (m/day) 

x = Distance downstream of effluent (m) 

Several assumptions accompany this model: 

1. KT, the 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 is mixed instantaneously with the 

river (i.e., no mixing zone or lateral and vertical 

concentration gradients). 

4. Dispersion is negligible in the longitudinal direction 

(i.e., only advective transport is considered significant: 

plug flow). 

13
5. Average flow, average cross-section area, and average depth 

sufficiently represent conditions within a single reach. 

Figure 2.3 depicts this model graphically. This model is 

directly analogous to 800 disappearance in a classical 

Streeter-Phelps 00 model. The model equation is 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 is 

determined by significant changes in river geometry, flow, and 

location of point source tributaries. 

The overall decay coefficient is both site- and timespecific, 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 KT to the 

observed rate of disappearance. That is, KT is adjusted until 

the calculated concentrations reasonably match the measured 

concentrations along the length of the river downstream of the 

effluent. As in 800 modeling, it 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, 

it is fairly straightforward, with few degrees of freedom to 

manage. A key limitation is that is sheds no light: on the 

factors that control KT. In contrast to the situation with 800 

and ammonia, there is little experience to indicate reasonable 

values for loss rates of most priority pollutants. 

Consequently, the analyst has little basis for recognizing or 

evaluating the uncertainties of applying the model, 

particularly under conditions not yet observed (for example, 

under conditions of improved wastewater treatment). A more 

theoretical model, although still tied to some calibration

parameters, provides a better basis for dealing with the 

uncertainties inherent in such applications. 

14
DISTANCE X

FIGURE 2.3 SIMPLE FIRST ORDER DECAY ANALYSIS 

FOR TOTAL POLLUTANT 

15
Nevertheless, this general approach has been applied to 

phenols and cyanide in the Mahoning River (EPA 1977). 

Application of this approach to the settling of metals in the 

Flint River is described in Appendix A. 

2.5 ONE-DIMENSIONAL. STEADY-STATE MODELS FOR SEPARATE 

DISSOLVED AND SUSPENDED PHASES. HAVING BED INTERACTIONS 

AND MULTIPLE PROCESS RATES 

Unlike the approaches described previously, this approach 

was 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, it provides a more complete understanding of 

pollutant behavior. The trade-off is that there are more 

parameters to specify, and it is 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 physical properties of the site and to 

known chemical properties of many pollutants, some model 

predictions may be attempted without having surveyed the 

pollutant's downstream profile at the site. 

This level of analysis is sufficiently complex that a 

computer program is helpful (but not essential) for executing 

the computations. The Simplified Lake and Stream Analysis 

(SLSA), which is available as both a calculator algorithm and 

a computer program, is 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 particulate 

bound pollutant. This program was developed by the EPA Large 

Lakes Research Station, Grosse Ile, specifically for WLA 

purposes. 

2.5.1 Model Framework

The framework for this type of model is illuminated in 

Figure 2.4. The model discerns two media, water and bed 

sediment, and two forms of pollutant within each media, 

dissolved and particulate bound. Process 

16
FIGURE 2.4 MICHRIV FRAMEWORK 

17
rates are specific to the media and to the pollutant form: for 

example, only the participate phase in water settles to the 

bed, and only the dissolved phase in water volatilizes. 

Derivation of the model equations and listing of assumptions 

are presented in Appendix A. 

In summary, the MICHRIV program predicts the dissolved and 

particulate concentrations in water and bed sediment, using 

the 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 MICHRIV'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, m1(x). Is 

related to the initial concentration, m1(0), and to the 

settling and resuspension velocities, ws and wrs, by the 

expression: 

for which all parameters are defined in Table 2.1. It is 

assumed that the bed solids concentration, m2, is constant 

throughout the reach. (SLSA differs from MICHRIV in that it 

also treats m1 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: 

 

(2.5)

18
TABLE 2.1: NOMENCLATURE FOR WATER/SEDIMENT MODEL 

19
TABLE 2.1: NOMENCLATURE FOR WATER-SEDIMENT MODEL (Continued) 

*In terms of bulk volume. 

20
The sedimentation or burial velocity, wd, reflects the 

rate of change in elevation of the benthal surface at a 

particular point over time. A positive value for wd indicates 

that the channel is 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 downcutting 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 follows the movement 

of solids. The fraction of total toxicant that is adsorbed on 

particulates (fp1 in water, fp2 in bed) and the fraction that is 

dissolved (fd1, fd2) depend on the partition coefficients 

applicable to the water and bed (w1, and w2, respectively), and 

the solids concentrations (m1 and m2 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 particulate 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: wd = 0 and K2 = 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 w1 = w2 were imposed, a net movement 

of particulate 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: 

 (2.8)

The overall removal rate coefficient, KT (l/day), can be 

expressed by the function: 

 (2.9)

where Kd1 and Kd2 are the toxicant decay coefficients in the 

water and bed, respectively, and Ks is the sedimentation loss 

coefficient, which is related to the burial velocity by the 

expression: 

KS = Wd/H2 (2.10)

The aggregate decay coefficients K1 and K2 are simply the sum 

of the coefficients specific to each competing process, such 

as hydrolysis, biolysis, photolysis, and volatilization. 

The parameter group βr2/r1 controls the importance of 

sediment decay and loss processes in Equation 2.9. The 

parameter β called the sediment capacity factor (DiToro et al. 

1982), is defined in terms of solids masses in water and 

sediment (proportional to mH) and fractions particulate, fp: 

 

 (2.11) 

The ratio of toxicant concentrations on particulates, r2/r1, is 

determined from the expression: 

 

 (2.12) 

22
This ratio is controlled by sediment exchange velocities, wrs + 

wd (or ws through Equation 2.5); the diffusion coefficient, XL, 

for exchange between the water column and the interstitual 

water of moderate to high porosity sediments; the decay 

velocity 1n sediment, K2H2: and the fractions dissolved and 

particulate. 

Finally, the total concentration of toxicant in the bed, CT2 is 

given by: 

(2.13) 

It should be noted that Equation 2.9 and 2.11 utilize fd1 and 

fp1 as if they were constant throughout the reach, when in fact 

they vary with m1(x). Consequently, in order to treat them as 

constants, the MICHRIV program divides each reach into small 

computational increments, within which m1 is virtually 

constant, and solves the equations for each increment, moving 

downstream. 

It can be seen that the model is not entirely simple. Most 

first-time users may find some aspects of Us behavior not 

intuitively obvious; some sensitivity runs coupled with 

examination of the formulating equations may be helpful to 

obtain a good feeling for how the model responds to its input 

parameters. DiToro 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 (1984) suggests a simplification of this type 

of model. 

2.5.2 Relationship with Other Approaches

In predicting the total pollutant in the water column, it 

can be seen that MICHRIV and SLSA use the same first order 

decay formula (question 2.8) at the simple empirical approach 

(Equation 2.3). In 

23
MICHRIV, however, unlike SISA and the empirical approach, the 

decay coefficient, KT1, is not necessarily constant within a 

reach. Rather, it is a function of the fractions dissolved and 

particulate (per Equations 2.9), which in turn vary with any 

change in the suspended solids concentration moving downstream 

(per Equations 2.6 and 2.7), as previously mentioned. 

Consequently, if 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 degradative 

processes or volatilization. 

For metals and other nondegradable, nonvolatile substances 

the sole mechanism for reduction of the water column load is 

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) steady pollutant loading to (b) a graded 

stream with (c) insignificant bed lead and (d) steady flow. A 

graded stream is one where neither downcutting nor 

sedimentation is significant; the bed elevation is not 

significantly changing over time. Solids settling and 

resuspension 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 uncontaminated 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 field surveys) may not be conserved. During periods of 

high concentrations in 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 resuspension 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 

resuspension 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 MICHRIV (or SLSA) 

are that they discern between dissolved and particulate phases 

and they predict the 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; in 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 1t is one-dimensional, steady-state, 

and plug flow. In addition, MICHRIV and SLSA lack complex 

kinetic routines for Internally deriving a chemical 

degradation rate from input data. 

25
FIGURE 2.5 TYPICAL BEHAVIOR PREDICTED BY MICHRIV FOR (A) 

METAL, AND (B) DEGRADABLE ORGANIC. 

26
For studies of far field impacts (as opposed to mixing 

zone impacts), lateral and vertical variations in 

concentration are seldom sufficient in rivers and streams to 

justify modeling in more than one dimension. Longitudinal 

dispersion is likewise seldom sufficient in rivers and streams 

to discourage use of a plug flow assumption (Driscoll et al. 

1983). The plug flow assumption does deter applying such 

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 is independent of 

the previous run. Unlike a dynamic model, the steady-state 

model has no memory of the previous state of the system: it 

assumes that the modeled conditions have persisted since time 

Immemorial. To the extent that the real system can "remember" 

its previous condition, for example through longitudinal 

dispersion and a long hydraulic retention time, a modeling 

error is generated. In this case the steady-state model would 

tend to overpredict during periods of high or steadily rising 

concentrations and underpredict during periods of low or 

steadily decreasing concentrations. 

Mulkey et al. (1982) 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 HSPF to a situation of a constant effluent

load discharging to a river with variable flow. (Both models 

are described in 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 daily flow record. They found the 

frequency distribution produced by the steady-state model to 

be nearly identical to that produced 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 it does not apply to 

concentrations in the bed sediment (which similarly has a long 

retention time). Also, it 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 

SPECIATION 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 assumptions and contain more degrees 

of freedom. They tend to involve more process-oriented 

descriptions of chemical transport, sorption, speciation, and 

transformation. Enhanced process descriptions can provide a 

more 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 in 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 steady or unsteady periods. The use of 

more complex models requires more 

28
technical competence and resources to obtain predictions, but 

not necessarily more wisdom and experience to interpret the 

predictions and gain insight into the problem. 

A variety of fairly complex models exist or will soon be 

available. Those general purpose toxic chemical model codes 

described in this section include: EXAMS, EXAMS 2, and 

TOXIWASP, developed by Athens Environmental Research 

Laboratory; HSPF, developed by Hydrocomp and Anderson-Nichols 

for Athens ERL; SERATRA, TOOAM, 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, MICHRIV and 

SLSA. the slightly less complex models described 1n 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, 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 (steadystate, seasonal, dynamic), and their availability. Attachment 

II of this document contains additional Information. 

Clearly, a range of models is available with widely 

differing capabilities. Table 2.3 summarizes what components 

of these models can be considered more complex or more general 

than those of MICHRIV and SLSA: their transport, sorption, 

speciation, or transformation 

29
Table 2.2 General Categorization of Computer Models 

(listed in alphabetical order) 

G - General Aquatic System; R - River 

O - Generalized Pollutant; M - Metal, Specifically; D - 

Daughter Product 

O - Descriptive Input, Not Simulated 

B - Box Approach, Pseudo 3-Dimensional; 2V - Two Dimensions 

(x-z) 

FD - Finite Difference; FE-Finite Element; SS - Steady State 

S - Seasonal; D - Daily 

A - Available from EPA Center for Water Quality Modeling, 

Athens, GA 

C - Available from Chem. Manuf. Assoc.; GI - Available from 

EPA Gros 

See Attachment II for additional information. 

30
Table 2.3 Model Components which could be considered somewhat 

more complex or general than MICHRIV 

S - Frequency-Duration Summary 

F - Food Chain Model 

(F) - Food Chain Model from WASTOX Expected to be Compatible 

31
algorithms, or their linkage to hydrologie, flow, and/or 

effects models. Those components not labeled as more complex 

may be roughly equivalent to, or even simpler and more 

restrictive than MICHRIV and SLSA. 

Generally it is sound practice to use the simplest 

approach that will properly handle the problem. Nevertheless, 

to satisfactorily resolve some WLA problems, it 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 particulate 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 

HICHRIV and SLSA, transport 1n the water column follows the 

one-dimensional steady-state solution to the advective 

transport equation for chemical and suspended sediment. 

Settling and resuspension velocities are specified for 

particulates (and thus sorbed chemical). Less restrictive 

transport and bed/water exchange assumptions can allow 

multiple sediment size fractions with different sorption 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 

exfiltration, 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 benthic water column segments 

carrying a high suspended solids load. These properties are 

tabulated 1n columns 4 and 5 of Table 

2.4 for each model. 

32
Table 2.4 Transport and Bed/Water Exchange Properties 

* - Capable 

0 - Descriptive Input, Not Simulated 

8 - Box Approach, Pseudo 3-Dimensional; 2V - Two Dimensions 

(x-z) 

M - Multiple Bed Layers 

W - Bed Load Approximated with Lower Water Layer 

P - Pore Water Dispersion; T - Enhanced Diffusion From 

Bioturbation or Physical Mixing; S - Direct Sorption 

between Bed and Water Column 

C - Calibrated (Emphirical) Scour-Deposition Parameters 

F - Functional Scour-Deposition Parameters 

33
In MICHRIV and SLSA, 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 sorption 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 bioturbation 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 Sorption

Sorption of a chemical onto sediment is generally 

considered to proceed rapidly compared to other transport or 

transformation processes. MICHRIV and SLSA assume adsorption

and desorption are completely reversible, and proceed rapidly. 

Mathematically, these two assumptions lead to the use of a 

partition or distribution coefficient for sorption/desorption. 

This coefficient can be measured in the laboratory and 

adjusted for conditions in the environment. 

Many of the other models also use partition coefficients

adjusted for organic carbon content of the sediment. One model 

also automatically adjusts the coefficient for sediment 

concentration based on higher partitioning at lower sediment 

concentrations. Some models make use of the Langmuir or 

Freundlich isotherms widely used in soil science. These 

empirical relationships predict progressively less additional 

sorption 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 concentrations are expected. 

34
Other models assume a linear isotherm at equilibrium, but 

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 

minutes 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 in the Flint River case 

study), or vice versa. 

Three types of theoretically-based sorption algorithms 

have been used in these models: ion exchange, constant 

capacitance double layer, and triple layer site binding. The 

ion exchange technique can be useful for Ionic 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 sorption-pH dynamics are 

important, and where experimental work is possible. 

Table 2.5 tabulates the sorption properties of the general 

purpose 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 Speciation

Many chemicals or metals discharged into an aquatic 

environment will be found in several species or complexes. A 

common speciation process is ionization, which is controlled 

by pH. Both chemical reactivity and toxicity can be 

significantly affected by the extent of ionization. 

35
Table 2.5 Sorption properties 

36
For metals, an important process is inorganic 

complexation. Table 2.6 gives, for example, the possible 

dissgived species of lead in water containing nitrate, 

chloride, sulfate, chloride, and carbonate (Felmy et al. 

1983). To calculate these species, one needs experimental data 

on the equilibrium constants and environmental data for pH, 

chloride, sulfate, flouride, nitrate, and carbonate. A 

reliable thermodynamic data base is available for inorganic 

complexation of some metals, and incorporated into some 

geochemical models, including MEXAMS, the only geochemical 

speciation model listed in this section. 

Another important process for metals in many natural 

waters is organic complexation. The experimental data base is 

more limited than for inorganic complexation. Some data for 

fulvic and humic acids are incorporated into certain 

geochemical models (including MEXAMS). Experimental data from 

a particular site, however, would be better for waste load 

allocations at that site. 

EXAMS and EXAMS 2 have the capability of discerning up to 

5 ionic species of any organic pollutant under study. Unlike 

MEXAMS, it does not predict metal speciation. The other models 

have no accounting of ionic species. 

2.6.4 Transformation

Transformation of a compound within an aquatic environment 

can result from physical, chemical, or biological reactions. 

The standard set of reactions includes photolysis, hydrolysis, 

oxidation, and biolysis. Because volatilization (transfer into 

the atmosphere) is handled in a mathematically equivalent way, 

it too can be treated as a transformation reaction. These 

processes are discussed in Section 3.3 of this guidance 

document. 

The simplest approach, such as that used in MICHRIV and 

CTAP, is to utilize a single first-order rate coefficient 

specified in the program 

37
Table 2.6. Dissolved Species of Pb 

From Felmy et al. 1983. 

38
input data. The analyst may obtain this rate coefficient by 

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 this addition 

internally. 

The other ten models 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 acidic hydrolysis), or bacterial concentration (in 

biolysis). 

Rate = KCACB 

where, K = Second-order coefficient 

CA = An environmental parameter 

CB = Concentration of toxicant 

With respect to the toxicant, the product KCA is sometimes 

called a "pseudo" first-order decay coefficient: a first-order 

coefficient which 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 here, 

the overall reaction rates can vary in response to the time 

variation of the relevant environmental properties, such as 

temperature, pH, light, wind or current velocity. These four 

models are EXAMS2, HSPF, TOXIWASP, and UTM-TOX. 

Lastly, EXAMS 2 and HSPF are able to handle daughter 

products along with parent compounds in a single simulation. 

Other models require two separate simulations, with internal 

loadings from the parent compound specified as external Input 

to the second simulation. 

39
SECTION 3.0 

ESTIMATION AND USE OF MODEL PARAMETERS 

The purpose of this section is to provide information for 

estimating parameters for a model of intermediate complexity, 

such as MICHRIV, described in Section 2. Some discussion of 

the basis for estimating 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 chemical properties; and Mill et al. 

(1982) present laboratory protocols for evaluating the fate of 

organic chemicals. 

The section will cover partitioning between aqueous and 

particulate 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 adequately 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 in 

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 the most significant mechanisms 

for the movement of toxic chemicals through the 

40
aquatic environment is the adsorption or uptake of the 

chemical by both nonviable 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 biotic 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 the 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 particulate 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 in 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 is related to the particulate 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 MICHRIV modeling framework, the variables or 

parameters which control the exchange of particles between the 

bed and the water column are: m1 and m2, the slids 

concentrations in the water and

41
bed; and ws, wrs, and wd, the velocities (m/day) of settling, 

resuspension, and burial, (sedimentation). The thickness of 

the active bed is assumed constant. Specifying any four of 

these five variables allows the last one to be calculated 

from: 

 

(3.1) 

As MICHRIV 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 MICHRIV and SLSA, 

the bed solids concentration, m2, is a user specified constant. 

For a typical river bottom with water content 60-90% by 

weight, and a typical solids density of 2.5 g/cm3, m2 will vary 

in the range 50,000-500,000 mg/l (in terms of bulk volume). 

Flint River bottom sediments were measured at 200,000 mg/

(Section 5). 

l

In most models (including MICHRIV but excluding SLSA and 

EXAMS) the solids concentration in the water column, m1, is a 

state variable predicted from the solids loadings and the 

settling and resuspension velocities. In MICHRIV (as described 

in Section 2), m1, is given by: 

(3.2) 

where all parameters are constant. 

It is useful to identify three basic conditions of 

particle exchange. First, a condition may exist where m1

remains constant (moving downstream) and the sediment bed is 

neither accumulating nor scouring away. Although solids 

settling and scour may be occurring, they 

42
are in a state of equilibrium (i.e., they balance each other); 

consequently, wd = 0. 

In the second condition, m1 decreases moving downstream 

because the settling flux exceeds the resuspension flux. As 

the bed cannot move horizontally in MICHRIV, the resulting 

excess in settled solids is buried at velocity wd > 0. In the 

third condition, m1 increases because the resuspension flux 

exceeds the settling flux; for the resulting net scour, wd < 0. 

These three possibilities are depicted in Figure 3.1. 

It must be noted that in models which allow downstream 

movement of the bed (bed load), wd could be given a different 

interpretation. For example, when settling exceeds 

resuspension (wd > 0), an increase in bed load (at the expense 

of the suspended load) could be permitted to remove the excess 

sediment. In this case wd could reflect the rate of increase of 

the bed load. As noted in Appendix 8, however, bed load is 

seldom expected to be an important transport mechanism for 

adsorbed toxicants. 

If the downstream profile of m1 follows Equation 3.2, then 

it may be possible to evaluate ws and wrs (or wd.) indirectly. 

The procedure is analogous to determining a first order decay 

coefficient from the disappearance profile, but has the 

complication of an additional degree of freedom. In the 

following discussion it is assumed that m2, U1 (velocity), H1

(depth), and Q1 (flow) are known constants throughout the 

reach, m1(x) has been measured at several points through the 

reach, as illustrated in Figure 3.1, ws and wrs are unknown but 

constant, and wd(x) is 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 nonpoint loads and no phytoplankton

growth). 

By evaluating Equation 3.2 for large values of x1, ws can be 

related to the m1 asymptote: 

43
FIGURE 3.1 

44
(3.3) 

where m1(∞ ) is the asymptote (m1(x) at x = ∞ ), estimated 

visually from a profile such as shown in Figure 3.1(b). If 

Equation 3.2 is normalized for the asymptote and put in 

logarithmic form, the derivative (slope) of the resulting 

expression is directly related to ws: 

(3.4) 

This in analogous to determining a decay coefficient from 

the slope of a semi-logarithmic plot of concentration versus 

time. Having thus determined ws and wrs, the value of wd(x) is 

given by Equation 3.1. 

Alternatively, the value of wd(x) at any particular point 

can be expressed in terms of the linear (rather than semilogarithmic) slope of the profile: 

(3.5) 

The value of ws would then be given by combining Equation 

3.1 and 3.3, and the value of wrs 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 U1, and H1) vary 

along the length of the river, then ws and wrs 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 

45
for estimating ws and wrs. 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 ws (or wd) 

controls the distance needed to approach the asymptote, while 

the ratio wrs/ws controls the value of the asymptote. 

A direct estimate of the settling velocity, ws, can be 

made using Stokes' equation. Figure 3.2 illustrates the 

solution of this formula. 

(3.6) 

where, vs = Stokes' settling velocity (cm/sec) 

g = Gravitational acceleration (approx. 980 cm/sec2) 

d = Particle diameter (cm) 

v = Kinematic viscosity (cm2/sec) (Figure 3.28) 

Ss = Specific gravity of particle (dimensionless 

ratio) 

Sw = Specific gravity of water (1.0) 

This calculation is intended to apply to noncohesive spherical 

particles in a quiescent medium. Substantial differences may 

exist between the calculated Stokes' velocity, vs, and the 

effective settling velocity, ws, 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 Stokes' 

settling velocity. The inter-particle collisions necessary to 

bring this about may result from (a) Brownian 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'Mella 1980, Hayter and Mehta 1982). 

The inter-particle cohesiveness needed to produce aggregate 

from colliding particles may result from (a) van der Waals 

forces, (b) electric charges on particle 

46
FIGURE 3.2 OBTAINING THE STOKES SETTLING VELOCITY (V

47
surfaces, (c) interactions of aqueous ions attracted to the 

charged surfaces (double layer), (d) chemical bonds, and (e) 

other mechanisms (Partheniades 1971). As a result of 

coagulation into larger particles the observed settling 

velocity may be orders of magnitude larger than the Stokes' 

velocity of the disaggregated particles (Uchrin and Weber 

1980). 

Hayter and Mehta (1983), in modeling fine sediments in 

estuaries, indicate that the effective deposition velocity, 

ws, decreases with increasing shear stress, ?, produced by the 

fluid passing over the bed. Shear stress in an open channel is 

determined as Follows (Graf 1971): 

(3.7) 

where τ = Shear stress (Newton/m2) 

γ = Specific weight of water (approximately 9807 

N/m2

) 

R = Hydraulic radius, approximately equal to stream 

depth (m) 

S = Slope of the energy grade line, approximately 

equal to the bed slope (m/m) 

If the stream velocity, U, is assumed to follow Manning's 

equation, then the bed shear can be expressed as follows: 

(3.8) 

where n is Manning's roughness coefficient. Under this 

condition, bed shear is related to the square of velocity. 

Parthenaides (1971) notes that there is some critical 

velocity or shear above which no deposition of fine particles 

will occur. As velocities drop below this critical value a 

rapidly increasing proportion of the fine particles are 

capable of depositing. 

In this vein, HydroQual (1982) recommends settling ws

equal to 25-50% of the Stokes' velocity in most rivers, and to 

as little as 10% of the Stokes' velocity in shallow streams. 

They also point out that the Stokes calculation should be 

treated as a preliminary estimate, to be modified by 

calibration to the suspended solids data. 

48
For quiescent waters, Thomann (1982b) and Richardson et 

al. (1983) have calculated ws in the range 0.1-5 m/day. In the 

Flint River case study, ws was calibrated at 0.25 m/day during 

low flow and 0.6 m/day during a higher flow period. (An 

increase in settling velocity with an increase in flow is the 

result of suspension of larger or denser particles by the 

higher flows). The manual Sedimentation Engineering (ASCE 

1975) 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 ws. HydroQual (1982) briefly discusses such 

measurements, summarizing the findings of Blomquist and 

Hakansom (1981) on the accuracy of various types of sediment 

traps. 

The resuspension velocity, wrs, also depends on the shear 

stress, τ, as well as on the strength of bed to resist shear. 

The strength of the bed is related to the nature the particles 

and the deposition history-(including age and degree of 

consolidation). Below some critical velocity or shear, little 

or no resuspension may take place, while above this critical 

value, resuspension may increase rapidly (Parthenaides 1971, 

Hayter and Mehta 1983). The parameters determining 

resuspension rates must generally be empirically measured. 

Bonazountas and Mathias (1984) discuss data and methods, 

and propose algorithms for determining both deposition and 

resuspension. Their computer model, SEDIM, 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 Toffaleti, 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 water column 

and the sediment interstitial water operates to move material 

from the region of 

49
higher concentration to the region of lower concentration, in 

accordance with Fick's Law. If no dissolved concentration 

gradient exists then diffusion is 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 in 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, if partition 

coefficients are lower in the bed than in the water column due 

to the higher solids concentration in the bed (O'Connor and 

Connolly 1980), then deposited participate pollutant may have 

the opportunity to desorb and diffuse back into the water 

column in 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 copper, cadmium, and zinc, sediment diffusion is an 

important process in waters having suspended solids

concentrations less than about 10 mg/l. 

The exchange coefficient describing dissolved exchange 

between the bed and water column has been termed KL, in Section 

2. HydroQual (1982) notes that this parameter is difficult to 

measure directly. They believe that KL 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 

KL. Where only molecular diffusion is important KL can be 

estimated from the expression (HydroQual 1982): 

(3.8.1) 

 where: 

DL = molecular diffusivity of chemical in water (cm2/day) 

ф = sediment porosity (dimensioness) 

δ = length of vertical concentration gradient in sediment 

(cm) 

n = a power 

50
The sediment porosity ф appears the numerator to account for 

the reduction in diffusion caused by the tortuosity in 

sediments. It appears in the denominator to account for the 

conversion of concentration units from interstitual volume to 

bulk volume. The vertical concentration gradient length ф may 

be taken to be equivalent to the active sediment thickness H2

used in Section 2.5. The value of n is a sediment dependent 

property indicating the relationship between porosity and 

tortuosity. HydroQual (1982) assigns the value n = 2; however 

Chapra and Reckhow (1983) indicate that other values may also 

apply. 

Chapra and Reckhow (1983) present the molecular diffusion 

coefficient data of L1 and Gregory (1974). For priority 

pollutant metals DL is frequently between 6 x 10-6 and 12 x 10-6

cm2/sec at 25*C. Diffusivity 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 physical 

stirring of the bed sediment caused by currents and benthic

animals (bioturbation). Heathershaw (1976) and Fisher et al. 

(1980), among others have noted the importance of bioturbation 

in increasing the effective rates of diffusion. 

Additional explanation of sediment diffusion processes 

and formulations are provided by Berner (1980), Chapra and 

Reckhow (1983). and DiToro and Connolly (1980). 

3.2 PARTITIONING PROCESSES 

3.2.1 Metals Partitioning

The interaction between dissolved metal species and riverine 

particulate matter, under normal physicochemical 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 is in the solid phase, the fate, 

transport, and bioavailabilty of the metal are 

51
altered considerably. There is ample evidence in the 

literature that metal associates with participate matter; 

however, theoretical (as opposed to empirical) approaches have 

not been widely applied to quantifying this process in natural 

systems. The purpose of this section is 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 in aquatic solid 

substances can be characterized by the following five major 

mechanisms (Gibbs 1973): 1) adsorptive bonding on fine-grained 

substances. 2) precipitation of discrete metal compounds. 3) 

coprecipitation of metals by hydrous Fe and Mn oxides and by 

metal carbonates, 4) association with organic molecules, and 

5) 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 adsorption is 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 is 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 

supersaturation of the adsorbate ion must exist. 

In a system where the ultimate result is the formation of a 

precipitate, however, the strict assumption of an adsorptiondesorption equilibrium may be invalid. 

In most cases encountered 1n river systems it would seem 

that adsorption of metals to inorganic surfaces is the 

dominant binding mechanism. However, in situations where there 

is a large fraction of biological solids, sorption into 

biomass or binding by organic surface 

52
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 twodimensional, surface phenomenon. 

Adsorption models developed from a theoretical basis are 

generally a composite of surface complex formation theory 

(Schindler et al. 1976; Huang and Stumm 1973) 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 by Davis, James and Leckie (Davis et al. 

1978; Davis and Leckie 1978; James et al. 1978), combine 

surface complexion with electric double-layer theory and 

interpret adsorption phenomena in terms of a knowledge of the 

speciation of the adsorbate and the adsorption site. 

Current adsorption models have been reviewed by a number 

of authors (Westall and Hohl 1980; James and Parks 1981; 

Schindler 1981; Morel 1981) and have been shown to have a 

sound theoretical basis. The application of these models to 

well-characterized laboratory metal-ligand-surface systems 

have shown excellent agreement with experimental observations 

(e.g., Davis and Leckie 1978a; James et al. 1981; Theiss and 

Richter 1980; Benjamin and Leckie 1980). However, without 

further basic research and experimentation with natural 

aquatic sediments, it will be difficult to apply the 

theoretical models to adsorption in natural systems. The 

application to natural systems is 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 of 

adsorption reactions. Metal adsorption is considered to be 

53
analogous to the formation of soluble complexes, with the only 

difference being that the ligand in the reaction is a surface 

site (Stumm and Morgan 1981; Benjamin and Leckie 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 speciation 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 acidity 

constants. 

At the same time the metal ion will undergo hydrolysis as pH 

increases. The resultant surface association of metal ions 

with hydrous oxide surfaces tends to demonstrate a rapidly 

increasing metal ion adsorption as pH increases over a very 

narrow range of 1-2 units (James and Healy 1972a; and many 

others). 

This “pH adsorption edge”, as it is commonly called, often is 

demonstrated by a plot of percent metal adsorbed versus pH. 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 right) as total metal concentration (MeT) in 

the system increases, other conditions being constant 

(Benjamin and Leckie 1980). This effect is most often evident 

at low adsorption densities, when excess surface sites are 

available. 

In situations where-complexing ligands (either organic or 

inorganic) are present in an adsorbing system, the above 

generalization for the relationship between metal adsorption 

and pH is not always true. In fact, depending upon the 

particular metal, ligand, adsorbent and pH 

54
FIGURE 3.3 TYPICAL pH-ADSORPTION EDGE FOR METAL ADSORPTION 

HYDROUS OXIDE SURFACE 

55
range, fractional metal adsorption has been observed to 

decrease as pH increases (MacNaughton and James 1974). 

Benjamin and Leckie (1981; 1982) have proposed a conceptual 

model to explain this behavior, where the possibility exists 

for free metal, a metal-ligand complex, or free ligand to be 

associated with a surface. Then the percent adsorption of a 

metal-ligand complex will increase with pH if it behaves as a 

free metal in its surface interaction ("metal-like") and will 

decrease with pH if its adsorption reaction is similar to that 

of a free ligand ("ligand 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 is difficult, and observations range 

from inhibition to no effect. At any rate the effect certainly 

seems to be smaller than those due to variations in pH and 

ligand levels. 

Competitive adsorption inhibition among adsorbing trace 

metals is observed even at very low total adsorption densities 

(at most a few percent of all surface sites occupied). 

Benjamin and Leckie (1981) have suggested that the explanation 

for this phenomenon is the presence of several distinct groups 

of surface sites. Then the 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 

sites. The existence of multisite surfaces may also partially 

explain the variation of pH-adsorption 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 instigating the uptake of trace metals by 

natural aquatic sediments is 

56
relatively small. The results of these adsorption experiments 

of trace metals partitioning measurements made in natural 

aquatic systems are generally quantified in terms of 

relatively simple empirical expressions, including general 

exchange equilibrium expressions (Langmuir 1981) and 

adsorption isotherms (Oakley et al. 1981) of the Freundlich or 

Langmuir 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 Freundlich or 

Langmuir isotherm (Figure 3.4). The Freundlich isotherm is an 

empirical equation having the general form 

(3.9) 

where KF and n are fitting constants. The Langmuir equation has 

a more theoretical base and may be deduced from either kinetic 

or thermodynamic considerations (Weber 1972). The Langmuir 

equation assumes that maximum adsorption density corresponds 

to a saturated monolayer surface covering of adsorbate, that 

the energy of adsorption is constant regardless of adsorption 

density, and that there is no migration of adsorbate in the 

surface plane. 

As shown in Figure 3.4 the Langmuir equation can be expressed 

(3.10) 

where Γ = Specific adsorption density of the metal [Moles 

 Me/mg Adsorbent] ("gamma"), 

Γm = Maximum adsorption density in forming a complete 

monolayer [Moles/mg] (i.e., number of usable 

surface sites per unit mass. of solid), and 

[M] = Equilibrium dissolved metal concentration 

[Moles/l ]. 

The constant rm/Χa, which normally is written 1/b in the 

Langmuir equation, is shown in this manner because be thought 

of as a 

57
EQUILIBRIUM DISSOLVED Me CONC. (MOLES Me/L) 

FIGURE 3.4 EXAMPLE OF PARAMETERIZATION OF LANGMUIR ADSORPTION 

 ISOTHERM FOR METAL ADSORPTION ON A SURFACE 

58
conditional adsorption equilibrium constant ( /mg) for a 

surface-metal complexation reaction of the form: 

l

Ka is termed a conditional equilibrium constant because its 

value in only a constant for specified surface and bulk 

solution chemical conditions. 

It is convenient to express the Langmuir adsorption 

isotherm equation in 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 rm /Ka = [M], Equation 3.10 reduces to 

(3.11) 

where [M-S] = Concentration of metal in metal-surface complex 

 (mole/l ) and 

ST = Total concentration of interacting (adsorbing) solids 

(mg/l ). 

Converting the Langmuir nomenclature to the toxicant

modeling nomenclature of Section 2.5 (Table 2.1), it can be 

recognized that Xa is the same as v ( /mg), [M] (mole/l ) 

corresponds to C

l

d (ug/l ), [M-S] (mole/l ) corresponds to Cp

(ug/l ), ST is m (mg/ ), and Γ (mole/mg) corresponds to Γ

(ug/mg). Thus, Equation 3.11 can be written: 

l

(3.12) 

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
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, pr, ionic strength, 

concentration of completing organic and inorganic ligands, 

concentration of competing surfaces, and concentration of 

competing adsorbate species. The use of v is, therefore, 

limited to conditions very similar to those for which it was 

determined. If a wide range of environmental conditions are 

encountered, then v 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 is highly dependent on the type and 

relative amount of each solid phase making up the solids in an 

aquatic system. Oakley et al. (1981) demonstrated this 

postulate using bentonite clay, amorphous iron oxide, hydrous 

manganese oxide, and humic acid. Sediment organic content can 

be highly correlated with metal partitioning for those metals, 

such as copper, that have a high affinity for humic acids 

(Oakley et al. 1981; Ramamoorth and Rust 1978; Suzuki et al. 

1979). 

2) Metal partition coefficients depend on the size 

distribution and concentration of adsorbing aquatic sediments. 

It is 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 is 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 organics (O'Connor and Connolly 

1980; DiToro et al. 1982). 

3) Of course, pH and other water chemistry parameters 

(particularly the presences and concentration of metalcomplexing and adsorbing ligands) will affect partition 

coefficients. Many of the same observations made on 'model' 

systems in controlled laboratory studies have been observed in 

studies of natural aquatic sediments. (Gardiner 1974; Huang et 

al., 1977; Vuceta and Morgan 1978; Tada and Suzuki 1982; Brown 

1979). 

60
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 Organics Partitioning

Most organic contaminants of concern tend to be 

relatively hydrophobic, non-polar compounds. Such organic 

compounds tend to have strong affinity for natural aquatic 

particulate material, making solution/sediment distribution of 

these chemicals as important in predicting their fate and 

transport as it is for heavy metals. 

 The sorption of hydrophobic organics is considered by 

most researchers to be a true equilibrium partitioning between 

the water and sediments. The linearity of sorption 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 sorption of nonionic organic compounds (Chiou et 

al. 1982). This being the case, much emphasis on the 

characterization of this sorption process has been focused on 

the properties of the chemicals 

61
FIGURE 3-5. PARTITION COEFFICIENT AS FUNCTION OR SUSPENDED 

SOLIDS ALL METALS IN STREAMS 

62
related to their solubility and hydrophobicity 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 Freundlich or Langmuir isotherms previously 

described (Equations 3.9 and 3.10). For typical environmental 

pollutant concentrations, sorption isotherms in the 

sediment/water suspension are very close to linear and both 

Freundlich and Langmuir equations can be reduced to: 

Γ = vCd 

(3.13) 

where Γ = chemical concentration in solid phase (ug/mg) 

 Cd = chemical concentration in aqueous phase (ug/l ) 

v = partition coefficient (l /mg) 

The prediction of v for a given chemical/suspension 

system has relied on correlations with chemical solubility, 

octanol/water partition coefficients (KoW) and the organic 

carbon/water partition coefficient (KOC), coupled with the 

organic carbon content (weight fraction) of the sediments. In 

general, the more insoluble and hydrophobic a chemical it the 

more likely it is to have a larger v. Likewise, in comparing 

sorption of a given chemical among various sediments, the 

sediment with the highest organic content is likely to sorb 

the most chemical and produce the largest w. 

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; Chiou et al. 197/; Banerjee et al. 1980; 

Mackay et al. 1980). 

63
Given either the aqueous solubility (S) or the 

octanol/water partition coefficient (Kow) of a compound, a 

correlation can be developed between v and S or Kow for a range 

of chemicals. More often than not, however, recent 

experimental interpretations have included several sediments 

with a range of organic carbon content. Then, by dividing the 

measured partition coefficient (Kp) by the organic carbon 

weight fraction of the particular sediment (O.C.), a sedimentindependent partition coefficient (Koc) between the aqueous 

phase and the organic portion of the sediments may be 

obtained. 

(3.14) 

Table 3.2 contains a summary of empirical correlations for 

predicting sediment partitioning and biological partitioning 

(bioconcentration factors) of nonpolar organic compounds. 

Care must be taken in applying the above (or other) 

empirical correlations to unstudied systems. While these 

correlations will probably give reasonable estimates for 

application to a WLA problem, there are a number of potential 

pitfalls that should be considered. Some of these 

considerations are discussed below: 

1. These relationships are all log-log correlations; 

therefore, what may seem 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 could not be applied to semipolar or polar organic 

compounds, where electrostatic interactions may become 

significant (Pavlow, 1980; Leenheer, 1980). 

64
TABLE 3.2. CORRELATIONS BETWEEN Koc OR βF AND AQUEOUS SOLUB

OR OCTANOL/WATER PARTITION COEFFICIENT (Kow) REP

 RECENT LITERATURE (a) Bf = bioconcentration factor in trout 

(b) Bf = bioconcentration factor in fathead minnows, rainbow trout, blueg(c) Kpz = partition coefficient for a particular nonpolar molecule; fOC = 

Note that fish bioconcentration (given by βf) is very slow compared t

adsorption (given by Koc). 

65
3. The water chemistry of the aquatic system (in addition to 

the water concentration of the compound of interest) can 

alter the empirically predicted partition coefficient, for 

example, 1f the compound interacts by 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 hydrophobic compounds 

the presence of other dissolved organic matter has been 

shown to reduce sorption by river and sewage particulate 

matter (Hassett and Anderson 1982). 

4. Properties of the sediments other than their organic carbon 

content may influence sorption. Hiraizumi et al. (1979) 

found a good correlation between partition coefficients of 

PCB and the specific surface area of adsorbing marine 

particulates'. Of course, the concentration of adsorbing 

solids has been observed to affect partition coefficients 

for organics as well as metals (O'Connor and Connolly 

1980). 

5. Finally, the question of kinetics and hysteresis arises. In 

all adsorption-desorption problems. Karickhoff (1980) has 

found that the kinetics of approach to equilibrium in 

sediment suspensions (either during adsorption or 

desorption) could be characterized by 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 DiToro and Horzempa (1982) and DiToro et al. 

(1982), in that the lack of reversibility in PC8 

adsorption-desorption 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 is assumed 

irreversible for the second component. DiToro et al. (1983) 

presents the mathematical formulation for this model and 

shows the results of further laboratory testing. The 

practical difference between modeling with the classical 

reversible expression and modeling with the reversibleirreversible expression is that the former may predict 

higher dissolved concentrations, particularly in dynamic 

models. 

66
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 

coefficient: 

(3.15) 

where Kd = Aggregate degradation coefficient (l /day) 

 KB = Biolysis coefficient (1/day) 

 KH = Hydrolysis coefficient (1/day) 

 Kp = Photolysis coefficient (1/day) 

 Kv = Volatilization coefficient (1/day) 

Some models also distinguish non-biological oxidation, KO, 

separately, although this process is not important for most 

organics. 

In models with simple first-order kinetic structures 

(such as MICHRIV), the analyst either enters the aggregate Xd

or enter: the individual process coefficients KB, XH, Kp, and 

KV. In models with more elaborate kinetic routines (such as 

EXAMS), each individual first-order coefficient (Xβ, etc.) is 

internally calculated as a function of several other 

parameters which the analyst must enter. 

3.3.1 Biodegradation

Biological transformations (biolysis) are enzyme mediated 

reactions usually performed during metabolic activity, 

primarily by bacteria and fungi. The catalyzed transformations 

include oxidation, reduction, and hydrolysis. The rates of 

biologically mediated transformations can be very rapid in 

comparison to chemical transformations that lack enzymatic 

catalysts. It is precisely because of these accelerated rate; 

that the biogratuation of organic contaminants is often the 

most important transformation loss process in aquatic 

ecosystems. 

67
Conceptually, biodegradation 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 biodégradation, 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 Description

When heterotrophic 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 microbial community must adapt itself to the 

chemical. This acclimation period is most often termed a lag 

phase. The lag phase is marked by enzyme induction, selected 

population increases, and a progressive increase in the rate 

of observed biodégradation. 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 its structure, concentration, and 

history within its environment; 2) the characteristics of the 

acting microbial community, such as community diversity, size 

and general health; and 3) the status of the environment in 

terms of temperature, the presence of additional organics or 

supporting growth 

68
requirements and especially, the dissolved oxygen status: 

Within the framework of a model designed to describe the 

fate of an organic contaminant, it is necessary to formulate 

and define a kinetic expression of biodegradation. This task 

is made difficult because of the great complexity of factors 

inherent in a natural system. Contemporary fate models have 

simplified this task by representing the loss or decay of an 

organic by first-order kinetics or in some cases second-order 

kinetics. 

First-order kinetic representation in the WLA model is 

described as: 

(3.16) 

where k1 = First-order biodegradation rate constant (time-1) 

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, the respective particulate and dissolved 

fractions must be quantified. This expression describes the 

loss of an organic due to biological activity and is analogous 

to expressions commonly used for the decay of 800. 

Larson (1981), among others, has shown that first-order 

kinetics represent the decay of organics reasonably well at 

bacteria concentrations evident 1n many environmental 

situations. 

In many respects representation of second-order kinetics 

is a simplification of a modified Monod expression (Paris et 

al. 1981), represented as: 

(3.17) 

where β = Magnitude of bacteria (count or biomass/volume) and 

k2 = Second-order biodegradation rate constant 

 (volume/organism time). 

69
The decay of a contaminant is seen as not only a function of 

its concentration, as is the case in first-order kinetics, but 

also a function of the bacteria population. However, bacteria 

count has not always proven a reliable indicator of bacteria 

activity, especially in regard to a specific contaminant 

organic. This development in biodegradation 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 biodegradation is recognized as being 

influenced by ambient temperature, process representation can 

include a function relating the biodegradation rate to the 

temperature regime. The analyst may derive a temperature 

specific rate by making use of an Arrhenius function such as: 

(3.18) 

where KT = Temperature specific biolysis rate, 

 K20 = Expected rate at 20*

 centigrade, 

T = Characteristic temperature, and 

θ = Temperature correction factor. 

Theta (θ) is frequently between 1.04 and 1.095. 

Rate Selection

In nearly every circumstance the 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 is compared to incremental changes in the 

decay rate over the range of expected values. 

70
A list of relevant considerations or approaches to 

estimating a decay rate is 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 Mabey et al. (1982) have estimated the general 

susceptibility to biodegradation. 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 

toxicity, if intermediates form that are toxic and 

possess different decay characteristics. Therefore, the 

analyst would want information regarding the toxicity and 

biodegradability of probable intermediates. In cases 

where the knowledge of an organic is very sparse, it may 

be necessary to compare the structure and physical 

characteristics of the organic of interest to a host of 

better known contaminants. When approached in great 

detail, this procedure is called structure activity 

analysis. 

B. System Examination 

Information regarding the trophic level and pollution 

level of the target system may assist in defining the 

expected decay rate. More highly impacted waters have 

demonstrated shorter lag phases and greater decay rates 

of organics (Spain et al. 1981, Rodgers and Salisbury 

1981). Previously reported field data may, to varying 

degrees, yield insight into the spatial and time 

distribution of the 

71
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 biodegradation 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 organics 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 (Giddings 

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 microbial population, then 

the magnitude of the microbial population must be assessed. 

D. Field Program 

 

The Waste Load Allocation process may involve a field 

program. To assist in the accurate evaluation of a 

biodegradation rate, the measured parameters should include 

both total and soluble concentrations of the pollutant of 

interest, solids concentration, dissolved oxygen, C800, 

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 might influence bacterial activity. 

The spatial and temporal scale of sampling will impact the 

calibration and accuracy 

72
of the biodegradation term, as well as all 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 in terms of hydraulic 

residence (flow regime) and the major forcing functions

(temperature, loading, light, etc.). Some attempt to reflect 

the seasonal variation 1n forcing functions may be especially 

helpful. 

3.3.2 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 in the aquatic environment. Zepp (1980) 

provides a more complete discussion of this process. 

Process Theory

A summary of the theory Involving the transformation of 

organics via photolysis is outlined from Mills 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 in the ultraviolet and 

visible portions of the spectrum 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 depends on several chemical and 

environmental factors. 

73
The 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 ultraviolet 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 1s 

determined by Lambert's Law: 

where I = Irradiance

(3.30) 

 Sb = Backward scattering of light 

X = Diffuse light attenuation coefficient, given by:

 K = aD + sb

where a = Absorption term 

D = Radiance distribution function

74
The value of K (diffuse light attenuation function) depends 

on variations in amounts and types of particulates and dissolved 

substances, i.e., suspended solids, chlorophyll a, dissolved 

carbon. The value of D, which represents variable light path 

lengths, is 1.2 where scattering is ignored. Average value for 

natural waters is 1.6 as reported by Miller and Zepp (1979). 

An empirical relationship developed by Burns et al. (1981) 

enables the attenuation coefficient to be estimated based on 

system status: 

K = D(AW + AaChla + AdocDOC + ASSSS) (3.21) 

where K = Diffuse light attenuation coefficient 

 AW = Absorptivity of water, (m-1) 

 Aa = Absorptivity of chlorophyll a pigment, (mg/l) (m-1) 

 Adoc= Absorptivity of dissolved organic carbon, (mg/l)-1 

(m-1) 

 Ass = Absorptivity of suspended sediments, (mg/l)-1 (m-1) 

 Chla = Concentration of Chlorophyll-a pigment, (mg/l). 

 OOC = Concentration of dissolved organic carbon, (mg/l)and 

 SS = Concentration of suspended solids, (mg/ ι) 

Mills et al. (1982) tabulate the values of A W, Aa, Adoc, and Ass

for different wavelengths. 

D. Quantum yield: 

Not every absorbed photon induces a chemical reaction. The 

fraction of adsorbed photons resulting in the desired reaction 

is termed quantum yield, Φ. 

Φ= moles of given species formed or destroyed

 moles of photon absorbed 

75
Environmental factors affecting quantum yield include: 

a) molecular oxygen -- as a quenching agent. 

b) suspended solids -- change reactivity of compounds 

adsorbed (usually negligible). 

c) chemical speciation -- photolysis rates may vary with 

pH, especially important when pKa is 7 + 2. 

d) temperature effect -- until further research is 

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 it 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 absorbs light. Various reactions can occur: 

fragmentation, reduction, oxidation, hydrolysis, add-base 

reaction, addition, substitution, isomerization, 

polymerization. Quantum yield data obtained from 

experimentation can assist the WLA analyst in determining 

whether or not to include direct photolysis in the analysis. 

Sensitized (indirect) photodegradation occurs when a 

light-absorbing molecule transfers its excess energy to an 

acceptor molecule causing the acceptor to react as if it had 

absorbed the radiant energy directly. Natural humic acids (and 

synthetic organic compounds) can mediate such reactions, for 

example. 

Rate Estimation

Photolysis follows a psuedo-first-order reaction: 

76
where Kp = Rate constant = Kd + Ks (Time-1), 

 Kd = Direct photolysis rate, and 

 Ks = 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 in 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): 

where K d = Direct photolysis rate constant (day-1), 

 K do = Near surface rate constant (measured) (day-1), 

I = Total solar radiation (langleys-day-1), 

Io = Total solar radiation under conditions at which K do

was measured (langleys-day), 

D = Radiance distribution function, 

Do = Radiance distribution near surface (approximate 

value = 1.2), 

K(λ*) = Light attenuation coefficient calculated from 

Equation 3.20 for λ*, the wavelength (nm) of maximum 

light adsorption, and 

Z = Depth of water in meters. 

The second method involves evaluating the rate constant 

integrals. if certain data are available for a substance 

(i.e., absorption spectrum c(λ) or as(λ), and the quantum 

yields, Φd. or Qs), it is possible to estimate the photolysis 

rate for a specific site from the following (Mills et al. 

1982): 

77
where l = wavelength interval index, 

W = Photon irradiance near surface (photons cm-2 sec nm-1) 

W' = W - ∆ λ

j = Conversion factor = 1.43 x 10-16 (mole cm3 sec nm-1

day -1) 

c = Base 10 molar extinction coefficient (ιmo1-1 cm-1 of 

toxicant), 

Φ = Disappearance quantum yield. 

K = Diffuse light attenuation near surface (m-1), 

Z = Mixed water depth (m) and, 

£ = Base e absorption coefficient of the sensitizer (mg1 cm). 

For toxicants for which photolysis may be significant, Mabey 

et al. (1982) 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 likely alter 

other characteristics of the chemical including its 

susceptability 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. Microbial influence is covered in Section 3.3.1; 

consequently only direct, abiotic hydrolysis will be examined 

here. 

78
Abiotic hydrolysis is normally represented by a first 

order reaction which in its most simplified form is: 

where 

C = Concentration of an organic (Mass/volume) and, 

KH=Specific first-order hydrolysis rate constant(Time-1). 

In the scientific literature KH is typically represented as: 

where 

Kn = Neutral hydrolysis rate constant (Time-1), 

ka = The add catalyzed hydrolysis rate constant (Molar-1

Time-1), 

kb = The base catalyzed hydrolysis rate constant (Molar-1

Time-1), 

[H+

] = Molar concentration of hydrogen ions and, 

[OH-] = Molar concentration of hydroxide ions. 

This representation conveys the strong pH dependence 

often observed in hydrolysis reactions and is a convenient 

method of representing detailed laboratory results. 

The adsorption of an organic onto solids often removes 

the particulate 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 

particulate phases, then the observed partitioning should be 

used in adjusting the magnitude of the rate constant. 

Rate Selection

A great deal of data has been reported in the chemical 

literature regarding the observed hydrolysis of chemicals in 

distilled water. Natural waters, however, contain organics and 

metals which may catalyze and accelerate hydrolysis. 

Consequently, the query which consistently 

79
emerges is, how applicable are distilled water rates to field 

conditions? Research designed to answer this question has been 

reported within the last several years (e.g., Zepp 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 ultrafiltration to remove the biological community, thereby 

eliminating biolysis. 

Specific hydrolysis coefficients for many organics or 

classes of compounds are reported 1n the professional, 

governmental, and industrial publications. Recent sources 

include Wolfe (1980), Mabey and Mill (1978), and Mabey 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 is 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 input, it should be noted that some values are reported 

as second-order coefficients because they are a function of 

either the hydrogen or hydroxide ion 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 ion 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 its mathematical similarities to 

these processes. Actually, 

80
however, it 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 the chemical as well as the 

characteristics of the water body and possibly the atmosphere. 

The chemical properties favoring volatilization are high vapor 

pressure, high diffusivity, and low solubility. The 

environmental conditions favoring volatilization are high 

surface-to-volume ratio and turbulence. 

The partitioning of pollutant between water and air is 

described in terms of an air/water partition coefficient, Hc: 

Hc = Cg(eq)/Cd(eq) (3.27) 

Where: 

Hc = Henry's law constant (dimensionless, mass/vol. basis), 

Cg (eq) = Gas phase concentration at equilibrium (mg/ ι), and 

Cd(eq) = Dissolved aqueous concentration at equilibrium (mg/ 

ι). 

The value of Hc can be determined by measuring Cg and Cd

in an equilibrated system. More commonly, however, it is 

calculated from the toxicant vapor pressure (equivalent to the 

gaseous concentration in equilibrium with the pure toxicant 

phase) and solubility (aqueous concentration in equilibrium 

with the pure toxicant phase): 

Hc = 16.04 PM/TS (3.28) 

where P = Vapor pressure (torr), 

 M = Molecular weight (g/mole), 

 T = Temperature (K°), and 

 S = Solubility (mg/ ι). 

81
It should be noted that Hc may be reported in an 

assortment of units or nonequivalent dimensionless bases. One 

useful conversion is: 

Hc (dimensionless) = Hc (atm - m3

/mole)/RT (3.29) 

where R = 8.206 x 10-5 atm - m3

 /°K - mole. For the organic 

priority pollutants the values for P, M, and S in Equation 

3.28 are provided by Callahan et al. (1979), and the values of 

Hc provided directly by Mabey et al. (1982). For other 

substances data may be available in Mills et al. (1982), Perry 

and Chilton (1973), and Mackay et al. (1982). If vapor 

pressure data are not available, Mackay et al. (1982) suggests 

the following equation for estimating P (torr) for 

hydrocarbons or halogenated hydrocarbons with boiling point 

greater than 100°C: where:

T = Ambient temperature (K) 

TB = Boiling point (K) 

TM = Melting point (K) 

If the melting point TM is less than the ambient temperature T, 

than the third term is eliminated. 

The net rate of transfer (mg/ ι - day) from water to air 

is governed by the difference between (a) the gross transfer 

from water to air, proportional to the actual dissolved 

concentration Cd and (b) the gross transfer from air to water, 

proportional to the air concentration, Cg: 

Rate: -Kv(Cd-Cg/Hc). (3.30) 

where KV = Volatilization rate coefficient (1/day). 

82
The term Cg/Hc is the water concentration which would be 

in equilibrium with (saturated with respect to) the local air 

concentration. Unlike common gases like oxygen, the 

environmental concentrations of toxicants, Cd and Cg /Hc, 

typically vary over many orders of magnitude. Consequently, it 

is usually the case that either (a) Cd = Cg /Hc and the net 

input from the atmosphere is a constant load, essentially 

independent of the modeled Cd, or (b) Cd » Cg /Hc, and the 

volatilization rate is essentially independent of the air 

concentration: 

Rate = kyCd (3.31) 

Most computer models incorporate Equation 3.31 rather than 

Equation 3.30. 

The rate coefficient Ky (1/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 film and a 

gas surface film as controlling the mass transfer (Canale and 

Weber 1972; Liss and Slater 1974; Mills et al. 1982). 

Reciprocals of mass transfer coefficients are used to 

represent these resistances: 

 

where kl = liquid film transfer coefficient (m/day), and 

 kg = gas film transfer coefficient (m/day). 

83
It is useful to discern three basic cases, (a) When kl « 

Hc kg, then Ky in Equation 3.33 is essentially equal to kl

(liquid phase controlled); (b) when kl = Hc kg, then kv is 

essentially equal to Hc kg (gas phase controlled); and (c) when 

kl and Hc kg are of the same magnitude, then both contribute 

significantly to kv. 

As the chemical-to-chemical variability of Hc tends to be 

greater than the site-to-site variability of kl and kg, the 

value of the Hc tends to be more important than the 

environmental conditions in determining whether the liquid or 

gas phase resistance controls the volatilization rate. 

Gas Phase Resistance

The movement of air causes a mixing of the air surface 

film which results in an increase in kg. Because the 

evaporation of water is controlled by kg, and because this 

process has considerable engineering importance, data are 

available relating kg (for water vapor) to the ambient 

windspeed. Such data are presented by O'Connor (1960) and 

HydroQual (1982). By including theoretical effects of 

diffusivity and viscosity, they arrive at an expression 

applicable to any substance: 

kg = 0.0001 (Dg/Vg)0.67 W (3.34) 

where Dg = Diffusivity of substance in air (cm2 /sec), 

 Vg = Kinematic viscosity of air (= 0.15 cm2 /sec), and 

 W = Wind speed (L/T). 

As the expression is dimensionally correct, consistent 

units will result in kg having the same units as W. Average 

windspeeds tend to be in the neighborhood of 5 m/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 3.35) may 

not produce a realistic result. 

84
Mills et al. (1982), using a similar type of data and 

analysis as O'Connor (1980) and HydroQual (1982), suggest the 

general relationship: 

kg = 170 (18/M)1/4 W (3.35) 

where W is in m/sec. 

Molecular weight, M, enters the expression because of its 

relationship to diffusivity. For water vapor, Equation 3.34 

and 3.35 are nearly identical. Since Dg/vp = 1.6 for water 

vapor equation 3.34 reduces to kg = 140W while Equation 3.35 

becomes kg = 170W. Thus, at a typical wind speed of 5 m/sec, kg

(water vapor) would be around 700-800 m/day. 

Liquid Phase Resistance

In the environment the transfer of oxygen, a well studied 

WQ parameter, is always controlled by kl, the liquid transfer 

become coefficient, rather than Kg, the gas transfer 

coefficient. Because Hckg(O2) >> kl (O2), equations 3.32 and 

3.33 become where Kl (02). in 1/day, is the commonly used 

reaeration coefficient. Consequently, it is useful to relate Kl

(toxicant) to Kl (O2), through an expression involving liquid 

diffusivities: 

where n may vary from 0.5, in flowing waters, possibly up to 

1.0 in standing waters (Mills et al. 1982). 

Mills further suggests that Dl is inversely proportional to the 

square root of molecular weight, and thus, for flowing waters: 

85
Revised 

10/85 

Mills notes, however, that in field studies using radioactive 

tracers (Rathbun and Tai 1981), such relationships were 

difficult to discern. Rather, the volatilization rate could be 

adequately predicted by: 

 kl (toxicant) = 0.655 kl(O2) (3.39) 

Mabey et al. (1982), using a more complicated procedure 

relating Dl 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, kl(O2), correctly. This coefficient is a 

function of water turbulence, which may be generated either by 

water flow or by wind. 

In free flowing rivers, water turbulence is generated by 

the flow, and numerous formulas are available for calculating 

kl(O2) (i.e., kv(O2)) from hydraulic parameters such as 

velocity, depth, and slope. Wilson and Macleod (1974) and 

Rathbun (1977) review many of the reaeration formulas which 

have been proposed over the last three decades. One example of 

such a formula is that of O'Connor: 

kl = (=Dl) u/H)0.5 (3.40) 

where u is stream velocity and units for parameters on both 

sides of the equation are chosen to be consistent. 

Dl for O2 is 1.81 x 10-4 m2

 /day. The equation can be used to 

directly calculate kl (toxicant) if Dl (toxicant) can be 

estimated. 

In impounded waters and other slow moving water bodies, 

water turbulence may be generated by wind. O'Connor (1980) and 

HydroQual (1982) summarize data relating kl to wind speed, W. 

These dates suggest a relationship: 

 kl =(Dl u/h)0.5 (3.40) 

86
where CD = Drag coefficient (unitless), and 

v ι = Kinematic viscosity of water (=0.0100 cm2 /sec). 

The units of all other parameters must be chosen to be 

compatable. CD also appears to vary with windspeed, W, but may 

maintain a value around 0.001 for W less than 10 m/day. As 

with using Equations 3.34 and 3.35, sustained periods of 

little or no wind are not common; kl(O2) values substantially 

less than about 0.5 m/day are not usually expected. Table 3.3 

illustrates parameters needed to determine a wind controlled 

volatilization rate for two toxicants. 

Hydroscience (1971) and EPA (1976) present data and a 

nomograph for estimating kl(O2) for a variety of hydraulic 

conditions. Their data suggest that kl (O2) 

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. 

Identifying the Important Parameters

Equation 3.33 can be examined in light of the observed 

relationships of kl and kg versus windspeed, and the reasonable 

range of kg suggested by Hydroscience (1971) and EPA (1976). 

Some simplifications of the two film analysis are thereby 

indicated. 

If Hc is less than about 3 x 10-4, then the gas phase 

coefficient will control the overall transfer coefficient Hv in 

all aquatic environments, even standing waters. This is 

because Hc kg will increase much more slowly than kl as a 

function of windspeed. In 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, 

dieldrin, and pentachlorophenol are examples of compounds in 

this class. 

87
Revised 

10/85 

TABLE 3.3 VOLATILIZATION PARAMETER VALUES 

 

87.5
If Hc is greater than about 3 x 10-1, then the liquid 

phase coefficient kl will control the overall transfer 

coefficient kv under nearly all conditions, even when the water 

is very turbulent (kl (O2) = 12 m/day) and the air calm (W = 2 

m/sec). For Hc in this range the analyst need not consider the 

air phase parameters. It is also important to note that among 

substances with a high Hc, the volatilization rate is 

independent of Hc; rather, it is dependent on the substance's 

water diffusivity. As diffusivities 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 tri-and tetrachloroethylene. 

If Hc falls between about 3 x 10-4 and 3 x 10-1, there can 

be some environmental conditions under which resistance in 

both the liquid and the gas phase controls the rate of 

volatilization. Nevertheless, under other environmental 

conditions only one phase may still control the overall rate. 

For example, under conditions of moderate turbulence 

(kl (O2) = 2 m/day) and wind (W = 5 m/sec), the liquid phase 

solely controls for any Hc greater than about 2 x 10-2. 

88
SECTION 4.0 

GUIDANCE FOR MODEL APPLICATION 

4.1 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 is, "How much of a specific 

substance can be allowed to be discharged in a receiving 

water, yet not 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 desire and legal means to allow use 

of the assimilative capacity of water systems. The models may 

assist in choosing some optimal mix of treatment, production 

modification, standard modification, and time schedule for 

Implementation. It is important that 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 within a cooperative rather than 

adversarial context. Considering the level of uncertainty 

inherent in estimating allowable ambient concentrations, 

allowable recurrence intervals, and allowable effluent

loadings, such agreement may be helpful for successfully 

completing the endeavor. 

89
FIGURE 4.1 WASTE LOAD ALLOCATION PROCESS 

90
An important issue confronting WLA analysts and managers 

concerns the 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 hydrologically 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 WLA 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, it is 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 that 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 USGS or previous pollutant reports for the area, or 

they may be estimated from the drainage area. Upstream 

concentration may be available from STORET or other water 

quality records; in many cases, the upstream toxicant 

load may be nearly zero compared to the effluent load. 

 

91
Stream concentrations 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 DiToro 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 if (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 the dilution calculation. 

Phase 2: Downstream Estimates

The purpose of Phase 2 would usually be to estimate 

the spatial extent of the problem or, for multiple 

discharges, to estimate the additivity of loads. Beyond 

Phase I, this entails predicting the downstream behavior 

of pollutants using a fate and transport model (such as 

described in Sections 2.5 and 2.6). The model's input 

parameters 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, IFO, 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 

summarizes the contents of these data bases and how to 

obtain access. 

 

 

92
93
94
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 are 

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 may often produce data 

that are impossible 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 model predictions 

and field observations for one 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 is to suggest & realistic, achievable data 

collection plan. 

The reader is referred to Book II, Streams and Rivers, 

Chapter 1: Biochemical Oxygen Demand/Dissolved Oxygen and 

Ammonia Toxicity, 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 is 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 WLA 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 Model Input Data 

Table 4.2 summarizes 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; in 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 resuspension velocities, and bed characteristics) 

are needed for pollutants which readily adsorb to 

participates. Degradation rate data are needed for organic

pollutants, depending on which processes (hydrolysis, 

photolysis, etc.) are applicable to the particular compound. 

References like Callahan et al. (1978), Mabey 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-specific 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 flow with little dilution. 

b. high flow, with nonpoint loadings and 

sediment resuspension. 

c. periods critical to fish survival. 

5. Production and treatment schedules and cycles. 

 

97
98
99
100
Whenever possible point source surveys should be 

scheduled for seasons when the system is likely to be most 

stable, unless specifically designed to evaluate time 

variability. 

4.2.2 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 2.5 

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 1s 

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 is considered best if a single set of decay, 

partition, and sediment exchange coefficients fit both 

(or all) surveys adequately; however, it may be the case 

that some coefficients may need to vary between surveys, 

as illustrated in the Flint River study. If this is the 

case, then it is 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 several days. It may include some master station 

to 

101
discern diurnal variations, particularly for those 

organic compounds which photplyze readily, and for sites 

where waste flows comprise a large fraction of the river 

flow. Station locations depend on 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 might 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 in 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. 

While the supporting site-specific data are a key element 

of any WLA 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 its input values, and the general 

understanding demonstrated by the analyst are at least as 

important. 

102
Model 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. 

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 analysis 

shows the sensitivity of the results to errors in 

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 distributing 

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 firstorder variance propagation, the variance in the output 

distribution is calculated directly from variances of the 

input distributions. Burges and Lettenmaier (1975) 

illustrate application of both techniques to 800-00 

models; Scavia et al. (1981) illustrate their application 

to eutrophication 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 values for the calibration/ 

103
verification runs of 15 dissolved oxygen models indicated 

an overall median relative error of 10%. Median relative 

error of individual models ranged from a few percent to 

about 60 percent. For a eutrophication model of Lake 

Ontario with complex kinetics and fine spatial scales, 

median relative error over a 10-year simulation period 

for 5 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 is the computed 

average concentration. This statistic, it should be 

noted, behaves poorly for small x, and tends to weight 

overprediction more heavily than underprediction. 

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 Flint River case study, 

the calibration/verification accuracy seemed quite 

satisfactory by conventional WLA 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 be difficult to estimate to what 

degree lower stream flow and improved effluent quality 

will affect parameters such as the settling and 

104
resuspension velocities (flow and particle-size 

dependent) or partition coefficient (also particle-size 

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 is beyond the scope of this 

volume (apparently along with the other volumes of this 

Manual, thus far). Chapra and Reckhow (1983), however, 

provide a more thorough treatment of the subject. 

In actual WLA 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 firstorder variance analysis has been set up for the 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 in 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: 

105
 a) Hardness and alkalinity: to interpret toxicity 

and determine metals criteria. 

b) Conductivity: to confirm transport. 

c) Total organic carbon. 

d) Dissolved oxygen, ammonia, and chlorine residual: 

to interpret toxicity and biotic status. 

e) Qualitative description of sediment bed: to 

support estimates of bed/water exchange. 

f) Concentration of pollutant in biota: to indicate 

long term exposure. 

Furthermore, it 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 is violated for a certain 

time period may provide no indication of how the 

integrity of the ecosystem is being affected. In addition 

to demonstrating the impairment of use, a biological 

survey, coordinated with a chemical survey, can help in 

identifying the culprit pollutants and in 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 Assurance

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 

WLA analyst should meet with the laboratory director and 

quality insurance officer to 

106
request a quality assurance proposal. The proposal should 

consider sample collection, handling, preservation, 

preparation, and analysis. Of particular concern to the 

WLA analyst would be the detection (quantitation) limit 

for each toxicant. 

Some production laboratories, although very 

reputable, may not report concentrations at levels at or 

below criteria limits because doing so requires 

additional care and quality control, reduces the 

productivity in terms 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 

WLA 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 to prevent 

sample contamination from collection devices and 

containers. This is not a trivial concern. 

Samples that will be filtered for particulate 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 in the data base. 

4.3 FORECASTING 

The purpose for developing a site-specific model is to 

forecast the environmental consequences of pollution 

abatement alternatives. Environmental goals for a stream 

reach are, of course, embodied in the beneficial uses 

designated in its water quality standards. The water 

107
quality needed to protect the designated uses may be specified 

as numerical criteria, which indicate acceptable chemical 

concentrations (if known). Criteria are generally derived from 

laboratory tests in which particular species are exposed 

continuously to a toxicant. As the tested concentrations do 

not vary over time, it is 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 

degradative 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 800 and ammonia WLAs, 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 the 7Q10 low flow. 

In judging pollution control alternatives within such a 

framework, the measure of effectiveness generally applied is 

the change in concentration during the single rare event. 

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. 

108
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 decisionmaker 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 framework 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 in all 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 1n terms of the probability of exhibiting discrete 

values. Correlations between parameters may need to be taken 

into account. 

109
 A Monte Carlo simulation can then be performed by 

randomly selecting model input values from the assigned 

distributions. By tallying the water quality predictions 

resulting from each set of randomly selected inputs, the 

overall distribution of resulting water quality is generated. 

A simple illustration of applying this procedure to a few of 

the input parameters for a model of a stream with two 

dischargers is shown in 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 DiToro 

(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 SERATRA. A several year sequence of flow, 

temperature, loading, and other input is used to generate a 

time sequence of water quality, which may be summarized into a 

frequency plot or possibly evaluated in other more 

toxicologically relevant ways. While daily records for flow 

are usually readily available, time sequences for other model 

inputs may be more difficult to construct. 

Compared with the deterministic analysis 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). 

110
111
In situations where rules require that the WLA be designed for 

a particular flow, such as the 7Q10, the Monte Carlo technique 

can be applied to all input parameters except flow. Control 

alternatives can then be evaluated in terms 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 1s made automatically, grounded more on 

past precedent than on technical rationale. Its level of 

protection, however, may vary from site to site somewhat 

haphazardly, unrelated to use attainment. If such a 

conventional design condition is not used, the level of 

protection may become a technical question; that is, 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 term (possibly life-time) average exposures. 

If 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 is 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 

exceedance frequency is 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 species is less constrained 

112
in the field than in the laboratory, relatively little 

technical data can be brought to bear on the question. In the 

past the question of exceedance frequency has probably not 

received the attention it deserved. It should be recognized 

that the uncertainty in the entire waste load allocation 

analysis is 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 Ile, Michigan, in developing and applying a 

toxic substance model to heavy metals in the Flint River and 

PCB surveys and model development for Saginaw 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 major discharges. 

2. 50-mile river reach. 

3. Three metals and three organic compounds. 

4. Sampling points at bridges. 

5. Organic substances 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 WLA analyst, office 

support, and the laboratory and field personnel. The estimates 

exclude standards promulgation, permit negotiation, 

management, and overhead. 

113
 The resource estimates are summarized in Tables 4.3 and 4.4. 

It is obvious that the most costly component 1s 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 nonpoint 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
TABLE 4.3 RESOURCE ESTIMATES FOR WASTE LOAD ALLOCATION AN

(See text and Table 4.3A for Assumptions and Related C

115
TABLE 4.3 RESOURCE ESTIMATES FOR WASTE LOAD ALLOCATION ANALYSES

(See Text and Table 4.3A for Assumptions and Related C

116
TABLE 4.4 RESOURCE ESTIMATES MONITORING RESOURCES (MAN 

117
118

TABLE 4.4 (Cont'd.) 
SECTION 5.0 

CASE STUDY: MODELING HEAVY METALS TRANSPORT IN THE FLINT 

RIVER 

5.1 INTRODUCTION 

The Flint River project, discussed in 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 MICHRIV model. This section contains 

the results of application of the MICHRIV 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 with the Flint data set. The project also 

serves as an example of data acquisition methods for the 

application of the model to a WLA problem. 

Section 5.2 describes the study reach of the Flint River. 

Sections 5.3, 5.4, and 5.5 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, is a 

major tributary to the Saginaw River, a major tributary to 

Saginaw Bay. The Saginaw watershed had been identified as one 

of several national priority sites. The Flint River is also 

considered a high priority site for development of toxicant

WLA procedures by the Michigan Department of Natural Resources 

(MDNR). 

The Flint River watershed occupies 3,500 square 

kilometers (Figure 5.2.1) and contains significant 

agricultural and urban development. The north and south 

branches of the river join in Lapeer Counts and flow in a 

 

119
FIGURE 5.2 FLINT RIVER BASIN

120
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 in the summer months. Downstream from the City of 

Flint, the river flows northwest before joining the Shiawassee 

River in Saginaw County. Municipalities downstream of Flint 

include Flushing, Montrose, and 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 city of Flint, the reach selected 

for the model application was the 60 kilometers from Mill Road 

(Km 71.9) to Cresswell Road (Km 11.0). This reach, shown 1n 

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 five point source 

discharges were sampled during the two week survey. A list of 

the stations, their distance from the river mouth in 

kilometers and the sampling schedule for each station are 

presented in Table 5.3.1. The August survey is, 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 in the

121
122
TABLE 5.3.1 SAMPLING STATIONS FOR AUGUST, 1981 FLINT 

RIVER HEAVY METALS SURVEY

 

123
field. Hardness, suspended solids and total and filtrable 

zinc, cadmium and copper were analyzed at the Grosse Ile 

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 time-of-travel information (Cummings and Miller 

1981). 

5.3.1 August Survey Data 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 USGS gaging stations 

near Flint and Fosters (Figure 5.3.1) illustrate the event. 

The water quality in the river responded predictably to the 

event, as illustrated by the hydrograph and various time 

profiles at Station FR08 (Figure 5.3.2). Suspended solids and 

particulate 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 resuspension 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 

hydrological 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 from 

the Mill Road station (FR06) to the Cresswell Road station 

(FR16) for the four day steady-state, low-flow period in 

August. These values have been established primarily from 

measurements made by USGS (Cummings and Miller 1981) during 

the August survey. 

 

124
FIGURE 5.3.1 HYDROGRAPHS OF THE FLINT RIVER NEAR FLI

 AND NEAR FOSTERS, AUGUST 4 -15, 1981

125
FIGURE 5.3.2 FLINT RIVER WASTE LOAD ALLOCATION SURV

126
TABLE 5.3.2 SEGMENTATION, FLOWS, AND GEOMETRY FOR FLINT 

RIVER DURING AUGUST 4-7, 1981 

 

 

127
For this model application the river reach from Mill Road 

to Cresswell has been divided into 9 segments. The 

segmentation was primarily governed by location of point 

sources and tributaries, although changes in river geometry 

also contributed to segment boundary selections. The 

segmentation is also presented in 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 in 

Table 5.3.3. The two municipal plants represent the major 

source of metals to the river. Or total metal loads are 

reported, because equilibrium partitioning with solids is 

assumed. It should be noted, however, that in reality the 

metals discharged from the Flint plant were primarily in the 

dissolved phase while those from the Ragnone plant were 

primarily in the particulate 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 since 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. 

Given the input data presented 1n the previous section, 

the only parameters at one's disposal for calibrating the 

suspended solids submodel are suspended solids settling rate 

(ws), solids resuspension rate (wrs), and solids concentration

in the bed (m2). Since the solids entrapment rate or 

resuspension flux (g/m2

 -d) is the product of wrs and m2, an 

extra degree of freedom exists in the model framework. This 

degree of freedom was eliminated by fixing the bed solids 

concentration based on direct measurements. 

128
TABLE 5.3.3. INITIAL CONDITIONS AND POINT SOURCES TO FLIN

MEAN LOADS FOR AUGUST 4-7, 1981

129
The typical river bottom will have a water content 

between 60 and 95% by weight; therefore, if the solids have a 

specific gravity of 2.5, the solids concentration in the bed 

will 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 m2 = 200,000 mg/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 resuspension rate (wrs) 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 ws 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 

ws. The calibration with ws = 0.25 m/d is shown in Figure 

5.3.3a. 

The calibration in Figure 5.3.3a is quite good until just 

downstream of the Ragnone treatment plant (about Km pnt. 35). 

From this point downstream it seems that the model 

underpredicts suspended solids. One possible explanation is 

that resuspension was occurring in the lower portion of the 

river. By applying a very small entrapment rate of 4.0 g/m2

 -d 

in segment 7-9 (Km 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 entrainment rate corresponds to 

a resuspension velocity of 2.0 x 10-5 m/day. 

Justification for applying a resuspension factor in 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 among segments of the river. 

Lee et al. (1981) list five factors on which resuspension 

depends: (1) turbulent shear stress at the sediment-water 

interface; (2) water content of bottom sediments; 

 

130
KM FROM MOUTH 

 

FIGURE 5.3.3 a & b 

131
(3) composition (mineralogy, organic content, size 

distribution) of sediments; (4) activity of benthic organisms; 

and (5) 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 resuspension 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 in a particular river may be predicted from an 

empirical relationship between entrapment rate and shear 

stress. 

In the case of the Flint River the best justification for 

increasing the resuspension 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 is 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/cm2

. 

It should be noted, nevertheless, that resuspension 

is not the only possible explanation for increasing suspended 

solids profiles in rivers. Growth of phytoplankton biomasss 

can also produce this phenomenon, with each additional 5 μg/ ι

chlorophyll-a equivalent to 1 mg/l suspended solids (Canale 

1983). Unlike resuspension, phytoplankton growth should 

increase suspended solids concentrations without increasing 

total metals concentrations. While both interpretations seem 

compatible with the 

132
TABLE 5.3.4. BOTTOM SHEAR STRESS IN SEGMENTS OF FLINT RIVER 

DURING AUGUST 1981 STEADY STATE PERIOD

133
August survey data resuspension would be a more viable 

explanation during the winter surveys (described later). For 

the Flint River the model's overall results are not 

particularly 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 field data indicate that the partition 

coefficient for zinc should fall between 0.1 and 0.3 l/mg. 

There is a great deal of variation in observed cadmium 

partitioning; this variation, between about 0.05 and 0.45 

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 is 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 in 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 91%, 84%, and 75% dissolved phase for zinc, cadmium, 

and copper, respectively. As the MICHRIV model does not 

consider adsorption

134
FIGURE 5.3.4 

135
FIGURE 5.3.5 

136
kinetics, the way to handle 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 

are presented in 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 83% of the Ragnone copper discharge was in a 

participate phase. 

It is encouraging to note 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 

Saginaw Bay modeling effort (Dolan and Bierman 1982). Even the 

absolute calibration values were quite similar. The 

calibration values for Saginaw Bay were 0.225, 0.145, and 0.05 

l/mg for zinc, cadmium, and copper, respectively. The Flint 

River ultimately flows into Saginaw Bay via the Shiawassee and 

Saginaw Rivers. 

5.3.3 August Survey Sensitivity Analysis

As indicated above, the main calibration coefficients for 

metals in the Flint River are the suspended solids settling 

(ws) and resuspension (wrs) 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. 

137
KM FROM MOUTH 

FIGURE 5.3.6

138
KM FROM MOUTH 

FIGURE 5.3.7

139
FLIMT (8/4 - 7/81) 

KM FROM MOUTH

FIGURE 5.3.8

140
FLIMT (8/4 - 7/81) 

FIGURE 5.3.9 

141
FLINT (8/4 - 7/81) 

KM FROM MOUTH FIGURE 5.3.10

142
TABLE 5.3.5. CALIBRATION VALUES OF PARTITION COEFFICIENTS IN FLINT RIVER 

DURING AUGUST 1981 STEADY-STATE PERIOD

 

143
 Results of varying the above model coefficients have been 

evaluated in terms of percent change of total and dissolved 

metal concentrations (and suspended solids when applicable) in 

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 

(ws). (One hundred percent on the x-axis represents the 

calibration value of ws for the August survey.) Suspended 

solids is the most sensitive state variable; with a value of ws

= 0 overpredicting the suspended solids concentration by a 

factor of 2. No solids settling would lead to an 

overprediction of total zinc by 50 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 in 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 in water column 

partitioning, unless w 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-axis over 

which the coefficient perturbation is 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 35, about 5 km downstream from the Ragnon 

discharge; and (4) kilometer point 10, about 30 kilometers 

downstream of Ragnone. For the settling 

144
Model Response to Change in ws

 

Parameter (% of Calibr.) 

FIGURE 5.3.11

145
Model Responses to Change in π1 

Parameter (% of Calibr.) 

FIGURE 5.3.12 

146
velocity and partition coefficient, the results are 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 river 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 the river by eliminating much of the 

confounding influence of diurnal loading variations. 

Conducting the December survey in this manner provided model 

calibration, data in a shorter period of time and 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, in 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 1t 

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
TABLE 5.3.6. MODEL SENSITIVITY TO VARIATION OF SUSPENDED SOLI

VELOCITY (ws) FOR THE FLINT RIVER AUGUST 4-7, 1

*Value not determined. 

148
TABLE 5.3.7. MODEL SENSITIVITY TO VARIATION OF WATER COLUMN 

COEFFICIENT FOR THE FLINT RIVER AUGUST 4-7, 1

*Value not determined. 

149
TABLE 5.3.7. MODEL SENSITIVITY TO VARIATION OF WATER COLUMN 

COEFFICIENT FOR THE FLINT RIVER AUGUST 4-7, 1

*Value not determined. 

150
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 fluorometric 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, 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 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 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 runs, as 

indicated in the figure. 

A record of the dye slug time-of-travel and sampling 

times for both sampling runs has been reconstructed in Table 

5.4.2. A very fortunate occurrence 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 fluorometric 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, 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 H-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 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 runs, as 

indicated in the figure. 

A record of the dye slug time-of-travel and sampling 

times for both sampling runs has been reconstructed in Table 

5.4.2. A very fortunate occurrence is evident from this table. 

The river flow conditions were 

152
TABLE 5.4.2. COMPARISON OF DYE CLOUDS TIME-OF-TRAVEL WITH SAMPLING SCHEDULE 

FOR DECEMBER, 1981 SURVEY OF FLINT RIVER 

Dye dumped at Grand Traverse Street (Km. Pt. 79.3) at 7:00 a.m. on 12/1/81 and 12/3/81.

153
very 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 time-of-travel over the study reach from 

Mill Road to M-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 km/hr (0.653 m/s) 

and 2.25 km/hr (0.625 m/s) for runs 1 and 2, respectively. 

Based on discharge measurements, time-of-travel data, and 

cross-sectional area data provided by USGS, the river reach 

from Mill Road to Cresswell Road was segmented and the 

hydrological 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 is 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. SEGMENTATION, FLOWS, AND GEOMETRY FOR FLINT RIVER 

DURING DECEMBER 1981 SURVEY 

155
TABLE 5.4.3. (Cont'd.) 

156
TABLE 5.4.4. UPSTREAM CONDITIONS AND POINT SOURCES TO FLIN

FOR RUN 1 DECEMBER 1-2, 1981 

157
TABLE 5.4.5. UPSTREAM CONDITIONS AND POINT SOURCES 10 FLIN

FOR RUN 2 DECEMBER 3-4, 1981

158
model would perform under similar river flow conditions with 

drastically different loads - an exercise often required in 

performing waste load allocations. Finally, it should be noted 

that once again the metals discharged from the Flint outfalls 

were primarily dissolved, while those from the Ragnone 

discharge were largely particulate. 

5.4.2 December Survey Model Calibration

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 ws and wrs; this 

step was followed by calibration of the metals system using 

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 particulate 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 in greater bottom shear stress. 

Assuming that the other factors governing entrapment are the 

same, the December solids resuspension velocities (wrs) should 

also be greater than those determined in August. Depending, of 

course, on the magnitude of change in ws and wrs, 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 in the higher 

flow river system, the longitudinal distribution of suspended 

159
solids in December will not exhibit as great a variation as 

was observed in August. 

The hypotheses presented in the above paragraph were 

largely confirmed by the calibration of the model to the 

December run 1 data. Calibration values for ws and wrs for each 

f 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 

settling velocity, ws, among segments. Because of the greater 

uncertainty in ascertaining the factors governing sediment 

erosion, it was felt that there would more likely be 

intersegment variability in wrs than in ws. A settling rate of 

0.6 m/d (as opposed to 0.25 m/d in August) does not seem 

unusually high 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 μm compared with 1.8 μm for 

a settling velocity rate of 0.25 m/d. 

The bottom shear stress in the various river segments 

calculated in the same manner as in the August survey ranged 

from 25 to 54 dynes/cm2. 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/cm2

, range 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 resuspension values for 

December are greater in each segment (see Table 5.4.6) and 

greatest in 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 et al. 

(1981). The downstream segments of the Flint River pass 

through an almost exclusively agricultural area, perhaps 

160
TABLE 5.4.6. CALIBRATION COEFFICIENT FOR SOLIDS TRANSPORT SYSTEM 

USING FLINT RIVER DECEMBER 1-2, 1981 (RUN 1) DATA 

 

161
 

KM FROM MOUTH

FIGURE 5.4.2

162
resulting in different bottom sediment characteristics. 

Furthermore, the downstream segments tend to have steeper, 

more loosely packed banks. It is also possible that the 

depositional pattern downstream of the Ragnone treatment 

plant, which tends to discharge high solids concentrations, 

might favor high erosion rates. Temporal variations in recent 

depositional history for any river reach may lead to 

variability in bottom sediment resuspension 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 is presented in Figures 5.4.3-5.4.7. 

The partition coefficients used to generate these simulations 

are very similar to those obtained in 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 having a smaller surface area to mass 

ratio. 

Once again the high fraction of dissolved solids in the 

Flint discharge forced a calibration with lower partition 

coefficients for all three metals in segments 2 and 3. Also, 

the copper in the high particulate metals load from Ragnone 

seemed to remain in a particulate phase through the end of the 

study reach. These necessary adjustments from segment to 

segment reflect a need to characterize the partitioning of 

metals in the effluent streams as well as in the river. 

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 same flow 

regimes. This test was performed by 

163
TABLE 5.4.7 CALIBRATION VALUES OF PARTITION COEFFICIENTS 

IN FLINT RIVER DURING DECEMBER 1-2, 1981 SURVEY

 

164
FLINT (12/1 - 2/01) 

KM FROM MOUTH 

FIGURE 5.4.3

165
FLINT (12/1 - 2/01) 

KM FROM MOUTH 

FIGURE 5.4.4

166
FLINT (12/1 - 2/01) 

KM FROM MOUTH 

FIGURE 5.4.6

167
FLINT (12/0 - 2/01) 

KM FROM MOUTH 

FIGURE 5.4.6

168
FLINT (12/1 - 2/01) 

KM FROM MOUTH 

FIGURE 5.4.7 

169
applying the model to the December 3-4, 1981 (run 2) loading 

data (Table 5.4.5) and hydrologic data (Table 5.4.3) without 

adjusting the calibration coefficients (ws, wrs, πzn, πcd, πcu) 

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 particulate 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 recalibration. The 

only significant falling of the model was the over-prediction 

of dissolved copper downstream from the Ragnone discharge. 

Obviously, with particulate 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 In segment 6-9 from 0.07 ι/mg to 0.12 

ι/mg. 

5.5 FLINT RIVER MARCH 1982 SURVEY 

The primary goal of the Flint 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 m3

/s), roughly 4 times 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 in the values of those parameters, 

such as sediment settling and resuspension velocities and 

partition coefficients, that appear to be flow dependent. 

170
FLINT (12/3 - 4/01) 

KM FROM MOUTH

FIGURE 5.4.8

171
FLINT (12/3 - 4/01) 

KM FROM MOUTH 

FIGURE 5.4.9 

172
FLINT (12/3 - 4/01) 

KM FROM MOUTH FIGURE 5.4.10 

173
FLINT (12/3 - 4/01) 

KM FROM MOUTH FIGURE 5.4.11 

174
FLINT (12/3 - 4/01) 

KM FROM MOUTH 

FIGURE 5.4.12

175
TABLE 5.5.1. SAMPLING STATION FOR MARCH 1981 FLINT RIVER 

HEAVY METALS SURVEY

 

176
FIGURE 5.5.1 

177
TABLE 5.5.2. HYDROLOGIC AND MORPHOMETRIC INPUTS FOR FLINT RIVER 

MARCH 1982 HEAVY METAL MODELING APPLICATION

 

178
TABLE 5.5.3. UPSTREAM CONDITIONS AND POINT SOURCES TO FLI

MEAN LOADS FOR MARCH 23-26, 1981 

179
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 time variations 

during the monitoring, the survey strategy was to sample fewer 

river stations more frequently. Also, more tributaries were 

added to the list 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 is presented in 

Table 5.5.1. 

5.5.1 March Survey 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 furthest 

downstream station (M-13) dropped from approximately 4500 cfs 

to 3800 cfs during the survey. This roughly 15 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 values at each river station, as illustrated in Figure 

5.5.1. Based on the hydrological 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. 

5.5.2 March Survey Model Calibration

The results of calibration of the model to the March 

survey data are presented in Figures 5.5.2 - 5.5.6. The 

calibration was done using 

180
FLINT (3/23 - 26/02) 

KM FROM MOUTH

FIGURE 5.5.2 

181
FLINT (3/23 - 26/02) 

KM FROM MOUTH FIGURE 5.5.3 

182
FLINT (3/23 - 26/02) 

KM FROM MOUTH 

FIGURE 5.5.4 

183
FLINT (3/23 - 20/02) 

KM FROM MOUTH 

FIGURE 5.5.5 

184
FLINT (3/23 - 20/02) 

KM FROM MOUTH 

FIGURE 5.5.6 

185
larger settling and resuspension velocities than used in 

previous surveys. The more significant Increase, however, was 

in the resuspension velocity, resulting in 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 in a system transporting larger 

particles. The increase in resuspension 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 resuspension 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 in 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 particulate 

metals tend to Include a certain portion that does not readily 

exchange with the bulk solution. 

186
TABLE 5.5.4. CALIBRATION COEFFICIENTS FOR SOLIDS 

TRANSPORT DURING MARCH 1981 FLINT RIVER SURVEY

187
BOTTOM SHEAR STRESS. (DYNES/cm2) 

FIGURE 5.5.7 RELATIONSHIP BETWEEN CALIBRATED RESUSPENSION VELOCITY AND 

BOTTOM SHEAR STRESS UNDER THREE FLOW REGIMES IN THE FLINT 

RIVER

188
FIGURE 5.5.8 

189
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APPENDIX A 

DEVELOPMENT OF MODEL EQUATIONS 

August 1984
APPENDIX A 

DEVELOPMENT OF MODEL EQUATIONS 

A.1 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 in the 

receiving water. Only external sources of the contaminant, or 

inflow of dilution water by advection or dispersion can alter 

the contaminant conconcentration, according to this 

conservative assumption. 

Under the conservative substance assumption, and assuming 

that longitudinal dispersion is negligible relative to 

advective transport, the general river transport equation 

reduces to 

 

Furthermore, application of Equation A1 is almost always made 

under the assumption that the river reach in question is at 

steady-state with a constant, continuous point discharge of 

the contaminant in question. Also, flow 1s assumed to be 

constant over the reach. Under these assumptions, the solution 

for Equation A1 is simply 

(A2) 

(A1)

(A2) 

 

where Cu = upstream river concentration of contaminant and Co = 

river contaminant concentration at x = 0 after mixing upstream 

river water with point discharge. The concentration, Co, is 

determined by performing a mass balance for C at x = 0, 

assuming instantaneous mixing at that point. Therefore, Co is 

A-1
calculated from Cu, the point source concentration (Cw), the 

point source flow (Qw), and upstream river flow (Qu) as 

follows: 

(A3) (A3) 

Given the above assumptions, Co is 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 

uncontaminated diluting water. Multiple point discharges can 

be handled reapplying 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 in 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 (Dolan and 

Bierman, 1981; Richardson, et al., 1983; and Rodgers, 1981). 

Flint River data (presented later in this section) collected 

by Michigan DNR in 1978 demonstrated that total zinc and 

copper did not behave conservatively in certain stretches of 

the river. Unless advective 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 is likely to vary with longitudinal distance. 

A.2 NONCONSERVATIVE POLLUTANT - SIMPLE WATER COLUMN ANALYSIS 

Often the net result of the combined effects of transport 

and transformation forces acting on a chemical substance is a 

first-order die-off of the substance with distance (or timeof-travel) downstream from a discharge. This type of 

concentration profile can be simulated by lumning several processes into a single first-order loss term applied to the 

general river transport equation. Given this approach 

A-2
(A4) 

where KT [time-1] is an aggregate first-order decay coefficient 

for the 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 is negligible in the longitudinal 

direction; that is only advection 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 

(A5) 

The solution to Equation AS is 

(A6) 

where, U = Average river velocity in reach [length/time] 

C(0) = Initial concentration of the modeled 

 substance at x = 0 [mass/length3

]. 

This approach limits itself to only the water column and 

only one form of the solicitant. 

There are two methods for applying Equation A6 to a 

problem of multiple discharges in a river system. Since 

Equation A5 is an ordinary, 

A-3
linear differential equation, the independent solutions for 

individual point sources can be additively 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 is modeled 

sequentially moving downstream. The initial (upstream) 

concentration of each segment is 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. KΓ for a 

particular pollutant may vary from site to site, or may vary 

over time due to changes in controlling parameters like flow 

or river cross-sectional geometry. 

Application of Model to Flint River August 1978 Data-- 

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, KΓ, is 

assumed to be an apparent net settling velocity from the water 

column; therefore, 

 KΓ = vs/H 

(A7) 

where, KΓ = First-order loss rate coefficient of total metal or 

suspended solids [time-1], 

 vs = Apparent net settling velocity [length/time], 

H = Mean depth of river [length]. 

The study reach of the Flint River used in this 

investigation was from the Utah Street Dam in the city of 

Flint (Km 83.6) to the bridge at Grosswell Road (Km 11.0). 

Data on river metals and solids concentrations 

A-4
and point source inputs were obtained from a Michigan DNR 

survey conducted in August of 1978 (Roycraft and Buda, 1979). 

Table A1 is a summary of the point discharges considered in 

this study, and Table A2 contains the river hydrology and 

geometry at the time of sampling. As indicated in Table A2, 

the river reach has been divided into four segments. 

Figures A1 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, vs, which determines 

the stream concentration through Equations A7 and A6. Of 

course, when vs is set equal to zero, it implies that the 

pollutant is transported conservatively down the river. All 

other parameters, those in Tables A1 and A2, and the initial 

upstream conditions were held constant. 

It is 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 WWTP and the Ragnone 

plant (Km 70.7-41.1), total zinc appears to settle at an 

apparent rate of 1.0 m/d, while total copper is lost at a rate 

close to 0.5 m/d. The apparent settling rate for zinc in 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 between Flint and Ragnone 

plants, solids are settling at a rate between 1.0 and 1.25 

m/d. The larger net settling rate observed for solids is 

consistent with the assumption that not all 

A-5
TABLE A1. POINT DISCHARGES FOR AUGUST 1978 SURVEY 

TABLE A2. FLINT RIVER HYDROLOGY AND GEOMETRY FOR AUGUST 1978 

SURVEY 

A-6
KILOMETERS FROM MOUTH

FIGURE A1 TOTAL ZINC CONCENTRATION IN FLINT RIVER VERSUS DISTANCE F 

ROM MOUTH. DATA POINTS ARE FROM MICHIGAN ONR SURVEY OF AUGUST 1978. 

KILOMETERS FROM MOUTH 

FIGURE A1. TOTAL ZINC CONCENTRATION IN FLINT RIVER VERSUS DISTAN

DATA POINTS ARE FROM MICHIGAN DNR SURVEY OF AUGUST 1

A-7
KILOMETERS FROM MOUTH

FIGURE A2. TOTAL COPPER CONCENTRATION IN FLINT RIVER VERSUS DISTANCE FROM 

DATA POINTS ARE FROM MICHIGAN DNR SURVEY OF AUGUST 1978 

A-8
KILOMETERS FROM MOUTH

FIGURE A3. SUSPENDED SOLIDS IN FLINT RIVER VERSUS DISTANCE FROM MOUTH. 

DATA POINTS ARE TAKEN FROM MICHIGAN DNR SURVEY OF AUGUST 1978 

A-9
the metals in the river are in a particulate form; therefore, 

the apparent settling rate for metals should be somewhat less 

than for suspended solids. Furthermore, the finer-grained, 

slower-settling particulates probably have a higher metals 

content than the larger, sand size particulates. 

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 resuspension in this 

segment due to higher water velocities. This behavior is 

addressed further in Section 5.0, which is a case study of the 

more extensive 1981-82 Flint data. 

Finally, it is quite apparent that the metals' behavior 

in the river is 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 MODEL HAVING SEPARATE PARTICULATE AND 

DISSOLVED CONTAMINANT PHASES 

One of the most significant mechanisms for the movement 

of pollutants through an aquatic environment is the adsorption

or uptake of the chemical by both nonviable and viable 

particulate matter, followed by the transport of the 

Interacting particulates. Association with suspended matter 

thus significantly alters the transport regime of a chemical 

by introducing additional transport processes, such as 

settling and resuspension. Furthermore, the association with 

suspended matter can indirectly affect the rate and extent of 

chemical transformations and biotic 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 its toxicity. 

Accordingly, determination of the fate and potential toxicity 

of 

A-10
pollutants in 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 

resuspension) as affected by hydraulics and particulate 

physical properties. 

A conceptual diagram of the MICHRIV model is presented in 

Figure A4; nomenclature is presented in Table A3. Note that 

the calculation scheme permits the estimation of the 

equilibrium partitioning of total chemical between dissolved 

and solid phases in 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 is used 1n the SUSA model; the 

latter approach, described below, is used in the MICHRIV 

model. 

Settling, resuspension, 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 in streams. For organics, however, 

the loss rate can include volatilization, hydrolysis, 

photolysis, chemical oxidation, and biodegradation (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 

biodegradation may often be far more rapid in the dissolved 

than in the adsorbed phase, there seems to be no consensus 

that this is true in all cases. Consequently, to maintain 

generality the decay coefficient for total pollutant, K, has 

been formulated below as the weighted sum of dissolved and 

particulate phase decay coefficients, Kdfd + Kpfp (with 

appropriate subscripts 1 or 2). 

A-11
FIGURE A4. MICHRIV FRAMEWORK 

A-12
TABLE A3: NOMENCLATURE FOR WATER-SEDIMENT MODEL 

A-13
TABLE A3: NOMENCLATURE FOR WATER-SEDIMENT MODEL (Continued) 

*In terms of bulk volume. 

A-14
A-15

Within the conceptual framework of the model shown in 

Figure A4, the following assumptions are used to develop mass 

balance equations: 

1. Constant hydrological and morphological conditions for 

each river segment: 

2. Steady-state conditions exist: 

3. Vertical and lateral uniformity in water column and 

sediments; no mixing zones 

 4. Dispersion is negligible in the longitudinal 

direction; 

5. No longitudinal (downstream) movement of the bed: Q2 = 

0; 

6. No spatial variation of the solids content of the bed: 

m2 is constant (although m1 is not constant). 

7. Partitioning between dissolved and solid phases is 

rapid relative to transport and other transformation 

kinetics. 

The solution for pollutant concentrations in such a onedimensional, 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 in the water column and in 

the bed. 

Using the subscript 1 for water column variables and the 

subscript 2 for sediment variables, the mass balance for 

solids suspended in the water column (m1) takes the form: 

(advection) (settling) (resuspension) 

(A8)

Assuming that m2 is not a function of x, and that ws and wrs are 

constant, this equation has the solution: 

(initial solids) (resuspended solids) 

(A9) 
It can be seen that m1 is a function of the travel time

downstream (x/U1), the settling velocity (ws) or its associated 

depth-dependent rate coefficient (ws/H1), and the resuspension 

flux (wrs m2). When the resuspenslon velocity (wrs) is zero, the 

second term drops out of Equation A9, and m1 is no longer 

dependent on m2. In comparing Equation A9 predictions with 

field data it is important to account for all external and 

internal sources of suspended solids. One potential internal 

source is phytoplankton growth: the concentration of 

phytoplankton solids may 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: 

 (advection) (settling) (resuspension) (burial) 

(A10)

It is assumed that the bed does not move (O2 = 0) and that m2

is constant (dm2/dx = 0). Either of these assumptions causes 

the advection term to drop out. Consequently, Equation A10 

reduces to an algebraic equation: 

(settling) (resuspension) (burial) 

(A11) 

The sedimentation velocity, wd represents the movement of 

material downward and out of the active sediment layer, the 

thickness of which (H2) does not change with time. This 

velocity thus represents the rate of change in elevation of 

the surface of the bed. Ignoring any effect of compression of 

the deep sediment. If the resuspending flux exceeds the 

settling flux, then wd is negative. implying that channel

downcutting is occurring. If the downward flux exceeds the 

upward flux, wd is positive, implying that the chanrel bed is 

rising over time, for the conditions being modeled. Where 

chemical within the bed is rapid, however, a positive wd can 

approximate the situation where the settled solids are 

transported out of the system as a bed load. 

A-16
The third mass balance equation is for the toxicant in the 

bed: 

 (advection) (settling) (diffusion in) 

(A12) 

(resuspension and burial) (diffusion out) (decay) 

The "diffusion out" term above assumes that the sediment

porosity is high, such that dissolved concentrations are 

almost the same whether expressed in terms of bulk sediment 

volume or pore water volume. If sediment porosity Φ is not 

high, fd2 in Equation A12 (as well as in A13 and A20) is 

replaced by Φfd2 in order to convert dissolved concentration

from a bulk volume basis to an interstitual volume basis. 

Since the advection term in Equation A12 is again zero, 

the equation reduces to an algebraic equation establishing the 

relationship between CT2 and CT1: 

(A13) 

Before writing a fourth mass balance equation and solving for 

CT1, it is worthwhile to express Equation A13 in other terms. 

Since: 

(A14) 

thus, 

(A15) 

A-17
If a term β, the "sediment capacity factor" (DiToro et al. 

1982), is defined as: 

(A16) 

then 

(A17) 

Combining Equations A13 and A17, and solving for r2/r1

(A18) 

The first term in the numerator is modified by noting the 

Equation A11 relationship between ws, wrs and wd. The second 

term is modified by noting that: 

(A19) 

Consequently. 

(A20) 

The ratios CT2/CT1 and r2/r1 thus depend on water-sediment 

particle exchange rates, the water-sediment diffusion rate, 

and the decay rate within sediment. They do not depend on the 

decay rate within the water column. 

A-18
The fourth mass balance equation 1s for the toxicant in the 

water column: 

 (advection) (decay) (settling) (diffusion out) 

(A21) 

(resuspension) (diffusion in) 

Combining Equations A21 and A13 results in: 

All terms on the right side of the equation are constant for a 

particular reach except for CT1, fd1, and fp1 (and subsequently 

β). The fractions fd1 and fp1 are functions of m1 per Equation 

A19; m1 is a function of x per Equation A9. However, if the 

increments of x are small enough, then m1, fd1 and fp1 are 

essentially constant. Consequently within small 

increments of x. Equation A22 has a simple solution: 

(A23) 

where 

(decay) (settling) (diffusion out) (resuspension) (diffusion in) 

 

(A24) 

By stepping down the reach to small increments of x, CT1 can be 

computed from the input parameters s, rs, kL, W1, H1, H2, m2, K1. 

A-19
K2, and the input solids and toxicant loads using Equations A9, 

A19, A20, A23, and A24. Then CT2 can be computed from CT1 using 

Equation A13 or A15. For the solution to be valid, the 

increments of x must be short enough that the relative change 

in m1 is small within each increment. That is, the increments 

Λx must be shortened until Λm1/m1 is small. 

While Equation A24 is satisfactory as written, some 

simplification of it is helpful for better understanding the 

model. Using the relationships shown in Equations A19 and A16, 

the "diffusion in term of Equation A24 can be put in terms of 

fd2 and combined with the "diffusion out" term. Using Equations 

A11 and A16, the "settling" term can be expressed 1n terms of 

resuspension and burial. The resulting equation is: 

(decay) (settling) (resuspension) (net diffusion) 

 

(A25) 

Meanwhile, Equation A20 can be solved in terms of KL as 

follows: 

(A26) 

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 

Ks = wd/H2 the equation can be expressed as: 

(A27) 

This result expresses KT, 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: decay in water, decay in sediment, and 

burial. The rates of sediment decay and burial are 

A-20
modified by βr2/r1, which is 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 be noted that for a condition where w1 = 

w2, K1 = K2 = 0, and wrs = 0, this model reduces to the simpler 

model expressed by Equation A7. 

A-21
APPENDIX B 

SEDIMENT TRANSPORT CONSIDERATIONS 
APPENDIX B 

SEDIMENT TRANSPORT CONSIDERATIONS 

 The pollutant fraction associated with particulate 

material is determined (at equilibrium) by the partition 

coefficient and the solids concentration. In many natural 

waters the particulate 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, zinc, 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 

particulate material. 

Predicting the transport and fate of particulateassociated pollutants requires an understanding of the 

behavior of particles. Predicting particle behavior is, 

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 streambed rather than to the smaller 

particles likely to adsorb many of the toxic pollutants. 

Consequently, future findings 1n this area may significantly 

improve predictive abilities. 

B.1 SEDIMENT PROPERTIES 

An individual sedimentary particle may be characterized 

by its 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 same all velocity 

(Richardson 1971, Guy 1970). Table B1 and Figure B1 show the 

size ranges corresponding to particle classifications. 

B-1
TABLE B1. KINDS OF SEDIMENT MATERIALS AND SIZE CLASS 

TRANSPORTED IN STREAM (from Culbertson 1977) 

Sediment Size Class Mode of Transport 

Boulders >256 mm Bed Load 

Cobbles 64-256 mm Bed Load 

Gravel 2-64 mm Bed Load 

Sand 0.062-2 mm Bed Load or Suspended 

Silt 4-62 μm Suspended 

Clay 0.2-4 μm Suspended 

Organic Detritus

Including leaves, 

trees biological 

remains, etc. 

 Bed Load or Suspended 

Biota 

Including floating 

and bottom dwelling 

organisms 

 Bed Load or Suspended 

B-2
Figure B1. Size Range of Sediment Particles and Filter Pores 

 (from Stumm and Morgan 1981). 

B-3
Fall 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. 

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 is used as somewhat of a benchmark. 

Nevertheless, substantial variations in density may be 

observed, with organic particles especially tending toward 

lower density. 

 For sediment transport the most useful expression of 

particle shape 1s given by the Corey shape factor, c/ ab, 

where a, b, and c are the lengths of the longest, 

intermediate, and shortest mutually perpendicular axes, 

respectively (McNown and Malaika 1950). 

Bulk sediment is 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 cohesiveness. 

Measured size distributions may be expressed in a number 

of different ways. Frequency distribution histograms 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 quartile values, d25, 

d50, and d75 (where dx is the diameter greater than x percent of 

the particles). Table B2 shows particle size distributions 

observed in raw sewage, primary effluent, and secondary 

effluent of one municipality (Evnazountas and Mathias 1984). 

Figure B2 shows the size distributions observed in stream beds 

of 11 rivers (Guy 1970). Mills et al. (1982) also presents 

some sediment data for several rivers. 

B-4
TABLE B2. MASS DISTRIBUTION OF FILTRABLE SUSPENDED SOLIDS IN RAW A

aAll samples from the University of California. Davis wastewater treatmenbBased on the total mass of filtrable suspended solids retained on an 0.1 cSize range between 12-35 

μm. 

Source: Tchobanoglous et al. (1983) as reported by Bonazountas (1984). 

B-5
TECHNIQUES OF WATER-RESOURCES INVESTIGATIONS 

PARTICLE SIZE, IN MILLIMETERS 

7 - Seneca Creek near Rockville, Md. 

8 - Brandywine Creek at Lenape, Pa. 

9 - Brandywine Creek at Cornog. Pa. 

10 - Yellowstone River at Billings, Mont. 

11 - W. Fork Rock Creek near Red Lodge. Mont.

1 - Mississippi River at Head of Passes, La. 

2 - Mississippi River at Cairo. Ill. 

3 - Missouri River at Omaha, Nebr. 

4 - Republican River at Clay Center, Kans. 

5 - South Plane River at South Platts, Colo. 

6 - Pembina River at Walhalla, N. Dak. 

Figure B2. Particle-size Distribution of Streambed Material Typical of 

Indicated Streams in the United States (from Guy 1970). 

B-6
The size distribution of natural sediment is ordinarily 

expected to plot as a straight line on log probability paper. 

If this is the case, then the median will equal the geometric 

mean, and the ratio d50/d16 and d84/d50 will equal the geometric 

standard deviation or "gradation 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 

solids (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 Dossis and Warren (1980) observed 

higher particulate 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 in place. Porosity 

may vary between 0 and 1, with 0 signifying 100% solid and 1 

signifying 100% water in the bed. Porosity affects the shear 

strength of the bed, which in turn affects the rate of 

resuspension under various shear stresses or current 

velocities. Bed porosity must be distinguished from individual 

particle porosity. 

Cohesiveness describes the attraction the individual 

particles have for each other. Noncohesive sediments are 

composed primarily of sand and gravel. Cohesive sediments 

consist of silts and clays. The behavior of cohesive sediments 

differs from that of noncohesive sediments in some important 

ways. 

B-7
Figure B3. Variation of Metal Concentration with Sediment Particle 

Size (from Hayter and Mehta 1983). 

B-8
In suspensions of noncohesive material the basic settling 

unit is the individual grain. Particle interactions are 

strictly mechanical, such as momentum transfer between 

colliding grains. Noncohesive sediment beds resist erosion by 

the submerged weight of the individual grains, which may 

provide mutual support by interlocking or by friction 

(Partheniades 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, (b) surface electric charges, (c) chemical bonds, and 

(d) Interactions of the double layer (counter-ions attracted 

from the solution). These forces are only partially understood 

and may vary with the water environment (Parthenlades 1971). 

For clay particles in distilled water the net effect of 

these forces may be repulsion, allowing enormous 

concentrations to be suspended at small current velocities. 

However, even small amounts of dissolved salt will bring about 

particle attraction (through double layer compression), 

resulting in the aggregation of colliding particles into flocs 

having size and fall velocity much larger than those of the 

individual clay particles. The basic settling unit is thus the 

floc, the size distribution of which may depend on the flow 

conditions and on the physico-chemical properties of the water 

and sediment: Cohesiveness provides a sediment bed with 

additional shear strength to resist erosion. Partheniades 

(1971) notes that fresh waters ordinarily contain enough salt 

to bring about clay particle flocculation. Nevertheless, 

Edzwald et al. (1974) and Hayter and Mehta (1983) found 

estuarine salinity to measurably increase fall velocity over 

that in ordinary fresh water. 

B.2 TRANSPORT OF SEDIMENT LOADS 

The gravity-driven downhill movement of stream flow is 

resisted by the friction of the fluid passing over the stream 

bed. This results in a variation of velocity with depth: 

velocity decreases near the bottom of 

B-9
the water column. For the stream flow to keep particles in 

suspension the flow turbulence must counter the tendency of 

the particles to settle. Consequently, the stream flow tends 

to carry heavier particles near the bed, while it is likely to 

carry fine particles more uniformly throughout the water 

column, as illustrated in Figure 84. 

 The total 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 load 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 intermittant 

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 is 

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 term measured load refers to that portion of the 

sediment load that can be measured with sampling equipment. 

The unmeasured load is 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 in the Stream Bed: 

This division is based on particle sizes. Wash load is 

that portion of the total load comprised of grain sizes finer 

than those found in 

B-10
CONCENTRATION: 1 SPACE - 100 MC/L BY WEIGHT 

Figure B4. Flow-weighted Concentrations of Different Particle 

Sizes for the Missouri River at Kansas City (Guy 

1970). 

B-11
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 limited by its availability in the watershed. 

This part of the load is designated as wash load. The coarser 

part of the load, i.e., the 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 demarkation is possible since it 

depends on flow conditions and on sediment sources. 

The bed material load is of great importance in 

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 slope to increase, which in turn increases the 

velocity or bottom shear stress, thereby increasing 

resuspension until a new equilibrium is attained. Degradation, 

on the other hand, occurs when the sediment carrying capacity 

of the flow exceeds the upstream supply rate. 

B-12
B-13

The resulting net erosion reduces the slope, which in turn 

reduces the velocity or bottom shear stress, thereby reducing 

resuspension until a new equilibrium is 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 al. 1982), many procedures require data on the 

suspended solids 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 is 

determined by external sources and is thus not predictable 

from the stream's sediment carrying capacity (Nordin and 

McQuivey 1971). Nevertheless, it can be noted that many 

sediment transport formulas can be put in the form (Gessler 

1971): 

gs = a(τ-τc)P 

 (B1) 

where gs is sediment load per unit width, τ is shear stress, τc 

is a critical shear stress at which sediments start to move, a 

is some coefficient, and p some power. 

Shear stress, τ (Newton/m2

), is given by: 

τ =γ (γRS) 

 (B2) 

where γ is the specific weight of water (approximately 9807 

N/m3), R is the hydraulic radius (m), and S is the slope of the 

energy grade line (m/m). To obtain τ in dynes/cm2

 multiply N/m2

by 10. The importance of shear stress in controlling both 

settling and resuspension will be further discussed later. 

Shen and Hung (Shen 1971) have suggested a simple 

empirical 
B-14

regression formula for predicting the suspended bed material 

concentration. Using flume and river data they obtained: 

where C is bed material load concentration (mg/2), V is the 

average flow velocity (ft/sec), S again the energy slope

f l v oci he regression

= 0.004288 

withi

1n

mplication, toxicant bound to wash

imited interaction with the bed.

E

nd the

1

0 = ws m1 

(B4) 

(ft/ft), and W the al el ty (cm/sec). T

values are: 

a0 = - 107404.459 i = 0.007502 

a1 - 324214.747 j 

a2 = -326309.589 k = -0.002400 

a3 = - 109503.872 

The standard error of log C was 0.217 (68% of the data was 

n 0.217 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 surface-to-mass 

ratios) comprising the wash load. By definition of the wash 

load, these finer particles are not found in the stream bed

substantial quantities. By i

load particles would have l

B3. DEPOSITION AND EROSION 

The net particle flux (μg/cm2/sec) across the bed-water 

interface can be expressed as the difference between the 

deposition (settling) flux, SD, and the erosion (entrainment or

resuspension) flux S (Fukuda and Lick 1980). The deposition 

flux 1s related to the settling velocity, wS (cm/sec), a

water column solids concentration, m (mg/2, by: 

S
B-15

The erosion flux is related to the resuspension velocity, wrs, 

and the bed solids concentration, m2, by: 

SE = Wrs m2 

(B5) 

The assumption here is that deposition and resuspension are 

independent processes that can occur simultaneously. 

B.3.1 Deposition 

In evaluating the settling velocities of sediment

particles, three physical processes can be considered (O'Melia 

1980): (a) gravity, (b) Brownian motion or molecular 

diffusion, and (c) turbulent or laminar 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 be 

expresses in terms of Stokes Law: 

 (B6) 

where vs is the Stokes settling velocity (cm/sec), g is the 

acceleration of gravity (980 cm/sec2 ), ps - p is the 

difference in the densities of the particle and of water, d is 

the spherical particle diameter, and μ is the absolute 

viscosity of water (poise, g/cm - sec). (Note that μ = νp, 

where ν is the kinematic viscosity (cm/sec) tabulated 1n 

Figure 3.2.8). The Stokes velocity, vs, applies to a single 

particle in a quiescent medium. The effective settling 

velocity in the field, ws, may differ from νs. 

The Stakes settling velocity is quite sensitive to the 

particle diameter; for example, a doubling of particle 

diameter would quadruple the fall velocity. For noncohesive 

particles, the size distribution is simply a property of the 

sediments. For cohesive particles, the size distribution 

depends on the properties of both the particles and the 
B-16

fluid. Aggregation into flocs occurs when particles having 

sufficient physico-chemical attraction collide with each 

other. Such collisions may result primlarily from Brownian 

motion for small particles, and fluid shear and differential 

settling velocities for larger particles. Valloulis and List 

(1984) have modeled these processes in a sedimentation basin. 

Disaggregating of flocculant particles may also occur through 

fluid shear and through collisions (Lick 1982). Uchrin and 

Weber (1980) noted that laboratory measured settling 

velocities were substantially more rapid than expected from 

the Stokes velocity of the 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 Brownian diffusion is 

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 ts = H/νs, where H is depth of water. A 

characteristic time for turbulent diffusion is td = H2

/20v, 

where Dv is the vertical eddy diffusivity. The dominant 

mechanism is that with the shorter characteristic time. 

Increasing the particle size 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 vs in shallow 

streams seems consistent with this reasoning. 

In this vein Hayter and Mehta (1983), constructing a 

general model of particle behavior in estuaries, applied the 

relationship: 

 (B7) 

where τC is a critical shear stress above which little 

deposition of the sediment world occur (as measured in flume 

tests). They suggest a minimum value of wS being 5% of v. 
B-17

Click (1982) applies a different line of reasoning to a 

thin film of water near the bed-water interface, where 

turbulent diffusion is assumed to decrease. The flux through 

this film can be written: 

 (B8) 

where SD - SE (ug/cm2

 - sec) is the net downward flux (per 

Equations B4 and B5), Dv is vertical eddy diffusivity 

(cm2

/sec), m1 (mg/1) is the particle concentration near the 

interace, z is vertical distance from the bottom, and DB is the 

Brownian diffusivity for the particles, given by: 

 (B9) 

where k is the Boltzmann constant (1.38 x 10-16 g - m2/K - sec) 

and T is absolute temperature. DB and vs are thus functions of

particle size; Dv is a function of shear stress and distance z

from the bed-water Interface. When resuspension is negligible,

the concentration 

In this

x due to particle diffusion adds to the flux due

t s

d s when only 

(1982) determined vd to be: 

gradient is positive within the thin film,

because particles are being lost to the bed surface.

case the flu

to gravity settling, thereby increasing the rate of 

deposition. 

Combining Equations B4 and B8, Lick (1982) determined the 

effective settling veloci y w : 

Ws = vs/1 - exp(-vs/vd)

 (B10) 

where v is the minimum limiting value of w

diffusion (both Brownian and turbulent) is considered. Lick 
B-18

 (B11) 

Figure 85 illustrates the solution of ws, vd, and vd over a 

range of particle sizes for a shear stress, τ, of 10 dynes/cm2. 

For large particles ws - vs and the effect of diffusion through 

the boundary film is negligible. For small particles ws = vd

and the effect of 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 

it. This limitation might be related to why increasing τ

increases ws for small particles, a contrast to the previously 

described empirical approach (Equation B7), where increasing τ 

decreases ws. 

B.3.2 Bed Erosion 

Erosion or entrainment is the scour of sediments from any 

part of the stream bed into suspension 1n the water column. To 

remove material from the bed the flow-generated forces must 

overcome the stabilizing forces, which consist of the immersed 

weight and (for silt or clay beds) the cohesive strength. Lee 

et al. (1981) and Lick (1982) list five factors controlling 

entrainment: (a) turbulent shear stress at the bed-water 

Interface, (b) water content (porosity) of the bed, (c) 

sediment composition, including minerology, organic content, 

and size distribution, (d) activity of benthic organisms, (e) 

vertical distribution of sediment properties, related to the 

manner of deposition. 

Lee et al. (1981) and Fukuda and Lick: (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 depositional 

period, the freshly deposited sediments will tend to have a 

smaller mean 
B-19

Figure B5. Deposition velocity ws as a function of particle size 

for a shear stress of 10 dynes/cm2 (from Lick 1982) 
B-20

size and a higher water content; therefore, these surface 

sediments will be more easily entrained when shear stress 

increases. 

The erosion rate may be formulated in terms of the shear 

stress on the bed, τ, and the erosion resistance of the bed. 

The erosion resistance of the bed 1s generally empirically 

estimated; it cannot be predicted solely from the basic 

properties of particle size distribution and porosity. 

Figure 86 illustrates a typically measured relationship 

between erosion flux and shear stress. Once beyond critical 

shear stress, τc, the erosion flux, SE, increases rapidly. In 

modeling consolidated estuarine beds, Hayter and Mehta (1983) 

estimate 

(B12)

ere

D

d an

d solids concentration would be given 

by m1

noncohesive sediments, Einstein

(1950) demonstrated (using dyed particles) that conservation 

of load was the result of an equilibrium balance between 

deposition and resuspension. 

wh both a and τc are empirically derived constants. 

B.3.3 Particle Exchange: Continuous Versus Discontinuous 

The conservation of sediment load through a stream reach 

may occur under two conditions: (a) deposition and 

resuspension are occurring continuously, but at equal rates (S

= SE, or wSm1 = wrs m2), or (b) deposition and resuspension 

rates are both zero. The former situation can be considere

equilibrium state; the latter cannot. For the equilibrium

condition, the suspende

= m2 wrs/ws. For the zero rate situation, whatever 

concentration exists at the head of the reach is carried 

downstream unchanged. 

In flume experiments with
Figure B6. Example of Relationship between Erosion Race SE

and Bed Shear Stress τ (after Hayter and Mehta 1983). 

B-21
B-22

Although the concentration did not change, a continuous 

exchange of particles was occurring through the simultaneous 

processes of deposition and resuspension. 

Whether cohesive sediments exhibit the same behavior is 

open to question. For clay particles Partheniades (1971) and 

Hayter and Mehta (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 is, there appeared to be a shear stress range within 

which neither erosion nor deposition is significant, within 

this range the velocity was sufficient to prevent the 

suspended particles from flocculating and adhering to the bed 

but insufficient to break the 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, which are likely 

to be finer 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 the bed. 

Lick (1982) thus notes that erosion and deposition are 

not completely reversible and that a hysteresis effect is 

often present. For a particular shear stress, the steady state 

concentration will be higher if the shear stress (and 

suspended concentration) had been decreasing over time than if 

it had been increasing over time. 

B.4 SEDIMENT SOURCES 

External sources of suspended sediments can originate 

from either paint or nonpoint sources. Point sources of 

sediments are generally minimal, and in any event, are easily 

quantifiable. Nonpoint sources of 
B-23

concern are governed by natural and culturally accelerated 

erosion processes. Although urban runoff can have significant 

localized impacts on streams (Porcella and Sorenson 1980; 

Tomlinson et al. 1980), the preponderance of sediments 

delivered to U.S. streams by accelerated erosion are derived 

by sheet erosion from agricultural lands (Omernik 1977). Sheet 

erosion is the wearing away of a thin layer of land surface. 

Sheet erosion rates depend on rainfall and flow 

properties, soil 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 by Wischmeier and 

Smith (1960), has been extensively used to estimate average 

annual soil loss 1n tons/acre. To predict sediment yield of a 

watershed, the USLE is coupled with a "sediment delivery 

ratio", the fraction of an area's soil loss that actually 

reaches the stream. Table B3 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 

McElroy et al. (1976), U.S. EPA (1976), and Zison et al. 

(1977). That material will not be repeated here. However, it 

can be noted that for many water quality modeling purposes, 

the utility of the USLE is constrained by being limited to 

annual average soil loss. It is not intended for event 

modeling (Wischmeier 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), Geiger and 

Dorsch (1980), and Klemetson et al. (1980). For agricultural 

runoff they Include Williams (1980), Novotny (1980), and 

Donigian and Crawford (1976). Given sufficient resources, the 

method of choice might be the Agricultural Runoff Management 

Model (ARM) (Donigian and Davis 1978). 
B-24

TABLE 83. SEDIMENT YIELD FROM DRAINAGE AREAS OF 100 

SQUARE MILES OR LESS OF THE UNITED STATES (Todd 1970) 

Region Estimated sediment yield 

High Low Average 

tons/sq mi/yr 

 

North Atlantic 1,210 30 250 

South Atlantic Gulf 1,850 100 800 

Great Lakes 800 10 100 

Ohio 2,110 160 850 

Tennessee 1,560 460 700 

Upper Mississippi 3,900 10 800 

Lower Mississippi 8,210 1,560 5,200 

Souris-Red-Rainy 470 10 50 

Missouri 6,700 10 1,500 

Arkansas White-Red 8,210 260 2,200 

Texas Gulf 3,180 90 1,800 

Rio Grande 3,340 150 1,300 

Upper Colorado 3,340 150 1,800 

Lower Colorado 1,620 150 600 

Great Basin 1,780 100 400 

Columbia-North Pacific 1,100 30 400 

California 5,570 80 1,300 
APPENDIX C 

FIELD AND LABORATORY METHODS 

FOR FLINT RIVER SURVEYS 

by 

Cranbrook Institute of Science 

9311 Groh Road Grosse Ile, Michigan 48138 

August 1984 
C-1

Metals in the ambient environment frequently occur at 

levels below the detection limit of many of the analytical 

methods commonly employed by State and Federal agencies. 

Consequently, in 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, which are believed to 

influence metal speciation or to Interact with solids, were 

also analyzed. The parameters Included were suspended solids, 

pH. 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 minimize contamination. 

River water was collected using a half-gallon linear 

polyethylene wide mouth Nalgene 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 lid suspended from the rope 

just above it. The purpose of the lid was to keep out debris 

as the sample was pulled up. 
C-2

Sampling was usually done from bridges at three marked 

positions that are at 1/4 the 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 is from this composite sample 

that an aliquot for analysis was taken. 

A sample processing scheme 1s presented 1n Figure C1. All 

filtering operations were conducted in the mobile laboratory 

as well as pH, conductivity, alkalinity, and metal 

preservations. Temperature and dissolved oxygen were measured 

in-situ. Total metal analysis, dissolved metal analysis, and 

hardness were analyzed at the EPA Large Lakes Research 

Station. 

Trace Metals

Trace metal samples were collected in new linear 

polyethylene bottles washed with hot water in a dishwater, 

rinsed with deionized water, with 30% v/v nitric acid, and 

with deionized water; then they were soaked in 2% v/v nitric 

add for two weeks, rinsed six times with deionized 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-3

FIGURE C1 SAMPLE PROCESSING SCHEME FOR FLINT RIVER WATER 
C-4

Every tenth sample included a duplicate aliquot or split 

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 where d = difference 

between the sample 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 C1 

for results. Detection limits 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 C3). The variability 

of these replicates 1s 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 81 and 83 suggests that the variability of the results 

for all the metals was mainly due to laboratory and 

Instrumental procedures. 

Total river water (unfiltered) was collected in a 500 m2 

linear polyethylene bottle pre-cleaned as above. A 100 m2 

portion of that water was filtered through a .45 μm Sartorius 

cellulose acetate filter. The filtering apparatus was a 

Millipore polycarbonate 

Sterifil filtration system. Before use, the system was 

soaked in 4% v/v HNO3, then rinsed well with deionized water. 

The filter was set in place, and 50 m2 of deionized water was 

filtered, then discarded. Fifty m2 of sample was then filtered 

and disscarded. Sample water was then filtered until the 

filter began to clog. Before filling the 175 m2 bottle with 

filtrate, the first 50 m?? of filtrate was used to rinse it 

out. 
C-5

TABLE C1. RESULTS OF FIELD DUPLICATES (SPLITS) 

NOTE: If 

2

1 [Detection Limit] <- [Measured Metal Concentration] 

< [Detection Limit], then the 'result is recorded as "T" 

preceding the detection limit. 

If [Measured Metal Concentration] < 

2

1 [Detection Limit], then 

the result is recorded as "W" preceding a value - the 

detection limit. 

TABLE C2. DETECTION LIMITS 
C-6

TABLE C3 RESULTS OF BETWEEN-RUN REPLICATES 
C-7

The models 603 and 460 (Perkin Elmer) atomic absorption 

instruments equipped with graphite furnaces were used to 

analyze the samples. The drying, charring, and atomization 

program were optimized for river and effluents using 

optimization as described in "Analytical Methods for Atomic 

Absorption Spectrophotometry Using the HGA Graphite Furnace," 

Perkin Elmer (1977). See Table C4 for Information on 

analytical conditions. All nameless analyses were done 1n 

duplicate while the flame analysis (zinc) was done in 

triplicate. 

If chemical interferences were present which enhanced or 

suppressed the analytical atomization 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 in order to monitor recovery. Recovery here is defined 

as the slope of the standard addition calculation on a sample, 

times 100, divided by 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 m?? 

HMO3/2). 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-8

TABLE C4 ANALYTICAL CONDITIONS 

•Background correction was especially important to take care of non-specifor Cd and Zinc analysis. 
C-9

Atomic Absorption Spectroscopy, Perkin Elmer, 1980). The 

digested blanks consisted of deionized water plus the same 

amount of HNO3 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 then based on these 

corrected absorbences. The filtered samples (dissolved) 

received no sample pretreatment. 

In order to determine contamination Introduced 1n the 

filtering process, two filter blanks were taken 1n each eighthour shift in the field. This involved filtering an aliquot of 

deionized water. An unfiltered sample of this deionized water 

was also taken at the same time. This unfiltered sample is the 

batch blank in Table C5. The analytical results of the two 

types of samples were compared; if they were equal, then no 

filtering contamination was believed to occur. Equality here 

is confirmed by a T test. The results 1n Table C5 show that no 

correction was required for the filtering process in the 

August, December, and March surveys. 

Since the filter blank results from both a bottle blank 

and a blank for the filtering process. 1t 1s assumed that if 

the filter blank were 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 μg/?? Cu in the sample of batch water was due to 

the copper in the batch water only. 

Trace metal water samples were preserved by adding 3 m2 

of HNO3 per liter of sample. Samples were refrigerated at 7°C. 
C-10

TABLE C5. FILTRATION BLANKS 

(Note: B.B. - Batch Blank and F.B. = Filter Blank) 

 

*At the 95% confidence level the mean batch blanks and filter 

blanks were equal; therefore, no blank correction was needed 

for the filtering process. 
C-11

Results of several intercomparison studies are presented 

in Table C6. In both of these series, performance was 

considered 'good.' The true values of the unknowns fell within 

our 95% 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 spliced with 

known standards (standard additions). The remaining standards 

were spiked at the end of the day. The average slope of these 

standard additions to standards was used in the denominator of 

the recovery formula. 

Conventional Parameters

Methods used for non-metal parameters are described 1n 

Table C7. Dissolved oxygen and temperature were in-situ 

measurements. Specific conductivity, pH, total alkalinity, and 

total non-filterable residue (suspended solids) were analyzed 

in the mobile laboratory. Hardness was analyzed at the Grosse 

Ile. Lab. 
C-12

TABLE C6 INTERCOMPARISON WITH U.S. EPA ENVIRONMENTAL 

MONITORING AND SUPPORT LABORATORY, CINCINNATI 

(Concentrations in μg/2) 
C-13

TABLE C7. ANALYTICAL METHODS - CONVENTIONAL 

PARAMETERS 
APPENDIX D 

BEHAVIOR OF HALOGEN DISINFECTIONS RESIDUALS 
D-1

APPENDIX D 

BEHAVIOR OF HALOGEN DISINFECTIONS RESIDUALS 

This appendix presents information on the aquatic fate of 

wastewater disinfections residuals. This discussion has been 

added at the 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 (b) chlorine is not discussed in the 

portion of the Guidance Manual covering 800,00, and ammonia

(Driscoll et al. 1983), and since it is not a "priority 

pollutant", it is not covered by Mabey et al. (1982) and 

Callahan et al. (1979). 

As chlorination is by far the most common disinfections 

practice in this country, the emphasis is on chlorine 

residuals; nevertheless, some information on bromine chloride

is also included. The discussion is intended to apply to fresh 

water; halogen chemistry in saltwater, described by Haag and 

Lietzke (1981), is not identical to that in fresh water. 

The discussion is limited to the fate of halogen 

oxidants. It does not deal with the formation of halogenated 

organic by-products; such formation is of minor importance in 

determining the half-life of the disinfectant itself. Although 

some of these by-products may be carcinogenic, their 

production 1s 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 trihalomethanes (the byproducts of greatest concern) appears to be depressed by the 

presence of ammonia, a usual constituent of municipal 

wastewaters that have not undergone complete nitrification

(Metcalf & Eddy 1982). 

The following discussion has been edited from the Metcalf 

& Eddy (1982) report. Impacts of Wastewater Disinfections 

Practices on Coldwater 
D-2

Fisheries. Additional details on disinfectant chemistry can be 

found in Weber (1972). 

D.1 AQUATIC FATE 

Most 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 diffusers. 

A common method of estimating the dilution of wastewater 

effluent is to calculate 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 to average sized rivers, and 1 or 2 for plants 

discharging to small tributaries. 

The pitfall of using the ratio of flows to estimate 

dilution is that complete mixing (lateral and vertical) is 

implicitly assumed. In cases of small tributaries with low 

dilutions (e.g., 1 or 2) this may be a reasonable assumption. 

However, for higher dilutions (e.g., 100 or more), a long 

distance is 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 in solution as shown below: 

This reaction is rapid and essentially complete if the pH is 

greater than 6. Application of sodium or calcium hypochlorite 

will yield hypochlorite 
D-3

ion (Ocl-) initially, which will rapidly establish equilibrium 

with hypochlorous acid (HOC1): 

NaOCl + H2O = HOCl + Na+

 + OHChlorine present in wastewater or receiving waters is 

usually measured as total residual chlorine (TRC). TRC is the 

sum of free residual chlorine and combined residual chlorine. 

Free residual chlorine (FRC) is the free available oxidant in 

solution consisting of hypochlorous add (HOCl) and 

hypochlorite ion (OCl-). Combined residual chlorine (CRC) 

generally refers to the chloramines formed when hypochlorous 

add reacts with ammonia. Free chlorine can also react with 

other organic compounds containing amino groups to form 

organic chloramines. Bactericidal strength 1s in the order: 

hypochlorous acid > hypochlorite ion > chloramines. 

Chlorine demand occurs both in wastewater and the 

receiving waters. Chlorine demand is the difference between 

the appplied chlorine dose and the free residual chlorine. It 

is due to a variety of reactions including oxidation and 

decomposition. Some important reactions of chlorine in the 

freshwater environment are presented in Table D1 and a 

schematic summary of the important reaction pathways is 

presented in Figure D1. 

The principal determinants of the likely reaction 

products in municipal wastewaters and fresh surface waters are 

concentrations of chlorine and ammonia and pH. Chlorine enters 

the receiving water mainly in the form of combined chlorine 

(mono- and di-chloramine) due to the typical ammonia 

concentrations found in wastewater. For highly nitrified 

effluents, free chlorine would be the main constituent. This 

residual chlorine is lost rapidly from the receiving water due 

to several reactions including: demand from oxidation 

reactions; combination with organic material; photochemical 

decay; and decompositon. Although measurements of chlorine 

decay rates are not common, some results are presented in 

Table D2. Several generalizations can be made from results 

presented in the table, including (1) rates are widely 

varying, (2) decay 
D-4

TABLE D1. PRINCIPAL REACTIONS OF CHLORINE IN SOLUTION 
D-5

FIGURE D1 DIAGRAM OF CHLORINE REACTION PATHS IN 

FRESHWATER 
D-6

TABLE D2. CHLORINE DECAY RATES 
D-7

of free chlorine is an order of magnitude faster than of 

monochloramine and (3) field decay rates are normally an order 

of magnitude faster than laboratory rates. 

Bromine Chloride

Reactions of bromine chloride in freshwater are more 

complicated since two halogens are involved. Reactions in 

freshwater include the production of both hypobromous acid and 

hypochlorous acid: 

BrCl + H2O === HOBr + HCL 

Br2 + H2O === HOBr + H+

 + Br 

Cl2 + H2O ===HOC1 + H+ + Cl 

As with chlorine, hypobromous add will react with ammonia

to form bromamines. Roberts and Gleason (1978) presented data 

on the decay of bromine residual in seawater with ammonia, 

concentrations of 0.2 mg/1. Decay was extremely rapid since 

bromine residuals were not detected after only two hours. No 

other data on bromine residual decay were available. 

D.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 Fairfield 

Hills Sewage Treatment Plant on Deep Brook in Newtown (CT DEP 

1981). The plant provides advanced treatment with the main 

treatment units being primary and secondary settling tanks, 

trickling filters, and Intermittent gravity and sand filters. 

Current plant flow averages about 0.3 mgd. Plant effluent

concentrations of TRC typically range from 0.8 to 3.0 mg/1. 

Deep Brook is a fast flowing, well oxygenated tributary

of the Pootatuck River with an average flow of 0.27 m3/s and a 

7-day 10-year low flow of 0.014m3/s. The plant effluent 

discharges Into Deep Brook about 610 meters above the 

Pootatuck River. Pootatuck River average flow 
D-8

is 1.1 m3/s and 7-day 10-year low flow is 0.12 m3/s. 

Measurements of in-stream and effluent TRC were conducted on 

August 29, 1980. 

Mean values of TRC concentrations measured using the 

amperometric titration method are presented in Figure D2. On 

the date of these measurements, river flow was 0.025 m3

/s and 

plant flow was 0.014 m3/s. Total mixing 1s said to occur 15 

meters downstream of the discharge point, although the basis 

for this statement (i.e., visual, dye study, etc.) is 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 in Figure 02, at a point 15 

meters downstream the effluent concentration of TRC had been 

reduced from 3.8 to 2.0 mg/l, or diluted 1.9 times. This tends 

to support the 15-meter complete mixing assumption. 

The TRC concentrations decreased to 0.2 mg/l (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 (Equation 2.3 in Section 2.4 of the text), a rate 

coefficient of about 100 per day is calculated. (To obtain 

this value, a stream velocity of 0.3 m/s has been assumed, as 

the actual value was not given.) This Indicates that the instream loss of chlorine is extremely rapid. However, the value 

of 0.2 mg/l is 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 

in Deep Brook downstream of the discharge. 

In summary, a simple dilution calculation followed by a 

first order reaction coefficient was adequate in this case to 

estimate the ostoline. 
DISTANCE DOWNSTREAM FROM WWTP EFFLUENT – m Pootatuc

FIGURE D2. TRC CONCENTRATIONS IN DEEP BRO

D-9
D-10

concentrations in Deep Brook. However, this method must be 

used with caution for several reasons. If stream-flows are 

higher, complete mixing would not occur as quickly, and this 

method does not apply in the zone of incomplete mixing. Also, 

as discussed earlier, in-stream reactions are extremely 

variable depending on environmental factors such as light, 

temperature, and streamflow. 
ATTACHMENT I 

WATER-SEDIMENT PARTITION COEFFICIENTS

FOR PRIORITY METALS 

by 

HydroQual, Inc. 

1 Lethbridge Plaza 

Mahwah, New Jersey 07430 

November 1982 
I-1

ACKNOWLEDGEMENTS 

The technical analysis reported in the study was 

performed by Michael T. Kontaxis, Project Scientist. Dominic 

M. DiToro and Donald J. O'Connor served as Project Consultants 

and provided technical guidance. John P. St. John served as 

Principal-in-Charge and drafted the report. 

William L. Richardson of the U.S. Environmental 

Protection Agency, ERD-L at Grosse Ile, served as Project 

Officer for the Government. 
I-2

 ABSTRACT 

The Office of Water of the U.S. Environmental Protection 

Agency is responsible for managing waste load allocation (WLA) 

activities throughout the nation. These procedures generally 

involve the application of mathematical modeling activities 

which require specialized information for 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 particulate 

materials in the natural environment. As realistic modeling 

frameworks must properly track both dissolved and particulate 

forms of substance in the receiving water environment, it is 

important to determine partition coefficients for the priority 

metals for use in these analyses. 

It was the purpose of the investigation reported herein 

to retrieve information and data by which to document 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 relationships 

among partition coefficients and various environmental water 

quality variables. 
I-3

TABLE OF CONTENTS 

Section Page 

No. No. 

1 INTRODUCTION................................ 1-1 

1.1 BACKGROUND............................. 1-1 

1.2 OBJECTIVES OF THE STUDY................ 1-2 

1.3 SCOPE OF THE REPORT.................... 1-3 

2 CHEMICAL PARTITIONING....................... 2-1 

3 SUMMARY OF AVAILABLE DATA.................. 3-1 

3.1 DATA REQUIREMENTS........................ 3-1 

3.2 SOURCES OF DATA........................ 3-2 

3.3 AVAILABILITY OF DATA................... 3-3 

3.4 CLASSIFICATION AND DISTRIBUTION OF DATA.... 3-5 

4 METHODS OF ANALYSIS........................... 4-1 

4.1 TECHNICAL OVERVIEW..................... 4-1 

4.2 BIN ANALYSIS........................... 4-1 

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 
I-4

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 

1. The available technical literature contains very limited 

useable data for determination of partition coefficients

for priority metals. 

2. Of the various computerized data bases investigated in 

this study, the water quality file of STORET contains the 

largest amount of pertinent information by a large 

margin, approximately 20,000 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. 

3. Retrieved data were in the following order of abundance 

by priority metal: zinc, copper, lead, arsenic, nickel, 

chromium, cadmium, mercury, and silver. Analysis was 

confined to data collected in streams and lakes. 

Insufficient 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 

representative 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 of data indicated a pronounced apparent 

relationship between partition coefficients for the 

various priority 
I-5

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 among partitioning and other environmental 

measures such as pH, alkalinity, temperature or BOD. 

Partition coefficient values for lakes were determined to 

be consistently greater than for streams for all priority 

metals except mercury. 

6. The partition coefficient values determined for the 

various priority metals are satisfactory for application 

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 

physical, chemical and biochemical factors which may 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. 
I-8

SECTION 1 

INTRODUCTION 

1.1 BACKGROUND 

The Office of Water of the U.S.. Environmental Protection 

Agency is technically responsible for managing waste load 

allocation (WLA) activities within the organization and for 

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 costeffectively 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 area 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 properly determined WLAs, which in turn must be 

based on mechanistically realistic assessments of the 

transport and fate of these materials in the aqueous 

environment. An important characteristic of metals in this 

regard is the affinity to complex and/or 
I-9

be adsorbed (partition) to natural particulate materials. 

Realistic modeling frameworks must have the capacity to track 

both the dissolved and particulate forms of metal in the 

receiving water as each phase may be transported by different 

mechanisms. Fundamental information which is required for this 

purpose is appropriate partition coefficients for metals, 

which define the fraction of metal in dissolved and 

particulate phases for specific environmental conditions. 

1.2 OBJECTIVES OF THE STUDY 

 Specific objectives of the study are summarized as 

follows: 

1) identify and retrieve appropriate reference material 

which contains information on partition coefficients for 

certain priority heavy metals. 

2) identify and retrieve appropriate laboratory and field 

data which are satisfactory for calculation of partition 

coefficients for the priority metals. 

3) calculate partition coefficients for priority metals from 

available data, display the results, and wherever 

possible, relate coefficients to appropriate 

environmental variables. 

4) assess the quality of the data collected, the 

representative- ness of reported or calculated partition 

coefficients and recommend methods for improvement. 

5) provide a report summarizing the results of all work and 

including an appendix containing basic information 

resulting from the evaluations. 
I-10

The specific priority metals of concern which are 

considered in the study are: 

- Arsenic 

- Cadmium 

- Chromium 

- Copper 

- Lead 

- Mercury 

- Nickel 

- Silver 

- Zinc 

1.3 SCOPE OF THE REPORT 

The report summarizes various technical procedures 

which were implemented to obtain, categorize and evaluate 

data for priority heavy metal partition coefficients. 

Theoretical considerations are presented to orovide a 

background for the analysis and to indicate data 

requirements. Required data are summarized by source and 

availability and classification procedures are described. 

Methods of analysis are described for the categorization 

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 are presented and 

discussed. 
I-11

SECTION 2 

CHEMICAL PARTITIONING 

One of the major characteristics which differentiates 

many 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 mass of soluble chemical is placed in a laboratory beaker 

of water, an initial concentration of dissolved chemical, cd, 

will result. If particulate material is then added to the 

beaker and stirred, a portion of dissolved chemical will be 

sorbed onto the particulates and some of the chemical 

concentration will then be in particulate form, cp. If this 

process is monitored with time as shown on the diagram, 

dissolved chemical will be reduced and particulate chemical 

will increase in a reversible reaction until an equilibrium is 

achieved at some point. The total chemical concentration at 

any time is equal to the sum of the dissolved and particulate 

concentrations: 

 (2-1) 

in which ct is total chemical concentration and all 

concentrations are expressed, on a bulk volume (liquid plus 

solid) basis. 

The rate with which this reaction takes place and the 

relative relationship between the dissolved and particulate 

chemical, that is, the water-sediment partitioning, are both 

chemical specific. In most cases, reaction between the 

dissolved chemical and particulates occurs very rapidly, 

minutes to hours, and equilibrium is achieved quickly relative 

to the time characteristics of the environmental setting. The 

tendency to sorbs is highly chemical specific and will range 

from very weak to string, in the case of materials with low 

solubility. 
I-12

SCHEMATIC DIAGRAM OF CHEMICAL SORPTION
I-13

The affinity of a particular chemical or heavy metal to 

sorb can be quantitatively expressed by a sediment-water 

partition "coefficient, Kp. A series of experiments of the type 

schematically indicated on Fiqure 2-1 may be conducted with 

differing initial dissolved concentrations of a specific 

chemical. After equilibrium is achieved, the particulate 

chemical concentration to suspended solids ratio, cp/ss, 

expressed as micrograms of chemical per gram of particulate 

material (μg/g), may be plotted as a function of the dissolved 

chemical remaining, cd, expressed on a volumetric basis as 

microgram per liter of water (μg/l).

Figure 2-2 schematically illustrates the results of this 

laboratory experiment. A specific chemical will produce 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 which can be plotted and correlated as shown on 

Fiqure 2-2 behave according to the Freundlich isotherm defined 

as:

 (2-2) 

lationship, the partition coefficient is defined as:

in which n is a constant characterizing the slope of the 

relationship. If the slope is near 1 indicating a linear

re

 (2-3) 

on

Figure 2-3 for a specific type of sorbing particulate 

material. However, 

As indicated, a specific chemical or heavy metal will

yield one of the relationships indicated schematically
I-14

FIGURE 2-2 

EXAMPLE ISOTHERMS AND PARTITION COEFFICIENTS
I-15

different relationships, and therefore, different partition 

coefficients, may be observed for the sane chemical with 

various types of sorbants. For example, organic particulates 

or silty materials may attract a certain chemical more 

strongly than sandy materials. Further, different size classes 

of particulate material, as they may reflect different classes 

of particulates as sands, silts, clays, etc., may exhibit 

differing affinities, and partitioning, for a specific 

chemical. In principle, it is most advantageous, therefore, to 

perform experiments and determine a chemical's partitioning 

characteristics with the type of particulate material 

(suspended and bed sediment) to which it will come in contact 

in the natural environment. 

As described, the nature of the sorbant may have a 

hearing on the magnitude of the partition, coefficient for a 

particular substance. It has also been observed (O'Connor and 

Connoll) that partition, coefficients may vary in accordance 

with the concentration of the sorbant as well as its nature. 

Figure 2-3 presents some empirical relationships between 

partition coefficients and sediment concentration for a 

variety of substances. For certain chemicals, it is observed 

that partition coefficients may be expected to vary by an 

order of magnitude or more depending upon the solids 

concentration. In the case of heavy metals, other factors such 

as pH, alkalinity or hardness, temperature, and conductivity 

may have an effect on the partition coefficient due to the 

complex chemical reactions which occur with these substances 

in the natural environment. 

Much valuable information with which to. define partition 

coefficients and relationships with various environmental 

variables can be determined from carefully developed and 

controlled laboratory experiments. It is noted, however, that 

such values are to be used subsequently in calculations to 
VARIATION OF PARTITION COEFFICIENT WITH SOLIDS 

CONCENTRATION

FIGURE 2-3

 

I-16
I-17

estimate what occurs in the field, this is, in the natural environment. Although the laboratory 

data are quite useful, it is appropriate to utilize field data wherever available in order to 

calculate partition coefficient values from those settings to which they will be subsequently 

applied. These natural conditions are likely to be far more physically, chemically, and 

biologically complex than the laboratory setting, but the information derived, therefore, is 

realistic in the natural sense and can serve to indicate how well or poorly partition 

coefficients can be defined for the natural setting. 
I-18

SECTION 3 

SUMMARY OF AVAILABLE DATA 

3.1 DATA REQUIREMENTS 

The minimum basic data requirements for calculation of 

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 solids - 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 sorbing 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 

suspended solids, biochemical oxygen demand (BOD), chemical 

oxygen demand (COD), and chlorophyll-a. Flow data, whether 

low, average, or high in a particular stream, nay provide some 

indication of the type of particulate material (sand, silt, 

clay) likely to comprise suspended sediment in the water 

column in the absence of other information. 
I-19

The foregoing type of information was sought from both 

laboratory and field investigations. In the case of field 

data, data were sought for both water column and bed sediment

and for different types of water body, particularly streams 

and lakes. A distinction between partition coefficients

calculated for 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 from various types of 

surveys resides on computerized data bases. The following data 

bases were examined for data availability: STORET (USEPA), 

NASQAN (USGS) , the data base maintained by NOAA, and STAR 

(Canadian Centre for Inland Waters). In addition, the 

computerized reference service DIALOG was utilized to identify 

and secure additional reference material including both field 

and laboratory investigations. These data bases were reviewed 

to avoid potential multiple counting of samples. The following 

is a brief description of these data bases in relation to this 

project: 

1) STORET 

The water quality file of this data base contains water 

quality information obtained at numerous stations located 

in all states and operated by various agencies. It is the 

largest data base for water quality information by far. 

2) NASQAN 

Water quality data of this file are contained in STORET. 

NASQAN data updates are transferred to STORET on a biweekly 

basis. Data can be retrieved in the USGS format. 
I-20

This data base contains data for ocean, near-shore are 

estuarine samples. The estuarine samples cover the areas of 

Puqet Sound and New York Bight. A search of these areas was 

requested. The search for Puget Sound revealed no metals 

data. The search request for New York Bight did not result 

in information in time for processing. 

Environmental Bibliography 

Instructional Resources Information Systems 

3) NOAA 

4) STAR 

This data base contains two subsystems. One subsystem 

contains data exclusively for the Great Lakes. The other 

subsystem, NAQUADAT, contains river water quality data for 

Canada. The NAQUADAT data are not included in STORET and it 

has not been updated in the last four years. The cost per 

retrieval request was prohibitively expensive and beyond 

the scope of this investigation. 

5) DIALOG 

This reference service contains numerous citations of 

publications in a great variety of technical fields. The 

citations are arranged in subject-related files. A strategy 

. was devised to search relevant files in order to identify 

sources containing data consistent with the requirements of 

the study. The files searched were: 

National Technical Information Service 

Water Resources Abstracts 

Pollution Abstracts 
I-21

Enviroline 

Compendex 

Ocean Abstracts 

Comprehensive Index (Dissertation Abstracts) 

A small number of usable publications were identified and 

obtained. 

5) Miscellaneous 

A search of all pertinent in-house publications was 

conducted. 

3.3 AVAILABILITY OF DATA 

The small number of references obtained from DIALOG contained 

no significant amount of useful information. Only a modest 

/amount of pertinent data was obtained from in-house sources 

of reference material. On this basis, and in accordance with 

the evaluation of the various computerized data bases as 

described above, it was concluded that the water quality file 

of STORET would provide the vast majority of usable samples, 

and almost all effort should be directed to this source. A 

retrieval strategy was then devised to search the data records 

of all stations in all states, and identify ambient, remarkcode-free samples which contain, at minimum, the concentration

of total and dissolved species of metal and suspended solids. 

For such samples, data retrieval also included temperature, 

pH, alkalinity, hardness, flow, COD, BOD, volatile nonfilterable residue (volatile suspended solids), chlorophyll-a 

corrected, and uncorrected chlorophyll. No data were available 

on the size distribution of suspended solids in the water 

column. Although data are available on bed sediment samples, 

the information is not 
I-22

sufficiently complete (dissolved chemical missing) 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 STORET retrievals that the 

useful data base for some of the priority metals was quite 

large with samples of interest numbering in the thousands. 

Data handling and subsequent analysis was therefore performed 

by computer. All of the basic minimum data which were 

retrieved for each priority metal permitted calculation of a 

partition coefficient in accordance with Equation (2-3). 

However, a purpose of the study was to assess any 

relationships between partitioning and various environmental 

variables as discussed. In order for these determinations to 

be valid, the basic data must be measured simultaneously on 

the same sample or at the same time. Hence, the data base for 

each priority metal was sorted into various types of records, 

each of which was characterized by the simultaneous 

determination of various parameters. In this manner, a large 

data base could be examined for sampling 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-23

Type Data Content of Record

0 Total and dissolved metal and 

suspended solids concentrations 

1 data of type 0 and pH 

4 data of type 3 and (COD or BOD) 

suspended solids, or chl-a corrected 

or chl-a uncorrected) 

2 data of type 1 and alkalinity 

3 data of type 2 and temperature 

5 data of type 4 and (volatile 

6 data of type 4 and 5 

Hardness was judged to be redundant with alkalinity for 

correlation purposes and thus not included in the data 

records. Flow information was judged to be too meager for 

meaningful, correlation and also excluded from evaluation. 

For each priority metal, the total number of records and 

the distribution of these records per type was determined for 

each station in each state. At the end of the first stage of 

data processing, a summary table was developed for each metal 

which contains an aggregation of records and stations per 

state, a distribution of records per type for each state and 

the total number of all records. 

Table 3-1 is an example data summary for the priority 

metal, 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 for zinc is 5397. 

These records include data from streams, lakes, estuaries, 

coastal zones, manholes, and other miscellaneous origins. 

Figure 3-1 is a 
I-24

TABLE 3-1

ZINC DATA SUMMARY 
I-25

FIGURE 3-1 
I-26

summary of the geographical distribution of the available 

records by state. Similar information is presented for each of 

the priority metals on Figures A-1 through A-9 in the 

Appendix. 

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 3166 

arsenic 2035 

nickel 1998 

chromium 820 

mercury 631 

cadmium 799 

silver 50 

These records formed the basis of the subsequent 

technical analysis. 
I-27

4.1 TECHNICAL OVERVIEW 

A substantial amount of data for the priority metals is 

available for analysis. Approximately 20,000 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 strategy for data handling 

and analysis. As a result, specialized software was developed 

to process STORET information for each priority metal which 

was contained on a series of maqnetic tapes. This software was 

used to select appropriate data, compartmentalize it as 

appropriate into a series of "bins" and calculate partition 

coefficients. The values were then subsequently processed by 

various statistical techniques to search for relationships 

among partitioning and various environmental variables. 

SECTION 4 

METHODS OF ANALYSIS 

4.2 BIN ANALYSIS 

An objective of the study is to determine correlations, 

if any, which exist among calculated partition coefficients 

and ambient environmental conditions as represented by various 

physical, chemical and biochemical measurements simultaneously 

performed or observed. The data records for each priority 

metal 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 serves 

of compartments, or bins, each of which would be specifically 

defined by a particular range of environmental variables. For 

example, a bin 
I-28

may be defined as that portion of the data base which has 

limits. of suspended solids of 10 to 30 mg/l, a pH range of 

6.0 to 7.0, and alkalinity from 20 to 50 mg/l, etc. All data 

records falling within various bins defined in a similar 

manner could then be used to calculate partition coefficients

for those ranges of the environmental variables. The resulting 

information could then be evaluated by direct observation as 

well as statistically to assess any relationships which may 

exist. 

The computer program developed to implement the bin 

analysis selects data for each priority metal on the basis of 

station type, so that the data base can be segregated for 

streams, lakes, etc. A series of bins are then established 

with the dimensions of the bins specified as appropriate 

values for the environmental variables under consideration. 

The dimensionality of the bin is related to the type of data 

record; that is, number of variables, under consideration. As 

a general rule, the greater the number of variables under 

consideration, the greater is the number of bins and therefore 

the less amount of partition coefficient information within 

each bin. The data base for increasing numbers of concurrently 

measured environmental variables also becomes more restricted. 

This is of concern from a statistical standpoint. The total 

number of bins is the product of the number of intervals 

established for each of the variables. The analysis proceeds 

with the calculation of partition coefficients from the data 

records falling within each bin, the bin mean partition 

coefficient and the coefficient of variation. The bin 

statistical parameters are calculated under the assumption 

that the individual partition coefficients are log normally 

distributed within the bin. The assumption of log normality 

was tested with zinc and was found to be true. 
I-29

4.3 STATISTICAL ANALYSIS 

At the conclusion of the bin analysis, a statistical 

analysis was performed to assess any empirical functional 

relationships between the calculated partition coefficients

and the environ-mental variables selected to define the bins. 

A multiple correlation routine was used for this purpose. The 

following generalized regression equation was applied. 

χ , χ = environmental variables (mg/l) 

H = hydrogen ion concentration (as a measure of pH) 

Equation (4-1) as stated, provides for an analysis of the 

relationship between partition coefficients, temperature, pH, 

and two environmental variables. Any number of environmental 

variables could be specified as appropriate. In the regression 

analysis, all bins with population five or less are rejected 

as statistically not significant. For each of the remaining 

bins, the means of the intervals defining the bins were 

correlated with the mean of the partition coefficient of the 

bin. These quantities were used in the linearized form of the 

above relation: 

(4-2) 

(4-1) 

in which: 

Kρ = partition coefficient (l/kg)

1 2

T = temperature (°C)

Kpo, χ, β, γ, τ = coefficient and exponential constants 

determined from regression
I-30

The regression analysis includes appropriate statistical 

measures such as correlation coefficient, r, and standard 

deviation ??, ?? the exponential constants. 
I-31

RESULTS OF ANALYSIS 

5.1 PARTITION COEFFICIENTS

The bin analysis was used to calculate partition 

coefficients for each priority metal 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, the bin analyses were restricted to three 

dimensional arrays. The 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: 

Suspended solids (mg/l)

SECTION 5 

pH

10 to 30; 30 to 50; 50 to 100; 100 to 200; 200 to 500 

5.0 to 6.0; 6.0 to 7.0; 7.0 to 8.0; 8.0 to 9.0 

Alkalinity

0 to 20; 20 to 50; 50 to 100; 100 to 200; 200 to 500 
I-32

Thus, the initial analysis of stream data consisted of 

100 compartments 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/l. 

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. 

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 relationship, 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. 

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. 

Table 5-2 presents a summary of the statistical 

parameters obtained for zinc in streams. The table indicates 

an overall geometric mean partition coefficient of 

approximately 55,000 l/kg 
I-33

TABLE 5-1 

BIN ANALYSIS

ZINC IN STREAMS 
I-34
I-35

TABLE 3.2 

SUMMARY OF STATISTICAL PARAMETERS FOR ZINC 

REGRESSION WITH SOLIDS, PH, ALKALINITY 

0 1 2 3 4 5 6 Total 

Records 

Records by Type 1988 379 686 861 663 674 146 5397 

Sort by Type _>24 by Water Body Stream: Records Analyzed 1782 

FIRST BIN ANALYSIS 

No. of Acceptable Bins (> 5 Records) 50: Bin Avg Kρ (l/kg) = 

54,916

CORRELATION COEFFICIENTS (partial) 
I-36

TABLE 5-2 (continued) 
I-37

in which ss suspended solids, ALK is alkalinity with other 

terms as previously defined. The exponential constant, α, 

taken as that determined in the first bin analysis. The 

product of the partition coefficient and suspended solids, the 

partition parameter, as shown above was then regressed with 

alkalinity and pH. Simple and multiple regressions were 

performed. 

Table 5-2 indicates relatively weak correlation 

coefficient and exponential constants resulting from this 

analysis. 

from all data. Simple correlation coefficients are presented 

for partition coefficient variation with suspended solids, pH, 

and alkalinity, respectively. These values indicate a very 

strong dependency of partitioning with suspended solids and 

weak relationships with the other variables on a simple basis. 

Statistical parameters are also shown for the multiple stepwise regression. Exponential constants, multiple correlation 

coefficients, and the standard deviation of the exponential 

constants are presented for the sequential regression of 

partition coefficients with suspended solids, suspended solids 

and pH, and finally suspended solids, pH and alkalinity.

It is observed from Table 5-2 that the exponential 

constants for pH, as represented by the hydrogen ion 

concentration, and alkalinity are relatively small. Further, 

the multiple correlation coefficient is not markedly improved 

by the inclusion of these variables in the regression. It is

possible that the strong dependency of partitioning with 

solids is masking a correlation with these variables. Hence, a 

second bin analysis was performed where the solids dependency 

of partitioning was removed by rearranging the regression 

equation: 
I-38

A similar type of regression analysis was performed to 

determine any relationship between the partitioning parameter 

and other environmental variables: temperature, and BOD 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 time 

constraints of the investigation to assess other potential 

interrelationships. 

From the foregoing analyses, it 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 

strong correlation was indicated between the mean partition 

coefficient value 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 was based on analysis of all available data records 

which contained the basic information necessary for 

calculation of the partition coefficient. The only data needed 

to determine these relations 

Graphical presentation of the results supports the 

observation. Figure 5-1 shows the partitioning parameter Kρ

.

ss

-

α

, for zinc plotted as a function of alkalinity for various pH 

levels in bot.. streams and lakes. Similarly, Figure 5-2 

presents the same data with the partitioning parameter plotted 

as a function of pH for various ranges of alkalinity. No 

consistent trends are observed from these diagrams. The 

multiple regression analysis was performed on stream and some 

lake data for all priority metals with similar results. 
I-39

FIGURE 5-1

Kρ* AS FUNCTION OF ALKALINITY
I-40

FIGURE 5-2 

Kρ AS FUNCTION OF ALKALINITY
I-41

TABLE 5-3 

Records by Type 1988 379 686 861 663 674 146 5397 

SUMMARY OF STATISTICAL PARAMETERS FOR ZINC 

 0 1 2 3 4 5 6 Total Records 

Sort by Type 0 & by Water Body streams: Records Analyzed 2253 

FIRST BIN ANALYSIS 
I-42

ships are dissolved and particulate chemical, and suspended 

solids. The lowest order of data records, Type 0, and above 

could be used, thus substantially expanding the data base for 

final analysis. Hence, the final correlations between 

partitioning and suspended solids concentration in streams and 

lakes were based on all available data records in STORET 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 from 

estuarine, ocean, miscellaneous sources, etc., not included in 

the analysis. 

An illustration of the results of this analysis is 

presented on Figures 5-3A and 5-3B, for the priority metal 

zinc in streams and lakes, 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 coefficients were calculated from the data records 

falling within each compartment, and the bin means and other 

statistical parameters were determined. The bin means were 

then regressed with bin mean suspended solids concentrations. 

The logarithmic diagrams present the bin mean partition 

coefficients plotted with bin average suspended solids 

concentration and 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. 
I-43

FIGURE 5-3A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

ZINC IN STREAMS 
I-44

FIGURE 5-3B 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

ZINC IN LAKES 
I-45

The analysis indicated above was performed for each 

priority metal for both streams and lakes. The executions were 

silver (streams and lakes) and arsenic (lakes) for which 

sufficient information was not available. Figures 5-4A and 5-

4B present a summary of the regression lines for partition 

coefficients and suspended solids for the various priority 

metals for streams and lakes, respectively. These diagrams can 

be used for the best estimate of a partition coefficient value 

for a particular priority metal based on suspended solids 

concentration. Figures A-10 through A-17 in the Appendix 

present the data and the regression lines for each priority 

metal in streams and lakes. These diagrams should be consulted 

in the selection of a partition coefficient value in order to 

indicate the degree of variability which may exist around a 

particular value on the basis of the analysis of available 

data. 

Table 5-4 is a summary of the statistical properties of 

the regression equations for partition coefficients and 

suspended solids as developed for the priority metals. The 

number of data records in each evaluation is indicated along 

with the exponential constant, correlation coefficient, and 

standard deviation of the slope. It is evident from the table 

that the bin mean partition coefficients are very highly 

correlated with suspended solids in all cases but lead. 

It is noted that the calculated partition coefficients 

from lakes are consistently greater than values in streams for 

the same priority metal, in all cases except for mercury. This 

may be due, in general, to a more organic nature of suspended 

materials in lakes than in streams. 
I-46

METALS: 

1 - ARSENIC 

3 - CHROMIUM 

6 – MERCURY 

8 - ZINC 

2 - CADMIUM 

4 – COPPER 

5 – LEAD 

7 - NICKEL 

FIGURE 5-4A

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

ALL METALS IN STREAMS
I-47

2 - CADMIUM 

4 - COPPER 

6 - MERCURY 

FIGURE 5-4B 

METALS: 

1 - ARSENIC (NO DATA FOR LAKES) 

3 - CHROMIUM 

5 - LEAD 

7 - NICKEL 

8 - ZINC 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

ALL METALS IN LAKES
I-48

STATISTICAL PARAMETERS FOR VARIOUS METALS REGRESSION WITH 

SUSPENDED SOLIDS

TABLE 5-4 

* Insufficient bins to perform regression 
I-49

APPENDIX 
I-50

TABLE A-1 STATE CODES 

Alaska 02 Montana 30 

Arkansas 05 Nevada 32 

Colorado 08 New Jersey 34 

Delaware 10 New York 36 

Hawaii 15 Oklahoma 48 

Illinois 17 Pennsylvania 42 

Alabama 01 Missouri 29 

Arizona 04 Nebraska 31 

California 06 New Hampshire 33 

Connecticut 09 New Mexico 35 

District of Columbia 11 North Carolina 37 

Florida 12 North Dakota 38 

Georgia 13 Ohio 39 

Idaho 16 Oregon 41 

Indiana 18 Rhode Island 44 

Iowa 19 South Carolina 45 

Kansas 20 South Dakota 45 

Kentucky 21 Tennessee 47 

Louisiana 22 Texas 48 

Maine 23 Utah 49 

Maryland 23 Vermont 50 

Massachusetts 25 Virginia 51 

Michigan 26 Washington 53 

Minnesota 27 West Virginia 54 

Mississippi 28 Wisconsin 55 

Wyoming 56
FIGURE A-1 

ARSENIC DATA RECORDS AVAILABLE BY STATE

I-51
FIGURE A-2 

CADMIUM DATA RECORDS AVAILABLE BY STATE

I-52
FIGURE A-3 

CHROMIUM DATA RECORDS AVAILABLE BY STATE

I-53
FIGURE A-4 

COPPER DATA RECORDS AVAILABLE BY STATE

I-54
FIGURE A-5 

LEAD DATA RECORDS AVAILABLE BY STATE

I-55
FIGURE A-6 

MERCURY DATA RECORDS AVAILABLE BY STATE

I-56
FIGURE A-7 

NICKEL DATA RECORDS AVAILABLE BY STATE

I-57
FIGURE A-8 

SILVER DATA RECORDS AVAILABLE BY STATE

I-58
FIGURE A-9 

ZINC DATA RECORDS AVAILABLE BY STATE

I-59
I-60

FIGURE A-10A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

ARSENIC IN STREAMS 
I-61

CADMIUM IN STREAMS 

FIGURE A-11A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
I-62

FIGURE A-11B 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

CADMIUM IN LAKES 
I-63

FIGURE A-12A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

CHROMIUM IN STREAMS 
I-64

FIGURE A-12B 

CHROMIUM IN LAKES 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
I-65

COPPER IN STREAMS 

FIGURE A-13A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
I-66

COPPER IN LAKES

FIGURE A-13B 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
I-67

LEAD IN STREAMS 

FIGURE A-14A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
I-68

LEAD IN LAKES 

FIGURE A-14B 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
I-69

MERCURY IN STREAMS 

FIGURE A-15A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
I-70

MERCURY IN LAKES 

FIGURE A-15B 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
I-71

FIGURE A-16A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

NICKEL IN STREAMS 
I-72

FIGURE A-16B 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

NICKEL IN LAKES 
I-73

FIGURE A-17A 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

ZINC IN STREAMS 
I-74

FIGURE A-17B 

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS

ZINC IN LAKES
II-1

ATTACHMENT II 

Catalogue of Waste Load Allocation

Models for Toxic Compounds 

EPA Contract No. 68-01-6160 

Work Order No. 8 

Prepared by: 

Versar Inc. 

6850 Versar Center 

Springfield, Virginia 22151 

Under Subcontract to: 

Arthur D. Little, Inc. 

Cambridge, Massachusetts 02140 

Prepared for: 

U.S. Environmental Protection Agency 

Monitoring and Data Support Division 

Washington, D.C. 20460 

April 1984 
II-2

List of Model Summaries 

TABLE OF CONTENTS 

Page No.

Introduction ........................................................................................................................ II-1 

Simple Desk Calculator Method

Water Quality Assessment Methodology (WQAM)................................. II-2 

Steady State Models 

Simplified Lake/Stream Analysis (SLSA) ............................................. II-7 

Michigan River Model (MICHRIV) ..................................................................... II-11 

Chemical Transport and Analyses Program (CTAP) ..................... II-14 

Exposure Analysis Modeling System (EXAMS) .................................... II-18 

Metals Exposure Analyses Modeling System (MEXAMS) ............ II-24 

Time-variable Models

Estuary and Water Quality Model (WASTOX) ....................................... II-28 

Chemical Transport and Fate Model (TOXIWASP) ........................... II-31 

Toxic Organic Substance Transport and Bioaccumulation 

Model (TOXIC) ........................................................................................................................ II-35 

Channel Transport Model (CHNTRA) ............................................................... II-39 

Finite Element Transport Model (FETRA) ............................................. II-43 

Sediment-Contaminant Transport Model (SERATRA) ..................... II-48 

Transient One-Dimensional Degradation and 

Migration Model (TODAM) .......................................................................................... II-53 

Hydrologic Simulation Program-FORTRAN (HSPF) ........................... II-57 
II-3

Simple Desk Calculator Method
II-4

Introduction

Water quality based effluent limitations, as envisioned by 

Section 303 of the Clean Water Act, call for an analysis of the 

capabilities 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 be accomplished on a sitespecific basis using mathematical models. 

The desirability of controlling toxic pollutant discharges has 

lead 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 summarize the characteristics and capabilities of 

several of these simulation routines and thereby assist the 

potential user in identifying, selecting, and applying the 

documented and supported mathematical toxic pollutant fate models 

appropriate for the problem at hand. It is understood that final 

model selection will be a function of several important factors: 

complexity of the aquatic system simulated (e.g., number and nature 

of pollutant sources, system hydrodynamics, physico-chemical 

properties of pollutant, etc.); available resources (i.e., relevant 

data bases, technical expertise, economic, and hardware); and the 

ultimate use of the simulation results (e.g., permit writing vis-avis waste load allocation analysis, scientific research in pollutant 

behavior, etc). It is up to the analyst to prioritize each of these 

factors according to the constraints of his particular analysis. 

The models discussed in this catalogue do not represent the 

universe of available toxic simulation routines; however, they do 

represent an adequate cross-section exhibiting a wide range of 

complexity and may be considered representative of the types of 

models likely to find use. Furthermore, this catalogue does not 

include models with toxicant simulation capabilities limited to the 

processes of pollutant delution and simple first-order decay in the 

water column. 

All of the models listed here are general in nature; they take 

on the characteristics of a specific site only by specifying the 

parameter values appropriate to that site. They are all readily 

available and at least moderately well documented. All predict 

toxicant concentrations in the water column an in the bed sediment. 

As a group, they apply to streams, rivers, impoundments, lakes, 

estuaries, and coastal waters, although some individual models are 

limited to particular types of waters. 

Each abstract summarizes the model's theoretical 

characteristics, Input requirements, output information, primary 

strengths, limitations, and response requirements. It also indicates 

where the model has been applied, and how to obtain further 

information and user support. 
II-5

Water Quality Assessment Methodology (WQAM)

Summary

The methodology was designed as a screening procedure 

that makes use of available generic data. The analyses require 

little external Input since much of the needed information is 

provided by tables and figures within the manual. It predicts 

far field, average steady-state pollutant concentrations in 

rivers, lakes, and estuaries as a function of long term 

average maximum and minimum non-point source and point source 

loads. 

Calculations performed by WQAM are divided into four 

sections. The first set is for waste load estimations of toxic 

and conventional pollutant loadings from both point and nonpoint sources. Procedures include load estimations for single 

event and annual loads from agricultural, forested, and urban 

areas. The Universal Soil Loss Equation (USLE) is used for 

agricultural areas; the URS Urban Water Quality Management 

procedure (Amy et al. 1974) and the Stormwater Management 

Model (SWMM) Level One Screening procedure are used for urban 

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 by the second set of calculations. 

Variations in longitudinal pollutant concentrations are 

estimated. The calculations are based on steady-state, plug 

flow solutions to the conservation of mass equation. 

Conventional pollutant interactions presented include 800,00, 

temperature, coliforms, nutrients, and sediment transport. 

Procedures for toxic chemicals include methods for point and 

non-point sources as well as for large single event spills. 

The fate and transport of toxic chemicals are assessed using 

volatilization, sorption, and first order degradation.

The water Quality Assessment 

Methodology (WQAM) (Mills et al. 1982) 

was developed by Tetra Tech Inc. of 

Lafayette, California; Monitoring and 

Data Support Division, OWRS; and the 

Center for Environmental Research 

Information (CERI). The methodology 

was designed to perform preliminary 

(screening) assessments of surface 

freshwaters. The original methodology (Zison et al. 1977) 

addressed the identification of problems associated with 

sediment, nutrients, dissolved oxygen, and some urban 

pollutants in streams, lakes, and estuaries. The updated 

version now includes provisions for the assessment of toxic 

chemicals in the environment. WQAM is a simplified methodology 

in that all of the calculations are intended to be solved by a 

desk calculator.

Capsule Summary: WQAM

• Far-field, steady scare, 1-

dimensional model. 

• Procedures for assessing 

river, lake, and estuary 

water quality. 

• First-order decay kinesics. 

• Requires only a desk top 

calculator for calculations.
II-6

Methods for assessing water quality and physical 

conditions in lakes are addressed in the third section. They 

are based on empirical stratification relationships and mass 

balances. In addition to toxic materials, sediment

accumulation, thermal stratification, 800-00 interactions, and 

eutrophication are covered. The fate of toxic pollutants is 

estimated with respect to biological uptake and 

bioconcentration in addition to the physico-chemical 

properties of the water and the chemical. 

The last section provides methods for estuarine water 

quality assessment and prediction. The procedures include 

means for estuary classification (vertically stratified or 

vertically well mixed), turbidity, sedimentation, thermal 

pollution, transport of conservative and non-conservative 

pollutants, and flushing time prediction of pollutants. 

Analysis of longitudinal pollutant distribution 1s estimated 

by two different methods, the far field approach and the near 

field approach. The near field approach accounts for the 

buoyancy and momentum effects of the pollutant while the far 

field approach ignores them. 

WQAM is designed to operate with minimum data, 

recognizing that the more data available, the more accurate 

the analysis. Although WQAM provides much information, most of 

it is general in nature. 

Input Data Requirements

WQAM provides most of the data required for the calculations. 

In general, the only data not provided are climatic and 

hydrologic. Climatic data needed Include precipitation, cloud 

cover, and humidity. The type of hydrologic information 

required includes runoff quantities, statistical flows such as 

7010, stagnant regions, stratification, and estuarine tidal 

prism. Other basic information is also needed such as land 

use, stream lengths, lake depths and volumes, and estuary 

salinity distributions. 

Output Descriptions

WQAM's 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 800, 00, total N, total P, and 

temperature. 

• lake nutrient concentrations, eutrophic status, and 

hypolimnion 00 concentrations. 

• estuarine concentrations of 800, 00, and ota. N and P. 
II-7

Advantages and Limitations

The major advantage of WQAM lies in its simplistic 

approach to waste load assessments. All the equations in the 

methodology are algebraic, and they can be solved using a desk 

calculator. This is a major advantage over other models in 

that the user does not need any programming experience. WQAM 

provides "typical" data which can be used in lieu of actual 

data for predicting chemical concentrations. Another advantage 

of WQAM is that it can be used for waste load assessments of 

estuaries as well as lakes and rivers. 

Because of its simplistic approach, WQAM cannot include 

all the physico-chemical processes acting upon a pollutant. It 

is designed for long-term pollutant loading and average 

steady-state conditions and does not address the short-term 

effects that may be associated with toxics loadings. The 

methodology does cover the assumptions under which the 

algorithms are developed and provides the user the limitations 

of some of the tools presented. 

WQAM closely relates the loading of conventional and 

toxic pollutants to receiving waters to the loss of soils and 

sediments and the amount of runoff. The non-point source

loading section utilizes the Universal Soil Loss Equation for 

agricultural areas. It was developed primarily for croplands 

and does no: predict erosion from streambanks, ditches beside 

roads, or gullies. The stream and river section is based on 

steady-state. plug flow solutions. It assumes dispersion to be 

small compared to advection. The calculations assume the lotic 

system to be vertically and laterally mixed and that any decay

of the pollutant to be first order. 

In estimating the fate and transport of pollutants in a 

lake, the methodology accounts for biodegradation, 

volatilization, and sedimentation. However, the model neglects 

several important physico-chemical processes (e.g., 

photolysis, oxidation, hydrolysis, coagulation-flocculation. 

and precipitation). 

The transport of pollutants in estuaries assumes 

continuous, steady-state discharges of pollutants. The 

distribution of pollutants is based on 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. 
II-8

Model Applications

The original WQAM was applied and tested on the Sandusky 

River Basin and four Chesapeake Bay sub-basins: the Patuxent, 

Chester, Ware, and Occoquan. 

The model was used for simulating sedimentation, 

stratification, eutrophication, and 00 depletion. 

The modeling of toxic pollutants has been done using data 

from Coralville Reservior, Iowa. The Insecticide dieldrin had 

accumulated in the reservior from its use on agricultural 

fields. The data from Schnoor's (1981) report on the reservlor 

was used to test the accuracy of WQAM. The results showed WQAM 

to be in agreement with Schnoor (1981) for dieldrin 

concentration in both water and fish tissue. 

Resource Requirements

All of the calculations 1n WQAM are algebraic expressions 

requiring only a hand calculator for solving. A programable 

calculator might be useful for the numerous advectivedispersion equations in the estuary section. 

User Support Activities 

User assistance may be obtained by contacting: 

Copies of WQAM (EPA-600/6-82-004a-b) are available from 

the CERI, U.S. EPA, Cincinnati, Ohio, (534-684-7562). 

Robert Ambrose 

USEPA 

EPA Athens Environmental Research Laboratory 

Center for water Quality Modeling 

Athens, Georgia 30613 

FTS 250-3585 COM 404-546-3585 

General References

Amy G. Pitt R. Singh R, Bradford WL, LaGraff MB. 1974. Water 

quality management planning for urban runoff. U.S. 

Environmental Protection Agency, Washington, D.C. EPA440/9-75-

004; PB 241 689/AS. 

Mills WB, Dean JD, Porcella DB, et al. 1982. Tetra Tech, Inc. 

water quality assessment: a screening procedure for toxic and 

conventional pollutant Part ... Athens, Georgia: Environmental 

Research Laboratory. Office of Research and Development, U.S. 

Environmental Protection Agency. EPA-600/6-82-004a. 
II-9

Zison SW, Haven K, Mill WB. 1977. Tetra Tech. Inc. water 

quality assessment: a screening methodology for nondesignated 

208 areas. Athens. Georgia: Environmental Research Laboratory. 

Office of Research and Development, U.S. Environmental 

Protection Agency. EPA-600/9-77-023.

Mills WB, Dean JD, Porcella DB, et al. 1982. Tetra Tech, Inc. 

water quality assessment: a screening procedure for toxic and 

conventional pollutants. Part 2. Athens, Georgia: 

Environmental Research Laboratory. Office of Research and 

Development, U.S. Environmental Protection Agency. EPA-600/6-

82-004b. 

Schnoor JL. 1981. Fate and transport of dieldrin in Coralville 

Reservior: residues in fish and water following a pesticide 

ban. Science 211:840-842. February 20. 
Steady State Models
II-9

Simplified Lake/Stream Analysis (SLSA) 

Summary

The Simplified/Lake Stream 

Analysis (SLSA (HydroQual 1982) is 

a simplified waste load assessment 

model developed by HydroQual Inc., 

Mahwah, New Jersey, for the 

Chemical Manufacturers 

Association, Washington, D.C. It 

analyzes organic and inorganic 

chemicals in simplified lake and 

stream settings. SLSA calculates 

the dissolved and sorbed steady state concentrations of a 

pollutant in the water column and bed sediment using an 

analytical solution. The model provides a less rigorous 

approach to pollutant simulation than complex computer 

programs. It is most applicable to single (or bunched) point 

source loadings. The intent of this model is to make the 

chemical fate of a pollutant in a freshwater system 

understandable to an unspecialized analyst.

SLSA only considers advection in 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 methods for evaluating the interactions 

between the water column and bed sediment particulate 

sedimentation and resuspension and diffusive exchange are 

provided. 

SLSA is essentially a l-dimensional, steady-state model; 

however, it is capable of three quasi-time varying, analyses 

of lakes in which the pollutant discharge rate is at steady 

Capsule Summary: SLSA

• Steady state and time varying, 

1-dimensional, compartment 

model. 

• Freshwater, non-tidal aquatic 

systems. 

• Simple first-order kinetics. 

• Suitable for hand calculation 

or simple FORTRAN program. 

• Easy to set up and use.

SLSA models streams and rivers as being well mixed in 

cross-section and as having a relatively constant flow and 

geometry. An analytical solution is given for pollutant 

concentration as a continuous function of distance downstream 

from the loading source. The model also estimates pollutant 

concentrations in the water column and bed sediment of 

unstratified Impoundments or lakes. SLSA simplifies the 

hydrodynamics of the systems; aqueous transport is a function 

of the mean inflow rate of water, the depth and volume of the 

segment modeled, and the stream velocity or lake hydraulic 

retention time. Sedimentation and exchanging bed conditions 

are accounted for, however, the bed is assumed to be 

completely uniform.
II-10

state. The first time-variable evaluation pertains to the 

water column and bed sediment pollutant responses to an 

instantaneous chemical load. The other two evaluations deal 

with the water column and bed sediment responses to either an 

Initiation or cessation of long term pollutant loading. 

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 small 

core requirements of the program and the speed of execution 

make the program very compatible with microcomputers. 

Input Data Requirements

Input data requirements for SLSA are limited and are 

arranged in three groups: 

• Pollutant Input - pollutant loadings to the receiving 

water, 

• 

• Transfers and kinetics - water column and sediment 

partition coefficients; water column photolysis, 

volatilization, hydrolysis, oxidation, and 

biodegradation first-order coefficients; sediment 

hydrolysis, oxidation and biodegradation rates. 

Transport Information - flow rates, water and 

sediment depths, length of stream, solids in water 

column and sediments, sedimentation and resuspension 

velocities, diffusive exchange. 

The model defines the data requirements and explains how to 

evaluate and prepare it for use 1n the calculations. 

Output Descriptions

SLSA output includes summations of the model's inputs and 

diagnostic information that aids in determining the relative 

importance of the transport mechanisms. The receiving water's 

response to the pollutant load is presented 1n tabular form. 

The response is presented in terms of pollutant concentrations 

in the water column and bed sediment. The concentrations can 

be further refined to estimate the dissolved and particulate 

concentrations of each phase as well. 

Advantages and Limitations 

The major advantage of SLSA is that it is a simple and 

easy-to-use model which can be applied using hand calculations 

or small microcomputers. SLSA requires only minimal data 

input. Model set up and 
II-11

use can be accomplished in relatively short time as compared 

to other models. 

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 it has several 

limitations. Dispersive flow is not accounted for as in other 

models such as CTAP, thus limiting its use to relatively 

simplistic systems. The bed sediment is assumed to be 

completely mixed and undergoes no movement. Decay mechanisms 

are all first-order. Suspended solid concentration is kept 

constant at the input value and only a single particle size is 

considered. Only one reach with a single point source loading

and no additional inflows are permitted. 

Model Applications 

The model was applied to reflect diurnal load variability 

for long-term loading. The predicted water column 

concentrations were slightly lower than the actual 

concentrations, but there was good agreement between the 

predicted and actual concentrations in the bed sediment. 

SLSA has been applied to a 90 km reach of Rapid Creek in 

Rapid City, South Dakota. The study area was located 

downstream of a municipal wastewater treatment plant. The 

pollutant considered was the surfactant, linear alkylbenzene 

sulfonate (LAS). The treatment plant was the only known source 

of LAS. 

Evaluation of the model Inputs showed the accuracy and 

validity of these estimates to be good. There were however, 

discrepancies in the known flows and LAS concentrations. The 

known concentration values of the plant effluent were based on 

only five samples taken over a two-year period, necessitating 

calculations of alternative effluent concentrations. The 

estimates in those calculations probably contributed to any 

deviations between SLSA-computed concentrations and the actual 

concentrations. 

Resource Requirements

SLSA requires a short (280 Bytes) FORTRAN program and a small 

storage capacity, if programming is desired. 
II-12

User Support Activities

Copies of the SLSA user manual, as well as documentation, 

can be obtained from: 

William Gulledge 

Chemical Manufacturers Association 

2581 M Street, N.W. 

Washington, D.C. 20037 

202-887-1183 

Other assistance can be obtained by contacting: 

John St. John 

Domenic DiToro 

HydroQual Inc. 

1 Lethbridge Plaza 

Mahwah, New Jersey 07430 

201-529-5151 

General References

HydroQual, Inc. 1981. Analysis of fate of chemicals in 

receiving waters Phase I. Prepared for: Chemical 

Manufacturers Association. Washington, D.C. 

HydroQual, Inc. 1982. Application guide for CMA - HydroQual 

chemical fate models. Prepared for: Chemical Manufacturers 

Association, Washington, D.C. 
II-13

_________________________________________ 

Michigan River Model (MICHRIV) 

Summary

sediment. It has the ability to model successive reaches 

(instead of just one) using an analytical solution. It is 

fairly similar to SLSA but is less simplified and more 

flexible. MICHRIV is comparable to SLSA except for: 

1) MICHRIV predicts particulate concentrations in the water 

column (state variable); SLSA treats it as an input data 

constant. 

3) MICHRIV is not intended for lakes whereas SLSA is. 

MICHRIV simulates the advective transport of dissolved and 

adsorbed pollutants. The model employs first-order decay

mechanisms for predicting pollutant distributions. An 

aggregate first-order loss rate coefficient, representing the 

sum of a number of processes, including volatilization, 

hydrolysis, photoyisis, oxidation, and biodegradation, is used 

in the model. Bed-water interactions include settling, 

resuspension, burial of particulates, and diffusion of 

dissolved constituents. 

The Michigan River Model 

(MICHRIV) (DePinto et al., n.d.) was 

developed at the EPA's Environmental 

Research Laboratory - Duluth, Large 

Lakes Research Station, Grosse Ile, 

Michigan, specifically for use in the 

waste load allocation program. 

MICHRIV simulates steady-state concentrations of pollutants 

from loadings into rivers for the water column and bed

Capsule Summary: MICHRIV 

• Steady state, 1-dimensional. 

• Models rivers/screams. 

• Simple first-order kinetics. 

• Easy to set up and use.

2) MICHRIV can model successive reaches; SLSA can handle only 

one. 

The model is written in FORTRAN, is user oriented, and 

provides guidance for input data preparation and model option 

selection. MICHRIV has flexible batch input routines suitable 

for multiple reaches. 

Input Requirements

MICHRIV requires basic Information for modeling: 

• 

 river. 

• 

Loading rates of pollutants and solids to the receiving

Flow rates, length of reach, water depth, and crosssectional area.
II-14

• Partition and first-order decay coefficients for both 

the water column and bed sediment. 

• Sediment/water exchange parameters; sediment solids 

concentration. 

Output Description

Advantages and Limitations

MICHRIV predicts pollutant concentrations as a function 

of distance from the loading source. Total and dissolved 

pollutant concentrations for both the water column and bed 

sediment are reported. Suspended sediment concentrations are 

predicted as well. 

MICHRIV was developed specifically for riverine waste 

load allocations; its level of complexity was intended to be 

suitable for widespread application. It requires less than two 

dozen input parameters per reach, therefore, model set up time 

is relatively rapid. 

MICHRIV is designed for single river systems and is not 

appropriate for river networks, lakes, or estuaries without 

modifications. Other limitations of MICHRIV include: 

• The model is steady-state with respect to flow and 

loads. 

• Decay processes are first-order; it has no specialized 

second-order organic decay routines. 

• 

• 

Dispersion is assumed to be negligible. 

Sorption/desorption are assumed to be instantaneous. 

• Bed load is not permitted. 

Model Applications

MICHRIV was tested and applied to a 60 Km reach of the 

Flint River, Genesee County, Michigan. The application of the 

model dealt with the distribution of zinc, 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 reasonable 

enough to demonstrate MICHRIV's ability to accurately simulate 

sediment and water column concentrations of a pollutant. 
II-15

Resource Requirements

User Support

MICHRIV is written in FORTRAN. The user manual and 

documentation are contained within an EPA draft report on 

technical guidance for waste load allocation studies. 

Bill L. Richardson 

Environmental Research Laboratory - Duluth Large Lakes 

Research Station 

Joseph V. DePinto 

Clarkson College of Technology 

Potsdam, New York 13676 

General References

MICHRIV is currently under review and should become 

available in the near future. Technical assistance for MICHRIV 

can be obtained by contacting: 

US EPA 

Grosse Ile, Michigan 48138 

or 

DePinto JV, Richardson WL, Rygwelski K, et al. n.d, Technical 

guidance manual for performing waste load allocations. Draft 

report. Washington, DC: U.S. Environmental Protection Agency. 
II-16

_____________________________________________________ 

Chemical Transport and Analysis Program (CTAP) 

Summary

The Chemical Transport and 

Analysis Program (CTAP) (HydroQual 

1981) was developed by HydroQual 

Inc., Mahwah, New Jersey for the 

Chemical Manufacturers Association, 

Washington, D.C. CTAP is an 

extension of the Simplified 

Lake/Stream Analysis (SLSA), also 

developed by HydroQual and was 

designed for more complex problem 

settings. 

CTAP, like SLSA, is designed to account for the dissolved 

and sorbed steady-state concentrations of organic and 

Inorganic pollutants in both the water column and bed 

sediment. However, its greater complexity allows it to model 

stratified lakes, rivers, tidal rivers, estuaries, and coastal 

embayments. CTAP is essentially like SLSA in that it is a 

compartment model in which each compartment is equivalent to 

one SLSA "lake". However, CTAP is more complex in that these 

compartments (up to 425) may be arranged in any 1, 2, or 3-

dimensional configuration (spatial concentration variations 

may exist in one, two, or three dimensions). Whatsmore, the 

compartments ar Interactive with each other via advective and 

dispersive transport. Mass balance equations are written for 

each compartment of both the water column and bed sediment and 

are interconnected to adjacent compartments. The result is a 

matrix of equations which are solved by digital computation. 

CTAP accepts multiple chemical load inputs from different 

locales to the aquatic system. It can also account for 

tributary Inflows and withdrawals as well. 

CTAP can be used to simulate multi-dimensional bed 

sediment conditions. In addition, it allows for a moving bed, 

where the upper-most layers are subject to movement 1n the 

direction of water flow. 

CTAP utilizes the same first-order reaction kinetics as 

SLSA. The coefficients for photolysis, hydrolysis, oxidation, 

and biodegradation are supplied by the user and then summed 

for an aggregate rec constant. The sorption-desorption

mechanisms are assumed to occur instantaneously; as it is 

assumed that soluble and particulate chemicals within each 

compartment are in a state of local equilibrium. Interactions 

between the water column and bed sediment include settling, 

resuspension, burial of particulates, and diffusive exchange 

of dissolved constituents. 

Capsule Summary: CTAP

• Steady-state, 3-dimensional 

• compartment model. 

• Streams, stratified rivers, 

lakes, 

• estuaries, and coastal 

embayments. 

• Multiple waste inputs. 

• Simple first-order kinetics.
II-17

Input Data Requirements

• 

• 

• Solids types, distributions, loadings and 

concentration. 

• Partition coefficients by phase and segment. 

• First-order coefficients for - water column 

photolysis, volatilization, hydrolysis, oxidation, and 

biodegradation; sediment hydrolysis. oxidation, and 

biodegradation. 

Data requirements are described by the model along with a 

discussion of how to prepare data for input. 

Output Descriptions

CTAP data requirements are more intensive than those of 

SLSA. In addition to the standard physico-chemical parameters 

of the aquatic system, CTAP requires: 

Sources and amounts of pollutant loading. 

• Segment volumes and lengths. 

Segment flows per phase and dispersion rates. 

The CTAP output presents less diagnostic information than 

SLSA, but prints out more computed chemical concentrations in 

the dissolved and particulate phases. The concentrations are 

presented in tabular form for both the water column and 

sediments. The output is also arranged so that the 

concentrations for each segment-compartment are reported. 

Advantages and Limitations

CTAP is a compartmental model, very flexible in 

configuration (up to three dimensions in both water column and 

bed sediment), and applicable to most types of water bodies. 

It can account for multiple point source waste inputs, but no 

non-point sources. Spatial variable flows can be handled, 

though the 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 firstorder coefficients of photolysis, hydrolysis, oxidation, 

biodegradation, and volatilization, is used to predict 

chemical fate. Bed-water interactions are articulated with an 

intermediate level of complexity. CTAP allows for up to five 

different particle sizes; it also allows for bed load. 
II-18

Model Applications

CTAP was applied to the data collected by Games (1981) 

from a 90-km reach of Rapid Creek, Rapid City, South Dakota. 

The study area was located downstream of a municipal 

wastewater treatment plant. The chemical, considered was the 

surfactant linear alkylbenzene sulfonate (LAS). The treatment 

plant was the only known source of LAS. This is the same 

scenario used to apply the SLSA model.

The results of the CTAP 

modeling of long-term loading are 

shown in Figures 1 and 2 for 

concentrations in the water column 

and sediments, respectively. The 

circled points 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. When diurnal load

variability was accounted for, the 

predicted sediment values were in 

better agreement.

Although not all the 

capabilities of CTAP were used in 

this application, the input was 

sufficient to accurately predict LAS 

concentrations. Evaluation of the 

inputs showed the accuracy and 

validity of these estimates to be 

good. 

Resource Requirements

CTAP is written in Fortran IV and is suitable for operation, 

with slight modification, to the IBM 360/370, Univac 1108, CDC 

6600 mainframe computers and to minicomputer systems such as 

the PDP 11/70, VAX 750/780, IBM 1130, and DSC Meta/4. The 

minicomputer version of CTAP requires 32K bytes of storage 

with subroutine overlay and disk scratch files for temporary 

storage. 

User Support Activities

Copies of the CTAP user manual, as well as documentation, can 

be obtained for a fee from: 
II-19

William Gulledge 

HydroQual Inc. 

Mahwan, New Jersey 07430 

Chemical Manufacturers Association 

2581 M Street. N.W. 

Washington, D.C. 20037 

202-887-1183 

Technical assistance can be obtained by contacting: 

John St. John or 

Domenic DiToro 

1 Lethbridge Plaza 

201-529-5151 

General References

HydroQual Inc. 1981. CTAP documentation - chemical transport 

analysis program. Prepared for the Chemical Manufacturers 

Association, Washington, D.C. 

HydroOual Inc. 1982. Application guide for CMA-HydroQual 

chemical fate models. Prepared for the Chemical Manufacturers 

Association, Washington, D.C. 

Games L. 1981. Practical applications and comparisons of 

environmental exposure assessment models. Presented at the 

ASTM Sixth Symposium of Aquatic Toxicology. St. Louis, 

Missouri. 
II-20

Summary

Exposure Analysis Modeling System (EXAMS)

_________________________________ 

The Exposure Analysis Modeling 

System (EXAMS) (Burns et al.. 1982) 

is a steady-state water quality

model designed by the U.S. 

Environmental Protection Agency's 

Environmental Research Laboratory in 

Athens, Georgia. The model was 

designed to allow for the rapid 

screening and evaluation of the behavior of synthetic organic

chemicals in freshwater aquatic ecosystems. With a description 

of the physical and chemical properties of the compound of 

interest, and the relevant transport and physical/chemical 

characteristics of the aquatic system, EXAMS computes the 

exposure (steady-state environmental concentration), 

persistence (e.g., pollutant removal system half-life), and 

fate (distribution in the system and fraction consumed by each 

removal process) of each compound modeled. These calculations 

are based on the assumptions that the loadings are long term 

and time averaged. 

The EXAMS program is an interactive modeling system that 

allows the uses to specify and store the physical/chemical 

properties of both the chemical compounds and the aquatic 

environment. 

The aquatic system is user specified and is represented 

by a set of segments or distinct compartments (water and 

sediment) in the system. As many as 100 compartments can be 

handled by EXAMS. 

Capsule Summary: EXAMS

• Steady state, 3-dimensional 

compartment model. 

• Fresh water, non-tidal 

aquatic systems. 

• Comprehensive second-order 

kinetics for organic 

chemical decay.

The program is 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 algebraic 

sum of (1) external loadings, (2) 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.
II-21

EXAMS computes the kinetics of transformations 

attributable to direct photolysis, hydrolysis, biolysis, and 

oxidation reactions. The input chemical data for hydrolytic, 

biolytic, and oxidative reactions can be entered either as 

single valued second-order rate constants or as pairs of 

values defining the rate constant as a function of the 

environmental temperature specified for each segment. EXAMS 

includes two algorithms for computing the rate of photolytic 

transformation. The first requires an average pseudo-firstorder rate constant applicable to near-surface waters; and the 

second computes the photolysis rate directly from the 

absorption spectra of the compound and its ions, measured 

values of the reaction quantum yields, and the environmental 

concentrations of competing light absorbence (chlorophyls, 

sediments, etc.). 

Internal transport and export occur via advective and 

dispersive movement of dissolved, sediment-sorbed, and 

biosorbed materials, and by volatilization losses at the airwater interface. EXAMS provides a set of vectors that allows 

the user to specify the location and strength of both 

advective and dispersive transport pathways. EXAMS can compute 

transport of a chemical via whole-sediment bedloads, suspended 

sediment washloads, groundwater infiltration, transport 

through the thermocline of a lake, losses in effluent streams, 

etc. 

EXAMS is available in both a batch and interactive 

version. 

Input Data Requirements

External loadings of a chemical can enter the ecosystem

via point sources, non-point sources, dry fallout or aerial 

drift, atmospheric wash-out, and groundwater seepage entering 

the system. 

EXAMS requires an extensive amount of environmental data. 

However, the 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 transformation processes. For 

example, pH and pOH data can be omitted in the case of neutral 

organics that are not subject to acid or alkaline hydrolysis. 

Six "canonical" environments are included with most model 

versions and can be used for non-specific screening 

investigations. 

Input parameters include: 

• A set of chemical loadings on each sector of the 

ecosystem. 

• Molecular weight, solubility, and ionization constants of 

the compound. 
II-22

• 

• 

• Photolysis parameters: reaction quantum yields, 

absorption spectra, surface scalar irradiance, 

cloudiness, scattering parameters, suspended sediments, 

chlorophyl, and dissolved organic carbon. 

• Hydrolysis: 2nd-order rate constants or Arrhenius 

functions for the relevant molecular species: pH, pOH, 

and temperatures. 

• Oxidation: rate constants, temperatures, and oxidant 

concentrations. 

• Biotransformation: rate constants, temperatures, total 

and active bacterial population densities. 

• 

Sediment-sorption and biosorption parameters: Kp, Koc or 

Kow, biomasses, benthic 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, windspeeds, and reaeration rates. 

• Parameters defining strength and direction of advective 

and dispersive transport pathways. 

System geometry and hydrology: volumes, areas, depths, 

rainfall, evaporation rates, entering stream and nonpoint source flows and sediment loads, and groundwater

flows. 

Output Descriptions

EXAMS' 17 output tables include an echo of the input 

data, and tabulations giving the concentration, fate, and 

persistence of the chemical. Printer plots of longitudinal and 

vertical concentration profiles can be invoked by the 

interactive user. 

Advantages and Limitations

The major technical strength 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 is modeled in a way that is consistent with 

accepted mass transfer processes. Thus the model's strength is 

in evaluating the chemical's kinetics. 
II-23

• 

• EXAMS evaluations do not include the effects of sedimentcontaminant absorption/desorption kinetics on transport 

or transformation processes. 

Model Applications

From the user's standpoint, the model can be run in an 

interactive mode for rapid evaluation of scenarios reflecting 

varying system physical and chemical conditions. Furthermore, 

the model contains a built-in, on-line 'help-file' to explain 

the command options and required input data. 

EXAMS is a steady-state model and as such was not 

designed to evaluate the short-term variations of an aquatic 

system. 

EXAMS does not account for sediment and contaminant loss 

by burial in the benthic layer. Furthermore, it has only a 

single exchange coefficient lumping the process of watersediment particle exchange and the process of water-pore water 

diffusion. 

EXAMS has the capability to model ponds, rivers, and lakes. 

It does not have the capability of modeling estuarine aquatic 

environments. 

The model does not simulate sediment-pollutant loads from 

point and non-point sources. Solid concentrations must be 

derived from external sources. 

A field validation study of EXAMS was conducted for an 

anionic surfactant, linear alkylbenzene sulfonate (LAS), in 

Rapid Creek, South Dakota (Games, 1982). The single point 

source input to the creek is a sewage treatment plant located 

in Rapid City. Effluent to the creek is 7 to 8 mgd, which 

results in about a 3:1 dilution factor. Influent and effluent 

concentrations of LAS in the treatment plant were 3.7 mg/l and 

0.51 mg/l, respectively, over the period 1978 to 1980. 
II-24

may be inherent in chemical and aquatic systems and may not be 

a problem unique to EXAMS. 

In other applications, EXAMS has been successfully used 

to model volatilization of organics in specific field 

situations, and for a general assessment of the behavior of 

phthalate esters in aquatic systems. EXAMS has been 

implemented by a number of manufacturing firms for 

environmental evaluations of newly synthesized materials and 

has been used in an academic setting for both teaching and 

research. 

Simulations of LAS steady state 

concentrations in water and sediment

were compared with observed 

concentrations. In a qualitative 

sense, agreement was good (see Figure 

1). However, this agreement was only 

obtained by assigning an arbitrary 

value to the dispersion coefficient at 

the sediment/water Interface. The 

value chosen was in the expected 

range, but little or no rationale for 

the value could be provided. Since 

this term is fairly Important (as 

determined by a sensitivity analysis) 

and is seldom measured, it acquires 

the characteristics of a calibration

parameter.

Model sensitivity analyses with 

respect to errors in measurement of 

creek flow rate, biodegradation rate 

constants, and adsorption coefficient 

were also conducted. Results indicated 

that model calculations are most 

sensitive to the least understood 

parameters, that is, the 

sediment/water exchange coefficient 

and the sediment biodegradation rate 

constant. However, this phenomena

Resource Requirements

EXAMS is available from the EPA Athens Environmental 

Research Laboratory in either a batch or an interactive 

version. The batch version requires 64K bytes (overlaid) of 

memory (for aquatic systems of up to 17 segments); this 

version does not require mass storage capability. The 

interactive version also requires 64K bytes (overlaid) of 

memory, plus an additional mass storage capability. The 

interactive version of EXAMS requires 100K bytes of mass 

storage for utility files, 2K bytes, for each chemical in the 

active files, and 2.5K bytes for each active defined 

environment. An overlay capability is 
II-25

User Support Activities

required to implement EXAMS on small computers such as PDP-11 

or HP 3000 systems. Execution times range from a few seconds 

to several minutes depending on the problem to be solved. The 

software is distributed on magnetic tape; the source code 

consists of about 16,000 care images. 

It has been estimated that approximately one to two manmonths of effort are required to setup the model (not 

including the effort required to evaluate the results). This 

estimate is based on the following assumptions: (1) all data 

necessary to meet the input requirements of the model are 

available and (2) qualified personnel are available to 

implement the model. 

User assistance can be obtained by contacting: 

Environmental Systems Branch 

Environmental Research Laboratory 

Athens, Georgia 30613 

FTS 250-3123 COM 404/546-3123 

Automatic Data Processing 

General References

Free copies of the user's manual and system documentation 

are available from ORD Publications, Center for Environmental 

Research Information, USEPA, Cincinnati, Ohio 45268 

(Telephone: 513/684-7562; ask for publication No. EPA-600/3-

82-023). The computer tape of the program (provided for the 

requestor to copy and return) is available from Center for 

Water Quality Modeling. Environmental Research Laboratory, 

USEPA. College Station Road, Athens, Georgia 30613 (Telephone: 

404/546-3123). 

Lawrence A. Burns 

U.S. Environmental Protection Agency 

College Station Road 

David M. Cline 

U.S. Environmental Protection Agency 

Environmental Research Laboratory 

College Station Road 

Athens, Georgia 30613 

FTS 250-3123 COM 404/546-3123 

Games L. M. 1982. Field validation of exposure analyses 

modeling system (EXAMS) in a flowing stream. In: Modeling the 

fate of chemicals in the aquatic environment. Dickson KL, Make 

AW, and Cairns J, Jr. eds. Ann Arbor Science Publishers. 

Burns L. Cline DM, Lassiter RR. 1982. Exposure analysis 

modeling system (EXAMS) user manual and system documentation. 

U.S. Environmental Protection Agency, Athens, Georgia. 

Publication No. EPA-600/3-82-023. 
II-26

__________________________________ 

Metals Exposure Analysis Modeling System (MEXAMS)

Summary

The Metals Exposure Analysis 

Modeling System (MEXAMS) (Felmy et 

al. 1982) is a synthesis of two 

existing computer models that 

accounts for the chemical and 

physical processes affecting the fate 

and transport of metals in aquatic 

systems. This is accomplished by 

linking the geochemical model, 

MINTEQ, with the Exposure Analysis 

Modeling System (EXAMS) (Burns et al. 

1982), an aquatic exposure assessment model. Combined, these 

models provide the capability to (1) estimate the quantities 

of metal likely to be in solution and (2) consider the effect 

of chemical speciation on adsorption or precipitation of 

metals, both of which can act to reduce the amount of metal in 

solution.

Capsule Summary: MEXAMS

• Steady-state, 3-dimensional, 

compartment model. 

• Calculates speciation, and 

dissolved, adsorbed and 

precipitated metal 

concentrations. 

• Equilibrium constants and 

ancillary data available for 

a several metals in the 

models data base.

The chemical interactions are handled by MINTEQ, using 

thermodynamic equilibrium relationships and water quality data 

to calculate speciation, are dissolved, adsorbed, and 

precipitated metal concentrations.

Speciation is calculated using an equilibrium constant 

approach wherein a series of mass action expressions are 

solved subject to mass balance constraints on each chemical 

component. An estimate of aqueous speciation 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, toxicity and 

bioavailability of individual metal species can vary by 

several orders of magnitude; therefore, estimates of metal 

speciation are required to predict aquatic impacts.

Adsorption is treated as being analogous to aqueous 

speciation. There-fore, mass action expressions can be 

formulated for adsorption reactions. MINTEQ 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).
II-27

The model is user oriented in that it contains an 

interactive program that assists the user in preparing data 

for input to MINTEQ and EXAMS. 

The coupling of MINTEQ and EXAMS was accomplished in such 

a way as to (1) retain all of the original EXAMS options and 

capabilities and (2) bypass unnecessary calculations or 

calculations either not applicable to metals or duplicated by 

MINTEQ. For Instance, there 1s no need for EXAMS to compute 

adsorption since MINTEQ will provide the quantity of metal 

sorbed to sediments and biota. Another example is chemical 

degradation which is applicable to organics but not to metals. 

Through the proper specification of EXAMS inputs, most of 

these calculations can be bypassed. Thus, the user will not be 

required to maintain two versions of EXAMS, one for organics 

and one for metals. 

MEXAMS was designed primarily to be used in performing 

screening level assessments using generally available water 

quality data. It can also be used to interpret data collected 

during bioassays and as a framework for guiding research 

related to the aquatic impacts of pollutant metals. 

At present, the model's data base contains data necessary 

for the evaluation of the following metals: arsenic, cadmium, 

cooper, lead, nickel, silver, and zinc. 

Input Data Requirements

MEXAMS requires input data for both the MINTEQ and EXAMS 

components of the model. The reader is directed to the EXAMS 

review in this catalogue for a discussion and list of EXAM's 

input requirements. MINTEQ requires two types of data: (1.) 

thermodynamic data and (2) water quality data. The 

thermodynamic data are equilibrium constants, heats of 

reaction, and other Information required to predict the 

formation of each species or solid phase. The water quality 

data are physical and chemical properties of the water body 

being analyzed (e.g., pH, pOH, temperature). 

The user need only to generate the water quality data in 

order to implement MINTEQ. The thermodynamic data (for the 

specific metals currently handled by the model) are contained 

in a data base that accompanies the model, 

Output Descriptions

The model output is divided between the EXAMS and MINTEQ 

components. EXAMS provides tabulations presenting estimates of 

the exposure, fate, and persistence of the metal. The MINTEQ 

outputs give details on the chemical Interactions occurring in 

each compartment of the simulated aquatic system. 
II-28

Advantages and Limitations

MEXAMS represents an improvement in metals modeling in 

that it accounts for the complex chemistry affecting the 

behavior of metals as well as the transport processes that 

affect their migration and fate. Specifically, MEXAMS 

considers the effect of chemical speciation on adsorption or 

precipitation of metals. 

Organic complexation can have a significant impact on the 

speciation of metals. Although MINTEQ is capable of handling 

organic complexation, the thermodynamic data base does not 

contain the necessary equilibrium constants and ancillary data 

to evaluate this phenomena. 

The modeling system is user oriented. It contains an 

interactive program that helps the user prepare water quality

data for input to MINTEQ. It also queries the user to obtain 

user run information which is then used to control the 

operation of MINTEQ and EXAMS and the transfer of simulation

results between the models. 

The thermodynamic data base associated with MINTEQ 

contains equilibrium constants and ancillary data for only a 

limited number of pollutant metals (i.e., As, Cd, Cu, Pb, N1, 

Ag. and Zu). 

MINTEQ considers precipitation/dissolution, 

oxidation/reduction, and adsorption as equilibrium processes 

when in fact they may not be in equilibrium. 

EXAMS does not describe vertical changes in pH, and 

oxidation-reduction reactions in the bed sediment. The latter 

can be very significant in simulating the fate of metals in 

lakes and polluted riven. 

The MEXAMS methodology is currently under development and 

has not been applied in the field. 

Model Applications

Although MINTEQ and EXAMS have been applied independently, as 

they are currently linked in the MEXAMS program, they have not 

been applied in an environmental analysis. 
II-29

Resource Requirements

MEXAMS will require a system with 32K memory. An overlay 

capability is required to implement MEXAMS on small computers 

such as a POP 11/70 or HP3000 system. 

User Support Activities:

Battelle, Pacific Northwest Laboratories 

General References

Copies of the user manual and system documentation will 

be available sometime during the summer of 1983. At that time, 

it is anticipated that user support will be provided by the 

Center for Water Quality Modeling, ERL, USEPA, Athens, 

Georgia. 

Additional information concerning the model can be obtained 

by contacting: 

Yasuo Onishi 

Richland, Washington 99352 

Burns LA, Cline DM, Lassiter RR. 1982. Exposure analyses 

modeling system (EXAMS): user manual and system documentation. 

U.S. Environmental Protection Agency, Athens, Georgia. 

Publication No. EPA-600/3-82-023.

Felmy AR, Brown SM, Onishi Y, Argo RS. Yabusaki SB. 1982. 

MEXAMS - The metals exposure analyses modeling system. 

Battelle. Pacific Northwest Laboratories, Richland, 

Washington. Contract No. 68-03-3089. 
Time-Variable Models 
Estuary and Stream Quality Model (WASTOX)

_________________________________ 

Summary

WASTOX (Connolly 1982) was 

designed as a time-variable 

compartment model for simulating 

the transport and transformation 

of organic chemicals in the water 

column and the sediment of 

streams and estuaries; although 

the model is generally applicable 

to all types of water bodies. 

WASTOX belongs to the WASP model (DiToro et al. 1981) 

family and there-fore has capabilities and features similar to 

TOXIWASP (Ambrose et al. 1983). The major differences between 

WASTOX and TOXIWASP are: (1) WASTOX can account for three 

sediment size fractions; TOXIWASP accounts for one. (2) WASTOX 

sediment partition coefficients are expressed as a function of 

the solids concentration; TOXIWASP assumes a constant 

partitioning coefficient. (3) WASTOX assumes certain system 

properties being constant spatially; TOXIWASP accounts for a 

spatial distribution. (4) WASTOX employs a finite difference 

scheme for its mathematics; TOXIWASP employs the EXAMS 

compartment approach. (5) WASTOX does not separately account 

for pollutant oxidation, as TOXIWASP does. (6) In WASTOX, 

pollutant on sediment is transported via an advective sediment 

process; in TOXIWASP it is transported via the above process 

and an exchange (diffusive) process not requiring bedload 

(sedimentation) exchange between the water column and the 

sediment. (7) WASTOX has been mainly designed for 1- or 2-

dimensional configurations of streams and tidal estuaries, and 

the kinetics of volatilization are oriented towards flowing 

systems. (8) An aquatic food chain routine is to be 

incorporated into the model. 

Capsule Summary: WASTOX 

___________________________ 

• Time-variable 3-dimensional 

compartment. 

• Streams and estuaries, fresh 

and saline water. 

• Comprehensive second-order 

kinetics.

WASTOX is a water/sediment quality program only, and as 

such, it requires the water body and sedimentation dynamics 

(e.g., flow, velocity, bed sedimentation velocity) as user 

inputs (see discussion in TOXIWASP model). WASTOX can be 

applied to time-variable simulations. Chemical kinetics are 

treated similarly to the EXAMS approach. 

Input Data Requirements

Input requirements for WASTOX Include: 

II-33
o Exchange coefficients and fields between segments (such 

as dispersion).

• Segment volumes and flows including time of flow 

duration, and velocities in compartments. 

• Environmental and pollutant parameters such as geometry 

of the system, sedimentation transport/dynamics 

parameters, pH, temperature, concentration of compound 

degrading bacteria in water, second order biodegradation 

constants for dissolved and adsorbed toxicant, first-and 

second-order alkaline hydrolysis ratio, other first-order 

decay rates, Henry's constant, molecular weight of 

toxicants, temperature correcting parameters, solids 

dependent partitioning coefficients. 

• Initial conditions, boundary conditions, and waste loads.

Output Descriptions

A finalized output format does not exist, since WASTOX is 

at a developmental stage. The output is expected to be similar 

to the WASP output, consisting of a listing of input data, and 

tabulations giving transport, fate, and persistence of the 

chemical in all water and sediment compartments of the water 

body.

Advantages and Limitations

Resource Requirements 

The major advantages of WASTOX are its multi-dimensional 

capabilities which allow application to a variety of water 

bodies; capability to simulate time-variable loads and 

response; relatively sophisticated kinetics; and the 

capability to simulate the important features of sediment 

transport. 

The technical limitations of WASTOX are similar to those 

of TOXIWASP. WASTOX has not been applied or validated yet and 

has not been formally released by the EPA. This may not be a 

limitation to model use, since the theory of WASTOX is 

conceptually sound and model predictions can be expected to be 

within certain limits. WASTOX, similar to most of the models 

described 1n this catalogue, requires detailed inputs 

regarding sediment transport characteristics of the water 

body. These requirements are difficult to meet from routinely 

available data. 

WASTOX is available in a batch/tape version, is written 

in FORTRAN IV, and uses up to a 32K-byte user area on a POP 

11/70 machine. Execution times range from a few seconds to 

II-34
several minutes depending on the temporal and spatial 

resolution of the environment analyzed. At this stage, it is 

estimated that approx. two man months of effort are required 

to have an operational model with a rough understanding of its 

overall behavior or performance. 

II-35
User Support Activities

To obtain the WASTOX documentation along with sample data 

sets and support software, write or contact: 

Dr. Parmely H. Prichard 

Dr. John P. Connolly 

(212) 920-0276 

Environmental Research Laboratory 

Gulf Breeze, Florida 32561 

(904) 932-5311 

Environmental Engineering and Science 

Manhattan College 

Bronx, NY 10471 

General References

Ambrose R. Hill S, Mulkey L. 1983. 

User's manual for the chemical transport and fate model 

TOXIWASP, Version I. Draft document, U.S. EPA, Office of 

Research and Development. Athens Research Laboratory, Athens, 

Georgia. 

DiToro DM, Fitzpatrick JJ, Thomann RV. 1982. Water quality

analysis simulation program (WASP) and model verification

program (MVP) documentation. Hydroscience, Inc., Westwood, NY. 

For U.S. Environmental Protection Agency. Duluth, MN, Contract 

No. 68-01-3872. 

Connolly TP. 1982. Preliminary estuary and stream version 

documentation of WASTOX. EPA Cooperative Agreement No. R807 

827-02. EPA, Gulf Breeze, Florida. Manhattan College, Bronx, 

New York.

II-36
Chemical Transport and Fate Model (TOXIWASP) 

___________________________________ 

Summary

The Toxics Water Analysis Simulation Program (TOXIWASP) (Ambrose 

et al. 1983) was designed as a timevariable compartment model for 

simulating the transport and 

transformation of organic toxic 

chemicals in the water column and the sediment of stratified 

lakes and reservoirs, large rivers, estuaries, and coastal 

waters. 

The kinetic pollutant components of TOXIWASP were 

modified from EXAMS, EXAMS formulates a total transformation 

rate from the chemical characteristics of a compound and the 

environmental parameters of the system. In TOXIWASP and EXAMS 

this rate is based on a simple addition of the rate constant 

of each process, and the kinetic time derivative is calculated 

from this rate, yielding a time varying chemical concentration

for a user-specified spatial network. EXAMS uses a kinetic 

structure that allows the study of five different ionic forms 

of a chemical, several ways to calculate photolysis, and other 

capabilities. In TOXIWASP, all those features have been 

aggregated in one formulation, but with an expanded library of 

kinetic subroutines. In that respect, TOXIWASP allows 

simulations of toxic organic chemical behavior in the aquatic 

environment resulting from loading pulses that cannot be 

modeled via steady-state coder. 

Capsule Summary: TOXWASP

• Time-variable, 3-dimensional 

compartment model. 

• Rivers, lakes, estuaries. 

• Comprehensive second-order 

kinetics. 

TOXIWASP was created by first adapting the kinetic 

structure of the EXAMS model (Burns et al. 1982) to the 

transport framework provided by the WASP program (DiToro et 

al. 1981), and by then adding simple sediment balance 

algorithms along with special input and output software. 

Since TOXIWASP uses the compartment modeling approach, 

whereby segments can be arranged in a 0-, 1-, 2-, or 3-

dimensional configuration. TOXIWASP is a water/sediment 

quality program only, and as such, it requires the water body 

and the sedimentation dynamics (e.g., flow, velocity, bed 

sediment velocity) as user Inputs. TOXIWASP can be employed 

for analyses requiring more dynamic transport and loading

capabilities than EXAMS, but less detailed and mechanistic 

sediment predictions than SERATRA (Onishi and Wise 1982).

II-37
Regarding the chemical transport and fate processes 

considered. TOXIWASP can account for volatilization, 

photolysis, hydrolysis, oxidation, biolysis, sorption on both 

sediment and biomass, advection, and diffusion. Sorption on 

sediments and biomass is calculated assuming local equilibrium 

using a constant partition coefficient and spatially varying 

environmental organic carbon fractions. For each compartment, 

one differential equation for the pollutant dissolved phase 

and one differential equation for the adsorbed phase are 

formulated and solved. As contrasted to WASTOX (Connolly 

1982). the effective first-order decay rate can vary with 

time. 

Exchange between the water column and the bed can occur 

by settling or resuspension of particulates, diffusion (of 

dissolved pollutant) between the water column and the pore 

water, by direct adsorption/desorption between the water 

column and the bed surface, and by percolation or 

infiltration, within the bed, the pollutant can move 

vertically by diffusion, turnover (dispersion), percolation, 

and burial. Also within the bed, the pollutant cannot move 

horizontally (i.e., no bed load), 1n contrast with WASTOX.

Input Data Requirements 

Input requirements for TOXIWASP include: 

• Exchange coefficients between compartments such as 

dispersion between segments, water column and sediment, 

sediment and water in bed material. 

• Segment volumes and flows. 

• 

• 

Boundary conditions.

Environmental and pollutant characteristics such as 

number of constituents, temperature, cloudiness, 

bacterial population, biomass, hydroxide ion activity, 

molar concentration of oxidants, organic carbon, pH, 

decay coefficients. Arrhenius constants, second-order 

rate constants for biolysis in the benthic environment, 

octanol water partition coefficient, Henry's law 

constant, vapor pressure, and solubility.

Output Descriptions

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 the water 

body. 

II-38
Chemical Transport and Fate Model (TOXIWASP) 

____________________________________ 

Summary

The Toxics Water Analysis Simulation Program (TOXIWASP) (Ambrose 

et al. 1983) was designed as a timevariable compartment model for 

simulating the transport and 

transformation of organic toxic 

chemicals in the water column and 

the sediment of stratified lakes and reservoirs, large rivers, 

estuaries, and coastal waters.

Capsule Summary: TOXIWASP

• Time-variable, 3-dimensional 

compartment model. 

• Rivers, lakes, estuaries. 

• Comprehensive second-order 

kinetics.

TOXIWASP was created by first adapting the kinetic 

structure of the EXAMS model (Burns et al. 1982) to the 

transport framework provided by the WASP program (DiToro et 

al. 1981), and by then adding simple sediment balance 

algorithms along with special input and output software. 

Since TOXIWASP uses the compartment modeling approach, 

whereby segments can be arranged in a 0-, 1-, 2-, or 3-

dimensional configuration. TOXIWASP is a water/sediment 

quality program only, and as such, it requires the water body 

and the sedimentation dynamics (e.g., flow, velocity, bed 

sediment velocity) as user inputs. TOXIWASP can be employed 

for analyses requiring more dynamic transport and loading

capabilities than EXAMS, but less detailed and mechanistic 

sediment predictions than SERATRA (Onishi and wise 1982).

The kinetic pollutant components of TOXIWASP were 

modified from EXAMS. EXAMS formulates a total transformation 

rate from the chemical characteristics of a compound and the 

environmental parameters of the system. In TOXIWASP and EXAMS 

this rate is based on a simple addition of the rate constant 

of each process, and the kinetic time derivative is calculated 

from this rate, yielding a time varying chemical concentration

for a user-specified spatial network. EXAMS uses a kinetic 

structure that allows the study of five different ionic forms 

of a chemical, several ways to calculate photolysis, and other 

capabilities. In TOXIWASP, all those features have been 

aggregated in one formulation, but with an expanded library of 

kinetic subroutines. In that respect, TOXIWASP allows 

simulations of toxic organic chemical behavior in the aquatic 

environment resulting from loading pulses that cannot be 

modeled via steady-state code.

II-39
Regarding the chemical transport and fate processes 

considered. TOXIWASP can account for volatilization, 

photolysis, hydrolysis, oxidation, biolysis, sorption on both 

sediment and biomass, advection, and diffusion. Sorption on 

sediments and biomass is calculated assuming local equilibrium 

using a constant partition coefficient and spatially varying 

environmental organic carbon fractions. For each compartment, 

one differential equation for the pollutant dissolved phase 

and one differential equation for the adsorbed phase are 

formulated and solved. As contrasted to WASTOX (Connolly 

1982), the effective first-order decay rate can vary with 

time.

Exchange between the water column and the bed can occur 

by settling or resuspension of particulates, diffusion (of 

dissolved pollutant) between the water column and the pore 

water, by direct adsorption/desorption between the water 

column and the bed surface, and by percolation or 

infiltration, within the bed, the pollutant can move 

vertically by diffusion, turnover (dispersion), percolation, 

and burial. Also within the bed, the pollutant cannot move 

horizontally (i.e., no bed load), in contrast with WASTOX.

Input Data Requirements

Input requirements for TOXIWASP include: 

• Exchange coefficients between compartments such as 

dispersion between segments, water column and sediment, 

sediment and water in bed material. 

• Segment volumes and flows. 

• 

• 

Boundary conditions.

Environmental and pollutant characteristics such as 

number of constituents, temperature, cloudiness, 

bacterial population, biomass, hydroxide ion activity, 

molar concentration of oxidants, organic carbon, pH, 

decay coefficients, Arrhenius constants, second-order 

rate constants for biolysis in the benthic environment, 

octanol water partition coefficient, Henry's law 

constant, vapor pressure, and solubility.

Output Descriptions

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 the water 

body.

II-40
Advantages and Limitations

TOXIWASP is designed for multi-dimensional, time-variable 

analyses, using sophisticated organic chemical decay kinetics. 

It does not incorporate a hydrodynamic simulation model; the 

user must input flows between compartments. 

Adsorption/desorption is instantaneous.

TOXIWASP is in a developmental stage and as such has not 

been applied or validated yet. This may not be an inherent 

limitation, since the basic science of the model originates 

from the EXAMS and the WASP packages for which some 

application experience exists. 

TOXIWASP does not account for corrective bed load

sediment transport between compartments.

Similar to most other models reviewed in this catalogue 

TOXIWASP input requirements are not easily met by routinely 

available data. 

Model Applications

TOXIWASP has not been applied to a real situation; 

however, the EXAMS model has been applied in numerous 

situations (see EXAMS description) and the WASP program, 

available since 1970, has been applied in more than 20 

environments (DiToro et al. 1981). 

Resource Requirements

TOXIWASP is not an interactive modeling package; rather, 

it is a standard software package in FORTRAN, operational via 

a standard CRT unit or a card deck. TOXIWASP requires an IBM 

370 (OS/MVS Operating System), or a POP 11/70 (IAS Operating 

System), programmed in FORTRAN IV+ or FORTRAN IV. The first 

version can accommodate 100 compartments, the second 50 

compartments. The POP 11/70 computer utilizes an IAS operating 

system and allocates a 32K word (54K byte) user area for 

execution of a program. TOXIWASP occupies at least 32K words 

of memory in either machine. Execution times range from a few 

seconds 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 manmonths of effort are required to have an operational model, 

with a rough understanding of its overall behavior / 

performance.

II-41
User Support Activities

TOXIWASP is available from the EPA Athens Environmental 

Research Laboratory in a batch/tape version. To obtain the 

TOXIWASP documentation along with sample data sets and support 

software, write or contact: 

Mr. Robert Ambrose 

General References

Center for Water Quality Modeling

Environmental Research Laboratory 

U.S. EPA, College Station Road 

Athens, Georgia 30613 

(404) 546-3546 

Burns LA, Cline DM, Lassiter RR. 1982. Exposure analysis 

modeling system (EXAMS), user manual and system documentation. 

Athens, Georgia: U.S. Environmental Protection Agency. 

Connolly TP. 1982. Preliminary estuary and stream version 

documentation of WASTOX. EPA Cooperative Agreement No. R807 

827-02. EPA Gulf Breeze, Florida, Manhattan College, Bronx, 

New York. 

DiToro DM, Fitzpatrick JJ, Thomann RV. 1981. Hydroscience, 

Inc. Water quality analysis simulation program (WASP) and 

model verification program (MVP) - documentation. Duluth, MN: 

U.S. Environmental Protection Agency. 

Contract No. 68-01-3872. 

Onishi Y, Wise SE. 1982. User's manual for the instream 

sediment contaminant transport model, SERATRA. Athens, 

Georgia: U.S. Environmental Protection Agency. EPA-600/3-82-

005 (in press). 

Ambrose R, Hill S, Mulkey L. 1983. User's manual for the 

chemical transport and fate model TOXIWASP, Version I. Draft 

document, U.S. EPA. Office of Research and Development. Athens 

Research Laboratory, Athens, Georgia. 

Bonazountas M, Fiksel J. eds. 1982. Arthur D. Little, Inc. 

ENVIRO: Environmental mathematical modeling 

handbook/catalogue. Washington, D.C.: Office of Policy of 

Resource Management, U.S. Environmental Protection Agency.

II-42
Toxic Organic Substance Transport and Bioaccumulation Model 

(TOXIC) 

_____________________________________

Summary

The Toxic Organic Substance 

Transport and Bioaccumulation 

Model (TOXIC) (Schnoor and McAvoy 

1981) is a quasi-dynamic water 

quality model designed to simulate 

the behavior of pesticides In a 

reservoir and bio-concentration of 

pesticides in aquatic life. 

The quasi-dynamic approach utilities: (1) steady, annual 

average flow (from dally averages) for long-term simulations; 

(2) steady, annual average flow-weighted solids (from dally 

suspended solids measurements) and (3) time-variable toxicant

loadings. 

TOXIC includes a routine which calculates a mass balance

on sediments and the adsorbed chemical. Sediment deposition 

and scour are included, as is diffusion of toxics from 

sediment pore water to the overlying water. The model also 

computes contaminant uptake and depuration by fish. 

TOXIC considers the aquatic system being simulated as 

being divided Into a number of compartments (In one 

application of the model up to 100 compartments have been 

utilized). Each compartment Is considered to be a completely 

mixed system. 

The concentration of the contaminant through time is 

described by a set-of ordinary differential equations, one for 

each compartment. The basic equation is written to Include the 

sum of the first-order or pseudo-first order reactions 

(hydrolysis, biological degradation, biological uptake, 

photolysis, and volatilization) as well as adsorption and 

desorption kinetics as a function of particle size 

distribution. The coupled equations are then solved via a 

variable step size fourth order Runge-Kutta numerical 

technique. 

Input Data Requirements

The Inputs to the model can be classified "Jet the 

following categories: 

• Geometric properties, such as volumes of 

compartments, distances between them, surface areas, 

and locations with respect to other compartments.

Capsule Summary: TOXIC 

• Quasi-dynamic, 3-dimensional 

compartment model. 

• Designed for reservoir and 

impoundment aquatic systems. 

• First-order kinetics with 

biological degradation and 

biouptake and depuration.

II-43
• Flows between compartments and between each 

compartment and the outside of the system. 

• 

• 

• 

• Simulation parameters such as step size and time of 

simulation. 

Reaction rates, settling rate constants, and 

partition coefficients.

Solids concentrations in each compartment.

Bulk dispersion coefficients between compartments.

Output Descriptions

Output from TOXIC includes: 

 

• Solids balance description listing the concentration of 

the solids in the water column and In the sediment, 

and the net flux between the sediment and the water 

column over time. 

• Description of the Inflow and outflow volumes associated 

with each compartment. 

Advantages and Limitations 

• Dissolved, particulate, and total concentration of the 

contaminant over time within each compartment. 

TOXIC includes a routine which calculates a mass 

balance on contaminant-sorbed and unsorbed sediments. 

Sediment deposition and scour are also included, as is the 

diffusion of toxics from sediment pore water to the overlying 

water. 

Coefficients and rate constants must be supplied by the user 

thus requiring a working knowledge of kinetic processes, 

sediment transport mechanisms, and the ability to adjust the 

model's computer code. 

TOXIC is intermediate between fully time-variable models 

(e.g.. HSPF) and steady-state models (e.g., EXAMS). Flows and 

sediment loads are steady-state, while toxicant loading values 

are time-variable. It was designed as a pesticide transport 

model to be applied to reservoirs and impoundments. In 

addition to chemical reaction pathways, fish uptake and 

depuration (excretion and metabolism) are included in the 

model. Previous models have not combined fate and transport 

modeling with the biological effect (bioconcentration).

II-44
The model's simulation capabilities was designed to be 

applied to reservoir or impoundment aquatic ecosystems and may 

therefore be unattractive for use in situations where multiple 

aquatic systems (e.g., rivers, streams, and impoundments) 

exist.

User support for the model is rather limited. A user's 

manual is unavailable at this time and the model is not 

currently supported by the Center for Water Quality Modeling. 

ERL, USEPA, Athens, Georgia

Model Application

TOXIC has been applied to Iowa reservoir data to simulate 

the behavior of the Insecticide dieldrin and the herbicidesalachior and atrazine. Steady-state analyses and quasi-dynamic 

simulations with time-variable flows and loadings were 

undertaken.

Laboratory measurements for alachior were used in the 

model simulations with good agreement between model 

predictions and measured concentration. Laboratory 

measurements were also used in the atrazine and dieldrin 

simulations with the results well within an order of magnitude 

of field data.

Resource Requirements

The computer code for TOXIC is written in FORTRAN. TOXIC 

can be implemented in a batch mode on a IBM 370 or a 750 Prime 

system. No system overlay capabilities are required.

User Support Activities

The user's manual for the TOXIC methodology is not 

currently available, nor is its availability planned for the 

near future. However, copies of the model code (computer tape) 

and technical assistance on applying the model may be obtained 

by contacting: 

J. L. Schnoor 

Civil and Environmental Engineering 

Energy Engineering Division 

University of Iowa 

Iowa City, Iowa 52248 

(319) 353-7262 

II-45
General References 

Schnoor JL, HcAvoy DC. -1981. A pesitcide transport and 

bloconcentration model. Journal Environmental Engineering 

Division. ASCE, Volume 17, No. EEC 

Schnoor JL, 1981. Fate and transport of dieldrin in 

Coralville Reservoir: Residues in' fish and water following a 

pesticide ban. Science. 211. pp. 840-842. 

Schnoor JL, et al. 1983. Verification of a toxic substance 

transport and bioaccumulation model. EPA 600/3-83-007. 

Environmental Research Laboratory. Athens, GA 30613. 

Schnoor JL. 1982. Field validation of water quality criteria

for hydrophobic pollutants. In: Proceedings of the 5th 

symposium. Aquatic Toxlclty, ASTM.

Schnoor JS. 1982. Fate and transport modeling for toxic 

substances. In: Modeling the fate of chemicals in the 

aquatic environment. Pellston Conference Proceedings, Ann 

Arbor Science Publishers. 

II-46
Channel Transport Model (CHNTRN)

_____________________________________

Summary

wide variety of aquatic systems, that include: tidal and nontidal rivers, lakes, and reservors, streams, estuaries, and 

coastal seas. A particular feature of CHNTRN is its capability 

to deal with a network system that may consist of any number 

of Joined and branched streams/rivers of comparable size. 

CHNTRN, combined with the Channe'l Hydrodynamic Model (CHNHYD) 

(Yen 1982) which models the hydrodynamic computations of flows 

and water depths, constitutes a software package for 

predicting the transport, transfer, and transformations of 

organic pollutants in a stream/river system. 

Capsule Summary: CHNTRH 

The Channel Transport Model 

(CHNTRN) (Yen 1982) was developed 

by the Environmental Science 

Division of the Oak Ridge National 

Laboratory, Oak Ridge, Tennessee, 

for the EPA's Office of Pesticides 

and Toxic Substances. The purpose 

of CHNTRN is to simulate time 

varying distributions of sediments 

and chemicals in receiving waters. 

CHNTRN can model the transport and fate of a pollutant in a

• Time-varying, 1-dimensional 

compartment model. 

• Models rivers, lakes, estuaries, 

coastal waters. 

• Comprehensive second-order 

kinetics for organics. 

• Complex and data intensive.

CHNTRN can model complex problem settings that can be 

approximated with 1-dimensional segments. Modification of the 

model to treat 2-and 3-dimensional problems is relatively easy 

because of the integrated compartment approach. The spatial 

scale of segments can vary from meters to kilometers, and the 

temporal scale can vary from seconds to hours. 

CHNTRN uses the chemical kinetics of EXAMS (Exposure 

Analysis Modeling System) to account for hydrolysis, 

oxidation, photolysis, volatilization, biodegradation, and 

adsorption by biota. Consequently, other physico-chemical 

factors (e.g., temperature, 00. pH) are also required. 

Sediment transport, deposition, and scouring are simulated for 

three particle types, sand, silt, and clay. Provisions for 

adsorption/desorption and pollutant accumulation In the bed 

sediment are included.

The model code is in basic FORTRAN language. It consists 

of a main program and 15 subroutines. The equations that 

govern the system's kinetics are derived from 3-dimensional 

mass balance equations. An integrated compartment method (Yeh 

1981) is used to solve the differential equations. In this 

method, the link matrices are derived based on the fluxes of 

mass along each of the links that intertwine the compartments 

of the river system. The global matrix associated with spatial 

derivatives is assembled from these link matrices.

II-47
The result is a system of ordinary differential equations 

with respect to time that govern the dynamic evolution of 

suspended sediment, bed sediment, dissolved chemical 

concentration, particulate chemical concentration, and bed 

sediment chemical concentration. Chemical concentrations for 

both the wat column and bed sediment are solved by the time 

split scheme. Two options for solution are provided; one is 

the explicit scheme for fast computation; the second is the 

implicit scheme which generates stable solutions for large 

time steps.

CHNTRN user manual and documentation are currently in 

draft form and it has yet to be field verified. 

Input Data Requirements

CHNTRN is a sophisticated model requiring extensive data 

input. Before CHNTRN can be executed, hydrodynamic variables 

such as flow rates, water depth, cross-sectional area, width, 

and wet perimeter must be obtained from actual data if 

available; if not available, this Information can be estimates 

using CHNHYO. Other data input includes: 

• 

• Biological Information - bacterial population density, 

biotemperature for activation energy, and the 

bacterial portion involved in degradation. 

• 

• 

• 

Environmental parameters - air temperature, solar 

radiation, wind speed, vapor pressure, water 

temperature, extinction coefficient, pH, pOH. oxidation

radicals.

Coefficients for photolysis, hydrolysis, oxidation, 

biodegradation.

• Sediment types and distributions. 

Transport Information - solids in water column and 

sediment, sedimentation and resuspension velocities, 

partition coefficients, dispersive coefficients between 

phases, and volatilization rates.

• System geometry - areas, depths, volume. 

Sources and amounts of pollutant.

II-48
Output Descriptions

CHNTRN calculates and presents the following for 

individual compartments in tabular form:

Dissolved chemical concentration in the water column as 

a function of distance from the source.

• 

• Particulate concentration in suspended and bed 

sediment.

• Suspended sediment concentration and amount of bed 

sediment remaining in a unit bed association. 

Advantages and Limitations

The major advantage of CHNTRN is its capacity to simulate 

time-varying distributions in all types of water bodies. 

CHNTRN accounts for both advective and dispersive flows and 

the total flux of an aquatic system. The chemical kinetics are 

the second-order rate expressions of EXAMS.

CHNTRN is a complex model and as such is very data 

Intensive. Considerable time might be needed for the 

acquisition of data not readily available. Computations for 

complex systems will also require large amounts of time for 

solution, as will the simulation execution time.

If hydrodynamic information is not-available. It can be 

estimated by first using CHNHYO and then supplying the results 

to CHNTRN, Although CHNTRN would not be defined as 'userfriendly', It does make provisions to allow the user to make 

some modifications. 

CHNTRN has yet to be field validated. 

Model Applications

CHNTRN has been applied to two river network sample 

problems for demonstration purposes. The first sample is 

a single river system, and the second is a network of five 

rivers. Typical data were used for the simulations. In each 

example, the rivers are divided up into compartments.

II-49
The first scenario produced seemingly unrealistic 

results. Closer analysis of the input data revealed 1t to be 

erroneous and Illustrated the "garbage in, garbage out" 

results. The second scenario showed reasons results for 

clay, silt, sand, dissolved chemical, clay-adsorbed chemic 

silt-adsorbed chemical, and sand-adsorbed chemical 

concentrations. Because no analytical solutions were 

available, it is not possible to assess the accuracy of the 

results by comparing them with analytical results. However, 

the results Intuitively Indicate that the model can 

realistically simulate the behavior of the sediment and 

chemical variations in a stream/river network.

Resource Requirements

CHNTRN is written in FORTRAN IV and has been Implemented 

on an ISM 3933 computer. Simulation execution time may be 

extensive. 

User Support Activities

Copies of CHNTRN's draft user manual and documentation as 

well as assistance may be obtained from: 

G.T. Yeh 

Oak Ridge National Laboratory 

P.O. Box X 

Oak Ridge, TN 33830 

(615) 574-7285 

Environmental Sciences Division 

References

Yeh GT. 1982. CHNTRN: A channel transport model for 

simulating sediment and chemical distribution in a 

stream/river network. Oak Ridge National Laboratory, Oak 

Ridge, TN. ORNL-5882.

Yeh GT. 1982. CHNHYO: A channel hydrodynamic model for 

simulating flows and water surface elevations in a 

stream/river network. Oak Ridge National Laboratory, Oak 

Ridge, TN. ORNL-5701.

Yeh GT. 1981. ICM: An Integrated compartment method for 

numerically solving partial differential equations. Oak Ridge 

National Laboratory, Oak Ridge, TN. ORNL-5701.

II-50
Finite Clement Transport Model (FETRA)

_____________________________________

Summary

The Finite Element 

Transport Model (FETRA) is a 

time-varying, 2-dimensional 

(longitudinal and lateral) 

transport model developed by 

Battelle Pacific Northwest 

Laboratories. FETRA utilizes a 

finite element solution technique and consists of three 

submodels coupled to simulate the transport of sediments and 

contaminants in rivers and estuaries through the mechanisms of 

advection, diffusion/dispersion, adsorption/desorption, and 

degradation/decay. FETRA can be applied to rivers, estuaries, 

coastal, and unsatratified lake systems.

Capsule Summary: FETRA 

• Time-varying 2-dimensional 

(longitudinal, lateral) 

• Complex sediment transport 

capabilities. 

• Comprehensive second-order 

kinetics for organic decay. River, 

estuary, and coastal systems.

The sediment transport submodel simulates sediment 

movement for three sediment size fractions or sediment types. 

This submodel includes the mechanisms of: (1) advection and 

dispersion of sediments, (2) fall velocity and cohesiveness, 

and (3) deposition or erosion from the bed. It also calculates 

changes in bed conditions, including bed elevation changes due 

to scouring or deposition, and gives a 3-dimensional 

distribution of sediment sizes within the bed.

The dissolved contaminant transport submodel simulates 

the dissolved contaminant Interaction with sediments in motion 

and with stationary bed sediments. The submodel includes the 

mechanisms of: (1) advection and diffusion/dispersion of 

dissolved contaminants; (2) adsorption of dissolved 

contaminants by both moving and stationary sediments or 

desorption from the sediments Into water; and (3) chemical and 

biological degradation or radionuclide decay of contaminants. 

The particulate contaminant transport submodel simulates 

the transport of sediment-attached contaminants for each 

sediment size fraction. It includes the mechanisms of: (1) 

advection and dispersion of particulate contaminants; (2) 

adsorption/desorption of dissolved contaminants with sediment; 

(3) chemical and biological degradation or radionuclide decay 

of contaminants; and (4) deposition of particulate 

contaminants on the bed or erosion from the bed. 

II-51
The temporal scale of FETRA is on the order of minutes 

to hours. Hydrodynamic data are supplied by exterior models 

such as CAFE-I (ocean currents) and 1030 (wave refractions) 

for coastal waters applications, and EXPLORE-I (velocities and 

flow depths) for estuarine and riverine application

EXPLORE-I is a comprehensive mathematical water quality

model to be used in river basin planning and water resource 

studies. This generalized river basin water quality model can 

predict the hydrodynamics and water quality dynamics for 

rivers and well mixed estuaries. The EXPLORE-I model is an 

extended and modified version of the Storm Water Management 

Model, receiving water component, which was developed for 

studies of 00/800 dynamics. The model is capable of 

simulating a number of hydraulic regimes in either a dynamic 

or steady-state mode, and it has been set up. calibrated, and 

verified on a portion of the Willamette River Basin, 

consisting of major tributaries. EXPLORE-I was developed by 

Battelle-Northwest Laboratories for the EPA.

Input Data Requirements

The Input data requirements for FETRA are quite 

extensive. The three submodel data requirements include: 

• Common data requirements for all the submodels: 

- Channel geometry, 

- Discharges during the simulation period, 

- Discharges of tributaries, overland runoff, and 

other point and non-point sources. 

• 

 

- Critical shear stresses for erosion and deposition 

of cohesive sediment (silt and clay). 

- Erodibility coefficient of cohesive sediment.

- Lateral and longitudinal dispersion coefficients. 

Additional Requirements for sediment transport 

submodel:

- Sediment size fraction. 

- Sediment density and fall velocities for sand, silt, 

and clay. 

II-52
- Sediment concentration for each sediment size 

fraction.

- Bottom sediment size fraction. 

- Sediment concentration at the upstream end of the 

study reach. 

- Contributions of sediments from over land, 

tributaries, and other point and non-point sources.

• Additional requirements for the dissolved contaminant 

and particulate contaminant transport submodels: 

- Distribution coefficients and transfer rates of 

contaminant with sediment in each sediment size 

fraction (I.e., sand, silt, and clay). If values of 

distribution coefficients are not available, it as 

necessary to know clay mineral and organic sediment 

content to estimate these values.

- Second-order decay rates of contaminants

- Boundary conditions.

- Contributions of dissolved, and particulate 

contaminant concentrations from tributaries, 

overland, and other point and non-point sources. 

Output Descriptions

With the input data described above. FETRA simulates the 

following: 

• Sediment simulation and longitudinal/lateral 

distributions of total sediment and size fractions and 

changes in bed elevation.

• Contaminant simulation and longitudinal/lateral 

distributions of dissolved contaminants, contaminants 

adsorbed by sediment and In the bottom sediment for 

each sediment size. 

Advantages and Limitations

TETRA is designed for time-variable analyses of 1- or 2-

dimensional (horizontal) water bodies. Its sediment transport 

routines are sophisticated and will predict the resuspension 

velocities and bed load given the sediment and hydraulic 

characteristics. The model can be coupled with a hydrodynamic 

model in order to generate flows and velocities.

II-53
Input data requirements for PETRA are extensive, and 

computational time for long term continuous simulations may be 

high. Resource requirements for set up and execution are 

expected to be substantial. 

Model Applications

FETRA cannot discern water body stratification.

FETRA has been applied to the 

James River estuary in Virginia 

(Onishi 1981) and to the Irish Sea 

(Onishi et al. 1982). The purpose of 

the James River application was to 

simulate sediment movement and the 

transport of the pesticide Kepone 

which was discharged to the river in 

substantial quantities during the 

early 1970s. The purpose of the Irish 

Sea application was to evaluate 

exposure levels of radionuclides, 

heavy metals. and other toxic 

chemicals in coastal waters. Results 

of the Irish Sea application have not 

yet been published. A discussion of 

the James River application follows.

The James River application was 

a calibration and verification study 

of FETRA. Sediment transport was 

modeled for three sediment types: (1) 

cohesive (silt and clay); (2) 

noncohesive (sand); and, (3) organic

matter. These results (see Figure 1) 

were compared to field data which 

indicated that a considerable amount 

of particulate Kepone was transported by organic materials 

moving independently with other sediments. Predicted 

particulate Kepone concentrations associated with each type of 

sediment and weighted average particulate Kepone are shown 

together with measured field data of average particulate 

Kepone concentrations in Figure 2. The computed results and 

the field data closely agree in both Figures.

II-54
Resource Requirements

The computer program for FETRA is written in FORTRAN IV 

language. FETRA can be used on IBM, VAX, or COC-7600 

computers. Execution times and run costs vary, depending on 

the characteristics of the system to be modeled. For the James 

River application, computer time required to calculate all 

seven substances per computational mode per time step was 

0.0028 cp second on the CDC-7600 computer. 

User Support Activities

The user's manual and system documentation are still 

undergoing review at USEPA and are not yet available for 

publication. The FETRA model is operational, has been 

implemented in selected applications, and is available to the 

public. 

Model Information can be obtained by contacting: 

Yasuo Onishi 

Battelle - Pacific Northwest -Laboratories 

Richland, Washington 99352 

FTS 444-8302 COM 509-376-8302 

General References

Onishi Y. 1981. Sediment-contaminant transport model. 

Journal of the Hydraulics Division, ASCE, Vol. 107, No. HY9. 

Proc. Paper 16505. pp. 1089-1107.

Onishi Y, Mayer OW, Argo, RS. 1982. Sediment and toxic 

contaminant transport modeling in coastal waters. In: Finite 

Element Flow Analysis. Proc. Paper, pp 733-740.

Onishi Y, et al. 1981. Critical review: Radionuclide 

Transport, Sediment Transport, and Water Quality Mathematical 

Modeling; and Radionuclide Adsorption/desorption Mechanisms. 

Richland, Washington: Pacific Northwest Laboratory, Battelle 

Memorial Institute. NUREG/CR-1322, PNL 2901.

USEPA, 1982. EPA Environmental Modeling Catalogue. 

Abstracts of Environmental Models, pp. 363-366.

II-55
Sediment-Contaminant Transport Model (SERATRA)

_____________________________________

Summary

Capsule Summary: SERATRA The Sediment-Contaminant 

Transport Model (SERATRA) 

(Onishi and wise 1982a) is a 

time-varying 2-dimensional 

(longitudinal and vertical 

resolution in the water column 

and bed) sediment and 

contaminant transport model 

developed by Battelle-Pacific 

Northwest Laboratories. The model predicts distributions of 

sediments and toxic contaminants in rivers and some 

impoundments. The model consists of the following three 

coupled submodels which describe sediment-contaminant 

interactions and migration: (1) sediment transport submodel; 

(2) a dissolved contaminant transport submodel and (3) a 

particulate contaminant transport submodel. 

• Time-varying, 2-dimensional 

(longitudinal, vertical}. 

• Complex sediment transport 

capabilities. 

• Comprehensive second-order 

kinetics. 

• River, lake, and impoundment 

systems.

The sediment transport submodel simulates transport, 

deposition, scouring and armouring for three size fractions 

(or sediment types) of cohesive and noncohesive sediments. The 

transport of particulate contaminants (i.e., contaminants 

absorbed by sediment) is also simulated for each sediment 

size. Dissolved contaminants are linked by the 

adsorption/desorption process to the sediment and particulate 

contaminants. The contaminant submodels account for (1) 

advection and dispersion of dissolved and particulate 

contaminants; (2) chemical resulting from hydrolysis, 

oxidation, photolysis, biological activities, and radionuclide 

decay where applicable; (3) volatilization; (4) 

adsorption/desorption; and (5) deposition and scouring of 

particulate contaminants. SERATRA also computes changes in 

riverbed conditions for sediment and contaminant 

distributions.

Required input includes channel and sediment 

characteristics and adsorptlon/desorption properties of the 

contaminants. In addition, SERATRA requires discharge and 

depth distributions which can be obtained by a hydrodynamic 

model such as EXPLORE-I. EXPLORE applications for use with 

SERATRA do not require reprogramming; however, some 

reformatting and recalculation of the input parameters may be 

required. EXPLORE is discussed in further detail in the FETRA 

summary.

II-56
SERATRA is similar to TETRA in that both consist of the 

same three coupled submodels; and provide time-varying, 2-

dimensional transport simulation using comprehensive secondorder decay kinetics. Both provide longitudinal resolution, 

whereas the other dimension for FETRA is lateral rather than 

vertical (as in SERATRA).

Input Data Requirements

SERATRA consists of the same three coupled submodels that 

comprises PETRA and therefore requires Identical Input data. 

Refer to the FETRA summary for discussion of SERATRA's Input 

data requirements. 

Output Descriptions

SERATRA provides output Identical to the FETRA output 

except that longitudinal and vertical, rather than 

longitudinal and lateral, distributions and resolution are 

provided. 

Advantages and Limitations

Like FETRA, SERATRA provides the capability of simulating 

the complex mechanisms involved in contaminant migration by 

coupling contaminant transport and degradation with sediment

transport. SERATRA also handles time-variable analysis of 

stratified (2-dimensional) water bodies.

Adsorption/desorption mechanisms are expressed by a 

distribution coefficient and a transfer rate which describes 

the rate at which dissolved and particulate contaminant 

concentrations reach their equilibrium condition. Unlike most 

other models, SERATRA uses different distribution coefficients 

for adsorption and desorption and treats adsorption/desorption 

mechanisms as not being fully reversible.

SERATRA requires extensive input data, which may limit 

its applicability. It also requires rather extensive computer 

time, in which long-term, continuous simulations can be 

expensive. 

II-57
The model cannot be applied to estuary systems because 

longitudinal diffusion is 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.

SERATRA requires an exterior hydrodynamic model to supply 

required hydrodynamic data. EXPLORE applications require 

adjustment of several parameters for use with SERATRA.

Model Applications

SERATRA has been applied under both steady and unsteady 

flow conditions and has also undergone field applications with 

calibration and verification data (Onishi et al. 1982). Also, 

SERATRA has been field tested as an Integral component of the 

Chemical Migration Risk Assessment (CMRA) Methodology (Onishi 

et al. 1981).

Under steady flow conditions, SERATRA was applied to the 

Columbia River in Washington and the Clinch River in Tennessee 

(Onlshl et al. 1982). The Columbia River application simulated 

the transport of sediments, radioactive 65Zn, and a heavy 

metal. The Clinch River application simulated instantaneous 

and. continuous releases of radioactive 137Cs and 90Sr. 

Reasonably good agreement between predicted and measured 

results was obtained in both applications. 

Under unsteady flow conditions, SERATRA was applied to 

two small streams with rapidly changing flows (Onishi 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 SERATRA 

simulated the transport of sediment and four radionuclides in 

the Cattaraugus Creek watershed in New York (Onishi et al. 

1982). Although there were some discrepancies between 

predicted and measured values, considering the complexity of 

the modeling system and field data accuracy, agreement between 

predicted and measured results were judged to be reasonable.

SERATRA, as part of CHRA, was applied to the Four Mile 

Creek watershed in Iowa for a three-year field study (Onishi 

and Wise 1982b). Migration and fate of a herbicide were 

simulated in this application. Simulation result revealed a 

strong seasonal pattern of herbicide transport.

II-58
Resource Requirements

The computer program for SERATRA is written in FORTRAN 

preprocessor language, FLECS. A standard FORTRAN IV version of 

SERATRA is 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 $0.0088 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 $200 per run 

per one year simulation (for all four of the CMRA components). 

This time estimate is based on the following assumptions: (1) 

all data necessary to meet the Input requirements are 

available; and (2) qualified personnel are available to 

implement the model.

User Support Activities

Copies of the user's manual are available from ORO 

Publications. Center for Environmental Research Information, 

USEPA, Cincinnati, Ohio 45268 (telephone 513/684/7562; ask for 

publication EPA-600/3-82-055). 

Robert Ambrose 

User assistance can be obtained by contacting: 

USEPA 

EPA Athens Environmental Research Laboratory

Center for Water Quality Modeling

Athens, Georgia 30613 

(404) 546-3546 

Model Information can be obtained by contacting: 

Yasuo Onishi 

Battelle Pacific Northwest Laboratories 

P.O. Box 999 

Richland, Washington 99352 

(509) 376-8302 

The SERATRA model is operational, has been implemented in 

selected applications, and is available to the public. 

II-59
References

Onishi Y, Wise SE. 1982a. User's Manual for the Instream 

Sediment-Contaminant Transport Model SERATRA. EPA 600/3-

82-055. Environmental Research Laboratory, Office of Research 

and Development. USEPA. Athens, Georgia.

Onishi Y, Brown SM, Olsen AR, Parkhurst MA. 1981. "Chemical 

Migration and Risk Assessment Methodology." Proceedings of the 

Conference on Environmental Engineering. Proc. Paper, pp. 165-

172.

Onishi Y, Yabusaki SB, Kincaid CT. 1982. "Performance Testing 

of the Sediment-Contaminant Transport Model, SERATRA. 

Proceedings of the Conference Applying Research to Hydraulic 

Practice. Proc. Paper, pp. 623-632.

Onishi Y, wise SE. 1982b. Mathematical Model, SERATRA. for 

Sediment-Contaminant Transport in Rivers and its Application 

to Pesticide Transport in Four Mile and wolf Creeks in Iowa. 

EPA 600/3-82-045, Environmental Research Laboratory, Office of 

Research and Development. USEPA, Athens, Georgia.

II-60
Transiet One-Dimensiona1 Degradation and Migration Model (TODAM)

_____________________________________

Summary

Capsule Summary: TODAM The Transient One-Dimensional 

Degradation and Migration Model 

(TOOAM) is a time-varying, 1-

dimensional (longitudinal) 

transport model developed by 

Battelle Pacific Northwest 

Laboratories in Richland, 

Washington. TODAM includes the 

longitudinal dispersion term and can handle river/stream 

systems estuaries, and dry bed conditions. The model is 

suitable for many rivers where vertical stratification is not 

a concern.

• Time-varying, 1-dimensional. 

• Complex sediment transport 

capabilities. 

• Comprehensive second-order 

kinetics. 

• River and estuary systems. 

TODAM is a modified and simplified version of the 2-

dimensional transport model. Sediment Contaminant Transport 

Model (SERAFRA), also developed by Battelle. TODAM is composed 

of the following three submodels combined to describe 

sediment-contaminant Interaction and migration:

o Sediment transport 

o Dissolved contaminant transport 

o Sorbed contaminant (contaminants adsorbed by sediment) 

transport 

These submodels solve an advection-diffusion equation 

using a finite element solution technique with decay and 

sink/source terms with appropriate initial and boundary 

conditions.

The sediment transport submodel simulates transport, 

deposition, and erosion of three sediment size fractions (or 

sediment types) of cohesive and noncohesive sediments. The 

dissolved contaminant transport submodel includes mechanisms 

of contaminant adsorption/desorptlon, as well as radionuclide 

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.

TODAM includes the mechanisms of advection and 

diffusion/dispersion of sorbed contaminants; adsorption 

(uptake) of dissolved contaminants by

II-61
sediments or desorption from sediments; radionuclide decay; 

deposition of sorbed contaminants to the river bed or 

resuspension from the river bed; and contributions of sorbed 

contaminants from point and non-point sources into the system. 

TODAM also computes changes in river bed conditions, including 

bed elevation, sediment size distribution, and sorbed 

contaminant distribution within the bed. 

An exterior hydrodynamic model, such as EXPLORE, or the 

Distributed Kinematic Wave Model for Channel flows (DKWAV), is 

required to provide channel flow, cross-sectional area, depth, 

shear stress, and wetted perimeter for use Dy TOOAM. 

Reprogramming is not required if either EXPLORE or DKWAV is 

used. EXPLORE applications require some reformatting and 

recalculation of Input parameters; whereas, DKWAV applications 

can be directly Integrated for use with TODAM. 

DKWAV, also developed by Battelle, is an unsteady, 1-

dimensional, second-order, explicit, finite-difference model 

which simulates the hydrodynamics in dendritic river systems 

to obtain time varying distributions of depth and velocity in 

a channel. The model, which can be easily combined with 

overland flow models, routes flows through arbitrarily shaped 

channels in which the channel reach is divided into sections 

bounded by points called "nodes". Flow routing is performed 

from node to node by a marching solution. The equations of 

motion with the kinematic wave approximation are numerically 

analyzed via a modified version of the Lax-Wendroff, secondorder, finite-difference scheme. Numerical stability is based 

upon the Courant conditions. Point Inflow or continuous (or 

both) lateral inflow is included in which point inflow occurs 

at nodes or continuous lateral inflow occurs between nodes. 

Associated with each channel section is its own seepage 

velocity. A cross-sectional area versus discharge relationship 

exists for each 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. 

Input Data Requirements

The EXPLORE model is discussed in further detail in the 

TETRA summary. 

The following Hems are Input data requirements of TODAM: 

• Channel geometry

II-62
• Flow characteristics 

- Depth and velocity distributions 

• 

- Distribution coefficients 

Sediment characteristics

- Sediment size distribution 

- Density 

- Critical shear stresses and erod1b111ty coefficient for 

cohesive sediment 

• Contaminant characteristics 

- Transfer rates 

- Decay and degradation rates or associated parameters

- Initial conditions

- Boundary conditions

Output Descriptions

With the input data described above, TODAM provides the 

following output:

1. Sediment simulation and distributions of total 

sediment, sediment size fractions, and changes in 

bed elevation.

2. Contaminant simulation and distributions of 

dissolved contaminants, and concentrations adsorbed 

by each sediment size and within the bed. 

Advantages and Limitations

The major strength of the TODAM model is that like FETRA and 

SERATRA, it has very sophisticated sediment resuspenslon and 

bed load predictive capabilities. Its 1-dimensional framework 

makes TODAM more tailored to river applications. TODAM's 

kinetics are comprehensive second-order.

Also, TODAM can handle reversible flow and dry bed conditions. 

TODAM, as a simplified version of SERATRA, can be substituted 

for SERATRA in estuarine applications. 

II-63
TODAM requires extensive input data and computer time. 

The model can be used only in 1-dimensional applications.

Model Applications

TODAM was applied to Mortandad and South Mortandad 

Canyons in New Mexico to estimate in-stream flow, sediment

transport, and radionuclide transport in Intermittent streams. 

Transport of seven substances was simulated: sand, silt, clay, 

dissolved 239 Pu, and particulate 239 Pu adsorbed by each type 

of sediment. Results Indicate that the grid size was too large 

and produced numerical approximations that were too coarse to 

obtain accurate solutions.

Resource Requirements

The computer program for TODAM is written in the FORTRAN 

preprocessor language. FLECS. A standard FORTRAN IV version of 

TODAM is also available, TODAM can be implemented in a batch 

mode on VAX or POP 11/70 computers. Execution times and run 

costs vary, depending on the characteristics of the system to 

be modeled.

User Support Activities

The user's guide and system documentation are undergoing 

review at UScPA and are not yet available for publication. The 

TODAM model is in operation, has been Implemented in selected 

applications, and is available to the public. 

More information on this model can be obtained by 

contacting: 

Yasuo Onishi 

Battelle Pacific Northwest Laboratories 

P.O. Box 999 

Richland, Washington 99352 

(509) 376-8302

References

Onishi Y, Whelan G, and Skaggs RL. 1982. Development of a 

Multimedia Radionuclide Exposure Assessment Methodology for 

Low-level Waste Management. PNL-3370, Battelle-Pacific 

Northwest Laboratory, Richland. Washington.

II-64
II-65

Hydrological Simulation Program-FORTRAN (HSPF)

_____________________________________

Summary

The Hydrological Simulation Capsule Summary: HSPF 

Program-FORTRAN (HSPF) (Johanson 

et al. 1980) is a series of 

fully Integrated computer codes 

capable of simulating watershed 

hydrology and the behavior of 

conventional and organic

pollutants in land surface 

runoff and receiving waters. 

Simulations are performed on a 

time-varying, 1-dimensional 

basis and can be performed for 

streams and non-tidal rivers and for well mixed, nonstratified reservoirs.

• Time-variable, 1-dimensional model 

for non-tidal rivers and lakes. 

• Systematic modular framework allots 

a variety of operating modes, 

including continuous hydrologic 

simulation. 

• Comprehensive second-order kinetics 

for organics decay. 

• Stream routing uses /cinematic 

wave approximation. 

• Data intensive. 

HSPF has been modified from the original version, which 

considered only conventional pollutants, to an updated version 

which also considers the chemical processes associated with 

the simulation of organic pollutants. Kinetic processes for 

organics include hydrolysis, oxidation, photolysis, 

volatilization, and biodegradation. A generalized first-order 

decay rate is Included for sorbed substances. Furthermore, up 

to three organic pollutants can be simulated at any one time 

up to two may be "daughter" chemicals, produced from the 

"parent" chemical (or the other "daughter") by any or all of 

the degradation processes.

Sediment transport, deposition and scour are modeled for 

sand, silt, and clay particle sires; adsorption/desorption

rates are computed for each size of suspended and bed 

sediment.

The model incorporates structured programming and topdown design for a set of integrated modules which allow for 

the continuous simulation of hydrologic and water quality

processes. HSPF contains three application modules: PERLNO, 

IMPINO, and RCHRES. It contains five utility modules: COPY, 

PLTGEN, OISPLY, DURANL, and GENER.

The watershed hydrologic algorithm follow the assumptions 

made in the Stanford Watershed Model. The dynamic and 

continuous processes that affect the storages and outflow of 

pesticides and nutrients from agricultural lands are derived 

from the Agricultural Runoff Model (ARM), and the land surface 

washoff algorithms are from the Non-point Source (NPS) Model. 

Stream routing uses the kinematic wave approximation, and the 

water quality algorithms apply second-order kinetics to a 

finite difference solution technique.
Sediment-contaminant runoff contributions from rural and 

urban land surfaces can be simulated through the execution of 

the appropriate non-point source modules (user-specified) 

contained in the model code. These modules predict sedimentcontaminant loadings associated with pervious and impervious 

land surfaces" as a function of snow accumulation and melt, 

sediment production and removal, sediment-contaminant 

relationships, water budget, and soil Infiltration rates.

The PERLND module simulates snow accumulation and melt, 

water movement, erosion processes, scour, and water quality in 

a pervious land segment with homogeneous hydrologic and 

climatic characteristics.

For impervious land segments where little or no 

infiltration occurs, module IMPLNO can be applied. 

Module RCHRES simulates the processes that occur in a 

single reach (and one level of underlying bed sediment) of an 

open channel or a completely mixed lake.

The utility modules are designed to provide flexibility 

in managing Simulation input and output. COPY is used by the 

user to change the form of the time series. A 5-minute 

rainfall record may be aggregated to an hourly time interval 

for example. The PITGEN module creates a specially formatted 

segmental file for later access by a stand-alone plot program. 

DISPLY takes a time series and summarizes the data in a 

formatted table. DURANL computes several statistics on a time 

series. The GENER module is used to transform a time series to 

produce a new series or to combine two time series to create a 

new one.

An Interactive editor to prepare Input sequences for HSPF 

Vs under development and will be available from the 

Environmental Research Laboratory, Athens, Georgia. 

Input Data Requirements

If fully implemented, the HSPF 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 for the more sensitive, site-specific parameters. The 

time series, constant parameters, and water quality input 

requirements include: 

• Time series inputs which include: air temperature, 

precipitation, evapotranspiration, channel inflow, 

surface and groundwater inflow, and wind movement.

II-66
• 

• 

• 

• 

• 

Constant parameter inputs which include: channel

geometry, vegetative cover index, surface detention 

storage, groundwater storage volume, soil moisture 

content, overland flow slope, snow-pack data, 

Infiltration Index, and Interflow Index.

Land sediment factors: soil detachment coefficients, 

sediment Influx, surface cover, sediment washoff 

coefficient.

• Soil temperature data: air temperature time series, 

slope and Intercept of land temperature to air 

temperature equation. 

• Dissolved gas in land water: ground elevation, 

Interflow and groundwater 00 and CO2 concentrations. 

Quality constituents associated with sediment: 

washoff potency factor, scour potency factor.

• Quality constituents concentrations In Interflow and 

groundwater. 

Agrichemical quality constituents: solute leaching 

factors; soil layer depths; soil densities; and 

pesticide and nutrient sorption parameters, solubility 

factors, degradation rates.

Impervious land quality factors: surface runoff 

removal rates, solids washoff coefficient, rate of 

solids placement and removal on surface, and overland 

flow borne pollutant accumulation and storage rates.

• Reach and reservoir water quality characteristics, 

coefficients, and rates. 

Output Descriptions

HSPF 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 model also includes a frequency 

analyses which provides a statistical summary of time-varying 

contaminant concentrations and provides the link between 

simulated instream toxicant concentrations and risk 

assessment.

II-67
Advantages and Limitations

HSPF is designed for year-around simulation of river 

basin hydrology, pollutant runoff or discharge, and receiving 

water quality. Its modular structure allows it to be readily 

used in more restrictive ways, using streamflow 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 yeararound. perspective.

The sediment-contaminant kinetics routines have the same 

general characteristics as other complex water quality models; 

however, like EXAMS 1t has the added capability of simulating 

the production and Interactions of contaminant daughter 

products.

HSPF contains a code to calculate the frequency of 

occurrence and duration of contaminant concentrations 1n the 

receiving waters.

Because of Us 1-dimensional approach to pollutant 

simulation, HSPF does not discern stratification in the water 

column and bed sediments.

The model's code has been optimized for both mini-and 

mainframe computers. On minicomputers, usage of direct access 

files is maximized. On mainframes, maximum use is made of fast 

memory and direct access I/O is minimized. Versions of HSPF 

are available for both types of systems. 

Data requirements to implement HSPF are potentially 

extensive (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 its application and use as a planning tool 

in determining agricultural BMPs.

II-68
Resource Requirements

HSPF requires a FORTRAN compiler that supports direct 

access I/O. Twelve external files are required. The system 

requires 128K bytes of Instruction and data storage on virtual 

memory machines, or about 250K bytes with extensive overlaying 

on overlay-type machines. The system was developed on a 

Hewlett-Packard 3000 minicomputer and has been used on IBM 370 

series computers. It has been Installed on the following 

systems: IBM, DEC VAX and System 10/20, Prime 350 and above, 

Data General HV4000, CDC Cyber, HP3000 and HP1000, Burroughs 

and Harris. Installation notes are available for specific 

machines. 

User Support Activities

HSPF is in the public domain and can be obtained from the 

Center for Water Quality Modeling, Environmental Research 

Laboratory, USEPA, College Station Road, Athens, Georgia 30613 

(telephone 404.546-3583).

User assistance can be obtained by contacting: 

Thomas O. Barnwell 

U.S. Environmental Protection Agency 

Environmental Research Laboratory 

College Station Road 

Athens. Georgia 30613 

FTS 250-3175 COM 404-546-3175 

General References

Donigian AS, et al. ,1983. Guide to the Application of the 

Hydrological Simulation Program - FORTRAN (HSPF). Draft 

report, Environmental Research Laboratory, Athens, GA, 30613.

Johanson RC, Imhoff GC, Davis HH. 1980. User's manual for 

Hydrologlcal Simulation Program—FORTRAN (HSPF). EPA 600/9-9-

80-015, Environmental Research Laboratory, Athens, GA. 30613. 

Johanson RC, and Kittle JL. 1983. Design, Programming, and 

Maintenance of HSPF, Journal of Technical Topics 1n Civil 

Engineering, Vol. 109. No. 1, pp. 41-57. 

Imhoff JC, et al. 1981. User's Manual for Hydrological 

Simulation Program - FORTRAN (HSPF). Release 7.0 Draft Report. 

II-69
From: DIANE JONES 

To: VALENTINE-CONNIE, TURNER-YVONNE, BROWN-GEORGETTE, ... 

Subject: ELECTRONIC FREEDOM OF INFORMATION ACT (E-FOIA) 

Hello, 

We are under a tight deadline so act quickly.

Date: 7/22/9712:34pm 

Please read attachments carefully and distribute widely in 

your-Division. Specific groups of individuals have been 

identified in the attachment. 

CC: REGAS-DIANE, DOUGHERTY-CYNTHIA, DAVIES-TUDOR, COOK... 

Thanks. 

DJ 

II-70
July 22, 1997 

MEMORANDUM

SUBJECT: Implementation of the Electronic Reading Room and 

Electronic Index of All 

Public Records Requirements of E-FOIA Amendments of 1996 

FROM: Diane C. Jones 

 OW POI Coordinator 

TO: Connie Valentine, OGWDW 

Yvonne Turner, OST 

Georgette Brown, OWM 

Lou Gilbert, OWOW 

Tom Wall, AIEO 

By October 2, 1997, all Federal agencies are required by 

the Electronic Freedom of Information Act Amendments (E-FOIA) 

to implement procedures to disclose records in electronic 

format; maintain an electronic and paper reading room of 

public records and Agency policy, guidance, and interpretive 

documents; and to maintain an electronic index of all public 

records. This is a tremendous task for us at EPA because of 

our decentralized system of record maintenance. However, once 

these procedures are implemented, it will be an extraordinary 

tool not only for the public, but for those of us who respond 

to FOIA requests. 

To assist the FOIA Officer in this task, we have been asked to 

distribute and complete the attached survey form to all 

program FOIA personnel, records managers, database managers, 

subject matter experts, etc. in our offices. These survey 

forms are due to HWQ FOI Office by Monday, August 4,1997.

I cannot emphasize enough the importance of completing 

these survey forms and getting them back to the FOI Officer by 

August 4. If you have any questions, please contact me. 

Thanks your cooperation and assistance in this matter. 

Barbara Jarvis, CIHRS 

Attachments 

cc: Diane Regas, PRMO 

OW Office Directors 

II-71
July 9, 1997 

MEMORANDUM 

Headquarters FOI Coordinators 

 FOI Officer 

SUBJECT: Preparation for Implementation of the Electronic 

TO: Regional FOI Officers 

FROM: Jeralene Green 

Reading Room Requirements of the Electronic Freedom of 

Information Act (E-FOIA) Amendments of 1996 

We will be working with Regional and Headquarters Program 

Offices, and FOI Officers and FOI Coordinators throughout EPA 

to conduct a survey, perform selected regional site visits, 

and compile the results into electronic indices of Agency 

public information resources (as outlined in Peter D. 

Robertson's Memo, same subject, dated July 8, 1997). 

To meet implementation schedules, this work must be performed 

quickly and thoroughly. Please distribute the attached survey 

forms to all Program FOI personnel, Records Managers, Database 

Managers, Subject Matter Experts, etc.). Please note that the 

survey forms are due back by August 4, 1997. I am also 

requesting that you provide assistance to my contract 

assistants (Carol Brock and Bob Nawrocki). Carol and Bob will 

call you to interview you about frequently requested records 

and to establish the contacts you recommend they interview. 

Your timely efforts and cooperation will assure EPA's 

compliance with the new amendments. Thank you for your 

assistance. 

Attachment 

II-72
ENVIRONMENTAL PROTECTION AGENCY: E-FOIA PREPARATION SURVEY 

FORM 

NAME_______________________PHONE_____________________________

OFFICE/PROGRAM/MAIL 

CODE__________________________________________

LOCATION (HQ, Region, or 

Field)_________________________________

Directions: 

1. Respond to each query as completely as possible. Use 

separate pages, as necessary. 

2. Return the completed survey, by August 4, 1997, to the 

Agency FOI Officer, Ms. Jeralene Green (Attn.: E-FOIA). Paper 

responses should be addressed to Ms. Green at EPA HQ, Mail 

Code 1105. Electronic responses should be addressed to 

brock.carol@epamail.epa.gov. 

3. If you have any questions about the survey, please call 

202/260-4048. 

4. For reference purposes, "frequently requested" is defined 

as three or more requests for the same item. 

I. List all frequently requested records. For each record 

listed, provide the: 

- Record Title (and any other names for this item) 

- Record Format (paper, electronic, film, etc.) 

II. List all frequently requested databases. For each 

database listed, provide the: 

- Database Location (Program, contact name, telephone 

number) 

- Is this Database downloaded into a national database (or 

databases)? If so, which one(s)? 

- Does this Database contain releasable and non- 

- Does this Database contain a statement that some 

- Strengths of current process? 

- Record Description 

- Record Location (Program, contact name, telephone 

number) 

- Retrieval Method 

- Dissemination Method (paper copy, electronically 

- through the Internet, NTIS) 

- Strengths of current process 

- Weaknesses of current process 

Database Title (and any other names for this database) 

Database Description 

Data Elements (and a description of each element) 

- Is this Database disseminated (through the Internet, 

- Electronic Bulletin Boards, other)? If so, through 

which medium? 

releasable information? 

information is deleted? 

II-73
- Weaknesses of current process? 

III. Do indices of these frequently requested records and 

databases already exist? If so, where may the indices by 

obtained?

II-74
MEMORANDUM

TO: Assistant Administrators 

Inspector General 

Chief Financial Officer 

Associate Administrators 

Staff Office Directors 

Deputy Assistant Administrators 

Deputy General Counsel 

Deputy Inspector General 

General Counsel 

Regional Administrators 

Deputy Chief Financial Officer 

Deputy Regional Administrators 

Program Office Directors 

Regional Division Directors 

Headquarters FOI Coordinators 

Regional FOI Officers 

SUBJECT: Preparation for Implementation of the Electronic 

Reading Room Requirements of the Electronic Freedom of 

Information Act (E-FOIA) Amendments of 1996 

On October 2, 1996, President Clinton signed into law the E-FOIA 

Amendments. These Amendments require Agencies to implement, by 

October 2, 1997, procedures to disclose records in electronic 

format; maintain an electronic and paper Reading Room of public 

records and Agency policy, guidance, and interpretive documents; 

and to maintain an electronic index of all public records. To 

meet these requirements, the Agency FOI Officer, Ms. Jeralene 

Green, will be working with Program Offices and FOIA Coordinators 

throughout EPA. Ms. Green is conducting a survey, performing 

selected regional site visits, and compiling the results. 

Your efforts to assure timely cooperation with Ms. Green (and her 

contract staff) will assure EPA's compliance with the new 

amendments. Thank you for your assistance. 

Peter D. Robertson 

Chief of Staff
DISCLAIMER 

We have made efforts to ensure that this electronic document is an accurate reproduction 

of the original paper document. However, this document does not substitute for EPA 

regulations; nor is it a regulation itself. Thus, it does not and cannot impose legally binding 

requirements on EPA, the states, tribes or the regulated community, and may not apply to 

a particular situation based on the circumstances. If there are any differences between this 

web document and the statute or regulations related to this document, or the original 

(paper) document, the statute, regulations, and original document govern. We may change 

this guidance in the future. 

Supplemental material such as this disclaimer, a document abstract and glossary entries 

may have been added to the electronic document. 
Acute toxicity - A chemical stimulus severe enough to rapidly induce an effect; in 

aquatic toxicity tests, an effect observed within 96 hours or less is considered acute. 

When referring to aquatic toxicology or human health, an acute effect is not always 

measured in terms of lethality. 

Ammonia toxicity - Under specific conditions of temperature and pH, the un ionized 

component of ammonia can be toxic to aquatic life. The un ionized component of 

ammonia increases with pH and temperature. 

Background levels - Background levels represent the chemical, physical, and biological 

conditions that would result from natural geomorphological processes such as 

weathering or dissolution. 

Benthic - Refers to material, especially sediment, at the bottom of an aquatic ecosystem. 

It can be used to describe the organisms that live on, or in, the bottom of a waterbody. 

GLOSSARY 

Adsorption-desorption - Adsorption is the process by which nutrients such as inorganic 

phosphorous adhere to particles via a loose chemical bond with the surface of clay 

particles. Desorption is the process by which inorganic nutrients are released from the 

surface of particles back into solution. 

Advection - Bulk transport of the mass of discrete chemical or biological constituents by 

fluid flow within a receiving water. Advection describes the mass transport due to the 

velocity, or flow, of the waterbody. 

Aerobic - Environmental conditions characterized by the presence of dissolved oxygen; 

used to describe biological or chemical processes that occur in the presence of oxygen. 

Ambient water quality - Natural concentration of water quality constituents prior to 

mixing of either point or nonpoint source load of contaminants. Reference ambient 

concentration is used to indicate the concentration of a chemical that will not cause 

adverse impact to human health. 

Ammonia - Inorganic form of nitrogen; product of hydrolysis of organic nitrogen and 

denitrification. Ammonia is preferentially used by phytoplankton over nitrate for uptake of 

inorganic nitrogen. 

Anaerobic - Environmental condition characterized by zero oxygen levels. Describes 

biological and chemical processes that occur in the absence of oxygen. 

Aquatic ecosystem - Complex of biotic and abiotic components of natural waters. The 

aquatic ecosystem is an ecological unit that includes the physical characteristics (such as 

flow or velocity and depth), the biological community of the water column and benthos, 

and the chemical characteristics such as dissolved solids, dissolved oxygen, and 

nutrients. Both living and nonliving components of the aquatic ecosystem interact and 

influence the properties and status of each component. 

Assimilative capacity - The amount of contaminant load (expressed as mass per unit 

time) that can be discharged to a specific stream or river without exceeding water quality 

standards or criteria. Assimilative capacity is used to define the ability of a waterbody to 

naturally absorb and use waste matter and organic materials without impairing water 

quality or harming aquatic life. 

Bacteria - Microscopic, single-celled or noncellular plants, usually saprophytic or 

parasitic. 

Benthic organisms - Organisms living in, or on, bottom substrates in aquatic 

ecosystems. 
Best management practices (BMPs) - Methods, measures, or practices that are 

determined to be reasonable and cost effective means for a land owner to meet certain, 

generally nonpoint source, pollution control needs. BMPs include structural and 

nonstructural controls and operation and maintenance procedures. 

Calibration - Testing and tuning of a model to a set of field data not used in the 

development of the model; also includes minimization of deviations between measured 

field conditions and output of a model by selecting appropriate model coefficients. 

Channel - A natural stream that conveys water; a ditch or channel excavated for the flow 

of water. 

Chloride - An atom of chlorine in solution, bearing a single negative charge. 

Coastal Waters - Those waters surrounding the continent which exert a measurable 

influence on uses of the land and on its ecology. The Great Lakes and the waters to the 

edge of the continental shelf. 

Complete mixing - No significant difference in concentration of a pollutant exists across 

the transect of the waterbody. 

Concentration - Amount of a substance or material in a given unit volume of solution. 

Usually measured in milligrams per liter (mg/l) or parts per million (ppm). 

Conservative substance - Substance that does not undergo any chemical or biological 

transformation or degradation in a given ecosystem. 

Contamination - Act of polluting or making impure; any indication of chemical, sediment, 

or biological impurities. 

Conventional pollutants - As specified under the Clean Water Act, conventional 

contaminants include suspended solids, coliform bacteria, biochemical oxygen demand, 

pH, and oil and grease. 

Decay - Gradual decrease in the amount of a given substance in a given system due to 

various sink processes including chemical and biological transformation, dissipation to 

other environmental media, or deposition into storage areas. 

Decomposition - Metabolic breakdown of organic materials; the by products formation 

releases energy and simple organics and inorganic compounds. (see also respiration) 

Designated use - Uses specified in water quality standards for each waterbody or 

segment regardless of actual attainment. 

Detritus - Any loose material produced directly from disintegration processes. Organic 

detritus consists of material resulting from the decomposition of dead organic remains. 

Biochemical oxygen demand (BOD) - The amount of oxygen per unit volume of water 

required to bacterially or chemically oxidize (stabilize) the oxidizable matter in water. 

Biochemical oxygen demand measurements are usually conducted over specific time 

intervals (5,10,20,30 days). The term BOD generally refers to standard 5 day BOD test. 

Biomass - The amount, or weight, of a species, or group of biological organisms, within 

a specific volume or area of an ecosystem. 

Boundary conditions - Values or functions representing the state of a system at its 

boundary limits. 

Chlorophyll - Green photosynthetic pigment present in many plant and some bacterial 

cells. There are seven known types of chlorophyll; their presence and abundance vary 

from one group of photosynthetic organisms to another. 
Diagenesis - Production of sediment fluxes as a result of the flux of particulate organic 

carbon in the sediment and its decomposition. The diagenesis reaction can be thought of 

as producing oxygen equivalents released by various reduced species. 

Dilution - Addition of less concentrated liquid (water) that results in a decrease in the 

original concentration. 

Diurnal - (1) Occurring during a 24-hr period; diurnal variation. (2) Occurring during the 

day time (as opposed to night time). (3) In tidal hydraulics, having a period or cycle of 

approximately one tidal day. 

Drought - In general, an extended period of dry weather, or a period of deficient rainfall 

that may extend over an indefinite number of days, without any quantitative standard by 

which to determine the degree of deficiency needed to constitute a drought. Qualitatively, 

it may be defined by its effects as a dry period sufficient in length and severity to cause at 

least partial crop failure or impair the ability to meet a normal water demand. 

Dye study - Use of conservative substances to assess the physical behavior of a natural 

system to given stimulus. 

Dynamic model - A mathematical formulation describing the physical behavior of a 

system or a process and its temporal variability. 

Estuary - That portion of a coastal stream influenced by the tide of the body of water into 

which it flows; a bay, at the mouth of a river, where the tide meets the river current; an 

area where fresh and marine water mix. 

Dispersion - The spreading of chemical or biological constituents, including pollutants, in 

various directions from a point source, at varying velocities depending on the differential 

instream flow characteristics. 

Dissolved oxygen (DO) - The amount of oxygen that is dissolved in water. It also refers 

to a measure of the amount of oxygen available for biochemical activity in water body, 

and as indicator of the quality of that water. 

Domestic wastewater - Also called sanitary wastewater, consists of wastewater 

discharged from residences and from commercial, institutional, and similar facilities. 

Dynamic simulation - Modeling of the behavior of physical, chemical, and/or biological 

phenomena and their variation over time. 

Ecosystem - An interactive system that includes the organisms of a natural community 

association together with their abiotic physical, chemical, and geochemical environment. 

Effluent - Municipal sewage or industrial liquid waste (untreated, partially treated, or 

completely treated) that flows out of a treatment plant, septic system, pipe, etc. 

Eutrophication - Enrichment of an aquatic ecosystem with nutrients (nitrates, 

phosphates) that accelerate biological productivity (growth of algae and weeds) and an 

undesirable accumulation of algal biomass. 

Eutrophication model - Mathematical formulation that describes the advection, 

dispersion, and biological, chemical, and geochemical reactions that influence the growth 

and accumulation of algae in aquatic ecosystems. Models of eutrophication typically 

include one or more species groups of algae, inorganic and organic nutrients (N,P), 

organic carbon, and dissolved oxygen. 

Extinction coefficient - Measure for the reduction (absorption) of light intensity within a 

water column. 

Flocculation - The process by which suspended colloidal or very fine particles are 

assembled into larger masses or flocules that eventually settle out of suspension. 
Geochemical - Refers to chemical reactions related to earth materials such as soil, 

rocks, and water. 

Heavy Metals - Metals that can be precipitated by hydrogen sulfide in acid solution, for 

example, lead, silver, gold, mercury, bismuth, copper. 

Hydraulic Radius - The right cross-sectional area of a stream of water divided by the 

length of that part of its periphery in contact with its containing conduit; the ratio of area 

to wetted perimeter. Also called hydraulic mean depth. 

Initial conditions - A state of a system prior to an introduction of an induced stimulus. 

Describe conditions at the start up of system simulations. 

Interstitial water - Water contained in the interstices, which are the pore spaces or voids 

in soils and rocks. 

Kinetic processes - Description of the rate and mode of change in the transformation or 

degradation of a substance in an ecosystem. 

Flux - Movement and transport of mass of any water quality constituent over a given 

period of time. Units of mass flux are mass per unit time. 

Food Chain - Dependence of a series of organisms, one upon the other, for food. The 

chain begins with plants and ends with the largest carnivores. 

Forcing functions - External empirical formulation used to provide input describing a 

number of processes. Typical forcing functions include parameters such as temperature, 

point and tributary sources, solar radiation, and waste loads and flow. 

Gradient - The rate of decrease (or increase) of one quantity with respect to another; for 

example, the rate of decrease of temperature with depth in a lake. 

Groundwater - Phreatic water or subsurface water in the zone of saturation. 

Groundwater inflow describes the rate and amount of movement of water from a 

saturated formation. 

Heterotrophic - Pertaining to organisms that are dependent on organic material for food. 

Hydrodynamic model - Mathematical formulation used in describing circulation, 

transport, and deposition processes in receiving water. 

Hydrodynamics - The study of the motion of, and the forces acting on, fluids. 

Hydrograph - A graph showing variation of in stage (depth) or discharge of water in a 

stream over a period of time. 

Hydrolysis - Reactions that occur between chemicals and water molecules resulting in 

the cleaving of a molecular bond and the formation of new bonds with components of the 

water molecule. 

In situ - In place; in situ measurements consist of measurement of component or 

processes in a full scale system or a field rather than in a laboratory. 

Load allocation (LA) - The portion of a receiving water's total maximum daily load that is 

attributed either to one of its existing or future nonpoint sources of pollution or to natural 

background sources. 

Loading, Load, Loading rate - The total amount of material (pollutants) entering the 

system from one or multiple sources; measured as a rate in weight per unit time. 

Longitudinal dispersion - The spreading of chemical or biological constituents, 

including pollutants, downstream from a point source at varying velocities due to the 

differential instream flow characteristics. 
Natural waters - Flowing water within a physical system that has developed without 

human intervention, in which natural processes continue to take place. 

Numerical model - Models that approximate a solution of governing partial differential 

equations which describe a natural process. The approximation uses a numerical 

discretization of the space and time components of the system or process. 

Low flow (7Q10) - Low flow (7Q10) is the 7 day average low flow occurring once in 10 

years; this probability based statistic is used in determining stream design flow conditions 

and for evaluating the water quality impact of effluent discharge limits. 

Mass balance - An equation that accounts for the flux of mass going into a defined area 

and the flux of mass leaving the defined area. The flux in must equal the flux out. 

Mathematical model - A system of mathematical expressions that describe the spatial 

and temporal distribution of water quality constituents resulting from fluid transport and 

the one, or more, individual processes and interactions within some prototype aquatic 

ecosystem. A mathematical water quality model is used as the basis for waste load 

allocation evaluations. 

Mineralization - The process by which elements combined in organic form in living or 

dead organisms are eventually reconverted into inorganic forms to be made available for 

a fresh cycle of plant growth. The mineralization of organic compounds occurs through 

combustion and through metabolism by living animals. Microorganisms are ubiquitous, 

possess extremely high growth rates and have the ability to degrade all naturally 

occurring organic compounds. 

Modeling - The simulation of some physical or abstract phenomenon or system with 

another system believed to obey the same physical laws or abstract rules of logic, in 

order to predict the behavior of the former (main system) by experimenting with latter 

(analogous system). 

Monitoring - Routine observation, sampling and testing of designated locations or 

parameters to determine efficiency of treatment or compliance with standards or 

requirements. 

Monte Carlo simulation - A stochastic modeling technique that involves the random 

selection of sets of input data for use in repetitive model runs. Probability distributions of 

receiving water quality concentrations are generated as the output of a Monte Carlo 

simulation. 

Mouth - The exit or point of discharge of a stream into another stream or a lake, or the 

sea. 

Nitrification - The oxidation of ammonium salts to nitrites (via Nitrosomonas bacteria) 

and the further oxidation of nitrite to nitrate via Nitrobacter bacteria. 

Nonpoint source - Pollution that is not released through pipes but rather originates from 

multiple sources over a relatively a large area. Nonpoint source can be divided into 

source activities related to either land or water use including failing septic tanks, improper 

animal keeping practices, forest practices, and urban and rural runoff. 

Nutrient - A primary element necessary for the growth of living organisms. Carbon 

dioxide, nitrogen, and phosphorus, for example, are required nutrients for phytoplankton 

growth. 

Organic - Refers to volatile, combustible, and sometimes biodegradable chemical 

compounds containing carbon atoms (carbonaceous) bonded together and with other 

elements. The principal groups of organic substances found in wastewater are proteins, 

carbohydrates, and fats and oils. 
Partition coefficients - Chemicals in solution are partitioned into dissolved and 

particulate adsorbed phase based on their corresponding sediment to water partitioning 

coefficient. 

Plankton - Group of generally microscopic plants and animals passively floating, drifting 

or swimming weakly. Plankton include the phytoplankton (plants) and zooplankton 

(animals). 

Organic matter - The organic fraction that includes plant and animal residue at various 

stages of decomposition, cells and tissues of soil organisms, and substance synthesized 

by the soil population. Commonly determined as the amount of organic material 

contained in a soil or water sample. 

Outfall - Point where water flows from a conduit, stream, or drain. 

Oxidation - The chemical union of oxygen with metals or organic compounds 

accompanied by a removal of hydrogen or another atom. It is an important factor for soil 

formation and permits the release of energy from cellular fuels. 

Oxygen demand - Measure of the dissolved oxygen used by a system (microorganisms) 

in the oxidation of organic matter. See also biochemical oxygen demand. 

Phytoplankton - A group of generally unicellular microscopic plants characterized by 

passive drifting within the water column. See Algae. 

Point source - Pollutant loads discharged at a specific location from pipes, outfalls, and 

conveyance channels from either municipal wastewater treatment plants or industrial 

waste treatment facilities. Point sources can also include pollutant loads contributed by 

tributaries to the main receiving water stream or river. 

Pollutant - A contaminant in a concentration or amount that adversely alters the 

physical, chemical, or biological properties of a natural environment. The term include 

pathogens, toxic metals, carcinogens, oxygen demanding substances, or other harmful 

substances. Examples of pollutant sources include dredged spoil, solid waste, incinerator 

residue, sewage, garbage, sewage sludge, munitions, chemical waste, biological 

material, radioactive materials, heat, wrecked or discharged equipment, sediment, cellar 

dirt, hydrocarbons, oil, and municipal, industrial, and agricultural waste discharged into 

surface water or groundwater. 

Pretreatment - The treatment of wastewater to remove or reduce contaminants prior to 

discharge into another treatment system or a receiving water. 

Priority pollutant - Substances listed by the U.S. EPA under the Federal Clean Water 

Act as harmful substances and having priority for regulatory controls. The list includes 

metals (13), inorganic compounds (2), and a broad range of naturally occurring or 

artificial organic compounds (111). 

Quality - A term to describe the composite chemical, physical, and biological 

characteristics of a water with respect to it’s suitability for a particular use. 

Raw sewage - Untreated municipal sewage. 

Reaeration - The absorption of oxygen into water under conditions of oxygen deficiency. 

Receiving waters - Creeks, streams, rivers, lakes, estuaries, groundwater formations, or 

other bodies of water into which surface water and/or treated or untreated waste are 

discharged, either naturally or in man made systems. 

Residence time - Length of time that a pollutant remains within a section of a stream or 

river. The residence time is determined by the streamflow and the volume of the river 

reach or the average stream velocity and the length of the river reach. 
Sediment - Particulate organic and inorganic matter that accumulates in a loose, 

unconsolidated form on the bottom of natural waters. 

Stratification (of water body) - Formation of water layers each with specific physical, 

chemical, and biological characteristics. As the density of water decreases due to surface 

heating, a stable situation develops with lighter water overlaying heavier and denser 

water. 

Toxic substances - Those chemical substances, such as pesticides, plastics, heavy 

metals, detergent, solvent, or any other material that are poisonous, carcinogenic, or 

otherwise directly harmful to human health and the environment. 

Roughness Coefficient - A factor in velocity and discharge formulas representing the 

effects of channel roughness on energy losses in flowing water. Manning's "n" is a 

commonly used roughness coefficient. 

Scour - To abrade and wear away. Used to describe the weathering away of a terrace or 

diversion channel or streambed. The clearing and digging action of flowing water, 

especially the downward erosion by stream water in sweeping away mud and silt on the 

outside of a meander or during flood events. 

Sedimentation - Process of deposition of waterborne or windborne sediment or other 

material; also refers to the infilling of bottom substrate in a waterbody by sediment 

(siltation). 

Simulation - Refers to the use of mathematical models to approximate the observed 

behavior of a natural water system in response to a specific known set of input and 

forcing conditions. Models that have been validated, or verified, are then used to predict 

the response of a natural water system to changes in the input or forcing conditions. 

Sorption - The adherence of ions or molecules in a gas or liquid to the surface of a solid 

particle with which they are in contact. 

Steady state model - Mathematical model of fate and transport that uses constant 

values of input variables to predict constant values of receiving water quality 

concentrations. 

STORET - U.S. Environmental Protection Agency (EPA) national water quality database 

for STORage and RETrieval (STORET). Mainframe water quality database that includes 

physical, chemical, and biological data measured in waterbodies throughout the United 

States. 

Streamflow - Discharge that occurs in a natural channel. Although the term "discharge" 

can be applied to the flow of a canal, the word "streamflow" uniquely describes the 

discharge in a surface stream course. The term streamflow is more general than "runoff" 

as streamflow may be applied to discharge whether or not it is affected by diversion or 

regulation. 

Surface waters - Water that is present above the substrate or soil surface. Usually refers 

to natural waterbodies such as rivers, lakes and impoundments, and estuaries. 

Suspended solids or load - Organic and inorganic particles (sediment) suspended in 

and carried by a fluid (water). The suspension is governed by the upward components of 

turbulence, currents, or colloidal suspension. 

Tidal Prism - (1) The volume of water contained in a tidal basin between the elevations 

of high and low water. (2) The total amount of water that flows into a tidal basin or 

estuary and out again with movement of the tide, excluding any fresh-water flows. 
Unstratified - Indicates a vertically uniform or well mixed condition in a waterbody. See 

also stratified. 

Waste load allocation (WLA) - The portion of a receiving water's total maximum daily 

load that is allocated to one of its existing or future point sources of pollution. 

Wastewater - Usually refers to effluent from a sewage treatment plant. See also 

domestic wastewater. 

Toxicant - A substance that through its chemical or physical action kills, injures, or 

impairs an organism; any environmental factor which, when altered, produces a harmful 

biological effect. 

Transport of pollutants (in water) - Transport of pollutants in water involves two main 

process: (1) advection, resulting from the flow of water, and (2) diffusion, or transport due 

to turbulence in the water. 

Travel time - Time period required by a particle to cross a transport route such as a 

watershed, river system, or stream reach. 

Tributary - A lower order stream compared to a receiving waterbody. "Tributary to" 

indicates the largest stream into which the reported stream or tributary flows. 

Trickling filter - A wastewater treatment process consisting of a bed of highly permeable 

medium to which microorganisms are attached and through which wastewater is 

percolated or trickled. 

Turbidity - Measure of the amount of suspended material in water. 

Turbulence - A type of flow in which any particle may move in any direction with respect 

to any other particle and in a regular or fixed path. Turbulent water is agitated by cross 

current and eddies. Turbulent velocity is that velocity above which turbulent flow will 

always exist and below which the flow may be either turbulent or laminar. 

Verification (of a model) - Subsequent testing of a precalibrated model to additional 

field data usually under different external conditions to further examine model validity 

(also called validation). 

Volatilization - Process by which chemical compounds are vaporized (evaporated) at 

given temperature and pressure conditions by gas transfer reactions. Volatile compounds 

have a tendency to partition into the gas phase. 

Wastewater treatment - Chemical, biological, and mechanical procedures applied to an 

industrial or municipal discharge or to any other sources of contaminated water in order 

to remove, reduce, or neutralize contaminants. 

Water Pollution - Alteration of the aquatic environment in such a way as to interfere with 

a designated beneficial use. 

Water quality criteria (WQC) - Water quality criteria comprised numeric and narrative 

criteria. Numeric criteria are scientifically derived ambient concentrations developed by 

EPA or States for various pollutants of concern to protect human health and aquatic life. 

Narrative criteria are statements that describe the desired water quality goal. 

Water quality standard (WQS) - A water quality standard is a law or regulation that 

consists of the beneficial designated use or uses of a waterbody, the numeric and 

narrative water quality criteria that are necessary to protect the use or uses of that 

particular waterbody, and an antidegradation statement.