-------
/*'
              Sediment-contaminant  runoff   contributions   from   rural   ana  urban  land
          surfaces can  be  simulated through  the  execution of the  appropriate  non point
          source  modules  {user-sped fled)  contained  in  the  model  code.   These  modules
          predict sediment-contaminant  loadings, associated with  pervious  and impe-vious
          •an  riourly
                 The  PLTGEN  module creates  a  spec'ally  fo"nattec
                                                            takes  a
              An. interactive  editor  to  prepare
          development  and  will be available  from
          Athens, Georgia.
                                          Input   sequences
                                         the  environmental
                                               for  HS?f  'S   under
                                               Research  Laboratory.
                Saea Seoulremenea
          ^^^^hM^MI^^H^^^^^^^B^^M^—^^^^^^^                                ^

               If  fully  implemented,  the  MSPF methodology requires an extensive amount  of
          input   data.   However,  If  not  all  modules  are  selected   for  use   In   the
          simulation by  the  user,  the amount  of  input data will be reduced  accordingly.
          Furthermore,  many   parameters  may  be defaulted,  but default  values  are   not
          provided  fo-  the more  sensitive, site-specific  parameters.   The  time  series.
          constant parameters, and water Quality 1-nput  requirements  include:
                                                               •
               •   71m*  series  Inputs  which  Include:    air  temperature,   predpl-
                .  tatlon,   evapotransplratlon,   channel    Inflow.    surface    and
                 .groundwater  inflow,  and wind  movement.
                                            11-58

-------
    I  Constant  parameter   inputs  nn'cfi   include:    channe'   geometry.,
       vegetative  cover  index,   surface   detention  storage,  grauncwater
       storage  volume,   soil  moisture   content,   overland  f'o*  s^ape.
       snow-pacic data, infiltration indei. and interflo* index.

    •  Land  sediment  factors:    soil  detacnment  coefficients,  sediment
       influx, surface cover, sediment yashoff coefficient.

    •  Soil  temperature   data:   air  temperature  time  series,   s'ope  ana
       Intercept of land temperature to air temperature ecaat'cn.

    •  Dissolved  gas  in   land  water:   ground  e:evatian.  interf's*  anc
       groundwater 00 and COj concentrations.  ,
•  Quality  constituents  associated
   factor, scaur potency factor.
segment.    •as^c
                                                                   :cte":y
       QuaMty constituents concentrations  -n interf -ow and S'^yrc-at
                 a' 3ua'*ty  constituent!:   salute  ieacft'-g  "acta-s,  ic'1
       layer depths;  soil  densities, ana  pest'c'ie  a^C  lut-'er: $3'Dt'3r.
       parameters. soluOlllty factors, degradation rates

    •  Imoerv'cuS  land  quality  factors:    surface  runoff  -eticva :  -ate?.
       so'.'cs  -as^cf  c;ef f '.dent.   '3ie  of scl'cs  s'dcewe^t  and
       on  surface,  and  overland  f'ow  Some  sollutant   acc-jmu'at 'or.
       storage rates.

    •  Reacn  and  reservoir  water  quality  characteristics .  cce* ( ':' e
       and rates.
Oueguc Pscrlpeio/i
          output  consists  of  multiple printouts  Including  system state
variables,  pollutant  concentrations  at  a  point  versus  time,  and yearly
summaries  Describing   pollutant   duration   and   flux.    The  node1  a'so
includes  a  frequency  analyses which  provides  a  statistical  summary  of
tlfite-varylng  contaminant'  concentrations  and  provides  the  Unit   aet-een
simulated vnstream toxicant concentrations and risk assessment.
                                    11-59

-------
^
               HSPF 1j designed  for  year. around  simulation  of  river bas'.n hydrology.
           pollutant runoff  or  discharge,  and receiving water  quality.   Its  modular
           structure allows  it   to  be  readily used  in more restrictive  ways,  using
           streamfldw and  effluent  time series  inputs, without  the complications  of
           applying  the  rainfall/    runoff   simulation  module.   HSPF  provides  a
           frequency  distribution   summary   of   the   output,   thereby  providing   a
           year-around perspective.

               The  sediment-contaminant  kinetics  routines  nave  trie  'same  general
           characteristics  as  other  complei  water  quality  models,   however,  v.ke
           EXAMS  it  has   the added  capability  of  simulating  the  production  anc
           interactions of contaminant daughter products.


               HS?F  contains . a  code to  calculate  the  frequency of  occurrence  anc,
           durat'on of contaminant concentrations in the receiving Caters.


               Because  of  Its 1 -dimensional  approach  to  pollutant  simulation,
           does not discern stratification in the water column and bed  sed'merts


               The  mode''* cade  has  been  optimized  for   both  mini,   and  nw'n
           computers.   On  minicomputers,  usage of  direct  access  flies  Is max'.rm:ed
           On mainframes,  maximum use  is  made  of  fast memory  and  di-ect access  I/C
           is miniml:*d.   versions of HSPF are available for botn types of systems
               Data   requirements   to  implement  HSPF   are  potentially  ex
           (depending on  the  application  modules invoked) and may, therefore, result
           in high data production costs and significant manpower  requirements.
           Model Applications

               HSPF  has  been applied  on numerous  occasions  where  an  evaluation of
           best   management   practices   (BMP)   for  controlling   non-point  source
           pollution  from surface  land  runoff  was  needed.    In  this  context,  the
           model  was applied  to  the  Occoquan  River Basin  In Virginia  to project
           long-term  receiving  water quality  Impacts' from existing  and -future  land
           use patterns;  the Clinton River Basin  in  Michigan  to evaluate a proposed
           floodway,  estimate  the  Impact  of  developing  wetlands,  and investigate
           various  lake  operating  procedures;  and  various  EPA studies to evaluate
           Us application  and  use  as a  planning tool  in  determining agricultural
           BHPj.
                                               It-60

-------
                                                               •H-'
                Resource ffegu ir ttments

                   • HSPF  requires  a  FORTRAN  compiler  that  supports  direct  access  I/O
                Twelve  external   files  are  reaulred.  The  system requires  129* bytes  of
                instruction  and   data  storage  on  virtual  memory machines.  or  about  250K
                Bytes with  extensive  overlaying on overlay-type machines,   The  system wai
                developed on a  Hewlett-Packard 3000 minicomputer and has been ysec  on  ISM
                370  series  computers.   It  has  been  installed  on  trie  following  systems:
                IBM.  DEC  VAX  and System  10/20.  Prime  350 and above. Data Genera: «v<200.
                C3C  Cyber,  HP3000  and  HP1000,  Burroughs  and  Harris.  Instal 'at'cn  notes
                are available for sped.flc machines.                  .
                          's  'n  the puOl'.c domain  and  can ae oota'.nec from tne Center  far
                water Cuallty "cdellng.  Environmental  aesearcn Lasoratorv. ySE9«. C:''e-3e
                Stat'.cn ^cad. Atnens. Georgia  3C613 {telephone *C4 546-2533).
                    User assistance can be obtained by contact'r»g:
                    U.S. Environmental Protection Agency
                    EnvircMienta* 5esear:n laboratory
                    College Station aoad
                    Atnens. Georgia  30613
                        250-3175   C2H 40<-5<6-3l75
                        References
                Oonlg^an AS.  et  al.   1983.  Guide  to  the  Application of the Hyd.-oVog'ca'
                Simulation   Program   -   FORTRAN   (HSPf).    Draft   report.  Envl ronmenta"
                Researcn Laboratory. Athens. GA.   30613.

                Jonanson UC.  Imfioff  GC.  Davis HH.   1980.   User's  manual for Hyd-oloqica":
                Slmulat'on  Program- -FORTRAN  (HSPF).   EPA.  600/9-9-80-015.  Environmental
                Sesearcn Laboratory. Athens, GA.   30613.-

                Johanson RC.  and Kittle JL.   1983.   Design,  Programming,  and Maintenance
                of HSP?, Journal  of  Technical Topics  In CW11  Engineering,  vol. 109. NO.
                1. PP. 41-57.,

                Imhoff  JC,   et.  al.   1981.   User's  Manual  for  Hydrologlcal  Simulation
                Program - roftTRAN (HSPF).  Release 7.0 Draft Report.
                                                 11-61
.

-------

GPO's CGP - Record
                                  : • tY£i
                                                                                    Page 1 of2
 Search the CGP / BASiC /  ADVANCED/ EXPERT/ 9ROWSE  / NEW TrruES
 CATALOG OF
 U.S. GOVERNMENT PUBLICATIONS
                                                                            Ht-:up  / ABOUT
 Catalogs to Search:
 Congresstonal Serial Set
 Congressional Publications
                     GPO Access Publications  Periodicals
                     Internet Publications      Serials
                                                      My Options:
                                                      Bookshelf   Results list
                                                      Preferences  Previous Searches
 Catalog of U.S. Government Publications Home Page > National Bibliography of U.S. Government Publications
  Choose Record View: Stanti
  Record 1 out of 1
  Trtte
                           Add to Bookshelf
                          Short | .MARC
                                                                                E-mail
                 Technical guidance manual for performing waste load allocations book ll-streams
                 and rivers-chapter 3 toxic substances.
Publisher Info.     [Washington, D.C.]: United States Environmental Protection Agency, Office of
                 Water, [1984]
Internet Access    http://purl.access.gpo.gov/GPO/LPS67675
  SuDoc Number
  Item Number
  Description
  General Note
  Bibliography
  System Details

  Subject-LC
  Added Entry
                 :EP 2.8:440/4-84-022
                 0607-C (online)
                 424 p.: digital, PDF file.
                 Title from title screen (viewed oh Mar. 14, 2006).
                 "June 1984."
                 "EPA 440/4-84-022."
                 Includes bibliographical references.
                 Mode of access: Internet from the EPA web site. Address as of 3/14/2006:
                 http://www.epa.gov/waterscience/library/modeling/wlabook2chapter3.pdf; current
                 access available via PURL.
                 fffiuent gualitv - Government policy -- United States -- Handbooks, manuals, etc.
                 United States. Environmental Protection Agency. Office of Water.
Holdings          All items
Locate in a Library  (online) http://catalog-web2.gpo.QOv/LocateLibraries/locate.isp?ltemNumber=0607
                 C&SYS=000586329
                                      U.S. EPA Headquarters Library
                                            Mail Code 3404T
                                     1200 Pennsylvania Avenue, NW
                                         Washington DC 20460
                                             202-566-0556
  OCLC Number
  System Number
                 (OCOLQ64668450
   End Session - Preferences -Feedback - Help - Browse - Search - Results List - Previous Searches - Catalogs to Search - Bookshelf
http://catalog.gpo.gOv/F/7RUYBI2PG8DB6SRKUMK9UQD8VUB9Q4HHG9FFN2 YCX...   7/13/2006

-------
         GPO's CGP - Record View
Page 2 of2
                                  A service of the U.S. Government Printing Office. 2005-2006.
L
         http://catalog.gpo.gOV/F/7RUYBI2PG8DB6SRKUMK9UOD8VUB904HHG9FFN2YCX...,  7/13/2006

-------
         UnrtM Slltn
vvEPA
         QMici o* Mite R»guu»ani
         •no S«rxJ»n3«
         Monxormg fno Oitf Suooorr
                           Fin*
Technical Guidance
Manual for Performing
Waste Load Allocations
         Book II Streams and Rivers
         Chapter 3 Toxic Substances

-------
      Click here for
      DISCLAIMER

Document starts on next page

-------
  (SB)
     UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
                   WASHINGTON. D.C. 204«O



                   JUN  13  B84
  MDCRANDGM
  SUBJECT:
  FROM:
  TO:
Technical Guidance Manual  for Performing Waste Load
Allocations Book II,  Stream and Rivers, Cnapter 3,
Toxic substance Impacts
Steven Schatzow,  Director
Office of Water Regulations and Standards (VH-551)

Regional water Division Directors
Regional Btvircrwental Services Division Directors
Regional Wasteload Allocation Coordinators
       Attached, for national use,  is  the final version of the Technical
  Guidance Manual for Performing waste Load Allocations Book II,  streams  and
  Rivers, Chapter 3, Toxic Substance Impacts.  We are sending extra copies
  of this itenual to the Regional Wasteload Allocation Coordinators for
  distribution to the States to use in conducting uasteload allocations.

       If you have any questions or cements or desire additional information
  please contact Tin S. Stuart, Chief, Monitoring Branch, Monitoring and
  Data Support Division (W3-S53) on (FTS) 382-7074.

  Attachment
REVISIONS:

10/85 - pages 21,  86,  and 97.5

-------
             TECHNICAL GUIDANCE MANUAL FOR

           PERFORMING WASTE LOAD ALLOCATIONS.
              BOOK II  STREAMS AND RIVERS
              CHAPTER 3  TOXIC SUBSTANCES
                          by:

              Charles G. Oelos. EPA, OURS
win lam L. Richardson, EPA. Large Lakes Research Station
         Joseph v. DePlnto, CTarksan University
          Robert B. Ambrose. EPA. ERL - Athens
           Paul W. Rodgers, Llmno-Tech. Inc.
   Kenneth Rygwelskt. Cranbrook Institute of Science
           John P. St. John, HydroOuat. Inc.
             w.J. Shaughnessy. versar Inc.
                 T.A. Faha, versa'r  Inc.
               w.H. Christie, Versar Inc.
                      August 1984
       Office of Water Regulations and Standards
          Monitoring and Data Support Otvlston
             Water Quality Analysis Branch
          U.S. Environmental Protection Agency
      401 M. Street, S.W.. Washington, D.C.  20460

-------
                              ACKNOWLEDGMENTS
     The  development  of  this  document  grew  out  of  a  research  project,
modeling the  behavior of metals  in  the  Flint River,  undertaken  by  the
Office of  Research and  Development. Large  Lakes Research  Station.  Grosse
He.  Michigan.   In response  to the  needs of  the waste  Load Allocation
Section  of  OURS,  the scope of this  work was broadened  to  Include this
.volume of  the Guidance  Manual.

     Project direction was provided  by Nor&ert  Jaworski and Ne.ison  Thomas.
ERL-Ouluth. and Michael Sllmak.  OURS.   Victor  81erman  was  Instrumental
during the  Initial phases of the research  project.   The field and
laboratory work  for  the Flint River case .study was  oerfomed  by  the
Cranbrook  Institute  of  Science,  coordinated by V.E.  Smith, and  by  the
U.S.  Geological Survey, coordinated by  T.fi. Cunnings and  J.B. Miller.
Richard  Hobrla. Stephen Buda. and others at  the Michigan  Department of
Natural  Resources provided Invaluable guidance and  helped  keep  tne
development work  on  a practical  course.  Robert wethlngton of Computer
Sciences Corp. contributed to the modeling effort.

    The  following individuals contributed  to the  improvement and
completion of this document  through their  most ne1pfu> review and  comment
on the draft report:  Robert V.  Thomann, Raymond  P.  Canale.  Donald j.
O'Connor. Thomas  0.  Barbell, Bruce Zander, James S.,Kutzman. Gary
Williams, Gary Mllburn. £. Dale  wismer, Patrick J.  Harvey, tmory 3. tang.
James J. McXeown. Alexander NcBrlde,  John  Maxted, and  James  Bonner.

    Although several of the coauthor? contributed broadly  to the document
through  their critical  review, the primary responslbV'ty  for eacn  section
can be ascribed as follows:.
Section 2.1 - 2.5:
Section 2.6:
Section 3.1:
Section 3.2:
Section 3.3.1:
Section 3.3.2, 3.3.3:
Section 3.3.4:
Section 4.1 - 4.2:
Section 4.3:
Section 4.4:
Section 5:
Appendices A, B:
Appendix C:
Attachment 1 :
Attachment 2:
W.L. Richardson. J.V. OePlnto. and
R.B. Ambrose
C.G. Oelos and J.v. OePlnto
J.V. OePlnto
P.M. Rodgers
W.L. Richardson
C.G. Oelos. K. Rygwelskl. and W.L,
W.L. Richardson and C.G. Oelos
C.G. Oelos
W.L. Richardson
J.V. OePlnto
J.V. OePlnto and C.G. Oelos
K. RygwelSkl
J.P. St. John
w.1. Shaughnessy. T.A. Faha, and w.
C.G. Oelos





Richardson







N. Christie
W.L. Richardson coordinated the work of most of the coauthors and edited
the 1982 preliminary draft.  C.G. Oelos edited the 1983 and 1984 draft
and final versions and addressed comments.  The staff of versar Inc.,
coordinated by W.J. Shaughnessy, carried out the production of the 1983
and 1984 versions (Including the drafting of most figures); Oonna Barnard
typed the text and most tables.

-------
                                 CONTENTS
                                                                     Page

ACKNOWLEDGMENTS  	    1

t.O   INTRODUCTION  .  .  .	   1

2.0   BASIC MODEL FRAMEWORKS AND FORMULATIONS   	    5

    2.1  fieneral	    5
    2.2  Dilution  Calculations - Point of Discharge  	    8
    2.3  One Dimensional, Steady-State Model of
         Conservative Total Pollutant 	  11
    2.4  One Dimensional, Steady-State Mode! of
         Nonconservatlve Total Pollutant  	  13
    2.5  One Dimensional, Steady-State Models  For
         Separate -Dissolved and Suspended Phases,
         Having Bed  Interactions and Multiple  Process Rates 	  16
         2.5.1  Model Framework	16
         2.5.2  Relationship with Other Approaches   	  23
    2.6  Complex Models Having Multi-Dimensional, Dynamic, or
         Spedation  Capabilities.  .	28
         2.6.1  Transport and Bed/Water Exchange	  .  32
         2.6.2  Sorptlon	3*
         2.6.3  Spedation	:	  35
         2.6.4  Transfor.-natlon	37

3.0  ESTIMATION AND  USE OF MODEL PARAMETERS	40

    3.1  Exchange oet^een Bed and Water	  40
         3.1.1  Particle Transport and Exchange  	  41
         3.1.2  Diffusion of Dissolved Material  	  49
    3.2  Partitioning Processes 	  51
         3.2.1  Metals Partitioning	51
         3.2.2  Organlcs Partitioning 	  61
    3.3  Decay or Transformation Processes	 .  .  .  6?
         3.3.1  Blodegradatlon	67
         3.3.2  Photolysis	73
         3.3.3  Hydrolysis	78
         3.3,4  Volatilization	80

4.0  GUIDANCE FOR MODEL APPLICATION	  89

    4.1  Approach to Waste Load Allocation Problem   	  89
    4.2  Data Needs	96
         4.2.1  Obtaining Model Input Oata	97
         4.2.2  Calibration and Ver1f1cat'r»n:  Model Accuracy  .... 101
         4.2.3  Additional Oata	105
         4.2.4  Quality Assurance 	 106
    4.3  Forecasting	107
    4.4  Resource Requirements  	 113

-------
5.0 .CASE STUDY:  MCOttiNG HEAVY METALS TRANSPORT
     IN THE FLINT RIVER	119

    5.1  Introduction	119
    5.2  Description of Flint River Study Site   	  119
    5.3  Flint River August Survey	121
         5.3.1  August Survey Data Summary	124
         5.3.2  August Survey Node) Calibration  	  128
        " 5.3.3  August Survey Sensitivity Analysis  	  136
    5.4  Flint River December Survey  	  147
         5.4.1  December Survey. Data Summary  	151
         5.4.2  December Survey Model Calibration 	  159
    5.5  Flint River March 1982 Survey	170
         S.S.I  March Survey Data Summary .....  	  180
         5.5.2  March.Survey Model Calibration   	  180

6.0  REFERENCES (for Sections 1-5 and Appendices A - 0)	190

APPENDIX A.  DEVELOPMENT OF MODEL EQUATIONS 	  A-1

    A.I  Conservative Pollutant 	  A-1
    A.2  Nonconservatlve Pollutant  	  A-2
    A.3  Water-Sediment Model Having Separate PartUulate
         and Dissolved Phases	  •  A-iQ

APPENDIX 3.  SE3INE.HT TRANSPORT CONSIDERATIONS	3-1

    B.I  Sediment Properties  	  8-1
    8.2  Transport of Sediment Loads  	  8-9
    8.3  Deposition and Erosion	3-U
         8.3.1  Deposition	8-15
         8.3.2  Bed Erosion	  8-18
         8.3.3  Particle Exchange:  Continuous versus
            v   Discontinuous 	  8-20
    8.4  Sediment Sources 	  8-22

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

APPENDIX 0.  BEHAVIOR OF HALOGEN DISINFECTION RESIDUALS 	  0-1

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

ATTACHMENT II.  CATALOGUE OF MODELS 	  II-l

-------
                                SECTION 1.0
                                INTRODUCTION
     This  document  addresses  methods  For predicting  concentrations  of
 individual  constituents  resulting  from pollutant  loads  to  tne aauatic
 environment,   within  the context of  the waste  load  allocation (WIA)
 process,  tne methods  predict  the ambient concentrations expected to
 result  from existing  or  projected  pollutant loadings.   By  relating the
 predicted concentrations to  ecosystem  or human  health effects levels, an
 appropriate level  of  pollution abatement can be specified, tailored to
 protection  of  the  environment of a specific  site.

     As  the  focus of the  material is  the prediction  of ambient
 concentrations.  It will  not address  all  facets of the allowable load
 determination.   In order to use predictions effectively. It  is also
 necessary to establish (a) a  target  for  allowable concentrations, and (5)
 a target  frequency for not exceeding the allowable  concent.-attons.  3a:a
 on the  former  are  contained  in the water  Quality C.-iteMa documents: :a:a
 on the  latter  are  sparse.  Neither subject  Is within tne scope of this
 volume.

     The organization  Intended for the  first four volumes of  the complete
manual  is shown  In Table  1.1.  In order  to  reduce redundancy, mater'al
discussed 1n Book  II, Chapter 1 (900. dissolved oxygen ana ammonia) is
not  repeated here.   In particular,  U  Is assumed that the reader is
 familiar with  the concepts of advectlon  and dispersion,  variations  of
depth and velocity with  flow, first order reaction  rates,  surface
transfer of oxygen, and  steady-state versus time-variable analyses.  This
document 1$ Intended for use In conjunction with chemical  data references!
such as Nabey et al. (1982) and Callanan et al. (1979).

-------
       Table 1.1  ORGANIZATION OF GUIDANCE MANUAL FOR  PERFORMING OF
                          WASTE LOAO ALLOCATIONS
800* I     GENERAL GUIDANCE
           (Discussion of overall  WLA process,  procedure*.  considerations)

BOOK It    STREAMS AND RIVERS

           Chapter 1 - BOO/01ssolved Oxygen Impacts  and  Ammonia  To*1city
           Chapter 2 - NuiMent/EutropMcatlon  Impacts
           Chapter 3 - Toxic Substances Impacts

BOOK UI   ESTUARIES

           Chapter 1 - BOO/01ssolved Oxygen Impacts
           Chapter 2 - Nutrlent/EutropMcation  impacts
           Chapter 3 - Toxic Substances Impacts

BOOK IV    LAKCS, RESERVOIRS. IMPOUNDMENTS

           Chapter 1 • BOO/Qis solved Oxygen Impact
           Chapter 2 - Nutrient/EutropMcatlon  Impacts
           Chapter 3 • Toxic Substances Impacts

-------
    Because predictions are needed in a variety of different situation!.
 there 1s no one set of technically acceptable procedures that can be put
 forth as a standard method.  The appropriate level of effort, and thus
 the appropriate approach, depends on the difficulty with which pollutant
 controls can be implemented, the complexity of the environmental
 problems, the resources available, and the technical expectations of all
 parties Involved.  Consequently, the Intent of this document 1s to
 describe a variety of different approaches, covering a wide range of
 complexity, to help guide the analyst in choosing a cakulational
 framework, or model, appropriate to the specific problem.  Rather than
 recommending particular levels of effort as appropriate for analyzing
 particular wU situations, this document is intended to help guide the
 WLA analyst toward the most effective use of whatever resources are
 available.

    The remainder of this document Is organUed Into the following
 sections:

    Section 2.0 describes mathematical frameworks for predicting toxicant
 concentrations 1n rivers.  The approaches span a range of complex'ty,
 from dilution calculations to complex, multl-dimension*'. t'">e-*ary'ng
 computer models.  This section describes assumptions and limitations
 associated with each approach.

    Section 3.0 presents the mathematical formulation of important fate
 and transport process and provides background information for specifying
 the parameter values.

    Section 4.0 presents technical guidance for conducting waste load
allocations for toxicants.  It suggests that the analysis progress
 through three phases from simple to complex and discusses the associated
needs for and management rf supporting data.  Quality assurance and cost
estimates are covered for both field data and model parameters.   Thi«
 section also contains technical guidance in applying models and asse.smg
 the adequacy of site-specific model predictions.

-------
    Section 5.0 presents a case study of modeling metals transport In the
Flint River, Michigan.  Emphasis  is on the calibration of the toxicant
model with field data obtained under three very different flow regimes.
A sensitivity analysts of the model parameters relative to the flint
River calibration 1s also presented.

    Finally, there are appendices containing (A) derivations of model
equations. (8) a discussion of sediment exchange and transport modeling,
(C) a summary of Flint River (case study) survey methods, and (0)
chlorine behavior.  In addition, two other reports are attached.   One
                         «•                                   -i
contains metals partition coefficients derived from field data collected
nationwide.  The other Is a catalogue of 14 models designed for toxicant
Studies.  It briefly summarizes eacn model's theory, input and output,
strengths and limitations, and resource requirements.

-------
                               SECTION 2.0
                 BASIC MODEL FRAMEWORKS AND FORMULATIONS
2.T  GENERAL

    This section provides a summary of modeling frameworks, with
associated equations and assumptions, applicable to predicting
concentrations of discharged toxicants, as affected by stream hydrology
and morphology, reactions, and sediment Interactions.  Because the Intent
of this document 1s to present a range of approaches, 1t Is useful to
consider a means of categorizing water quality models according to their
components and characteristics.  In selecting an approach, a HIA analyst
1s likely to be Interested In environmental simulation capabilities,
which can be categorized as follows:
A.  System components
    -  Water column
    -  Bed sediment
    •  Terrestrial  watershed
B.  Processes modeled
    -  Dilution
    -  Advectlon.  dispersion
    •  Decay, transformation, speclatlon
    •  Transfer between water,  sediment, and air
C.  Spatial  variability or resolution
    •  0, 1, 2, 3-01mens1onal  variability
    -  Near or far  field
0.  Time variability
    -  Steady state
    -  Time variable

-------
     Tht analyst must also be concerned  with  the  Input  data  and  hardware
 requirements  associated w4th any approach.   These  tend to follow  from  the
 capability  characteristics  listed above.

     A  general  schematic framework for  Illustrating many factor;  that
 determine the  concentration of  toxicants  In  a  river 1s depicted  in  Figure
 2.1.   The conceptual  elements  Include:  (a) mixing  of effluent and
 upstream waters,  (b)  partitioning of toxicant  between  dissolved and
 participate phases  1n both  the  water column  and  the bed. (c> exchange
 between  the water column  and the bed, (d) decay  by irreversible chemical
 transformations,  (e)  losses  by  burial and volatilization, and (M
 downstream  transport  via  stream flow and bed load.  Simple  analytical
 frameworks  may  employ only  a few of the elements shown;  sophisticated
 computer codes, on  the other hand,-may articulate  more complex
 arrangements than shown  In  the  figure.

    the  selection of -any  approach  requires a trade-off  between system
 realism and analytical  efficiency.  The simplest approacnes tend  to v.nge
on a few critical assumptions   (as will be described shortly); the
 technical Issues and  uncertainties that surface  thus tend to be few *.n
number out  could be Intractable  In nature.  Furthermore, restrictions  in
 the form of their results can constrain the formulation of  the bas^c
                       <
questions they are  Intended  to answer.   Complex analyses, on the  otner
hand, with  their numerous Input parameters, call for the support  of
considerable laboratory and  field data.   The assumptions they rest on and
the uncertainties they surface may be greater 1n number but  more  subtle
 in nature than those of the  simpler approaches.  The complex approaches
are applicable to a wider range of questions than  the  simpler approaches.

    tt may  not be necessary  to choose a model at the outset  of a
project.  Rather, as discussed In Section 4.0,  It may be efficient to
apply the analysis In stages, starting  simply',  and then moving to the
appropriate  levtt of complexity, as the issues, costs,  benefits, and

-------
            LOAD
UCTMfAM
  LOAD
                                   i VOUTILIZAT10M
                               AIH
DJSSOtVEO
                           WATER
                           SCOIMC.1T
                                           4 TRANSFO
                ANSFORMAT1QN '•*—I
                —-'H
                                                CXCHAMCE
                                            /   CHEMICAL  ^.
                                            4 TRANSFORMATION^-
                            Otlf
                          SCOIMCNT
                                f

                             IURIAL
                    FIGURE 2.1  IMPORTANT FATE AND TRANSPORT PROCESSES
                               FOB TOXICANTS IN RIVERS

-------
decision  needs  evolve.  Nevertheless, because  the collection of
data  can  be  the most expensive project component, no major field survey;
should  be done  before the analytical Framework has been selected and tne
Input data requirements identified.

    The approaches covered In'this document can. for purposes of
discussion, be  placed in the following types of catagories:

    •   Point of discharge dilution calculations for total pollutant;
        steady-state or dynamic.
    *   One-dimensional, steady-state models for conservative total
        pollutant.
    •   One-dimensional, steady-state models for nonconservatlve tota'
        pollutant.
    •   One-dimensional , steady-state models for separate dissolved and
        partlculate phases; having bed interactions and multiple process
        rates.
    •  *ult1 -dimensional . and/or dynamic mode's for separate d's;o'*ed
       and partlculate pnases or multiple species; having aed
        interactions and multiple process rates.
    The approaches differ In discerning spatial and tempora- var*at'ons.
environmental media, and pollutant forms and behavior.   The approaches
are described in the sections that follow.   Mathematical derivat'sns °r~
fundamental equations are provided in Appendix A.

2.2  DILUTION CALCULATIONS - POINT OF DISCHARGE
    The mixing of the effluent flow with the river flow is tne first
process normally evaluated In predicting ambient concentrations of
toxicants.  At the point where mixing has been completed.  Che
concentration of the total pollutant Is  given by:
       CT,O,
                           ou          OT                          (2.D

-------
 where, C_(0) - Concentration of total pollutant immediately after
                complete mixing (ug/l)
        CT    * Efftuent concentration   (ug/l)
        CT(J   « Upstream concentration   (ug/l)
        0T    • Combined effluent Mow (Q^) plus upstream flow (0 ) (I/sec)
        Vij.    . Combined effluent plus upstream load (ug/sec)
 This formulation assumes that:
         \
     1. Nixing 1s relatively rapid.
     2. Decay or settling 1s slow compared to nixing.
 The combination of these two assumptions Implies that little decay has
 time to occur before mixing 1s complete.  The formulation says nothing
 about the size of or concentration within the mixing zone.   Nor does  t:
 say anything about the concentrations further downstream of the
 discharge.                    .

     Used by themselves without regard for downstream fate.  ci'ut'on
'calculations nave found considerable use In setting water aua*'ty  sasec
 effluent  limitations  for both  conservative and nonconservaMve
 pollutants.   This 1s  because Water  Quality Standards are often
 Implemented in  such a way that the  toxicant concentration 1s not
 permitted  to exceed the numerical  criterion at any  point (outside  the
 mixing rone), without regard for  the length of the  stream affected.
 Consequently,  for single dischargers In  a regulatory situation that gives
 no consideration  to the number of  stream miles affected, the analyst  may
 simply apply the'dllutlon formula  (Equation 2.1)  to determine  the
 concentration occurring Immediately below the discharge, before  any
 processes  (except for upstream dilution) can act to reduce  the
 concentration.

     This approach Is  not suitable  for situations where  two  or  more
 discharges,  separated by a  substantial distance, affect  trv toxicant
 cc «.enuat1on.   In  this case some consideration  of  the  pollutant

-------
behavior  in  the reach between  trie  two discharges  Is needed  In order to
predict the  concentration (Cj  ) above the second  discharger.  The
approach  1$  nevertheless applicable in the numerous situations where only
one of the several dischargers is  of Importance for a particular toxicant.

    Because  toilcants may rapidly  partition between the dissolved and
suspended solids phases  In the water column, or may rapidly  Irtterconvert
between different species or complexes, the concentrations  in Equation
2.1 are usually interpreted as being the total concentration of the
toxicant,   when only one form of the pollutant 1s biologically active
(such as unionized ammonia), it is customary to determine the dilution
concentration as total, and then to separately determine what fraction
will be biologically active.  For  example, the fraction of-unionized
ammonia Is determined by pH and temperature.

    Perhaps  the chief disadvantage of the dilution calculation at the
point of discharge 1s that it says nothing about  the spat'a'i extent of
the araolem, which in turn partiaV'y determines* the env'ronmenta'.
benefits of  pollution control.  The restricted vision of this approach
thus somewhat hampers the analyst's ability to respond to decision*
makers'•questions about environmental  benefits.

    The spatial restriction of this approach may  5e partially offset 3y
the comparative ease with which the temporal confines can be expanded.
It Is not  unduly difficult to determine the frequency distribution of the
output concentration (C^CQ)) using the frequency  distributions or rea'
time sequences of the four Input parameters (CJu. P/u. CTw, 0^).
Facile methods for determining the overall frequency of standards
violations at the point of discharge are being refined (DUoro and
MtzpatMck 1983)  and appear to promise Substantial  Improvements In the
evaluation of toxlclty problems.
                                    10

-------
 2.3  ONE,DIMENSIONAL. STEADY-STATE MODEL OF CONSERVATIVE TOTAL POLLUTANT
     This approach goes beyond that of the previous  section In that U
 predicts the concentration profile throughout the downstream reach.   This
 requires an assumption about downstream behavior.   In  this case the
 assumption  Is conservative pollutant  behavior because  the discharged  load
 1s  not reduced as It  travels downstream.   Consequently,  since dilution  1s
 the only process  affecting the concentration, the model  equation 1s  the
 previously  described  dilution formula (Equation  2.1).

 This  formulation  assumes:
     1.  The  pollutant  Is  essentially conservative (I.e..  does  not decay  or
        settle from the water).
     2.  The  system Is  represented by average  conditions over  some
        representative time period  so  that  the model equations  can  t>
-------
    a
 IM^/StC)
MAS LOAD
                                        Ow
                                    OlSTANCf I
COHCINTflATlOM
                                   OtSTANCI 3
                  FIGURE 2J  STIAOY.STATE. CONSERVATIVE BEHAVIOfl.

-------
     Nevertheless, where  the  spatial distance  under  study  Is  very  small,
 conservative assumptions nay hold  up quite well  for the total  form of
 most pollutants.  Thus,  It has been customary  to consider behavior within
 legal mixing zones  to  be conservative,  since  the time of passage  Is  so
 small.  Some other  conditions under which conservative behavior may  be
 predicted are described  In Section 2.5.2.

 2.4' ONC DIMENSIONAL,  STEADY-STATE MODEL OF NONCONSERVATIVE  TOTAL
     POLLUTANT
     This approach predicts the concentration of  the total form of a
 nonconservatlve pollutant 1n the water  column  throughout a
 one-dimensional stream reach under steady-state conditions.  The model
 formulation 1s:
                   /            •  •
                               x  "
       CT(x)  -  CT(0)  e   T  u                                     {2.3}

wnere. CT(X) • Concentration at points  downstream of effluent (ug/l)
       Cy(0) * Concentration Immediately below effluent (from Equation
       KT    « Overall loss coefficient (I/day)
       U     • Stream velocity (m/day)
       x     • Distance downstream of effluent (in)

Several assumptions accompany this model:
    1. KT« tn« overall first order decay coefficient, Includes net
       settling and all other losses or transformations.
    2. The average river and waste load conditions represent a
       steady-state condition (dc/dt » 0)  over some time  period.
    3. The pollutant discharge 1s mixed Instantaneously with the  river
       (I.e.,  no mixing zone or  lateral and vertical  concentration
       gradients).
    4. Dispersion Is negligible  1n the longitudinal  direction (i.e..  only
       advectlve transport Is cons lore  significant:  plug flow).
                                   13

-------
     5.  Average  Flow, average cross-section area, and average depth
        sufficiently represent conditions within a single reacn.
     Figure 2.3  depicts tnis model graphically.  This model 1$ directly
analogous to BOO disappearance in a classical Streeter-Phelps 00 model.
The  model equation 1s applied to each river reach with calculated
concentration from the end of an upstream reach becoming the upstream
boundary concentration for the next downstream reach.  The selection of
reaches 1$ determined by significant changes  in river geometry, flow, and
location of point source tributaries.

     The overall decay coefficient 1s both site- and time-sped Me.
possibly varying with changes In controlling parameters such as flow.
cross-sectional geometry, solids concentrations, aquatic vegetation,
temperature, sunlight, and pH.  usually this approach is applied to a
specific site where sufficient field data are available to calibrate K.
to the ooserved rate of disappearance.  That  1s. KT Is adjusted until
the  calculated  concentrations reasonably match the measured
concentrations  along the length of the river downstream of the effluent.
As In BOO modeling. U Is considered undesirable to spatially vary the
decay coefficient without a good underlying Justification.  The.observed
data must be expected to exhibit scatter about the predicted curve, due
to time variations and measurement errors.

    While this  empirical approach Is somewhat data intensive, 1t  Is
fairly straightforward, with few degrees of freedom to manage.  A key
limitation 1$ that 1s sheds no light on the factors that control  
-------
     (U
    HOW
      a
a»
    LflAO
     «
   (KG/SIC)
CfiMCfMTftArtOM
     CT
    (MG/U
                                      OlSTAMCf X
               FIGURE '3 SIMPLE FIRST ORDER DECAY ANALYSIS
                         i Oft TOTAL POLLUTANT
                                          15

-------
    Nevertheless, this general approach has been applied to phenols and
cyanide  in the Mahonlng River (EPA 1977).  Application of this approach
to the settling of metals  in the Flint River 1s described in Appendix A.

2.5  ONE-OINEXSIQNAL. STEADY-STATE MODELS POR SEPARATE DISSOLVED AND
    ; SUSPENDED PHASES. HAVING BED INTERACTIONS AND MULTIPLE PROCESS SATtS
    Unlike the approaches described previously, this approach
developed specifically for toxic pollutants which have important
Interactions with the bed sediments, and which may vary  In biological
activity and other behavior, depending on form.  Because this type of
model discerns multiple Individual processes. 1t provides a more complete
understanding of pollutant behavior.  The trade-off is that there are
more parameters to specify, and It 1s more difficult to rigorously
validate using field data.  On the other hand, because this type of model
relates some aspects of pollutant behavior to readily observable pnysica-'
properties of the site and to known chemical properties of .-"any
pollutants, some model predictions may be attempted without lav'ng
surveyed the pollutant's 'downstream profile at the site.

    This level of analysis is sufficiently complex that a compute'
program is helpful (but not essential) For executing the computations.
The SlmpllMeo Lake and Stream Analysis (SLSA). which is available as
both a calculator algorithm and a computer program. n perhaps the
simplest version of this type of model.  This program was developed by
Hydro-Qual and is available from the Chemical Manufacturers Association.
The computer program MICHRIV has a somewhat similar framework but is more
rigorous and flexible In Its handling of a partlculate bound pollutant.
This program was developed by the EPA Large Lakes  Research Station.
Grosse He, specifically for WLA purposes.

2.5.1  Model Framework

    *se framework for this type of model 1s Illu ^aMd tn Figure 2.4.
The iflodel  discerns two media, water and bed sediment,  and two forms of
pollutant  within each media,  dissolved and partlculate bound.   Process
                                     16

-------
AIR
                   LOAO (WT>
                                TOTAL SUISTAHCK - (
                             MftTICULATC
                             SUISTANCC!Cr1)
                               3U3HMOEO
                               SOLIDS M
ACTIVt
SIOIMfMT
OIW
U01MCMT
                  OIS5QLVEO
                  3UBSTANCZ
                     ICgt)
                                                  W
* *t Sf DIMCNTATION
                         FIGURE 2.4 MICHHIV FRAMEWORK
                                            17

-------
rates are specific to the media and to the pollutant form:  for example,
only the partlculate phase 1n water settles1 to the bed, and only the
dissolved phase In water volatilizes.  Derivation of the model equation*
and listing of assumptions are presented in Appendix A.

    In summary, the MICHRJV program predicts the dissolved and
partlculate concentrations In water and bed sediment, using tne following
types of input data:  flows and loads, hydraulic geometry, water.bed
exchange parameters, partition coefficients, and decay coefficients.
Nomenclature for the following discussion Is presented in Table 2.1.

    The first major step In HICHRlY's solution (after applying the
dilution formula, Equation 2.1) Is to predict the concentration profile
of suspended solids downstream of a point source.  The downstream solids
concentration, rn^x). Is related to the Initial concentration. m^O).
and to the settling and resuspenston velocities, w  and w    by the
expression:

                           W                           W,   X
                         \J_ J_            /     - J	\
       .,(»)  .  rn^O).    Hl    Ul  .  *" "*  f 1 -*  H1    Ul J  (2-4)
                                          "i   V                /
for which all parameters are defined in Table 2.1.  It Vs assumed  tnat
the bed solids concentration,  m_. Is constant throughout the reacn.
(SLSA differs from MICHRIV In that It also treats RL, as a constant,
rather than  a state variable,  and thus omits Equation 2.4.)

    The sediment exchange velocities are related by assuming that  the
mass (or thickness) of the active bed layer does not change over time.
Balancing the solids fluxes results In:
                                     18

-------
             TABLE 2.1:   NOMENCLATURE  FOR WATER/SEDIMENT MODEL
             i                                  «
 Parameters                            Water Column          Sediment

 Concentrations  and  Loads

 Total  toxicant  (j»g/D*                    Cyi                 C?2
 Dissolved toxicant  Ug/l)»                Cd]                 C^
 Partlculate  toxicant  (ug/D*              C ,                 C
                                           P 1                  02   .
 Partlculate  toxicant  («g toxicant/
  mg solids)                              r-j                  r2
 Total  solids (mg/i}'                      "v,                  m2
 Toxicant load (ug/sec)                    w_                  ---

 Partitioning

 Dissolved fraction                        f'                   f ,
                                           a'.                  C2
 Partlculate fraction                      f ,                 f
                                           51                  32
 Partition coefficient (l/mg)
  (. • r/C<} - Cp/mCd)                     .,                   ,2    ,

 Channel Geometry
Downstream distance (m)                    x                   x
Cross-sectional area (m )                 A                   ...
Depth (m)                                 H.                   H.
Flow (m3/sec)                             QI
velocity (m/sec) (U . Q/A)                ^
                                   19

-------
         TABLE 2.1:  NOMENCLATURE FOR WATER-SCO WENT MODEL (Continued)
                                      Water Column          Sediment

Rate Parameters

Aggregate decay rate coefficient
 - for dissolved (I/day)
 - for partlculate (l/day)
 - for total n/
-------
                                                                     Revised
                                                                      10/83
 The sedimentation or burial  velocity,  w..  reflects  the rate of  change
 1n  elevation  of  the  benthat  surface  at a particular point  over  time.   A
 positive  value  for w^  Indicates  that the channel  1s gradually  filling
 In  during the modeled  condition;  material  is  being  lost  to deep
 (inactive)  sediment,  beneath the  boundary  of  the  modeled system.   A
 negative  value  Indicates  downcuttlng of the channel,  and brings material
 Into  the  modeled  system.                               . ,

    This  type of  attention to solids behavior is  necessary because the
 movement  of adsorbed  toxicant fallows  the  movement  of solids.   The
 fraction  of total  toxicant that  Is adsorbed on  partlculates  (f.}  In
water.
            in bed) and" the  fraction  that  Is dissolved  (f
                                                        ^.
depend on the partition coefficients applicable  to  the water and bed
(«.j and »2- respectively), and  the  solids concentrations  (ia\
and «  respectively):
                                                                   (2.6)
                                                                   (2.7)
where all parameters apply together either to the water column or to the
bed.  Use of the partition coefficients assumes that the dissolved and
participate phases are In dynamic equilibrium within their respective
media.  It also assumes that the equilibrium adsorption Isotherms are
linear.  But It does not assume that any type of equilibrium exists
between the bed and the overlying water column.  (Such an equilibrium can
be set up under certain conditions:  w, • 0 and K- • 0 will cause the
fluxes of total pollutant between the.water column and bed to cancel each
other at steady-state; however, unless the additional condition »1 .
«2 were Imposed, a net movement of partlculate pollutant could occur,
for example, out of the water column, balanced by a net movement of
dissolved pollutant out of the bed.)
                                    21

-------
    The steady state solution for the total toxicant concentration in the
water column can be expressed in a familiar form:
               .  CTI(O,   .
The overall removal rate coefficient,  KT (I/day),  can be expressed  by
the function:
where Ktf1 and K^ are the toxicant decay coefficients in the water
and bed. respectively, and K{ 1s the sedimentation loss coefficient.
which 1s related to the burial velocity by the expression:

       s •  vH?                                                 {2JO)
The aggregate decay coefficients K-, and Kj are simply the sum of the
coefficients specific to each competing process,  such as hydrolysis,
biolysis, photolysis, and volatilization.

    The parameter group ^r./r^ controls the Importance of sediment
decay and loss processes in Equation 2.9.  The parameter fl, called the
sediment capacity factor (DUoro et al. 1982). Is defined 1n terms of
solids masses in water and sediment (proportional to mH) and fractions
partlculate, f:
       0
 The ratio of toxicant concentrations on partlculates, r2/r-|
 determined from the expression:
        fl      {wr»  *  Wd)fp2    *   \ fd2•

-------
 This  ratio is  controlled  by  sediment  exchange  velocities. w    * w.
                                                           rs    d
 (or w  through Equation 2.5);  the  diffusion  coefficient, K ,  for
 exchange  between  the  water column  and  the  InterstUual water  of moderate
 to  high porosity  sediments;  the  decay  velocity 1n  sediment, K2H2* and
 the fractions  dissolved and  paniculate.

    Finally, the  total concentration of  toxicant 1n  the bed,  CT,, Is
 given  by:
                    pl H2
                           cT1u)
{2. U)
 It should be noted that Equation 2.9 and 2.11 utilise f^and f . as
                                                       01     pi
 1f they were constant throughout the reach, when in fact they vary with
 m^x).  Consequently, in order to treat them as constants, the'MICHRlv
 program divides each reach into small computational increments, within
 which m^ Is virtually constant, and solves the equations for each
 increment, moving downstream.

    It can be seen that the model 1$ not entirely simple,  *ost
 first-time users may find some aspects of Its behavior not intuitively
 obvious; some sensitivity runs coupled with examination of the
 formulating equations may be helpful to obtain a good feeling for how tne
 model responds to Its Input parameters.  OlToro et al. (1982) discuss
 many aspects of the behavior of this type of formulation.  Appendix A of
 this document provides a more complete derivation of model formulation.
 Section 3.0 provides Information on selecting parameter values;  Section
 5.0 describes a case study using the model.   Thomann (1964)  suggests  a
 simplification of this type of model.

2.5.2  Relationship with Other Approaches

    In predicting the total pollutant  1n the water column,  U can be  seen
that  MICHRIV and SlSA use the same  first order decay formula
i"qft1on 2.8)  a  tht. simple empirical  approach (Equation 2.3).   In

-------
MICHftIV, however, unlike SlSA and the empirical approach, the decay
coefficient, KTI. Is not necessarily constant within a reach.  Rather,
U 1s a function of'the fractions dissolved and participate (per
equations 2.9), which 1n turn vary with any change In the suspended
solids concentration moving downstream (per Equations 2.6 and 2.?}. as
previously mentioned.

    Consequently, 1f the suspended solids levels do not vary within the
reach (such as would happen If deposition and resuspension fluxes
balanced each other), a steady-state loss of toxicant would occur only as
a result of degradatlve processes or volatilization.

    For metals and other nondegradable. nonvolatile substances the sole
mechanism for reduction of the water column load 1s burial beneath
depositing sediment (or possibly transport downstream as bed load).
Consequently, the behavior of such substances would be predicted to be
conservative under the conditions of (a) s'teady pollutant loading to ;a;
a graded stream with (c) Insignificant Ded Idad and ;d) steady ?*ow.  A
graded stream is one where neither downcuttlng nor sedimentation 1$
significant; the bed elevation Is not significantly changing over t*ne.
Solids settling and resusoenslon fluxes would-thus balance under the
above conditions.  When a pollutant loading first began, exchange
processes would cause a net transfer of pollutant out  of the water
column Into the previously uncontamlnated sediments.  After a period of
steady conditions, however, the bed concentrations would reach
equilibrium with (become saturated with respect to) the water column
concentrations.  Then the pollutant flux out of bed would balance the
flux Into the bed and no reduction of the water column load would take
place.
                                    24

-------
   .  In real .systems,  however,  time  variable  flows  and  loads would  produce
 unsteady  concentrations  and  disequilibrium between the water  column  and
 sediment  bed.  While  the long  term  loading of  the  total  form  of  a
 pollutant may  be  conserved within the  water  column,  short  term loadings
 (such  as  measured during Meld  surveys) may  not  be conserved.   During
 periods of high concentrations  1n the  water  column or  of net  deposition
 of sediment, the  stream  bed  may-act  as a  sink.   During periods  of  low
 concentrations In water  or of  resuspenslon of  the  bed, It  may  act  as a
 source.

     In evaluating metals or  other nonvolatile  nondegradable substances,
 MICHRIV differs from  simple  first order decay  models in  that  loss  through
 burial  only takes  place  until  the suspended  solids attain  their
 equilibrium concentration or (If resuspenslon  equals zero) until only the
 dissolved  fraction remains.  In any  case,  the  asymptote which  the  total
 metal  concentration approaches  is not  zero,  as illustrated in  figure 2.5.

     Overall, the  main advantages of  using 'HICHRIV  (or SlSA) are  t.ia:'they
 discern between dissolved and partlculate  phases and they  predict  tne
 degree of  contamination  of the  bed.  In addition,  they better  delineate
 the  factors affecting the overall loss rate, thereby allowing better
 utilization of previous  collective experience  In determining parameter
 values and producing a much better understanding of  the controlling
 factors.   The number of  degrees of freedom,  however, complicates the
 calibration procedure; 1n some  situations  more than one set of parameter
 values may fit the field  data.

     Relative to some of  the more complex models described  in the next
 section, the most  Important limitations of this approach might be that It
 1s one-dimensional, steady-state, and plug flow.    In addition. NICHRlv
and  SlSA  lack complex kinetic routines for Internally deriving a chemical
Degradation rate  from Input data.

-------
suvtaoio
  MUDS
OBBOIVIQ
                                                                 (A)
  T9TJU.
     FIGURE 15 TYPICAL BEHAVIOR PREDICTED BY MICHR1V FOR (A) METAL.

               AND (I) DS6RADASLC ORGANIC,

-------
     For studies  of far field, Impacts  (as  opposed to mixing rone Impacts),
 lateral and vertical  variations  In  concentration are seldom sufficient  1n
 rivers and  streams to Justify modeling In more than one dimension.
 Longitudinal  dispersion  Is  likewise seldom sufficient  1n rivers and
 streams to  discourage use of  a plug flow  assumption (Oriscoll  et al.
 1983).   The plug flow assumption  does  deter applying sucti  models to
 estuaries,  however.
     The steady-state  assumption affects the rigor with  which  time
 variability can  be analyzed.   When  successive  runs  of steady-state  model
 are .used  to simulate  a time sequence of events,  the output  of  each
 successive  run 1s  Independent  of  the previous  run.   Unlike  a  dynamic
 model,  the  steady-state model  has no memory of  the  previous state of  the
 system:   1t assumes that the modeled conditions  have persisted  since  time
 Immemorial.   To  the extent that the real  system  can  •remember•  Us
 previous  condition, for example through longitudinal dispersion and a
 long hydrau.llc retention time, a modeling  error  1$  generated.   In this
 case the  steady-state model would tend to  overpredic: during  ser'ods  of
 high or  steadily rising concentrations and  underpredlct  during  periods of
 low or  steadily decreasing concentrations.

   . Hulkey  et al.  (1962} have  compared the  frequency distributions of
 concentrations predicted by state-state and  dynamic models.  They applied
 the steady-state model EXAMS and the dynamic model  H$PF  to a situation of
 a constant  effluent load discharging to a  river with variable flow.
 (Both models are described 1n  Section 2.6 and in Attachment II.)  With
 the steady-state model, a frequency distribution of dissolved chemical
 concentration In the water column was generated by making several runs.
 each with a different flow having a known frequency of occurrence,  with
 the dynamic model, the frequency distribution was constructed from a
continuous, day-by-day simulation operated  from the dally flow  record.
They found  the frequency distribution produced by the steady-state model
 to be nearly Identical to that pr.iuc<»d by  the dynamic, continuous
                                   27

-------
simulation model, regardless of whether the chemical was assumed to be
strongly or weakly adsorbed by the sediments.  It is essential to note,
however, that this equivalence between the frequency distributions
generated by the two approaches applies only to rivers and only to the
water column.  It does not apply to waters having considerable
longitudinal dispersion and long hydraulic..retention times, such as
Impoundments and estuaries, and 1t does not apply to concentrations In
the bed sediment (which similarly has a long retention time).  Also, U
may not necessarily apply to ^situations where the effluent load or other
key factors are rapidly varying over a wide range.
2.6  COMPLEX MODELS HAVING MULTIDIMENSIONAL. DYNAMIC. OR SPECIATJON
     CAPABILITIES
    The waste load allocation models described in the previous section
were one-dimensional, steady-state, water column and sediment models with
equilibrium partitioning and linear transformation kinetics.  The models
described In this section employ less restrictive assumption? and contain
more degrees of freedom.  They tend to 1nvolv.e more process-oriented
descriptions of chemical transport, sorptlon. spedatlon, and
transformation.  Enhanced process descriptions can provide a more
                         r-
confident extrapolation of model results from the calibration conditions
to different conditions at the same site or to similar conditions at a
different site.  These models can also be operated to provide more
detailed resolution 1n time or space.

    Choice of a model will depend on characteristics and variability of
the waste load and the receiving environment, the level of certainty
required In model extrapolation, and the type of data available.   For a
given level of predictability, the more complex models generally require
a greater variety of data, but with fewer constraints than simpler
models.  For example, steady-state models require data averaged over
steady conditions, whereas dynamic models can use data taken during
                        i        f
steady or unsteady periods.  The use of more C-T.^U-. models require* no- .-
                                   28

-------
 technical  competence and resources  to obtain predictions,  but not
 necessarily more wisdom and experience to Interpret the  predictions  and
 fain Insight Into the problem.

     A variety of fairly complex  models exist or will  soon  be available.
 Those general purpose toxic chemical  Riddel  codes described In this
 section  Include:   EXAMS.  EXAMS 2, and TQXIHASP. developed  by Athens'
 Environmental Research Laboratory;  HSPF,  developed  by  Hydrocomp  and
 Anderson-Nichols  for Athens ERL; SERATRA,  TQOAM,  and MEXAMS,  developed by
 Battelle Pacific  Northwest  Laboratory for  Athens  ERL;  WASTOX.  developed
 by Manhattan College for  Gulf Breeze  ERL;  UTM-TOX.  developed  by  Oak  Ridge
 National Laboratory  for  the Office  of Toxic  Substances;  TOXIC, developed
 by University of  Iowa  for Athens ERL;  and CTAP.  developed  by  Hydro-Qual
 for  the Chemical  Manufacturers Association.   To assist comparison.
 NICHRIV and  SLSA.  the  slightly less complex  models  described  In  the
 previous section,  are  tabulated here  as well.

     Table 2.2 categorizes these computer codes.   General characteristics
 of concern are the type of  aquatic  system that  can  be  simulated  (general
 aquatic system or  river), the chemical capabilities (generalized
 pollutant that could be a metal or an  organic compound, metal  species,
     •          •                   n                                     •=
 and daughter  product}, the  sediment capabilities  (descriptive  input, one
 size fraction  simulated,  or  several size fractions  simulated}, the
 dimensionality (one-dimensional,  two-dimensional, or box, which can be
 arrayed as pseudo  three-dimensional),  the numerical solution technique
 (finite difference,  finite  element, steady-state algorithms), the time
 frame (steady-state, seasonal, dynamic), and their availability.
Attachment II of this document contains additional Information.
        f ' ,
    Clearly, a range of models 1s available with widely differing
capabilities.  Table 2.3 summarizes what components of these models can
be corsldered more complex or more general than those of MICHRIV and
SLSA:  their transport, sorptlon. speclatlon, or t an-formation

-------
                Table 2.2  General Categorization of Computer Models
                           (listed in alphabetical order)





CTAP
EXAMS
EXAMS 2
HSPF
MEXAMS
MiCHRIV
SERATRA
TQOAM
TOXIC
TQXIUASP
UTM-TQX
WASTQX


ase
H- U*
9 V»
' 0^
3c v»
6
S
6
R
6
R
R
R
6
6
R
6

i/i
0

Uri
5-
0
0
0.0
o.o
M
0
Q
Q
Q
Q
O.N
0


*
9C vi
SS
UJ —
IS* «/»
s
0
D
3
D
1
3
3
1
1
4
3
^
_j
S
o
«/>
x-
£
o
8
8
8
1
8
1
2V
1
8
8
1
8

•4 ~
51
« •—
uj a
ic!
Z v^
SS
ss
FO
FO
SS
ss
FE
FE
FO
FO
FO
FO

>;
u.

UJ
SS
SS
S
D
SS
ss
o
0
0
0
0
0
£
•~
i
^J
»
c
A
A
A
A
31
A


A

A
6  - general Aquatic System; R - River
0  - generalized Pollutant; N - Metal, Specifically; 0 - Daughter Product
0  - Descriptive Input. Not Simulated
8  - Boi Approach, Pteudo 3-Olnwnslonal; 2V - Two Dimensions (x-z)
FO - Finite Difference; FC-F1n1te Element; SS - Steady State
S  - Seasonal; 0 - Daily
A  - Available from EPA Center for Water Quality Modeling, Athens. GA
C  - Available from Chem. Manuf. Assuc.; fit - Available from EPA Grosse He Lab

See Attachment II for additional Information.

-------
        Table 2.3° Model Components which could be considered somewhat more
                          complex or general than
'


CTAP
EXAMS

EXAMS2

HSPF
MEXAMS

WICHRIV.SLSA
SERATSA
700AM
TOXIC
70X1 WASP
UTN-TOX
WAS TO A

5
o
f^
^k
i/l
OE
•


*

*



f
• *
•
'
*
*

s
1





*
•


'
»





|
• £

•

•


t








1 ! J
0
1
Si
— **
i
^
V

•

•



•
•
' •
• -
•
•



„
|



t
\
• s






•
*




s
s


-------
algorithms, or  their  linkage to hydrologlc, flow, and/or effects models.
       t
Those components not  labeTed as more complex may be roughly equivalent
to, or even simpler and more restrictive than PUCHRIV and SLSA.

    Generally It 1s sound practice to use the simplest approach that win
properly handle the problem.  Nevertheless, to satisfactorily resolve
some ULA problems, 1t may be necessary to apply a very complex analysis
to some facets of the modeled system.  To help discern the range of
analytical complexity available, the major model components are discussed
below.

2.6.1  Transport and Bed/water Exchange
    Movement of both dissolved and partlculate phase contaminants may
occur within the water column, within the bed, and between the bed and
the water column.  (Transfer between the water column and the air is
presented elsewhere In the guidance manual as "volatilization".)
In the less Intricate MICHRIV and SLSA, transport in the water column
follows the one-dimensional steady-state solution to the advectlve
transport equation for chemical and suspended sediment.  Settling and
resuspenslon velocities are specified for partlculates (and thus sorbed
chemical).  Less restrictive transport and bed/water exchange assumptions
can allow multiple sediment size fractions with different sorptlon and
settling properties, vertical or lateral resolution In the spatial grid,
and In many cases, unsteady flow.  These properties are tabulated in
figure 2.4.

    In place of a single mixed layer of bed, some models discern multiple
layers In the bed.  Dissolved chemical may be transported through the bed
by pore water percolation, or exflltratlon, or diffusion.  Sorbed
chemical  may be transported by sedimentation, erosion, or physical mixing
of the sediment.  Some models allow horizontal movement of the upper bed
layer (representing "fluid mud* or bed load).  Other models can represent'
this process with benthlc water colurn se-.nents carrying a high suspended
solids load.  These properties are tabulated In columns 4 and S of Table
2.4 for each model.

                                     32

-------
               Table 2.4  Transport and Bad/Water Exchange Properties





CTAP
CXAMS

EXAMSZ
HSPF

«IXA«S
HICHRIV.
SLSA

SERATRA
TODAM
TOXIC
TQXIWASP
UTN-TOX
MASTOX
^
a
ll

s £t



•
•





*
*
• •
*
*
•
»—
SS'
**
oS
UJ — •
wi vi
5
0

0
3

0

1

3
3
1
1
4
3
|
3?
i

3
8
8

8
. -\

B

1

2V
1
B
8
1
8
£
U4
«
O
LU
a
H
H

tt
1

H

1
o
o
o
UJ
OB


«i
u* ^
5.1S
3« ij*
o
at t/i
• a* •—.;—.
e*n ^ S
^*^ ^^ * i 	 * 	 i
« ° S S
P C
P.T
i
.

| S ' F

i
' P.T 1
i
P C
1
H
N
1
Pt
3
N


.W
W
•
•

S F
S
P
P.T
P
P .
F

C
F
C
•  - Capable
0  - Descriptive Input. Not Simulated ,
B  - Box Approach, Pseudo 3-01men$\onal; 2V - Two Dimensions (x-z)
«  - Multiple Bed Layers '
w  - Bed load Approximated with Lower water Layer
P  - Pore Water Dispersion; T - Enhanced Diffusion From Bloturbatlon or Physical
     Nixing; S - Direct Sorptlon between Bed and water Column
C  - Calibrated (Cmpnlrlcal) Scour-Deposition Parameters
F  - Functional Scour-Deposition Paramete s
                                          33

-------
     In  HICHRIV  and  SISA.  chemical  transport between  the bed and the water
 column  occurs through  pore water diffusion and  through steady scour and
 deposition  of sediment.   Some of the models considered here omit pore
 water diffusion and describe this  exchange as direct,  first order
 sorptlon between bed and  water column.  Mathematically, the results are
 the  same, given equivalent parameter values.  Other  models add a
 parameter to describe  enhanced dispersive exchange due to blotarbation or
 physical mixing.  The  value of this parameter can be specified in a
 qualitative sense only.   Finally,  In place of the above calibration input
 parameters, some models can Internally compute  sediment exchange
 parameters  from functional relationships between flow, shear stress, and
 scour.  These properties  are tabulated in columns 6  and 7 of Table 2.4.
 2,6.2  Sorotlon
    Sorptlon of a chemical onto sediment is generally considered to
proceed rapidly compared  to-other  transport or  transformation processes.
MlCHRIV and SISA assume adsorption and desorption are completely
 reversible, and proceed rapidly.   Mathematically, these two assumet'qns
 lead to the use of a partition or distribution coefficient for
 sorptlon/desorptlon.   This coefficient can be measured In the laboratory
and'adjus ted for conditions tn the environment.

    Many of the other models also use partition  coefficients adjusttfl  'or
organic carbon content of Che sediment.   One model  aUo automatically
adjusts the coefficient for sediment concentration based on higher
partitioning at  lower sediment concentrations.  Some models make use of
the langmulr or  Freundllch Isotherms widely  used >n soil  science.   These
empirical relationships predict progressively less  additional  sorptlon as
chemical concentrations become higher,  reflecting the saturation of
binding sites  on the sediment particles.   At low chemical  concentrations.
these Isotherms  approximate a linear isotherm, or partition coefficient.
Use of these Isotherms, then,  should be  Important only when relatively
high chemical  on >ntr tl  "is are  expected.
                                   34

-------
     Other mod* Is as SUM  a  linear  Isotherm at equilibrium, out  specify a
 first-order  rate at which  equilibrium  Is achieved.  This may be  Important
 when  transport or transformation  processes proceed as rapidly  as sorption
 (say, on the order of nlnutes to  an hour).  It can also be Important
 close to the point of discharge of an  effluent high in solids  entering a
 river low In solids (as  Illustrated 1n the Flint River case study), or
 visa versa.                             .          •

    Three types of theoretically-based sorptlon algorithms have been used
 1n these models:  Ion exchange, constant capacitance double layer, and
 triple layer site binding.  The Ion exchange technique can be  useful for
 1on1c compounds where selectivity coefficients For exchange reactions are
 available.  The constant capacitance and triple layer models consider
 charge-potential relationships at the  surface and the changing properties
 of the surface as. a result of changes  In pH or Ionic strength  (Felmy et
 al. 1983).  They require specific experimental work to obtain  the
 parameters, and are thus limited to applications where sorptlon-oH
 dynamics are Important,  and where experimental work 1s
    Table 2.5 tabulates the sorptlon properties of the general puraose
models considered here.  It Is Important to note that research is active
In this field, that other formulations have been described in research
models, and that these formulations will be tested and available In
general purpose models within a few years.

2.6.3  Soedatlon
    Many chemicals or metals discharged Into an aquatic environment will
bt found 1n several species or complexes,   A common speclatlon process is
lontzatlon. which 1s controlled by pH.  Both chemical reactivity and
toxUHy can be significantly affected by  the extent of loniration.
                                   35

-------
                          T*blt 2.5  Sorptlon properties.


                       . oe uj
                       
O
CTAP




EXAMS. EXAMS?



HSPF



HEXAHS



NICHRIV. SLSA




SESTRA '



TOOAM




TOXIC



TOXIWASP




UTM-TOX



WASTOX
 *
                                          36

-------
     For metals,  an  important  process  *s,i*s-gan1c  complexatlon.  Taole
 2.6  gives,  for  example,  the possible  d'sii-lved  species  of- lead  In water
 containing  nitrate,  chloride, sulfate.  **^or1de, and carbonate  (Felmy et
 al.  1983).   To  calculate  these species, cne needs  experimental  data on
 the  equilibrium constants ana env'.ronner:al data for pH,  chloride.
 sulfate,  flouMde,  nitrate-, 'an
-------
                   Tablt  2.6.   Dissolved  Species  of  Pb
                                                P6C1
               Pb
(AQ)
               PbS04 (AQ)
                                                    "
From Ftlmy et al. 1983.
                                  38

-------
 Input  data.   The  analyst  may  obtain  this  rate  coefficient  Oy  theoretical
 calculation  or  by calibration.   First, order  rate coefficients  for
 competing  processes are combined  by  simple addition  to obtain  an overall
 first  order  rate  coefficient  (with no  loss of  rigor).  SLSA performs  tnis
 addition Internally.

    The other ten (nodeIs  allow decay  to be formulated as a second-order
 process:   proportional to the toxicant concentration, and  proportional
 to some other concentration or environmental parameter, such as hydrogen
 Ion concentration (In addle hydrolysis), or bacterial concentration  (in
 biolysis).
      ' Rate  . KC^Cj
 where. K   •  Second-order  coefficient
       C.  •  An  environmental parameter
       C.  •  Concentration of toxicant

 With respect to the toxicant, the product KC&  is sometimes called a
 •pseudo' first-order decay coefficient:  a first-order coefficient «n'cri
 varies as a  function of another parameter.  To combine multiple
 processes, the  models Internally add together  the 'pseudo' first-order
 coefficients  In order to  obtain an overall first-order decay coefficient.

    In four  of  the eight  time-variable models considered nere, the
 overall reaction  rates can vary 1n response to the time variation of the
 relevant environmental properties, such as temperature, OH. light,  wind
 or current velocity.  These four models are EXAMS2, HSPF, TQxlWASP. and
UTM-TOX.

    lastly.  EXAMS  2 and HSPF are able to handle daughter products along
with parent compounds in a single simulation.  Other models reauire two
separate Simulations, with Internal loadings  from the parent compound
specified as external Input to the second simulation.
                                  39

-------
                                SECTION 3.0
                  ESTIMATION AND USE OF MODEL PARAMETERS

     Tht purpose of this section 1i to provide Information for estimating
parameter! for a model of  Intermediate complexity, such as MtCHRlv.
described  in Section 2.  Some discussion of  the basts for estimating
                                  t                              *
process rates will be presented; however, this document will not
duplicate  chemical-specific data and coefficients presented elsewhere:
Mabey et al. (1982) tabulate values for the  kinetic coefficients required
by EXAMS (and similar models) for the organic priority pollutants;
Callahan et al. (1979) review the fate characteristics of the 129
priority pollutants; Mills et al. (1982) summarize fate data for selected
pollutants; lyman et al. (1982) present methods for estimating cnemica<
properties; and M111 et al. (1982) present laboratory protocols for
evaluating the fate of organic chemicals.
                              ,                                           r
    The section will cover partitioning between aqueous and particular
phases, exchange between the water column and the bed. exchange between
the water  column and the atmosphere, and transformation or degradation of
the chemical.  In order to maintain focus on toxicant modeling, the
discussion will not cover  transport of the bulk fluid (advection and
dispersion); such transport is adeduately covered in conventional
pollutant  texts.  Furthermore, downstream movement of the bed will not be
dealt with.here, as this process is ordinarily not expected to be
important  for toxicant transport and is not  Included 1n most models.

3.1  EXCHANGE BETWEEN BED AND WATER
       In modeling the transport and fate of chemicals in aquatic
systems, It has. been Increasingly evident that knowledge of how a given
chemical is distributed among various  phases -solution,  suspension, air
and bottom sediment Interfaces,  biota  - Is essential.   One of Che most
significant mechanisms for the movement of toxic chemicals through the

-------
aquatic environment  1s the adsorption or uptake of the chemical by both
nonvlable and viable participate matter, followed by the transport of the
Interacting participates.  Association with suspended matter not only
alters the transport regime of a chemical - by Introducing additional
mechanisms such as deposition and entrapment - but the process can also
Indirectly affect the rate and extent of transformations and blotlc' *
accumulation.  For example, partitioning of a portion of a chemical in
suspended solids will reduce the flux of that chemical Into the
atmosphere via volatilization of tne soluble phase.  On the other hand,
such solid phase partitioning would be likely to Increase the chemical
flux Into the bottom sediments by deposition processes.  Accordingly.
accurate determination of the transport and fate of a chemical requires
concurrent knowledge of the transport and fate of Interacting partlculate
matter.

    Literature on sediment transport In riverine systems Is extensive:
however, accurate prediction of sediment dynamics from basic theory
appears tenuous.  Rather than attempt an extensive development of the
theory Involved 1n river sediment transport, the focus of this subsection
will be to provide some methods for estimating sediment transport
parameters.  A further development of concepts is appended (Appendix 3} .

3.1.1  Particle Transport and Exchange
    The capacity for particles to Interact with aqueous toxicants 1s
related to the partUulate surface area.  As small particles have greater
surface-to-volume ratios than large particles, it Is the smaller (silt
and clay sized) particles that tend to be more Important in determining
pollutant behavior.  Smaller particles are also more readily carried by
the streamflow than large particles.

    Within the MtCHRIV modeling framework, the variables or parameters
which control the exchange of particles between the bed and the water
column are:  m^ and m_, <*..-  .Me1* concentrations In the water and
                                  41

-------
bed; and w V w   , *nd wj- the'velocities  (m/day) of settling.
resuspenslon, and burial, (sedimentation).  The thickness of the active
bed  is assumed constant.  Specifying any  Four of these five variables
allows the last one to 6e calculated from:
    As HICHRIV and SLSA (as well as some more complex models) recognize
only one particle size or type, the parameters may represent average or
median values.

    In several model frameworks, Including MICHftiv and SlSA, the bed
solids concentration, m., 1j a user specified constant.  For a typical
river bottom with water content 60-90X by weight, and a  typical solids
density of. 2.5 g/cm . m  will vary in the range 50,000-500.000 mg/i
(in terms of bulk volume).  Flint River bottom sediments were measured ai
200,300 .ng/l (Section 5).

    In most models (Including NlCHRlv but excluding SlSA and EXAMS) the
solids concentration in the water column, n^. 1$ a state variable
predicted from the solids loadings and the settling and  resuspenslon
velocities.  In NICHRIV (as described 1n Section 2). m   M given by:
                        W  X
                                  w
       m^i) -m1(0)e   H1U1   •  "r^?     M - e   "lul  1        (3.2)

where all parameters are constant.

    It 1s useful to Identify three basic conditions of particle
exchange,  first, a condition may exist where m. remains constant
(moving downstream) and the sediment bed 1s neither accumulating nor
scou. tig  way.   Although solids settling and scour may be occurring, t'
                                 42

-------

are  in a state of equilibrium (i.e., they balance each other);
consequently, wfl » 0.          . .,  .

     In the second condition, m. decreases moving downstream because the
settling flux exceeds the resuspenslon flux.  As the bed cannot move
horizontally In MICHRIV, the resulting excess 1n settled solids Is "buried
at velocity w, > 0.  In the third condlton. m^ increases because the
resuspenslon flux exceeds the settling flux; for the resulting net scour.
w, < 0.  These three possibilities are depicted In Figure 3.1.
 o
    .It must be noted that 1n models which allow downstream movement of
the bed (bed load), w, could be given a different Interpretation.  For
example, when settling exceeds resuspenslon (wtf > 0). an increase in
bed load (at the expense of the suspended load) could be permitted to
remove the excess sediment.  In this case w^ could reflect the rate,of
Increase of the bed load.  As noted 1n Appendix 8. however, bed load 1$
seldom expected to be an important transport mechanism for adsoraec
toxicants.

    If the downstream profile of m. follows Equation 3.2, then it may
be possible to evaluate w  and w   (or wj Indirectly.  The
procedure 1s analogous to determining a first order decay coefficient
from the disappearance profile, but has the complication of an additiona"
degree of freedom.   In the following discussion 1t is assumed tnat'm .
U1 (velocity), HI (depth), and QI  (flow)  are known constants
throughout the, "reach, m.(x) has been measured at several points through
the reach,  as Illustrated In figure 3.1.  w$ and w^ are unknown but
constant, and w.(x) 1s unknown and variable (per Equation 3.1).  It is
also assumed that steady state conditions prevail and that the bed is the
only source of solids below the head of the reach (for example, no
nonpolnt loads and no phytoplankton growth).

    By evaluating Equation 3.2 for large values of -. wr can be related
to the m. asymptote:

-------
(«} STASH CONDITION
   (k) NIT SITTLING

(ONCT SCOUR
     MIVIR DISTANCE
         FIGURE XI

-------
       wrs.
(3.3)
where «.(•) 1s the asymptote (m^d) at x • •), estimated visually
from a prof lie.such as shown In Figure 3.1(6).  If Equation 3.2 1s
normalized for the asymptote and put 1n logarithmic form, the derivative
(slope) of the resulting expression 1s directly related to «s:
             -U1M1
  (3.4)
                             AX
    This in analogous to determining a decay coefficient from tne slope of a
semi.logarithmic plot of concentration versus Mn»e.  Having thus determined
w  and wrs< the value of w^x) 1$ given by Equation 3.1.

    Alternatively, tne value of *d(x) at any particular point can a«
expressed  In terms of the linear (rather trian semulogarunmic) s'ooe af
the profile:
                                                                     (3.5)
    The value of w  would .then be given by combining equation 3.1 and
3.3, and the value of w   by Equation 3.3.

    The Indirect estimation of solids exchange velocities, as described
above, may be difficult In many practical situations.  If hydraulic
conditions (such as U^ and H.,) vary along the length of the river.
then w  and w   may also vary.  Normal scatter In the suspended
solids data (such as caused by time variability) may make identification
of slope and asymptote ambiguous.  The analyst may thus need other means
                                   4S

-------
 for  estimating  w  and  w  .   Both  may  be  Independently  estimated,  or
 having  Independently estimated  one. the  other  can  be more  easily
 calibrated, using the solids  profile (as  Illustrated  in  the Flint  River
 case study).  In any.case, calibration  Is  assisted by  recognizing that
 the  magnitude of w  (or w.)  controls  the distance  needed to approach
 the  asymptote, while the  ratio  w  /w  controls the value of the
 asymptote.

     A direct estimate  of  the settling velocity, w   can be  made using
 Stokes'  equation.  Figure 3.2 Illustrates  the solution of  this formula.

      $ -  S|!   (S, -  SJ                                           (3-6)
v
 «    	
      18.
where,
          g
          d
          V

         Sy
    S
Stokes' settling velocity (cm/sec)
Gravitational acceleration (approi. 980 cm/sec2]
Particle diameter (cm)
Kinematic viscosity (cm2/sec) (Figure 3.29)
Specific gravity of particle -(dlmensionless ratio)
Specific gravity of water (1.0).
This calculation Is Intended to apply to noncoheslve spherical particles
1n a quiescent medium.  Substantial differences may exist between tne
calculated Stokes1  velocity, v$, and the effective settling velocity,
w ,  of natural particles under both laboratory and field conditions.
Such differences may result from particle Interactions and fluid
turbulence and shear.

    Coagulation or  clumping together of suspended particles creates
larger diameter particles  having higher Stokes1 settling velocity.   The
inter-particle collisions  necessary to bring this about may result  from
(a)  8rown 1 an motion (diffusion), (b) shear (velocity gradients)  internal
to the fluid, and (c)  differential  settling velocities, causing  more
rapidly settling particles to Intercept slower settling particles beneath
them (O'Hella 1980, Hayter and Hehta 1982).  The Inter.particle
coheslveness needed to produce aggregat'.* from colliding particles  may
result from (a) van der Waals forces,  \j) electric charges on particle

-------
s
S 9 S S S S S

s : s s s : s
• * s s a « a
                  1
5 -  s  1

sill
ill!. |
                         .  •   •
                                         SI «
                                         »l *


                                         H-
    s
                                                  u
                                                  e
    15 !
    2-
      Ss  238
                                                  IS
   I*  I  is
    a« *
   1*1 8  i   *!
   522 2  £2  25
                                                  v»
                                                  o
             8.—    •    *    '
                  S    8   S


          *•/«• 'AJJ30HA 9NI1X13S t*A)
                                  7 S
                                               _>
                                           i

-------
surfaces, (c)  Interactions of aqueous Ions attracted to the* charged
surfaces (double  layer), (d) chemical bonds, and (e) other mechanisms
(Parthenlades  1971).  As. a result of coagulation Into larger particles
the observed settling velocity may ae orders of magnitude larger than the
Stokes' velocity  of the  disaggregated particles (uchrln and Weber 1980).

    Nayter and Mehta (1983). in modeling fin* sediments in estuaries,
Indicate that  the effective deposit'o.i velocity, **  , decrease; with
increasing shear  stress, r. produced by the fluid passing over the
bed.  Shear stress -In an open channel 1s determined as Follows (Graf
1971):

    T .  T«S                                                          (3.7)
wnere, T • Shear  stress  (Newton/m2)
       T.- Specific weight of water (approximately 9807 N/m«)
       R • Hydraulic radius, approximately equal to stream depth (m)
       S • Slope  of the  energy g- 3,3.1 line, approximately equal to t.ie bed
           slope  (m/m)
If the stream  velocity.  U, 1s assumed to follow Manning's equation. men
the bed shear  can be expressed as "il
    T »   T (n U/1.*86)2/R1/3                                      (3.8)
wnere n 1s Manning's roughness coe'-'l :'.ent.  Under t.Vs condition, sed
shear 1s related to the square of velocity.

    Parthenaldes (1971) notes that tnere ts sonw critical velocity or
shear above which no deposition of fine- particles will occur.  As
velocities drop below thl; critical value a rapidly Increasing proportion
of the fine particles are capable of depositing.

    In this vein, HydroQual (1982) recommends settling w  equal to
25-50% of the Stokes' velocity in most rVvers, and to as little as 10% of
the Stokes' velocVtv in shailo-. st-ja.ns.   They a'so point out that the
Stokes ".U.latlon shou " bi treated as a preliminary estimate, to be
modified by calibration to Me scs^erded  solids data.

-------
    For quiescent waters, Thomann (19S2b) and Richardson et al. (1983)
have calculated w  in the range O.US m/day.  tn the Hint River case
study, w  was calibrated at 0.25 m/day during low Flow and 0.6 in/day
during a higher flaw period.  (Hn Increase in settling velocity with an
increase In flow 1s the result of suspension of larger or denser
particles oy the higher flows).  The manual SedJ men ta11 on Enc 1 ne_er ing
(ASCE 197S) presents a thorough discussion of the effects of sediment and
fluid properties on the settling velocity.

    Direct Instream measurements with sediment traps can be used to
estimate w .  HydroQual (1982) briefly discusses such measurements.
summarizing the findings of Bloraqulst and Hakansom (1981) on the accuracy
of various types of sediment traps.

    The resuspenslon velocity, w. also depends on the shear stress.
T, as well -as on the strength of bed to res. 1st shear.  The strength of
the lied is related to the nature the particles and the deposition history
(Including age and degree of consolidation).   Below some critical
velocity or shear, Uttle or no resuspenslon may take place, while aoove
this critical value,  resuspenslon may increase rapidly (Parthenaldes
1971. Hayter and Hehta 1983).  The parameters determining resuspenslon
rates must generally be empirically measured.

    Bonazountas and Mathlas (1984) discuss data and methods, and propose
algorithms for determining both deposition and resuspenslon.  Their
computer model, SCOIN, Is Intended to be used for estimating the Input
parameters of commonly used toxicant models.   It employs the formulations
of Einstein, Meyer-Peter and Mueller, and toffaletl. adapted to account
for the field data available to the analyst.

3.1.'2  Diffusion Between the water Column and Pore Water

    Diffusion of dissolved pollutant between the *»ate- column and the '  •
sediment Interstitial water operates to move mater la. from the region of
                                  49

-------
higher concentration  to the  region of lower concentration, in accordance
with Mck's law.   If  no dissolved ,concentration gradient exists then
diffusion  1s unimportant.  Water column and pore water would be expected
to attain  the same concentration under the following condition: .(a)
steady state with  (b) partition coefficients equal 1n the bed and the
water column, and  (c) no decay within the bed.

    Unsteady conditions produce concentration gradients that can make
diffusion  Important.  In addition. 1f partition coefficients are lower in
the bed than In the water column due to the higher sol Ids concentration
In the bed (O'Connor  and Connolly 1980). then deposited partUulate
pollutant may have the opportunity to desorb and diffuse back into the
water column 1n the dissolved phase.  Decay in the bed. on the other
hand, would tend to depress  pore water concentrations relative to water
column concentrations.  Thomann (1984) Indicates that for metals copoer,
cadmium, and zinc, sediment  diffusion.Is an Important process in waters
having suspended solids concentrations less than about 'Q ng/l.

    The exchange coefficient describing dissolved exchange between tne
bed and water column  has been termed K,  In Section 2.  HydroQual (1982)
notes that this parameter 1s difficult to measure directly.  They believe
that X.  In the range  10 - 100 cm/day may appear reasonable based on
field and microcosm calibration results.

    Both molecular diffusion and physical stirring of the bed may
contribute to the magnitude  of the exchange coefficient K .  where only
molecular diffusion Is Important K,  can be estimated from the
expression (HydroQual 1982):

                                                                    (3.8.1)
where:
    OL • molecular dlffuslvlty o* chemical  In water (cm^/day)
     • .  ec'ment porosity (dime1 * iQi.. ess)
     4  » length of vertical  concentration gradient in sediment (cm)
     n  • a power
                               SO

-------
 The  sediment porosity * appears the numerator to account for the
 reduction  In diffusion caused Dy the tortuosity  In sediments.   It appears
 1n the denominator  to account for  the conversion of concentration units
 from InterstUual volume  to bulk volume.  The vertical concentration
 gradient length 4 may be  taken to  be equivalent  to the active sediment
 thickness H. used in-Section 2.5.  The value of  n-ls a sediment
 dependent property  Indicating the  relationship aetween porosity and
 tortuosity. .HydroQual (1982) assigns the value  n • 2; However Chapra and
 Recknow (1983) indicate that other values may also apply.

     Chapra and Reckhow (1983) present the molecular diffusion coefficient
 data of U and Gregory (1974).  Tor priority pollutant metals 0. is
                          e             <   £                  t
 frequently between  6 x 10   and 12 x 10   cnr/sec at 2S*C.
 Qlffusivlty Is roughly linearly related to temperature; values at 0*C are
 about half of those at 25'C.

     Equation 3.8.1  does not Include the effect of ahys'cal st'.rring of
 the  bed sediment caused Dy currents and bent.i'.c an!maT$ (s'otwrsa:-an}.
 Heathershaw (1976)  and Fisher et al. (1980), among others have noted  t.ie
 importance of bloturbatlon In Increasing the effective rates of diffusion.

     Additional explanation of sediment diffusion processes and
 formulations are provided by Berner (1980), Chapra and Recfchow (1983),
 and  QIToro and Connolly (1980).

 3.2  PARTITIONING PROCESSES

 3.2.1  Metals Partitioning
     The Interaction between dissolved metal spedes and riverine
 partUulate matter,  under normal physlcochemical conditions, generally
 leads to a large fraction of the metal being associated with solids.
when a significant  fraction of the total metal in a system 1s in the
 solid phase, the fate, transport, and bloavailablHty of the metal  are
                                 51

-------
 altered  considerably.   There  Is  ample  evidence in  the  literature  that
 metal  associates  with  participate  natter;  however,  theoretical  (as
 opposed  to  empirical)  approaches have  not  been widely  applied  to
 quantifying this  process  In natural  systems.   The  purpose  of  this section
"1s  to  present  the theoretical  considerations  that  have led to  the simple
 parameterization  of metals partitioning  commonly used,  and to  indicate
 the  factors which Influence partitioning.

     The  accumulation of heavy  metals  1n  aquatic solid  substances  can  be
          •           *i
 characterized  by  the following five  major  mechanisms (Globs  1973):
 1)  adsorptlve  bonding  on  fine-grained  substances.  2). precipitation  of
 discrete metal  compounds. 3) copreclpUatlon  of metals by  hydrous ft  and
 Mn  oxides and  by  metal  carbonates, 4)  association  with organic  molecules.
 and  S)  Incorporation Into crystalline  minerals.  Inconsistent
 Interpretations of metal  solids  interactions  in natural  waters  can  easily
 arise  in situations where different  mechanisms are operating  under
 different environmental conditions.   For example,  even though  adsorjt'on
 1s  a necessary  first step for  heterogeneous  surface precipitation,  Corey
 {1981} distinguishes between the two as  follows:   1} adsorption Is  a
 two-dimensional,  surface  layer process while  precipitation involves
 three-dimensional crystal buildup, and 2)  in  adsorption,  solution
 adsorbate concentration 1s controlled  by surface site  concentration
 whereas  the degree of  precipitation  Is controlled  by solution
 concentration,  for heterogeneous  precipitation to  occur  conditions
 leading  to  a critical  supersaturatlon  of the  adsorbate ion must exist.
 In  a system where the  ultimate result  1s the  formation of  a precipitate.
 however, the strict assumption of  an adsorptlon-desorptlon equilibrium
 may  be Invalid.

     In most cases encountered  1n river systems 1t  would  seem  that
 adsorption  of metals to Inorganic  surfaces Is  the  dominant binding
 mechanism.   However. In situations where there Is  a 1arg»  fraction  of
 biological  solids, sorptlon Into blomass or binding by c.ganu  surface

-------
 functional  groups  can  play  an  Important  role.   In  fact, most of  the
 models  describing  the  interaction  of  adsorbate  ions and surfaces have an
 Implicit  definition  of adsorption  as  a two-dimensional, surface
 phenomenon.
          •  •          •                                      <
    Adsorption models  developed  from  a theoretical basis are generally a
 compos He of  surface complex formation theory (Schlndler et al.  1976;
 Huang and Stumm  7973)  and various  electrostatic models
 (Gouy-Chapman-Stern model In Shaw  1978;  Grahame 1955; James and  Healy
 1972).  More  recent models, such as the  one proposed ay Oavis, James and
 Uckle  {Oavis et al. 1978;  Oavts and  Leckle 1978;  James et al. 1978).
 combine surface complexion with electric double-layer theory and
 Interpret adsorption phenomena In  terms  of a knowledge of the speclatlon
 of the adsorbate and the adsorption site.

    Current adsorption models have been  reviewed by a number of authors
 (Westall  and  nohl  1980; James and  Parks  1981; Schlndler 1981; Morel 1981)
 and have  been shown to have a sound theoretical  basts.  The application
 of these models to well-characterized laboratory metal-ligand-surface
 systems have  shown excellent agreement with experimental observations
 (e.g., Oavis and leek1e I978a; James et al. 1981;  Theiss and aichter
 1980; Benjamin and Lecxle 1980).   However, without further baste  reseaVch
 and experimentation with natural  aquatic  sediments, it will be difMcu't
 to apply  the  theoretical models to adsorption In natural systems.  The
 application to natural  systems 1s mainly  hampered by the need for data-on
 numerous  Intrinsic parameters for each adsorbent phase in  the system of
 Interest.

    Despite the lag that currently exists between the development of
 theoretical  metal adsorption models and their practical  application to
natural  systems,  there has arisen (through model development and
experimental observation)  general agreement on many of the characteristic
features o'  idf^rptlon  reactions.  Metal  adsorption is considered to be
                                  53

-------
analogous to the formation of soluble complexes, with the only difference
being that the llgand 1n the reaction 1s a surface site (Stumm and Morgan
1981; Benjamin and Leckle 1981).  Therefore, the same factors affecting
soluble complex formation also affect the Interactions at surfaces.

    Of course, pH Is one of the most Influential parameters in governing
metal.adsorption, affecting both the type of surface sites and the
speclatlon of the metal ion In solution through hydrolysis reactions.
For example, surface hydroxyl groups can exist  In three possible charge
states, with the relative distribution depending on the pH and acldiry
constants.
               ,S       rS
At the same time the metal Ion will undergo hydrolysis as 0H increases.  The
resultant surface association of metal ions with hydrous oxide surfaces
tends to demonstrate a rapidly Increasing metal ion adsorgtion as 3H
Increases over a very narrow range of 1-2 unl'ts (James and Mealy 19~2a: and
many others).

    This 'pH adsorption edge,* as It  1s commonly called, often is
demonstrated by a plot of percent metal adsorbed versus DM.  An example
of a typical metal adsorption edge Is shown in Figure 3.3.  In most
cases, fractional adsorption decreases (the pH edge shifts to the rignt)
as total metal concentration (Me.) 1n the system Increases, other
conditions being constant (Benjamin and lecxle 198Q).  This effect Is
most .often evident at low adsorption densities, when excess surface sites
are available.

    In situations where complex?ng Ugands (either organic or Inorganic)
are present 1n an adsorbing, system, the above generalization for the
relationship between metal adsorption and pH  ,s not always true.  In
fact, depending upon the particular metal, llgand, adsorbent and pH
                                 54

-------
   100
    to
    •0
u
5   40
                     «!»
                 HIGHER Mt
                                                         to
      FIGURE 34  TY-'CAI pH-ADSORATION EDGE FOR METAL ADSORPTION
                           r  H 1ROUSOXIOE SURFACE
                                  55

-------
range, fractional metal adsorption has been observed to decrease as pH
Increases (NacNaughton and James 1974).  Benjamin and leckle (1961; 1982)
have proposed a conceptual model to explain this behavior, where the
possibility exists for free metal, a metal-Hgand complex, or free Ugand
to be associated with a surface.  Then the percent adsorption of a
metal-Ugand complex will Increase with pH 1f It behaves as a free metal
1n Us surface Interaction ("metal-1Hte") and will decrease with pH 1f
Its adsorption reaction 1s similar to that of a free llgand ("llgand
like').

    Another characteristic feature of metal adsorption Is competition
between adsorbate metals.  Major cations, such as calcium and magnesium,
have been shown to Influence the adsorption of a given metal ion.
Predictability of the Influence of major cation 1$ difficult, and
observations range from Inhibition to no effect.  At any rate the effect
certainly seems to be smaller than those due to variations Vn JH and
Ugand levels.

    Competitive adsorption Inhibition among'adsorbing trace metals U
observed even at very low total adsorption densities (at most a few
percent of all surface sites occupied).  Benjamin and Leckle (1981) nave
suggested that the explanation for this phenomenon 1s the presence of
several distinct groups of surface sites.  Then tne possibility exists
for two metals to be competing for the same group of preferred binding
sites, which may represent only a small fraction of all  surface sUes.
The existence of multlslte surfaces may also partially explain the
variation of pH-adsorpt1on edge among different metals adsorbed
Individually to the same adsorbent.  In this case different binding sites
are preferred by each metal.

    Although numerous experimental  adsorption studies with "model"
adsorbents have been conducted, the number of laboratory studies
'n»  stlgatlng the uptake of  trace metals by natural aquatic sediments is
                                 56             U.S. EPA Headquarters Library
                                                      Mai! Code 3404T
                                                1200 Pennsylvania Avenue, NW
                                                      .  >iibn DC 20460
                                                       ix... 366-0556

-------
 relatively  small.   The  results  of  these  adsorption  experiments  of  trace
 metals  partitioning measurements made  1n natural  aquatic  systems  are
 generally quantified in  terms of relatively  simple  empirical  expressions.
 Including general  exchange  equilibrium expressions  (Langmulr  1981}  and
 adsorption  Isotherms (Oakley et. aj..  1981)  of  the  freunclic-i or  Langmulr
 type.     .         •                   -    .

    The data  from  a  typical metal  adsorption  Isotherm  run at  specified
 environmental  conditions, when  plotted as  the  adsorption  density  versus
 equilibrium dissolved metal concentration, generally can  be fit to  a
 Freundllch  or  Langmulr  Isotherm (Figure  3.*}.   The  Freundllch  isotherm  1s
 an empirical equation having the general  form
                  ,1/n
               r»n
(3.9)
where K^ and n are  fitting constants.  The Langmulr eouat'on has a more
theoretical base and-may be deduced  from e1t..ier kinetic or  thermodynamic
considerations (Weber  1972).  The Langmulr equation assumes  t.tac •nax'mufn
adsorption density  corresponds to a  saturated mono layer surface covering  of
adsoroate. that the  energy of adsorption is.constant regardless of adsorption
density, and that there is no migration of adsoroate In the  surface
As shown In Figure  3.4 the Langmulr  equation can be expressed
where   r • Specific adsorption density of the metal [*ole* We/mg
            Adsorbent] ('gamma').
        ra • Maximum adsorption density In forming a complete mono layer
             (Moles/mg] (i.e., number of usable surface sites per unit
             mass.of solid), and
       [W] • Equilibrium dissolved metal concentration [Moles/l].
The constant r A . which normally Is written Vb 1n the Langmulr
              ma.'                                      '
equation. 1s shown 1n this manner becaus/  ..  .an be thought of as a
                                  57

-------
 r.
lm/2
                       I       I
                  MAXIMUM SKC1FIC ADSORPTION
                              I	 I       i      I
                CaullllRIIIM OI3SOLVJD Mi CONC. (MOLES Mt/l)
   FIGURE 3.4  EXAMPLE Of PARAMETERIZATION OP LANGMUIH ADSORPTION
                  ISOTHERM FOR METAL ADSORPTION ON A SURFACE
                                    58

-------
 conditional adsorption equilibrium constant (l/mg) for a surface-metal
 complexatlon reaction of the Form:
X  Is termed a conditional equilibrium constant because  Us  value  in
only, a constant For specified surface and bulk solution  chemical
conditions.

    tt Is convenient to express the Langmulr adsorption  Isotherm equation
1n the above form because, due to low metal  adsorption densities in
natural systems, adsorption Is often linear  with respect to  dissolved
metal concentration.  Then, since r.A  » [H], Equation 3.10
                                   ma
reduces to
                  or
(3.11}
where  [N-S] * Concentration of  metal  In metal-surface  cample*
               (mole/1) and
       Sj   • Total concentration of Interacting (adsorbing)  solids
               (mg/l).

    Converting the Langmulr nomenclature to  the  toxicant modeling
nomenclature of Section 2.5 (Table 2.1). It  can  be  recognized that K
Is the same as * (l/mg), [H] {mole/1)  corresponds to  C^ (ug/t).
(H-SJ (mole/1) corresponds to C   (ug/l), ST  is m (mg/t), and
r (mole/mg) corresponds to r (ug/mg).   Thus.  EQuatlon 3.11  can be
written:

              '   .  c»
       ..   j-    j-                                        (]..?)

The distribution of metal  between dissolved  and  solid phase can  therefore
be determined by specifying the  partition coefficient and  the
concentration of Interacting solids.
                                   59

-------
    It should be emphasized that the value of the partition coefficient

for a given metal Is dependent on a number of environmental conditions
such as pH, PC. ionic strength, concentration of complexlng organic and

Inorganic Ugands, concentration of competing .surfaces, and concentration

of competing adsorbate species.  The use of * ts, therefore, limited to

conditions very similar to those for which 1t was determined,  tf a wide

range of environmental conditions are encountered, then » must be

Quantified (either experimentally .or theoretically} for the conditions of

Interest in order to accurately compute the soluble/solid phase metal

distribution.  This point became apparent .during the model application to

the Flint River system.


    The partition coefficient might be adjusted as a function of

environmental factors, as described below:


1}  Metal adsorption 1s highly dependent on the type and relative amount
    of each solid phase making ,up the solid* In an aquatic system.
    Oakley et al. (1981) demonstrated this postulate using bentonite
    clay, amorphous iron oxide, hydrous manganese oxide, and numic add.
    Sediment organic content can be highly correlated with metal
    partitioning for those metals, such as copper, that have a high
    affinity for humlc adds (Oakley et al. 1981,; flamamoorth and Rust
    1978; Suzuki et al. 1979).

2)  Metal partition coefficients depend on the size distribution and
    concentration of adsorbing aquatic sediments.  It 1s obvious that
    smaller particles would have a larger surface area-to-mass ratio.
    thus having a higher capacity for the metal (Tada and Suzuki 1982).
    It 1s not obvious why a higher solids concentration gives a lower
    calculated partition coefficient, although It has been observed in a
    number of studies on both metals and organic* (O'Connor and Connolly
    1980; OIToro et al. 1982).

3)  Of course, pH and other water chemistry parameters (particularly the
    presences and concentration of metal-complexlng and adsorbing
    Ugands) will affect partition coefficients.  Many'of the same
    observations made on •model1 systems In controlled laboratory studies
    have been observed In studies of natural aquatic sediments. (Gardiner
    197'; Huang et al. 1977; Vuceta and Morgan 1978; Tada and Suzuki
    1982; Brown 1979).
                                  60

-------
    An Alternative to adjusting metals partitioning as a function of
environmental conditions based on theory and laboratory experimentation
would be to empirically derive correlations from extensive field data.
By measuring partition coefficients for a range of water Quality In a
given system or by reviewing partition coefficient data from many
different river systems, a multiple regression might be found for a given
metal partition coefficient versus such Important water quality
parameters as temperature, pH, hardness, alkalinity, suspended solids.
dissolved organic carbon, and chlorophyll a..  Attachment 1 contains a
summary of data retrieval of field measurements of metals partition
coefficients with pertinent water quality parameters.  Figure 3.5
summarizes the regression results for the major metals for data obtained
from streams.  These data are useful for estimating metal partition
coefficients for specific river systems where actual field data are not
available.
3.2.2  Oroanlcs Partitioning
    Most organic contaminants of concern tend to be .relatively
hydrophoblc, non-polar compounds.  Such organic compounds tend to have
strong affinity for-natural aquatic partlculate material, making
solution/sediment distribution of these chemicals as important in
predicting their fate and transport as It 1s for heavy metals.

    The sorptlon of hydrophoblc organicJ Is considered by most
researchers to be a true equilibrium partitioning between the water and
sediments.  The linearity of sorptlon Isotherms In dilute sediment/water
systems and the lack of competitive effects between two sorbates have led
to the proposition that partitioning to sediment organic matter is the
primary mechanism of sorptlon of nonlontc organic compounds (Chlou et al.
1962).  This being the case, much emphasis on the characterization of
this sorptlon process has been focused on the properties of the chemicals
                                61

-------
         •0'
         10'
         10s
                          ..i.i
j_
                             iO     .           iOO

                          SUSPENDED SOLIDS (mg/i)
                 iOCO
METALS >
2 -CADMIUM

3 • CMflQMIUM
9- LC*0
r-
• - ZINC
                            FIGURE 3-5.

      PARTITION  COEFFICIENT AS FUNCTION ^F  SUSPENDED  SOLIDS

                      ALL METALS IN  STREA.'tS

-------
related to their solubility and hydropnobidty and on the size and
organic content of the sorbents.

    Data relating the chemical concentration in the aqueous phase to that
In the solid phase are frequently expressed in terms of the Freundlicn or
langmulr Isotherms previously described (Equations 3.9 and 3.10).  For
typical environmental pollutant concentrations, sorption isotherms m the
sediment/water suspension are very close to linear and both Freundlicn
and langmulr equations can be reduced to:
       r . «C                                                -      (3.13)
where  r  • chemical concentration In solid phase (ug/mg)
       C. . chemical concentration in aqueous phase (ug/l)
        a
       »  . partition coefficient (l/«g)

    The prediction of « for a given chemical/Suspension system has
relied on correlations with chemical  solubl-Mty. octanol/water partition
coefficients (KQWK
                        the organic carbon/water partition coefficient
(K  }. coupled with the organic carbon content (weight fraction) of the
  oc
sediments.  In general, the more Insoluble and hydrophobic a chemical  is
the more likely It 1s to have a larger ».  Likewise, in comparing
sorption of a given chemical among various sediments, the sediment wit.i
the highest organic content 1$ likely to sorb the most chemical ana
produce the largest «.

    In quantifying the above relationships the first useful correlation
Is between the octanol/water partition coefficient and the chemical's
aqueous solubility.  This work was pioneered by pharmacological Interests
In the partitioning of drugs Into the aqueous and fatty phases of living
tissues (Hansch et al. 1968).  More recently correlations have been
developed for organic chemicals of environmental Interest In aquatic
systems /Freed et al. 1977; Chlou et al. 1977; Banerjee et al. 1980;
Nackay  t ai. 1980).
                                63

-------
     Given either  the adueous  solubility  (S)  or  the  octanol/water
 partition coefficient (KQU) of  a  compound, a correlation  can  be
 developed between  *  and  S  or  K  •  For  a range of  chemicals.  More
 often  than not, however, recent experimental  interpretations  have
 Included  several  sediments with a  range  of organic  caroon content.   Then.
 by dividing  the measured partition coefficient  (K )  by  the  organic
 carbon weight  fraction of  the particular sediment (O.C.), a sedlment-
•independent  partition coefficient  (K..)  between  the  aqueous phase and
                                    oc
 the  organic  portion  of the sediments  may be  obtained.
Table 3.2 contains a summary of empirical correlations  for  predicting
sediment partitioning and biological partitioning  (bloconcentratlon
factors) of nonpolar organic compounds.
  ,  Care must be taken  in applying  the above (or other)
correlations to unstudied systems.  While these correlations will
probably give reasonable estimates  for application to .a WIA problem.
there are a number of potential -pitfalls that should be cons'de*ed.  Some
of these considerations are discussed below:

1.  These relationships are all log-log correlations; therefore, *nat *»ay
    seen like a small deviation 'from the regression  line could produce a
    rather large error  In final fate and transport' determinations .

2.  These relationships are useful  only for nonpolar compounds; they
    sould not be applied to semlpolar or polar organic compounds, where
    electrostatic Interactions nay  become significant (Pavlow, 1980;
    leenheer. 1980) .
                                   64

-------

tat
—i
^ **
Sa
l/"l P*
§i
** Q»
Oat
||
i^""*
flO P™
oc ^^
w
X w.
«s
 OB ^ ^
• ""* *7 e d •— *~ *o
* e *" «*" i^ — i
^^ O ^ At
I/I F» •— Oi • »/>
at d F» — w
* o •* i a
50 } d «o e »
MO S . • M •«
m M i e •
J d « j . J *
i ^ « i».
*** »» o , »
•o «r a e • • •

o n o

v XX X
i*» VN> |t^ O O O Q
flO fld 4B M» ME !• &^
j o^ CP o^ o* o* 5^ ^*
O O O O O Q O
^* ^ ^ *™
« m A
0
«]
"§

"S
A
\
l
w
VI













^^
•i
•?
^
^f

.
e>
e

JJ
»
o


w
M
Ol
O


ep
Wl
•e
- *
W
J3
e
a
w
I/I

*










O*
• •
a • .
w
e
Ol
o
.'
Ol

£f
*
o


i«» a.
M
' Bi
^f9
. O
o-«
O






,


^
' ^H
«
3
*
"3
i
|
c
2
•
5
§
e
^
1 1
e • c
h. U
e o
W W

o e
e e
w w
e e
o a
o o

S 3
ti it
to ea
t ^.^ ^ ^
i i • '£-
4


0)' >
•u
• e
^
e
w
U
u
^
en
u
o

3
• •'
41
"3
V
§
V
"S
"O
e
*i
tj~
u,
„ *
J

w
s
coeffic
•
o
^J
,<^
*J
$•>
*o
tl
V-
^^
o

-


^_








Ol
f
w
1
I/I
w
>
.^

ca
^" -^
e o
^•o
, c
C 01
o >
"* * "^^
15 «s
k.
** e
e a
o w
e a.
0 U
U O
*" ™
£ 01
^rt
<*T ^»
3
^ W
£ "w

^y ^^
9 O



-------
3.  The water chemistry of the aquatic system (1n addition to the water
    concentration of the compound of interest) can alter the empirically
    predicted partition coefficient,  for example, 1f the compound
    Interacts Dy surface adsorption or Ion exchange, then solution
    properties such as pH. Ionic strength, and temperature will affect
    uptake on solids (Hollander et al. 1980).  Even with nydrophoblc
    compounds the presence of other dissolved organic matter has been
    shown to reduce sorptlon by river and sewage participate matter
    (Hassett and Anderson 1982).

4.  Properties of tne sediments other than-their organic caroon content
    m«y Influence sorptlon.  HlraUural et al. (1979) found a good
    correlation between partition coefficients of PCS and the specific
    surface area of adsorbing marine partlculates'.  Of .course, the
    concentration of adsorbing sol ids has been observed to affect
    partition coefficients for organlcs as well as metals (O'Connor and
    Connolly 1980).

5.  finally, the question of kinetics and hysteresis arises in all
    adsorptlon-desorptlon problems.   Karlckhoff (1980) has Found that the
    kinetics of approach to equilibrium 1n sediment suspensions (either
    during adsorption or desorption)'could be characterized Dy a rapid
    component and a much slower component that may require days or weeks
    to reach equilibrium.  A possibly related problem has been observed
    by OIToro and Horzempa (1982)  and OIToro et al. (1982). 1n that the
    lack of reversibility In PC8 adsorptlon-desorptlon reactions could be
    described by Invoking a two-component formulation.  Adsorption and
    desorptlon are assumed completely reversible for the first component.
    following Equation 3.13.   Adsorption  1s  assumed Irreversible for the
    second component.   OIToro et al. (1983)  presents the mathematical '
    formulation for this  model  and shows  the results of further
    laboratory ,,'sfing.   The practical  difference between modeling with
    the classical  reversible'expression and  modeling with the
    reversible-Irreversible expression  Is that the former may predict
    higher dissolved concentrations, particularly in dynamic models.
                                 66

-------
3.3  TRANSFORMATION PROCESSES
    transformation processes are those in which the toxicant is
essentially irreversibly destroyed, modified, or eliminated from the
system.  In most cases these processes apply only to organic compounds.

    First-order decay coefficients for Individual processes are additive;
together they form an aggregate degradation co fHdent:

       Kd "  *B *  *H *  S *  *V                                  (3>15)
where  Kd • Aggregate degradation coefficient {'/day)
       K. • Biolysis coefficient (I/day)
       KH . Hydrolysis coefficient (I/day)
       Kp . Photolysis coefficient (I/day)
       Ry • Volatilization coefficient (I/day)

Some models also distinguish non-biological oxidation, KQ. separately.
although this process Is not Important for most organic*.

    In models with simple first-order kinetic structures (such as
N1CHRIV). the analyst either enters the aggregate Ktf or enter: the
individual  process coefficients K-. KH> JCp, and Ky.   In models
with more elaborate kinetic routines (such as CXANS). each ind'  dual
first-order coefficient (K.. etc.) Is Internally calculated as j
function of several other parameters wftlch the analyst must* enter.

3.3.1  aiodegradatlon

    Biological transformations (biolysis) are enzyme mediate* reac.lons
usually performed during metabolic activity, primarily by bacteria and
fyngl.  The catalyzed transformations Include oxidation, reduction, and
hydrolysis.  The rates of biologically mediated transformations c. .1 be
very rapid 1n comparison to chemical transformations that lack'enzymatic
catalysts.   It 1s precisely because oP these accelerated rat*; that the
blograf *.iou of organic contaminants Is often the most Important
transformation loss process in aquatic ecosystems.
                                67

-------
    Conceptually. blodegradatlon should not be thought of as a single
step process.  Rather, It,.is a multi-step process where intermediate
products may accumulate.  The total conversion of organic substances to
inorganic products, including carbon dioxide, is termed mineralization.
The process termed blodegradatlon. however, often involves only the
partial metabolism of an organic.  For Instance, detoxification of a
contaminant may involve only the transformation to an innocuous
Intermediate compound.

Process Oescr1pt1on

    When heterotrophlc microbes degrade organic compounds, energy and
carbon are frequently obtained for growth, thereby accomplishing
metabolism.  Occasionally a compound may be biologically transformed
without the responsible microbes acquiring growth requirements.
Typically, this process of cometabolism will proceed at relatively slower
rates and will not Impact the activity of the .decomposer community.

    Frequently; when an organic contaminant is first introduced to an
aquatic community an acclimation period is observed when the mlcrooial
community must adapt Itself to the chemical.  This acclimation per'od .'.*
most often termed a lag phase.  The lag phase Is marked by enzyme
induction, selected population Increases, and a progressive increase \n
the rate of observed blodegradatlon.  Once the microbes have become
acclimated to an organic pollutant, the rate of specific decay becomes of
Interest.

    There are three primary factors that determine the extent and rate of
biological decay of an organic In a natural system.  These are:   1) the
properties of the organic contaminant. Including Us structure.
concentration, and history within Us environment; 2) the characteristics
of the acting mlcroblal community, such as community diversity, size and
general health; and 3) th*e status of the -r--'ironment In terms oF
temperature, the presence of additional o.panics or supporting growth
                                66

-------
 requirements  and.  especially,  the  dissolved  oxygen  status:

    witnln  the  framework  of a  model designed  to describe  the  fate  of  an
 organic contaminant,  U  1s necessary to  formulate and define  a kinetic
 expression  of blodegradatlon.  This task  Is made difficult because  of  the
 great complexity of  factors Inherent 1n a natural system.  Contemporary
 fate models have simplified this task by  representing the loss or decay
 of an organic by first-order kinetics or  1n  some cases  second-order
 kinetics.

    First-order kinetic representation In the WlA model 1s described as:
       £   . -k,C                                                   (3.16)

 where  k? • First-order olodegradatlon rate constant (time' }
       C  • Concentration of an organic contaminant (mass/volume)

    The concentration. C, susceptible to decay may be the dissolved
 fraction.   Therefore, if a contaminant partitions onto  solids. trie
 respective participate and dissolved fractions must be Quantified,  r.its
 expression describes  the  loss  of an organic due to biological activity
 and Is analogous to expressions commonly used for the decay of BOO.
 Larson (1981). among  others, has shown that first-order kinetics
 represent the decay of organic* reasonably well at bacteria
 concentrations, evident In many environmental situations.

    In many respects  representation of second-order kinetics  1s a
 simplification of a modified Honod expression (Paris et al. 1981).
 represented as:                                           .
                                                                   (3.17)
where  B  • Magnitude of bacteria (count or blomass /volume) and
       kj • Second-order blodegradatlon" rate constant
            (volume/org;* sfl.Mme).

-------
The decay of a contaminant  is seen as not only a function of Us
concentration, as 1s the case In First-order kinetics, but also a function
of tne bacteria population.  However, bacteria count has not always proven a
reliable indicator of bacteria activity, especially in regard to » specific
contaminant organic.  This development  In blodegradatlon process
representation has been offered by many models (e.g.. EXAMS) as a way of
increasing the application of a single, contaminant-specific decay rate to a
wide variety of environments.  Since blodegradation ts recognUed as being
Influenced by ambient temperature, process representation can include a
function relating the blodegradatlon rate to the temperature regime.  The
analyst nay derive a temperature specific rate by making use of an Arrhenius
function such as:

       KT .  K2Q e                                         (3.18)

where  K,  • Temperature specific biolysis rate.
       *?. * Cxpected rate at 20* centigrade,
       T   > Characteristic temperature, and
       e   * Temperature correction factor.

Theta (d) Is frequently between 1.04 and 1.095.

Sate Selection
    In nearly every circumstance tne selection of an appropriate decay
rate is constrained by incomplete Information regarding the contaminant
or the system of Interest.  However, there are a number of approaches and
relevant considerations to guide prudent selection of a representative
decay rate.   Inherent In this selection process is the realization that
no rate is applicable to all conditions for a specific contaminant.
Instead a range of estimates will more  likely emerge that will  impart to
the analysis a range of output.  The Importance of this range may be
established by a sensitivity analysis, whereby variability in model
output 1s compared to incremental changes In the decay rate over tt,«
range of expected values.

                               70

-------
     A  11st  of  relevant  considerations  or  approaches  to  estimating a decay
 rate 1s  offered  below.   Although  a  ranking of  these  considerations by
 order  of  Importance  could only  be made on a problem  specific basis.
 awareness of an  overall  ranking  is  evident In  the presentation.  •

 A.   Properties of  Contaminant

     A  thorough literature survey  of the properties of the organic
     contaminant  of Interest  should  logically be an Initial step.
     Previously reported  data relating  decay rates for the organic in the
     laboratory or  the field would be an Important step  in defining a
     probable range of decay rates,  for each of the  organic priority
     pollutants flabey et  al. (1982) have estimated the general
     susceptibility to blodegradatlon.  Definition of likely metabolic
     pathways may also be helpful  In several regards.  Aerobic pathways
     will generally be more rapid  and complete  than anaerobic pathways.
     Also, the loss rate of one chemical may not be indicative of changes
     In toiidty,  If Intermediates form that are toxic and possess
    different decay characteristics.  Therefore, the analyst would -ant
     Information regarding the toiidty and blodegradabllIty of  probable
     Intermediates.  In cases where the knowledge of an.organic  1s very
    sparse,  1t may be necessary to compare the structure and physical
    characteristics of the organic of Interest to a host of better known
    contaminants,  when approached 1n great detail,  this procedure ts
    called structure activity analysis.

B.  System Examination

    Information regarding the trophic level  and pollution level  of  the
    target system may assist 1n defining  the  expected decay rate. More
    highly Impacted waters  have demonstrated  shorter  lag phases  and
    greater  decay rates  of  organlcs  {Spain et  al..  1981.  Rodgers  and
    Salisbury  1981).   Previously reported  f'c'd <1ata  may,  to  varying
    degrees, yield Insight  Into the  sp't.al and time  distribution of  the
                                 71

-------
    organic, as well as Important environmental factors (temperature
    regime, volume and flow).  IF available In sufficient Quantity and
    quality, this Information may allow the user.to 'calibrate' a
    b1ode$radat1on decay rate by accounting for other components of a
    mass balance and then solving for the magnitude.of the biolysis term.

C.  Experimental Program

    Laboratory measurement of decay rates may be necessary in evaluating
    decay rates for organic; for which no Information is available.
    Methods for measuring decay rates have been demonstrated for both
    batch (Paris et al. 1981) and continuous cultures or by. use of
    microcosms (Biddings et al. 1979).  In site specific applications it
    is the practice to use the natural waters as the test media.
    Sterilization of, the water before Introduction of the test organic
    serves as a control.  The batch cultures yield a decay rate by
    plotting the log concentration of organic vs.  time, while continuous
    cultures can yield a decay rate via a mass balance approach since
    other sources and sinks can be controlled and thereby Quantified.
    Should a second-order formulation be invoked, relating the rate to
    both the pollutant concentration and the microblal population,  then
    the magnitude of the mlcroblal population must be assessed.

0.  Field Program

    The Waste Load Allocation process may Involve a field program.  To
    assist in the accurate evaluation of a blodegradation rate,  the
    measured parameters should Include both total and soluble
    concentrations of the pollutant of Interest, solids concentration,
    dissolved oxygen, C300, flow, temperature, and basic physical
    dimensions.  Basic  Information regarding the biology of the system  is
    desirable, especially bacteria counts and  Identification of toxic
    conditions which mlg >i IrMuence bacterial activity.  Th. sp'tial and
    temporal scale of sampling will impact  the calibration dtd accuracy

-------
    of the blodegradatlon term, as well as aU other kinetic processes.
    Data upstream and downstream of all major loads are important'.  The
    frequency of sampling should reflect the relative dynamic nature of
    the system 1n terms of hydraulic residence {Mow regime) and the
    major forcing functions (temperature, loading, light, etc.).  Some
    attempt to reflect the seasonal-variation in  forcing functions may be
    especially helpful.

3.3.?  Photolysis
    Some substances that absorb sunlight in the ultraviolet and visible
portion of the spectrum may gain sufficient energy to Initiate a chemical
reaction.  Some of these photochemical reactions  result in the
decomposition or transformation of the substance.  This process.
photolysis, can determine the fate of certain pollutants 1n the aquatic
environment.   Zepp (I960) provides a more complete discussion of tnts
process.

Process Theory

    A sunwary- of the theory Involving the transformation of organics via
photolysis  1s outlined from mils  et al.  (1982).   The basic
characteristics of photolysis are  as follows:

    »  Photolysis Is  an Irreversible decay process activated by  the
       energy of the  sun.
    •  Molecules  which absorb sunlight 1n the  ultraviolet and visible
       portions of  the spec'trum gain sufficient  energy to initiate
       chemical  reactions.
    •  Products of  photochemical decomposition may remain toxic;
       therefore,  decomposition  does not  necessarily  Imply  detoxification
       of the environment.
    •  The  photolysis  rate  d'.-pends  on several chemical  and  environmental
       facto.s.
                                   73

-------
     Tht chemical and  environmental  factors controlling the rate are as
 follows:

 A.   Absorption Spectrum of  the Pollutant:

     The probability of a photon being absorbed varies with wavelength of
 light  In a manner unique to every chemical species.  To change a
 molecule's structure, the absorbed  photon must be sufficiently energetic;
 generally, radiation  with wavelengths in the visible or ultavlolet range.
 or shorter, has sufficient  energy.  Consequently, the pollutant's
 visible/ultraviolet absorption spectrum Is most  Important.

 B.   Solar Radiation:

     Radiant energy from the sun depends on the composition of the
 atmosphere (cloud cover) and geographic location.

 C.   Light Attenuation:

     Light Intensity reduces with depth In water  column, due to reflection
 (< 10% reduction plus slight change In the spectrum) and absorption and
 scattering.  Absorption Is  determined by Lambert's Law:
                 XI                                                (3.19)

where  I > Irradlance
       1C • Diffuse light attenuation coefficient, given by:

       K  -  aO » Sb                  .                             (3.20)

where  a • Absorption tern
       0 • Radiance distribution function
         » Backward scattering of light
                                74

-------
    The value of K {diffuse light attenuation function) depends on
variations In amounts and types of partlculates and dissolved substances,
I.e., suspended solids, chlorophyll 4, dissolved carbon.  The value of 0.
which represents variable light path lengths, U 1.2 where scattering 1$
Ignored.  Average value for natural waters is 1.6 as reported by Miner
and Zepp (1979).

    An empirical relationship developed by Burns et al. (1981) enao'es
the attenuation coefficient to be estimated based on system status:
             (Aw * *ach1a * Adoc°OC* AssSS)
                                                        (3.21)
where R    • Diffuse .light.attenuation coefficient
      Y
      Adoc
      Ass
      Ciila
      DOC
      SS
• Absorptivity of water, (m* }
« Absorptivity of chlorophyll a pigment,  (mg/t]" (m" )
• Absorptivity Of dissolved organic carson,  (mg/l)"  (iT
                                             -1    .\
• Absorptivity of suspended sediments,  (fig/i)    (u  ,
. Concentration of Chtorophyll-a pigment,  (mg/l)
. Concentration of dissolved organic carbon, (mg/l) and
M Concentration of suspended sol ids, (mg/l)
Mills et al. (1982) tabulate tne values of A^. A^. A^, and ASJ
for different wavelengtr'S.

0.  Quantum yield:

    Not every absorbed photon induces a chemical reaction.  The fraction
of adsorbed photons resulting 1n the desired reaction is termed quantum
yield, +.

              moles of given species formed or destroyed
                       moles of photon absorbed
                                75

-------
    environmental factors affecting quantum yield Include:

    a)  molecular oxygtn -- as a quenching agent.
    b)  suspended solids — change reactivity of compounds adsorbed
        (usually negligible).      •              •   •
    c)  chemical spedatlon -- photolysis rates may vary with pH,
        especially Important when pxa 1s 7 ^ 2.
    d)  temperature effect -- until further research 1s completed this is
        assumed to be negligible.

    Type of photochemical reaction affects quantum yield.  Quantum yields
vary over several orders of magnitude depending on the nature of the
                                                    #• "
molecule which absorbs light and the nature of the reactions U
undergoes.  Two major classes of photochemical reactions of interest In
the aquatic environment are 'direct' and "sensitized"  photolysis.
    Direct photolysis occurs when the reacting molecule directly
light,  various reactions, can occur:  fragmentation.' reduction,
oxidation, hydrolysis, acid-base reaction, addition, substitution,
IsomeM ration, polymerization.  Quantum yield data obtained from
experimentation can assist the HLA analyst In determining whether or not
to Include direct photolysis in the analysis.

    Sensitized (Indirect) photodegradatlon occurs when a Hgnt-aosootng
molecule transfers Its excess energy to an acceptor molecule causing t."»e
acceptor to react as 1f It had absorbed the radiant energy directly.
Natural humlc acids (and synthetic organic compounds) can mediate
reactions, for example.

Bate estimation

    Photolysis follows a psuedo-Mrst-order reaction:
                                                                 (3-22)
                                76

-------
                            •K. *'K
                             0    S
where  X-  • Rate constant
        p
       X, • Direct photolysis rate, and
        y
       s
                                     >
            Sensitized photolysis rate.
    One .practical means of obtaining the appropriate photolysis rate is
to use experimental data from literature and extrapolate to the specific
site 1n question.  There are two methods reviewed below for using
environmental data to calculate the expected photolysis rate.

    One method Involves extrapolating near surface rate data to a
specific site (Mills et al. 1982):
                                       !-• Z
              do
J--2-
 •o  °o
                                                                   (3.23)
where
                                                  .1.
       do
            a Direct photolysis  rate .constant  (day*  ),
            a Near surface rate  constant (measured)  (day'
            « Total  solar radiation  (langTeys-day"1),
            • Total  solar radiation  under conditions
              at which K.  was measured (langleys«day"
                        90
                                                       ).
      D     • Radiance distribution function,
      0.    • Radiance distribution near surface (approximate
       o
                 value .1.2),
      K(X*) * Light attenuation coefficient calculated from Equation
              3.20 for x«, the wavelength (nm) of maximum light
              adsorption, and
      Z     * Depth of water 1n meters.
    The second method Involves evaluating the rate constant Integrals.
If certain data are available for a substance (i.e.. absorption spectrum
«(x) or a (X). and the quantum yields, », or 0,), It is
         »                              o     $
possible to estimate the photolysis rate for a specific site from the
following (Hills et al. 1982):
            2.3  •  J
                          0 •.!« • K'  1  - e
                                            -K Z
                                                  (3.23)
                                          K • Z
                               77

-------
where   1  • wavelength  interval Index,
       U  • Photon Irradiance near surface (photons cm"  sec nm" ).
      w  a u • &\
       J  . Conversion  factor . 1.43 x 10*   (mole cm  sec l"
           day"1)
       c  • Base 10 molar extinction coefficient {lmol~  cm"  of toxicant}.
     •    - Disappearance Quantum yield,
     K    • Diffuse light attenuation near surface (rn~ ).
     Z    • Nixed water depth (m)  and,
     •    • Base e absorption coefficient of the sensltlcer (mg~  cm).

for toxicants for which photolysis may be significant, Nabey et al.
       provides data on absorption spectrum and quantum yield.
3.3.3  Hydrolysis

    Certain organic compounds may be chemically transformed by direct
reaction with water.  This occurrence In an aquatic system is termed
hydrolysis.  A hydrolysis reaction may either be acid, neutral or base
dependent.  Essentially, this means that the concentration of hydrogen
and hydroxide Ions, and therefore pH, is often an Important factor in
assessing the rate of a hydrolysis reaction.

    Products of hydrolysis may be either more or less toxic than the
original compound.  For this reason one should be aware of the probable
products of transformation processes.  In addition, transformation via
hydrolysis will Hkely alter other characteristics of the chemical
Including Us susceptabllUy to other transformation processes.

Process Representation

    In a natural system hydrolysis may be either microbially mediated or
be abiotic and dependent only upon the status of the water.  Mlcroblal
Influence 1s covered In Section 3.3.1; consequently. only direct, abiotic
hydrolysis wl * bt examined here.
                                   78

-------
     Abiotic  hydrolysis  Is  normally  represented by a  first order  reaction
 which  In  Us most  simplified  form Is:
             - XMC                                                 (3.25)
       dt       H
where  C • Concentration of an organic (Mass/volume) and,
      JCU • Specific first-order hydrolysis rate constant (Time"
       n
    In the scientific literature KH 1s typically represented as:
       KH .  kn » ka [H*] » kb [OH-]                           (3.26)

where  k     -Neutral hydrolysis rate constant (Time  ),
                                                                  11
       k     • The acid catalyzed hydrolysis rate constant (Molar"  Time" ).
                                                                  li"
       k&    • The base catalyzed hydrolysis rate constant (Molar   Time  ).-
       [H*]  « Molar concentration of hydrogen ions and.
       [OH*] • Molar concentration of hydroxide Ions.
    This representation conveys .the strong pH dependence often observed
1n hydrolysis reactions and 1s a convenient method of-representing
detailed laboratory results.

    The adsorption of an organic onto sol Ids often removes t.ie
partlculate fraction from hydrolysis reactions.  Therefore, the
hydrolysis rates In Equation 3.25 and 3.26 are only applied to the
soluble fraction of the toxicant.  If the model being employed does not
discern between dissolved and participate phases,  then the observed
partitioning should be used In adjusting the magnitude of the rate
constant.

Bate Selection                                                            '

    A great deal of data has been reported 1n the chemical literature
regarding the observed hydrolysis of chtmlMls in distilled water.
Natural waters, however, contain  organlcs and metals which may catalyze
and accelerate hydrolysis.  Consequently, the querj which consistently

                               79

-------
emerges Is, how applicable art distilled water rates to
conditions?  Research designed to answer this question has been reported
within the last several years (e.g., Zeop et al.  1975).  The approach has
been to use field samples and to remove as many competing processes as
possible.  For example, dark conditions were used to eliminate photolysis
and ultra-filtration to remove the biological community, thereby
eliminating biolysis.

    Specific hydrolysis coefficients for many organic* or classes of
compounds are reported in the professional, governmental, and industrial
publications.  Recent sources Include Wolfe (1980). Nabey and Mill
(1979), and Nabey et al. (1982).  These coefficients should give the user
a range of values from which to calibrate the model or to guide a
sensitivity analysis.  Wolfe (1980) also reviewed a technique based on
linear free energy relationships (LFER) for estimating hydrolysis rate
coefficients when experimental values are not available.  When there 's a
paucity of reported values for a chemical of interest, other .measures may
be taken to estimate a rate.  The general format would be similar to that
presented for the biolysis rate constant in Section 3.3.1.

    Lastly. In translating literature values into computer model 'nout.
It should be noted that some values are reported as second-order
coefficients because they are a function of either tne hydrogen or
hydroxide 1on concentration {as represented In Equation 3.26).  In using
first-order kinetic models the analyst must translate these second-order
values Into pseudo-first-order rate coefficients by multiplying by the
appropriate 1on concentration.

3.3.4  volatilization

    Volatilization, loss of toxicants from the water column to the
atmosphere. Is customarily treated as an Irreversible decay process.
because of Us mathematical similarities to these processes.  Actually.
                                80

-------
 however. 1t Is- a reversible transfer or environmental  partitioning .
 process,, In which the concentrations in air and water  shift .toward
 equilibrium.  The volatilization rate depends  on the properties  of tne
 chemical as well as the characteristics of the water body and possibly
 the atmosphere.   The chemical  properties favoring volatl 11 ration are high
 vapor pressure,  high dlffusWUy,  and low solubility.   The  environmental
 conditions  favoring volatilization are high surf ace- to- volume ratio and
 turbulence.

     The partitioning of pollutant  between water and  air  1s  deserlaed in
 terms of an air/water partition coefficient.  H :
                                                                    (3.27)
where     H       • Henry's  law constant (dimenslonless, mass/vol. basis)
          C (eq)  • Gas phase concentration .at equilibrium  (mg/t), and
          CJeq)  • Dissolved aqueous concentration at equilibrium (mg/l)
    The value of H  can be determined by measuring C  and C^ In an
                .  c         •                        g      d
equilibrated system.  More commonly, however, it 1s calculated from the
toxicant vaoor pressure (equivalent to the gaseous concentration in
equilibrium with the pure.toxicant phase) and solubility (aqueous
concentration in equilibrium with the pure toxicant phase):

       He . 16.04  PM/TS                                         (3.28)
where  P • Vapor pressure (torr).
       M * Molecular weight (g/mole).
       T * Temperature (K*). and
       S - Solubility (mg/l).
                                 SI

-------
     It should be noted that H  may be reported In an assortment of
units or nonequlvalent dimension less bases.  One useful conversion is:
       Hc (dlmenjlonless) - Hc (atm - m/moU)/RT                  (3.29)
where  R • 8.206 x 10   atm - m /*K - mote.  For the organic priority
pollutants the values for P. M, and S 1n Equation 3.28 are provided by
Callahan et al. (1979). and the values of. H_ provided directly by Mabey
et al. (1982).  For other substances data may be available in Mills et
al. (1982), Perry and Chllton (1973), and Mackay et al. (1982}.  if vapor
pressure data are not available, Nackay et al. (1982) suggests the
following equation for estimating P (torr) for hydrocarbon* or
ha togenated .hydrocarbons with boiling point greater than 100'C:

    in (P/760) . - (4.4 » in T8). x (1.803 (T8/T - 1) - 0.803 tn (T8/T)
                 - 6.8 (TH/T - 1)
where:
     T * Anotent temperature {*)
    F| • floHtng point (K)
    TN • Melting point (K)
If the melting point TM Is less than the ambient temperature 7. than
                      ff
the third term Is eliminated.

    The net rate of transfer (mg/i • day) from water to air 1s governed
by the difference between (a) the gross transfer from water to air,
proportional to the actual dissolved concentration C.. and (b) the
gross transfer from air to water, proportional to the air concentration

V
       Hate . KV 
-------
     The tern C /H  Is the water concentration  which would  be  in  -
               g  c
 equilibrium with  (saturated  with respect  to)  the  local  air
 concentration.  Unlike common  gases  like  oxygen,  the  environmental
 concentrations  of toxicants, C^ and  CVHC,  typically  vary  over many
 orders  of  magnitude.   Consequently.  1t  Is usually  the case that  either
 (a)  C0  «  C-/HC,  and  the  net Input  from the atmosphere  is  a
 constant  load,  essentially Independent  of the  modeled C..  or  (b)  C,
                                                        a          a
 » C-/H .  and  the volatilization rate 1s  essentially  independent  of
 the  air concentration:

        Rate . XyCd                ,.                                (3.31)
 Most computer models  Incorporate Equation 3.31  rather than Equation 3.30.

     The rate coefficient  iCy (I/day)  Is  related  to  the mass transfer
 coefficient {or velocity), ky  (m/'day) by:

        Ky  . ky/H                                                   (3.32)
 where H is  the water  depth (the  Inverse of  the  surface  to  volume  ratio).

    The "two film* theory is generally applied  to  the calculation of the
 mass transfer coefficient.  This  theory envisions  diffusion resistances
 in a liquid surface flln  and a  gas surface  film as controlling the mass
 transfer (Canale  and  Weber 1972;  llss a/ifl Slater 1974; Mills et al.
 1982).  Reciprocals of mass transfer coefficients  are used to represent
 these resistances:

      _L_   .       1      „       1                             (3.33)

    Overall       Liquid  flln      6as flln
  resistance      resistance      resistance
where    k& • liquid  film transfer coefficient  (m/day),  and
         k  • gas film transfer coefficient (m/day).

-------
    It 1s useful to discern three basic cases.  («) When k  «
H k . then ky In Equation 3.33 Is essentially equal to kfc
(liquid phase controlled); (b) when ^ » Hck .  then ky 1s
essentially equal to H k  (gas phase controlled); and (c) when k&
and H k  are of the same magnitude, then both contribute
     c 9
significantly to k^,
    As the chemical-to-chemical variability of H( tends to be greater
than the site-to-site variability of k& and k .  the value of the
H  tends to be more Important than the environmental conditions In
determining whether the liquid or gas phase resistance controls the
volatilization rate.

Sa. s Phase Resist a nc e

    The movement of air causes a mixing of the air surface film which
results In an Increase in k .  Because the evaporation of water 1$
controlled by k  , and because this process has considerable engineering
Importance, data are available relating k  (for water vapor) to the
ambient windspeed.  Such data are presented by O'Connor (1980) and
HydroQual (1982).  fly Including theoretical effects of dlffuslvlty and
viscosity, they arrive at an expression applicable to any substance:

       kg - 0.001 (Dg/wg)*)-*.7 W                                      (3.34)
where  0  • D1ffus1v1ty of substance In air (cm /sec).
        9                                         2
       v  • Kinematic viscosity of air  (.0.15 cm /sec), and
        U • Wind speed U/T).

    As the expression Is dlmtnslonaUy correct, consistent units will
result 1n k  having the same units as W.  Average windspeeds tend to be
1n the neighborhood of 5 ra/sec.  Although transient periods of no wind
are common In many localities, such periods are not long.  Consequently,
use of a steady  state condition of little or  no wind in Equation 3.34 (or
i.3S) may not produce a realistic result.
                                   84

-------
    mils  et  al.  (1982), using a similar type of data and analysis as
 O'Connor (1980) and HydroQual (1982), suggest the general relationship:

       k,  . 170 (18/*)1/4«        •                                 (3.35)
where  W is 1n m/sec.
    Molecular weight. «. enters the expression because of Us
relationship to 
-------
                                                                  Revised
                                                                   10/85
Mills notes, however. that 1n field studies using radioactive tracers
(Rathbun and Tal 1981), such relationships were difficult to discern.
Rather, the volatilization rate could be adequately predicted by:

       kt{ toxicant) • 0.655 kt(02)                                 (3.39)

Nabey et al. (1982), using a more complicated procedure relating 0&
to molar volume, has calculated the toxicant/oxygen transfer rate ratios
for all volatile priority pollutants.

    In any case, the difficult step in this approach is not to obtain the
above ratio, but rather to predict the oxygen transfer coefficient,
k^O,), correctly.  This coefficient 1$ a function of water
turbulence, which may be generated either by water flow or by wind.

    In free flowing rivers, water turbulence Ts generated by the flow,
and numerous formulas are available for calculating k^Oj) (I.e..
J4K (0,)) from hydraulic parameters such as velocity, depth, and
$Too«.  Wilson and Madeod (1974) and Rathbun (1977) review many of  the
reatratlon formulas which have been proposed over  the  last three
decades.  One example of such a formula 1s that of O'Connor:
       kt,  • (Ot u/H)0-5                                            (3.405

where  u  1s stream  velocity and units  for parameters on both sides of the
equation are chosen  to be consistent.  Ot  for 02  Is 1.81 x 10'
(i2 /day.  The equation can be used  to  directly calculate kt( toxicant) 1f
BI( toxicant) can be  estimated.

     In Impounded waters  and other  slow moving water bodies, water
turbulence may be  gene-4ted by wind.  O'Connor  (1980) and HydroQual
(1982)  summarize data /elating k%  to  windspeed. u.  These dat
suggest  a  relationship:
        kt •  0.17  C0 (Oi/n)0'6  U                                   (3.41)
                                86

-------
where   C.  • Drag  coefficient (unities;), and
        •   • Kinematic viscosity of water {•0.0100 cm /sec).

    The units of  all other parameters must be chosen to be computable.
C. also appears to vary with wlndspeed. W, but nay maintain a value
around 0.001 for  u less than 10 m/day.  As with using Equations 3.34 and
3.35. sustained periods of little or no wind are not common; *.(02)
values substantially less than about O.S m/day are not usually expected.
Table 3.3  Illustrates parameters needed 'to determine a wind controlled
volatilization rate for two toxicants.

    Hydrosdence  (1971} and EPA (1976) present data and a nomograph for
estimating k (Q_)  for a variety of hydraulic conditions.  Their
data suggest that k.(0j) would not be expected to be much less than
about 0.6 m/day nor much more than about 12 m/day, except under unusually
stagnant or turbulent conditions.

Identlrylnfl. thf__Jmeortant Parameters

    Equation 3.33 can be examined In light of the observed relationships
of k. and it  versus wlndspeed. and the reasonable range of k.
    I      3                                                l
suggested by Hydrosdence (197:) and EPA (1976).  Some simplifications of
the two film analysis are thereby indicated.

    If H   Is less  than about 3 x 10"4. then the gas phase coefficient will
control the overall transfer coefficient k  In all aquatic environments, even
standing waters.  This is because H k  will increase much more slowly than
k^ as a function of wlndspeed.  [n this case, the analyst need not consider
the turbulence of  the water body at all.  Furthermore,  surface transfer will be
slow for substances of this type,  and the rate will decrease as HC decreases.
Benzo(a)pyrene, dleldrln, and pentachlorophenol are examples of compounds In
this das*.
                                87

-------
                                                       10/85
TABLE 3.3  VOLATILIZATION PARAMETER VALUES
Parameter
o
ot

y!
^
w
p
cs
M
T
H
Olffn$1vlty of PCS In Air
Olffuslvlty of .PCB In Water
Kinematic Viscosity of Air
Kinematic Viscosity of Water
Drag Coefficient
wind Speed
vapor Pressure
Saturation Concentration
Molecular weight
Temperature
Henry's Law Constant
c«2/sec
cm^/see
c»2/$ec
cnr/sec
-
N/sec
m Hg
«g/£
g/nol
K
.
Aroclor
0.
5.
0.
0.
0.
s.
4.
0.
261
289
0.
04652
387xlO*6
15
or
001
0
06*10-4
35


OU7S
Aroclor
0
4
0
0
0
5
4
0
372
289
0
.03673
.253x10
.15
.01
.001
.0

-6




.06ilO"5
.027


.0309




                     37.3

-------
     If H   1s greater than about 3 x 10, then the liquid phase
coefficient k^ will, control, tht overall transfer coefficient k^
under nearly all commons, even when the water 1s very turbulent
(k1(0j) -12 m/day) and the air calm (W « 2 m/sec).  For H( In
this range the analyst need not consider the air phase parameters.  It 1s
also Important to note that among substances with a high H . the
volatilization rate 1s Independent of H ; rather, It Is dependent on
the substance's water*dlffuslvlty.  As dlffuslvHIes vary relatively
little among most toxicants, the volatilization rates of all highly
volatile toxicants are nearly Identical.  Examples of such compounds are
vinyl chloride and trl- and tetrachloroethylene.

    If HC falls between about 3 x 10~4 and 3 x TO*1,  there can be
some environmental conditions under which resistance in both the liquid
and the gas phase controls tht rate of volatilization.   Nevertheless.
under other environmental  conditions only one phase may still  control  the
overall rate.   For example,  under conditions of moderate turbulence
(k.(0.) - 2 m/day) and wind (U • S m/sec).  the liquid phase solely
                          '                   2
controls for any H  greater .than about 2 x 10  .
                                     80

-------
                               SECTION 4.0
                      GUIDANCE FOR MODEL APPLICATION

4.)  APPROACH TO WASTE LOAD ALLOCATION PROBLEM
    Within the pollutant-by-pollutant modeling context considered by this
document, the basic question confronting the waste load allocation
analyst 1s, 'How much of a specific substance can be a Mowed to be
discharged 1n areceiving water, vetnot violate the numerical water
quality, standard?'  This section of the guidance document provides some
principles and direction to answering this question.  The intent here is
not to provide a standard method to be followed verbatim.  The various
models and example application are provided as guides to be used to gain
Insight Into the process.  Modeling results, as depicted in Figure 4.1
should be used by decision makers In conjunction with water quality
standards to develop waste discharge permit limitations.  This process
assumes that the decision makers (e.g.. state water quality boards or
administrators) have the the desire and legal means to allow use of trie
assimilative capacity of water systems.  The models may assist 1n
                          i
choosing some optimal mix of treatment, production modification, standard
modification, and time schedule for Implementation.  It .1s important tnat
the analyst be Involved In this process and communicate modeling results
Including estimates of accuracy and uncertainty to others involved.

    furthermore, it is desirable to get the affected parties Involved in
the process early In order to Identify the most Important Issues.  By
obtaining agreement on the approach for defining and evaluating the water
quality problem, the regulatory agency, the dischargers, and any
Interested citizen groups may be able to work with In a cooperative rather
than adversarial context.  Considering the level of uncertainty inherent
1n estimating allowable ambient concentrations,  allowable recurrence
Intervals,  and allowable effluent loadings, such agreement may be helpful
for successfully completing the endeavor.

-------
                          WO STANOAROS OEVELflP^ENT

                          IOEMT1PY POTENTIAL USES ANO
                         PQSSIILE LEVELS OF PROTECTION.
                         OIVELOP ALTERNATIVE CRITERIA.
             WASTCTATER TtCHMOLOSY gVAlUATIQM
             lOEimrr A^itcASLE TECHNOLOGICAL
               COMTROU ANO ASSOCIATED COSTS.
                   WOtHKB

         PREDICT AMSICMT CONCENTRATIONS
           RESULTING FROM ALTERNATIVE
                EFFLUENT LOADINGS.
                    WEIGHING ALTERNATIVES

         CON90ER ItNEFITS Of f ARTICULAR USES ANO LEVELS
          Of PROTECTION. CONSIDER COSTS FOR ATTAINMENT.
                       DECISION MAKING

               DESIGNATE USE. LEVEL OP PROTECTION.
              ANO ASSOCIATED CRITERION. ASSIGN THE
               CORRESPONDING PERMIT LIMITATION.
4.1  WASTE LOAD ALLOCATION PROCESS
                90

-------
     An Important Issue confronting  ULA analysts  and  managers  concerns  tht
 amount of efFort needed to make a  sound,  scientifically  credible
 analysis.  The appropriate level of effort  depends partially  on the
 complexity of  the environmental  problem.  Single discharger,  single
 toxicant, and  relatively uncomplicated river  problems  can  be  expected  to
 require less analysis  effort  than multiple  discharge,  multiple toxicant.
 and  hydrologlcally complex problems.   Nevertheless,  the  appropriate level
 of effort depends  on other factors-as  well:   such as  the expectations  of
 the  decision makers, affected dischargers,  and other  parties.  These
 expectations may be related to  their, previous ULA experiences, to  the
 anticipated costs  and  the potential  benefits, and to  the resources
 available.  The  appropriate level of effort depends  heavily on the
 consequences of  a  wrong decision.

     Thus.  1t 1s  not desirable for this  document  to attempt to specify
 from afar  a particular  level  of effort  as appropriate  to a particular
 environmental  problem.   Rather, Section 2 has suggested  a  range of
 analytical approaches.-  Furthermore, the discussion  tnat Follows suggests
 a phased  procedure for efficiently  approaching whatever  type of analysis
 Is finally selected.
     Phase  1;   Dilution  Calculation
     A dilution formula  calculation  (Section 2,2}  determines the
 concentration at the point  of discharge, before any fate processes  can
 act  to remove or destroy  the  pollutant.  The Inputs required are the
 effluent flow and  concentration and  the upstream  flow and concentration.
 The effluent data  should  be available  from the permit application.
 Upstream flow may  be available from U565 or previous pollutant reports
 for the area, or they may be  estimated from the drainage area.  Upstream
concentration may  be available from STQRET or other water quality
 records; In many cases, the upstream toxicant load may be nearly zero
compared to. the effluent  load.  .
                                  91

-------
    Stream concentration? can be provided for flows and  loads associated
with a particular design event or for any number of events having various
return frequencies (as described by DlToro 1982).  This analysis for the
point of discharge, however, provides no Information on the downstream
concentration, the area-of Impact, or the fate of the pollutant.  These
factors may need consideration primarily If (a) the load must be
allocated among two or more dischargers spread out along the reach (in
order to assess the degree of depuration occurring between dischargers).
or If (b) the environmental benefits must be assessed (since they depend
on the size of the Impact area}.  Less probable reasons for pursuing fate
modeling are 1f (c) sensitive downstream reaches require special
protections, or (d) the pollutant produces hazardous degradation products.

    For many permits, there may be little reason to proceed beyond tne
dilution calculation.
    Phase 2:  DownstreamEstimates
    The purpose of Phase 2 would usually be to estimate .the saat'a!
extent of the problem or. for multiple discharges, to estimate the
addltlvlty of loads.  Beyond Phase I. this entails predicting the
downstream behavior of pollutants using a fate and transport model (suc.i
as described In Sections 2.5 and 2.6).  The model's Input parameter:
would be estimated from whatever data are already available on the
hydraulic and water quality characteristics of the reach, together with
published Information on the chemical characteristics of the pollutants.
The model could then be used to estimate concentrations throughout the
reach, for various control alternatives under various environmental
conditions.

    Computer data bases, such as STORET. 1FO, Reach File. CHEM FATE.
ISHOW, and Individual state Information systems, allow rapid retrieval of
some types of Information needed to apply the model.  Table 4.1
           the contents of these data bases and how to obtain access.
                                 92

-------
« i
w •«-

3 —
              k •«

              aS
                           u
                           41
                                         « —

                                         •* 4)
                                                               w •»
                                                *• «
                                                U BE
                                                41
                                                               a
                                            •* 41
                                            u OB
                                            41
                                                                       a <•
Ml


7
t/>
*   a
«ri   U
IS*   41

X


*   S
41   3
                           4t •

                           3^—
           S
               «• 4>  • 41
               ^ Ol Ml **
              *• k O
               £ *
              iS|
               e 3 0.43
               — *W ft

               323!
               < IM 9 M
  e
M»   Ml'
c* .g1
*—^
** ». >

So
u *•
                                 §,
                                                 u •
                                             CM O • W
                                         _.g^.s
              O c o   a <•
                —   e u 4i
              4»   >«•»•  W
              — a o —.- —

              2 3   •»•  


                                      e

                                    ^i
                                    Ml  •
                                    « e
                                                 s-a
                                    5*2
                                      W t
                                    a:?
                                                               > — e
                                                                        4*
                                                                        
                                          *• •••
                                          V *»
                                                          —
                                                               iE
                                             « w
                                             Ml •—


                                               Ml

                                             O »

                                             1s

-------
     «>
     u

        <• u

        "
•o
•
W


 §
        e *
        3. >
        o «
        •to **
        e e
    u  •
        £8
        is
     k
si
2t

                k
                a

              >  • Ol
      *   i  •

      ?Iri


      — **•«§
      wa e 8 **
      ^ OJS. «J
      •C U U TO
  O 141





O U «V
      8

      i
                       4= •
                        U OE

                        £•0

                       -S =
                        «* u

                        il
                        w 2
                '  •

                ££•
                 o
                       flj
                         JB i
                         u —

                         2!
i
                                          1!
                               a
                                ex
                                M*  IA
                                3 f 
                               il
                               o w
                                       • e
                                       U *»
                                       «• u
                                  a •£
                                  o«si
                                                  w u
                                          Is

                                          o —"
                                          W Ol U
                                          *» e e

                                          "•"a 2


                                          ssl
                                             i
                                             o
                                             u
                                             «l
                                             u
                                             a
                                            CM
        •s
        ^
                                                            o
                                                            •g
                                                            X
                                                            «
                                                    u
                                                    01
                                                      u
                                                    u —
                                                  o s e
                                                       >i
                                                       to
                                                            «*

-------
     This  phase,  relying  on  existing  information, does  not  undertake  the
 collection  of  new field  data.  While  published  Information on  the
 chemical  characteristics of many  toxicants  Is. .often  reasonably  sound,  the
 site-specific  environmental data  are  often  sparse.   In particular,
 bed/water exchange parameters, partition coefficients,  some pollutant
 degradation  parameters,  and even  the  channel depth and  velocity may  be
 uncertain.   Depending*on potential environmental benefits  and  treatment
 costs estimated  (using the  model) to  hinge  on the WLA,  It  may  be
 desirable to Implement a monitoring program designed specifically to
 calibrate and  verify the model, thus  proceeding to Phase 3.  A
 sensitivity analysis of  model parameters can be used to identify the key
 uncertainties.
     Phase 3;   Monitoring and Model Validation
  •   When modeling results Indicate that the WlA decision Is  sensitive  to
 poorly defined or understood parameters, then more Intensive data
 collection may be warranted.  Unlike  the Phase 2 gathering  of  existing
 Information, the  Phase 3 monitoring program would be designed and
 Implemented  for  the specific purpose  of relating the receiving water
 response to the  pollutant Input,  through calibrating and verifying the
 model.

     Such monitoring of rivers Is most effective If performed as Intensive
 surveys.  Their  success  requires careful design and substantial
 resources.  Survey programs can vary  greatly 1n magnitude,  from single
 •plug flow" surveys, such as Illustrated by the December 1981 Flint River
 survey (described In Section 5} ranging to  large regional  programs, such
as the Delaware Estuary  study (Thomann 1972).

    The resources needed for a single Intensive survey a^e determined
 largely by the size and complexity of the system.   As the normal
variability of the environment and effluent can be considerable, a
fragmentary survey fay often produce data that a:* lT"os$lble to
                                  95

-------
reconcile satisfactorily with modeling results.  Consequently, If
undertaken at all. an Intensive, survey should be tailored to the needs of
the Model, and designed to be Insensitive to temporary aberrations in the
system, as will be discussed further,

    Having demonstrated accord between the node! predictions and field .
observations for on* or two or more conditions, the Phase 3 model can be
used'to forecast entirely new conditions with somewhat greater confidence
than-the Phase 2 model.

4.2  DATA NEEDS
    Site-specific calibration of a toxic substance model for a Phase 3
analysis requires (a) waste load and boundary condition data,
(b) environmental and chemical data for process rate estimation, and
(c) calibration and verification data.  The amount of data needed to be
collected In time and space depends on the particular site, the
variability of the system, the accuracy desired, and the resources
available.  The desire here 1s to suggest a realistic, achievable data
collection plan.                        .

    The reader Is referred to Book II. Stream* and givers. Chapter 1;
Biochemical Oxygen Demand/Dissolved Oxygen and Aimronla TotUUx. Section
4, for a thorough discussion of general problem definition and data
requirements for stream models.  The toxic substance problem should be
considered as a special case of stream modeling, building upon a
historical base of conventional monitoring and research.

    In the following discussion. It 1s assumed that the WLA analyst has
defined the problem, reviewed historical data, made preliminary modeling
calculations, presented the Initial findings to management, and developed
a consensus to proceed with the collection of additional field data.  It
is also assumed that the ULA analyst can direct or at least recommend a
monitoring plan and that he or she has visited the site and obtained a
•feel" for the situation.
                                 96

-------
4.2.1  Obtaining »odel Input Data
    TtbIt 4.2 suinnarUes typical data needs for setting up and
calibrating a toxicant model.  Not all Items are applicable to all
pollutants.  Generally, channel data are needed for all types of
pollutants; 1n addition, velocity and depth ordinarily have significant
flow dependencies which must be ascertained.  Effluent and boundary
concentrations and flows are likewise needed for all pollutants.
Sediment related data (partition coefficients, settling and ^suspension
velocities, and bed characteristics) are needed for pollutants which
readily adsorb to partlculates.  Degradation rate data are needed for
organic pollutants, depending on which processes (hydrolysis, photolysis,
etc.) are applicable to the particular compound.  References like
Catlahan et al. (1978). Habcy et al. (1982), and the CHEM FATE data base
can be consulted to determine what processes are Important for particular
chemicals and to provide selected non-site-speclflc coefficients or
data.  Once the Initial estimates are made, adjustments may be necessary
during model calibration.

    Site-specific environmental parameters can be obtained or Inferred
from direct measurements over the appropriate time period.  The time
frame selected would be determined by considering:
    1. Residence time of the pollutant In the system.
    2. Time variability of the system.
    3. Time and frequency qualification to the water quality standard or
       criteria.
    4. The expected critical time period -
          a. low /low with little dilution.
          b. high flow, with nonpolnt loadings and sediment resuspenslon.
          c. periods critical to fish survival.
    S. Production and treatment schedules and cycles.
                                  97

-------












A
£J
X
<
2
i
a
?














Ml
C
e

|
•J
CMrkt/Ou
at







i
«•*)
I






^
*
5







*
at
3
3
VI
8
4^
G
• «tf
o

to
2
a
i
|
^
o





,
i 1
1 1
41 4K

S
.
•it
•
^ *
M§ Jjjpl
Ml
* 3
O
I ••
81
** 4to
«• ^
is
«* •*
w
to. o
5-5
* .2
a a
I1
X w
u
^
»






£
w
e
*
8

•
1
W ,
e
jj»
fll

e
"5
w
Si
S

0 6
!• <*
»
• *•
II

*•
*



<•
b.
S
^»
S
Ml
e
u
*
t M»
"w t»
*• VI
1?
w w
8
1
w
1
Mi
»»•
4
1 ,
f M
k. «"
A*
• ^
3 *• 5
*£ i
m «• w

«
-





4b*
I
:
*
I


t



I

w
^
e
*• 4S
w ••*
b* ^^
« *
• €t
W W
a 3
M> M
11

*
r




•
u
<•• 1*
o e
1 I
0 1










* *
C* "e
!9
t 8
a a
M> M
ii

w w
w w
^
<»y
JT



X Ml
W V
li !

«*•«*«
<• ** VI «
* C— 0,
W « M •»
«• W O 3
w> e N> t/i
cv o
a u
5
w e a
«• O "
3?i?
S 3 «| «
5 o' e —
Bt ** W  €1 W
031.
= §C^*
b 0 > •«
o — ^ •«
•» a «> >»
** T« £ ^ *>. *
5 Ji=^-
O *« «» O v *»
to -• — 3 -<
W >» >»
*• •< -o — -o
IK S5
_33-ii «M
O«w 3 S
M> e
r 1
'e jj ^
^> • ^*
o a MI ^
§••- e ^ "«• t*
e o e — «
MI • «• *• v* at
* • V ** (J «-
U ••«*<« '. O
h> to W to Wi **l
3 ** o «* a u K*
a e t— e v* m *
I/I « « > Ob «|
3 w •« v -o e
•M a c 4 e<« ^ ^
c— e o 90 e _u
— t*. W -» -< 
-------
              3
              5
                        I
l> w» •
** • A
«* J« <•

TS **
e vt e



e o w
o  o
                                                  "^ ^
                                                 *• 3
                                                  *_
                                                 5i 5
                                                    >
           ,
           o   w *
                                                  w *  •
                                                    3 c
                                   5S2-5    c   S   3   5
                                   £.«-   -  - i.-   .

                                   .^  .5  2
                                                    >, ••   «*

v» 3 O ^   <•   >«    O  ••
i^ «« o. <«   W   O  » *.  —I
                                              •n .          •"•
                                              <«   n       «v

                                             I   !       1
                                                                                 i

                                                                                 a
                                                                                 Ml
                                                                                 <«
                                                                     •
                                                                   >.**  •
                                                                                                W   O
                                                                                                «^  ^^

                                                                                                i   «
                                                                                                S  +t
                                                                            ••
                                                                   • Q e   «.   *•
                        1   it   *
                         W   *•**   •
                         g >»«« ut   3>
                         e *•   —
                        w — «• a   9
                           •*•«     «
                         wt O«- •   —
                        ^ b. •— ••   •»•
                        2 o o —   ••
                        MO ^ V) VI   **
                         e         «
                        VI         VI

           e    e
           41    <•
                        ••   ""^

                            ^
                                                          vi  -en
§
                                                                       «.-      3  *•
                                         to
                   •- C vi * w   «•
                   — <•   OK -a   «•

                   £ W «l » "w   OC
                     &o -w a a
                       C •> VI   «•
                   W  • «l w     ••
                   O to Q. <« k.   «•


                   iiiv^i   ?

-------
^v^
I
M
Jtf

i
                   3
—  ** A  s
<•  ^ <•  •»
9$    «fc
H»  A ««
a    «  o
                      IO «>   U
                      u «•   u
                        «   •*
                   V  w 1
                                         o
                                         o
                           ..

                           S  S
             if
        ^•*



        *  I
        ^  .«*
        <—  <«
                   o  e
                   MO
                   •5  e *•
                   •c  « o .
                   •  w »-
                              ^  i
                «j

-------
    Whenever possible, .point source surveys should be scheduled for
seasons when the sy?t«n is likely to be most stable, unless specifically
designed to evaluate tine variability.
4t2.2  Calibration and Verification:  Comparing Prediction with Observation
    Calibration refers to the procedure of adjusting the Input parameters
until the output predictions (e.g., dissolved and total toxicant profile
and suspended solids profile) reasonably match the observed
concentrations.  In multi-parameter models such as described in Sections
?.S and 2.6, numerous different combinations of Input values may allow a
fit between predictions and observations.  -Consequently, before
attempting to fit the data. It Is customary to fix the values of as many
parameters as possible, based on direct measurements.  It may then be
feasible to adjust the values of a small number of parameters, within the
range of uncertainty for those parameters. In order to match the
observations.  '

    Verification generally, refers.to comparing predictions with
observations for a second Independent survey or time period.  In
practical WLA contexts (In contrast to some academic or research
contexts), the distinction between calibration and verification may
become'hazy; the Initial calibration may be modified or compromised such
that the model can reasonably match both surveys.  It 1s considered best
If.a single set of decay, partition,'and sediment exchange coefficients
fit both (or all) surveys adequately; however, 1t may be the case Chat
some.coefficients may need to vary between surveys, as Illustrated In the
Flint River study.  If this Is the case, then it 1s essential that the
values vary In consistent, reasonable, readily justifiable ways.

    Ideally, then, the WLA monitoring program would include at least two
Independent surveys.  One survey might be more Intensive because of the
requirements for calibration.  This survey may cover a longer time
period,  perhaps *»v *al days.  It.may Include some master station to
                                 101

-------
discern diurnal variations, particularly for thost organic compounds
wnlch photolyze readily,-and for sites where waste flows comprise a large
fraction of the river  flow.  Station locations depend an the- sources.
tributaries, and stream characteristics.  At a minimum, there should*be
one station,to define  boundary concentrations upstream from  the first
point source, one station just downstream of the mixing zone, and at
least one some distance (travel time) downstream,.reflecting the effect
of the loss processes.  The final plan would reflect the complexity of
the system and the resources available.
         ..        •                              •             ^ ,   -
    A second survey night be less Intensive, covering a shorter time
period or perhaps employing a 'plug flow* or 'slug sampling" survey
Strategy.  -This strategy. Illustrated In the December 1981 Flint River
survey. Involves sampling the point sources and river according to the
passage of a plug of flow marked by a dye tracer.  Although this method
entails considerable coordination 1n the field, fewer samples are
required to be analyzed and, as a result, it Is less costly.  This method
also has the advantage of filtering out many variations, which Is ideal
for steady state models.  Resource estimates for survey options are
discussed further In Section 4.4.

    Many WLA studies have not used two or more surveys for support.
Obtaining complete.and unambiguous data is more Important than performing
a particular number of surveys.  Faced with a situation where resources
are sufficient for only a single good comprehensive survey, the analyst
may be better off with Implementing the one survey than with splitting
the resources between  two abbreviated or fragmented surveys.
                                                                V
    While the supporting site-specific data are a key element of any HLA
analysis, the ability  of the model to curve fit a verification data set
is hardly the only measure of adequacy.   Its consonance with aggregate
modeling experience, the overall reasonableness of Us Input v^ues. and
the general understanding demonstrated by the analyst are at \east as
.mportant.
                                 102

-------
     Hodyl  Accuracy
     The  question will  undoubtedly arise concerning  the accuracy of  the
model.   Without any calibration  or verification data, the question  for
any  site-specific situation may  never be answered satisfactorily,   with
or without water quality data, however, the appropriateness  of the  model
Input values  (and possibly the model formulation) may always be
questioned.
     /                    <                 V
     Some Indication of  predictive reliability can be obtained by
sensitivity analysis:   varying,  one at a time, the  key model parameters,
such as  partition and decay coefficients, over a reasonable  range.  Such
an analysts shows the sensitivity of the results to errors m estimating
model parameters.  For a more thorough evaluation,  all key model
parameters can be varied at the  same time using either of two
approaches:   (a) Monte Carlo simulation and (b) first-order variance
propagation.  Both techniques require specifying a  probability
distribute of values for each Input parameter of the model. In the Monte
Carlo simulation, parameter values are selected randomly from the
specified distributions, and the model run over and over again, each time
with a different set of parameter values.  The model output at each
station can then be described .by a frequency distribution.  In
first-order variance propagation, the variance In the output distribution
                                  -v
Is calculated directly from variances of the input distributions.   Surges
and Lettenmaler (1975) Illustrate application of both techniques to
500-00 models; Scavla et al. (1981)  Illustrate their application to
eutrophlcatlon models.  Chapra and Reckhow (1983)  provide a more detailed
description of these techniques.

    For comparing model predictions  with field observations, several
measures  of model  accuracy have been suggested by  Thomann (1982).   These
include regression analysis  of observed  and predicted values, relative
error,  t-test comparison of  means, and root mean square error.   An
analysis  of observed and predicted value:  ror  the  calibration/
                                   103

-------
verification runs of IS dissolved oxygen models indicated an overall
median relative error of 10%.  Median relative error of Individual models
ranged fron a few percent to about 60 percent.  For a eutrophlcatlon
model of Lake Ontario with complex.kinetics and fine spatial scales,
median relative error over a 10-year simulation period for S variables
was 22 to 32%.  Relative error Is defined as
where c Is the relative error, x Is the average observed concentration
at each station, and c 1s the computed average concentration.  This
statistic. It should be noted, behaves poorly for small i. and tends to
weight overpredlctlon more heavily than underpredlctlon.
    Typical accuracies of toxicant model applications have not been
evaluated.  Because the aggregate experience with toxicant modeling is
less extensive than with dissolved oxygen modeling, and because typical
levels of almost any toxicant vary over a far wider range than do  levels
of dissolved oxygen, toxicant models may not always attain the accuracy
of dissolved oxygen applications.  However, as the effect levels for
toxicants are so much more uncertain than effect levels for oxygen
depression, the need for very high accuracy seems less pressing.
Nevertheless, In the Hint River case study, the calibration/verification
accuracy seemed quite satisfactory by conventional wiA yardsticks.
    Predictive accuracy of either conventional or toxic pollutant models
can be expected to be less for a new survey for which the model has not
been calibrated.  This Is particularly true for an event with conditions
outside the range of those for which the model was calibrated.  Thus.
predictive accuracy for conventional design events (extreme drought flows
coupled with hypothetical Improvements 1n effluent quality) may be
somewhat less than the calibration/verification accuracy.  In particular,
It may oe difficult to estimate to what degree lower stream flow and
Improved effluent '..-a" ty will affect-parameters such as the settling and
                                104

-------
 resuspenslon velocities (flow and particle-size dependent) or partition
 coefficient (also part1cU-slze dependent).  Such model adjustments must
 be based on analyst Judgment.
    In concluding this section it must be noted that an adequate
 discussion of approaches for evaluating model accuracy and uncertainty 1s
 beyond the scope of this volume (apparently along with the other volumes
 of tnls Manual, thus far).  Chapra and Reckhow (1983). however, provide a
 more thorough treatment of the subject.
    In actual UUk practice, the analysis of model uncertainty is seldom
 quantitative.  It Is most common to compare observation and prediction
 graphically, declare the model 'validated,* and proceed to apply the
 model for determining the allowable waste load.  Although a sensitivity
 analysis may be performed on some of the Input parameters, the results
 are unlikely to Influence the decision-making process.  Where the WLA is
 being done within an adversarial context. It is perhaps understandable
 that the analyst may not consider It helpful to spotlight the
 uncertainties.  However, If the model verification is not treated as a
 pass/fall proposition, then quantitative estimates of model uncertainties
 can be more readily Incorporated into the decision-making process.  Once
 a Monte Carlo or first-order variance analysis has been set up for  tfie
 model, pollution control alternatives can be evaluated in terms of  their
 probability of bringing about particular water quality outcomes.  Section
 4.3 further discusses the use of Monte Carlo simulation For this purpose.

 4.2.3  Additional Data
    The data presented 1n Table 4.2 are directly applicable to setting up
 the model.   Some additional parameter measurements may be useful for
 Interpreting results and substantiating the actual existence and cause of
 the reach's use Impairment..  Incremental costs of this work would be
 small, since the major expense for the survey would be for the field crew
and the chemical analyses of toxicants.  The additional measurements
could Include:
                               105

-------
    a) Hardness and alkalinity:  to Interpret toxldty and determine
       metals criteria.
    b) Conductivity:  to confirm transport.
    c) Total organic carbon.
    d) Dissolved oxygen, ammonia, and chlorine residual:  to Interpret
       toxldty and blotlc status.
    e) Qualitative description of sediment bed:  to support estimates of
       bed/water exchange.
    f) Concentration of pollutant In biota:  to Indicate long terra
       exposure.
    Furthermore. H Is preferable to coordinate the chemical sampling
with a biological survey.  As the numerical criteria of water quality
standards are mostly derived from single-species laboratory tests, an
observation that a criterion H violated for a certain time period may
provide no Indication of how the Integrity of the ecosystem 1s being
affected,  tn addition to demonstrating the Impairment of use, a
biological survey, coordinated with a chemlcai survey, can help in
Identifying the culprit pollutants and 1n substantiating the criteria
values.  The resulting data base may also provide Information
transferable to other sites.  For multi-faceted surveys. It may be
advantageous 'to try to coordinate efforts with universities, research
Institutions,  or Industries, especially If they can contribute their own
resources.
4.2.4  Quality As stance
    The WLA analyst should refer to Book II, Chapter 1, Section 4.3, for
a general discussion of quality assurance requirements for waste load
allocation studies.  This discussion will focus on the unique
requirements for toxic substances.

    During the development of the monitoring plan, the UlA analyst shoulj
•*eet with the laboratory director and quality i'ju^ance officer to
                                106

-------
request a Quality issuranct proposal.  The proposal should consider
sample collection, handling, preservation, preparation, and analysis.  Of
particular concern to the UU analyst would be the detection
(quantltatlon) limit for each toxicant.

    Some production laboratories, although very reputable, nay not report
concentrations at levels at or below criteria limits.because doing so
requires additional care and quality control, reduces the productivity in
term of numbers of analyses performed and may require alternate
analytical methods.  water quality managers need to recognize this
possibility and make special concessions for lower productivity during
UU comprehensive surveys.

    Samples to be used for toxic substance analyses require special
collection and handling procedures unlike those for conventional
parameters.  Depending on the specific chemical, precautions should be
taken ta prevent sample contamination from collection devices and
containers.  This is not a trivial concern.

    Samples that will be filtered for partlculate and dissolved fractions
should be delivered to the field laboratory for filtration within the
shortest period possible (one or two hours maximum For metals samples) or
filtered and preserved on site.  For unstable chemicals, samples should
be preserved using prescribed methods.

    Key to the entire effort Is proper sample logging, recording of
results. Input of Information Into a computerized data based such as
STORET, and verification and correction of data 1n the data base.*

4.3  FORECASTING
    The purpose for developing a site-specific model  ts to forecast the
environmental  consequences of pollution abatement alternatives.
Environmental  goals for a stream reach are. of course, embodied in the
'*
-------
quality needed to protect the designated uses my bt sped fled as
numerical criteria,, which Indicate acceptable chemical concentrations (if
known).  Criteria are generally derived fron laboratory tests in which
particular species are exposed continuously to a toxicant.  As the tested
concentrations do not vary over time. 1t 1s not obvious precisely how
they should be related to ambient concentrations, which often vary
considerably over time.  It Is not clear how often the criteria can be
violated without Impairing the use.

    In actual practice, lacking a firm technical basis for specifying a
target frequency of attainment, WLA analyses have often Incorporated the
convention of designing for the criteria to be met during the 7-day, once
In 10-year (7Q10) low flow.  This assumes that upstream dilution has a
dominant Influence on water quality, a premise which Is correct for many
water courses and pollutants, but not true for all.  Indeed, several
other time-variable parameters may also affect the modeling results;  for
example, temperature affects most degradatlve processes, pH affects add
and basic hydrolysis, wind velocity affects volatilization in sluggish
waters, solar radiation and turbidity affect photolysis, and suspended
solids affect partitioning.  In BOO and ammonia UUs, the other key
parameters, usually temperature, upstream concentrations,  and pH,  have
been specified by various procedures; depending on the procedure used,
the values may either frequently or seldomly be expected to accompany th«
7Q10 low flow.

    In Judging pollution control alternatives within such a framework,
the measure of effectiveness generally applied Is the change 1n
concentration during the single rare tvent.  Other measures are not
easily applied because the conventional procedure generally obscures both
the expected frequency of violation and the overall toxicant exposure
level, due to:
    a.. The use of a single rare event.
                                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 decision-maker may realize
what level of protection the design condition Is providing.  Indeed, they
may not even realize that 7Q10 design conditions provide different levels
of protection In different streams.  For example. In a large river the
upstream dilution flow may be less than or equal to the 7Q10 only 1% of
the time, but In many small streams It may be at a zero flow 7Q10 for a
substantial percentage of the time.

    An alternative framewo'rk for model forecasting has been proposed by
Freedman and Canale (1983).  They suggest a conceptually simple Monte
Carlo technique which can account for both the time-variability and the  •
uncertainty 1n alt model parameters:  (a) environmental conditions, (b)
effluent quality, (c) rate coefficients, and (d) water quality criteria
values.  By generating a probability distribution of water quality
outcomes for each pollution control alternative, the framework can
provide a more realistic comparison of their likely effectiveness.

    The analyst begins by describing the probability distribution for
each of the key model Input parameters.  Statistical evaluation of the
historical data can define the variability of parameters such as flow.
upstream concentrations, effluent loads, PH. and temperature (using
dally,-weekly, monthly, or any other averaging periods).  Published data
and analyst judgment can suggest the uncertainty of parameters such as
decay and partition coefficients.  The distributions can be defined 1n
terms of standard statistical functions such as normal, log normal,
gamma,  or uniform distributions, or they can be numerically defined in
terms of the probability of exhibiting discrete values.  Correlations
between parameters may need to be taken Into account.
                                  109

-------
    A  Monte  Carlo  simulation  can  then  be  performed  by  randomly  selecting
model  Input  values  from  the assigned distributions.  By  tallying  the
water  qua-Hty predictions  resulting from  each  set of randomly selected
Inputs,  the  overall distribution .of resulting  water quality  1$
generated.   A simple  Illustration of applying  this  procedure to a few of
the Input parameters  for a model  of a  stream with two  dischargers 1$
shown  1n Figure 4.2.
    Some other methods can also provide probability distributions of
water  quality, accounting  for time variability but  not necessarily
parameter uncertainty.  A  computationally  simple technique has been
suggested by OIToro (1982).  Using log normal  distributions  for flow,
loading; and other environmental  parameters. It generates a  log normal
distribution of concentration Immediately  below the outfall;  The method
was Intended for dilution calculations, not downstream fate predictions.

    Perhaps  the most  straight-forward means of addressing time
variability  Is to apply a continuous simulation model  such as HSPF or
SEBATRA.  A  several year sequence of flow, temperature,  loading, and
other  Input  Is used to generate a time sequence of water quality, whlcn
nay be summarized Into a frequency plot or possibly evaluated in other
more toz1co1og1cally  relevant ways.  While dally records for flow are
usually readily available, time sequences  for  other model inputs nay be
more difficult to construct.

    Compared with the deterministic analysts of a single rare event,
probabilistic and continuous simulation techniques provide a broader
perspective over the  entire water quality  response.   In comparing
different control options, the measure of  effectiveness can be the
probability of exceeding the criteria, or  It can even be the frequency
coupled with the severity of violation (as Illustrated by the shaded area
exceeding the criteria in Figure 4.2).
                               no

-------
    Is
                        Ss
                                     ail
                                     II
                                     WI _
                                     52
                                     £s
                         I
«
a

-------
     In situations  where rules  require that the  WLA  be designed  for  a
 particular  flow,  suet) as the 7Q1Q,  the Monte Carlo  technlaue can  be
 applied to  all  Incut  parameters  except flow.  Control alternatives  can
 then be evaluated  In  terns  of  probable outcomes for that  particular flow
 event,   In  situations where the  analyst wishes  to construct  a single
 event corresponding  to a particular recurrence  Interval.  Book VI  of the
 guidance Manual  (USEPA 1984) describes a method for selecting flow,
 temperature,  and  pH.   The method does not consider  all variable inputs
 and  may be  restricted to single  discharger situations.

     With the  7Q10  (or similar) design convention, a level  of protection
 decision Is made automatically,  grounded more on past precedent than on
 technical rationale.   Its level  of  protection,  however, nay  vary  from
 site to site  somewhat haphazardly,  unrelated  to use attainment.   If such
 a  conventional design condition  1s  not used,' the level of  protection may
.become  a technical Question; that 1s,  it must be determined  what
 frequency (or other measure) of  standards  attainment will  protect
 particular  uses.

     For protection of human health,  the decision can often be based on
 readily available  Information.    Many health  criteria are  based on  long
 tern (possibly life-time) average exposures,  tf the long  term  mean
 concentration were appropriate for  the criterion, and If  probabilistic or
 continuous  simulation approaches were not used,  specifying a design
 condition that produces  the mean  concentration  is still not  necessarily  a
 trivial  task.  For example. It 1s the harmonic  (not arithmetic) mean flow
 that produces the arithmetic mean concentration below a single  discharger
 (because concentration Is proportional  to the Inverse of dilution flow).

     For the protection of aquatic life the allowable exeeedance frequency
 1s a particularly difficult technical  question.  As the criteria  are  .
 based on laboratory tests with constant rather  than time-variable
 concentrations, and because mobility  for many sp'-les  1s  less constrained
                                  112

-------
 in. the  field  than  in  the  laboratory,  relatively  lUtle technical data can
 bt brought  to bear,an  the question.   In the past the question of
 exceedance  frequency  has probably not  received the attention  1t
 deserved.   It should be recognized that the uncertainty  in .the entire
 waste Uad  allocation  analysis 1s a combination of the uncertainty  In the
 target  concentration,  the uncertainty  in.the target attainment frequency.
 and the uncertainty in the model predictions.

 4.4  RESOURCE REQUIREMENTS
    In  this section, estimates are presented for conducting a water
 quality analysis for a hypothetical river.. The estimates are based on
 the experience of  the  EPA Large Lakes Research Station at Grosse He.
 Michigan. In  developing and applying a toxic substance model  to heavy
 metals  In the flint River and PCS surveys and model development for
 Saglnaw Bay.  Lake .Huron.

    The estimates  apply to setting up a model comparable to MICHRIV,
 using two Intensive surveys for calibration/verification.  The following
 presents the  assumptions for which the costs were estimated:
    1  Two ma)or discharges.
    2. 50-m11e river reach.
    3. Three metals and three organic compounds.
    4. Sampling points at bridges.
    S. Organic subttances readily photolyze according to literature.
    6. All capital equipment such as laboratory, field, and computer
       equipment is installed and available.
The estimates apply to an experienced ULA analyst,  office support, and
the laboratory and field personnel.   The estimates  exclude standards
promulgation, permit negotiation, management, and o-erhead.
                                  113

-------
    The  resource  estimates art  summarized  In  Tables  4.3 and 4.4.   rt  is
obvious  that  the  most costly component  U  for the chemical analyses for
surveys, particularly the synoptic-type approach for Survey No.  1.  These
costs could vary  widely depending on unit  costs, analytical procedures.
quality  assurance, etc.  The cost for organic analyses assumes that high
resolution capillary column GC's are used.  Metals are assumed to  be
analyzed using graphite furnace atomic adsorption.

    There may be  Instances where the system Is extremely complex,  with
nonpolnt sources, complicated hydraulics, multiple and Intermittent
discharges, and multiple pollutants that would warrant surveys over a
year's time frame Including event sampling covering a range of
conditions.   In these cases. If the costs of  the surveys are compared to
the potential cost of remedial controls, they  should be minor.   In many
situations, the regulatory agency may suggest  or require that the
permittee assist with the collection of the necessary data.

    In summary, a waste load allocation project may vary from very simple
to very complex.  The resources estimates presented herein consider a
typical problem setting.  In the final analysis, the use of surveys and
models depends on the site and chemical-specific problem.
                                  114

-------
ft
H-
S3
I1
Is
i3
    • w
   *in
   «•- 5 3 ej
ft

I
3
           «   I
    OOOOOO O   00  0000
    o « o o o ^  x>
     d   oo
op e o
nt d d «M
                 o   o o p o
         o o o
                   f!
         41  .:
               I  Ji,.* ~

                 i Hi J}  i
                 * lil ! ! 5
                   s ss-,s « f
                   ~liSii8
                    1  e S fr ""
                   i|2£S ££
                                 in

-------
I*
~g|

I'


i!
a J
if

11
II
itMirt*

MM-flauer
       I
    !r-
    Ill
   ssis
         - 8
              118
           1  §  IS
        I ...
                  I
                          1 "*
                          5 * -
                          * S-
                         u3|S

                         P

                         !*U
                         I s If

                         Ssii
                         15i s
                         :i«i
                         II  !
                          »-e
         i*3S£!t°
                         3m *O ••» J*





                     ™ J  ^ ^» T

-------
   «n
o o
£8
 o
                                                s
                                                            (V        O*
                 rt ^
                                       M        e w            *••»«•»«• w    *
                                       e        o •            »*•
                 a   «*   w   «§««w«»   «
              I

             o»
              **e»   «    K» — •• e  i —   >w « — « —   x
   « e    *•    w^       •—  i 01 1» ••• -•   **
w a      e    • •—   w»    o jt •»•>•* ^
•> •*> b    •>   **A   M   •• w — *••»<»   l/l
> ^ I    •   •« C   «    o •» c o e —   -<
— «, —    ••    « <«   w   JKQ a * «B  a   <
at M >-    v   t/i ui   e   c^**(^i« — »   ^

-------
       «V>   CM
 i
4
e



»
             •A   ift
       a  a
|

to
     s
     I
55-.-. £
srlscls
«  s
n

                      s
                    ii
                    H
                    £5

                    c

-------
                               SECTION 5.0
               CASE STUDY:  MODELING HEAVY METALS TRANSPORT
                            IN THE FLINT RIVER
S.I  INTRODUCTION
    Tlit Flint R1v*r project, discussed 1n this section, was undertaken as
a demonstration study for the development of procedures that can be used
In regulating point source discharges of priority pollutants.  The
results of the one year study have served as a technical basis for the
preparation of this document.  Specifically, the field data aided
considerably In the development of the NICHRIV model.  This section
contains the results of application of the MICHRlV model to the Flint
River survey of zinc, cadmium, and copper.  The emphasis will be on the
calibration and, to a certain extent, the field testing of the model wUh
the Flint data set.  The project also serves as an example of data
acquisition methods for the application of Che model to a HLA problem.

    Section 5.2 describes the study reach of the Flint River.  Sections
5.3, 5.4, and S.5 describe the application of the model to the August,
1981, the December, 1981. and the March, 1982, survey data.

5.2  DESCRIPTION OF FLINT RIVER STUDY SITE

    The Flint River, located In Southeastern Michigan, 1s a major
tributary to the Saglnaw River, a major tributary to Saglnaw Bay.  The
Saglnaw watershed had been Identified as one of several national priorUy
sites.   The Flint River Is also considered a high priority site for
development of toxicant WLA procedure? by the Michigan Department of
Natural Resouroes (MONR).

    The Flint River watershed occupies 3",500 square kilometers (Figure
5.2.1)  and contains significant agricultural and uiban development.  The
north and south branches of the river join in Lapeer Counts  nd flow In, ,_
                                  119

-------
a
V

-------
southwesterly direction to the City of Flint,  within this reach are two
Impoundments. Holloway Reservoir and Mott Lake, which are used for
recreation and occasionally for low flow augmentation 1n the summer
months.  Downstream from the City of Flint, the river flows northwest
before Joining the Shlawassee River in Saglnaw County.  Municipalities
downstream of Flint include Flushing, Montrose, arid Fosters.

    Because the purpose of this project was to study a river system in
enough detail to develop a metals transport model, and because there were
Insufficient resources to quantify,all sources to the river in the dty
of Flint, the reach selected for the model application was the 60
kilometers from N111 Road (Km 71.9)  to Cresswell Road (Km 11.0).  This
reach, shown In Figure 5.2.2, contains two major point discharges of
metals - Flint wastewater treatment plant (Km 70.7) and Genesee Co. No. 2
(Ragnone) wastewater treatment plant (Km 41.1).  Several tributaries.
also monitored. Join the river along the study reach.

5.3  FLINT RIVER AUGUST SURVEY

    The Flint River August Survey, conducted during August 4.14, 1981.
was intended to develop a quantitative cause-effect relationship between
metals loadings and resulting concentrations during summer, low flow
conditions.  Thirteen river stations, four tributary streams, and Mv«
point source discharges were sampled during the two week survey.  A lut
of the stations, their distance from the river mouth 1n kilometers and
the sampling schedule for each station are presented in Table 5.3.1.  The
August survey 1s an example of a routine monitoring schedule.  Most river
stations were sampled dally; however, four 'master* stations were sampled
at more frequent Intervals as a check on diurnal variations.

    Temperature, dissolved oxygen, pH, alkalinity, and conductivity were
measured In the field.  Samples were also filtered and preserved 1n the
                                  121

-------
U4  'LINT RiVM ITUQV MUCK - H.IHT TO
                                     122

-------
          TABLE 5.3.1  SAMPLING STATIONS FOR AUGUST,  1981 FLINT RIVER
                              HEAVY HETALS SURVEY
Station/Description
Km. Point  , Sampling Schedule
FROOQl/Utah Street river station
FEQOQ1/GH Plant discharge
FR0002/Ha«11 ton Street river station
FR0003/Grand Traverse Street river station
FR0004/Swart* Creek tributary
FRGQOS/OievroItt Street river station
FR0006/N111 Road river station
FEOQ02/F11nt WWTP discharge
FROQQ7 /Linden Road river station
FEOOAI/Fllnt fly. ash pond discharge
FEQOA2 /Flint My ash pond discharge
FROOQS/Elms Road river station
FROOQ9/Na1n Street river station
FROOIO/Ht. Norris Road river station
FROOU /Vienna Road river station
FR0012/Brent Run tributary
F£OOQ3/Ragnone wwTP -discharge
FRQ013/East Surt Road river station
FR0017/P1ne Run tributary
FROOU/SDver Creek tributary ,
FRQ01S/M-13 river station
FROOl6/Cres swell Road river station
83.6
93.4
81.9
79.3
79.1
77.9
71.9
70.7
70.5
70.0
70.0
66.3
61.2
52.6
43. 5
41.6
41.1
32.1
29.7
25. 2
14.9
11.0
Grab - every 2« ^ours
24 hour composite
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
24 hour composite
Grab - every 24 hours
Single grab
Single grab
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
Grab - every 24 hours
24 hour composite
Grab * every 24 hours
Grab - every 24 hours
Grab • every 24 hours
Grab - every 24 hours
Grab - every 24 hours
                                   123

-------
field.  Hardness, suspended  solids and  total and  flltrable  zinc,  cadmium
and cooper were analyzed at  the Grosse  lie Laboratory.  Field  sampling
and analytical work was the  responsibility of Cranbrook Institute of
Science.  The USGS, Lansing  Office participated In the field work and
provided flow and t1me-of-travel Information (Cummlngs and Miller 1981).

5.3.1  August SurveyOata Summary

    During the survey a precipitation event interrupted the steady-state
conditions that existed for  the first four days, of the survey.  The hydro-
graphs from the USSS gaging  stations near Flint and Fosters (figure
5.3.1} Illustrate the event.  The water .quality 1n the river responded
predictably to the event, as Illustrated by the hydrograph and various
tine profiles at Station FRQ8 (Figure 5.3.2).  Suspended solids and
partlculate metals (as reflected In the total metals peaks with no change
In dissolved phase concentrations) peaked In response to the Flow event.
As discussed later this phenomenon represented resuspenslon of sediments
from the river bottom caused by higher shear stress.  Also, dissolved   .
constituents not particularly associated with sediment material were
diluted by the Increased flow.  This process Is Illustrated by the
conductivity and hardness profile.

    Although the event phenomena are quite Interesting, the model applied
                                     /
is steady-state.  Consequently, the modeling described here Is restricted
to the first four days of the August survey.  Observations at each
station will be reported as  four-day means plus or minus one standard
deviation.

    The necessary Input data for the model Include basic hydrologlcal and
morphological Information on the river and loads of suspended solids and
total metals to the system.  Table 5.3.2 is a summary of the flows and
river geometry froa the H111 Road station (FR06) to the Cresswell Road
station (FR16) for the four day steady-state, low-flow period ir, August.
These values have been established primarily from measu'tments made by
US6S (Cummlngs and Miller 1981) during the August survey.
                                  124

-------
    1	I	L
i   i    i    i    i
                                                   i
                                                   •4
                                                   « «.
                                                   3 5

                                                   Is
                                                   MM
                                                   0
                                                   0 g
                         HI

-------
j
I]
5 V
•* • ^^^
;
S
i
88888
I* * 9,1
I \ \\

\ {
i
= J
5 _-"(/
5 - / 1
m
ttt
i •
3?









8
!•






i







V
f
•j "
«
a i
•t *
•L
888889
• • • • • 4
3 I S S !

\
*»
•. .
« •
+ m
5 •%
aE •**
s *
i }
2 *
•


M
9t *^
j •
S
5 .*
&
S -•;
2 2 :


•
•
*
/
— •
•• *
^ ^
S |
S *
•**


I
& f
^k *
s
S
s • :
I i
22!


•
*
*
*
* *
i :
w *
• 5
I '
S «
• 4


1 •.
^ (
9 *,
3B «
,
%
•«
8838 S S


4
#
*
a . ''
a
1
W 1
• 1
1 '••'•
** «1
• •*
8 S 8
ox
»ct
ctz

m
l
• f

I i


0

• B*
*
*
2 •

5 J
888*88888*89
'!!25s'?I2!i*


«
.
4 *
•
i
U m
I 'i
f I 8
S * •


*
•
•*•'."
* «•
^ •
i
* 2 H
ta
tec
«
«
tzz
oez

•u
•1C

-------
         TABU 5.3.2  SEGMENTATION.  FLOWS.  AND GEOMETRY FOR FLINT RIVER
                            DURING AUGUST 4-7, 1981
Segment
  No.
Boundary
•  Km.
Point
Segment
 How
(m3/s)
 Cross-
Sectional
Area
                    Mean
                    Depth
                     (m)
   1
   2
   3
   4
   s
   6
   7
   a
   9
mil Road                   71.9
Flint WUTP                  70.7
Flint fly*ash oonds         70.0
Downstream of Elms Road     65.0
Brent Run                   41.6
Ragnone WWTP                41.1
Upstream of E. Burt Road    36.0
Pine Run                    29.7
Silver Creek                25.2
Cresswell Road              11.0
2.66
4.34
4.38
4.38
4.53
5.22
5.22
5.28
5.36
   14.5
   20.5
   17.8
   15.7
   22. B
   24.5
   19.5
   17.S
   17.8
                        0.45
                        Q.M
                        0.47
                        0.34
                        0.47
                        0.64
                        0.56
                        0.56
                        0.70
                                  127

-------
    For this modal application the river reach from Mill Road to
Cresswe 11 has been divided Into 9 segments.  The segmentation was
primarily governed by location of point sources and tributaries, although
changes In river geometry at so contributed to segment boundary
selections.  The segmentation Is also presented 1n Table 5.3.2. where
flows and geometry are given by segment.

    The upstream boundary conditions and the effluent and tributary loads
for the steady-state period are presented 1n Table 5.3.3.  The two
         fc                                      t
municipal plants represent the major source of metals to the river.  Only
total metal loads art reported, because equilibrium partitioning with
solids 1s assumed.  It should be noted, however, that 1n reality the
metals discharged from the Flint plant were primarily 1n the dissolved
phase while those from the Ragnone plant were  primarily 1n the
participate phase.  This Information will be discussed further In the
model calibration section.

5.3.2  August Survey Model Calibration

    The model calibration was performed In two stages.  First, the
suspended solids simulation was calibrated to the existing data.  This
could be done independently of the metals calibration sine* the solids
submodel  does not depend on metals Interactions or transport.  The second
stage consisted of calibrating the three metal predictions without
altering the suspended solids calibration.

    ilven the Input data presented In the previous section, the only
parameters at one's disposal for calibrating the suspended solids
submodel are suspended solids settling rate (w 1. solids resuspenslon
rate 
-------
               IM  o   «»  «  O  «  a»
               *-  a   o  •»  o  —  <*
               -  -   2
       l»
                «   •    •   •   •   #   «
I-
               o  ^   S —  ^
                •   <    *  •   *
               o  o .  »• o  *»
 . l/t-
  3
v r «•»
oS
^ Ml
ii
-5
 I
"ol^r
1!
                       CM
                                e  a
.
      &
      3S£
               ^  «A  «»
                •   *    •   •
               *  —    o  e
             i      *
             :i  i  £
                             §

-------
     The  typical  river  bottom Ml 11  have  i  water  content  between  60  and  95X
 by weight;  therefore.  1f  the sol Ids  have  a  specific  gravity  of  2.5,  the
 solids concentration 1n  the bed  win  vary from  approximately 50.000  -
 500,000  mg/l  of  bulk sediment.   Based on  some bottom sampling conducted
 during the  August  survey,  a value  of  «2 • 200.000 rag/l  was selected
 for  the  river reach.

     Since the August survey was  during  a  relatively  low flow period, the
 first solids  calibration  attempt was  made assuming the  resuspenslon  rate
 (*rs) was equal  to zero.   Furthermore,  since there was  no reason to
 suspect  that  the settling  rate would  vary along the  river, a single  value
 of w$ was used In all  segments.  It  Is  possible that  the solids
 settling rate would be a  function  of  flow In the river; however, the flow
 differences along the  river were not  considered to be significant  enough
 to Justify  segment-to-segment variation of w$.  The calibration with
 w$ . 0.25 m/d 1s shown In  Figure 5.3.3a.

     The  calibration In Figure 5.3.3a  Is quite good until just downstream
 of the ftagnone treatment plant (about Km  pnt. 35).  From this point
 downstream  It  seems that the model underpredlcts suspended solids.  One
 possible explanation Is that resuspenslon was occurring 1n the  lower
 portion  of  the river.  By  applying a  very small entrapment  rate of 4.0
 g/m -d In segment 7-9 (Xm  36.0  11.0) on  top of the settling rate  of
 0.25 m/d throughout the reach, the calibration shown  In Figure 5.3.3b was
 obtained.   The above entralnment rate corresponds to a  resuspenslon
 velocity of 2.0 x 10"* m/day.

    Justification for applying a resuspenslon factor 1n segment 7-9 comes
 from a review of the experimental work of Lick (Lee et al. 1981; Fukuda
and Lick 1980) and from a  comparison of calculated bottom shear stresses
arnon? segments of the river.  Lee et al.  (1981) 11st five factors  on
which resuspenslon depends:  (1) turbulent shear stress at the
 sediment-water Interface;  (2) water content of bottom sedl'—nts;
                                130

-------
I
                         i
                        1U
41.7
IU
                       i
                      tu
 r         r
tU      41.7


KJMHOtt MflUTM




PI6URI 5JJ * 4  b
            r
          iu
            04
 i
n.0
                               L31

-------
(3) composition  (mineralogy, organic content, sire distribution) of
sediments; (4) activity of benthlc organisms; and (S) vertical
distribution of  sediment properties. I.e.. manner of deposition.

    The effects  of the first two factors are qualitatively understood.
Lee et al. (1981) found that for the western basin of Lake Erie, bottom
sediment resuspenslon rates were directly proportional to shear stress
and water content.  Sediments with a fine-grained (clay size) fraction
deposited at the surface were more easily erodable than vertically
well-mixed sediments with the same composition.  These considerations
suggest that resuspenslon 1n a particular river may be predicted from an
empirical relationship between entrapment rate,and shear stress.

    In the case of the Hint River the best Justification for Increasing
the resuspenslon velocity below Km 36 comes from comparing the bottom
shear stress among various river segments, using Equation 3.6 (Graf
1971).  For the August steady-state conditions, bottom shear stress
values for several segments of the flint River are presented in Table
5.3.4.  Although the absolute values of shear stress are only estimates.
the relative differences should be valid because of the consistent method
of calculation.  Note that the three downstream segments have greater
shear values  than the four upstream segments.   There 1s  typically a
threshold value of shear for a given sediment  condition  above which
entrapment rate Increases rapidly.  It Is possible that for the Flint
system the threshold value Is In the neighborhood of 10  dynes/era2.

    It should be noted,  nevertheless, that resuspenslon  Is not the only
possible explanation for Increasing suspended solids profiles In rivers.
Growth of phytoplankton blomasss can also produce this phenomenon, with
each additional S »g/l chlorophyll-a equivalent to 1 rag/t suspended
solids (Canale 1983).  Unlike resuspenslon, phytoplankton growth should
Increase suspended solids concentrations without Increasing total metals
concentrations,  while both Interpretations seem compatable with the
                                   132

-------
         TABU 5.3.4.  BOTTOM SHEAR STRESS IN SEGMENTS Of FLINT RIVER
                    DURING AUGUST 1961 STEADY.STATE PERIOD
Segment
   1
   3
   4
   6
   7
   8
   9
Segment Boundaries
   (Kn. Points)
   71.9 - 70.7
   70.0 .65.9
   65.0 - 41.6
   41.1 - 36.0
   36.0 - 29.7
   29.7 - 25.2
   25.2 - 11.0
Shear Stress
(dynes/cms)
     3.91
     6.92
     9.87
     4.70
    10.54
    13.4
    12.5
                                  133

-------
August survey data, resuspenslon would be a more viable explanation
during the winter surveys (described later).  For the Flint River the
model's overall results are not parted tarty sensitive to the question,
however.

    Once the suspended solids submodel was calibrated, only the metal
partition coefficients were used to calibrate the metals predictions.
Sediment-water diffusion of dissolved metals was considered to be
Insignificant.

    Calibration of the metals system began with observed partition
coefficients and adjusted these values within reason in order to match
total and dissolved metal profiles.  There are so many Factors that can
affect metals partitioning that Insufficient Information Is available In
this case for a. priori establishment of partition coefficients.  Plots of
the observed partition coefficients for the three metals In question
during the modeling period are presented in Figure 5.3.4.  These
data indicate that the partition coefficient for zinc should fa'l
0.1 and 0.3 l/mg.  There 1s a great deal of variation In observed
cadmium partitioning; this variation, between about 0.05 and 0.4$ l/mg.
Is probably due to dissolved cadmium values being near the detectable
limit.  Finally, copper demonstrated the lowest partitioning with a range
of approximately 0.02 - 0.10 l/mg.

    It 1s worthy of note that for all three metals the Linden Road
samples, which are from a site Just downstream from the Flint STP
discharge, tended to have lower partition coefficients than the
downstream sites.  One possible explanation is that the Flint discharge
contained metals primarily 1n a dissolved (filterable) state and that an
equilibrium partitioning had not been attained In the first few
kilometers downstream.  The metals In the Flint discharge averaged 91X,
84%. and 75% dissolved phase for zinc, cadmium, and copper,
respectively.  As the MICHRIV model does not consider adsorption
                                   134

-------
                1      J
t
           i
                tl.

I   i  I
                       §•

                11.   t  *••
                             181

                      r;
                i  Si I 15513
     |I
                ti.
     I


     r,
 \  *'
 !  1  i
          •ICUNE

-------
I     1     I     I     I
          I      I     J     I     I
                                    •    3    2    S    2
                                         sonos

-------
 kinetics,  the way  to  hand It  this  phenomenon was  to  lower  the
 partition  coefficient for  approximately  four  kilometers downstream of  the
 Flint discharge.

  "  The  results of  the model calibration  for  the three metals  art
 presented  1n Figures  5.3.5-5.3.10.  Table 5.3.5  summarizes  the partition
 coefficients used for calibration.  Recall that  once  the  solids model  had
 been calibrated, metals partitioning was .the  only remaining calibration
 parameter  for the metals.  For all three  metals  the lower partition
 coefficients In segments 2 and 3  downstream of the Flint  discharge*are
 necessary  to simulate the  higher  proportion of dissolved  metals in this
 region.  Also, an Increased partition coefficient for copper downstream
 of the Ragnone discharge was employed In  the  calibration.  This was
 justified  by the observed  data (Figure 5.3.4) as well as  the Fact  that an
 average  of 83X of the Ragnone copper discharge was 1n a participate phase.

    It Is  encouraging to not* that the relative  magnitude of calibration
 partition  coefficients among the  three metals for the August 1981  Flint
 study Is the same as  was found In a Saglnaw Bay  modeling  effort (Dolan
and Blerman 1982).  Even the absolute calibration values were  quite
similar.  The calibration values  for Saglnaw Say were 0.225, Q.US, and
6.OS l/mg for zinc, cadmium, and  copper,  respectively.  The Flint  River
ultimately flows Into  Saglnaw Bay via the Shlawassee and Saglnaw Rivers,

 5.3.3  August Survey  Sensitivity  Analysis

    As Indicated above, the main calibration coefficients for metals In
tiie Flint River are the suspended solids  settling (w ) and resuspenston
(wp$) rates and the partition coefficients for the respective .metals.
A sensitivity analysis on these model parameters would be Instructive  in
determining the accuracy necessary In defining these parameters for a
given model prediction accuracy.   It would also confirm the need for the
respective terms In the model framework.
                                136

-------
     L     I     1
I
i
                     >     i      i
         J     8     S    I    !
1 1 1 » 1 '



H-
-4
^^
i-
^
^._


<
«->



"" <
                                                                          a
                                                                          i
»     i     .
                                                    3HIZS&0

-------
I      I
                                                           I      I
                                                                            •*  o»
                       1           n    i                       I
                        83333333
      ii/**n nnmava

-------
s
I

I
to
  2I  I  3
                             I    <
                                       { •
KM
. ° C
' a.
»— B

k
!-&(
— H

^9-4



1^
^
^
                                          I

                                       -a I
£  8 '2
•  •  . ^
s  i
             urn

-------
f       I      I       )      I
 I
s
s
      I	>       I      I       I	I   •
5532
 1
a
                                                                              i
 I      I
s      s
1     3
                                                   SOI1QS 4$nS

-------
    I       I
s
i
     r
    s
lit!
              I     !
£
»"H
K- «
H-S
I—
•a-^S""1
M
— »
— *
a — i

                                                                                             i
                                                                                         •  a     -
 I


ll/«")
s      s
 1

3

-------
TAILE 5 3.5.  CALIBRATION VAUJES OF PARTITION COEFFICIENTS IN FLINT RIVER
                  DURING AUGUST 1981 STEADY.STATE PERIOD
Segment
1
I
3
4
5
6
7
8
9

Zinc
0.2$
0.10
0.10
0.25
0.2S
0.25
0.25
0.25
0.25
Metal Partition Coefficient ft/ma)
Cadmium
0.20
0.03
0.03
0.20
0.20
0.20
0.20
0.20
0.20

Copoer
O.OS
0.03
0.03
O.OS
0.05
0.09
0.0$
0.09
0.09
                                143

-------
    Results of varying the above model coefficients have been evaluated
la term of percent change of total and dissolved metal concentrations
(and suspended solids when applicable) \n the river for a given percent
change of each parameter  Individually.  Percent changes of both model
coefficients and model output are related to the final calibration run
presented In the previous subsection.  Figure 5.3.11 presents the
predicted response of suspended solids, and total and dissolved zinc at
Km 45 to changes In the solids settling rate (w ).  (One hundred
precent on the x-axis represents the calibration value of w  For the
August survey.)  Suspended sol Ids 1s the most sensitive state variable;
with a value of w  • 0 overpredlctlng the suspended solids
concentration by a factor of 2.  No solids settling would lead to an
overpredlctlon of total zinc by SO percent; the extent of this variation
depends on the partition coefficient.  Dissolved zinc (and other
dissolved metals) are relatively Insensitive to vertical solids flux
rates.

    An example of the model response to the water column partition
coefficient Is presented 1n Figure 5.3.12.  In this case the dissolved
zinc Is very sensitive to the choice of partition coefficient, with the
sensitivity among metals depending upon the relative value of the
calibration partition coefficient.  Total metal levels are relatively
Insensitive to changes tn water column partitioning, unless « Is
drastically underestimated or omitted altogether.

    Since steady-state concentration profiles are not constant in the
longitudinal direction, the percent change of model output depends on the
distance along the x-ax1s over which the coefficient perturbation 1s
applied.  To demonstrate this concept, the sensitivity analysis results
for the August survey are given at four different locations along the
river:  (1) kilometer point 65. 5 km downstream from the Flint discharge;
(2) kilometer point 45. 25 km downstream of Flint; (3) kilometer point
3d. about 5 km downstream from the Ragnon  discharge; and (4) kilometer
point 10. about 30 kilometers downstream uf Ragnone.  For the settling
                                  144

-------
 Model  Response to  Change in Ws
               1       I
            8/4-7/81 at Km. 45
0      50     100    150     200

 Parameter  (%of Calibr.)
           FIGURE S.3. 11

                KS
U S EPA Headquarters Library
  '  Mail Code 3404T
1200 Pennsylvania Avenue^ NW
   Washington DC 20460
     202-566-0556
                          \

-------
CD
c
a

o
CD
c
o
Q.
cn
Q}
cr
  \
  \
   \
h \
    50
   -50
   -100
       Model Response to Change rn TT1
            I
                  1       II

                 8/4-7/81  at Km.45
                         I
                               I
            50
                  100
                  150   200
       Parameter (%of Calibr.)
                FIGURE 5.3.12


                   146

-------
velocity and partition coefficient, the results art presented  in Tables
5.3.6 and 5.3.7.

5.4  FLINT RIVER DECEMBER SURVEY

    Another survey, conducted during the period December 1.4,  1981,
studied metal profiles In the Mver during a relatively high-flow
period.  It was also felt that calibration of models for toxic substances
In rivers under different flow regimes was an essential step in
developing a model that could be applied to WlA problems with  confidence.

    Another benefit derived from the December survey was the
demonstration of data collection' for a steady-state system via the  slug
sampling method.  In this sampling method a finite slug of river water is
sampled periodically as It moves downstream.  Any tributaries  or point
sources contributing materials to the slug are also sampled as the  slug
passes these points.  This approach can provide an efficient (In terms of
number of samples required) way to obtain a steady-state longitudinal
profile of tne river by eliminating much of the confounding influence of
diurnal loading variations.  Conducting the December survey in this  •.
manner provided motel calibration data In a shorter period of  time  ar.d
with many fewer samples than the August data.

    The parameters measured In the December survey were the same as those
In the August survey, with the exception that dissolved oxygen analysis
was omitted.   The USGS once again participated In the field'work;  This
time, 1ft addition to providing discharge measurements, they conducted the
dye dump and monitoring so as to coincide with the water quality
sampling.   By following the dye slug downstream, the sampling crew was
assured of collecting water from the same slug as It passed the various
sampling locations along the study reach.   A list of the sampling
Stations for the December survey Is presented In Table 5.4.1.
                                    147

-------
irt  •

*£
-*»
               t
              X    M
              I o <•?
              — w a

              I <• «fc <•
              I— O >
                               I   I  I  I
                                 «  1  1
                              i  i  i  i
                                                                 *- 0» OCB
                                                i   i  i  i
                                               ^ ft O 
-------
-I «/•»
             X    S

O hM
ۥ> O

              w* e
                 «* o

              5 »
                             •-** rt rt
                              i   i   i   i
                              »•»
i  r»   i  O
<  CD   i  —
                                                                                         « «W  V 4*1

-------
Z i/l
2
a
                   ^^



                   5
                   5
            >    e
                              1  1   .   1
                              1  1   1   1
                             I'll
                             1  1   1   1
                                               ««0  — 0

                                               C3 ^  c*i m
                                               •— -•  (V 
-------
S.4.1  December Survey Data Summary

    The December survey actually consisted of two distinct slug
monitoring runs down the river.  On December 1, 1981, at 7:00 a.m.  the
dye was dumped at Grand Traverse Street, a point 7.4 kilometers upstream
of the Initial water Quality 'sampling station (Mill Road).  This
permitted the dye slug to adequately mix over the river cross-section by
the time It reached Hill Road.  At Mill Road and at all subsequent  river
stations and point source locations, estimates were made (based on
average river velocity estimates) of the time of travel between sampMng
points along the river.  These estimates were confirmed by following the
dye slug along the river and sampling on-s1te at each location via
Huorometric analysis when the leading edge and peak of the dye slug was
passing.  Three water quality samples were collected at each location,
separated in time by about 1/2 hour, as the dye was passing.  An attempt
was made, :m most cases successfully, to obtain one water quality sample
prior to passage of the peak of dye, one at the peak, and one after
passage of the peak.  In this way a good representation of the water
 • *£
quality 1n the dye slug could be obtained.

    Hydrographs of the Flint River at the M-57 (Vienna Road) and M-13
sampling locations during the week of the December survey are presented
In Figure 5.4.1.  These hydrographs Indicate two major things.  First,
the discharge of the river during the December survey was an order  of
magnitude larger than the August low-flow survey.  Second, the
hydrographs are reasonably flat. Indicating that the river flow was dose
to steady-state during the survey.  There was a small peak 1n each
hydrograph due to a brief rainfall late Tuesday afternoon; however, this
event occurred between two sampling runs, as Indicated In the figure.

    A record of the dye slug tlme-of-travel and sampling times for  both
sampling runs has been reconstructed In Table 5.4.2.  A very fortunate
oceurrp«ce Is evident from this table.  The river flow conditions were
                                151

-------
5.4.1  December Survey Data Summary

    The December survey actually consisted of two distinct slug
monitoring runs down the river.  On December 1. 1981. at 7:00 a.m. the
dye was dumped at Grand Traverse Street, a point 7.4 kilometers upstream
of the Initial water quality sampling station (Mill Road).  This
permitted the dye slug to adequately mix over the river cross-section by
the time It reached Mill Road.  At Mill Road and at all subsequent river
stations and point source locations, estimates were made (based on
average river velocity estimates) of the time of travel between sampling
points along the river.  These estimates were confirmed by following the
dye slug along the river and sampling on.site at each location via
fluorometrlc analysis when the leading edge and peak of the dye slug was
passing.  Three water quality samples were collected at each location.
separated 1n time by about 1/2 hour, as the dye was passing.  An attempt
was made, in most cases successfully, to obtain one water quality sample
prior to passage of the peak of dye. one at the peak, and one after
passage of the peak.  In this way a good representation of the water
quality In the dye slug could be obtained.

    Hydrographs of the Flint River at the M-57 (Vienna Road) and N-13
sampling locations during the week of the December survey are presented
In Figure 5.4.1.  These hydrographs Indicate two major things,  first.
the discharge of the river during the December survey was an order of
magnitude larger than the August low-flow survey.  Second, the
hydrographs are reasonably flat. Indicating that the river flow was close
to steady-state during the survey.  There was a small peak in each
hydrograph due to a brief rainfall late Tuesday afternoon: however, this
event occurred between two sampling runt', as Indicated in the figure.

    A record of the dye slug t1me-of-travel and sampling times for both
sampling runs has been reconstructed 1h Table 5.4,2.   A very fortunate
oecurr^ce 1$ evident from this table.   The river flow conditions  were
                                151

-------
        TABU 5.4.2..
COMPARISON OF DYE CLOUDS TIME-OF-TRAVEL WITH SAMPLING SCHEDULE
    FOR DECEMBER. 1981 SURVEY OF FLINT RIVER
Site to. Point
till Road
' 300061
• int WWTP
fFEQ002^
'.'idtn Road
' 30007}
Eiras Road
TROQ08)
< \n Street
'.20009}
4t. Morris
» id fFRQOIOI
i »nna Road/-
«<-57 (FR0011)
Vjnone WWTP
-.00031
_ake Road
•c»noi8
Run
,,aa (FR00191
-<-l3.
00151
71.9
70.7
70.5
66.3
61.2
52.6
43.5
41.1
38.3
30. 5
14.9
Arrival of
Peak Dye
Date Concentration*
12/1/81
12/3/81
12/1/81
12/3 /fll
12/1/81
12/1/81
12/1/81
12/3/fll
12/1/81
12/3/81
12/1/81
12/3/81
12/1/81 .
12/3/81
12/1/81
12/3/81
12/1-2/81
12/4/81
12/2/81
12/4/81
12/2/81
12/4/81
1005
1010
1030
1035
1045
1050
1220
1220
142Q
1430
1745
1800
2120
2150
2200
2220
2345
0015
0330
03SO
1020
1130
Sampling Times
0945;1005;1030
0950:1010:1030
1005; 1035; 1105
1005:1025:1045
101S;104S;1U5
1020:1040:1100
1200:1230:1250
1200:1230:1300
1400;H25:1455
1400:1430:1500
1700:1730;1600
1730:1800:1830
2020:2050:2145
2115:2145:2215
2120:2220:2250
2215:2245:2315
2345:0020:0045
0000:0030:0100
0300:0330:0400
0315:0400:0445
0930:1000:1030
1015:1100:1145
Tlme-of-Tra
of Peak (hr
3.1
3.2
3.5
3.6
3.75
3.8
5.3
5.3
7.3
7.5
10.75
11.0
14.3
14.8
15.0
15.3
16.75
17.25
20.5
20.8
27.3
28.5
Dye dumped at Grand Traverse Street (K«. Pt. 79.3) at 7:00 a.m. on  12/1/81 and 12/3/81.
                                              153

-------
 vtry  similar during the two sampling runs,  effectively providing a
 replicate experiment that permitted a certain degree of field testing of
 the,model.   The table Indicates  the success attained in sampling river
 stations  near the peak of the dye slug.   The t'1me-of-trave1  over the
 study reach  from Mill Road to H-13 was  24.25 hours  for run 1 and 25.33
 hours for run 2.   These travel  times corresponded  to average velocities
 through the  study reach of 2.35  tcm/hr (0.653 m/s) and 2.25 kra/hr (0.625
 n/s)  for  runs 1  and 2,  respectively.

    Based  on  discharge  measurements, t1me-of-travel  data,  and cross-
 sectional area, data provided  by  USGS. the river  reach from Mill  Road to
 Cresswell  Road  was  segmented  and the hydrologlcal and morphological  Input
 data  were compiled  by segment.   This Information is  presented in Table
 5.4.3.  The  same  nine segments used In  the  August model  application  were
 sufficient for  the  December survey;  however,  a flow  balance  (based on .
 available flow  and  gaging station  measurements)  showed that  there were
 tributary or  groundwater sources of water to some segments for which no
 accounting-was  available.   The segments of  concern are shown In  Table
 5.4.3 with two  entries  under  the 'segment flow"  column;  the  first entry
 1s the flow at  the  upstream boundary, and the second entry 1s the flow at
 the downstream  boundary  of the segment.   The model was set up to handle
 this  situation  by distributing the flow Increment of any given segment
 uniformly along the  length of the  segment.

    The Initial conditions  at Mill  Road and  the  point source loads at the
 time  the dye  cloud passed  each point are  presented in Tables 5.4.4 and
 5.4.5 for runs  1 and  2.  respectively.  Once  again the two  municipal
plants represented  the major source  of metals to the river.   Both plants
had higher discharge  flows  In December than  In August, with
correspondingly higher metals loads.  It  Is worthy of  note at this time
 that  the suspended solids and metals  loads  from  the  Ragnone  plant were
almost an order of magnitude greater during  run  2 than during run 1.
This occurrence provided  an excellent opportunity to determine how the
                                  154

-------
TABLE 5.4.3.  SESMENTATION. PLOWS. AND GEOMETRY FOR FLINT RIVER
                  DURING DECEMBER 1981 SURVEY
Segment
Number
•Boundary
Ka.
Point
Segment Flow Cross -Sectional Mean Oep*
(m»/s) Area (m*) (a)
Run l' - 12/1-2/81

1

2

3

4

5

6

7

a

9

H111 Road

flint UUTP

Flint Fly Ash Ponds

Downstream of Elms Road

Brent Run

Ragnone WUTP

Upstream of East Burt Rd.

Pine Run

Silver Creek

Cres swell Road
71.9

70.7

70.0

65. 0

41.6

41.1

36.0

29.7

25.2

11.0

26.3

28. 5

26.6-29.3

29.3-32.3

32.9

34.0-34.9

34.9-36.1

36. a

37.4


39.5

44.0

41.3

46.3

47.4

62.8

44.6

59.5

60.9


1.2

1.2

1.0

o.as

1.0

1.2

1.2
-
1.2

1.3

                              155

-------
                                 TABLE 5.4.3.  (Cont'd.)
Segment
Number
 Boundary
    
-------




1
at
M
^
^
0
M4
u
OE — •
a «
X eJ
5-
si
< X
o a
mm
3 X
o ac
14
< to.
IM
at
i
»
*
*
• •
•ft
t*l
-J .
^f
m^
»•



1





•e
«B
e
Q
t_
• •*
~ e
•i
• w
<_>
8
hta
JC
u
W
*•»
O




S
M



WJ
«*1
Q
U
<•
iAM
o


§
•«
M
"»
J
M
*i

w
••>
e-
1/1
S S
i/k O
^r (•>

ef*%
CM?

o o
r s
*n oa
O *^
0 0
o
0 0
cv ff>
« a*

o n
* 5
*
i 5
2" s-
0 —
. ** ^
« ^
<« <0
ts«
i
»»
f] *
W w*
W «*
d^Mi MP* AS

Sr> o rw •«
v cv «N e»
— — op _

A O' O
OtfVt kfl «MP ^B
W* *»^ *^ WP

» O «fl
(«» mf a is« rg

** ¥• ' ** O «
eiflBBI ^K «• Aft ^fc
^•F GP G» <• 3*
^ O W f« *• ^
«.» 4^ a or a. ^

-------






•
»
1
e

Ml
U
e 0*
S «•»
o -j
«.
Wt*
a a
— «v
a * •
u
at
x a
«c ••
at
3
•
4
^
|













|
*


«
S
w
Q
cr
w
"fl
JC
«*
•"•









1
w






w
•t
0
«
«••»


J
•MM

^>


W
*""
~

"o
1
MB
^^
•P*
•HP

W
e
o
3
I/I

ta.




-»

*x
B
01
•Mr
•f
o»
^te.
1
-.
PH
0*
a
***
•o
1


•Mh
!

^i
•x.
w
— '
^
<
•
*"*
Mi
*"*


*
W
3

* * •
00 0
^™" *•* Cwi

.n w •
3 «
•- «v O
. IV A*
(V
* ~ 9»
S 2 5
— o *»
o — —
00 (^

00 0
o o 2


P«M
^ 0
IS*
o
O O rt

• fV •»
•"•

•ft un iv
44 ^
***
7 " 0
at
^9 ' Ml
5 a. *
gS 1 -
WOK
"52 ~S 3 '
«« ^^ _ ..^ &
v
. o
O> O »
 e
9*

Jo

-.
s

-
~
(V
0
, o

^
*"*



o
*
•^B
9ft
"•


4
^

o

•*
«
«

                                           tc
                                           If
">  i  ox   zr  ^

-------
model would perform under similar river Flow conditions with drastically
different loads - an exercise often required in performing waste load
allocations,  finally. It sftould be noted that once again the metals
discharged from the Flint outfalls were primarily dissolved/while those
from the Ragnone discharge were largely participate.

S.4.?  Dec ember Survey Model Calibration
    . -              >                        .                 i
    The procedure taken In calibrating the model to,the December data was
to  first calibrate the model using data from run 1 only; then the
calibrated model was applied to run 2 data as a field test of the model
performance under similar Flow conditions with very different loadings.
As with the August survey, calibration of the suspended solids transport
system was performed First by adjustment of w$ and wr?; this step was
followed by calibration of the metals-system us.lng the respective
partition coefficients.  Degradation and sediment-water diffusion of  .
dissolved metals have again been considered Insignificant. .

    In calibrating the suspended solids system .one should not expect the
sediment transport regime to be the same in December as It was In
August.  In the higher flow regime of the December survey, one might
expect the river to.have the capacity to carry larger participate matter.
which would have a larger intrinsic settling velocity (per Stokes
formula).  On ,the other hand, higher flows lead to higher stream
velocities and depths, and thus result 1n greater bottom shear stress.
Assuming that the other factors governing entrapment are the same,
the December solids resuspenslon velocities («  ) should also be
greater than those determined In August.  Depending, of course, on the
magnitude of change in w  and w  . it Is possible that the net flux
of solids between bottom sediments and overlying water many not be
significantly different From the August results.  It Is likely therefore,
that because of the characteristically shorter detention time 1n the
hlghtr fl v river system, the longitudinal distribution of suspended
                                   159

-------
 solids  In  December will  not  exhibit  as  great  a  variation  as  was  observed
 1n .August.

    The hypotheses presented  1n  the  above  paragraph were  largely
 confirmed  by the  calibration  of  the  model  to  the  December run  1  data.
 Calibration values for w  and w    for each of the nine  segments  are
 presented  in Table 5.4.6.  Illustration of the  suspected  relative
 flatness of the solids longitudinal  distribution  and the  comparison  of
model simulation with field data for run 1 are  presented  In  Figure 5.4.2.

    Once again the calibration was made without varying the  sett!ing velo-
city, w$. among segments.  Because of the  greater uncertainty  In ascer-
 taining the factors governing sediment  erosion, 1t was felt  that1 there
would more likely be Intersegment variability In w   than In w .  A
 settling rate of 0.6 m/d (as  opposed to 0.25 m/d  In August) does not seem
unusually nigh for a river flowing at about Five  times the discharge
rate.  Assuming the river  suspended  solids had  a  specific gravity of 2.5.
the effective Stokes diameter for 0.6 m/d  settling velocity, would be 3.0
urn compared with 1.8 urn  for a settling  velocity rate of 0.2S m/d.

    The bottom shear stress in the various river  segments calculated in
the same manner as 1n the August survey ranged  from 25 to 54
dynes/ca.  Once again the lower segments  (7-9) had slightly higher
values than the upper reaches.  All  these  shear stress values are
considerably higher than the  4.13 dynes/en  'ange calculated for the
August flow conditions.  In fact, both  stream velocities  and shear
stresses In December are roughly three  times the August values.  It seems
logical, therefore, that the calibrated resuspenslon values for December
are greater In each segment (see Table  5.4.6) and greatest 1n the
downstream segments again.  There are other possible reasons for greater
downstream erosion rates, related to some of the other governing factors
mentioned by Lee f£ aj..  (1981).  The downstream segments  of the Flint
River pass through an almost *«lu$1ve1y agricultural area, perhaps
                                   160

-------
TABU 5.4.6.  CALIBRATION COEFFICIENT FOR SOLIDS TRANSPORT SYSTEM
        USING FLINT RIVER, 0£CE*6£R 1-2, 1981 (RUN 1) DATA
Segment
1
2
3
4
5
6
7
1
9
Settling Velocity
(ra/d)
0.6
0.6
0.6
0.6.
0.6
0.6
0.6
0.6
0.6
Resujoenston Velocity
(m/d)
0.2xlQ-4
O.ZxlO-4
0.4xlO-4
0.4xlO-4
0.4x10-4
0.2xlO-4
\.2x10-4
l.OxlO-4
l.Oxlfl-4

-------
t      I      >      I      1
  i      a  /   i
                                 - s
III      T
      5s      *     t    i     i
      as-"-

-------
resulting 1n different bottom sediment characteristics.  Furthermore, the
downstream segments tend to have steeper, more loosely packed banks.  It
1s also possible that the deposition*! pattern downstream of the Ragnone
treatment plant, which tends to discharge high solids concentrations.
might Favor high erosion rates.  Temporal variations in recent
deposition*! history for any river reach may lead to variability In
bottom sediment resuspenslon rates for a given flow regime.

    Once the solids transport submodel has been calibrated, calibration
for the metals Is performed by adjustment of partition coefficients.  The
final calibration values for the three metals in each segment are
presented In Table 5.4.7.  A comparison of the model simulations using
these coefficients with the run 1 field data 1s presented In Figures
S.4.3-S.4.7.  The partition coefficients used to generate these
simulations are very similar to those obtained 1n the August
calibration.  Where they do differ, such as for zinc and for copper
downstream.of the Ragnone discharge, they tend to be slightly lower for
the higher flow case.  This result might be expected, since the solids
being transported In December are probably slightly larger, thus navlng a
smaller surface area to mass ratio.

    Once again the high fraction of dissolved.solids in the Flint dis-
charge forced a calibration with lower partition coefficients for all
three metals in segments 2 and 3.  Also, the copper In the high
partlculate metals load from Ragnone seemed to remain in a partlculate
phase through the end of the study reach.  These necessary adjustments
froa segment to segment reflect a need to characterize the partitioning
of metals 1n the effluent stream as well as 1n the Mver.

    As Indicated earlier, the second plug flow survey provided an
excellent opportunity to field test the model for Its ability to simulate
variation In river-solids and metals levels for different loading
conditions under the san.* flow regimes.  This test was performed oy
                                  163

-------
TABU 5.1.7.  CALIBRATION VALUES OF PARTITION COEFFICIENTS
     IN fllNT RIVER DURING DECEMBER T-2. .1981 SURVEY
Segment
1
2
3
4
5
6
7
a
9

Zinc
0.20
0.08
0.08
0.20
0.20
0.20
v 0.20
0.20
0.20
Ketal Partition Coefficient (l/mc5
.Cadmium
0.20
0.05
O.OS
0.20
0.20
0.20
0.20
0.20
0.20

Copper
0.05
0.03
0.03
0.05
0.05
- 0.07
0.07
0.07
0.07

-------
                                                     s  I
                                                   - a
I2St*StSlS13
S85-**SS2-»-

-------
i     i    i     i    r
                 J
i
I
c
f
9
i
i
                                      r
                                             (I •
                                   '     o
T
r
I
i
a
s
 2   1
 t»   •


amznio
                                                  -s
                                                  — •  a
                                                             to
                      \


                      *

-------
s
S
 I      I      I
!     s      ;
 II/W) M3M03 1*101
                            I
                            *
I
8
i
96
                                                                                         00
1
•A
                                           (I/*") HfllNOVS 1UV4

-------
J	L   	L

' 	 *"" ""*» Q, J


^
>—
*•
1
<*•
S
H
^4
.«
»fl
*-
i— i
M
— (

9
^
»


*- i 	 ±e —
t* »•
-H i e
^_
i i
" 7

e-i
•^
_e— i
5— 1
N9H
t L
— < L, o
k '

8
«
~a
*
"t
•
"a
•
"a

•
„»
•
a     a
 ll   n     »     liiii
s     s    s     s    s     s    s    s     s
•„••••     •••     •
                                   (!/••»)
                                    IV 101

-------
  1    I -   I   1 .   t    t
s
                   J
         I   T   I

' °^


^
^

•



3

5'
^
^

1
i
D

s


a
a ^
— • S ""
5s- "*
a M
* c'
* 3
a a
M —
"s
m
                        i    i
  *  *   *   ;   2   *   *   *   2   !

-------
          the model  to the December 3-4,  1981  (run 2)  loading oata (Table
 5.4.5)  and hydrologlc data (Table 5.4.3}  without adjusting the
 calibration coefficients  (wj(  wrj, «zn>  *cfl,  ,^) obtained using
 run  1 data.   The  results  of this  model  run  are presented  in Figures  5.4.8
 -  5.4.12.   The  main  difference In the two data sets,  of course,  was  the
 large Increase  in solids  and participate  metals discharge from the
 Ragnone WWTP when the second dye  cloud  (run 2} was  passing.   Based on the
 comparison of model  predictions with  field  data,  the  model performed
 quite well,  without  a need for recallbratlon.   The  only significant
 falling of the  model  was  the over-prediction  of dissolved copper
 downstream from the  Ragnone discharge.  Obviously,  with participate
 copper  from the Ragnone discharge comprising  over half of the  total  load
 of copper  In segments 6-9,  a higher partition  coefficient would  have
 helped  to  simulate the dissolved  copper profile.  In  fact.  If  one were
 actually calibrating  the  model  to the run 2 data, the only change from
 the  run 1  calibration would be to raise the copper  partition coefficients
 1n segment 6-9  from 0.07  1/mg  to  0.12 t/mg.

 5.5  FLINT RIVER  MARCH 1982 SURVEY

    The primary goal  of the Mint River March  Survey  (March  23-26,  1982)
was  to  evaluate the Status  of  metals  and  solids  transport in the river
during  a period that  would  likely represent the highest discharge flow  in
an annual  cycle.  It  was  fortunate that the survey  was scheduled about
 two weeks  after a major snowmelt  period In  southeastern Michigan.  At the
 time of the  survey the study reach was discharging  water  which had
previously collected  In upstream  reservoirs during  the snowmelt.   The
flow In the  study reach during the survey was  about 4000  cfs (113
• /s).  roughly 4  tints the  December flow and 20  times the August flow.
Studying the  river under  this  range of flow conditions provided  a  good
Idea of  the  range that Is  likely  to exist 1n the  values of those
                                                  •V.
parameters,  such as sediment settling and resuspenslon velocities  and
partition coefficients, that app -a- *•> be flow dependv.it.
                                   170

-------
I	I
                                    I      I     I      I
I
*
*
 t
>
S

It/*")
 1
3
                                                      -s
a     *     «
S     I
                                         t'       >*

-------
I
i
      J	L
                  J
I      i      I
?     I    s
                              t      I     I      I      t
    amz uiv4
                                                                     -
 I     I      I      I  ~  .
I     «     1    •     1
S     S,    ".  .*

((/•'H 9HIZSBO

-------
I	I	I
I	I       I	I	I	j	I	I
_«
>_r
V—

V— 4

L


' ,0
1
i
«»
i»
i i i i i
a 2 s s s
c
*«•

5

Li






3






r . c \


, J
1
1 i :
1 S S 5


^^




fcr"



1 1
S Z


^








s
rs
*


s
"5
X
a
_ e
L3 a e
w 9 •*
e «
• ^»
. i 1
— • 43
tto
-S

-5
i
                                                 it/if)

-------
    I	I	I	f	I	I      I	I	I	I
5
i      i      i       i       r
i      *      •      s     •     s      s
    i       i
                                                                 r
                                                                S
                                                                                           ^^
                                                                                           ji  r-
i       i
           II/««J M1M03 1V101
(l/ll)

-------
I       I        1  	I
t    _  L      I





1
s


i






»
^
MP
"t
'
i

-

—
V
^PW
i^S*
31
3
B-t
8
*B-4
'~a
^-,
»w

_
*
~s
_•
s
Hinonnonjn:
• •« t«»
i i
• *
"a
"a

•
™«
_*
•


M
i
^





i 1 1 1 1,1 ( 1 1 1 1 "
IfltXtxiSS.S!
aas»»"a •"**••
                                                  ((/•"I

-------
                      TABLE 5.5.1.  SAMPLING STATION FOR MARCH 1981 FLINT RIVER
                                         HEAVY METALS SURVEY
                 Station/Description                                       Km. Point
FR0006/wm Road river station
FE0002/F1lnt wwTP discharge
FEOOAl/FUnt Fly Ash Pond discharge
F£OOA2/F1int Fly Ash Pond discharge
FROOOa/Elns. Road river station
FR0022/Mud Creek tributary
FRQ023/Co1e Creek tributary
FROQIQ/Mt. Morris Road river station
FR0024/Brent Creek tributary
FR0025/Armstrong Creek tributary
FRQOIl/Vlenna Road (N-57) river station
FR0012/Brent Run tributary
FE0003/Ragnone WWTP discharge
FR0020/B1rch Run Road river station
FRQ017/P1nc Run tributary
FROOU/snver Creek tributary
FROQ1S/N-13 river station
71.9
70.7
70.0
70.0
66.3
64.4
60.2
52.6
50.6
44.6
43.5
41.6
41.1
30.5
29.7
25.2
14.9
                                         176
.

-------
i
g 100.000
5
11 a
40&OOQ I
J2 200.000
u
O
o
«. a.
o
§"

J I
HINT
1 I
Mjnw I I
3 tBJMO
S
I
i. a.



s





S S B


11. n. 10
RIVtH KM MIT
JM
8
4f
|
j/s-am
i




o
I i °
* « °
h Si
a .51

i H



SI. 71. 1Q
NfVf R KM MIT
s
s |
a •
I
s
i
5
s
S S S
« o 5
! ii

-------
             TABU 5.5.2.  HYOROL06IC AND HORPHOMETRIC INPUTS FOR FLINT RIVER
                       MARCH 1982 HEAVY METAL MODELING APPLICATION
Numter
 Boundary
 K«.     Segment Floy
Point      («»/s)
Cross-Sectional
  Area (m»)
                               Mean Oep
                                   («)
   1
   2
   3
   4
   5
   6
   7
   8
   9
H111 Road                   71.9
Flint WHIP                  70.7
flint Fly Asn Discharge     70.0
Cole Creek         •         60.0
Armstrong Creek             44.6
Ragnone UUTP                41.1
Upstream of Cast Burt Rd.   36.0
Pine Run                    29.7
Silver Creek                25.2
PM3                        14.9
  93.4
  96.1
96.25-99.9
100.7-106.4
 107.7
110.3-111.7
111.7-113.5
 115.5
 117.5
     97.5
     95.0
     90.0
    110.
     96.0
    109.
     65.0
    US.
    120.
                                             1.8
                                             1.8
                                             1.5
                                             1.5
                                             1.6
                                             1.6
                                             1.8
                                             2.2
                                             2.2

-------
o ^
**
§s
       e a

                  §
                  
-------
    Because of the expected  short  travel  time  For the  study  reach  in
March - it turned out  to be  about  16 hours - and because of  the high
possibility of encountering  rapid  tine variations during the monitoring.
the survey strategy was to sample  fewer river  stations more  frequently.
Also, more tributaries were  added  to the  11st  In case  surface runoff  in
the study reach was a  significant  source  of solids and associated
metals.  All river stations  were sampled  every 4 hours, tributaries were
sampled every 12 hours, and  4-hour composites were collected from  point
source effluents during the  72-hour survey.  A list of the sampling
stations for the March survey 1s presented in Table S.S.I.

S.S.I  March Survev Data Summary

    During the 72-hour March survey the Flint  River flow was receding
somewhat from the flood stage recorded during  the rapid snowmelt.  for
example, the discharge at the farthest downstream station (fl-13) dropped
from approximately 4500 cfs  to 3800 Cfs during the survey.   This rougnly
IS percent flow decrease, however, did not appear to negate  the
possibility of applying the'steady-state  model to the  data,  as evidenced
by the relatively narrow spread of conductivity and suspended solids
value* at each river station, as Illustrated In Figure 5.5.1-.  Saved  on
the hydrologlcal and morphological data collected during the survey,
therefore, the segmentation, river flow and geometry used as input for
the steady-state model are given In Table 5.5.2.

    Initial conditions and loads obtained for  the March survey are
presented In Table 5.5.3.  It Is apparent that flow and loads at the
upstream boundary exceed the flow  and  loads of all point sources and
tributaries combined.                                   .

5.S.2  March Survey Model Calibration          1

    The results of calibration of  the model to the M*rch survey data
are presented In Figures 5.5.2 - 5.S.6.   The calibration was done  using
                                  180

-------
I     I    I     I    I    )
8
             2
         i    i
t   1
                          r
i
z
                                                  'S
                                                    *
                                                  • a
                                                  •5 2
S   I    1
                                 sonos am

-------
1 i r 1


-
S
| |
i
i i i i
a -a- i s
i
j
J




i
i i i it t •

^^ f
h-€
™ L
^»
H-!
IB^

^ > Oi
^v
i— e
•»
^
•f
— i
M

•^
— ^

I 1 I 11 1
«•• ill <•• III II
ROMMOIIIH
iutu fcii
a Z
"S
-a
_• _
!!!!!!
                                                           :  »
9NI2
                               ami sio

-------
                   K—Q
I      I     I     I      I
                                  I	t     I
                                                              -s
                                    n/*») Miri*

-------
       L      t      I	!     !_    I
 r      i      i      T     r
3     J     2
            I     I
                                                                   •2  a
                                                                    "  3
                1*101
r      f     r     i     i
3     !     !     5    5
 lis*"l nniNgvq uivj

-------
Ill i. !
•




i
2
/






^^^J^^ 1 • '


i

»-€

• ^**1
> 8
Ml
J
' '
-^

'


»— 1
— ^

"8

"8
• s
*
5 i
. i
*
"JS
_ «,
3*333** *• S3*
• • « v •• • £ •• ^ ft m
(I/I")
^ o

-------
larger settling and resuspenslon velocities  than used  1n previous
surveys.  The more significant  Increase, however, was  In the  resuspenslon
velocity, resulting 1n a net  Flux of solids  from sediment to  overlying
water throughout the study reach.

    The calibration coefficients for the solids transport submodel are
given In Table 5.5.4.  A large  gross settling velocity would  be expected
1n a system transporting larger particles.   The Increase in resuspenslon
rates in all segments Is Justified by the large Increase in calculated
bottom shear stress over the  December values, as shown in Table 5.5.4.
In general, there Is a reasonably good correlation between bottom shear
stress and calibrated resuspenslon rate for  a given river segment.
Although only three data points were available (one for each  survey).
they did seem to follow a linear trend, as shown for three sample
segments shown 1n Figure 5.5.7.

    Calibration of the metals data by adjusting water column  partition
coefficients also produced reasonably predictable results,   with
presumably larger solids being  transported with much of the material
originating from the river bottom, one might expect to see slightly lower
water column partition coefficients than were observed at lower flow
conditions.  This hypothesis was confirmed by comparing the final
calibration partition coefficients to average observed values for each
metal at each river station during the survey (Figure 5.5.8).  The
calibration values tend to be slightly lower then the measured values;
however, this, observation Is  often made In natural systems since measured
values of total partlculate metals tend to Include a certain  portion that
does not readily exchange with  the bulk solution.
                                     1B6

-------
TABU 5.5.4.  CALIBRATION COEFFICIENTS FOR SOLIDS TRANSPORT
            DURING MARCH 1981 FLINT fllVCR SURVEY
Segment
1
2
3
4
S
6
7
a
9
Settling velocity
(m/d)
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
Resuspenslon Velocity
(m/dk.
1.0x10-*
1.0x10-*
2.0x10-*
2.0x10-*
2.0x10-*
2.0x10-*
4.0*10-*
3.0x10-*
3.0x10-*
Bottom Shear Stress
(dynes/cm )
92.5
92.5
127.
94.3
108.
108.
176.
115.
115.

-------
\
                                           vt
                                           •M

                                           Ml

                                           3
                                                   N- >
                                                   I/I JJ
                                                   •» s
                                                   IM. s
                                                   SC <9
                                                             O
                                                             o
                                                   5 «!
                                                   a g
                    ouvain«

-------
                 FLINT 3SZS.»/n
B.MD

~ 0.4W
*
S OJM
2 o.tso
N
0,000
— • CAUMATtON VALUI


f
1


|
!


T • 1
! i
i i



M
                    28.          SI.         71.
                          PHVIH KM «»T
I
101
1

• ? I
a S
^^•^ ^AkltJlAl
.MO r
Jno . |
T 1
DM . I
S
S s' .
II
S •• 5
> a «
'ION VAUUC
'
j t
, ,«


S
^
-





'* 7 .' ' *-J3~
0. 29.


3
I
s 5
a s
SI.
MlVf N KM M|T
e
•f
N ;
m » •
> a £
— CAtltdATION VALUI
OJ80
&1M
J

i , ,
7«. 1O



w
!
»

!

™o. a

S
I
2 I
a i
* 11.
(HVtPt KM »Nf
S
a S
i! i
> 5 s
* 7t. 10


i
i
FIGURE 5.5.3

189


-------
6.0   REFERENCES  (for Sections. 1-5 and Appendices A-0)


Alonso, C.V.  1981.  Stochastic model? of suspended sediment dispersion.
Jourr». Hydraulics 01v., ASCE. 107(HY6):  733-757.

Antsyferov, S.M. and R.O. Kos'yan.  1980.  Sediments suspended in stream
flow.  Journ. Hydraulics 01v.. ASCE. 106(HY2):  313-330.

ASCE.  1975.  Sedimentation Engineering. V.A. vanonl (ed.}. Amer. Soc.
Civil Engrs., N.Y.

Banerjee. S.. S.H. Yalkowsky, and S.C. Valvanl.  1980.  water soluoility
and octanol/water partition coefficients of organlcs.  Limitations of the
solubility-partition coefficient correlation.  Environ. Sci. Techno 1..
14(1(1}:  1227-1229.

Bender, N.C., H.H. Roberts. R. Olaz and R.J. Huggett.  1975.  Proceedings
technology and ecological effects of blofoullng.  Maryland Power Plant
Siting Program.  Baltimore. Maryland.

Benjamin. M.M., and J.O: leckle.  1981.  Conceptual model for
metal-Mgand-surface Interactions during adsorption.  Environ. Sci.
Technol.. 15(9):  1050-1057.

Benjamin. H.M.. and J.O. Leckle.  1980.  Adsorption of metals at oxide
Interfaces:  effects of the concentrations of adsorbate and competing
metals.  In:  Contaminants and Sediments, Vol. 2.  R.A. Baker (ed.).   Ann
Arbor Science. Inc.  pp. 305-322.

Benjamin, M.M.. and J.O. leckle.  1982.  Effects of complexatlon by
CljSO^ S^On on adsorption behavior of Cd on oxide surfaces.
Environ.  Set. Technol., 16:  162-170.

Berner, R.A.  1980.  Early Olagenesls.  Princeton Univ. Press.
Princeton, NJ.

Blonqulst, S.. and L. Hakanson.   1981.  A review on sediment traps In
aquatic environments.  Arch. HydrobloK. 91(1):  101-132.

Bonazountas. H. and S. Hathlas.   1984.  SEOIM - a model for estimating
sedimentation Input parameters of selected stream quality models.  U.S.
EPA. Office of Water Regulations and Standards, Washington, O.C.
Contract No. 68-01-6160.

Brown. O.H.  1979.  Adsorption of lead from solution on the quartz- and
feldspar-containing »11t fraction of a natural str ambed sediment.  In:
Chemical  Modeling V- .kqu.ous Systems.  ACS Symposium Series 93. E.A.
Jenne (ed.). pp. 238-260.
                                     190

-------
 Surges, S.J., and O.P. Lettennialer.  1975.  Probabilistic methods  1n
 stream Quality management.  Water Resources Bull.  11:  115-130.

 Burns. 1.A.. D.H. CUne, and R.R. LassUer.  1981.  Exposure analysis
 model ing system  (EXAMS):  User manual and  system documentation.  Draft.
 U.S.  EPA, Environmental Research Laboratory.  Athens. Georgia.

 Callahan. N.A..  N.w. Slimaic, N.W. Gabel, I.P. Nay. C.F. fowler. J.R.
 Freed, P. Jennings. R.L. Durfee, F.C. Whltmore, 9. Maestri, U.K. Nabey.
 8.R.  Holt, and C. Gould.  1979.  water related fate of 129 priority
 pollutants.  U.S. EPA, Office of Water Planning and Standards.
 Washington. O.C.  EPA-440/4-79-029.

 Canale, R.P., and W.J. Weber, Jr.  1972.   Aeration and gas transfer.
 In:   Physlcochemlcal Processes For Water Quality Control.  W.J. weber.
 Jr.,  (ed.).  WHey-Intersdence. New York.

 Canale, ft.P.  1983.  Unpublished communication.

 Chapra. S.C. and K.H. Reckhow.  1983.  Engineering Approaches  for  Lake
 Management, Volume 2:  Mechanistic Modeling.  Butterworth Publishers.
 Boston, MA.                                              .

 Chlou. C.T., V.H. Freed, O.W. Scheddlng. and R.L. Kohnert.  1977.
 Partition coefficient and 'bloa'ccumulatlon  of* selected organic  chemicals
 Environ'. Scl. Techno 1.. 11(5): 475-478.

 Chlou. C.T.. P.E. Portor and I.J. Tlnsley.  1982.  Resolution  of soil
 sorptlve mechanisms In aqueous and nonaqueous systems.  Paper  presented
 at 3rd Annual SETAC meeting, Arlington. Virginia.

 Connecticut OEP.  1981.  Impact of a chlorinated sewage treatment  plant
 effluent on the aquatic habitat of Deep Brook, Newtown.

 Corey. R.B.  1981.  Adsorption vs. precipitation.  In:  Adsorption of
 Inorganics at Solid-liquid Interfaces.  M.A. Anderson and A.J. Rubin
 (eds.), Ann Arbor Science Publishers, Inc.. Ann Arbor.

 Culbertson. J.   1977.  Influence of flow characteristics on sediment
 transport with emphasis on grain size and mineralogy.  In:  The Fluvial
Transport of Sediment-Associated Nutrients and Contaminants.
 International Joint Commission,  pp.  117-133..

Cunnings.  T.R.. and J.8. Miller.  1981.  Time of travel of the Flint
River. Utah Cam to Highway N-13, Michigan:  August 4-8, 1981.   USSS
open-file.  Lansing,  M1ch.
                                       191

-------
                                           V
Davis, J.A., R.O. James, and J.O.  Leckle.   1978.   Surface  lonlzatlon and
complexatlon at the oxide/water interface:   1.   Computation  of  electrical
double layer properties in simple  electrolytes.   J.  Colloid  Interface
Sc1., 63:  480-499.

Davis. J.A., and J.O.,Leckle.  1978a.   Effect of  adsorbed  complexlng
Ugands on trace metal uptake by hydrous oxides.   Environ. 3d. Tecnnol..
12:  13Q9-1315.
Oavls. J.A., and J.O. Leckle.
at the oxide-water Interface:
Interface- Sc1., 67:  90.
1978.   Surface 1on1zat1on  and  complexatlon
3.  Adsorption of  anlons.   J.  Colloid
Dietrich. W.  1982.  Settling velocity of  natural  particles.
Resources Research. 18(6):   1615-1626.
                               Water
OUoro. O.N. and J.C. Connolly.   1980.   Mathematical  models  of water
quality In large lakes.  2.   Lake Erie.   U.S.  EPA.  Environmental  Research
Lab - Ouluth.  EPA-600/3-80-065.

OUoro. O.M., and L~M. Horzempa.   1982.   Reversible and  resistant
components of PCS adsorptlon-desorpMon:   Isotherms.   Environ. Set.
Techno1., 16(9):  594.602.

OUoro. P.M., L.M.  Horzempa.  M.M.  Casey.  and-w.L.  Richardson;  1982.
Reversible and resistance components  of  PCB  adsorptlon-desorptlon:
adsorbent concentration effects.   J.  Great Lakes  Research, 8(2):   336-349

OUoro. O.M.. Q.J,  O'Conner.  R.V.  Thomann. and J.P.  St.  John.  1982.
Simplified model of the fate  of  partitioning chemicals  in  lakes and
streams.  In:  Modeling the  Fate of Chemicals  1n  the  Aquatic
Environment.  K.L.  Olckson,  A.M.  Makt,  and J.  Cairns.  Jr.  (eds.)  Ann
Arbor Science. Ann Arbor.

OUoro, O.M., and J.J Fltxpatrlek.  1983.  Verification  analysis  of the
probabilistic dilution model.  U.S. EPA,  Office of Water Programs
Operations.  Washington* O.C.  Contract No.  68-01-6275.

OUoro, O.H., J.J.  FltzpatMck,  and R.V.  Thomann.   1983.  Documentation
for water quality analysis  simulation program (WASP)  and model
verification program (MVP).   U.S.  CPA.  Urge Lakes flesearch  Station.
Srosse lie. HI.  EPA-600/3-81-044.

OUoro. O.N., L.H. Horzempa.  and N.C. Casey.  1983.   Adsorption and
desorptlon of hexachloroblphenyl.   U.S.  EPA, Large Lakes Research
Station.  Srosse He. MI.  EPA.600/S3-83-088.
                                      IBZ

-------
 Dolan, D.N., and V.J. Blermaiy.Jr.  1982.  Mass balance modeling of heavy
 metals in Saglnaw Bay, Lake Huron.  J. Great Lakes Research. 8(4):
 616-694.

 Oonlglan, A.S., Jr. and N.H. Crawford.  1976.  Simulation of aricultural
 runoff.  U.S. EPA.  Washington. O.C.  EPA-600-9-76-016.

 Oontglan. A.5.. Jr. and H.H. D«y1s. Jr.  1978.  User's manual for
 Agricultural Runoff Management, tARM) Model.  U.S. EPA.  Washington, O.C.
 EPA-600/3-78-080.

 Oossls P., and L.J. Warren.  1WO.  Distribution of heavy metals between
 the minerals and organic debru 1n a contaminated marine sediment.  In:
 Contaminants and Sediments. Volume I.  P.A. Baker (ed.).  Ann Arbor
 Science.

 OMscoll, E.O., J.L. Nanclnl. and P.A. Mangarella.  1983.  Technical
 guidance manual for performing waste load allocations.  Book II streams
 and rivers.  Chapter 1 biochemical oxygen demand, dissolved oxygen and
 ammonia toxldty.  U.S. EPA. Office of Water Regulations and Standards.
 Washington, O.C.

 Edzvald. J.K.. J.B. Upchurcn  and C.R. O'Mella.  1974.  Coagulation m
 estuaries.  Environmental Science Techno 1. B.

 Einstein, H.A.  1950.  The bed load function for sediment transportation
 In open channel flows.  Technical Bulletin No. 1026.  U.S. Department of
 Agriculture, Soil Conservatd   Service.  Washington, O.C.

 Einstein. H.A.   1964.  River hydraulics.  In:  Handbook of Applied
 Hydrology.  V.T. Chow (ed.)-.uyMcGraw-Hlll. N.Y.

 Felmy. A.R.. S.N. Brown. Y. Qn1$h1. R.S. Argo and S.B. Yabusaki.  1982.
 MEXAHS - The metals exposure analysis modeling system. 'U.S. EPA. Office
 of Research and Development.  Contract No. 68-03-3089.  Athens. Georgia.

 Fisher. J.B., W.J. Ucx, F.L. JlcCall. J.A. Robblns.  1980.  Vertical
mixing of lake sediments by tuBHIdd ollgochaetes.  J. Geophys. Res..
 85: 3997-4006.

 Forstner. U.  1977.  Fonts and sediment associations of nutrients and
 metals.-  trace metals.  In:  Th« Fluvial Transport of Sediment-Associated
 Nutrients and Contaminants,  u  Shear and A.E.P.  Watson (eds.).
 Internatlnal Joint Commission

 Freed, V.H.. C.T. Chlou. and R. Hague.  1977.  Chemodynamlcs:  transport
 and behavior of chemicals 1n the environment - a problem In environmental
 "«.3lth.  Environmental Health .Serspe* * ,vt.. ZO: 55-70.

 Freedman. P.L.. and R.P. Canale.  1983.   Modeling uncertainty and
 variability for waste load allocations.   U.S. EPA. office of water
 Regulations and Standards. Washington. O.C.


                                   193

-------
Fukuda. M.K.. and w. lick.  1980.  The entrapment of cohesive sediments
In frcsn water.  Jour. Geophysical Research. 85{C5):  2813-282*.

Garde. R.J. and K.G. Ranga Raju.  1977.  Mechanics of Sediment
Transportation and Alluvial Stream Problems.  John Wiley & Sons.  N.r.

Gardiner, 0.  1974.  The chemistry of cadmium in natural water - II.  The
adsorption of cadmium on river muds and naturally occurring solids.
water Research. 8:  157-16*.

Gelger, W.F. and H.R. Dorset).  1980.  Quantity-quality simulation (QQS):
a detailed continuous planning model for urban runoff control.  U.S.
EPA.  Washington. O.C.  EPA-600/2-8Q-011.

Gessler. J.  1971.  Aggradation and degradation.  In:  River Mechanics.
H.W. Shen (ed. and ouol.).  Fort Collins, CO.
                            V                         .                -
Glbbs. 8.J.  1980.  Mechanisms of trace metals transport In rivers.
Science. 180:  71-73.

Glddlngs. J.M., B.T. Walton. G.K. Eddleman and K.G. Olson.  1979.
Transport and fate of antlacene m aquatic microcosms.  In:  MUroolal
Degradation of Pollutants in Marine Environments.  EPA-&00/9-79-GU.

Graf. W.H.  1971. .Hydraulics of Sediment Transport.  McGraw-Hill, New
York.

Grahame, O.C.  1955.  On the specific adsorption of ions in the  "•
electrical.double layer.  J. Chem. Phys., 23:  1166.

Guy. H.P.  1970.  Fluvial sediment concepts.  In:  Techniques of
water-Resources Investigations of the United States Geological Survey,
Book 3. Applications of Hydraulics.  U.S. Geological Survey.

Haag. W.R.. and N.H. Uetzke.  1961.  A kinetic model for  predicting  the
concentrations of active halogens species In chlorinated saline  cooling
waters.  Oak Ridge National Laboratory.  ORNL/TM-7942.

Haas. C.N.  1981.  Application of predator.prey models to  disinfection.
Journal WPCF S3:  378-386.

Hansch. C., J.E. Oulnlan. and G.L. Lawrence.  1968.  The linear
free-energy relationship between partition coefficients and the  aqueous
solubility of organic liquids.  The Journal of Organic Chemistry.  33(1):
347.350.

Hassett, J.P., and M.A. Anderson.  1981.  Effect' if dissolved organic
matter on adsorption of hydrophoblc organic comp  4nd^ by river-  and
sewage-borne particles.  Water Research.  16:  681-686..
                                    194

-------
Hayter, E.J., and A.J. Henta.  1983.  Modeling fine sediment transport In
estuaries.  U.S. EPA, Environmental Research Laboratory - Athens.
EPA-60Q/S3-83-045.

Heathershaw. A.O.  1976.  Measurements of turbulence In the Irish Sea
bentnlc boundary layer.  The Benthlc Boundary Layer.  I.N. McCave (ed.j.
Plenum Publishing Corp., New York.

Helnemann, T.J., G.F. Lee, R.A. Jones, and 8.W. Newberg. .1981.
environmental chemistry - fate modeling of domestic wastewaeer treatment
plant effluent - derived chloramlnes In Colorado front range rivers.
Fourth conference on Water ChloMriatlon Environmental Imoact and Health
Effects.  Abstracts of Oral Presentations.

Hlrafzuml. r., N. Takahashl. and H. Nlshlmura.  1979.  Adsorption of PCS
onto sea bed sediment, marine plankton, and other adsorbing agents.
Environ. Scl. Technol.  13(5):  580-584.

Hollander, A.F.. P. Somasundaran, and C.C. firyte.  1980.  Adsorption of
polyacrylanlde and sulfonated PAH on Na-Kao11n»te.  In:  Adsorption from
Aqueous Solutions.  Plenum Press. N.Y., 143-162.

Huang. C.P.. and W. Stumm.  1973.  Specific adsorption of cations on
hydrous T-AljOj.  J. Colloid Interface Scl.  43:  409.

Huang. C.P., H.A. Elliott, and A.M. Ashmead.  1977.  Interfaclal
reactions and the fate of heavy metals in so 1.1-water systems.   J. Water
Pollution Control Fed... 49:  745-756.

HydroQual. Inc. 1982.  Application guide for CMA-HydroQual chemical fate
models.  Chemical Manufacturers Association.  Washington. D.C.

Hydrosdence. Inc.   1971.  Simplified mathematical modeling of  water
quality.  U.S.. EPA, Office of Water Program.   Washington. D.C.

James, ft.O.. and, T.w. Htaly.  1972.  Adsorption of hydrolyza&le metal
Ions at the oxide-water Interface - III.  A thermodynamlc model of
adsorption.  J. Colloid Interface Set,, 40:  65..
                          •  t  i          •         "•
James, R.O., and T.W. Healy.  1972a.  Adsorption of hydrolyzable metal
Ions at the oxide-water Interface:  I.   Co(II) adsorption on 5102 and
TIOj as model system.  J. Colloid Interface Scl.. 40(1):  42-52.

James, R.O., J.A. Oavls,  and J.O.  Leckle.   1978.  Surface 1on1zat1on and
complsxatlon at tie oxide-water Interface: II.  Surface properties of
amorphous Iron hydroxide and adsorption of metal Ions.   J. Colloid
Interface Set.. 65:  331.
                                  195

-------
James. R.O., and 6.A. Parks.  1981. .Characterization of aqueous colloids
by their electrical double layer and Intrinsic surface charge
properties.  In:  Surface and Colloid Scl.  Vol. 12.  t.  Mat1Jev1c (ed).
Plenum Press. Mew York.

James. R.O., P.J. Stlgllch, and T.w, Healy.  1981.   The T102/aqueouj
electrolyte system - applications of colloid models and  model colloids.
In:  Adsorption from Aqueous Solutions.  P.M. Tewarl  (ed.).   Plenum
Press. M.t.  pp. 19-40.

Johnson. O.J.  1978.  Measurement and persistence of  chlorine residuals
In natural waters.  In:  Water Chlor1nat1on Environmental Impact and
Health effects. Volume 1.  R.L. Jolley (ed.)  Ann Arbor  Science
Publishers.  Ann Arbor, HI.

Jolley. R.I.  1975.  Chlorine-containing organic constituents In
chlorinated effluents.  J. Water Pollut. Control Fed.. 47(3):  601-608.

KaMckhoff, S.W.. O.S. Brown, and T.A.  Scott.  1979.   Sorptlon of
hydroghoblc pollutants on natural sediments.  Water Research 13(3):
241-248.                                                  '

KaMckhoff. S.w. • 1980.  Sorptlon kinetics of hydrophobic  pollutants in
natural sediments.  In:  Contaminants and Sediments,  Vol.  2. R.A. Baker
(ed.).  Ann Arbor Science,  pp. 193-205.

Klemetson. S.I.., T.N. Keefer. and R.K.  Simons.  7980.  Movement and
effects of combined sewer overflow sediments in receiving  waters.  U.S.
EPA.   Washington. O.C.  EPA-600/2-80-126.

Krenkel. P.A; and V. Novotny.  1980.  water Quality Management.  Acedemtc
Press. M.Y.

Langmulr. D.  1^81.  The power exchange function:  a  general model for
metal adsorption onto geological materials.  In:  Adsorption from Aqueous
Solutions.  P.M. Tewarl (ed,).  Plenum Press,  pp.  1-18.          .,

Larson. R.J.. C1lnckema11He and 1. van Belle.  1981.  Effect of
temperature and dissolved oxygen on blodegradatlon of nltrllotrlacetate.
Water Research. 15:  615-620.

Lee.  O.Y., W. L1ck. and S.W. Rang.  1981.  The entralnment and deposition
of fine-grained sediments 1n lake Erie.  Journal of Sreat  Lakes Research,
7(3):  264-275.                                           .

Leenheer, J.A.  1980.  Study of sorptlon of complex organic  solute
mixtures on sediment by dissolved organic ca Son fractlonatlon analysis.
In:  Contaminants and Sediments, Vol. 2.  ».A. Baker (ed.).   Ann Arbor
Science,  pp.  267-278.
                                  196

-------
Leytham, K.M.. and ft.C. Johanson.  1979.  Watershed erosion and sediment
transport model.  U.S. EPA. Environmental Research Laboratory.  Athens,
GA.  EPA-60Q/3-79-028.

LI, Y.H. and S. Gregory.  1974.  Diffusion of Ions In sea water and in
deep-sea sediments.  Geochlm.  CosraocMn. Acta. 38:703*714.

Lick, U.  1982.  Entralnment, deposition, and transport of fine-grained
sediments In lakes.  Hydroblologla 91:  31-40.

Llss, P.S., and P.G. Slater.  1974.  Flux of gases across the air-sea
interface.  Nature. 247:  181-184.

Lyman, W.J.. et al.  1982.  Handbook of Chemical Property estimation
Methods.  McGraw-Hill.  Mew York.

Mabey. H.. and T. mil.  1978. • Critical review of nydro.lysls of organic
compounds In water under environmental conditions.  J. Phys. Chem. Ref.
Data. 7(2):  383-41S.

Mabey. W.R.. J.H. Smith, R.T. Podoll. H.L. Johnson, T. mil. T.w. Chou.
J. Gates, I.W. Partridge, and 0. Vandenberg.  1982.  Aquatic fate process
data for organic priority pollutants.  U.S. EPA, Office of water
Regulations and Standards, Washington. O.C.  EPA 440/4-81-014.

Nackay, D.. A. flobra, and U.Y. Sh1u.  1980.  Relationships between
aqueous solubility and octanol/water partition coefficients.
Chemosphere, 9:  701-711.

Mackay. 0.. A Bobra. O.W. Chan, and w.Y.'Shln.  1982.  vapor pressure
correlations for low-volatility environmental chemicals.  Environ. Scl.
Technol. 16: 645-649.          .

MacNaughton, M.S., and R.O. James.  1974.  Adsorption of aqueous Hg(ll)
complexes at the oxide/water Interface.  J. Colloid Interface Scl.. 47:
431-440.

NeElroy, A.O.. S.Y. Chin, J.H. Nebgen. A. Aletl. and F.u. Bennett.
1976.  Loading functions for assessment of water pollution from nonpolnt
sources.  U.S. EPA, Office of Research and Development.  Washington,
O.C.  EPA-600/2-76-151.

McNown, J.S. and J. Malalka.  1950.  Effect of particle shape on settling
velocity at low Reynolds number.  Transactions American Geophysical
Union, 31:   74-82.

Means. J.C.. S.S. Wood. J.J. HMsett. and W.I. .Banwart.  1982.  Sorptlon
of amlno- and carbexy-substlti *.d ^Jlynuclear aromatic hydrocarbons by
sediments and soils.  Environ. Sc1. Technol., 16(2):   93-98.
                                    197

-------
Metcalf & Eddy.  1982.  Impacts of wastewater disinfection on coldwater
fisheries.  U.S. EPA, Region I.  Boston, MA.

Miller, 6.C., and R.G. Zepp.  1979.  Effects of suspended sediment on
photolysis rates of dissolved pollutants,  water Research. 13:  453-459.

M111. T., W.R.Mabey, O.C. Bomberger, T.W. Chou, O.C. Hendrey, J.H.
Smith.  1982.  Laboratory protocols for evaluating the fate of organic
chemicals In air and water.  U.S. EPA, ERL-Athens.  EPA-600/3-82-0220.

Hills, W.B.. J.O. Dean, O.B. PorceMa. S.A. Gherlnl, R.J.M. Hudson.
w.E. FMck, G.L. Rupp, and G.L. Bowie.  1982.  Water quality assessment:
a screening procedure for toxic and conventional pollutants.  U.S. EPA,
Office of Research and Development, Athens, Georgia.  EPA-60Q/6-82-004.

Morel. F.M.H.  1981.  Adsorption models:  A mathematical analysis In the
framework of general equilibrium calculations.  In:  Adsorption of
Inorganics at Solid-liquid Interfaces,  pp.  263-294.

Mulkey, 1.*.. R.S. Ambrose. Jr., and T.O. Barnwell. Jr.  1982.  Aauatlc
fate and transport modeling techniques for predicting environmental
exposure to organic pesticides and other toxicants - a comparative
study.  International Workshop on the Comparison of Applications of
Mathematical Models.  UNESCO.  laCoruna. Spain.  (Available from authors
at EPA Environmental Research Lab. Athens. GA).

National Research Council..  1979.  The chemistry of disinfectants in
water:  reactions and products.  U.S. Environmental Protection Agency.
(ITIS:  Pfl-292 776.

Neely. U.B.. O.R. Branson, and 6.E. Blaw.  1974.  Partition coefficient
to measure bloconcentratlon potential of organic chemicals in Hsn.
Environ. Sc1. Techno!., 8(13);  1113-1115.

Nordln, C.F.. and R.S. McQulvey.  1971.  Suspended load.  In:  River
Mechanics.  H.w. Shen (ed. and publ.).  Fort Collins, CO.

Novotny, V.  1980.  Delivery of suspended sediments and pollutants from
nonpolnt sources during overland flow,  water Resources Bulletin 16(6):
1057-1065.

Oakley, S.N., P.O. Nelson, and K.J. Williamson.  1981.  Model of true
metal partitioning 1n marine sediments.  Environ. Scl. Technol.. IS:
474-480.

O'Connor, O.J.  1980.  Physical transfer processes.  In:.  Mode11 no of
Toxic Substances  In Natural U«eer Systems.  Manhattan College (Sun er
Institute).
                                   198

-------
O'Connor,-O.J., and J.P. Connolly.  1980.  The effect of concentrations
of adsorbing solids on the partition coefficient,   water Research, H:
1511..

O'Mella. C.R.  1980.  Aquasols:  the behavior of small particles in
aquatic systems.  Environ.  Sd. Techno). 14(9):  1052-1060.

Omerntk. J.M.  1977.  Monpotnt source stream nutrient level
relationships:  a nationwide survey.  U.S. EPA, Office of Research and
Development,  EPA-600/3-77-105.

Paris, O.F., H.C. Steen, 6.L. Baugnman. and J.T. Barnett, Jr.  1981.
Second-order model to predict mlcroblal degradation of organic compounds
In natural waters.  Applied and Environmental Microbiology. 41(3):
603-609.                                '

Parthenlades, E.  1971.  Erosion and deposition of cohesive materials.
In:  River Mechanics.  H.W. Shen (ed. and publ.}.   Fort Collins. CO.

Pavlow, S.P.  1980.  Thermodynamlc aspects of equilibrium sorptlon of
persistent organic molecules at the sediment-seawater interface:  a
framework for predicting distributions In the aquatic environment.'  In: .
Contaminants and Sediments, Vol. 2.  R.A. Baker (ed.).  Ann Arbor
Science,  pp. 323-332.

Perry. R.H.  and C.H. CMHon.  1973.  Chemical Engineers Handbook.  5th
Edition.  McGraw-Hill. New York.

Porcella. D-B., and O.L. Sorensen.  1980.  Characteristics of nonpoint
source urban runoff and. Its effects on stream ecosystems.  U..S.  EPA.
Office of Research and Development.  EPA-600/3-80-032.

Ramamoorth,  S., and B.R. Aust.  1978.  Heavy metal exchange processes,in
sediment-water systems.  Environ. Geol., 2:  165-172.

ftathbun, R.E.  1977.  ReaeratIon coefficients of streams - state of the
art.   Journal of the Hydraulics Division. ASCE. 1Q3(HY4);  409-424.

Rathbun, R.E.. and O.Y. Tal.  1981.  Technique for determining the
volatilization coefficients of priority pollutants In streams.  Water
Research. 15(2).

Richardson.  E.V.  1971.  Sediment properties.  In:  River Mechanics.
H.W.  Shen (ed. and'publ.}.  Fort Collins, CO.

Richardson.  W.{.., V.E. Smith., and R. Wethlngton.  1983.   Dynamic mass
Balance of PC8 and suspended sol Ids In Sa^lnaw Say - a case study.  In:
•i.yi.cal Behavior of PCBs In the Sreat Lakes.  »*ckay, Paterson.
EUenrelch,  and Simmons (ads.).  Ann Arbor Sdev».
                                 199

-------
Roberts. M.H. Jr., and R.A. .Gleeson.  1978.  Acute toxUHy of(
brombchlorlnated seawater to selected estuarlne species with a comparison
to chlorinated seawater toxlclty.  Marine Environ. Res. 1:  19-30.

Rodgers, P.M., and 0. Salisbury.  1981.  water quality modeling of Lake
Michigan and consideration of the anomalous 1ce cover of 1976-1977.  j.
of Great Lakes Res.. 7(4):  467.480.

Scavla. 0., W.F. Powers, R.P. Canale. and J.L. Moody.  Comparison of
first-order error analysis and Monte Carlo simulation In time-dependent
lake eutrophUatlon models,  water Resources Res. 17:  1051-1059.

Schlndler, P.M., 8, Furst. R. Olck. and P.U. Wolf.  1976.  Llgand
properties of surface sllanol groups - I.  Surface complex formation with
Fe*5, Cu*2, Cd*2. Pb*2.  J. Colloid Interface Sc1.. 55:  469.

Schlndler, P.w.  1981.  Surface complexes at oxide-water, interfaces.
In:  Adsorption of Inorganics at Solid-liquid Interfaces. Anderson and
Rubin (eds.). Ann Arbor Science,  pp. 1-50.
                                             v
Schwarzenback. R.P.. and J. Westall,.  1981.  Transport of nonpolar
organic compounds from surface to groundwater:  laboratory sorptlon
studies.  Environ. Scl. Techno 1., 15(11):  1360-1367.

Shaw, D.J.  1978.  Introduction of Colloid and Surface Chemistry, 2nd
edition, Butterwortfts. London.

Shen, H.W.   1971.  Wash load and bed load.  In:  River Mechanics.  H.M.
Shen (ed. and publ.).  Fort Collins. CO.

Shen, H.W.  1971.  Total sediment load.  In:  River Mechanics.  H.W. Shen
(ed. and publ.).  Fort Collins. CO.

Salomon. S.I.. and S.K. Gupta.  1977.  Distributed numerical model for
estimating runoff and sediment discharge of ungaged rivers.  2.  Model
development.  3.  Comparison with other simple techniques,  water
Resources Res. 13(3):  619-636.

Spain. J.C., P.M. Prltchard and A.w. Bourguln.  1980.  Effects of
adaptation and blodegradatlon rates In sediment/water cores from
estuarlne and freshwater environments.  Applied and Environmental
Microbiology. 40(4):  726-734.                      ,

Stum, W.. and J.J. Morgan.  1981.  Aquatic Chemistry:  An Introduction
emphasizing Chemical Equilibria 1n Natural Waters. 2nd edition.  John
Wiley and Sons. New York.

.Suzuki. M.. T. Yamada. T. Mlyazakl. and X. Kawazoe.  :*»79   Sorptlon and
accumulation of cadmium 1n the sediment of the Tama p>.
-------
Tada, f. and S. Suzuki.  1982.  Adsorption and desorotion of heavy metal $
in bottom mud of urban rivers,  water Research, 16:  U89.

Tchobanoglous G.. A.O. Levlne, and J.R. Koltz.  1983.  mtraO'e solids
as a design parameter for yastewater treatment processes, prepared for
presentation at the Stxty Symposium on Waste Treatment. Montreal Canada,
November 16-17.  1983.

Thelss, T.U, and R.O. Rlchter.  1980.  Adsorption reactions of nickel
species at oxide surfaces.  In:  Participates m water.  M.C. Kavanaugn
and J.O, Leckle (eds.).  Advances 1n Chemistry Series #189. ACS.
Washington.  DO. 73-96.

Thomann, R.V.  1972.  Systems Analysis and water Quality Management.
Environmental Science Services 01v., New York.

Thomann. R.V.  1982.  Verification of water Quality models.  Journal of
the Environmental Engineering Division. ASCE, lOB(EES):  923-9*0.

Thomann, R.V.  19826.  Physico-chemical model of toxic substances in the
Great lakes.  U.S. EPA, Office of Research and Oevelooment.

Thomann, R.V.  1984.  Simplified heavy metals mode! for rivers.  Paper
presented at ACSE National Conference on Environmental Engineering.  LOS
Angeles, 'CA.

Thomas. W.A.  1977.  Sediment transport.  In:  Hydrologlc Engineering
Methods for Water Resources Development.  Hydrologlc Engineering Center,
Corps of Engineers.  Davis. CA.

Todd, O.K.   1970.   The Water Encyclopedia.  Water Information Center.
Port Washington, N.Y.

Tomllnson,  R.O., et al.  1980.  fate and effects of participates
discharged  by combined sewers and storm drains.  U.S. EPA, Office of
Research and Development.  EPA-600/2-80-111.

Uchrln, C.J., and W.J. Weber. Jr.  1980.  Modeling the transport
processes for suspended solids and associated pollutants in
river-harbor-lake systems.  In:  Contaminants and Sediments. Vol. 1.  Ann
Arbor Science.

U.S.  environmental Protection Agency.  1976.  Areawlde assessment
procedures  manual, Volume I.  Municipal Environmental Research
Laboratory.   Cincinnati,  OH.  EPA-600/9-76-014.

U.S.  Environmental Protect'JP Agency.  1977.  Mahonlng River waste load
allocation  study.   M1ch^>n-0n1o District Office,   west Uke.
                                 201

-------
U.S. Environmental Protection Agency.  1964.  Technical guidance manual
for performing waste load allocations.  Book VI, design conditions. •
Office of water Regulations and Standards, Washington. O.C.

valloulls. I.A., and E.J. List:  1984.  Numerical simulation of a
sedimentation basin.  1.  Model development.  Environ. Sd. Technology
18:242-247.

Velth. G.O., O.L. DeFoe. and B.V. Bergstedt.  1979.  Measuring and
estimating the bloconcentratlon factor of chemicals in fish.  J. Fish
Res. Bd. Can.. 36(3):  1040.1048.

Vueeta, J., and J.J, Morgan.  1978.  Chemical modeling of trace metals in
fresh waters:  role of eomplexatlon and adsorption.  Environ. Scl.
Techno!., 12(12):  1302-1319.

Weber, W.J., Jr.  1972.  Physlcochemlcal Processes for Water Quality
Control.  Wlley-Intersdence, New York.

Westall. J.. and K. Hohl.  1980.  A comparison of electrostatic models
for the oxide/solution Interface.  Adv. Coll. Interface ScL. 12:
265-294.

Williams, J.R.  1975.  Sediment yield prediction with universal equation '
using runoff energy factors.  In:  Present and Prospective Technology for
Predicting Sediment Yields and Sources.  U.S. Department of Agriculture.
AftS-S-40.

Williams. J.R.  1980.  SPNM. a model for predicting sediment, phosphorus,
and nitrogen yields from agricultural basins,  water Resources Res.
16(5):  843-848.

Wilson. G.T., and N. Macleod.  1974.  A critical appraisal of empirical
equations and models for the prediction of the coefficient Of reaeratlon
of deoxygenated water.  Water Research. 8:  341-366.

wvschmeler. W.H., and D.D. Smith.  1960.  A universal soil-loss equation
to guide conservation farm planning.  Seventh International Congress of
Soil Science.  Madison, HI.

Wischmeler. W.H.  1976.  Use and misuse of the universal soil loss
equation.  J. Soil Hater Cons. 31(1):  S-9.

Wolfe. N.t.  1980.  Determining the role of hydrolysis In the fate of
organIcs In natural waters.  In:  Dynamics. Exposure and Hazard
Assessment of Toxic Chemicals.  R. Haque (ed.).  Ann Arbor Press.

Zepp. 9 ft.  N.I. Wolfe. J.A. Gordon, -id 6.1. "Baughman.  1975.  Dynamics
of 2.4-0 esters In surface waters.  **..1r«,i. Sc1. Technol.. 9(13):
1144-1150.
                                  202

-------
Zepp, R.6.  1980.  Assessing the photochemistry of organic pollutants 1n
aquatic environments.  In:  Dynamics, Exposure and Hazard Assessment of
Toxic Chemicals,  ft. Hague (»d.).  Ann Arbor Press,  pp. 69*110.

Zlson, S.W., K.F. Haven, and W.B. mils.  1977.  water duality
assessment, a screening method for nondeslgnated 208 areas.  U.S. EPA,
Environmental Research Laboratory.  Athens, SA.  EPA-600/9-77-023.
                                203

-------
          APPENDIX A



DEVELOPMENT OF MODEL EQUATIONS
        August 1984

-------
                                  APPENDIX  A
                         DEVELOPMENT  OF MODEL  EQUATIONS

A.I  CONSERVATIVE  POLLUTANT

    The basic assumption  In  the conservative  substance model  is  that  .
there are no Internal  source/sink reactions that significantly affect the
toxic substance concentration  1n the receiving water.  Only external
sources of  the contaminant,  or Inflow of dilution water by advectlon or
dispersion  can alter the  contaminant conconcentratlon. according  to this
conservative assumption.

    Under the conservative substance assumption, and assuming that  longi-
tudinal dispersion  Is  negligible relative to  advectlve transport, the
general river transport equation reduces to
       dC     1  d(OC)
Furthermore, application of Equation Al 1s almost always made under the
assumption that the river reach In question is at steady-state with a con-
staht, continuous point discharge of the contaminant In question.  Also, flow
Is assumed to be constant over the reach.  Under these assumptions, the
solution for Equation Al 1s simply
       C(x)  -  CQ;  x > 0
                                                                           (A2)
       C(x)  -  Cu;  x < 0 .

where C  * upstream river concentration of contaminant and C  • river
       u                                                    o
contaminant concentration at x • 0 after nixing upstream river water with point
discharge.  The concentration, C . Is determined by performing a mass balance
for C at x • 0, assuming instantaneous mixing at that point.  Therefore, C  is
                              A-l

-------
calculated from C . the point source concentration (C^),  the  point  source
flow  (Qw). «nd upstream river flow (0 }  as follows:
       p     [y  CuHi-M
           *  " —       —                                        U3)
Given the above assumptions, CQ 1s Independent  of  x  downstream of  the
effluent unless there Is another downstream discharge  of  the  substance or a
    (
dilution of the substance by inflow of uncontamlnated  diluting water.
Multiple point discharges can be handled reapplylng  Equation  A3 at each
successive discharge point, using the result of  the  previous  discharge mass
balance as the upstream boundary conditions.

    There Is a large body of literature which suggests  that most priority
pollutants do not behave conservatively 1n  water bodies.  Recent results
from dynamic mass balance modeling studies  of heavy  metals and several
synthetic organics in the Great Lakes have  indicated nonconservative
behavior (Oolan and Blerman, 1981; Richardson,  et  al..  1983;  and Rodger*,
1961}.  Flint River data (presented later In this  section) collected by
Michigan ONR In 1978 demonstrated that total zinc  and  cooper  did not
behave conservatively 1n certain stretches  of the  river.  Unless
advectlve transport In a given reach Is rapid relative  to the transport
and transformation processes discussed In Section  2.2  or unless relevant
Internal source/sink fluxes just balance, the Instream  concentration of a
pollutant 1s likely to vary with longitudinal distance.

A. 2  NONCONSERVAWE POLLUTANT - SIMPLE WATER COLUMN ANALYSIS

      Often the net result of the combined  effects of transport and transfor
mation forces acting on a chemical substance Is a  first-order die-off of the
substance with distance (or t1me-of -travel)  downstream  from a discharge.
This type of concentration profile can be simulated  by  lumping several pro-
cesses Into a single first-order loss term  applied to  the general  river
transport equation.  Given this approach
                               A-2

-------
        dC     f2  d2C     Q  dC     K .                                  ....
        ai  '  e        -         •  *C •                                 (A4)
               .r
 where K. [time"  ] 1s an aggregate  first-order  decay coefficient for tie

 substance in question.          '    .


     Several  further assumptions are often Involved in applying the above

 equation to  a specific  site.   They are as follows:


       1} The river Is at steady-state with respect to flow and loads;

       2) Concentration  of  the modeled substance  is uniform over the.
          cross-section  of  the river (I.e.. one dimensional system); thus,
          any point discharge  Instantaneously mixes with the river  flow at
          the point of discharge;

       3} Dispersion 1s  negligible  in the longitudinal direction;  that  1s.
          only advectlon Is considered significant  in the direction of  flow;
          thus,  E « 0 In equation A4;

       4) Flow,  cross-sectional area, and mean  depth are constant  over  the
          reach  In question.     •'

 Given the above  assumptions.  Equation A4 reduces  to

        0  ..  -     '     -  KC .          ,                                (A5)
•The solution  to Equation  AS  Is


                        *  *T*
        C(x)   -  C(0)exp(—i-J                                            (A6)
 where.     U *  Average  river  velocity  In  reach  [length/time]

        C(0) •  Initial' concentration of  the modeled  substance  at  x  •  0

               [mass/length3].


     TM$  approach  limits  Itself  to only  the water column and  only  oi.e

,fom of the • M* :tant.
                 *                         -            t

     There are  ,two  methods  for  applying Equation  A6  to  a problem  of

 multiple  discharges  In  a  river system.   Since  Equation AS  1s  an  ordinary.
                                A-3

-------
 linear differential equation, the Independent solutions for Individual point
 sources can be addltlvely superimposed to obtain a total concentration
 profile along the river.   Alternatively,  the river reach in question can be
 segmented according to significant changes In river geometry or flow, or at
 locations of point sources.   Then each, segment 1s modeled sequentially
 moving downstream.  The Initial  (upstream) concentration of each segment 1s
 determined by the concentration  entering  from the upstream segment,
 augmented by any  effluent load entering at the segment  boundary.

     Great care must be taken  In  applying  this type of model to a specific
 site without enough field data to confirm the validity  of the  aggregate
 decay  coefficient.  Kf for a  particular pollutant may vary from site to
 site,  or  may vary over time due  to changes 1n controlling parameters like
 flow or river cross-sectional  geometry.

 Application  of Model to Flint  River August 1978  Oata--

     As a  brief example of  analyzing a  system  with  first-order  decay  model
 of the water column, metals and  suspended  solids  data obtained  during a
 preliminary  survey of  the  Flint  River  will  be compared  to the model
 presented  above.   In this  application  the  aggregate  first-order
 coefficient,  K^,  Is assumed to be  an apparent net  settling  velocity
 from the water column;  therefore.                                  --
       *7    •   *t/*  •                                                   (A7)
                                                                         •,.
where. KT- First-order  loss rate coefficient  of total metal or  suspended
              solids [t1mv-1].
       vj. Apparent net settling velocity  [length/time].
       H  • Mean depth of river [length].
    The study  reach of the Flint River used 1n this  Investigation was
from the Utah Street Oam 1n the city of Flint (Km 83.6)  to  :he bridge at
Crosswell Road (Km 11.0).  Oata on river metals and solids concentrations
                               A-4

-------
and point source  Inputs were obtained  from a Michigan  ONR.survey
conducted 1n August of 1978 {Roycraft  and-Buda,  19^9).  Table  Al  is  a
summary of the point discharges considered 1n this study,  and  Table  A2
contains the river hydrology and geometry at the time  of sampling.   As
Indicated In Table A?, the river reach has been divided Into four
segments.

    Figures Al through A3 contain the  survey data and  model predictions
for total zinc, total copper, and suspended solids, respectively.  In
attempting to simulate the data points In these figures, the only
parameter that was varied was the net apparent settling velocity. v$.
which determines  the stream concentration through Equations A7 and A6.
Of course, when v  1$ set equal to zero. It Implies that the pollutant
1s transported conservatively down the river.  All other parameters.
those In Tables Al and A2, and the Initial upstream conditions were  held
constant.

    It 1$ apparent that none of the three substances behaved
conservatively within the entire study reach; the conservative assumption
considerably over-predicts the downstream concentrations.  This type of
error could be especially Important In situations where a  waste load must
be allocated among multiple discharges along a river reach, since the
conservative pollutant assumption omits the effect of  depuration
occurring between points of discharge.

    In the segment between the Flint UUTP and the Ragnone  plant {Km
70.7-41.1), total zinc appears to settle at an apparent rate of 1.0 ra/d,
while total copper Is lost at a rate close to fl.S m/d.  The apparent
settling rate for zinc 1n this segment may be slightly less than 1.0 m/d,
or there may have been an unaccounted for source of zinc at about
kilometer 46. The available data base did not permit this  distinction.
The suspended solids data and simulations (Figure A3)   confirm the metal
findings.  In the segment bet*, en Hint and ftagnone plants, solids are
settling at a rate between 1.0 and 1.2$ m/d.   The larger net settling
rate observed for sol Ids Is consistent with the assumption that not all
                               A-5

-------
         TABU Al,  POINT DISCHARGES FOR AUGUST 1978 SURVEY
Source
G.«./Bu1ck
Flint WWTP
Ragnone WWTP
Km
83.4
70.7
41.1
Flow
fm /s)
0.09
0.86
0.84
Loadlnas (ka/d)
Total Zinc
0.77
29.. 0
11.0
Total Cu
0.48
3.6
4.0
Susoended Solid s
.
2.710
8,000
TABLE A2.  FLINT RIVER HYDROLOGY AND GEOMETRY FOR AUGUST 1978 SURVEY
Seamen t
1
2
3
4
.Starting
Point
(Km)
83.4
81.9
70.7
41.1
Segment
Length
(Km)
1.S
11.2
29.6
30.1
Mean
Depth
fm)
3.0
0.66
0.66
1.0
Cross-Sectional
Area
140
30
30
30.6
Flow
6.2
6.2
7.06
7.9
                        A-6

-------
   I I
   i
   *
§11
      1	
                                §
MM
MM


i
                                   a
                                   a
   si
   11
   s3
                                   i
                                   U hU

                                   X CB

                                   o <

-------
TT:
  i	

          •
          L.
                                                   cs >•
                                               5

                     I
          s          s
          ()/•") U1M09 1V1U

-------
                                  - s

                                             l
                                             _ U
                                             il
(«/••) SOU OS

-------
 the metals  'in  the  river  are  In a  participate Form;  therefore,  the
 apparent  settling  rate  For metals should  be  somewhat less  than for
 suspended solids.   Furthermore,  the finer-grained,  slower-settling
 participates probably have a  higher metals content  than  the  larger,
 size participates.

     Some  difficulty was  encountered In  simulating  the  data downstream  of
 the Ragnone discharge For all  three substances.   IF  the  data  set  is  in
 fact representative of a steady-state condition  In  this  segment (a fact
 which cannot be established  from  such a small  sampling}, then  it appears
 that the  net loss of metals and solids  in this reach was close to zero.
 This  could have been the result of  sediment  resuspenston 1n this segment
 due to higher water velocities.   This behavior 1s addressed further  in
 Section 5.0. which  1s a  case  study  of the more extensive 1981-82 Flint
data.

     Finally. 1t 1s  quite apparent that the metals' behavior in the river
 1s  closely related  to the suspended  solids'  behavior.  This observation.
 coupled with the need to know  the exposure of aquatic  biota to dissolved
 contaminant concentrations, leads to the rationale for the somewhat,  more
 complicated approach described next.

A.3  WATER-SEDIMENT WOOEL HAVING  SEPARATE PARTICIPATE  AND DISSOLVED
      CONTAMINANT PHASES
    One of the most significant mechanisms for the movement of pollutants
through an aquatic  environment 1s the adsorption or uptake of  the
chemical by both nonvlable and viable partlculate matter, followed by the
transport of the interacting particulars.  Association with suspended
matter thus significantly alters the transport regime of a chemical  by
Introducing additional  transport processes, such as settling and
resuspenslon.   Furthermore, the association with suspended matter can
Indirectly affect the rate and extent of chemical transformations and
I'otic accumulations.   For example, partitioning of a portion of a
chemical In suspended solids  could reduce the flux of  the chemical's
dissolved phase Into the biota, thus potentially reducing Us toxlclty.
Accordingly, determination  of the fate and potential toxlclty of
                                 A-10

-------
pollutants  1n aquatic systems requires knowledge of two  Important
processes:   1) partitioning of metals between dissolved  and particulate
phases In aquatic systems, and 2} transport of particulate matter  (i.e.,
settling and resuspenslon) as affected by hydraulics and particulate
physical properties.

   .A conceptual diagram of the NICHRIV model is presented in Figure A4;
nomenclature Is presented  In Taolt A3.  Note that the calculation  scheme
permit; the  estimation of  the equilibrium partitioning of total chemical
between dissolved and solid phases 1n both the"water column and the
sediment bed.  With this approach It Is necessary either (a) to specify
the (water column) suspended solids concentration as a parameter,  or
(b) to model suspended solids as a state variable.  The  former approach
1s used In the SLSA model; the latter approach, described below. 1s used
1n the HICHRIV model.

    Settling, resuspenslon. and burial apply only to the particulate
bound pollutant.  Diffusion between the sediment pore water and water
column applies only to the dissolved phase.  The first-order decay
coefficient represents the sum of a number of potential  processes, most
of which are Insignificant for metals 1n streams.  For organlcs, however,
the loss rate can Include volatilization, hydrolysis, photolysis,
chemical oxidation, and blodegradatlon (described in Section 3).

    In the current version of the MICHRIV model the decay coefficient
applies only to the dissolved phase.  Volatilization is  a process  that
clearly .applies only to the dissolved phase;  While hydrolysis,
photolysis, oxidation, and blodegradatlon may often be far more rapid in
the dissolved than In th* adsorbed phase, there seems to be no consensus
that this Is true 1n all casts.  Consequently, to maintain generality the
decay coefficient for total pollutant, I, has been formulated below as
the weighted sum of dissolved and particulate phase decay coefficients.
*dfd * *P'P ^w1th appropriate subscripts 1  or 2)
                               A-11

-------
Aii
                   IOAO (WT)
WATER
                                                                     DECAY (K4t)
                                                                           TRANSPORT
 ACTIVE     "2
 SfOIMiNT
StOUUNT
                               TQTAi SUUTAMCX - 4Cj)
PAKTICULATE
SUBTAMCf  C.|
              SCTTIINC W,
                         H6URE A4. MICHRIV FRAMEWORK

-------
                 TABLE  A3:   NOMENCLATURE  FOR WATER-SEDIMENT MODEL
 Parameters                            Water  Column           Sediment

 Concentrations

 Total, toxicant (i.g/l)«                    CTI                  CT2
 Dissolved toxicant {ug/D*                C^                  Cd2
 Partlculate toxicant (»g/l)'              C                    C  .
 Participate toxicant ( g toxicant/
  mg solids)                              f]                   r2
 Total solids (mg/l)»                      m^                •  »2
 Toxicant load (kg/day)                    «T
 Sediment porosity                         —                  *

 Part_1_t1_on_1nq

 Dissolved fraction                        f.,                  f ..
                                           di                  az
 Partlculate fraction                      f_                  f  .
 Partition coefficient (l/mg) (» • r/Cd)  .^                   «2

 Channel Geometry

 Oownstream distance                        x                   x
 Cross-sectional area (m )                 A.                   ---
 Oepth (m)                                 H.                   H
 Flow («3/sec)                              Q.J
Velocity (n/sec)  (U . 0/A)                 u.
                                  A-13

-------
          TABLt A3:  NOMENCLATURE FOR WATER-SEDWENT MODEL (Continued)
Parameters                            water Column          Sediment

Hate Parameters

Aggregate decay rate coefficient (I/day)
 - for dissolved                          Kai
 . for participate                        Rpl
 - for total (K • Kflffl » Kpfp)            PC}                  Kg
Settling velocity (m/day)                 w           ;        ---
fiesusoenslon velocity (in/day)             —                 u
Sedimentation (burial) velocity (m/day)   —                 w
                                                               9
Sedimentation loss coefficient (I/day}    ---                 K
Diffusive exchange coefficient (fli/day)    K                   n
•In terms of bulk volume.

-------
    HUMn  the. conceptual  framework of  the model  shown  in  Figure A*,  the
 following assumption's are  used  to develop mass balance  eauatlons:

       1.  Constant hydrologlcal and morphological conditions  for eacfi  r'ver
          segment;
                                          dCT    • am
       2.  Steady-state conditions exist:     i  .      .  0;
      3.  Vertical and  lateral uniformity 1n water column and sediments; no
          mixing zones
      4.  Dispersion  1s negligible  In. the longitudinal direction;
      S.  No longitudinal (downstream) movement of the bed: Oj  • 0;
      6.  No spatial  variation of the sol Ids content of the bed:  mj  Vs
          constant (although vr\ Is not constant).
      7.  Partitioning between dissolved and solid phases 1s rapid relative
          to transport and other transformation kinetics.
      The solution for pollutant concentrations in such a one-dimens'onal .
steady-state system is developed below.  The solution is based  on four
coupled, differential  equations representing mass balances for solids  in the
water column and in the bed, and for the toxicant 1n the water  column and  -n
the bed.

    using the subscript 1 for water column variables and the suoscript 2
for sediment variables, the mass balance for solids suspended In the
water column (ut) takes the form:
              (advectlon)   (settling)   (resuspenslon)
              °i  dml         ws             wrs
                  ar    -   iq- *i •   *  •  —  mz
Assuming that m. 1s not a function of x. and that w  and w   are
constant, this equation has the solution:

                    (initial solids)         '   (resuspended sallds)
                       - w    x                       - w  x,
                       	n    i. " '     w  * m_    r       ..  M 1
                                         w$     L            J
                              A. is

-------
 It  can  be  seen  that m.  1s a  function  of  the  travel  time downstream
 (x/U^).  the  settling  velocity  (w  ) or Us associated death-dependent •
 rate  coefficient  (w /H  ), and  the  resuspenslon  flux  (*   m  )..  when
 the resuspenslon  velocity (w  ) Is zero, the  second  term drops out of
 Equation A9, and  m. Is  no longer dependent on m..   In comparing Equation
 A9  predictions with.field data 1t* 1s  Important  to account for all external
 and Internal sources  of  suspended  solids.  One  potential .internal source  is
 phytoplanfcton growth:   the concentration of phytoplahkton solids nay be 200
 fold  greater than the concentration of chlorophyll-a (Canale 1983).

    A second mass balance equation, this one  for solids in  the bed. can  .
 be  written:
           (advectlon)    (settling)     (resuspenslon)   (burial)
It 1s assumed that the bed does not move (Q. • 0) and that m^ 1s
constant (dm./dx • 0).  Cither of these assumptions causes the advectlon
tern to drop out.  Consequently. Equation AID reduces to an algeorak
equation:
     (settling)    (resuspenslon)  (burial)

       -s»7    •       -n    '     "«•                       •'           (A11)
The sedimentation velocity, w.. represents the movement of material downward
and out of the active sediment layer, the thickness of which (H ) does not
change with tint.  This velocity thus represents the rate of change 1n elevation
of the surface of the bed, Ignoring any effect of compression of the deep
sediment.  If the resuspendlng flux exceeds the settling flux, then w. 1s
negative. Implying that channel downeuttlng Is occurring.  If the downward flux
exceeds the upward flux. wtf is positive. Implying that the chanrel bed 1s
rising over time, for the conditions being modeled.  Where chemical 
-------
    The  third mass  balance equation  1s  for the  toxicant  in  the  bed
           (advectlon)   (settling)     (diffusion  in)
               °2  
-------
 If a  term fl. the 'sediment capacity factor- (OUoro et al.  1982).  1s  defined
 as:
       a .
then
                 flV2
Combining Equations A13 and A17. and solving for r  /r
                          p2
                     V rp2
The first term 1n the numerator 1s modified by  noting  the  Equation'An
relationship between w ,  w  ,  and wfl.   The second  term 1s  modified by
noting that:
                           and   f.
and   f.
m«f.
Consequently.
                         o2
(A19
                                                                          (A20)
The ratios CT2/CT1 and r./r1  thus depend on  the water.sediment
P4rtlc1e exchange rate*, the water.sediment  diffusion  rate,  and the decay
rate within sediment.  They do not depend on the decay  rate  within the water
column.
                                A-IS

-------
     The fourth mass balance equation Ms for tne toxicant in the water
column:
           (advectlon)     (decay)      (settling)     (diffusion out)
                             r  -
                  -3x—  *  *1 CT1 *


       (rtsuspenslon)    (diffusion 1n}
Combining Equations A21 ana A13 results in:

dCT1    T            wf        K.f.,,       flr. /               \T   C-,
_I1  .   .r   .   -121 .   -L51  .   -J"(w  f    .  K  f   )   -II
«!      t             Mi         «i        Vi ^               'J    yi

All terms on tne right side of the equation  are constant for a  particular
exceot for CTI, fd1 , and f ^ (and sofiseauently 0).  The fractions  f
and f . are functions of m. per Equation AH;  m.  Is  a  function  of  z per
Equation A9.  However, If the Increments of  x  are  small enough, then m, ,
f., , and f , are essentially constant.   Consequently «1tnln  small
 01       pi
Increments of x. Equation A22 has .a  simple solution:
                           K.
                            T
                                                                         (A22)
                                                                           (M35
where
   (decay)  (settling) (diffusion out)        (resuspenslon)  (diffusion  in)

By stepping down the react) 1~ small  increments  of x,  C-.  can  be computed
from the input parameters •  .  p^,  K ,  »^,  »j,  HI,  H_.  m ,  KI,
                                A-19

-------

K., and the input solids and toxicant loads using Equations A3,  Aig,  A20,
A23, and A2«.   Then CT2 can be computed from CTI  using Equation  A13 or
A1S. .For the solution to be valid, the increments of x must be  short enough
that the relative change in m^ is small within each increment.   That  U.
the Increments Ax must be shortened until ftm./ra  Is small.
                                                                            i
    While Equation A24 1s satisfactory as written, some simplification of
It Is helpful  for better understanding the model.  Using the relation-
ships shown in Equations A19 and AU. the "diffusion in" term of Equation
A24 can be put In terms of f,. and combined with  the "diffusion  out"
term.  Using Equations All and A16, the "settling* term can oe  expressed
In terms of resuspenjlon and burial.  The resulting equation Is:
       (decay)       (settling)        (resuspenslon)   (net diffusion)
         , Equation A20 can be solved in terms of K  as follows:
Substituting this relationship Into the "net diffusion* term of Equation A25
causes several terms to cancel out; then, after defining the sedimentation
or* burial rate coefficient as K$ . ^/H^, the equation can be
expressed as:
                    6%  '
This result expresses K_, the overall rate coefficient for disappearance of
the toxicant from the water column, In terms of the three avenues for
elimination of -the toxicant from the water-sediment system:  deca* In water.
decay in *edlment, ai.J burial.  The rates of sediment decay and burial are
                               A-20

-------
modified Dy 3r /r   wMch 1s a function of the water-sediment mass ratio,
the partitioning parameters, the sediment-water exchange parameters, as well
as the sediment decay rate Itself.   It might also De noted that for a
condition where *-,  • «j, KI  • K. »  0,  and w   • 0, this model
reduces to the simpler model expressed by Equation A7.
                                 A-Zl

-------
            APPENDIX 6





SEDIMENT TRANSPORT CONSIDERATIONS

-------
                                APPENDIX P

                    SEDIMENT TRANSPORT CONSIDERATIONS
     The pollutant  fraction associated with participate material  is
determined (at equilibrium) by the partition coefficient and the solids
concentration.  In many natural waters the participate phase on average
contains a small percentage of the alkali and alkali-earth metals such as
sodium and calcium. 20-30% of the     strontium and boron. 30-70% of the
cadmium, fine, copper, and mercury, 70-85% of the chromium and lead, and
98%  of the aluminum and Iron (Forstner 1977). .The bulk of many
pollutants Is thus carried on participate material.

     Predicting the transport and fate of partlculate-assodated
pollutants requires an understanding of the behavior of particles.
Predicting particle Behavior 1s. however, one of the most'difficult and
uncertain aspects of water quality modeling.  Much of the existing
knowledge pertains to the larger particles which control the
configuration of the streamoed rather than to the smaller particles
likely to adsorb many of the toxic pollutants.  Consequently, future
findings In this area may significantly Improve predictive abilities.

8.1  SEDIMENT PROPERTIES

    An Individual sedimentary particle may oe characterized by Us  size,
shape, density, fall velocity, mineral composition, surface texture, and
other properties.  Particle size can be described by a number of
different measures, Including but not limited to (a) nominal  diameter -
the diameter of a sphere having the same volume as the particle, (b)
sieve diameter - size of sieve opening through which the particle will
pass, approximately equal  to the nominal diameter, and (c) fall diameter
- diameter of a sphere with specific gravity 2.65 (quartz) that has the
s«me  all velocity (Richardson 1971, Guy 1970).   Table 81  and Figure 81
show the size ranges corresponding to particle classifications.
                                  8-1

-------
    TABLE 81.  KINDS OF SEDIMENT MATERIALS ANQ SIZE CLASS TRANSPORTED
                     IN STREAM (from CulDertSon 1977)
Sediment
Boulders
Cobbles
Gravel
Sand
sin
Clay
Organic Detritus
Size Class
>2S6 am
64-256 nm
2-64 mo
0.062-2 am
4.62 nm
0.2-4 wfl

Mode of Transport
Bed Load
Bed Load
Bed Load
Bed Load or
Suspended
Suspended
Bed Load or



Suspended


Suspended
  Including leaves,
  trees biological
  remains, etc.

Biota

  Including floating
  and bottom duelling
  organ1sns
Bed Load or Suspended
                                  B-2

-------
10- 1Q    10'*
10-4
                         10
                          -7
                                            10-*
                                                 »0
                                                  '*
'*
                      COILOIOS
                                                 1
                                    SUSFENOCO ^ARTICLES
                              •ACTIHIA
              vmus
                                                 I
                                         i
                                         I    MICRO        '
                              _ _ » •i'-. {if v£S — — —* —• —
                                        . I        . SIEVES  .
F1L 7T» TY*tS

         MOL«CULA«.
                      MCMVNANI
                                                 1  SANO
                                   OlArOMACEOUS
                                      EANThS
                                                 | ACTIVATED
                                                 . CARBON 'CAAlNSi
           MICRO-   'OHC OPENINGS
      Figun 81.   Size Rang* of Sediment Par-tides and Filter Pores
                  (from Stumm and Morgan 1981).
                             B-3

-------
     Fill velocity is the average terminal velocity of a particle falling
 alone in Quiescent distilled water.  It is related to a number of
 particle and fluid characteristics Including particle and fluid
 densities,  fluid viscosity, and particle diameter, shape, surface
 texture, and tumbling frequency.
9
     Mineral  composition influences density,  size,  shape,  and thus fall
 velocity.  Most mineral sediments carried by stream flow  have a specific
 gravity  of  around 2.65  (Culbertson 1977).  Consequently,  the fall
 velocity of  quartz spheres having specific gravity 2.65 1s  used as
 somewhat of  a benchmark.   Nevertheless, substantial  variations in density
 may be observed,  with organic particles especially tending  toward lower
 density.

     Tor  sediment transport the most useful expression of  particle snape
 1s  given by  the Corey shape factor, c//ib".  where  a.  b. and  c are the
 lengths  of  the longest. Intermediate,  and Shortest mutually  perpendicular
 axes,  respectively (Ncflown and NalaUa I960).

     Sulk  sediment 1s  a  complex mixture of differing  individual
 particles.   Bulk  properties are related to the above  individual
 properties and to the way  they are distributed.  Bulk properties  of
 particular  Importance may  be the size  distribution,  specific gravity,
 porosity, and conesIveness.

     Measured size distributions may be expressed  In a number of different
 ways,  frequency  distribution histogram show  the  prevalence of material
 within given class Intervals.  Cumulative distribution plots show the
 total  percentage of  material with size smaller than  particular values.
 Cumulative distribution plots can be used to specify  guartlle values.
 d2s<  djg, and d?$ (where dz Is the diameter  greater  than  x
 percent  of  the particles).   Table 82 shows particle  size  distributions
 ^bserved in  raw sewage, primary effluent, and  secondary effluent  of  one
 Municipall v (f  nazountas  and Kathlas  1964).   Figure  B2 shows the size
 distributions observed  In  stream beds  of 11  rivers (Guy 1970).  mils et
 al.  (1982) also presents some sediment data  for several rivers.
                                 B-4

-------
                            o
t/t
«M
         et
        »•    A
         el
         •it
         w*
 •      a.         js
                                                         tt    *•    «*    w
                                                         ••          a*   >•

                                                         *'    f    S
                                                         <«          w    ..
                                                         w    TI          «*
                                                               «J    «l    W
                                                         c^     w>    •»    l_
                                                         v.     *    .    9

                                                        J6   ^»    ?   5

-------
           Silt
   100
s
o
X
s
o
z
i
-   »
<
TECHNIQUES OP WATER-RESOURCES INVESTIGATIONS

  Sand                 Gravel          Coboi*          Boulder
     0.01
                                            SIZI. IMMIkLIMITtllS
          1 - Mnamipp* Riwr at H«aeJ 9t
          2 - MffMHBO* Riw atCiwo. III.
          3 - Mtacwn Rnw n Omafca, N«cjr.
          4 — RcpwMiean Ri««r at day Ctnrar.
          S — South han» Rhwr at Sowdi Hatw. Colo.
          • - PwnCMia RIMT at WtfhMU. N. Dak.
                           7 - S«n»q C/Mk .war Rock«ill«, Md.
                           I — SrandywiiM Ot*k it U«n«O«. ^a.
                           9 — Brantfywrma C/MM it Cornoq. PI.
                          10 - YrtlowttoiM Rnr«r at Billing. Mant.
                          11 - W. •eric Rock C/«*k n*ar R«J Loaq«. Mont.
               Figur* B2.   P«rtid«-*ia» Oinhbution of Stnambod Matwial Typical of
                            Indierad Strains in tira United Statn (from Guy 1970).
                                             B-6

-------
    The sUe distribution of natural sediment 1s ordinarily expected to
plot as a straight line on log probability paper.  If this is the case,
then the median wltl equal the geometric mean, and the ratio dcn/d.,
                                                              9(J  . 1 b
and d../de. will equal the geometric standard deviation or 'gradation
     8*  50
coefficient.*  The complete distribution can thus be described By the
median (or geometric mean) and the geometric standard deviation.

    For a given shape, texture, and density, particle size Is Inversely
proportional to the particle surface-to-mass ratio.  As discussed in
Section 3.2, the partition coefficient of organic contaminants can be
related to the quantity of organic sol ids (i.e., the product of solids
concentration and percentage organic material) without regard for the
solids surface-to-mass ratio.  For metals, however, the partition
coefficient is likely to be related to the mineral composition and
surface-to-mass ratio of the solids.  Tada and Suzuki (1982) and to some
extent Oossls and Warren (1980) observed higher participate metal
concentrations in smaller particles.  Hayter and Mehta (1983) present
similar data, as shown In Figure 83.  Thus, the smaller size fractions,
particularly the readily transported silt and clay fractions, are
expected to more strongly affect contaminant behavior.

    The porosity of bed sediment Is a measure of the Interstitial volume
per unit of bulk volume 1n place.  Porosity may vary between 0 and 1,
with 0 signifying 100* solid and 1 signifying 10QX water In the bed.
Porosity affects the shear strength of the bed. which 1n turn affects the
rate of resuspenslon under various shear stresses or current velocities.
Bed porosity must be distinguished from individual particle porosity.

    Coheslveness describes the attraction the Individual particles have
for each other.  NoneonesIve sediments are composed primarily of sand and
gravel.  Cohesive sediments consist of silts and clays.  The behavior of
cohesive sediments differs from that of noncoheslve sediments in some
Importar* ways.
                                 8-7

-------

      40      80
     %< Hum
                             £  2
                                         I   I   I
                                                         v    I    I
   0

   (bl
                  «o      so
                                  80
           OJ4



        «  0.20
        •
        u
           aot
           0.04
            0.0
               a
              (cl
40      80       80      IOC
F'igun 83.  Vwittion of Meal Concentration with Scdimtnt Partieit Siz«
           (tarn H«yar mtt Mctra Y983>.
                             B-8

-------
     In  suspensions of nonconesive material  the baste setting. ynU  is  the
 individual grain.  Particle  Interactions are  strictly mechanical, sucn as
 momentum  transfer between colliding grains.   Noncoheslve sediment beds
 resist  erosion by the submerged weight of  the Individual grains, wnich
 may.provide mutual support by Interlocking  or by friction  (°artneniades
 1971).

     Cohesive sediments consist of particles small enough, with
 surface-to-mass ratio large  enough, that their surface physico-chemical
 forces may become much more  important than  their weight.  These forces
 may  Include (a) van der Waals forces. {&)  surface electric charges, (c)
 cheaical  bonds, and (d) Interactions of.tne double layer (counter.ions
 attracted from the solution).  These forces are only partially understood
 and may vary with the water  environment (Parthenlades 1971).

     For day particles tn distilled water  the net effect of these forces
                                                 i
 may  be repulsion, allowing enormous concentrations to be suspended at
 small current velocities.   However', even small amounts of dissolved salt
 will or ing about particle attraction (through double layer compression).
 resulting m the aggregation of colliding particles Into floes having
 size and  fall velocity much  larger, than those of the Individual day
 particles.  The basic settling unit Is thus the floe, the size
 distribution of which may depend on the flow conditions and on the
 physico-chemical•properties  of the water and  sediment:  Cohestveness
 provides a sediment bed with additional shear strength to resist
 erosion.  Parthenlades (1971) notes that fresh waters ordinarily contain
 enough salt to bring about day particle flocculatlon.  Nevertheless,
 Edzwatd et al.  (1974) and Hayter and Kenta  (1983) found estuarlne
 salinity to measurably Increase fall velocity over that in ordinary fresh
water.

 8.2  TRANSPORT OF SEDIMENT LOADS

    The gravity-driven (4-vnh1l1  movement of stream flow 1s resisted by
 the friction of the flu.d  passing over the stream bed.  This results in a
variation of velocity with depth:  velocity decreases near the bottom of
                                 8-9

-------
the Water column.  Tor the stream How to keep particles m suspension
the flow turbulence must counter the tendency of the particles to
settle.  Consequently, the stream flow tends to carry heavier particles
near tne bed. while 1t Is likely to carry fine particles more uniformly
throughout the water column, as illustrated 1n Figure B«.

    The tgtal sediment load (mass/time) passing a river cross section can
be split Into two parts using any of three related but nonequivalent
schemes (Thomas 1977):

    Based on Mode of Transport:

    The suspended load consists of sediment particles that are
transported entirely within the body of fluid with very little contact
with the bed.  The bed Toad consists of particles either rolling and
sliding along the bed as surface creep or Intermittently leaping into the
flow, settling to the bed. and resting on the bed (Shen 1971; ASCE
1975).  Such intermlttant movement is called saltation.. As there is no
sharp distinction between saltation and suspension,  there  is  likewise no
sharp boundary between suspended load and bed load.  The bed  load ts
usually a small fraction of the suspended load (Thomas 1977).  The
suspended load plus the bed load equals the total sediment load.

    Based on Sampling Capabilities:

    The tern measured load refers to that portion of the sediment load
that can be measured with sampling equipment.  The   unmeasured load 1s
the portion that would escape detection.  Current equipment can sample
over the entire range of depth to within Inches of the bed.   All but a
small percentage of the total load is usually measurable (Thomas 1977).

    Based on Availability 1n the Stream Bed:

    This division  1s baseu on particle sizes.  Wash  load is  that portion
of the  total load  comprised of grain sizes  finer  than  those  found 1 -
                               B-10

-------
              CQNCINTftATIOM: 1
Figure B4.   plow-weighted Concentrations of Different Particle Sizes
             for the Missouri River at Kansas City (Guy 1970).
                            B-U

-------
significant quantities  In the stream bed.  The magnitude of  the wash  load
Is controlled by the rate of entry of these particles from the
terrestrial watershed.  The bed material load consists of coarser
particles, readily found In the bed; the magnitude of this load is
determined by the ability of stream flow to move the bed particles.   H.A.
Einstein (1964) describes this distinction as follows:

    Either the availability of material in the watershed or  the
    transporting ability of the stream may limit the sediment load at a
    cross section.  In most streams the finer part of the load, i.e., the
    part which the flow can easily carry In large quantities. Is United
    by Its availability in the watershed.  This part of the  load is
    designated as wash load.  The coarser part of the load.  i.e.. tne
    part which Is more difficult to move by flowing water, is limited in
    Its rate by the transporting ability of the flow between the source
    and the section.  This part of the load is designated as bed material
    load.
wash load Is often considered to be silt and clay, while bed material
load would be sand, gravel, and larger material.  However, no uniform
line of demarcation Is possible since It depends on flow conditions and
on sediment sources.

    The bed material load Is of great Importance 1n determining the shape
and stability of stream channels.  For this reason considerable
engineering research has been directed toward Its prediction.  Einstein
(1950), using dyed particles, was able to demonstrate that a continuous
exchange of particles between the bed and the water column takes place in
a reach where the number of particles leaving the downstream end equals
the number of particles entering the upstream end.   Gessler (1971) notes
that aggradation occurs when the upstream sediment supply exceeds the
capacity of the flow to transport sediment out of the reach.  Given
sufficient time, the sediment depositing at the upstream end of the reach
causes the bed slop* to Increase, which In turn Increases the velocity or
bottom shear stress, thereby increasing resuspenslon until a new
equilibrium Is attained.  Degradation, on the other hand, occurs when the
sediment carrying capacity of the flow exceeds the upstream  sapp1* rate.
                               B-12

-------
The resulting net erosion reduces the slope, wMch \n turn reduces the
velocity or bottom shear stress, thereby reducing resuspenslon until a
new equilibrium 1s attained.  Thomas (1977) notes that degrading reaches
may tend to become incised while aggrading reaches may tend to meander.

    Despite the amount of study that has gone Into sediment transport.
accurate predictions remain difficult.  As discussed briefly in the
Guidance Manual Book VIII, Screening Procedure (Mills et at. 1982). many
procedures require data on the suspended sol Ids concentration at some
reference depth.  The Einstein (1950) procedure and its modifications do
not require such data but are rather complex.  The Einstein procedures.
furthermore. Involve only bed material load; wash load 1s determined by
external sources and Is thus not predictable from the stream's sediment
carrying capacity (Nordln and McQulvey 1971).  Nevertheless, it can be
noted that many sediment transport formulas can be put in the form
(Sessler 1971):

    gs • »(T - TC)P                                                  (Bl)
wnere g  1s sediment load per unit width, T is shear stress, t
1s a crHlcal shear stress at which sediments start to move, a is some
coefficient, and g some power.

    Shear stress. T (Newton/m ), 1s given by:

    t - T«                                                          (82)
where T Is the specific weight of water (approximately 9807 N/ra ). R
1$ the hydraulic radius (m), and S is the slope of the energy grade line
(m/ra).  To obtain T 1n dynes/cm  Multiply N/mZ by 10.  The
Importance of shear stress In controlling both settling and resuspenslon
will be further discussed later.

    Shen and Hung (Shen 1971) have suggested t simple empirical
                                 B-13

-------
regression formula for predicting the suspended bed material
concentration.  Using flume and river data they obtained:
    log C - a0 «• ai* •• a2x  » *3x                                   (83a)
    x • v1 sJ w*                                                    (83b)
where C 1s bed material load concentration (mg/i), v 1s the average
flow-velocity "(ft/sec), S again the energy slope (ft/ft), and w the fall
velocity  (OT/ sec).   The regression values are:

    a0 •  -107404.459          1 • 0.007502
    ai .  324214.747  '          J . 0.004288
    12 •  -326309.589          k . -0.002*00
    a3 .  109501.872
The standard error of log C was 0.217 (68X of the data was within 0.21?
base 10 logarithmic cycles of the predicted value).

    Equation 83 and all of the numerous other approaches for predicting
bed load and bed material load may be of limited value for toxicant
modeling.   Much of the toxicant may be adsorbed to the finer particles
(with higher surf ace-to-mas s ratios) comprising the wash load.  By
definition of the wash load, these finer particles are not found in the
stream bed 1n substantial quantities.  By Implication, toxicant bound to
wash load  particles would have limited Interaction with the bed.

S3.  DEPOSITION ANO EROSION

    The ntt particle flux (vg/cm /sec) across the bed-water Interface
can be expressed as the difference between the deposition (settling)
flux. Sg,  and the erosion (entralnaent or resuspenslon) flux S.
(Fukuda and lick 1980).  The deposition flux Is related to the settling
velocity.  w$ (cm/sec) , and the water column solids concentration, m.
(mg/l). by:

    S0 • ws «i                                                      (P4)
                                 B-14

-------
Th« erosion flux 1s related to the resuspension velocity, w   , and  the
bed solids concentration, n>2. by:
The assumption here 1s that deposition and  resuspenslon  are  independent
processes that can occur simultaneously.

S.3.1  Deposit Von
'""^^^•^••'••'••""•""^"''^^^•^^^ i^B*           _                    i
    In evaluating the settling velocities :of  sediment particles,  three
physical processes can be considered (O'Mella  1980):  (a)  gravity,
(p) Brewnlan motion or molecular diffusion, and (c)  turbulent  or  laming
fluid shear (velocity gradients).  The degree  to which each  of these
processes governs particle behavior depends 'on  the  characteristics  of
both the fluid and the particles.

    The effect of gravity on .particle settling can  3e expresses  in  terms
of Stokes Law:
    vs • (g/lBw) (PS  - p)d2                                           (86)
where v, 1s the Stokes settling  velocity  (cm/sec),  g  1s  the
       5                           2
acceleration of gravity  (980 era/sec ). t   •  p  is  the  difference
in the densities of the  particle and of water. «
                              8-15

-------
        Aggregation  Into  floes occurs when particles having  sufficient
physico-chemical attraction collide with each other.. Such coHsions may
result pMmlaMly from Brownlan motion for small particles,  and fluid
shear and differential settling velocities for larger particles.
Valloulis  and List  (1984) nave modeled these processes in a
sedimentation basin.  Olsaggregatlon of flocculant particles may a'so
occur through fluid  shear and through collisions (Lick 1982).  ucftrm and
Weber (1980) noted that laboratory measured settling velocities were
substantially more rapid  than expected from the Stokes velocity of tne
individual particles, apparently due to particle aggregation.

    vertical movement of  particles may also be brought about by
dispersion, consisting of BrownIan motion and turbulent diffusion
(resulting from eddies produced by fluid shear).  Away from  the bed-water
boundary Brownlan diffusion 1s expected to be negligible compared with
turbulent diffusion.  The Importance of turbulent diffusion  relative to
settling can be directly  compared (Lick 1982).  A characteristic time for
settling to occur is t  » H/v . where H 1s depth of water.   A
                                                     7
characteristic time  for turbulent diffusion Is ttf « H /2Q , where
0  Is the vertical eddy d1ffu$1v1ty.  The dominant mechanism is that
with the shorter characteristic time.  Increasing the particle sUe and
the depth favors settling as the dominant mechanism; Increasing the
turbulence favors diffusion (Lick 1982).   Thus, the HydroQual (1982)
recommendation to reduce w  to perhaps 10% of v  in shallow  streams
seems consistent with this reasoning.

    In this vein Hayter and Nehta (1983). constructing a general model of
particle behavior In estuaries, applied the relationship:

    w$.(1-   J-)V                                                  (67)

where TC Is a critical shear stress above which little deposition of
the sediment wo Id occur  (as measured In flume tests).  They suggest a
minimum value of w   being SX v v .
                               8-16

-------
    Uck (1982) applies a different  line of reasoning to a thin f»'m of
water near the bed-water interface, where turbulent diffusion  1s assumes
to decrease.  The  flux through  this  film can be written:
            (0
                        dm,
                        __  *  v m,
                        —      s 1
where SQ - S- (ug/cm  - sec) is the net downward flux (per

Equations B* and 85). Oy U vertical eddy diffusivity (cm2/sec). 
-------
    vd - 0.06 (T/*)     (»/Otaii)                                      (811)
Figure 85 Illustrates the solution of w ,  v .  and v  over a range
of particle sizes for a shear stress, T, of 10 dynes/cm .   For large
particles w  • v  and tne effect of diffusion  through trie boundary
film Is negligible.  For small particles w  «  v  and the effect of
                                          s    d
gravity settling Is negligible.  The particle  size at which control of
deposition shifts from diffusion to settling depends on shear stress.
This theoretical approach assumes that all particles that hit the bed
surface adhere to 1t.  This limitation night be related to why increasing
T Increases w  for small particles, a contrast to the previously
described empirical approach (Equation 87), where increasing T
decreases w$.

8.3.2  Bed Erosion

    Erosion or entrapment Is the scour of sediments from any part of the
stream bed Into suspension In the water column.  To remove material from
the bed the flow-generated forces must overcome the srtabUUIng forces.
which consist of the Immersed weight and (for  jilt or clay beds) the
cohesive strength.  Lee et al. (1981) and Lick (1982) 11st five factors
controlling entrapment:  (a) turbulent shear  stress .at the bed-water
Interface, (b) water content (porosity) of the bed. (c) sediment
composition, Including nlnerology, organic content, and size
distribution, (d) activity of benthlc organisms, (e) vertical
distribution of sediment properties, related to the manner of
deposition.

    Lee et al. (1981) and Fukuda and tick (1980) found entrapment rates
to be directly proportional to shear stress and water content.  Also.
sediments with a fine-grained (clay size)  fraction deposited at the
surface were more easily erodable than vertically well-mixed sediments
with the same composition.  For example, after a brief net deposUVonal
period, the fresnly deposited sediments will tend to have a smaller mean
                               B-18

-------
               10-3
            r4
                 0.1        1.0       10.0
                    PARTICLE SIZE
Ftgur* BS.  Deposition velocity w} as a function of p*mel«
           for a sh«w stren of 10 6yrm/an2 (from Lick 1982).

-------
,     sUe and  a  Higher water content;  therefore,  these surface sediments  will
     be wore easily entrained when shear stress  Increases.

         The erosion rate may be formulated In terms  of the shear stress  on
     the bed.  T.  and the erosion resistance of the bed.  The erosion
     resistance  of  the bed is generally  empirically estimated; U cannot  be
     predicted solely from the basic properties  of particle size distribution
     and porostty.

         Figure  B6  Illustrates a typically measured relationship between
     erosion flux and shear stress.   Once beyond  critical  shear stress,
     T  .  the erosion flux, S-. Increases rapidly.   In modeling
     consolidated estuaMne beds,  Hayter and Mehta (1983)  estimate

                                                                          CB12)
     where  both  a  and  T   are  empirically  derived  constants.

     6.3.3Particle  Exchange:   Continuous  yersuf Discontinuous

         The conservation of  sediment  load  through a stream reach may occur
     under  two conditions:   (a)  deposition  and resuspenslon are  occurring
     continuously, but at equal  rates  (SQ • S^.  or w{m^  • wrj  m^),
     or (b)  deposition and  resuspenslon rites  are both zero.   The former
     situation can be  considered an  equilibrium  state; the Utter cannot.   For'
     the equilibrium  condition,  the  suspended  solids concentration would be
     given  by m.  » m,  «../«.•  p<>r *n* z*r(> r*te  situation,  whatever
     concentration exists at  the head  of  the reach Is carried  downstream
     unchanged.

         In flume experiments wlttr noncoheslve sediments, Einstein (1950)
     demonstrated (using dyed particles)  that  conservation of  load was the
     result of an equilibrium balance  between  deposition and resuspenslon.
                                    B-20

-------
                0.02
             I
            N
             g
                0.01
                              I     I     I     I
                         I     t     I    t
                             0.2        0.4        0.6        0.8        1.0        1.2
Figur* 86.   Examplt of Relationship b«cw««n Erosion  !Utt S£ and Bed Shear  Stress
             (afc«r Hayear and M«hc« 1983).

-------
 Although tne concentration did not change, a continuous exchange of
 particles was occurring through tne simultaneous processes of deposition
 and resusoenslon.

     whether cohesive sediments exhibit the same behavior 1s open to
 question.  For clay particles Parthenlades (1971) and Hayter and Menta
 (1983) note evidence that the critical shear stress below which no
•erosion can occur Is greater than the critical shear stress above which
 no deposition can occur.  That 1s, there appeared to be a shear stress
 range within which neither erosion nor deposition is s'gniflcant.  *itn!n
 this range the velocity was sufficient to prevent the suspended partic'es
 from flocculating and adhering to the bed but Insufficient to break tne
 cohesion of the consolidated bed particles.   Above this range only
 erosion occurs, while below this range only  deposition occurs.  .

     In the experiments with lake sediments.  wMch are likely to be ?'ne-
 and more cohesive than river sediments. Lick (1982) observed' a  complex
 behavior seemingly Intermediate between the  continuous and simultaneous
 deposition and erosion observed for sand and the alternating deposition
 or erosion observed with clay.  He found that to a partial  degree a
 continuous exchange of particles was occurring through simultaneous
 deposition and erosion.  Some types of particles, however,  tended to
 remain only in the water column; others tended to remain only in tne bed.

     Lick (1982) thus notes that erosion and  deposition are not  completely
 reversible and that a hysteresis effect 1s often present.  For  a
 particular shear stress, the steady state concentration will be mgner if
 the shear stress (and suspended concentration) had .been decreasing over
 time than If 1t had been Increasing over time.

 8.4  SEDIMENT SOURCES

     External sources of suspended sediments  can or'gln.ce Front either
 ptMnt or nonpolnt sourr-s.  ?o1nt sources of sediments are generally
 minimal, and in any event, are easlly quantifiable.  Nonpolnt sources of
                               8-22

-------
concern ire governed by natural and culturally accelerated erosion
processes.  Although urban runoff can have significant localized impacts
on streams (Porcella and Sorenson 1980; Tomllnson et al.  1980), the
preponderance of sediments delivered to U.S. streams by accelerated
erosion are derived by sheet erosion from agricultural lands (Qmernlk
                                          i      •            •        r
1977}.  Sheet erosion Is the wearing away of a thin layer of land surface.
    Sheet erosion rates depend on rainfall and flow properties.
geomorphology and topography, and land use (Including vegetative cover
and soil management practices).  Although predicting soil loss is very
complicated, the Universal Soil Loss Equation, developed Dy wiscnmeler
and Smith (I960), has been extensively used to estimate average annual
soil loss in tons/acre.  To predict sediment yield of a watershed, the
USLE 1s coupled with a 'sediment delivery ratio", the fraction of an
area's soil loss that actually reaches the stream.  Taole 83 summarizes
the range of sediment yields expected 1n various regions of the country.

    Details on use of the USLE and sediment delivery ratio are contained
in Volume VIII of the Guidance Manual (Mills et al. 1982) and. in several
other EPA publications. Including HcElroy et al. (1976). uiS. EPA (W6).
and 21 son et al. (1977).  That material will not be repeated here.
However, it can be noted that  for many water duality modeling purposes.
the utility of the USLE Is constrained by being limited to annual average
soil loss.  It Is not Intended for event modeling (Wlschmeier 1976).  To
predict sediment yield from single events, Mills et al. (1982) describes
the Williams (1975) modification of the USLE.

    Several other approaches are available for predicting the sediment
and pollutant yield of events.  For urban runoff these  include U.S. EPA
(1975), Mills et al. (1982). Gelger and Oorsch (1980).  and Klemetson et
al. (-1980).  For agricultural  runoff they Include Williams (1980).
Novotny (I960), and Oonlglan and Crawford (1976).  Given sufficient
resources, the metbw'l c* choice might be the Agricultural RunofF
Management Model (ARM) (Oonlglan and Oavls 1978).
                              8-23

-------
TABLE 53.   SEDIMENT YIELD FROM DRAINAGE AREAS OF 100  SQUARE
       MILES OR LESS OF THE UNITED STATES (Todd 1970)
Region
i
North Atlantic
South Atlantic Gulf
Great Lakes
Ohio
Tennessee
Upper Mississippi
Lower Mississippi
Sourls-Red-Ra1ny.
Missouri
Arkansas wMte-fted
Texas Gulf
Rio Grande
Upper Colorado
Lower Colorado
Great Basin
Co lumo la-North Pacific
California
Estimated sediment velld
High

1,210
1,850
800
2,110
1,560
3.900
8.210
470
6.700
8.210
3.180
3.340
3.340
1,620
1,780
1,100
5.570
Low
tons/sq mi/yr
30
100
10
160
460
10
1.560
10
ID
260
90
150
150
150
100
30
00
Average

250
800
100
350
700
800
5.200
50
1.500
2.200
1.300
1.300
1.800
600
400
* 00
1.300
                           B-24

-------
          APPENDIX C

 fiUO AND LABORATORY METHODS
             FOR
     FlINT RIVER SURVEYS
Cranbroofc Institute of Science
        9311 Groh Road
 GrojJe He. Michigan.  48138
       August  1984

-------
    Metals in the ambient environment frequently occur at level* below the
detection limit of many of the analytical methods commonly employed by State
and Federal agencies.  Consequently. 1n order to assure obtaining data
useful for model calibration, the WLA analyst needs to be able to discuss
the overall adequacy of the methods used by laboratory and field personnel.
The key Issues are (a) the sensitivity, and perhaps accuracy, of the
analytical methods and (b) the freedom from detectable contamination during
sample handling, a problem If very sensitive analytical methods are used.
This appendix describes the sampling and analytical methods found to be
useful during the Flint River surveys.

    The sampling program began In August 1981 and ended in March 1982.
During this time, four sampling surveys were conducted on the Flint
River,  water was analyzed for the total and dissolved forms of cadmium,
copper, and zinc.  Chemical and physical parameters of the water, wnic.i
are believed to Influence metal spec 1 at 1 on or to interact with solids,
were also analyzed.  The parameters included were suspended solids. on.
specific conductivity, hardness, dissolved oxygen, total alkalinity, and
temperature.  River flow and velocity were also estimated.

    All aspects of sample collection, filtration, and preservation were
evaluated so that the final analytical results reflected actual quality
of the river water sampled.  Care was taken to choose equipment made of
materials that would minimise contamination.

    River water was collected using a half-gallon linear polyethylene
wide mouth Malgene  bottle fixed to a polypropylene rope with
stainless steel clamps.  The bottle was weighted from below with lead,
and the bottle mouth was sheltered with a plastic awning or Hd suspended
from the rope just above It.  The purpose of the 1ld was to keep out
debris as the sample was pulled up.
                                C-l

-------
    Sampling was usually done from bridges at three market) positions that
are at 1/4 trie distance across the stream, at 1/2 the distance, and at
3/4 the distance.  The sampling device was lowered quickly below the
surface of the water, .rinsed once, emptied, then filled again.  Three
such samples from the various bridge positions were combined  in a ten-
liter polyethylene carboy which was previously rinsed with some water
from the first sample.  It 1s from this composite sample that an aliquot
for analysis was taken.

    A sample processing scheme Is-presented In Figure Cl.  All filtering
operations were conducted in the-mobile laboratory as well as PH, con-
ductivity, alkalinity, and metal preservations.  Temperature  and
dissolved oxygen were measured In-sltu.  Total metal analysis, dissolved
metal analysis, and hardness were analyzed at the £PA large Lakes
Research Station.

Trace Metals

    Trace metal samples were collected 1n new linear polyethylene bottles
washed with hot water 1n a dishwater, rinsed with delonijed water,  with
30% v/v nitric acid, and with delonlzed water; then they were soaked  in
2X v/v nitric add for two weeks, rinsed six times with delonUed water,
and dried In an oven with the caps ajar.  Bottle blanks were  analyzed  to
Insure that contamination was kept to a minimum, and to provide a value
used to correct for low level background contamination.  Ten  of every  100
bottles were randomly selected and analyzed for background levels.  A
blank test was performed by filling the bottles with a pre-analyzed .-
acidified batch of water (3m nitric add/liter).  This batch  was
generally below the detection limit for each metal.  The solutions  in  the
bottles were then analyzed, and the resulting mean concentration  is the
bottle blank.  The stored bottle blank samples were analyzed  with the
river samples.
                                C-2

-------
               WHOLE »ATM
    FILTH
OISSOLVCO METALS
                             WHATMAN tiff
                                      RESIDUE
SUSPENDED SOLIDS
CONDUCTIVITY
                                                           TEM^CMATURE
                                                             DISSOLVED OXYGEN
                                                                MAROHESS
                                                             TOTAL ALKALimTY
                                                               TOTAL METALS
          FIGURE Cl  SAMPLE PROCESSING SCHEME FOR FLINT RIVER WATER



                                       03

-------
    Every tenth sample included a duplicate aliquot or spin of the
composite water which was processed the same way as any other sample
collected.  The standard deviation calculated for samples and their
duplicates gives an estimate of the overall precision, including both
field and instrumental variations.

    The following equation was used to calculate the standard deviation.
  Standard deviation  •  ,	u	-      where d • difference between the sample
                         / Td                    and its duplicate
                        *  *                 k • number of duplicates
Since the matrix of the sample can affect the precision, river water from
each survey was handled separately.  See Table CI for results.  Detection
limns for the metals analyzed are reported In Table C2.

    Certain samples'were analyzed once a day for a number of days as
"between run' replicates (Table Cj).  The variability of these replicates
is assumed to be due to laboratory and Instrumental procedures only.  The
field duplicates mentioned earlier have potentially higher standard
deviations since there is additional variability from field techniques.
I.e.. bottle blanks, filtering and possible non-homogenity of the water
in the 10  composite sample.  Comparing the results of Tables Bi and 83'
suggests that the variability of .the results for ail the metals was
mainly' due to laboratory and instrumental procedures.

    Total river water (unflltered) was collected in a 500 mi  linear
polyethylene bottle ore-cleaned as above.  A 100 ml portion of that
water was filtered through a .45  HA Sartorlus cellulose acetate
filter.  The filtering apparatus  was a Ml 111 pore polycarbonate
SterlMl   filtration system.  Before use. the system was soared  in
4X v/v UNO,, then rinsed well with delonUed water.  The  filter was  set
1n place, and SO ml of delonlzed  water was filtered, then discarded.
Fifty mi of  sa-,Me was then  filtered and  dVscarded.  Sample water   as
then  filtered until  the filter began to clog.  Before filling  the  175
mi bottle with  filtrate,  the first  50 mi  of  filtrate was  used-to
rinse  U out.

                                    C-4

-------
                       TABLE Cl.   RESULTS OF FIELD  DUPLICATES (SPLITS)
Metal ' •
Dissolved Cd
Total Cd
Dissolved Cu
Total Cu
Dissolved Zn
Total Zn
August 1981 Survey -
Flint River Samples
Number of
Pairs
19 '
18
19
18
20
.- 18
Standard
Deviation
fuO/l)
.05
.07
1.1
.9.
4
4 .
December 1981 Survey -
Flint River Samples
Number of
Pairs
6
7
7
7
7
" 7
Standard
Deviation
^t«q/l)
T.02
.04
.4
.3
S
W2
March 1982 Survey
F1»nt River Samole-
Number of
Pairs
14
14
13
U
U
13
Standar-
Oevlatl-.
(.iiO/ll
.03
.04
.5
.7
2
2
NOTE:  If 1 {Detection Limit] < [Measured Metal  Concentration) < (Detection Limit],
       then the result U recorded as "T" preceding the detection limit.

       If (Measured Metal Concentration] < . (Detection Limit], then the result is
       recorded as *W* preceding a value . the detection limit.
                       TABLE C2.  DETECTION LIMITS
Metal
Cadmlua
Copper
Zinc
Detection Limit (uS/l)
.02
. -08 ,
4

-------
TABLE C3   RESULTS OF BETWEEN-RUN REPLICATES
Metal ' Number of Samoles
Dissolved Cd
Total Cd
OU solved Cu
Total Cu
Dissolved In
Total Zn
6
IS
4
18
5
18
Standara Deviation fyq/i)
.03
.07
.6
1.2
W2
2

-------
    The models 603 and 460 (Perkln Elmer) atomic absorption  Instruments
 equipped with graphite furnaces were used to analyze the samples.  The
 drying, charring, and atomUatton program were optimized For river and ef-
 fluents using optimization as described  In  'Analytical Methods  for Atomic
 Absorption Spectrophotometry Using the HGA  GrapMte Furnace.* Perkin
 Elmer  (1977).  See Table C* for Information on analytical conditions.
 All fTameless analyses were done In duplicate while the flame analysis
 (zinc) was done  in triplicate.

    If chemical  Interferences were present  which enhanced or suppressed
 the analytical atomlzatlon signal, then  the standard method was used to
 calculate the sample concentration.  If  no  Interferences were present,
 then samples were calculated directly from  a linear regression  of the
 synthetically prepared metal standards.  The latter case still  involved a
 standard addition determination on every fifth sample 1n order  to monitor
 recovery.  Recovery here 1s defined as the  slope of the standard addition
 calculation on a sample, times 100. divided oy the mean slope of the
 standard addition on standards.

    No chemical  Interferences were found when analyzing copper  and zinc.
 However, since Interferences were present which suppressed the  analytical
 signal for.cadmium, standard additions were used to determine the
 concentration of this metal.

    Standards were prepared fresh dally and acidified (3 mi
HNOj/l).  Typically. 5 standards were digested along with every 20
 samples and 3 blanks.  The digestion procedure was a modified nitric acid
digestion for total metal determination  from 'Methods for Chemical
Analysis of Water and wastes' (U.S. E.P.A.. 1974).  Hydrochloric acid was
eliminated from  the EPA procedure due to the Interference of chloride ion
with the analysis of zinc and cadmium (Analytical Methods for Furnace
                                 C-7

-------
   <* «i rw
      §
   §3
   a »*
wt
e
            f  i-
      iS, 2- 'I3
      •/•a — -a M» ' ^
      •— x o e e e
      MM W X « » £
      •o i a "5 e IS
                   e

                   a
   e
   -^ 8
          in   o*
      j[

      e
              s
           e   e
       *
       I
              is
i'-
                   o
                    3 «»
                    e —
                    m e

-------
Atomic  Absorption  Spectroscopy,  Peru in  Elmer.  I960).   The  digested
consisted of  del on(zed water plus  the same  amount  of  HNO   added, to  the
samples and standards.  The median absorbence  of  the  blanks was  used  to
correct the samples.  Digested standards'were  corrected by a  standard
blank.  Calculations of concentrations  were  tnen  based on  these  corrected
absorbences.  The  filtered samples {dissolved)  received no sample
pretreatment.                            :

    In order  to determine contamination  Introduced  in  the  fi'ter'.ng
process, two  filter blanks were  taken in each  eight-hour shift  in the
field.  This  Involved filtering  an aliquot  of  deionUed water.   An
unMHered sample of this delonlzed water was  also  taken at tne  same  time.
This unflltered sample is the batch blank in Table CS.  Tne analytical
results of the two types of samples were compared;  if  they were  equal.
then no filtering contamination  was believed to occur.  Equality nere U
confirmed by a T test.  The results in  Table CS snow  that no  correction
was required  for the filtering process  in the  August,  Decemoer,  and *arc.i
surveys.

    Since tne filter blank results from both a  bottle  blank and .a Blank
for the filtering process, It Is assumed that  if  the  filter blank -ere
negligible, then the bottle blank would  also be negligible.   This was the
case for all  the metals during the surveys  except  for  copper  in  August
1981.  Bottle blanks were therefore checked  For copper in  the August  1981
.set.  The levels found in these  bottles were below  the detection  limit
for all three metals.  We therefore concluded  that  the .447 ug/t Cu
In the sample of batch water was due to  the  copper  In  the  batch  water
on 1 y.
    Trace metal water samples were preserved by adding 3 mt of  «NO
per liter of  sample.  Samples were refrigerated at  7»C.
                                 C-9

-------
                          TABLE C5.  FILTRATION BLANKS

                (Note:   3.a.  - Batch Blank and F.B.  • Filter Blank)
Metal .
Cadmium
Cadmium
Cadni urn
Coooer
Cooper
Cooper
Zinc
Zinc
Zinc
Survey
Aug. 81
Dec. 81
Mar. 82
Aug. 81
Dec. 81
Mar. 82
Aug. 81
Dec. 81
Mar. 82
Number
(B.fl.. F.B.)
47. 47
15, 13
18, 18
47. 47
15. 13
17, 17
47. 47
16. 14
18. IB
Standard
Mean
Ug/D
(B.B.. F.B.i
0.011. 0.022
0.001. -0.001
0.018. 0.038
0.447. 0.562
-0.067. . 0.092
•0.026. -0.104
0.681. 0.745
2.938. 3.143
•0.487. .0.394
Deviation
(ug/D
(B.B.. F.B.I
0.022. 0.027
T-Ttst
Result
•Same
0 . 008 0 . 009 Same
0.009. 0.108 Same
0.440. Q.563
0.209. 0.263
0.494. 0.239
1.476. 2.027
1.769. 2.107
1.889. 1.83
Same
Same
Same
Same
Same
Same
•At the 95X confidence level the mean batch blanks and Filter blank* were
 equal; therefore,  no blank correction was needed for the filtering process.
                                    C-1Q

-------
    Results of several intercomparlson studies are presented in Table
CS.  In both of tftese series, performance was considered 'good.'  The
true values of the unknowns fell within our 9SX confidence interval.
This interval Is defined as our reported result plus or minus two
standard deviations.
             *
    for both digested and dissolved samples, five standards were run at
the beginning and end of each day's run.  Half of the standards at the
beginning of the day were spiked with known standards (standard
additions).  The remaining standards were spitted at the end of tne day.
The average slope of these standard additions to standards was used in
the denominator of the recovery formula.

Convent 1ona j^Rarame t ers

    Methods used for non-metal parameters are described in Table C7 .  Dis-
solved oxygen and temperature were In-sltu measurements.  Specific con-
ductivity, pn, total alkalinity, and total non-MIterable residue
(suspended solids) were analyzed In the mobile laboratory.  Hardness was
analyzed at the Grosse tie. Lab.
                                 c-u

-------
     TABU Co   INTERCOHPARI50H WITH U.S.  EPA ENVIRONMENTAL MONITORING   AND
                         SUPPORT LABORATORY. CINCINNATI
                            (Concentrations In
 Sample
Q.C.. Series
475
  Sample 1

  Sample 2
O.C. Series
575
  Samp 1e 1
                       Cd
                                Cu
(Result. True value) I  (ttesult^Truc valued    (Result.  True
     11.3, V.5)
     (-31. .46)
(7.8. 6.0)
(1.4. 1.4)
 (65. 60)

(16.7, 12)
 (29. 30)
                                 C-12

-------
TABLE C7.  ANALYTICAL METHODS - CONVENTIONAL PARAMETERS
Parameter
Temperature
Dissolved
Oxygen
Specific
Conductivity
OH
Alkalinity.
Total
Residue, Total
Non-FMterao1e
Hardness
Method
Thermometry
Dissolved
Oxygen Probe
Electrical
Conductance
pH Electrode
Tltratlon to
pH 4.5 with
.02* H SO
Gravimetric
Measurement
mrlmetrlc
Equipment/
Instrumentation
Thermometer
Yellow Springs
Instruments Co. , Inc.
Beckman Conductivity
Bridge (Model RC-19]
Fisher Accumet (Model
520 pH/IOtt meter)
Fisher Automatic
TUratlon Model 471
GfF Filters (Wha titan)
SartoMous 2003 MP1
Balance
Fisher Automatic
TUratlon Model 471
Used 1n the Manual
Node
Source of Method
Standard Methods (1975)
Yellow Springs Instruments
Co., Inc.
Beckman Manual (1973)
Fisher Instrument Manual
No. 26285
EPA, Methods for Chemical
Analysis of Water and
Wastes (19?9)
EPA, Methods for Chemical
Analysis of water and
wastes. (1974)
Standard Methods for the
Examination of water and
Wastewater, 14th Ed. (1975)
                      C-13

-------
                APPENDIX 0
BEHAVIOR OF HALOGEN DISINFECTION RESIDUALS

-------
                                 APPENDIX 0
                 BEHAVIOR  OF  HALOGEN  DISINFECTION  RESIDUALS

     This  appendix  presents  information  on  the  aquatic  fate  of  was tester
 disinfection  residuals.   This  discussion has been added  at  tne request  of
 the  Office  of  water  Programs Operations  (Construction  Grants).
 recognizing that (a)  chlorine  residuals  are commonly discharged  in
 quantities  toxic to  aquatic  life,  and (o) chlorine  1s  not discussed  in
 the  portion of the Guidance Manual covering SOD,  DO. and ammonia
 (OMscoll et al. 1983). and since  U  Is  not a  "priority  pollutant',  it  is
 not  covered by MaBey  et a). (1982) and  Callahan et  al. (1979).
     As chlorlnatlon  is by far  the  most  common  disinfection  practice  in
 this country,  the  emphasis is  on chlorine residuals; nevertheless, some
 Information on bromine chloride  Is also  included.   The discussion 1s
 Intended  to apply  to  fresh water;  halogen chemistry In saltwater.
 described by Haag  and Lletzke  (1981). Is not identical to that in fresh
 water.
    The discussion Is limited  to the fate of halogen oxldants.  It does
 not deal with  the  formation of halogenated organic  by-products; such
 formation 1s of minor Importance In determining the half-life of the
 disinfectant Itself.   Although some of these by-products may be
carcinogenic,   their production Is of greater public health significance
during potable water treatment than during wastewater disinfection
 (Metcalf &  Eddy 1982).  Information on production of halogenated organIcs
 Is provided by the National  Research Council (1979) and Jolley (1975).
 It Is worth noting here,  however, that the formation of trlhalomethanes
(the by-products of greatest concern) appears  to be depressed by the
presence of ammonia,  a usual  constituent of municipal wastewaters that
have not undergone complete nitrification (Metea 1C & Eddy 1982).
    The follow,ng -"scirsslon has been edited from the PttcaVf & Eddy
(1982)  report.  Impacts of  Wastewater Disinfection Prar'ices  on Coldwater
                                0-1

-------
            Additional details on disinfectant chemistry can be found in
Weber (1972).

0.1 AQUATIC fATE
    Host wastewater treatment plants discharge effluent through an
outfall  pipe or through a small ditch which then combines with the
receiving water.  In such cases, Initial mixing of the effluent depends
upon the outfall or ditch characteristics, the river characteristics, and
the magnitude of flows of each,  for a few large treatment plants, waste
1s discharged through submerged multi-port dlffusers.    •   >
                          i.

    A common method of estimating the dilution of wastewater effluent U
to calcuate the ratio of river flow to effluent discharge flow.  This
number may range several orders of magnitude.-  Typical ratios may be 100
for small plants discharging ta average sired rivers, and 1 or 2 for
plants discharging to small tributaries.
                                     j..
    The pitfall of using the ratio of flows to estimate dilution 1s that
complete mixing (lateral and vertical) 1s Implicitly assumed.  In cases
of small tributaries with low .dilutions (e.g.. 1 or 2) thVs may be a
reasonable assumption.  However, for higher dilutions (e.g., 100 or
more), a long distance 1s often necessary to complete the lateral -mixing
process.  In most cases, complete vertical mixing may be a reaonable
assumption.
Chlorine
    The initial chemical reactions of chlorine In aqueous solution depend
on.the application form.  Chlorine gas hydrolyzes 1n solution as shown
below:

         C12 » H20—-HOC1 » H* » Cl"
This *eact1on 1s rapid and essentially complete  If  the pH 1s greater than
6.  Application of sodium or calcium hypbchloMte will yield BypochloHte
                              0-2

-------
 Ion  (OC1~)  initially, wnlch win rapidly establish equilibrium
 nypochlorous acid (HOC):    .                ,
         NaOCI
rHOC!  • Na  » OH
    Chlorine present 1n wastewater or receiving waters 1s usually
measured as total residua! chlorine (TBC>.  TRC 1s the sum of free
residual chlorine and combined residual chlorine.  Tree residual chlorine
(FRC) 1s the free available oxldant In solution consisting of   '
hypochlorous add (HOCT) •and hypochlorlte ion (OCl").  Combined
residual chlorine (CRC) generally refers to the chloramlnes formed wften
hypocnlorous acid reacts with ammonia.  Free chlorine can also react «Un
other organic compounds containing aialno groups to form organic
chloraralnes.  Bactericidal strength 1s 1n the.order:   nypocnlorous acid >
hypochlorlte Ion > chloraralnes/                '
    Chlorine demand occurs both' 1n wastewater and the receiving waters.
CMorlne demand is the difference Between the aopplted chlorine dose and
the free residual chlorine.  It fs due to a variety of reactions Ine1u
-------
TABLE 01.  PRINCIPAL REACTIONS OF CHLORINE IN SOLUTION
   Reaction type

Hydrolysis
Ammonia
  Substitution
  Oxidation
Inorganic oxidation
Decomposition
 (with sunlight)
Organic reactions
  Oxidation
  Substitution
                     C12 * H20— »HOC1 » HCL
HN3 » HOC1
I HNC1; * HjO
                                          * H2°
                                           HOC1 » 3 H*
                            HOCl * 2
                      » 3 H» » Cl-
                     2 HOCl
          •2 H* » 2 C1- *
                     RCHO » HQC1 —-.RCOQH • H* * Cl~
                     RNH2 *' HOCl --^ RNHC1 »
                       0.4

-------

AAPtO
REACTIONS
SLOW
REACTIONS '


i


FREE CHLORINE
HOC1»-MDC!" * M*
JNH,
CH LOR AMINES
/
CHLORO-ORGANICS
O
OXIDATION PRODUCTS

>'
«



OXIOANT
COMBINED
OXIOANT
MAINLY
' NON-OXIOANTS
- .
FISURE 01. DIAGRAM OF CHLORINE REACTION PATHS IN FRESHWATER

-------





0
o
B
b
0
th*
0
oe











0
6
^4
*
e
0
u
1
J*

^ .
4 0

04
O b








|
U
o
001

0
B
«*
O
1

B

C*
_4
— '
s
o
0
c
o




^j
B


o


0
0 »
2.2
JC ^P
0-4
0'B
0 a
«-4 0
0,
E -0
4 E
cn 4
•>
«
f^
- ^4
1
• o







o
*»

0
B

^g
O

^J
y
0 7*
0 -4

__ J __
« «D CB

01 C* 0*
^4 ^4 04
-* *- "•
•6 B 6
O 0 O
66 6
O O O
n *


(
o> u
•OB o
0-4 0 >
0 b  —-4*- O
o a 4J & U
•^ ** 0 «. ,M V^.
«* *• "O »o *• ^ «rt
6 r* Ef*
6 B 0 9« 6 0 at
O 0 flS *^ O ffl «H
0 0 ** 0 *-
B B B EC
£ £ 0 • £ O •
O O b -* O V* -*
^ TM 0 i^ *4 4
0
U
•9 B
0 0

ii
X U
8 3

0
,2^
a 4
. 4 **
0 £
0 «*
h -4
Ii
9 0
O **

* ' *"!
|0A p0)
1 1

• • •
10 ^4 •< r4




^^
^
Jr £
J 4 0
W " O, «
, 0-. 0 E
6019 -4
-« 00 e
U fa b 4
0006 b
- >«H 0 0
£ -4 £ -4 iH
o ae o 4J «

Ok > «— *4 X
4

4J
0

E
a
e_
32


•
0
*j
0
0

•a
^
0
0
*^
o
,£
4J
JC

m
m
. i
0)
•
^



X
ajg
0
6
^4
0 u
6 0

C **4
0 ft*
b
O O
••t *O
£ 4
O b
0 O
6-4

4

4^
0

6
4
| —
II


1
£
*t
«*
*
4

4 •
^
•4 •
0 B
— O
*4 «*
B 9
O rt
•a -a
0 **
4 3
oj a

o
^4
1
e
•
in






















ft.
04
*
-4
^*
'•
4






















O
fN

0
E
««
b
0
2
u
^4
4

•6
^(4
0
0
b

t-4
4
^

-------
 of free chlorine Is an order of magnitude faster than of monochloramlne.
 and (3) Htld decay rates are.normally an order of magnitude faster than
 laboratory rates.
 Bromine Chloride
     Reactions of bromine chloride 1n Freshwater are more complicates
 since two halogens are Involved.  Reactions In Freshwater Include the
 production of both hypobromous add and hypochlorous add:

     BrCl * H2Q   =   HOBr » HCl                               '
     Brj » HjO    ——-   H08r » H* » Br
     Clj »• H20    r	*"   HOC1 » H* » Cl
 As with chlorine, hypooromous acid will react with ammon'.a to Form
 bromamlnes.   Roberts and Gleason (1978) presented data on the decay of
 bromine residual in seawater with ammonia, concentration; of  0.2 mg/1.
 Decay was extremely rapid since bromine residuals were-not detected aFte-
. only two hours.   NO other data on bromine -residual decay were available.,
 0.2 CASE STUDY
     The concepts discussed above are applied here For demonstration
 purposes.   The Connecticut Department of Environmental Protection
 conducted physical,  chemical, and biological measurements to assess the
 Impact of the falrfleld Hills Sewage Treatment Plant on Deep Brook in
 Mewtown (CT  DEP  1981).  The plant provides advanced treatment with the  •
 main treatment units being primary and  secondary settling tanks,
 trickling Mlttrs,  and Intermittent gravity and sand filters.  Current  •
 plant flow averages  about 0.3 mgd.  Plant effluent concentrations of TRC
 typically range  From 0.0 to 3.0 rag/1.
     Deep Brook 1s a  fast flowing, well  oxygenated tributary  of  the
 Pootatuck  River  with art average Flow of 0.27 m^/s and a 7-day 10-year
 low flow of  0.014« /$.  The plant effluent discharges into Deep Brook
 about 610  meters above the Pootatuck River.   Pootatuck River average flow
                                0-7

-------
1s 1.1 n3/s and 7-day 10-year low flow 1s 0.12 IB /s.  Measurements of
tn-strea» and effluent TRC were conducted on August 29. 1980.
    Mean values of TRC concentrations measured using the amperoraetric
tltration method are presented 1n Figure 02.  On the date of these
measurements, river flow was 0.025 m /s and plant flow was 0.014
m3/s.  Total mixing Is said to occur 15 meters downstream of the
discharge point, although the basis for this statement (i.e.. visual, dye
study, etc.) 1s not stated.  It seems reasonable to expect that mixing
would be rapid with the plant flow nearly as large as the river flow
(dilution ratio of 1.8).  As shown 1n Figure 02. at a point IS meters
downstream the effluent concentration of TRC had been reduced from 3.8 to
2.0 mg/1. or diluted 1.9 tines.  This tends to support the 15-meter
complete mixing assumption.
    The TRC concentrations decreased to 0.2 mg/1 (a factor of 10) at a
point Just before the confluence with the Pootatuck River.  Since no
dilution water enters the brook In this reach, the loss of chlorine was
due to chemical reaction and decay.  When in-stream chlorine
concentrations are plotted on semi-log paper, a straight line gives a
reasonable fit with the data. Indicating that the die-off of chlorine for
this case Is approximately first order.  Using the formula for
first-order decay (Eauatlon 2.3 in Section 2.4 of the text), a rate
coefficient of about 100 per day 1s calculated.  (To obtain this value, a
stream velocity of 0.3 a/s has been assumed, as the actual value was not
given.)  This Indicates that the 1n-stream loss of chlorine is extremely
rapid.  However, the value of 0.2 mg/1 1s still more than an order of
magnitude higher than published maximum In-stream criteria.  In Pootatuck
River. TRC could not be detected after the Deep Brook confluence.
Biological measurements Indicated a highly stressed condition 1n Deep
Brook downstream of the discharge.
    In suranry. a slnple dilution calculation followed by a first order
reaction coefficient was adequate 1n this case to estl -cte the ol
                                0-8

-------
Is
 c
 i
               en
                       o
8
VI
               c

-------
concentrations 1n peep Brook.  However, tftls method must be used with
caution for several reasons.  If stream-flows are higher, complete ml
would not occur as aulckly, and this method does not apply in trie zone of
Incomplete mixing.  Also, as discussed earlier. 1n-streara reactions are
extremely variable depending on environmental factors such as light.
temperature, and streamHow.
                                 0-10

-------
             ATTACHMENT I

WATER-SEDIMENT PARTITION COEFFICIENTS
         FOR PRIORITY METALS
                  by

           HydroQual, Inc.
          1 letftbridge Plaza
      Maftwah, New Jersey  07430
           November 1982

-------
                         ACKNOWLEDGEMENTS

    The technical analysis  reported  in  she  study  was  performed by
•lichael T.  Kontaxis, Project  Scientist.   Dominic «.  OiToro  and
Donald  J.  O'Connor  served  as  Project  Consultants  and  provided
technical guidance.   John  P.  St.  John  served  *s  Princ:pal-;n-
Charge and drafted the report.

    William  L.  Richardson  of the  U.S.  Environmental  Protection
Agency, ERO-L at Gross*  lie, served as  Project   Officer  for -the
Government.
                                1-1

-------
                             ABSTRACT
    The  Office  of  Water of  the  U.S.  Env i ronnental   Protection
Agency  is  responsibly for inanaqinq waste  load  allocation  (WLA)
activities  throughout  the nation.   These  orocedures generally
involve the application of mathematical model inq activities  which
require specialized  information  Cor  proper  implementation.   One
area which  will  receive  increasing attention in  this regard is
the fate and  transport of toxic  pollutants, particularly certain
priority  heavy  metals.   An  important  characteristic of   these
materials   is an   affinity  to  complex  and/or  be adsorbed
(partition)  to partlculate materials in the natural environment.
As  realistic  modeling   frameworks  must  properly  traex  mth
dissolved and  particulate forms  of  substance in  tfte receiving
water  environment,  it   is  important  to  determine   partition
coefficients for  the priority  netals  for use  in these analyses.
    It w4s  the  purpose  of the  investigation  reoorted  herein  to
retrieve  information and  data  by which  to  docunent  and/or
calculate  water-sediment partition  coefficients  for  various
priority heavy metals.  In addition, the available data was to be
examined  to  determine  possible  functional   relacionshiss  5 -no no
partition coefficients  and  various environmental  water  quality
variables.
                              1-2

-------
                         TABLS OF CONTENTS

Section                                                 Paqe
No.                               •                      NJL?

  1    INTRODUCTION	    1-i

      -1.1  BACKGROUND	    1-1

       1.2  OBJECTIVES  OF  THE. STUDY	    i-2

       1.1  SCOPE  OF  THE REPORT	    i-3

  2    CHEMICAL PARTITIONING	    2-1

  3    SUMMARY OF  AVAILABLE  DATA....;.	    3-i

       3.1  DATA REQUIREMENTS	    3-1

       3.2  SOURCES OF  DATA	    3-2

       3.?  AVAILABILITY OF  DATA	    3-3

       3.4  -CLASSIFICATION AND DISTRIBUTION OF
            DATA	    3-5

  4    METHODS OF  ANALYSIS.	'.	    4-1

       4.1  TECHNICAL OVERVIEW	    4-i

       4.2  9 IN ANALYSIS.	    *-l

       4.3  STATISTICAL ANALYSIS	    4-3

  5    RESULTS OF  ANALYSIS...,	.	    5-1

       5.1  .PARTITION COEFFICIENTS	    5-1

       5.2  CORRELATION WITH  ENVIRONMENTAL
            VARIABLES	    5-2


       APPENDIX
                                1-3

-------
            SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
    The  available  technical   literature  contains  very   limited
    useable data  for  determination of  partition  coefficients  for
    priority metals.

    Of  the  various computerized data bases investigated   in  this
    study,  the  water  quality  file of STORET contains  the  lariest
    amount   of  pertinent   information  by   a   large  margin,
    approximately  29,090  useable records  from  various  water  body
    types.   The  most applicable data  were derived  from  water
    column  samples;  bed  sediment data, while  available,  did  not
    provide  sufficient information  for calculation of partition
    coefficients.

    Retrieved  data were  in the  following  order  of abundance  by
    priority  metal:  tine,   copper,  'lead,   arsenic,  nickel,
    chromium,  cadmium,  mercury,  and  silver.    Analysis  was
    confined  to data  collected  in  streams, and lakes.   Insuffi-
    cient  data were  available  for  analysis  of arsenic  in  lakes
    and  silver  in  both types of water bodies.
4.  Sufficient  data  are available for calculation of  represent-
       i
    ative values of  partition  coefficients  for  the  various
    priority  metals,  with  the  exceptions noted above.  Much  less
    information  is  available  by which  to assess  relationships
    among  partition  coefficients  and various  environmental
    variables other  than suspended solids.

5.  Analysis  oc  data  Indicated a  pronounced aoparent relationship
    between  partition coefficients  for  the v^iujs priority
                              1-4

-------
    metals and  suspended  solids  concentration.   However,  for any
    given solids concentration, calculated partition coefficients
    varied over  a  wide range of  values,  perhaps  multiple orders
    of  magnitude.    No  consistent correlation  was  found  amonq
    partitioning  and  other  environmental measures  such  as  pH,
    alkalinity, temperature or 800.  Partition coefficient values
    for lakes were determined to be consistently greater than for
    streams for all priority metals except mercury.

<.  The partition  coefficient  values  determined  for  the  various
    priority metals  are  satisfactory  for aoplication  analyses.
    However, the  values  resulting  from   the  regression  analyses
    developed in this study are order  of magnitude estimates .only
    and  the wide  range  of  calculated  partition  coefficients
    should be considered in practical  use.

    It is recommended that a refined data base be accumulated for
the  various  priority  heavy metals.    Such  a  data base  should
consist of controlled sampling of  a variety  of  natural  waterways
and  include' simultaneous  measurement  of  all ohysical,  chemical
and biochemical  factors  which  m*y have a bearing  on  heavy metal
partitioning.     Laboratory   studies  may  be  appropriate  to
supplement-  the  field   investigations.    These  data .should  be
evaluated to reassess the results of the present investigation.
                               1-5

-------
                            SECTION  1

                           INTRODUCTION

1.; BACKGROUND

    The Office  of  Water  of  Che  U.S.  Environmental  Protection
Agency  is  technically  responsible for  managing  waste  load
allocation  (WLA)   activities  within  the  organization   and   far
providing  technical assistance to the states.  In addition, this
office  also  has  technical  responsibility  to  review  various
advanced treatment  (AT) projects proposed  under the construction
grants  program  by  regional  offices and  the  states.    The  AT
projects often result  from  water  quality studies and mathematical
modeling  analyses  which are  used   to  establish  WLAs  indicating
that technology based effluent limitations are not sufficient to
achieve or  maintain water quality  standards.   It  is important
that WLAs be  established in a proficient and  technically  correct
manner so that recommended facilities are  properly developed  and
cost-effectively designed*

    In  the  performance  of  its mandate,  the  Office  of water  has
determined  certain  specific  areas whereby  assistance   to   the
States is advisable to help maintain and/or improve  the technical
bases  for  WLAs and recommended  AT facilities.   One  such are*
which will receive  increasing  attention  is  the fate  and transport
of  toxic materials,  particularly  certain  priority  pollutant
metals, as discharged  from  POTW's  and other sources.  Treatment
requirements  for  these  substances will  depend   upon   prooerly
determined WLAs, which  in  turn  must be  based  on  mechanistically
realistic assessments  of  the   transport  *nd   f*te of these
materials  in  the  aqueous environment.   An  inportAnt character-
istic of metals in  this  regard is the affinity to complex and/or
  •
                               [-6

-------
o«  adsorbed  (partition)   to  natural  particulate  Tiateriils.
Realistic  modeling  frameworks  must,  have the  caoacity  to  tr*c
-------
    The specific priority metals of concern -wnich  are  considered
in the'study are:

              Arsenic
              Cadmium
              Chrom ium
              Copper
              Lead
              Mercury
              Nickel                       .  .
              Silver
              Zinc

1.3 SCOPE OF THE REPORT-    ....

    The report summarises various technical procedures  wrier, were
implemented to obtain,  cateqorize  and  evaluate  data  fsr  sriority
neavy  metal  partition coefficients.   Theoretical  considerations
are  presented  to  orovide  a  background  for  the analysis  and  to
indicate data  requirements.   Required  data  are  summarized  hy
s^urr* *nd  availaoility  and  classification procedures  are
descr;5ed.    Methods  of  -snalysis  are  described for  tne  cateqor-
iration  of  data,  calculation  of  partition  coefficients,  and
statistical evaluation  of  relationships  between these  values and
various ambient environmental variables.  finally,  the  results of
the analysis *re presented and discussed.
                               1-8

-------
                             SECTION  2
                      CHEMICAL
                                                                of
    One  of  the  ma^or  characteristics  wnich  s i f fer en tia tas  -nan/
chemicals and heavy metals from classical water quality  variables
is  an  affinity  for  adsorption  to particulate  material.   Figure
2-1  schematically  illustrates   the   principle.     If  a  •nass
soluble  chemical  is placed  in  a  laboratory  bear  aid stirred,
portion  of  dissolved  chemical  will  be sorted s-ts  tne sart
ulates and  some of  the  chemical  concentr 31 io*.  w. 11 tner.  ^e
particulate form, c
                        If  this process is
                                              i its red with t l-ie  as
shown  on  the  diagram,  dissolved  chemical  will  ae  reduces  a-.-i
particulate chemical will increase  in a  reversible  reaction  ur.;:l
an equilibrium is  achieved  at some  point.   The  total  chenir>:
concentration  at  any time  is  equal to  the  sjm  -5 f  the -iissolve-
and particulate concentrations:

in which  -  is  total  chemical concentration  and  all concentra-
tions are expressed on a bulk  volume  (liquid plus solid)  basis.

    The rat* with which this reacion  taxes place and  the  relative
relationship between the dissolved and particulate chemical,  that
is, the water-sediment  partitioninq,  ire both chemical specific.
In  most  cases,   reaction   between   tht  dissolved   chemical   and
particulates  occurs  very  rapidly,  minutes  to   hours,   *r\A.
equilibrium  is achieved  quickly relative  to  the  time character-
istics  of  the environmental  s*ttinq.   rr *  tendency  t?  sorn  is
highly chemical specific and will ranqe  from very w«,»tc to  st
in the case of -naterfals with  Tow solubility.
                               1-9

-------
      a
      M
      z
•-

                                           UJ
                                           Z
c
u
?
I
G
VJ
            NOUVH1N33N03

-------
    The affinity of  a  oarticular  chemical  or heavy metal to sorb
can  be quantitatively  expressed  by  a  sediment-water  partition.
coefficient,  K  .   A series  of  experiments of  the tyoe schemat-
ically  indicated  on Figure  2-1 may be  conducted  with  differing
initial dissolved  concentrations  of a specific  chemical.   After
equilibrium  is  achieved,  the particulars  chemical concentration
to  suspended solids  ratio ,   c /s ,  expressed  as  microqrans  of
chemical per  gram  of particulate  material  {ag/g>,  may be clotted
as a  function of the dissolved  chemical  remaining, c . ,  exoressec"
                                                     G
on a volumetric basis as nicrograjn per liter of water («g/l).

    Figure  2-2  schematically illustrates  the  results of  this
laboratory experimenc.   A specific  chemical  will  oroduce  one  of
the lines shown on the logarithmic diagram, the relative position
of which determines  the  partition coefficient.   For a particular
dissolved  concentration, greater   particulate  concentrations
result  from  larger  partition coefficients  as shown schematically
by the  various  distributions.  Data from chemicals  w>-.i;h.  car.  be
plotted and correlated as shown on Figure 2-2 behave according  to
the Freundlich  isotherm defined as:


    Vss * Wl/n                                       f2'2''

in  which  n   is a   constant characterizing  the  slope  of  the
relationship.    If   the   slope  is  near   1   indicating  a  linear
relationship, the partition coefficient is defined  as:
    As indicated, a  specific  chemical  or heavy *etal  will  yield
one of  the  relationships  indicated  schematically on  Figure  2-^
( c *  specific  type of  sorbing  particulate -naterial.   However,
                               l-U

-------
  iQ.OOO
 9 i.OOO

ji.


in
v>
H   'CO
o
u

uj    "O
a:
<
a
      0 •
                                         Ct /SS
                                                     COC'
                 tO
DISSOLVED
                                                  (00

                                             ca •( Mg /
i COO
                         FIGURE  2-2


      'EXAMPLE ISOTHERMS AND  PARTITION COEFFICIENTS

-------
different  re lationsnips,  and  therefora,  different  oareitis-
coefficients, may be observed for the sane chenical with  various
types of sorbants.   For examole, orqanic  oarticulates or  silt>
materials may attract a  certain 'chemical  more  strongly  than  s*ndy
materials.   Further, different size  classes  of pirticulate
material, as. they may  reflect  different classes of particulars
as sands, silts, clays, etc.,  may exhibit differing affinities,
and partitioning, for a specific  cnemical.   In principle,  it  is
most  advantageous,  therefore,  to  perform  exser ime-.ts  ar.s
determine a chemical's partitioning  characteristics  with the  tyce
of particulate material (suspended and bed sediment' t» wh;c-.  ::
will come in contact in  the natural  environment.

    As described,  the nature  of  the  sorbant -nay have a  -e^r;n=  - r.
the  magnitude  of  the  partition, coefficient  for  a   par tic-Jl a:
substance.    It has  also  been  observed  .'O'Connor  and Conns'. I/
that  partition coefficients  may  vary   in  accordance  with  t~-.
concentration of the  sorbant as well as its nature.   Figure  "?-;
presents  some  empirical  relationsnips  between   'partitic-
eoefflcients  and   sediment  concentration  for   a  variety  -f
substances.  For certain chemicals,  it is observed  trat, part::io~.
coefficients may be  expected to  vary  by  an order af m aq r. i t ud e  sr
more depending  upon the solids  concentration.  In  the  case  sf
heavy metals,  other  factors  such as  pH, alkalinity or hardr.ess,
temperature, *nd conductivity may have an effect on tne partition
coefficient  due  to   the  complex  chemical  reactions  which  OCCJT-
with these substances in the natural  environment.

    Much  valuable   information  with  which  to define partition
  efficients  and   relationships with various   environmental
  riables   can be  determined  from carefully  •levelope'i  and
  ntrolled . lar.:-ra^ory experiments.    It  is  noted,  however,  that
coe
va
contro
«.<*•.*»«*•»— •*«.... i.a-.«ry experiments.   It  is  noted,  however,  that
such  values  
-------
 5  2,    1
 If lfh If
-ft. § ST 55 • w «
o«w s w ; «t o * ••
  W « 9 is u W *
  ' • 1 • • I  .
                                       O
                                       O
                                        z
                                        w

                                      . O
                                      O W
                                           o
                                           *
                                           o

                                           •4
                                           O
                                           w
                                           "o
                                                t.
                                                2
                                              K  -

                                              C
                                                <

                                                £

                                                o
                                                £

-------
estimate  what  occurs  in  the  field,  this   is,  in  tne  natural
environment.  Althouqh  the  laboratory  data   *r* quite  useful,  it
is appropriate to utilize field data wherever .wailaole  in  orier
to calculate  partition  coeffient values  from  those settings  -is
which  they  will  be  subsequently appl'ied.    These  natural
conditions are likely to be  far more physically, chemically, a-*
biologically complex   than  the  laboratory sett;-.q.  but t.-.e
information derived,  therefore,  is  realistic  in  the  natural  sens*
and  can  serve  to  indicate   how   well  or  poorl--  sirtitio-
so«fficients can  be  defined  for  the  natural  setting-.
                               1-15

-------
                            SECTION  3
                    SUMMARY OF AVAILABLE DATA
3.1 DATA REQUIREMENTS

    The  minimum  basic data  requirements  for  calculation  o £•
partition  coefficients for  the various  priority  metals  are
dissolved and  particulate  concentrations and  a  measure of  the
associated   particulates,  suspended  solids.    This  information
allows determination of partition coefficients in accordance with
Equation (2-3}  and also provides  for an examination of any soli-is
   dependent  partitioning  relationships  such  as  illustrated  on
figure 2-3.   Additional information  is  required  to  assess  other
potential   correlations  between  partitioning  and   ambient
environmental variables.

    In the  case  of  heavy metals,  other  pertinent  related  data
include pH,  alkalinity, hardness  and temperature, variables  which
may  influence  the chemical  reactions which  metallic  substances
undergo in  the natural  environment.  Further, some information on
the  nature   of  the  sorb ing  solids  in  terms  of  organic  and
inorganic  fractions  and  size  distribution  is  appropriate.
Measures which may provide some  information on the organic nature
of  the  sorbing   material  are  volatile   susoended   solids,
biochemical  oxygen  demand  (BOO),  chemical  oxygen demand  (COD),
and chlorophyll-*.  Flow data, whether low, average,  or high in a
particular  stream,  nay provide  some  indication  of  the  tyoe  of
particulate  material   (sand,  silt,  clay)  likely  to comprise
suspended sediment  in  the water  column  in  the absence  of  other
Information.
                              1-16

-------
                                                                         I
    The  foregoing  type  of  information   was  souaftt   fro*
laboratory and  field  investigations.   In  the case of field data,
data were sought  for  both water column and  sed  sedi-ieit and  for
different types of water body, particularly streams and  lakes.  A
distinction between partition coefficients calculated far each of
these  types  of  water  bodies  is  appropriate  as  the  nature  of
corresponding suspended sediment may  be different,  'that is, more
organic in the case of lakes.

3.2 SOURCES OF DATA

    A large amount of  field  data  resulting frnn  various ty?es 5?
surveys resides on  computer* zed data bases.   The following data
bases  were   examined   for  data  ava ilaoil i ty :     ST3R5T "JS£?v ,
NASQAN  {'JSCS^ ,  the  data  base maintained  by   NOAA,   arsrf  57^*
(Canadian  Centre  for   Inland   Waters}..    In  addition,   -..-«
computerized reference  service  DIALOG  was  utilized  to identify
and secure additional  reference material  including bo'th  field  -ar.rl
laboratory  investigations.  These   data  bases  were  reviewed  t=
avoid potential multiple countirtq of  samples.  The  fol!sw;nq is a
brief  description  of  .these  data  hases  in   relation t?  tr.is
project:  ?

1)  STORST

    The  water   quality file  of 'this data  base contains  water
    quality  information obtained  *t numerous .stations  located in
    all  states and  operated  by various  agencies.    It  is   the
    largest data base  for water quality information ^y  fir.

2)
    Water  quality 3ats  of this  file  *.e  contained  i-  STOSET.
    NASQAN data  updates  «re transferred  to  STORET on i "jiw-ekly
    basis.  Data can be retrieved in  the USGS  format.
                               1-17

-------
3}   NOAA
    This  data   base  contains  data  Cor  ocean,  near-shore  ar
    estuarine samples.   The estuarine samples cover  the  areas  of
    Puqet Sound and New Yor* Bight.  A  search of  these  areas WAS
    requested.    The  search  Cor  Puget  Sound  revealed no  metals
    data.  The  search  request  for  New York -Siqht did not  result
    In information in time for  processinq.
4)   STAR
                                                            other
This  data  base  contains  two  subsystems.    One  s
contains  data  exclusively for  the Great  La
-------
         Enviroline
         Compendex
         Ocean Abstracts
         Comprehensive Index  (Dissertation Abstracts)

    A  small  number  of  usable  publications  were  identified  and
    obtained'.

5)-  Miscellaneous

    A  search  of  all   pertinen-t   in-house  pu^l ica iisr.s  -as
    conducted.

3.3 AVAILABILITY  OF  DATA

    The snail number of references obtained  from OIALCC Toncair.ed
no  significant  amount  of  useful  information.    Only  a  modest
amount  of  pertinent data  was obtained  froi  in-house  sources 3:
reference  material.   On  this basis, and  in  accordance  with tr.e
evaluation of  the various  computerized  data  ftases  as descr:5«d
'above,  it  was concluded  that "the  water quality  file  of  STS3S?
would provide the vast majority of usable sa-ipl-s, and almost all
effort  should  be directed  to  this  source.   A retrieval strateay
was  chen 'devised to search the  data records  of  all stations in
all  states,  and  identify a-nbient, remar*-code-f ree  saiales w. lei-.
contain,  at  minimum,  the  concentration  of  total  *nd dissolved
species of tietal and suspended  solids.    For  such  samol-s, data
retrieval  also  included  temperature, pH,  «l»cal inity, h«r-iness,
flow,  COD,  800, volatile  non-filterable  residue  'volatile
suspended solids),  chloro phyl i-a  corrected,  *-ri  -jnco r r fz ted
chlorophyll.   No data were available on  the size d i str i* ;t i-an if
suspended solids  in ' th   water column.    A1, thoui*  l3t-^  
-------
sufficiently  comolete   (dissolved   chemical   •nissinq)   for
calculation of  partition  coefficients.   Data  retrieval  was
therefore restricted to water column data.   The results of these
retrievals  were  obtained  on  magnetic  tapes  for  subsequent
analysis.

3.4 CLASSIFICATION AND DISTRIBUTION  OF  DATA

    It was  determined  from  initial  STORCT retrievals  that  the
useful data base for some of the priority metals was quite larqe
with  samples of   Interest  numbering   in  the  thousands.    Data
handling   and subsequent  analysis  was  therefore   performed  by
computer.  All of  the basic  minimum  riata which were  retrieved for
each  priority  metal  permitted  calculation  of  a  partition
coefficient  in   accordance  with  Equation  (7-3).     However,  a
purpose  of  the  study  was  to  assess   any  relationshiss  =«:w««n
partitioning and  various  environmental variables  as disc-jssed.
In order  for  these  determinations  to  be valid* the  basic r*at*
must be measured simultaneously  on the  same sample or *t the same
time.  Hence, the  data base for each  priority  metal  was  sorted
into various types of records, each  of  which was characterLred by
the  simultaneous  determination  of  various  parameters.   In this
manner,  a  large  data base  could  be  examined  for  sanplir-3
information most  useful  for  cross-correlation  purposes,  and  the
appropriate data records  accessed  for analysis.

    The definitions established  for  the various data records are
as follows:
                               I -20

-------
Data Content of Record '

Total   and  ^issolve-1  -»etal   and
suspended solids concentrations
data of type 3 and oH
data of type 1 and alkalinity
data of tyoe 2 and temperature
data of type 3 and ?C2D or 9CD)
data   of   type   4   and   'vol-stil*
suspended solids, or cnl-a corrected
or chl-a uncorrectedi
data of type 4 and 5
    Hardness  was   judged  to  be  redundant  with  alkalinity  fcr
correlation purposes and  thus  not  included  in  the  data  records.
Flow  information   was  judqed  to  be  too  meager  far  nean;r.3fj'.
correlation and also excluded from evaluation.
    Type

     8

     1
     2
     3
     4
     5
    For each priority metal,  the  total  nunaer  of  records  and the
distribution o£  these  records  Per  cyae was determined  for  each
station in  each  state.    At  the end  of. the first  staqe  of  data
processing, a  summary  table  was  developed  for each  uetal  wric-.
contains  an aggregation  of   records  and stations  per state,  *
distribution of  records  per  type for  each state  and  the  totil
number of all records.
    Table 3-1 is an  example  data  summary for the priority -netal ,
zinc.   The  table  shows the  total  number  of  records and stations
and the distribution of record  types  within  each state reporting
data.   The  total  record count  Co.  zinc  is  5397,   These records
include  data  from   streams,  lakes,  est"-rUs,  coastal  zones,
manholes,  and other  miscellaneous  origins.   Figure  1-1   is   *
                              [-21

-------
                           TABLE 3-1


                       ZINC DATA SUMMARY
   STATE   TOTAL      8T «CCO«0 TTPC
          3CCOAOS  0    1    2    3    **    3
AX
A2
AR
CA
CO
TL
SA
:p
*«*
IN
:A
KS
KY
LA
MS
MA
MI

MS
MC
MT
NE
HV
NJ
NM
KY
NC
NO
OH
OK
OR
PA
RX
SC
SO
TN
TX
trr
VA
WA
w
MT
WY
 A




S

19
20
21
22

23
26

2*
29
30
31
32

33
36
37

if
«0
    «*^
     W9
     31
     33
     3*
    37
    • «
2
97
3
UA
79
7

a
126
ti
8
100
1036
12
ia
393

10
220

16
120
»6
27

it 33
i 7
20
1

13

43
139
12** t
in:
11

1

0

0
1 **
?3

7**
0
26
7
15
1
9
78
0
7
231

0
97
30
0
0
0
12

j

7
1
0

1
3
2
12
12**3

j
0
b
0
0
21
a
tt
u 6
y
0
ll
8
ll
1
6
1
11
2
0
1
1
3
1
1
2
^
9
Q
2

(4
0
1
1
0
^
1
0
1 1 0
c
32
1
U
3
C
y
1
0
0
0
0
0
0
0
7
0
1
0
1
6?2
0
0
2
2
5
1
0
1
1
0
0
a
i
<4


0
0
0
0
0
0
1ft
0
1
n-
3
1
0
1
0
0
1

0
0
0
210
9
3
7
6
12
1
1
3
20
8
0
313
27
0
0
6
a

9
73
j
8
S
2
7
21
1
2
1
57

)
0
2
a
«*
3
0
H
C
n
5
10
i*
1
0
0
71
3? I
0
2
10
2
it
0
?3
0
1 **
10B
22
1
n
u
1
0
1
0
0
f)
f)
3
1 ^
17
0
14
2
•
A
r.
0
0
0
12
o-
0
0
0
0
0
71
0
0
1
0
0
2
l n 9
0
?
1 ? S
0
0
Q
0
••4
0
0
322
1
0
7
0
0
3
1 ^
0
0
0
3
3
3
3
i
2
0
0
0
1 A
u
3
U
0
0
0
20
a
0
a
0
33
0
0
1
a
0
3
0
0
0
3
1
0
0
C
a
0
0
n
u
rj
u
72
j
a
w
IJ
I.
II
•*
•
                                                    STATIONS

                                                           1
                                                           7

                                                          2!
                                                          19
                                                           1
                                                           1



                                                          sl
                                                          2u
                                                           4
                                                           It
                                                           1

                                                          la
                                                           to
                                                          •  6
                                                           13
                                                            1

                                                            &
                                                           29
                                                          22
                                                           c.
   T3TAL
•U.S. Territories & Possessions
                              T-22

-------

-------
summary of the geographical distribution of the available records'
by  state.    Similar  information  is  presented  for  each of  th
                                             V
priority metals on Flqures A-l throuqh A-9 in th- Aopendix.

    In the  basis  of  this  analysis,  the total  number  of records
available for each metal is as follows:

    Metal                    Total Number of Records

    zinc                                 5397
    copper                               4557
    lead                                 31fA
    arsenic                              2335
    nickel                               1998
    chromium                              020
    cadmium                               799
    mercury                               *31
    silver                                 50

    These  records  formed  the  basis  of  she  subsequent technical
analysis.
                               1-24

-------
                            SECTION 4                  .

                      . METHODS OF ANALYSIS

 4.1 TECHNICAL OVERVIEW

    A  substantial  amount  of  data  for  the  priority  -netals  is
 available  for  analysis.   Approximately 20,030 data  records  were
 developed  for  the  priority  metals  in accordance  with  the  data
 classifications described previously.   This amount  of information
 required  the   application   of  computer  processing  -and   the
 development of   a  technical  strateqy  for  data  handling  anc
 analysis.   As  a  result, specialised  software  was developed  to
 process  ST3RET  information  for each  ariority metal  wnicn  was
 contained  on a series of magnetic tapes.   This  software was  used
 to  select appropriate data,  compartmentalize it   as  approoriate
 into  a  series of  "bins*  and  calculate   partition  coefficients.
 The   values  were  then  subsequently   processed  by  various
 statistical  techniques  to   search   for  relationships  amonq
 partitioning and various environmental  variables.

 4.2 3IN  ANALYSIS

    An  objective  of the study  Is  to determine correlations,  i*
 any,  which  exist  among  calculated  oartition  coefficients  *nd
 ambient  environmental  conditions  as represented  by  various
 physical,  chemical  4nd  biochemical measurements  simultaneously
 performed  or observed.   The data records  for  each  priority  «*etV.
 reflect  a wide range  of conditions for  analysis.   In order  to
 search  for  correlations,  the  procedure  ' selected  consisted  of
-segmenting the data progressively  into a  series of eomoar tien.i,
 or  bins,  «*ch of  which  would be specifically  defined  by  a
 o~ru.eular ranqe  of environmental variables.   For  exv..p\e,  a bin
                               1-25

-------
.-nay be defined as  that  portion  of  the  data  base which .-.as limits
of suspended  solids  of  13 to 39 mg/1, a  pH  range  of *.
-------
4.3 STATISTICAL ANALYSIS

    At Che conclusion of the bin analysis, * statistical analysis
was  performed  to assess  any empirical  functional  relationships
between  the  calculated partition  coefficients and  the  er,v;ron-
mental  variables  selected  to   define  the  bins.    A  multiple
correlation  routine  was used  for  this  purpose.    The  following
generalized regression equation was applied.


    KP ' Kpo <'«1»" {X2>"

in which:

       K     »   partition coefficient fl/'
-------
The regression analysis includes appropriate statistical measures
such as correlation coefficient, r, and  standard  deviation  a,  c*
the exponential constants.
                               1-28

-------
                            SECTION 5
                       RESULTS OF ANALYSIS
5.1 PARTITION COEFFICIENTS

    The bin analysis was used to calculate partition coefficients
for  each  priority  »etal   as  described.    The  data  base  was
segregated by  origin  of  sample  and  separate  analyses  were
performed on  data  reported from streams and lakes.   In order to
facilitate  the  identification of  possible  interrelationships by
direct  observation  as  well  as by  statistical  means,  ^he  bin
analyses  were  restricted   to  three   dimensional  arrays.    T^e
initial  analysis  was  focused on  variables  which could  exert  a
pronounced  affect  on  partitioning  for  heavy  metals,  suspended
solids, pH, and alkalinity.   Thus, all records  identified as Type
2 and  higher  order  in  Section 3 were selected   from the data base
for  initial  analysis.   The  bin  intervals  for  stream  data were
specified as  follows:

Susoended solids (mc/1)
    10 to 30;  30 to 50;  50 to 100;  100 to 200;  200 to  500
SSL
    5.8 to 4.0; fi.0 to 7.0;  7.0 to 8.0;  8.0 to 9.0
Alkalinity
0 to
to
                        50 to  130;  100 to  2&0;  20«  to  ?c
-------
    Thus,  the  initial  analysis of  stream  data consisted  of  10n
compart-nents in each of which  a series  of  partition  coefficients
were  calculated  depending  upon   data  availability.    The  bin
intervals  were  selected  to  represent  reasonable  ranges  of  the
indicated  variables   while  also  maintaining   a   sufficient
population  of  data  in  a  large  number of  bins for  statistical
reliability.   Analysis of lake  data  also  included  a  suspended
solids interval of 0 to 10 mg/1.

    Table 5-1 illustrates  the  results  of  this analysis  for  zinc
data  reported  for  streams.  The  table  presents  the  bin mean  of
the  partition  coefficients  calculated  within  each bin   in
liters/kilogram, the coefficient of  variation, and the  number  of
bin records.  For zinc, a  total of  1782 records were used  in  the
calculations.

    Similar  analyses  were  performed  for  all  priority  metals
except silver  (streams  and lakes)  and arsenic  (lakes)  for  which
sufficient  data do  not exist.   The  analysis  was  also  repeated
with other environmental variables  as subsequently  discussed.

5,2 CORRELATION. WITH ENVIRONMENTAL VARIABLES
    Observation of the calculated partition  coefficients  in  Table
5-1  indicates  an apparent  inverse variation with  suspended
solids,  but a  less  clear  relationshio,  if  any,  with  pH  and
alkalinity.  Similar results were obtained  for the  other  priority
metals.    Simple   and   multiple   regression  analyses  were   then
performed to better define any functional  relationships.

    Table 5-2  presents  a  summary of the  statistic*!  parameters
obtained  for  -*nc in streams.   The  table  indicates an  overall
geometric "tea.. i^rL.tion coefficient of  *oprox inately  55, 00^ l/kg
                             1-30

-------
                 TAS1E S-l


         BIN  ANALYSIS


           ZINC IN STREAMS
             .go to  ».te

             ,  »*.»3   SO.  »«.f«>   irj. lOO.r:
 S.M*16 84   *.a«OOC 00   J.300CC SJ   J.UV»C  "V   l.
 *T«|    1   I.fla
                             .
                        4   B,0»fl    •   3.008    I    O.t
             4.00 TO  Lot

           »«.  JJ.'o   «.  »

            o*   «.i«oic i«
            Jl   l.tji  »»   i.kr*    »   o.40«    1   ».aoe
                 '0  O.Ot
    «.«»«oe  •*   o.*i»oi o*   ».!«»»• :v   ».?.»tr •*   '.
    »;»••   i)   7.7i»  »o   /.»».  it*   ;.'»»  t-t   i.;
     : "0"  *.oo ft

     .T9   10.  70. >0
    o.ooooj  »o    .           .            .            .
    R.OOO    I   J.4T01'  »   I.IfO   II   >.«»^  »f*   J.«?«
       r»p»  «.«• rj  ».«•
o.oosat oo
o.ooe    3
                a.aafoi oo   J.s»o:: •:   :.r---r ::   i.—.t
                a.ooa    o   j.-«i    :   j.ier    9   ',:ne
 ji« '3  '•«•

»  O.'O   >«.  :o>*j   ^0.*
                     «.oo
                                               *»   -,-•»
                                               »»   *!»««
    o.ri

    f.r**
    j.oo*

-------
                                    4.:"  'a
    ,.  .

*t**»»Il  'r
                                                           1*1.

                          A. TO   J8.

                                  on
                                   ft
                                                      .'-J   1*1.
                                                           :  si    t.utir
                                                             d    i.n-
rt •»
                          ».TO   Jo.  ja.Tj   «o.  10. re  iJ?. na.ij  ?««. ??-.t;  «•-.

                          e.n*n B»   i.»9«ir~ «;   s.j^^^i ««   a.uasic :a   :.;::se v
                          1.11:    i   o.oos    a   B.IAJ    i   ;.ooa    a   e.jeg    ;
                                    .«: '3
                           ! "9»  ».:: '3


                           **s   **"  !"''1
                                               o;   s.
                                                                 o.aeo
                                                                         r*   -.seasr
                                                     .        <   0.:t5»' ^«   ».i «*r -•
                                   7   1.1*1   »»   !.'*>  UJ   l.Hfc   «»   ».'«•   '•
                                  ta
                                   o
                                               o*
                   »• »«•.*.»:  «•
-------
                       SUMMARY CF  STATISTICAL PARAMETERS "OR IIS

                         DEGRESSION WITH SOLIDS, PH, AL
                          0     1.2     3     u.     5     6      Total



Secoras Sy Type           1988   3?9   686   861    6*1]   67U   !46        5357



Sort &y Type >^2 & Sy  Water Body  Stream:   Records Analyzed *~32



FIRST BIS ANALYSE



So. of Acseptaole Slns^O  5 Records)  50:  Bin Avg <  (l/k?;  a *a.9'S



CORRELATION CC£?TICI£NTS  (partial)



riK, ss; s -:.333; rex.,  ?H)  a  o.i63: r(K., Ai) s O.:<;T
   ?                    p                   p


REGRESS 12 f«



i:  K  va as:



Kp s 1.05 « 1CS x OS)-0'685;  r      s fl.833.      . 3.065



2:  X  va as i CH];
    .A   . «.&    .  i '••Q * oo3    ».. i<"»Q «OooT           MB»£  —
s 0.32 » 10  *  (33)        I  [H]        ;  r-it1 . • O.B-»6. -t^



                                                              = 0.036C
 p                                           mi <*           ™* 3
3:  K  vs  ss &  [H'j  &  Al;



K  , 0.32  *  106  x  (33)"°-683 x [HT0'0687 i tUl'0'003; r     * 0.3U6, *
 p                                                        it ui *           espi



                                                                        *eip2
                                                                             _  s  2.372
                                                                         eip3
                                       r.-n

-------
                                ?ABL£ 5-2 'continued)
SSCONC 3IN ANALYSIS  ( ror *' 8 K SS~' !

                           P
                                                  •
No. of Acceptable Sins J> 5 Records) is; Bin Avg x  » 1.3



CORRELATION COEF'ICIEMT (partial): r(K*. pH) a 0.0«<5, r'.K*. Al) r 0.-36
                                      P                  P

3 EGRESSIONS
      *
3* . 0.6ia » (A1)°'1T9; r  . „ • 0.«86. a    » 0.362
r                        wui t           ex p






                                        , 0.52T. .
2:   K * v» U A
     P
                                      1*34

-------
from all dat,a.  Simple correlation  coefficients *re presented far
partition coefficient  variation  with  suspended  solids,  oH,  and
alkalinity,  resaectively .   These  values indicate a  very stronc
dependency  of  partitioning  with  suspended  solids  and  wea<
relationships with   the  other  variables  on  a  si-iple  Das s.
Statistical  parameters are also shown for the multiple step-w se
regression.     Exponential  constants,  multiple   cerrelat on
coefficients,   and  the standard  deviation   of   the  exponent al
constants  are  presented  for   the  sequential   recressian  of
partition coefficients  with  suspended   solids,  suspended solids
and pH, and- finally suspended  solids, oH and  alkalinity.

    It is o.bserved fron Table 5-2  that  the exoonential cs-.star.ts
for pK,  as  represented  by  the  hydrogen  ion  concentration,  ar.z
alkalinity  are   relatively  small.     Further,  the  m u 1 1 i s 1 e
correlation  coefficient is not  markedly  improved  oy the  inclusion
of  these variables  in the regression.    It  is  possible  that the
strong  dependency  of  partitioning  with  solids  is  mas*;. no;  a
correlation  with these  variables.   Hence,  a second  bin  analysis
was perfor-aed  where   the  solids  dependency  of  partitioning  was
removed by  rearranging the regression equation:
    K   • Kp'
-------
Graphical  presentation  of the results  supports  the  observation.
Figure  5-1  shows  the  partitioning  parameter  Kp-ss~0  for  zin-
plotted as a function of alkalinity fo.r various pH levels in bot.
streams and  lakes.   Similarly,  Fiqure  5-2 oresents  the same data
with the  partitioning  parameter  plotted as a  function of  pH for
variqus ranges of  alkalinity.   No  consistent trends  are  observed
from  these  diagrams.     The  multiple  regression  analysis  was
per famed  on  stream  and  some  lake data  for  all  priority  metals
with similar results.

    A  similar  type  of  regression  analysis  was  performed  to
determine any relationship between the  partitioning  'parameter and
other  environmental  variables:  temperature,  and  303  as  a
surrogate  parameter  possibly  representative  of organic material.
As with alkalinity and  pH,  no consistent  relationships  could  be
determined  among  the variables.   Table  5-3,  for  zinc  data  in
streams, is an illustration of the results of these  analyses.

    Sufficient  opportunity  was  not  available  within  the  t:n<
constraints of  the  investigation  to 'assess  other potential
interrelationships.

    From th«  foregoing  analyses,  i.t  was  concluded that  the only
clear  and  consistent   relationship, observed  between  partition
coefficients  and   the  environmental  variables  tested   was  with
suspended  solids.    For  almost  all  priority  metals,   a  stronq
correlation was indicated between  the  mean partition coefficient
valu*  within the  various  bins  and  the  average bin  suspended
solids value.   In  view of this consistent  dependency,  the final
functional relationships  developed for  these variables w*s based
on analysis  of ill  available data  records  which contained  the
basic  information  necessary  for  calculation  of the  partitio-
coefficlent.  The  only  data needed to  determine  these relatior.
                               1-36

-------
V)
trt
    O.i
                               PM
                                 7-9  9-9
                  -a 6* « o    a
                                   S
                  ..... I     .   ....... I
                      10              100

                  ALKALINITY {mq/l  CoC03)
lOOO
                   FIGURE  5-1

         K* AS  FUNCTION OF ALKALINITY
          P

-------
    iQC
     50
1 a
*
    0 i
     O i
; ALK -0-20
-
fe


mm
»
o o o
*
_.!.!_ ! J^
2O -30




.
O

O
1 1 T 1 1
SO-iOO
t



0 ° °



1 ! 1 1 1
iQO-200

O
O
o




1111!
200-3OC




o

o

I.I I !
36?«9967«9)«7ft9567l9*6?t
-------
                                      TA3L2 5-3



                      SUMMARY CF STATISTICAL PARAMETERS FOR ZINC



                            0     1     2     3     «*     5     6     Total ?e<



Records sy Type             1988  379   636   861   663   67«   i«6       529?



Sort Sy Type 3 4 Sy Water 9ody streams:  Records Analyzed 225?



FIRST SIM ANALYSIS



So. of Acceptaole Sins .(> 5 Records) 7:  3in Avg K  ( I/kg) r a5.300



:CRREUTI:N cctrriciENTS (partial)




r^<,. SS) s -0.995:
iC? vs S3;




K  ! 1.25 i 10* i (S3)"0'7035; •  „ « Q.03'7
 P                              ei p


StCCKD 3IN A.'fALTSIS  (For K*  * K SS"*)

                           P    P


So. of Aceeptaol* 31ns C> 5 Seccrds)  '5; 3ln Avg K  s 9. 7
                                                  P


CORRELATION :C£5?ICIEK7 (partial): r(K*. 900) s -O.U50, r(K , T) s -0.22«




3 £33 ESS 1C *S



':  .<   vs BOD;




    K/ , 30.2 i OODr0'4113: r     , O.«50. «    , 0.293






2:  K   vs SOD *> T;
          32.2 * (SOD)--.* lO-; r     . 0.«65.       ..0.316

-------
snips  are  Dissolved   and   particulate  chemical ,   and   susoenrled
solids,   The lowest  order  of data  records,  Tyoe  9,  and  above
could  be  used,   thus  substantially expanding the  data  b^se  for
final   analysis.    Hence,   the   final  correlations   between
partitioning and  suspended  sol\ds  concentration  in streams  and
lafces  were  based on  all available  data  records  in  ST3RET  for
these  types  of  water  bodies  from  which a partition coefficient
value  could  be  determined.    This  represents  a  relatively  large
fraction  of  the total  available  data  base with  only  data  Iron
estuarine,  scaan, miscellaneous  sources,  etr., not included  in
the analysis.

    An illustration  of  the  results of this analysis  is  presented
an Figures S-3A and S-3B, for  the  priority metal  sine in  streams
and la*es, respectively.  As before, the data  records were  sorted
into  a number  of  bins,  each of  which  was  characterized  by  a
specified  range  of  suspended solids.  Partition  coef fie ients. were
calculated from  the  data records  falling within  each comoartm*nt,
and  the  din means  and  other  statistical  parameters  were
determined.    The bin  means  were  then  regressed   with  bin  mean
suspended solids  concentrations.   The   logarith-sic  diagram's
present the bin  mean  partition  coefficients  plotted  with  hin
average suspended  solids concentration ar.d  the regression  line
which  correlates the  data.    Also  shown on  the  diagrams are  the
standard  deviations  of the  bin   values  for  the  log   normally
distributed partition coefficients.   The  strong  correlation
between  the calculated partition coefficients   and  suspended
solids is  evident from the diagrams.   The  analysis  also  indicates
a wide variation in the  partition  coefficients  calculated  within
the various  solids  intervals  as  indicated by the  large  standard
deviations.   Values  can vary by four orders  of magnitude.
                                1-40

-------
     •0
                       (0              100
                   SUSPENDED SOLIDS
«oco
                     FIGURE 5-3A
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED SOLID*
                   ZINC IN STREAMS

-------
                      lO              tOO
                   SUSPENDED SOLIDS  (mq/l)
'000
                     FIGURE  5*38

PARTITION COEFFICIENT AS  FUNCTION  OF  SUSPENDED SOLIDS
                     ZINC  IN  LAKES

-------
    The analysis  indicated  above  was performed far each  ariar::-/
metal  far  toth streams  and  lakes.   Tn*  excestions  were s:lv«r
{streams  and   lanes)  and  arsenic  (lakes   for  which sufficient
inf-jr-iation was not available.   Figures  5-4A and 5-43 sres*rst a
summary of  trie reqressiJn  lines  for  cartition  cjefficients  ar.d
suspended solids  for  the various  priority metals for streams  ar.c
lakes,  respectively.   These  diagrams can  5e used  for  tr.e   sest
escimate  af  a  partition  coefficient  value  far  a  sartic-'.ar
priority metal based  on  suspended' solids concentration.    "iz-res
A-10  through   A-i?  in   the  Appendix  ores^nt tr.e  iata   ar.c  tr.e
reqression  lines  for each  priority wetal  in streams ar.d la-ces.
These  diagrams   should   be  consulted  in  the -selection  -f 3
partition coefficient value  in  order to  indicate  the  dearee  sf
variasil:ty  which  may  exist  around a  particular value  or. • t*.e
basis of the.analysis of  available  data.

    Table 5-4  is  a summary  of the  statistical properties s:  z-.*
regression  equations  for  partition coef f ic ier.ts  ar.d   sussended
solids as developed for  the priority metals.  The number  of ia:a
records   in  each  evaluation   is  indicated  alons  -;t".  f«
exponential constant,  correlation coefficient, *-*•*.  stan-Jar-!
deviation of  the  slope.   It  is  evident  from trse tar-.'.e   tr.at  tr.e
bin mean  partition coefficients  are  very highly correlated  *i:h
suspended solids  in all  cases but lead.

    It is  noted  that the  calculated partition coefficients  from
lakes  are  consisteotly  greater   than  values   in  streams   for  tr.e
same  priority  metal,  in  all  cases  except for mercury.    Tw.is  -My
be  due,   in general, to   a   more  organic  nature  o'f   suspended
materials in lakes  Chan  in  streams.
                               1-43

-------
         •o4
         •01
METALS:
2 -
3 -
«-
9-
• -
r-
                           •o
                        SUSPENDED SOUOS ( mg / i )
                                                            
-------
                            >0               iOO
                         SUSPENDED SOLiOS  (mq/ I )
                                                              >OOO
METALS'

i -A*SEWiC (NO DATA
Z -CADMIUM
3 -
5- I.EAD
8- ZINC
                    LAKES 1
                          FIGURE  5-4B

     PARTITION  COEFFICIENT AS  FUNCTION OF SUSPENDED SOLIOS
                     ALL METALS  IN  LAKES

-------
                              t~   —  e   f   f~  <   «
                              e   —  —   c
                          i    i   c  e   c   e   c    10
                                  o
                                   1
                                           i   i    i
                                       e
                 •o
                                  e   o   e  «-   e    10
                                   r    i    i   i    i        i
                            <  
        to
i'      a
«*    **
a. a
o
u
                             Ol   

                                  e   o   o  c  c    i   e
                         (M
                         f


                         i   ,
                             o   c   «*<  m  in   —
     °-7'i'i?-i'?   lc?
                 =
                            o- *o *c -o *e -e
                         S  §
                                       X
                    S    s

                  (••  m
                  «v  ^
»   t  *^
9>      in
»«      fti
»      CM
                                  ?
                                  to


                                  5




                                  &
     a   e   i
     ••   3  -.
     e   «••   •
                 1*1
                                      to
                                      •

                                      I
                                                                                    e


                                                                                    a

-------
APPENDIX
1-47

-------

Alaoama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
district of Colum&ia
Florida
Georgia
Ha wa i i
Idaho
Illinois
Indiana
Iowa
Xar.sas
Ken;ac*y
Lou: s: ana
Wain*
Maryland
Massachusetts
Michigan
.Minnesota
Mississippi
                              TABLE A-l

                            STATS  cooes
ai
02
84
35
0<<
08
39
13
11
12
13
IS
18
19
20
21
22
23
23
25
2<5
27
28
Mi ssour i
Montana
Nebraska
Nevada
New Hampsnire
New Jersey
New Mexiro
New YorK
North Ca r a 1 : n a
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvan;a
Rhode Island
Souch Carolina
South 3a
-------
••» f "^ * «
          -  ^ ^'. / f :  /—
          "r/.^...',-)
          ***•».       • •*
                                 \

-------
   »•*
                          -* *
.  -.
•*
                                                                                            r-


-------
• *» *  • -s • •.  «• -. t ' •  *    ;••   —*       ,  • •  r v    **;•«».;   . •- -
                                                                                                                                             i        <

-------
                                                 •sszn
2
• •
                                                                           CM


                                                                            I
                                                                         <

                                                                         !~

                                                                         <
                                                   \

-------
-. ;
i   . ,.,.,..._.-j- ., a...-.,,......, s -i =
                 ..       ......
             "---      —4


-------
r
»«
e
                                                                                               <



                                                                                               >•
                                                  \

-------


-------
         I
               ' *  *  s  * —  •.  ;
                     »0 •«*._>•••««£
•    *
                                                                                                                                                                            <




                                                                                                                                                                 7         <

-------
•* •• —• —— €
                                                                                      <      <
                                                                                               >
                                                                                               5-

-------
   • C'
                     10              OOO
                    FIGURE  A-iOA

PARTITION COEFFICIENT AS FUNCTION  OF SUSPENDED SOLIDS
                ARSENIC  IN  STREAMS

-------
                  SUSPENDED SOLIDS  (m
-------
    •0*
                                                      
-------
     •cV
     •c
                         I
                                                I
                                                1
                       rO               '00
                   SUSPENDED SOLIDS

-------
    • 0'
                       iQ               '00
                   SUSPENDED SOLIDS ( m q / ' )
iCCO
                     FIGURE A-12B
PARTITION COEFFICIENT AS f'JNCTION OF SUSPENDED  SOLIDS
                CHROMIUM IN LAKES

-------
                    •  iO                '00
                  SUSPENDED SOL'CS
                       FIGURF: A-13A

PARTITION  COEFFICirN'T AS  rtT;CTION OF SCSPE.'.'DED SOLIDS
                   COPPER  IN STREAMS

-------
                      10               'OO
                  SUSPENDED SOL!CS (mq/
                  .FIGURE A-13D

PARTITION COEFFICIENT AS FUNCTION  OF  SUSPENDED SOLIDS
                  COPPER IN  LAKES

                         1-64
I

-------
                        •o1
                                     OMlTTCO OUt TO v£RY MIQM
                                COEf »ICIENT. 0' VARIATION.
                                                            'OO
                                       SUSPENDED SOtlOS  (m
1\
•ceo
                      •   "              < FIGURE  A-.14A

                    PARTITION  COEFFICIENT AS FUNCTION OF  SUSPENDED SOLIDS
                                       LEAD  IN  STREAMS

-------
                                                      OCO
                  SUSPENDED SOLiOS -(mq/i)
                   FIGURE A-148
PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED  SOLIDS
                   LEAD IN LAKES
i

-------
                       lO                '00
                   SUSPENDED SCL'OS  !mq/
OCC
                    FIGURE  A-!5A
PARTITION  COEFFICIENT AS FUNCTION OF SL'S?EN'OE3 SOLIDS
                 MERCURY  IN STREAMS

-------
                                       1 00
•oco
                   SUSPENDED  SOLtOS
                    FIGURE A-i5B

PARTITION COEFFICIENT AS FUNCTION OF SUSPENDED  SOLIDS
                  VERCL'RY IN LAKES

-------
    0*
   • 0'
                     <0               >OO
                  SUSPENDED soucs img/
                                                     ccc
                   FI-L'RE A-16A

PARTITION COEFFICIENT AS FUNCTION'  OF  SUSPENDED
                 NICKEL IN STREAMS

-------
   • 0*
                     iO               '00
                 SUSPENDED souos  (m
-------
     «0
    •o
    >c
                       iO               'OO
                   SUSPENDED SOLIDS  («
-------
    •O'l
                                             :«3«3t
                                                         'OCO
                   SUSPENDED SOLiOS
                    FIGURE A-I7B

PARTITIOK COEFFICIENT AS FUNCTION OF SUSPENDED SOLIDS
                    2I.VC  IN  LAKES
                        1-72

-------
 Catalogue of waste Load Allocation
     models for Toxic Compounds

    EPA Contract *o. 63-01-6160
          Work Order *o. 9
            Prepared by:
            Versar Inc.
         SflSO Versar Center
    Springfield, Virginia  22151
       Under Subcontract to:

       Arthur 0. LHtle. Inc.
  CaraeMdge, Massachusetts  02UO
           Prepared for:
U.S. Environmental Protection Agency
Monitoring and Data Support Division
      Washington. O.C.   20460
             April  1984

-------
                            TABLE OF CONTENTS

                         list of *odei Summaries
                                                                  ?aoe
Introduction

         s*

    water Oua''ty Assessment "etJiodology (*GA*;                .    \:-l

Steady State »odels

               lane/Stream Ara'ysis (SLSA)   ...      .  .     .:
              '.v«r «*ocei (MlCwfilv)  .............
              ransDort anfl Ana'yses Program  !CTA?:     .....   •'«
    exposure Ana'iys'.s "oae'lnq System (£*A*S;    ......   -18
    "etaU £xoosure Analyses ^cdeUng System i-tAAMS',        .    .   -2«

    -var.-aD'* "oce^s

    •stuary and water Cual'ty "odel (v«AS70X)   .  .            .      I -28
    ChemUa* Transport anc Fate ^oflel (TOxi'^AS?) ......    ; -3'
    Tox'c Organ U Suostance Transport and aioaccjrnu'af.on
      Model (TOXIC) .......................  I -:S
    Channel Transport "ode1. (CHNTRA) ..............    1-29
    f^nUe t'ement Transsort Podel (T£TRA) ...........    : -«3
    Sediment-Contaminant Transport Model (StSATRA,.        .  .      I -48
    Transient Qne-Oimens'.onal Degradation and
      Migration Model (TQOAH) ...........      .....  I -53
    HydroloqU Simulation Progranr-FORTRAH (HS?f)     .......  I -57

-------
:2r Me:ncc

-------
Introduction


    water quality based effluent  limitations, as envisioned by  Section
303 of the Clean water Act. call  for an analysis of  the capaoiiities of
water bodies to accept pollutant  loadings without  impairment of  their
beneficial uses.  Ambient water quality standards  indicate  the  pollutant
concentrations allowable for attaining the  use.  Predicting the  effluent
loading restrictions needed to prevent violation of  the ambient  standards
can ae accomplished on a site-specific basis using -nat.nematica'  models.

    The desirability of controlling toxic pollutant  discharges  has  >ad
to the recent development of a number of algorithms  and computer codes
which articulate the environmental transport and transformation  processes
relevant to toxicants.  The purpose of this catalogue is to s^nroar'je t.ie
' ^ ) C * Q I I • to W %WA*^4llfe£i   I I Hf ^ *p * ^ V * % V'  k i « " J *M*M'W^jw
-------
                  water QuaV.ty Assessment methodology
The water Quality Assessment
Methodology (WQAM) (Mills et al. 1982)
was developed by Tetra Tech Inc. of
Lafayette, California; Monitoring and
Data Support Division. OW8S; and the
Center for Environmental Research
Information (CER1). The. methodology
•as designed to perform preliminary
(screening) assessments of surface
'reshwaters. ' The original
methodc'cgy (ZUon et al, 1977) addr
associated with sediment, nutrients.
Debutants m streams.- lakes, and estua
Capsule Suraary w0A/f
» far-field, steady seace.
dljnen^ionaj jnodel
• Procedures for Assessing
JaJte, and escuaxy «accr
• Plrsc -order decay tuned
L Acquires only a desk ccp
i for caiculaclons -
essed the ident"icat'on
dissolved oxygen, ana
rles. The updated vers'on
I-

of srca'eiis
s;me u-sar
iow '^c'-jces
       by a
for the  assessment  of  toxic chemicals  in  the  environment.   *
'd  methodology in that  all  of  the  calculations  are  MtsncJes
 desk calculator.
    The  methodology was  designed  as a  screening- procedure  that  -naites  jse s>*
avai'aa'e  gener'.c  data.   The analyses reaulre little external ^nout s'^ce -nu-
•of  the  neeoea  information  is  provided   oy  taaUs  and  Mgures  -%tn'.n   t:.
•nanua',.   I:  arecicts  far  field,  average steady-state, ao« 'utant concentrat'cn?
'.i  rivers,  lakes,  and  estuaries as a function of 'ong term .average  xax*mum  anc
         non-solnt  source  and  point  source  loads.
     Calculations  performed by WQAM are  divided  Into four  sections.  The  f. rst
 set  Is  for waste load estimations of toilc and  conventional  pollutant  loadings
 from both, joint  and  non-oolnt  sources.   Procedures  Include load  estimations
 for  single  event  and  annual  loads  from agricultural,  forested,  and  yrsan
 areas.   The  Universal  Soil  LOSS  Equation  (USlE)   1s  used  for  agMcu'tura:
 areas;  the URS Urban  water Quality  Management procedure (Amy et  al. l^M  anc
 the  Stormwater Management Model  (S*HH)  level  One Screening procedure  are  used
 for  uroan  areas.   The estimations may then Be used  to  assess  the water quality
 Impacts  in rivers,  streams,  lakes, and  estuaries.
     The   response   of  rivers  and  streams  to  the  release  of  pollutants  is
 predicted oy   the  second  set  of   calculations.   variations  In  longitudinal
 pollutant  concentrations  are  estimated.    The  calculations   are   r*  s<    on
 steady-state,   plug  flow1  solutions  to  the  conservation  of 'mass   equation.
 Conventional  pollutant  interactions presented  include  BOO.  00,  temperature.
 conforms,  nutrients, and  sediment transport.  Procedures  for  toxic
 include  methods  for  point and  non point sources as  well  as for  large
 event spills.   The fate and  transport  of  toxic  chemicals are  assessed  uslr.
 volatilization, sorptlon. and first order degradation.

                                     II-2

-------
    Methods  for  assessing water quality  and  physical conditions  in  lakes are
addressed  In  the third  section.   They are  oased on  empirical  stratificat'on
relationships  and  mass  balances.    In  addition  to   toxic  materials,  sediment
accumulation,  thermal  stratification, 900-00  interactions,  and  eut-opnUatlon
are  covered.   The   fate  of  toxic  pollutants   is  estimated  with  respect  to
biological  uptake  and  Bloconcentratlon  in  addition' to  the  pnysico-cnem'ca'
properties of tne water and the chemical.


    The  last  section provides  methods  for estuar'-ne  -ate--  uaal'ty assessment
     prediction.   The  procedures   Include means for  estuary  classi^'cat'on
    The  last  section provides  methods  for estuar'-ne  -ate--  aaal
and  prediction.   The  procedures  Include means   for  estuary
(vertically  straMMed  or. vertical 1y well  mtxed).  turo'.dify.
thermal  pollution,  transport  of  conservative  and  non-csnservat*
thermal pollution,  transport  of conseryat
and  flushing   •<«»•  «^.*«^*^/»i«  «f   ««v.
pollutant di
approach and  tne near  rie'd  ai
the  buoyancy  and  momentum  ef
apprsach ignores them
                                                                 classi":a:'on
                                                                 sed'meritat'cn.
                                                                 ve po'-'jtants.
iiiutlon,  transport  of conservative  and  non-csnservat* ve po'-'jtants.
Ing   time  prediction  of   pbl'utants.    Analysis   of   1'ongitjd'na'
distribution  is estimated  by two different  methods.  :r,e  'ar f'e'c
md  the near  field  approach.  The  near field  acproac*^ a'ccsu-'.s  esr
ncy  and  momentum   effects   of  tne  pollutant  «n''e t!"e   ''A'  *'e'5
gnores them.
          is  designed  to operate  w
aata 'available,  the  more accurate
Information, most of It Is general In nature.
                         1th  minimum
                          the  analys1.
                                                ata.
                                                                            .o'«
         arovldes most  of  the  data  required for trie, ca'cu'atlons.   '.?.
the only data  not provided are c'imatic  and  hycrologic.   :''ma:'c data
•nclude  sreclp.1 ta'tlon,   cloud  cover,  and  humidity.    The  tyoe  of  .lydralsg^c
information  required  Includes  runoff  quantities,   statistical  f'aws  sjcri 4;
7010,  stagnant regions,  stratification,  and  eVtuarlne tidal  prism.     Qtr.er
basic  information is  also  needed  such as  land use,  stream lengths,  U*e c
and volumes, and estuary salinity distributions.
Pursue
             output,  includes   predicted   concentrations   of   a   pollutant
(conservative  and/or  non-conservative)  over distance  from  the  input  source
In addition to predicting pollutant concentrations, wQAM predicts:

    •  stream concentrations of BOO. 00.  total N,  total P. and temperature.

    •  lake  nutrient  concentrations,  eutrophlc  status,   and hypolimnion   DC
       concentrations.

    •  estuaMne concentrations of BOO, 00,  and  *lta> N and  P.
                                   rr-3

-------
           and H
    The major  advantage  'of wQAN  lies  in  its  simplistic approach to waste loaf
assessments.  All  the  eauations  in  the methodology are algebraic. and tney c
be  solved  using  a  desk  calculator.   TM$  is  a  major  advantage  over othei
models  in  that  tne  user  does  not  need  any  programing  experience.   *QAH
provides  "typical*  data  which  can  be  used .in  Ue'u   of   actual  data  for
predicting  chemical  concentrations.   Another  advantage of  wQAH  is  that  l t can
be used for waste  load assessments of  estuaries as  -ell as  '.axes  and rivers.


    Because   of   Us  simplistic   approach,   *QAH   cannot  'nc'ude  a*'  the
physico-chemical   processes  acting  upon  a  pollutant.   It   's  des'gnec  far
long-term  pollutant  loading and- average -steady-state  conditions anc  joes not
address tne snort-term  effects  that  may  be  associated  *ith tsx'cs 'oaangs.
The methodology  does  cover  the  assumptions  under  which  tne  a'gcr'trms  are
developed and provides the user the  limitations of  some of  the tso's presented.


    «CAi*  clase'y  relates  the  loading  of conventional  anc  tax^c  ao'-'utant?  tc
rece'^'ng  -aters  to  the  loss of  soils and  sediments anc tne amount af  "
"he non-ooint  source loading  section utVHce-s the  un1. versa 1  So'.' ^355
for. agricultural  areas.   It  ^as  develooed  primary  far croo'ancs ar.^ coes -c:
         erosion  from  streamoanfes,   ditches   oes'.de  'oacs.  3r  ;u''*es.   "he
        and river  section  is based  on steaoy:state,  s^yq  '•=*  sc'ut'sns.   It
assumes s'saerslon to  Se small  compared to advectlon.  The ca'.cj"dt';n$  ass-me
the '.otic  system  to  be  vertically  and  laterally  mixed and  that ary  2ecay  a-
the so'iutant to  be  first  order.


    In  estimating   the  fate  and   transport   of   pollutants' in  a   laice.  tne
methodology  accounts  for  biodegradatlon,  volatl Hiation.  ana   sec'.Tientat' :n .
However,   the   model   neglects  several  Important   physUo-che-mca'  srocesses
(e.g..   gnotolysU.    oxidation,   hydrolysis,   coagulation, f 'occupation.   and
precipitation) .


    The transport of  pollutants  in  estuaries assumes  cont'ni.ous. steady-state
discharges  of  pollutants.   The  distribution of   pollutants  '.s  bases  an the
fraction  of  freshwater  and/or  modified tidal  prism,  methods  for   calculating
flushing  times.   The  fraction of  freshwater method  assumes uniform salinity
and uniform mixing of freshwater.   The  modified  tidal  prism method models for
the entire  estuary,  regardless of where  the pollutant source  is  located.
                                   11-4

-------
      Aooli sa • e I ana

    The  original  wfjAH *as  applied  and tested on  the  Sandusky  RUe- Sasin and
four  Chesapeake  Say  sub-pasins:    tne  Patuxent,  Chester, ware,  and 3c:ocuan.
The  model  -as-, used  for  simulating  sedimentation,  strati? icat'on,  eutra-
phlcatlon. and 00 depletion.
     ne modeling of  toxic  pollutants nas ieen done  using  aata frjm Csra'
            Iowa.   The  insecticide  dleldrln  had  accamu'atsc  'r  tne  "ese
from its use on- agrVcu' tural  fields.   The  data  frcm Scinoor-* ;i3gi;  'eso
tne reservlor yas  used  to  test tne  accuracy  of wCAM.   The resu'ts  '^.cv.es
to  5e  in1  agreement   «itn  Scrmoor  ( 198' )  'or die'iflrin  concen:-at' :r  'i
•ater and fisn  tissue.
                                                                            : or.
    Aii 3f  the  calculations  in  wCAH are algecra'c soress'ens *ecy"'^"; en'/  *
land ca'c-jlator  *or  solving.  A programas'e calculator s^n: 3e jse'u'  'zr  t-e
•sumerous advecfve-OUpersion eauations  in  tr>e estuary sect'on
    Cao^es of <»CAM  (£?A-600/6-S2-OC«a-0)  a'e-ava'.iao-e
 'nclnr.at* .  Ohio, ( 534-634-7562) .
                                                             tne  :i=
    User assistance xay Qe. obtained 5y, contacting:

         t Amorose
    UStPA
    [PA Athens Environmental Research Laboratory
    Center for water Quality Modeling
    Athens. Georgia  30613
    FT5 250-3535   CDH 404-546-3535
        «efercneea

Amy  S,  Pitt  R.  Singh  R.  Bradford- WL.   LaGraff  MB.   1974.    ^ater  sua'ity
management planning  for urDan  runoff.  U.S.  Environmental  Protection Agency.
Washington, D.C.  EPA440/9-75-OQ4; f>B  241 689/AS.

Mills we,  Oean  jo,  Porcella  OS,  et al.  1932.  Tetra Tech,  Inc.  water quality
assessment:   a screening  procedure  for toxic  and conventional  pollutart
Part  ..   Athens,  Georgia:    Environmental  Research   laboratory.   QffU< of
Reset, ch    and   Development,     U.S.    Environmental    Protection    Agency.
£PA-600/&-32-Q04a.
                                    11-5
                                                                                    1

-------
•MUs i*a. Oean JO,  Porcel'a  OB,  et  al.   1982.   Tetra Teen,  Inc.   'water
assessment:   a  screening  procedure  for  toxic and  conventional  pollutants  •
Part  2.   Athens,   Georgia:    Environmental  Research  laboratory.   OfMce  of
Besearcn    and     Oevelooment.    U.S.    Environmental    Protection    Agency.
£PA-600/6-82-00
-------
State

-------
                      Simplified Lake/Stream Analyst (SLSA)
     The
                                                       Summary
                                            I -dimension*! .  ccmptrynenz .-node:
             Simplified/lake      Stream
          (SLSA  {HydroQual  198?)  is  a
simplified  waste load assessment model >   steady  scat*  *nd ci.-ne *aryi;
developed  by  HydroOual  Inc.,   Mahwah,
New    Jersey.    for    the   Chemical
manufacturers  Association,  nasnington.
O.C.     It   analyzes,   organic    and
Anorganic  chemicals  In simplified  lake
and  stream settings.   SLSA calculates
the  dissolved  and sor&ed  steady state
concentrations  of a  pollutant  in   the
water  column  and  bed  sediment  using
provides  a  less  rigorous  approach  to   pollutant
compute- programs.   It  1$  most applicable  to  single
loadings.   The  intent   of  this  model  rs  to  rca«e
po'lutant *.n a freshwater system unders"tandd5:e  to an
                                        I
                                            systems.
                                            Simple Slsac-oedee
                                            Suitable foe hard caJcuJacicr or
                                            Simple rCRTRAtf program-
                                                 zo sec j? and use
an  analytical   so'ut'on.
             s'mu'at'cn   •
            (or ounc.ied;
             tr»e
                                                                           •»cae
                                                                    pc'nt
                                                                  •S  ana  vs'
    SLSA models  streams  and  rivers  as oetng we'! mixed  id  cross-sect 'en  arc  a:
having  a  relatively  constant  flow  and geometry.   An  analytical  solution  is
given  for  pollutant  concentration  as  a  continuous   'unction   of  d'sta.ice
downstream  f^om  the  loading  source.    The  model   also  estimates   so''jtant
concentrations   1n   the  water  column  and   bee   sediment  of   unstraf'ed
Impoundments  or  lakes.   SLSA  simplifies  the  hydrodynamics  of  t.ie systems;
aqueous transport is a  function  of  the mean  infjow rate  of  water,   t^e depth
and volume  of the segment  modeled,  and the stream  velocity or ?ake  nydrau'^c
retention  time.   Sedimentation and .exchanging  oed  conditions  are   accounted
for, however4, the bed 1s assumed to be  completely uniform.
    SLSA only  considers  advectlon 1n the  transport  of a pollutant.  Pollutant
losses due to  degradation  processes  are represented by simple first-order rate
constants  supplied  by  the user.  The  constants are  then  summed  to  yield  an
aggregate  decay  value.    Practical  metnods- for  evaluating  the  interactions
between  the  water  column  and  bed  sediment   particulate   sedimentation  and
resuspenslon and diffusive exchange are provided.


    SLSA 1s  es>enMally a l-d1menr:inal ,  steady-state model;  however,  it  Is
capable of  th.ee quasi-time varyln, analyses  of lakes  in  which the pollutant
discharge  rate  1$  at  steady  state.   The  first   time-variable  evaluation
pertains  to   the water  column  and bed   sediment  pollutant  responses  to  an
                                     11-7

-------
instantaneous  chemical  load.  The  other two  evaluations  deal witfi  the water
column and bed  sediment  responses  to  either an initiation or cessation of long
term pollutant
    SLSA  is  amenable  to  desk  calculations,  though  a  computer  program  is
available for  convenience.   The program  Is  written in  FORTRAN  Iv  level G and
Is convertible  to  most standard computer systems  with  FORTRAN compilers.  The
relatively  snail  core requirements of  the  program and  the  speed of execution
make the program very compatible with microcomputers.
rout 0
-------
use  can
models.
          3e  accomplished  In  relatively  snort   time  as  compared  to  ot.ier
    SlSA  has   some   time  varying  capabilities  and  can  account  for   some
Interactions Between  the  water column and bed sediment.
    Because   of   SLSA's  simplistic  approach   U  has  several   limitations.
OUpersive  f'ow  1s  not accounted  for  as  in other -models  sucn  as CTAP.  t^us
limiting  its  use  to  relatively  Simplistic  systems.   The  bed  sedi-nent  's
assumed  to  De completely  mixed  and  undergoes  no movement.   Decay tiec"an'sns
are  all  first-order.   Suspended  solid concentration  's »ep:  constant  at  tne
input  value and  only  a single  particle s1?e  is  considered!.   Only  one  -
with a single point  source  loading  and  no acd'tiona'  inflows are  per--nittso.
ftodel Applications

    SLSA nas  Oeen  applied  to a 90 km reacfl of  Sao'd  Cr»e<  'n  Rao^ C'ty. Sogtn
Oaitota.   'he  study  area  -as  located  downstream of  a  munidDa'  «aitewa:e'
treat.-nen:   p'ant.    'he  pollutant   considered  -as   :">e  sjr'actan:.   '^ea»
a'
-------
'JS9t
           i Xc t 1 v I e I ea
    Copies  of  tne  SLSA user manual,  as  well as documentation, can  ae  ootalne*
from:

    will 1am Gull edge
    Chemical manufacturers Association
    2581 M Street. N.H.
    Washington, O.C.  70037
    202-887-1183

    Otner ass'stance can se obtained  by  contacting:

    John St. John
    Domenlc OiToro
    MyCroOual Inc.
    l Letfioridge Plara
    xanwan. ^ew Jersey  07430
    201-529-5'51
SoneraI Pefergncgj

-lydrcCua' ,   Inc.   1981.   Analysis  of Fate  of  c^eflUaU  '•"
3*iase I.  '"soared"for:  Cnemical manufacturers Assoc'at'on. «as?«'^gton.  D.;,

        ',   Inc.   1982.   Application  guide for  c*A -  HydroQual  ciemica'  ?4te
                  for;  Cnemlcal Manufacturers Association, wasVngtort.  DC
                                  11-10

-------
                         Michigan River *odei
trat'ons
                                                Capsule
    The   Michigan   River   Model   ...    .
jDePinto  et a)., n.d.)  was  developed at the
£?A's  Environmental   Research  UOoratory  -
3u-uth.   Large  Unes   Research   Station. ,
Srasse  I'e,  "icnlgan,  specifically  for -jse ,
in   tse   «a$:e  load   allocation   program.  ,
          simulates   steady-state   concen- j
          of  pollutants  from  loadings  into [
          or   t.ie  -ater   column  and   oed
secinent.   I:  nas  tne  aoi'ity  to  model  successive
one) using  an  analytical  solution.   It  Is falr'.y
sMiplif'ec and more flexible.
                                                      'caches
                              .
                           «ICHRIV 1* C3n?paraD> to
                                                               SvSA  5-
                                                             excest *
i;  MICHR:V predicts participate  concentrat isro  -n
   vartaoie);  SlSA treats it as  an input data csns
                                                            ware
    ?; "iC-iaiv can mode- successive reac.ies; SlSA can .larc'e

    3) "(ICHRlv is not intended for lakes wnereas SLSA is.
    ••ICHRlv  simulates  tS*  advective  transport  of  d'ssoivec  anc  ac:sr;e:
pol'ytants.   *he node*  employs  first-order  decay  mechanisms  far  s'ec'ct'"?
ponutanc  distrioutions.    An  aggregate   first-order   loss  rate  C3e*"c'ei:.
representing  tne  sum  of  a  numoer  of  processes.  Including  vo'ati "zat'an,
hydrolysis, pnotoylsis,  oxidation,  and eiodegradation.  is used  in  tne  -ncce'.
Bed-water  interaction*  include  settling,  resuspens ion.  ouriai  of partica'ates.
and dl'^asian of dissolved constituents.
    The model  Is  written In  FORTRAN.  Is  user  oriented,  and  prjvides  gu
for Input  data  preparation and model  option selection.   NICHRlv  has  f*«i?a*e
batch input routines suitable for multiple reacnes.
    KICHRIV requires oa$lc Information for modeling:

    •  Loading rates of pollutants and solids to the receiving river.

    •  flow rates, length of reach, water depth, and cross-sectional area.

-------
    •  Partition and  first-order  decay coefficients For  &otn  tne  water column
       and bed sediment.

    •  Sediment/water exchange parameters; sediment solids concentration.
OucsuC OeaerlPCion
    KICHRIV  predicts  pollutant concentrations  as  a function  of  distance from
the loading  source.   Total  and  dissolved  pollutant concentrations for act* trie
water column and bed  sediment  are resorted.   Suspended sediment concentrations
are predicted as well.
4dy«neaqea trd Llait
            was  developed  saedficaUy  for  riverine  waste .load
Its  level  of   complexity  was   Intended   to  be  suUaote   for
application.   It  requires  less  than  two  dozen  input  sarameters
tnere'ore. model set up time 1s relatively rapid.
                                                                          eac*.
    NtCHRI-V  Is  designed for  slnqte  river  systems  and 's not
river net^orits.  lakes,  or  estuaries without  modifications.   O
of «!:H»:V include:
                                                                       'a:e
        he
                    steady*sXate wltn resoect to flow ams
    •  Decay  processes  are  first-order;  H has  no  specialized
       organic decay routines.

    •  Dispersion is assumed to be negligible.

    •  Sorptlon/desoratlon are assumed to be instantaneous.

    •  Bed load is not permitted.
    MICHRIV  was  tested  and  applied to  a  60  Km  reach of  the  Hint  River,
Geres ee  County,  Michigan.   The  application  of   the   model  dealt  wVtn  tne
distribution  of  {Inc.   cadmium,  and  copper  from  point  sources.   The  main
purpose  of   the  study was  for  calibration  and  field  testing  of  the  model.
Calibrations  were  made   on   solids  transport  and  water  column  partition
coefficients  to  yield   reasonable   predictable   total   and  dissolved   metal
concentrations.   The  results  were   .-asonable enough  to demonstrate MlCHRZV's
ability  to  accurately simulate sediment  and water column  concentrations  of  a
ooUutant.
                                   11-12
                                                                                       <

-------
Pesource
              remen es
    XICHRIV  is  written  1n  FORTRAN.   The  user manual  and  documentation are
contained  within  an  E?A  draft  report  on technical  guidance for  waste  load
allocation studies.
User Support

    KICHR1V  1s  currently  under review and  should  aecome ava^'aole m the
future.  Technical assistance  for MTCHRIV can Sei ootainea ay contacting:

Bill L. Richardson
US CPA
Environmental Research Laboratory - Ouluth
large Lakes Research Station
Grosse He, Picf>lg«n  *8138

       or

Joseoh V. OePinto
Clarkson Col'ege of Technology
Potsdam. New ror*  1367S
De.Pinto  JV.   Richardson  WL,  Rygwelsitt  JC,   et
manual  for oerforfltng waste load .al local -ons .
U.S. Environmental Protection Agency.
                                                al.   n.d
                                                 Draft
                                                             "ecnn'ca*  g
                                                             .   nasritngton.  3C:
                                    11-13

-------
                 Chemical Transport ana Analysis Program (CTAP)
The  Chemical  Transport  and  Analysis
Program  (CTAP)  (HydroQual  1981)  was
developed  ay  HydroQual   Inc..  Mahwah,
New    jersey    for    the    Chemical
manufacturers  Association,  Washington.
D.C.   CTAP  1$  ah  extension  of  the
Simpltf'ed     Lake/Stream     Analyses
(SLSA).  also  developed  Sy  HydroQual
and  was   designed   for   more  complex
                             Capsuie
                                                                 CTAP
                       i
                       r
                       •
                       r
Steady-state. J-dimensional
comparonent model,
screams, stratified rivers,
estuaries, and eoastaj «m6aya»nts
Multiple waste inputs.
Simple first-order kinetics.
    CTAP.  lUe  SLSA.   is  designed  to  account  for   trie  dissolved  anc
steady-state  concentrations  of  organic  and  Inorganic  pollutants  *n  notn trie
•ater  column  and oed  sediment.   However;  its greater  complex'ty  aV.ows '. t ts
mode'   sfat'.fied   lakes.   r'.vers.   tidal-   rivers,  estuaries,   and   csasta".
emoa^fneits.   CTAP  Is  essentially  1 Ue SLSA   In tnat  it  ^s  a comoartment ^ooe'
•n «'n';n  eacn csmcartment is equivalent to  one  SLSA ">a*e'.  Mo^ever.  CTAP •«
more comolex  in  tf^at  these comoartments (up  to  <25)  may  be arranged in any !.
2. or  3-d'.menslonal configuration  (spatial  concentration  variations may ex".-
in   one,   two.  or   three  dimensions).   whatsmore.   the  ccmoartments  at
interactive  with  eacn other  via  advectlve  and  dispersive  transport.  *ass
sa'ance  ec^ations  are  written  for each compartment of aoth  tne  -ater ca-^nn
and  Ded,sed'ment and  are  interconnected to  adjacent compartments.  The -esu't
is a matrix of equations wnich are  solved By  digital computation.
    CTAP  accepts
aquatic  system.
well.
multiple chemical  load  Inputs from  different  locales to  the
It can also  account  for  tributary inflows and withdra^a's  as
    CTAP  can be  us«d to  simulate aiultl -dimensional  bed  sediment conditions
In  addition.  U  allows  for a  moving  bed.  where  the upper-most  layers are
subject to movement  in the direction of  water flow.
    CTAP  utilizes   the   same   first-order   reaction  kinetics  as  SLSA.    The
coefficients  for  photolysis,   hydrolysis,   oxidation,  and  blodegradatlon  are
supplied  by  the  user and  then summed  for  an aggregate  etc  ;  constant   "he
sorptlon-desorptlon  mechanisms  are assumed  to  occur  Instantaneously;  a.  It  1s
assumed  that  soluble and partlculate  chemicals within  each compartment are  in
a  state  of local  equilibrium.   Interactions between the  water  column anc  bed
sediment  include  settling,  resuspenslon, burial of partlculates , and  diffusive
exchange  of dissolved constituents.
                                                                 <
                                     ll-l*

-------
                                                                                          I
    CTAP data requirements are more  Intensive  than  those  of SlSA.   ".n-add't'cn
to  the  standard  physico-chemical   parameters   of   the  aquatic  -system.  :~A?
requires:

    •  Sources and amounts of pollutant loading.

    *  Segment volumes and lengths.

    •  Segment Mows per phase and dispersion rates.

    •  Solids types, distributions,  loadings and concentration.

    •  Partition coefficients by phase and segment'.

    •  First-order coefficients  for  -  water column  pnoto'ys's/- *o'at' '-' :at* zf ,
       hydrolysis.   oxidation,    and   biodegradation;   sec'me«>t   ^ycra'/s's.
       oxidation, anc blodegradatlon.


Data r-cuireaents are described  by  the model  along  «itn  a  s'scass'on s*  -c- ". :
prepare sata far input.


Output Descriptions

    The CTAP outsut  presents  less diagnostic  information  man  ScSA.  au:  pr'nt;
out  *ore computed  chemical   cancehtratlons  in  tne dissolves  anc  ;a":'c-"at?
pnases.  The  concentrations  are presented  in  tabuiar farm  for  potn  tne -«4t«-
column and  sediments.   The  output is also  ar'ranged  so  that tne  concert- afsns
for each segment^ compartment are reported.
           and Limieacions
    CTAP Is a compartmental model,  very  flexible  in  configuration (up to tir««
dimensions  in  both  water  column  and bed  sed'ment). and  applicable,  to  -ncit
types of water bodies.   It  can  account  for  multiple  point source waste inputs.
but no  non-point  sources.  Spatial  variable  flows  can be handled,  tnqugn  tne
user must specify them since they are not predicted.                  :


    The  model  has no  specialized  organic decay,  routines  of the type  used  in
EXAMS;  the  user  must  specify  first-order decay  rates.   A  single  decay  rate,
which  Is  the   sum  of  first-order   coefficients  of  nnotolysls.  hydrolysis,
oxidation,  biodegradation,  and  volatilization,  is  used  to predict  chemical
fate.    B> l-water  Interactions  are  articulated with  an   Intermediate  level  of
CPmptexUy.  CTAP allows  for  up  to five  different  partUle  sU«s;  U  also
allows for bed  load.
                                     11-15

-------
*odel Applications

    CTAP was  applied  to the data collected  by
of  Rapid  Creek.  Rapid  City.   South  Dakota.
downstream of a  municipal  wastewater  treatment
WAS  the  surfactant linear  alkylbenzene
was the only
SISA model.
                                                Games  (1981)  from a 9Q-*m. -ear-
                                                 The  study  area  was  'oca:
                                                plant.   The chemica', canside-es
                                         sulfonate  (LAS).  The  treatment  s'ant
             known  source  of  IAS.   This  Is the same scenario used to app'y tne
    The  results  of  the  CTAP  modeling
of  long. term   loading   are  shown  In
figures  1  and 2  for  concentrations in
the   -ater   column   and   sediments.
*espect1vejy.   The circled joints are
the    actual    concentrations.     The
predicted  concentrations  in  the  water
column  were  In  close  agreement  with
the  actual;   however,   the  predicted
sediment  values  were  slightly  higher.
•hen   diurnal    load   variability   was
                                                                  ac-3M« r a •«•
                                                   «. • &.*•«•
accounted  *or
values wepe 'n
                the  predicted sediment
               Setter agreement.
    Although  not  all  the capabilities
o? CTAP  were  used in this application.
the incut  -as  sufficient to accurately
predict    ,   LAS       concentrations.
(.valuation  of  the   Inputs   showed   the
accuracy    and    validity   of   these
estimates  to be good.
                                          .- •• -
Aesource
    CTAP  is  written in  Fortran  IV and  Is  suitable  For operation. «ith  s'*^:
modification/  to the  IBM  360/370. Unlvac  HC8.  COC  6600  mainframe  computer:
and to  minicomputer systems such  as  the POP 11/70,  VAX 750/730.  13*  1'30.  and
DSC Neta/4.   The minicomputer  version  of  CTAP requires 32K  bytes of  storage
with subroutine  overlay  and disk scratch files  for temporary storage.
     Support Acclv 1C lea
    Copies  of  the CTAP user manual,  as  *ell as documentation, can be  obtained
for a  fee from:
                                    11-16

-------
    William Gull edge
    Chemical manufacturers  Association
    2511 M Street, M.W.
    Washington,  O.C.   20037
    201-887-1183
    Technical assistance can  be  obtained  by  contacting:

    Joftn St. John  or
    Doi>en1c OlToro
    HydroOual Inc.
    1 lethOMdge Plaza
    *an*an, New Jersey  07*30
    201-529-5151
Genera 2
HydroCua!  Inc..    1981.   CTAP   documentation   -   cnemlca'   transsort  ar.
program.  Prtjared  For  tne Chemical  Manufacturers  Assoc'a.t ion.  Washington
           Inc.   1.982.   Application  guide  for  CJ*A-Hy
-------
                   Exposure  Analysis  KodeMng  System
                                            C4psuJ« Summary:
                                                                EXAMS
                                          Steady
                                          ecmparswnc  model
                                          systems.
                                          Coop «•« ens jve second-order *!
                                          for oryanic ertemjcaJ  decay
    The   Exposure   Analysis  Modeling
System  (EXAMS)  (Burns  et  al..  1982)
1s a  steady-state  water quality model
designed  by  the  U.S.  Environmental
Protection    Agency's    Environmental
Research    Laboratory    m   Athens.
Seorgla.   The model  was  designed  to
allow  for  the  rapid   screening  and
evaluation    of    the   behavior   of  	    	
synthet'c    organic    chemicals    In
freshwater  aquatic  ecosystems.   HUh  a  description  of  :ne  pnys<
chemical  properties  of  the compound  of  Interest,  and  trie  re'evant t
and  physical/chemical  characteristics  of  the  aquatic  system,  £*AMS
the  exposure (steady-state environmental  concentration).
pollutant removal  system haU.-Mfeh  and fate  {d1 str iaut '-on
fraction  consumed  by  each  removal  process)  of
calculations  are based on  tne assumptions  that
time averaged.
                                                                       ca.   an:
                                                                       ranspo-"1
                                                                    ^
                                                 eacn  compound -noce'ea
                                                 tne loadings  are  ';f>g
    Th«  £HA«S  program is  an  Interactive modeling  system  that a'-'ows
to  speedy and  store the  pnys leal/chemical  properties  of 3otn  tie
compounds  and the aquatic environment.
                                                                       tie  •j-'.e
                                                                       :r.emi:a
    The  aquatic  system  Is  user  specified  and  is  represented  by  a set  of
segments or distinct compartments (water and  sediment)  tn  tne system.  As :nany
as 100 compartments can be handled by EXAMS.


    The  program  1s based  on a  series  of mass  balances  that  give  rise  to  a
single  differential  equation for each  compartment.  Mass  balances accounting
for  all  compound  mass  entering  and leaving  are  calculated  by  EXAMS as  the
algeoralc  sum  of  (1)  external  loadings,  (?)  transport processes  that  export
the compound,  and  (3)  transformation processes within  the  system that convert
the   chemical   to  daughter  products.   working   from   the   transport  and
transformation process equations. EXAMS compiles an  equation  for  the net rate
of change of chemical concentration  in each compartment.
004 3 P
                                     IMS

-------
    tXANS  computes  the   kinetics  of  transformations  attrlbutaJ^e  to  dl*ect
photolys's. hydrolysis,  biolysis,  and  oxidation  reactions.   The input  criemlca*
data  for  hydrolytic,  blolytlc, and  oxidative  reactions can  be  entered  either
as  single  vaiyed  second-order  rate  constants or  as pairs  of  values  defining
the rate constant  as  a  function  of the environmental temperature specified for
each  segment.   CXAMS  includes   two  algorithms   for   computing  the  rate  of
pnotalytlc  transformation.   The  first requires  an  average pseudo-f1r$t-oraer
rate  constant  applicable to near-surface  waters;  and  the  second  computes trie
photolysis  rate  directly from the absorption  spectra  of  the  ccmcounc  ana ^ts
ions,  measured  values of  the  reaction quantum  yields, and  the  environment•
concentrations of competing light aosorbence (chloropny'5. seaimeits, etc.).


    Internal  transport  and  export occur via  advect've  and  a'spe-s^e  -nov-me":
of  dissolved,  sedlment-sorsed.  and  piosorsed  materials,  anc Sy vo'at'*':aticn
losses  at  the  air-water  interface.   EXAfS   provides  a set  of  vectors,  that
aT'ows  the user  to  specify the  location  and strength o?  sot",  acvect've s^c
d'spers've  transport  pathways.   SXA^S  can csmpute  transsc-t  3*  a  c"e^':a' ^'j
    e-sedraent    Dedloads.     suspended    ses'.me^t    -asn'oacs.     5-:gr2«a*.e-
    'tration,  transport  through  the  thermoc!'ne  of  a  'a"f*^e"t
streams, etc.


    External  1cadlngs of  a  chemical  can ente- trie eccsystefi v'a rc"t $3yr:»^.
ion-po'?t  sources,  dry   fallout  or  aer ".a l •' -r' f t,  afncssner': -a:^-:ut.  i".
grouncwater seepage entering the  system.
    CXAPS is available  m both a batch and  interactive
       * ca ffifgu 1 re.ign ta
           requires  an  extensive amount  of  environmental  data,   however,   tne
program  can be  run  with  a  much  reduced  data  set  when  the chemistry  of a
compound  of Interest  precludes   the  existence of  some of  the transfsr-nat'ci
processes,  "or example, pH  and  pOH data can be omitted  in the case of ieut'3'
organics  that are not  subject  to acid or alkaline hydrolysis.  Si* 'canor'ca'"
environments  are  Included  wHh most   model  versions  and  can   3e   used   for
non-specific screening  Investigations.

    Input parameters Include:

    •  A set of chemical loadings on each sector of the ecosystem.

    •  Molecular  weight,  solubility,  and   lonlzatton   constants  of  the
       compound.


0043?
                                  11-19

-------
    •   Sedlment-sorption .  and  biosorptlon  parameters;    Kp.  KOC  or  Row.
       biomasses.  aentnic  water  contents  and .bulk  densities,  suspended
       sediment concentrations,  sediment organic carbon,  and  ion exchange
       capacities.

    •   Volatilization parameters:   Henry's law constant  or  vapor pressure
       data,  wlndspeeds, and reaeratlon rates.

    e   Photolysis   parameters:    reaction   quantum   yields,   absorption
       Spectra,   surface    scalar   Irradlance,   cloudiness,   scattering
       parameters,  suspended sediments, cnloropnyl.  and  dissolved  organic
       carbon.

    •   Hydrolysis:   2nd-order  rate  constants  or  Arrhen'.us   functions  far
       the relevant  molecular species:   0H,  pOH.  and  temperatures.
    •  Oxidation:      rate
       concentrations.
constants.    temperatures,
and
oxidant
    •  Biotransfor-natVon:   rate constants,  temperatures,  tata'  and  active
       bacterial  population densities.
    •  Parameters  defining  strengtn
       dispersive transport pathways.
          and  d'rectlon  o?  advect^e
    •  System.geometry  and  hydrology:   volumes, areas,  depths,  rainfall.
       evaporation rates,  entering  stream and  non-point  source  Hows  and
       sediment loads, and groundwater flows.
OutSUt
    EXAMS' 17 output  tables  Include  an  echo  of  the input data, and tabulations
giving  tne  concentration.   Fate,  and  persistence of  the  chemical.   Printer
plots  of  longitudinal and  vertical  concentration  profiles  can be  invoiced Sy
the Interactive user.
    The major  technical  strengtn of  the  EXAMS program  lies  In  Its ability to
utilize   well    defined,   chemically   based   fate   process   information   in
second-order  rate expressions  for  the hydrolysis,  photolysis,  and  oxidation
processes.   Volatilization  1s  modeled  in  4 way   that   is  consistent  with
accepted mass  transfer  processes.   Thus the model's  strengtn  1s  In evaluating
the chemical's kinetics.
C043P
                                  11-20
                                                      I

-------
    From  the  user's standpoint,  the  model can  be.-un In an  interactive node
for  rapid  evaluation   of  scenarios  reflecting  varying   system physica'  and
cnemlcal  conditions.   Furthermore,   the  model  contains   a  built-in,  on-'^e
'help-file1 to eiplaln tne command options and required Input data.


    EXAMS  is a steady-state  model  and as  such was not designed to evaluate t*e
snort-term variations of an aquatic system.
    EXAMS does not  account for sediment and. contaminant  loss 5y bur'a'
bentMc layer.  Furthermore,  it has  only  a  single exchange :oe"*c%er.t
the process  of water-sediment particle exchange  and  the  process o* ..at
water diffusion.
                                                                            tne
   EXAMS has  the capability  to  model ponds,  -'ve^s.  anc
     the capaoillty of modeling estuarine aquat'c environments
       The  model  does  not  simulate  sediment-po1. 'utant  loass  e'3in  :c'-:t
       non-point  sources.   Solid concentrations  uost  ae  der
-------
    S'mu'ations  of  LAS  steady  state
concentrations  in  water  and  sediment
were     compared     with    observed
concentrations.    In   a   qualitative
sense, agreement was  good (see Figure
l).  However,  tms  agreement  was only
obtained  by  assigning   an  arbitrary
«alue  to  the  dispersion coefficient
at  tne  sediment/water interface.  The
value  chosen   was   in   the  expected
range. but  little  or no  rationale for
tne  value  could  be  provided.   Since
this  term  is   fairly  Important  {as
determined  by  a sensitivity analysis)
and  is   seldom measured,   it  acquires
tne  characteristics  of  a calibration
parameter.
                                                •  ••  It   .1  <•>«!•  1  tt
           sensitivity  analyses  with
"espect  to  errors  In measurement  of
cre«*  few  -ate.  oiodegradation  rate
constants, a.ic  adsorot'on coefficient
we's    a'so    conducted.     Results
indicated tnat  model  calculations are
most    sensitive    to    the     least
understood  parameters,  that   is.  the
     uen-t/water   exchange   coefficient
     the  seciment  biodegradation  rate
constant.   However,   this  pnenomena
may  De  inherent  in  chemical  and  aquatic  systems  and nay  iot se  a arob'em
jnique to EXAMS.
                                                      «n am i
 and
                                                        ''0»o»
     In   other  applications.   EXAMS   has   been  successfully   used   to   mode'
 volatilization  of  organics  in  specific   field  situations,  and  for  a  genera!
 assessment  of the  behavior  of  phthalate  esters m  acuatic  systems.
 been  Implemented  by  a  number  of  manufacturing  firms   for  *nv
 evaluations  of newly  synthesUed materials  and has  been uses  'n  an academ-c
 setting  for  both  teaching and  research.
 Reacutef
     EXAMS is  available from  the  EPA Athens  Environmental  Research Laboratory
 in eltn-r a  batch or  an  interactive version.  The  batch  version reauires 6*ic
 bytes (overlaid)  of  memory  (for  aquatic systems  of up to  17  segments);  this
 version  doc.-  fit reaulre mass  storage capaol 1 It -';.   The  interactive version
 also requ* es &•* bytes (overlaid) of memory, plus an additional mass  storage
 capability.    The interactive  version  of  EXAMS  requires   lOQK  bytes  of  mass
 storage  for utility  files.  2K  bytes  for each chemical  in the active Mies, and
 2.Sic bytes for each active defined environment.  An  overlay capability  is
 0043P
                                   11-22

-------
 requi'ed  to  implement  EXAMS  on  small  computers  such as  PDP-11  or  HP  3QCQ
 systems.   Execution  times   range  from  a   few  seconds   49   several   minutes
 depending  on   the   problem 'to  be  solved.    The  software  is  aistr'auted  on
 magnetic tape;  the  source code consists  of about 16.000 card images.


     It has been  estimated  that approximately  one  to  two man-months of  effort
 are  required  to setup the model (not  Including  the  effort  nequir.ed  to  evaluate
 the  results).   This  estimate  is based  on the  following assumptions:   (i)  a'l
 data  necessary to  meet  the  input  requirements  of  the model are  avai'ao'e  and
 (2) qualified  personnel  are available  to implement  tne model.


      Suooorr *cri»
     Free copies of  the user's  manual  and  system  documentat'on are  »va°aa'e
 from 080  Publications.  Center  for  Environmental  Resear;n  :n(3r^a:'or..  U*£=A.
 Cincinnati.  Ohio   45268  (Telephone:  513/684-7562;   ask  *or  3uD''cat'3n  No
 EPA-&CO/3-32-C23) .    The  computer  tape   of  the   program  ;?r2v«cea   'or   tre
 requestor  to  copy  and  return)   Is  available  from  Center  for  «ate'  :ua*':/
        g.  Environmental  Research  Laboratory,  USEPA,  College  Station  3cac.
         -Gecrg'.a   30613 (Telephone:- 404/5*6-3123).


     J:e' a:s'stance  can 5e ootalned 5y  contacting:


     ',awrence A.  Burns
     Environmental  Systems  Branch
     U.S. Environmental Protection Agency
     Environmental  Research Laooratory
     College  Station  Road
     Athens.  Georgia   30613
     FTS 250-3123  COM' 4Q4/546-3J23

     David  «.  Cline
     Automatic  Data Processing
     U.S. Environmental Protection Agency
     Environmental  Research Laboratory
     College  Station  Road
     Athens,  Georgia   30613
     FTS. 250-3123  COM 404/546-3123
 Burns  L'.  Cllne  OM,  Lasslter  RR.   1982.   Expo"ire  analysis  modeling  system
'(EXAMS)  «>ei  manual  and system  documentation.   ' .S  Environmental  Protection
 Agency.  Athens. Georgia.   Publication No.  EPA-600/3-82-023:


 Games  L  M.   1982.    Field  validation  of  exposure  analysis  modeling  system
 (EXAMS)   in  a flowing stream.    En:   Modeling  the  fate  of  chemicals  in  the
 aouatlc  environment.  OUkson  Kl,  Make AW. and  Cairns  J.  Jr. eds.  Ann  Arbor
 Science  Publishers.

 OO.JP                              "-»

-------
               "etaIs Exposure Analysis Modeling System
1982)  1s
computer
chemical
                     systems.   This  1s
                        Unking     the
                     MINTEQ.  with  the
                      Modeling   System
                       al.   1982).   an
acuat'c
Combines,
of metal
ssec'a
    The   Metals    Exposure   Analysis
Modeling System  (MEXAMS)  (Felmy et al.
          a  synthesis  of  two existing
         models  that accounts  for the
            and    pnyslcal    processes
affecting  the  fate and  transport  of
metals  in   aquatic
accomplished     by
geochemical   model.
Exposure   Analysis
         (Sums    et
         exposure   assessment  model.
          tnese  models  provide the  capability  to {1}
          line'/  to oe  in  solution  and  (?) consider
                                                     SUBIJIATy:
                                                       aodel.
                                                      *pec:*e.ion,
                                                      idsocbed *rtd
                                                  cazed .i»caJ  esnceneracions
                                                       constants *nd
                                                          for < jeve
                                                     c.*>* models daca
       'on on adsorption  or'precipitation -of metals,
       tne amount of metal in solution.
 estimate tne
  tne  effect  of
50th of .n':?!  :
                                                                       crienr
                                                                      an act
                                                                             a*
                                                                             to
    The  cnemical  Interactions  are  nan^led   ay  *IHT£Q,  us^ng  tn
eguiliorlum relationships  and  water quality data to  calculate  spec'at 'on
dusolved. adsorjeo. and precipitated metal concentrations.
    Spec'.ation  1s  calculated  using an equilibrium  constant  approach «nerein a
series  of  mass  action   expressions  are   solved   subject   to  mass  balance
constraints on  each  chemical  component.   An estimate  of  aqueous  spedat'.cn is
necessary  to  predict  the  Quantity of metal  that  will  be  taken out of solution
by  precipitation  and  adsorption,  and to  evaluate  environmental  Impacts.   In
the  case  of  the  latter,  toxldty  and  bioavaUabiHty  of  individual  metal
species can, vary  ay  several  orders of  magnitude;  therefore,  estimates of metal
$peda"on are  required to predict aquatic Impacts.
    Adsorption  is treated  as being  analogous  to aqueous  spedatlon.   There-
fore.  mass  action expressions  can  be  formulated, for  adsorption • reactions .
MINTCQ  contains  six  algorithms  for  calculating  adsorption.   It  computes  the
mass  of  metal  transferred  Into  or  out  of  solution  as  a  result   of   the
dissolution  or  precipitation  of solid phases.
    The  migration and  fate  of the  metal  are handled  by  the aquatic exposure
assessment model  EXAMS, a steady-state transport model developed primarily  for
use with organic  compounds (see EXAMS).
                                  11-24
                                                                                    ^

-------
     The coupling of MINTED  and  EXAMS was accomplished in such  a  way  as  to (1)
 retain  all  of  the original  EXAMS  options  and  capabilities  and  (2)  aypass
 unnecessary  calculations  or calculations  either  not  applicable  to  metals  or
 duplicated  by  MINTED.   For  Instance,  there is  no  need  for  EXAMS  to  compute
 adsorption  since MINTEQ will provide the quantity of  meta-1  soraed to sediments
 and  biota.   Another  example  Is  chemical  degradation which  is  applicable  to
 organic*  but  not to metals.  Through the proper specification of  EXAMS inputs.
 most  of  these  calculations can  be  bypassed.   Thus,  the  user Mill  not  be
 required  to  maintain   two  versions  of  EXAMS-,  one   for  organics-anq one  for
 metals.
    MEXAMS  was  designed  primarily  to  be  used  in  performing sc-«*t'ng  'eve'
assessments  using  generally available wate- sua'^ty data.   It  ca.~  a's;  se us«2
to  interpret data  collected during  bioassays  and  as  a framewjrK  *zr  gu'a'ng
research' related to  the aquatic  Impacts  of- pollutant metals
    The model    of  :ne
                                                        of reaction,  and otner
                                                        species or solid phase.
information  required  to predict the  formation  of  each species or  solid  p
The water  quality  data are physical  and  chemical  properties of trie water
being analyzed (e.g.. pH, pOH,  temperature).
    The  user  need  only  to  generate  trie  water  quality  data  in  oraer   to
implement  NINTEQ.   The  thermodynamlc  data (for  the  specific metals  currently
handled by the model) are contained  In a data base that accompanies  the model.


Ou CPU c D«3crl pti on j

    The  model   output  1s  divided  '.et.;en  the   EXAMS  and  MlN'-t*   imponents.
EXAMS  provides  tabulations  presenting  estimates of  the  expo>jre.  fate, and
persistence  of  the  metal.   The  MIMTEQ  outputs  give details  on  the chemical
interactions occurring In each compartment of the simulated  aquatic  system.
                                .11-25

-------
           And Llxl raco
            represents  an improvement  in  metals modeling  in that  U  accounts
for  the  complex  chemistry  affecting  the  behavior  of  metals  as  well  as  the
transport  processes  that  affect  their   migration  and  fate.   Specifically.
••EXAMS   considers   the  effect   of   chemical  spedation   on   adsorption   or
precipitation of metaU.                 ..
    The modeling  system  is  user oriented.   It  contains  an interactive program
that helps  the  user prepare water  aual'ty  data for  input  to  MINT?Q.   :t also.
queries the user  to ootaln  user run  information wnich  1s  then used to central
tne  ooe'-at'on  of  «INT£Q  and   EXAMS  and  the  transfer  of  simulation  results
ser-een the models.
    The  themodynamic  data  base  associated  with  "I.NTEC  conta* is ' ecu* ••':<• 'urn
 onstants  and  anci'*ary  data  for  only a  limited  numoer of  po'-utant meta's
 '.e.. As. Cd. Cu. ?5. Hi, Ag. and Zu).
     ic csmp'eiation  can nave  a  significant  impact  on tne
      Although  N-NT-fQ  is  capable  of  handMng  organic cim
          Sara  aase does  not  contain  the  necessary  ecui ' 'S
anc'"a-y iata  to evaluate tni.s pnenomena.
                                                                             of
                                                                      t ion,
                  df-s   precipitation/dissolution.   oxidat*on/r«suct'on.    and
            as   equlllBr'um   processes   -hen  In  fact  they  ^ay  *ot  3e   in
    EXAMS  does not  describe vertical  changes in  0H.  and ox'dation-reductlon
-eactions   in  the  bed  sediment.   The  latter  can  be   very   significant'  in
      t'nq  the fate of metals In  lakes and polluted  rivers.
    The  "EXAMS inetftodology  1$ currently  under  development  and  has  not  been
applied  in  the  field.
f*odfl Applications
    Although  MINTEQ and  EXAMS have  been applied  Independently, as  they  are
currently  linked  In  the  MEXAMS  program,  they  have not  been  applied  ir  an
environmental analysis.        :
                                 11-26

-------
            «1T'  require  a  system wUn  32* memory.  An  overlay caaaoi ". ty  '$
required  to  implement M£XAMS on  small computers  sucn as  a POP  !i/?0 or  HP3GCO
system.
t/3ft Supper ; Ac g j v i ti es -.

    Copies  of  tne  user  manual  and  system  oocamentat'on  wi *'   3e
sometime during trte summer  of  ^983.   At tnat  time, it '.s ant^c'satea :*a
support «m 5e providea  &y tne Center  for water Qua1':-/ "oae''"?. £=?•. ,
support «m 5e  providea  &y
Atnens, Georgia.
    Additional information concerning  the model can oe oDta'riea  ay contac
          Cnlshl
    Sattel'e. Pacific Hort^est  laooratories
    R'.cnlanc. -asMngton  99352
        References
Felffiy  AS.  8rswn  SM.  Qnlsni   t.  Argo  ?S.  YaOusaK^  S3.   '982.   «£.
-------
,
"

-------
                   Estuary  and  Stream  Qual'.ty  *ode!  (WASTQ.X)
    UASTQX    (Connolly     1982}    was
designed     as     a     -time-variable
compartment  model .for  simulating  tne
t-ansoort    anc    transformation   of
organ'c  cnemica's .m  tne  water  column
a«C   tne  sefl'ment
estuaries;   a'tnougn
genera' \y  app'iicao'te
-ater scales.
                                             CapsuJe Summary.
                                                               tes
                      of   streams   and
                        tne   model    is
                       to  all  types   of
                                  eornpdranvnc.
                                  Streams  *nd e5cuac
                                  *nd  a* lint oft tec
                                  Comprehensive  second-order
    -AS7CX  ae'onqs  to  the  WAS? model  (OiToro  et al.  198!)  «am'. ly anc  :ie*e-
     •>as  capat» titles  and features slml'.ar  to  TOXIWAS? (Am&rose et a'   -9935
    maior differences  between  WASTQX  and TOXIWASP are:  (11 wASTQx  can  account
                            and TOXIWASP are:   (1)  wASTOx  can account
            size  fractions;  TOXIWASP  accounts  for one.  (2;
sart't'on  coefficients  are  expressed  as  a  function  of
       T3x:»iAS?   assumes   a   constant   parV'
"ft« nwjor deferences  between
*cr  tnree  sedlfflent
isncentrav.on;  T3X'.'nAS?  assumes  a  constant  part/: '.on ing   coe"'c'*nt.
•ASTCx  assumes  certain  system orooerties  oeCM>iiicn»..  \ i )  MMJIUA uoi UCC" ina i n . f urii
-------
        Segment   volumes   ana  flows
        velocities  In  compartments.
                                      including  time  of  f'ow  duration,  and
     •   environmental  and pollutant parameters  such  as  geometry of  t.ie  system,
        sedimentation     transport/dynamics    parameters,    PH.    teape-atyre.
        concentration  of  compound  degrading  bacteria  In water,  sec ore -or:e»
        Siodegradatvon  constants  for  dissolved  and  adsorbed   toxicant,  f'rst-
        and  .second-order  alkaline  hydrolysis  ratio,  otner  first-order  decay
        rates,  Henry's  constant,  molecular  weight  of  tox'cants.
        correcting  parameters,  sol'ds  dependent  partitioning  coe* *'c'

     •   Initial conditions,  boundary conditions,  and  waste 'oads.
CutsuC
    A  finalized   output   Fornat   does  .tot  ex's:,   s'.ice
developmental stage.  The  output  is  expected  to  be similar t
consisting of a  listing of  input  data,  and tabu'at'ois givn
and aers'stence  of  tne' cneffllcal  In aH  water  and  sea*iter.t r
•ater aody.
                                                               AS"Ox   '$   ,
                                                                tne  «A$=  cyt
                                                                t-anuc-t .  *"
<e  requirements  are  diff'ca't  to -neet
from routinely available data.
ffeaoureg
    WASTOX  Is available  1n  a  batch/tape version,  is written in FORTRAN  [V, and
uses up to  a  32K-byte  user  area on a POP 11/70 machine.  Execution t'mes range
from a  few seconds  to several minutes  depending on the  temporal  a .id  spatial
r*c i.  two  man months  of  effort are required to have an operational model with
a rough understanding of Its overall behavior or performance.
                                                                                     !

-------
User Suoporr AczivjcifS

    To obtain the WASTQX documentation  along  with  sample  data sets and supper'
software, write or contact:

    Or. Parmely H. PMchard
    Environmental Research Laboratory
    Sulf .Breeze, Florida  32561
    (904) 932-5311

    Or. John P. Connolly
    Environmental Engineering and Science
    "annattan College
    Sronx, NY  10471
    (212) 920-0276
Ambrose a. H111  S,
and  fate  model  TOxiwASf
Research and development.
L.   1983.  User's manual  for  tne  cnera'ca'  fa
 version   I.   Draft  document,  U.S.  E?4,  3ff
Athens Research Laboratory. Athens. Georgia.
                                                                         ce  o-
nASTQX.
Florida.
         TP.   1982.   Preliminary  estuary  and stream version  documents:'on  3.
          £'*  Cooperative  Agreement  NO.   *8C7  927-02.   Ea&,  ^c'*  S!"??:?
          Kannattan College, Bronx. New York.
        3*.  ntzpatrlck   JJ,
Simulation   program   (WASP)
documentation. •  Mydroscience.
P-otectlon Agency.  Duluth. NN
    Thomann   3V.    1982.    water   cua'*t
    and    model    verification   program
     Inc..   westwood.   NY.   for . u.S.  En
      Contract No. 68-01-3872.
                                                                       ana /s
                                   11-30

-------
                  Chemical Transport and fate *ooe1 (TOxIwASP)
    The  foxier  water  Analysis  Simu-
lation  Program (TQXIWASP)  (Ambrose  et
al.   1983)   was  designed  as  a  time-
variable    compartment    model    for
simulating     tie     transport     and
transformation    of     organic    toiic
cftemtcais  In  the water  column  and  tfle
sediment   of    stratified   lares   and
reserves,  large  rivers,  estuaries.
and  coastal waters.
                                              Capsule Summary;   TS
                                             Rivers.  .IdJces.  estuaries
                                                        ve  second-order
    "OXIWAS? *as
model  (Sums  et
program  (OlTorg
algorithms along,
                  created "3y  first adapting  tne  k'netic  structure ^
                  al.  198?)  to  trie  transport  frameworic  sroviced 2y
                  et  al.  1981),  and  by  trier,  acting  slir.c'e
                 witfi special input and output softwa-e.
                                                                      t-ie
                                                                       the
    Since  TQXIWAS?  uses  tne  compartment  modeling  approach,  .nereoy
cart ae arranged  1n  a  0-.  1-,  ?-.  or 3-dlmens lonal conf igarat'cn. "Cx:«A:3  \\
water/sediment quality  program only, and  as sucJi,  it  requires  tne -ate-  aoc
and tne  sedimentation dynamics  (e.g..  flow, velocity,  aed  sed'ment ve'odty
as user  Inputs.   TOXIHAS? can 'oe employed  for  analyses
transport  and   loading   capabilities  than  EXAMS,
mecnanlstic sediment predictions than S£RATRA (Onlsnl
                                                                      ve
                                                         requiring -nore dynamic
                                                       but   less  aeta'. '.e
-------
               tne chemical  transport  an
-------
                  Chemical Transport and Fate ^odel {T
SUPUHLfM

    The  foxier  w«iter  Analysis  Simu-
lation  Program (TOXIWASP)  (Ambrose et
al.  1983)   -as  designed  as  a  tlme-
vaMable    compartment    model    for
simulating     the     transport •    and
transformation    of    organic    toxic
chemica's  m  the water  column  and  trie
sediment   of   stratified   lakes   and
reservlors,  large  rivers,  estuaries,
and coastal waters.
                                               CapsuJe
                                                                 TCx;v*£?
                                             cceparsnenc node2
                                             Rivers.  JdJtes,  *sclaries
                                             Comprehensive secsr.d-G:dez
             was
mode'  ;3urns  et
program  CM'oro
           a'ong
                  created  3y first adapting  :.ie  «'net'c strjct-'e 3'
                  al.   198?}  to  tne  transport frameworx. jrov'cec sy
                  et  a'.  1981).  and  by  t.ien acld'ng   s^.^c'e  cec'^e"
                 «'tn  saec'-al  input and output software.
                                                                      tie :.t4"C
                                                                       tie
    S'nce  TOXIWAS? uses  tne compart.-nent  nodeting aoproacn,  -fte-esy  5«^.e".t:
can J>e arranged  In a  0-,  1-. 2-, or 3-d1mensional canf'gurat'on.  "CxlfiA^?  -s  a
water/iedifnent  quality  program only,  and  as sucft.  it  requires tne  .a:*-  DCC<
and  t.ie  sedimentation  dynamics  (e.g.. flow, velocity,  3e<3 sec'sefit  
process,  and  the  kinetic  time  derivative  is   calculated  from  this   rate.
yielding  a  time  varying chemical  concentration   for  a  user.specif led  spatial
network.   EXAMS  uses  a  Kinetic   structure  that  allows  tne  study  of five
different ionic  forms  of  a  chemical, several ways to  calculate  photolysis,  and
other  capaollUles.   In TOXIWASP, all  tnose features, nave been aggregated  in
one formulation,  but  with  an  expanded library of kinetic subroutines.   In that
respect,  TOXIWASP allows  simulations of toxic organic chemical  behavior  m  the
aquatic  environment  resulting  from  loading pulses that  cannot  be modeled  via
steady-state cotfe..
                                                                                      1
                                    11-31

-------
    Se'garding  t.ie  cnem'ca'  transport  and fate  processes  cons'ce^es.  T:X:WA$?
can  account for  vo^at1 l 1 ;aHon.  pnotolysls.  hydrolysis,  ox'dat'on.  3'olys's,
sorptlon  on botn  sediment and  biomass.  advectlon,  and diffusion. . Sorptlon on
sediments   and  aiomass   1$  calculated   assuming   local  equrt'Sr'um   us'ng  a
constant  partition  coefficient  and'" soatVally  varying envlronnenta' organic
carbon  fractions.    for  eacn  compartment,  one  differentia'   equation for  the
pollutant dissolved  pnase  and  one differential'ecuation for trie  adsorbed  phase
are  formulated and  solved.   AS ' contrasted   to  wASTQx  (Connol'y  "9825.  tne
effective first-order decay  rate  can vary with time.


    Exchange  between tne  water column  and  tne  bed  can  occur  ay set:'*ig or
-esussension  of  participates,  diffusion (of  dissolved  goilytarst- sef-een  tne
•ater  co'umn  and  tne pore  water, by direct  adsoration/desorst'c.i ner-eei  t.ie
•ater  ca'amn  and  tne Sed sur*ace. and  3y percolation or infilt*at*on.  ^'tn-i
tte,  3ed,   tne   sol^utant   can   move  .vertically   5y   3"*'js'on,   turnover
;;'spersion).  serco'ation.  and  Durlal.   Also wit.iin  tne  aed.  :.-e  3o''jtant
cannot move horizontally (i.e., no bed  load),  in contrast witn «ASTCX.
'.TPU ; . Ja £a Pegu 1 re.'nen ;j

        t reculremeits for TQXIWAS?  include:
                  cseff'c'ents  between  ::mcar:.?er,t: -sjet as s'::€-:';--  :e:
         segments,  -ater  column  and  sediment,   sediment   arc  -at?-   '^   3ed
         material. .

         Segment volumes and flows.

         Soundary conditions.

         Envlronmenta'   and   pollutant   characteristics  sucn   as   number  of
         constituents,  temoerature,  cloudiness,  bacterial pcDuUt'on.. a''amass,
         hydroxide  ion  activity,  molar  concentration  of  ox'dants.   organic
         carbon,  pH,  decay  coefficients',   Arrhenlus   constants*  second-orae«-
         rate constants  for  biolysis in tne benthic  environment, octanol -a:er
         partition  coefficient.  Henry's  law  constant, vapor  pressure,   and
         solubility.
  '•Model  output  consists of  a listing  of  input data  and  tabulations giving
transport,  fate,  and  persistence  of  the chemical  in all water  and  sediment
compartments of tne water body.
                                   11-32

-------
Advantages dS  not  Oeen  acoMed  to  a  real  situation;  ftc-eve-.  :.-e  i^--;
.-node'  nai  seen  acplied 'n numerous, situations  (see £XA?<$ aescr'sr's.-.   a-c  :•*
       program,   available  since   1970.   nas  been   app''ed   *r»   -nore   :,".ar.   ;;
          :;  (3lToro et al.  1981).
              1s  not  an Interactive modeling  package;  ratner. 't  «s  a  srar.cari
software  package  in.  FORTRAN,  operational  via. a  standard  CRT  unVt  or a  can
deck.  TOXIWASP  requires  an IBM 370  (OS/MVS  Operating System).-or a  "QP  tl/70
(IAS  Operating  System),  programmed. In FORTRAN  [V» or  FORTRAN  IV.   The  firs:
version can accommodate 100 compartments.  the second 50 compartments.   The  'Q3
11/70 computer  utllUes  an IAS operating  system  and allocates  a  32*  «ord  '. M<
Dyte)  user  area  for  execution of  a  program.  TQXIWAS? occupies  at   least  22<
words of  memory  in either  machine,  execution times  range  from a few  secancs
to  several  minutes,   depending  on   the  temporal  and spatial  resolution of  the
environment  analyzed  and  the  machine  used.   At  this stage,  it  'is  estimated
that  one  to  two  man-months  of  effort  are  required  to  have  an operational
model, with a rough understanding of  Us overall oenavior/performance.
                                    11-33

-------
     support Activities
    TOX-WASP  '1s
Laboratory  In  a
along with sample
    available   from   the   EPA  Athens   Environmental   Research
   batch/tape  version.   To  obtain  the  TOXIWASP  docymentatio'
   data sets  and  support software.  write  or contact:
    Mr. Robert Ambrose
    Center for Mater Quality Modeling
    environmental Research Laboratory
    U.S. EPA. College Station Road
    Athens, Georgia  30613
    (404) 546-3546
Amorose R, H111  S.  MuUey L.  1983.   Uier's  manual  for the chemica' t-ansport
and  fate  model  TOXlWAS?.  Version  1.   Draft  document,  U.S.  E?A.  Qf«*ce  of
Research and-Development.  .Athens Research Laboratory. Athens. Georg'a.
3ona?ountas
£nvi f onmental
              eds.    '982.    Arthur-, 0.   LU.tle.   :nc.
•oathematica'  modeling .  handbook/cata^oQue.
Of*^ce of 'ol-Uy of Resource .Management . U.S. Environmental ?rot*ct'on
       LA.  CUne  OH.  Lasslter  RR.
(EXAMS},  user   manual   and   system
Environmental Protection Agency.
                      1982.    Exposure
                       documentation.
                                                      anal ys 1 s
                                                       Atnens.
•node'
                                                                         .NVISO:
                                                                          D.C.:
\ t em
J S.
Connolly  TP.   '982.   Preliminary  estuary  and stream  version  aocjDe".:a:'3n o
WAS7QX.   E?A Cooperative  Agreement  lo.  R807' 827-02.   £PA Gylf Sreere. f'or'ca.
"anhattan Col'ege,  Bronx, New York.

OUoro  DM,   FVtrpatrick  JJ.  Thomann  Rv.,' 1981.   Hydrosc lence.   Inc.   wter
quality  analysis  simulation  program  (WASP)  and  model  verification  program
(MVP)  -  documentation.   Oulutn.  MN:   U.S.  Environmental  Protection  Agency.
Contract  No. 68-01-3872.

Onlsnl  Y, Wise  SE..  1982.   User's  manual  for the Instream  sediment contaminant
transport- model,  SERATRA.   Athens,  Georgia:   U.S.  Envlronmenta'  Protection
Agency.   EPA-600/3-82-005 (\n press).
                                   U-34

-------
      Toxic Organic Substance  Transport  and Bioaccumulat'on  *odel  ;*3*IC;
                                               Capsule  Summary.-
    The    Toxic    Organic    Substance |
Transport  and   Bloaccumulation  *odel •  Quasi-dynamic.
(TOXIC) (Scnnoor  and  WcAvoy  1981) is a
quasi-dynamic   water   quality   model
designed  to  simulate  tne behavior  of
pesticides  in  }  reservoir  and  bio-
concentration of  pesticides  in aquatic
11 f e.
                                           iapouno
                                                    foe
                                                    nc
                                                         reservoir
                                                              c sys:es>s .
                                            biological
    The quasi-dynamic  approach ut^itUes:   ( '• )  'steady,  anr>ua
(from  da'ly  averages)   for  long-term simulations;  (2)-st*3Cy,
flow-«e'gnted
time-varlaole
               sol ids
              toxicant
                       (from  da 11y.
                       loac'nqs
suspended  so1! ids  -neasure-ne'
                                                                   av«-ace
                                                                       and
    TOx.'C  includes  a  routine «n)cn calculates  a  niass  Sa^ance an sea-men:$ ar.:
tne  adsorbed  cnemical.   Sediment  deposition   and  scaur  are  inc'uces.  as   ':
diffusion  of  toxics  from  sediment  oore  *ater  to  tne overlying  ..a:?1-.   "-••
model also computes contaminant uptake and depuration Qy fls.i.
    TOXIC considers t^e aouatlc  system being simulated as
numoer of compartments  {m  one  application of tne model
nave been utilized).   Each  compartment 1s  considered
system.
                                                             ing divided 'nti a
                                                         up to 100 compartment:
                                                       to  be  a completely m'.iec
    The concentration of  the  contaminant  tnrougn time is described by a- set  a*
ordinary differential equations,  one  for  each compartment.   The basic equat'on
Is  written  to  include  tne   sum of  the  first-order  or   pseudo-f Sr$t order
reactions  (hydrolysis,  biological degradation,  biological  uptake, photolysis.
and  volatttlration)  as  well  as  adsorption  and   desorptlon   kinetics as  a
function of particle  size  distribution.   The  coupled equations are tnen  solves
via a variable step Sl:e fourth order Runge-Kutta numerical  technique.
    The Inputs to tne model can be classified  ..Jet the following categories:

    •    Geometric  properties,  such  as  volumes   of  compartments,  distances
         between  them,   surfa'ce  ateas,  and  locations with  respect  to other
         compartments.
                                    11-35

-------
    •    flows  set ween  compartments  and  between   eacri   compartment  and  the
         outside of the system.

    9    Reaction  rates, settling rate constants, and partition coefficients

    •    Sol Ms concentrations In each compartment .

    •    Bulk dispersion coefficients between compartments.

   •'•    Simulation parameters such  as step size and time  of  simulation.


Oueguc Descriptions

    Output from TOXIC  Includes:

    •    Solids balance description  listing the concentration of tne soMds  in
         the  water column and  In the sediment., and the  net  £'u* .-e'.-e«". •?,*
         sediment  and  the water column over time

    •    Dissolved.  particuUte.  and. total  concentration of  tne   contam'-.
addition  to  chemical reaction pathways .< f )sn  uptake  and depuration  ;eicr»t'on
and metabolism)  are  Included in the  model.   Previous models have not  comomec
fate and transport 'modeling with the  biological effect  (aioconcentration)


    TOXIC   Includes   a   routine    which   calculates    a   mass   aalance   en
contamlnant-sorbed and  unsorbed sediments.   Sediment deposition and  scour  are
also  Included,  as  Is the diffusion of toxics  from sediment  pore water to  tne
overlying water.


    Coefficients  and   rate  constants  must be   supplied  by   the   user   thus
requiring  a   working   knowledge  of  kinetic  processes,  sedtm.-nt   transport
mechanisms, and  the  ability  to adjust  the model's  comput"r  code.
                                  11-36

-------
    The   -node^'s   sMiu'at'. on  caoaoilities  .as  designed   to   De   aoo-'-eo  :o
reservo'r  or  impoundment aquatic  ecosystems  anfl may  therefore  3e  unattract've
for  use  in situations  -here multiple aquatic  systems (e.g.,  rivers,  streams.
and  Impoundments)  exist.


    user   support   for  the  model  1s  rather   limited.   A  user's  manual  is
      l '.aole  at  this  time  ana  the  model  is   not  current'/ . supported 5y  trie
Center  for rfater Quality  Modeling.   CSi,  USE?*,  Athens.  Georgia.

            k

xcdel Application

    TOxlC  has  Seen applied to  lo*a  reservoir aata to simulate 't*.e  :«-a<';' a*
the  'fisect'ciae d'eldrin  and  the  heraic'.des a'acn-ar  and atrar'^.e    ::»4Cy-
state  analyses  and'  quasi-dynamic   simulations   with  tlme-^ar'aD'e  •":-?  310
         -ere  undertaken.
                measurements  for-alach'or  we'e  used  'n  the -nose'  : "ru/at' :n;
*!tn  scod  agreement  5et*een  nodel "  predict'ons  and  measwroc  canceit-at'cn
.atoratory   measurements   we'e   a'so   used   in   the  a:-a:'i«   e ootalnefl 5y contacting:

    J. L. Schnoor
    CW11 and environmental Engineering
    tnergy £ng1neerlng Division
    University of  Iowa
    Iowa City. Iowa  52248
    (319) 353-7262
                                    11-37

-------
Scnnoor  JL.  McAvby  OC .   -1981.   A  pesUldde  transport  and
model.  Journal Environmental Engineering Division.  ASCE. Volume 17. NO.  EEC

Scnnoor  JL.   1981.   Fate  and  transport  of  dleldrln in  Coralville  Reservoir:
Residues  in  Msn  and  water  following  a  pesticide ban.   Science. 21',  50.
840-842.
Scnnoor JL.  1982.   neld  validation  of  water cjual'ty criteria far *ysr3C
pollutants.  In:  Proceedings of the 5tn symoosium. aauat'-c 'ax^c'ty. AST

Scnnoor  JS.   1982.   fate  and  transport modeling  for  tax'.c  sjBstances.
XodeMng   tse   fate  of  cnemlcals   in   tne   aquatic  envi-onment .    3«'
Conference Proceedings, Ann Araor Science
Scnnoor  Ju.  et  al .   1983.   verification  of  a  toxic  suastancs  t* »'*<;c-t  a^
aioaccursulation  model.   t?A 600/2-83-C07 .   Environmental  ^c$ea':" .sDs-itsr/
        3A.  3G&13.
                                  11-38

-------
                        Channel  Transport Model  (CH.NTSN)
                                              Capsule SuBinary  CHHTM
                                           Tin*- varying,  l-tl
                                           eomparunenc ruodei
                                                   flyers.
     -order
    The    Channel    Transport    Model
(CHNTRN)  (Yeh  198?)  *as  developed  .by
tne  Environmental Science  Division  of
the Oak  Ridge National  Laboratory,  Oak
Ridge,  Tennessee,  for  the EPA's Office
of  Pesticides  and  Toxic  Substances.
~he  purpose  of  CHNTSN' is  to  s*mu'ate
time     varying    distributions    of
sediments  and  chemicals  .in  receiving
*aters.    CHNTSN   can    mode.    tne |	
transport and  fate  of  a  pollutant  In a
*'.ae  variety  of  aquatic  systems,  mat  include:   tida1  a.ic  icr.-
*a*es.  and  reservtors,  streams, estuaries,  and  csasta"  seas.   A  :
'eature  of  CHNTSN  is  its  capability  to  deal witn  a  oetwor*  s/stem
consist  of  any  numeer   of  Joined and  aranc.ied  streams/"'vers  3?  ;
s';e.   CHNTSN.   comotneti  «.stn  tne   Channel   Hydrodynamic
                                           Comprehensive
                                           for or9an^c5
                                                 ^  and da
                                                                    ia'
                                                                        •f.:u ar
                   i  tfte .lycrodynamlc computations  or  r  sws  ar>s
constitutes  a   software  package  for predicting  tne transport.
transformations of organic pollutants in a stream/river  system.
       transfer,  arc
    CHNTRN can  model  complex  problem settings  t^at  can
!-dimensional   segments.    Codification   of   tne  model
3-dimensional   proolems   is   relatively   easy   because
compartment  approach.   The  spatial scale  of  segments can
kilometers, and the temporal scale can vary  from  seconds  to hours
se ac;-"3x'^ates  -'t.i
  :o   treat   2-   ano
 of   t.ie   'ntsgrated
 vary from neter$
                                                                              to
    CHNTRN  uses  tne  chemical  kinetics  of  EXAHS (Exposure  Analysis *oce'1ng
System)  to  account  for  hydrolysis,  oxidation, photolysis,  volatilizat'on.
blodegradatlon. and  adsorption by biota.  Consequently, other pnys ico-cnemica1-
factors  (e.g..  temperature.  00.  pH)  are  also  reouired.   Sediment  transport,
deposition,  and scouring are  simulated  for three particle  tyoes,  cane.  silt.
and clay.   Provisions  for adsorption/ desorptlon and. pollutant accumulation  In
the bed sediment are Included.

    The  model   code  Is   In  basic FORTRAN  language.   It consists  of  a main
program and  IS  subroutines.   The equations that govern  tne system's kinetics
are  derived   from  3-dlmens1onal  mass   balance  equations.   An   integrated
compartment method (Yen  1981)  Is  used to solve the differential equations.   In
this method,  the  link matrlce?  ir»  derived based on  the  fluxes  of mass  a Ion-
each of  the links that  Intertwine  the compartments of  the  river  system.  Th>
global  matrix associated  with spatial derivatives is assembled From these link
matrices.
                                     11-39

-------
The resy't is a  system  of  ordinary  differential  eouat'ons  with respect to time
that  govern  the  dynamic  evolution  of  suspended  sediment,  aea  sediment,
dissolved chemical  concentration,  participate chemical  concentration,  and  oed
sediment  chemUal  concentration.   Chemical  concentrations  for ooth  the  wat
column and oed  sediment are solved By  the  time  split  scheme.   Two options  f-
solut'on  are  provided;  one  is  tne explicit  scheme for fast,  computat'on;  the
second is the  implicit  scheme which generates stable  solutions  for large time
steps.

    CHNTSN user  manual  and  documentation  are currently in draft  fom  and  it
    yet to ae field verified.
    CHNT3N  is  a  sophisticated  model  recuir'.ng  extensive sata  input.   3e*3r-
        can  se  executed,  hydrodynamU  variables  such  as  flow  rates,  .ate-
iecth.  crass-sect'.onal  area, width,  and wet  perimeter  must 5e  osta'riec  *-c^
actual  aata  if  availaBle;  if not avaiUole,  tn^s  infor-nat'on can se •st'^ate-:
using CHNHYO.  Otner data input includes:

    •   Cnvi-onmental  parameters  -  air  temperature,  solar  rac'at'on.  .'^fi
        speed,  vapor pressure.  »ate- te^pe-ature.  e*t'«ct'sp  esc" ': '«^t .  SH.
        sCM.  oi'.aation radicals.
    •  S'o'oqica'i  information .  sacteriai  population  aens'ty.  s'siem
       for   activation  energy,   and  the   bacterial   portion   ^rwo'v«s
       degradation.

    •  Coefficients  for pnotolysfs. hydrolys's. oiidation, a^oce

    •  Sediment  types  and distributions.

    •  Transport   Information  -   solids  in   water  column   anc
       sedimentation   and   resuspenslon  velocities,  partition   coe'f '.c'ents ,
       dispersive  coefficients between  phases, and  volatil i:ation  rates.

    •  System geometry - areas, depths, volume.

    •  Sources and amounts  of pollutant.
                                    I! -40

-------
Oucgue Jgserlpelona        •                •

     CHNTRN  calculates and  presents tne  following for  individual
1n  tabular  form:

     •  Dissolved  cnemlcal  concentration  1n  tne water  column  as a  funct'on  a?
       distance  from  tne source.

     •  ^articulate concentration  in suspended  and  bed  sediment.

     •  Suspended  sediment  concentration  anc amount of 3ed sec'^ei
       in a unit  bed  association.
    ~he  major  advantage  of  CHN'SN  is, its  casac'ty  '.z  s"nu'a:?  :'-
distributions  in  a*'  types . of  water  Socles.    CHNT?«»  accounts
advective and  dispersive  flows  and  tne total  fiux  sf  ar acuaf:  sys
cnemical Kinetics are tne second-order  rate eipression? of £
                                                                       cs'   ct
                                                                       tem.   ~*»
        SN   is   a   complex  .node'   and   as   suc.i   's   very  rata   '
Cons 'ceras'e  time  .uijnt  :e  neesea  far  .me  accui s ' t* 3-">  3'  data  ic:
availap'e.  Computations  *ar  comp'ex  systems *''  also  -ec-j'-e la-2e  ar
t'me 'or so'ution,  as will  tne simu'at'on execut'on  ti.-ne.

    If nydrodyna.-nic  infor-r^t'on  is  not  availaDle.  it can ae estimates
us'.ng CHNHY3  anc t^en  s-sstylng tne  -"Suits ts  :.-H*5,N.  Alt.-cuc.i  Tr
not ae  defined  as  'user-friendly*,  it  3oes tune  jrsv's'.sns  :z  al'sw
to -nase some modifications.

    CHNTRN Has yet  to De  field validated.
                                                                        sy f'-:t
                                                                        ^N «cu':
                                                                        :re  jse*
Model Appllci Ciena

    CHNTRN  has  been   applied   to   two  river  network  sample  profclefns   *«'
demonstration  purposes.   The first  sample  1s  a  single  river  system,  anc  :n-»
second  is  a  network  of  five  rivers.    Typical  data  were   used   for   tne
simulations.   In each example, the rivers are  divided up  into compartments.
                                   11-41
                                                                                     «

-------
    The   first   scenario  produced   seemingly   unrealistic   results.   Closer
analyses  of  the  input  data  revealed  U to  be  erroneous and  illustrated the
•garbage  in,  garbage  out*  results.   The   second  scenario  showed   reason?
results  for  day.   silt,   sand,  dissolved  chemical,   clay-adsorbed  c.iemic
silt-adsorbed chemical,  and  sand-adsorbed chemical concentrations.   Because no
analytical solutions were  available.  H is  not possible to assess the accuracy
of  the  results   by  comparing  them  with  analytical,  results.    However,  tne
results  intuitively  indicate  that  the model  can realistically  simulate the
behavior of the sediment and chemical variations  In a stream/rive' network.
    CHNTRN  Is  written In  FORTRAN  IV and nas  been  Implemented ="  an
computer.  Simulation execution time may ae extensive.
                                                                           2923
    Copies  of  CHNTSN's  draft  user  manual  and  documentation  as  -e"
assistance -nay be obtained from:
                                                                             3'.
    G. T.
    environmental Sciences Divisi
    Ca* aidge National Laoo-atory
    P.O. Box X
    Oak Ridge. T*  33830
    (615) 574-7295
Yeh GT.   1982.   CHNTRN:   A channel  transport model for simulating  sediment  and
ch««i1cal   distribution   in   a   stream/river J network.    Oak  R^dge   Hat'ona!
Laboratory, Oak Ridge, TH.  ORNL-5882.

feh 6T.   1982.   CHNHYO:  'A channel  hydrodynamlc  model for  simulating  flows  ar.c
water  surface  el'evatlons  1n  a  stream/river   network.    Oaic  Ridge   lat'ona'
Laboratory, Oak Ridge. TN.  ORNL-5701.

Yeh QT.   1981.   ICM:   An  Integrated compartment  method for numerically  solving
partial  differential  equations.   Oak  Ridge National  Laboratory.  Oak  Ridge.
TN.  ORNL-5701.
                                     11-42

-------
                     Finite Element Transport Model
SianarM

    The  Finite  Element  Transport
(FET8A)  1s  a  time-varying, J-dimenslonal
(longitudinal    and   lateral)   transport
model  developed   by    Sattelle  ?adfic
Northwest  laboratories.   F£TW  utilizes
a  finite  element  solution  technique  and
consists  of  three  submodels  coupled  to
simulate  the  transport of  sediments  and
contaminants   in   rivers   and   estuaries
tnrougn                               the
            o?   advectlon.
                    F£*RA
              ia«c systems
                                              C4p*uie Summary   FSTKA
                                             Complex sediment  :ra/?spor:
                                             capabilities.
                                             Csmprehenai*e second-order
                                          9  River, estuary. And
degrafiat 'on/decay .
jnsat.-a
can  be   appMed
svsce«. ;
to
ion. afi
rivers.
estuaries. ;sasta • , a--£

    Tne  sediment  transport  submodel  simulates  sediment --ncve^e":  fsf  :.--•?-
sed'ment   sire   fractions   or  sediment   types.    This   suomcde-   "sc'jdes   f-?
mechanisms of:   (1) advectlon and  dispersion  of sediments. !2) fa'.-  *-'3c*t*  a-:
cones iveness .  and  (3) deposition  or erosion  frsm  tne oed .   It  a'so ca'cuiatsi
cnanges  in bed   conditions,  including bed  elevation  c.nanges  due  ta  scour'nq ;r
decositlon. and  gives  a  3-dimef»$ional distribution of  sediment  s':?s .'ts'-".  t."e
bed.
    The  dissolved   contaminant  transport  submodel   simulates   t.ie  i
contaminant  interaction  with  sediments  In  motion  and  witn  stationary  ;ed
sediments.   The   submodel   Includes   the  mechanisms   of:   (1) advectlon  and
diffusion/dispersion  of  dissolved  contaminants;   (2)   adsorption   of  dissolved
contaminants by both  moving  and  stationary  sediments   or  desorpt'.on  f^sm :»*
sediments   Into  water;   and  (3)   chemical   and  biological   degradation  ;r
radlonuclide decay of contaminants.
    The  participate  contaminant  transport  submodel  simulates  the  transport of
sediment-attached contaminants  for  each  sediment  size fraction.   It includes the
mechanisms  of:   (1)  advectlon  and  dispersion  of  particulate  contaminants; (?)
adsorptlon/desorptlon of  dissolved  contaminants with  sediment;  (3) chemical and
biological  degradation or  radlonuclide decay  of contaminants;  and (4) deposition
of particulate contaminants on the bed or erosion from the. bed.
                                     11-43

-------
    ~ie  temporal  scale  of  FPTRA  u  on  the   order-  af  minutes  to  hours.
Hydrodynamic  data  are  supplied   by  exterior  models  such  as  CAFE-l  (ocean
currents)  and  1330  (wave  refractions)  for  coastal  Caters  applications.  and
EXPLORE-! (velocities and Mo* depths) for estuarine and riverine application


    £xPLQRE-I  -s  a  comprehensive  mathematical water  quality model  to  be used
in  river  oasin planning  and  water  resource  studies.   This  generalized river
basin  water Quality  model can' predict  the  hydrodynamics  and water   Quality
dynamics  far  rivers  and  well  mixed  estuaries.   The  EXPLORE-!  mode'  's  an
extended and  modified version  of  the Storm water  management "ccei. receives
-ater  component,  -nlch  was  developed   for  studies  of  DO/900  Syramics.   ~*e
model  is  capable  of  simulating  a  number of  hydraulic  regimes   in  e'tne'   a
dynamic or  steady-state  mode,  and  U has been set  up. ca'4bratea.  and  ve^'^ed
on  a  jort^on  of  the yi 1 1 '.amette 3iver  iasin.  conslst'nq Df -na^or  *.r*iuta''es .
tX?'.3R£-I -as developed by  Sattel'e^Northwest laboratories  for  the  EPA.


•Trpyg SJ:a Pe-ru-i rgr
         input data  reoui Cements  for r£TPA  a»"e  au".e  e*tens'-ve.
        ' iaia
    •  Csmnon 2ata resui rements  *ar all  the  sucmcdel s •.

       - Channel geometry.

       - Discharges during  the  simulation
       - Discharges  of  tributaries,  overland  r^noM.   anc  otne-  so'r.:   ars
         non-point sources.

       - Lateral and  longitudinal dispersion  coefficients.

    • . Additional Requirements  for .sediment  transport  submodel:

       - Sediment size  fraction.

       - Sediment density  and  fall  velocities  for  sand.  sVlt.  and  day.

       - Critical  shear   stresses   for   erosion,  ana  deposition   of  cones'.ve
         sediment (sll't ana  clay).

       - Credibility  coefficient  of  cohesive  sediment.
                                     II-44

-------
        -  Sediment concentration for eacn secernent si;e fract'on.

        -  3ottofn sediment sUe fraction. .

        -  Sediment concentration at the upstream end of trie study

        -  Contributions . of   sediments   from  over  land,   tributaries,  and
           other point and non-point sources.

     •  Additional   requirements    for   the   Dissolved   cantamtran:
        partlcglate contaminant transport
           Distribution  coefficients  and   transfer   rates  of  contan-iar.t
           with sediment  in  each  sediment  s'ze  f-action ;'..«..  sane. s''t,
           and  day).    If   values  of .d'st.-itjut'on  coef *'c *e"ts  ar»  not
           available,  it  is  necessary  to  know  c'ay  minera-  ano  organic
           sediment content to estimate t*es* ^a
           Second-order iecay rates of ccntaninarts

           Boundary conditions.

           Csnf'Syt'ons   of   dissolved   and   sa-ticj'ate   ::".t5n<
           concentrations f*cm  trioutaries,  cve-'ana.  and  otie'  so'^t
           non.po"'n: sources.
     m'.m tne input data descMoed aDove, r£'a^ simulates t.N.e fo'« 'o*'ic:

     •  Sediment   simulation  ard   longltudlna !/ latera'   d'.strtsut'sns   a?
        total sediment and size fractions and changes in oed elevat'on.

     •  Contaminant  simulation  and  1ongi tudinal/'atera l  distributions "of
        dissolved contaminants,  contaminants adsoroed  Dy sediment and  in
        tne DOttom sediment for each sediment sUe.
            And Llmltaciona
     FSTSA Is  designed, for  tlme-varlaole  analyses  of I-  or  2-dlmenslonal
 (nori'ontal)   water   bodies.    Its   sediment   transport  rogt'nes   are
 sopni sticated  and  will  predict  tne  resusoenslon  velocities  and  aed  "oad
 given   the  sediment*  and  hydraulic  characteristics.   The  model  can  &e
 coupled  with   a   hydrodynamlc   model  1n   order  to  generate   Hows   and
'velocities.
                                   11-45

-------
    Input  data  -eau'rements  for  FETSA  are  eitenstve.  ana  computational
time  for   long   term   continuous   simulations  may,  ae  high.   Sesource
Acquirements for set up and execution are eipected to De substantial.
          cannot discern.water body stratification.
                                                     ••>!••£• ••••<..•
      Applications
     FETRA   has   Been  applied   to   the
James    Slver    estuary    In    Virginia
(Onis.v,  1.981)   and  to  the  Irish,  Sea
(Cn'shi  et al.  1902).   The purpose  of
tne   James  River,  application   «as   to
Simulate   sediment  movement   and   the
transport   of   the   pesticide   Kepone
•f^cn  .as   d'scharged  to  tne   rUer  In
suostantia*   suantities    during   the
ear'y  '9'Cs.   The purpose  of the Ir'.sn
Sea    app'^cat'on    «as    to    evaluate
exposure    'evels    of    radionuc1 ides.
         •neta^s.    and    otner    toxic
       j's  in  coastal  -aters.    *esu>ts
a*  the ;-*sn Sea  application   nave  not
*e:  sesf  sup'ished.   A  discussion  of
"the  James  3'wer  application follows.
         James  9'ver  application -as  a
        ;*on  and  verification  study  o'
rt*»lA.   SeC'ment  transport was  modeled
for  three  sediment  types: (1) cohesive
(silt   and   clay);   (2} - noncpftes!ve
(sand);   and,   (3)   organic    matter.                  ...•..•»«—••«
These   results   {see  Figure  1)   were
compared   to   field  data  wnicn  Indicated  that  a   considerate   amount   o*
partlcuUte  Kepone  was  transported Oy  organic  mater^als mov'nq  'r\ceaendeit' y
-1th  other  sediments.  Predicted  part'culate Depone  concentrat'ons
witn  each  type of  sediment  and  weighted  average  partlculate
together with  measured field  data of  average partlculate
in  Figure  2.  The  computed  results  and  tne field data
Figures.
                                                               Kepone  are shown
                                                          (epone concentrations
                                                         closely agree  in

-------
ftgsoufge
     The  computer. program  Per  FETRA 1$ written  m  FORTRAN  [v  language    "'rSA
can  be  used  on IBM, VAX, or COC-7&OQ computers.  Execution  times  anc  run  costs
vary,  depending on  the  characteristics  of  the  system  to be modeled.   For  the
James   River   application;   computer   time  required  to   calculate  a"1.:  seven
substances  per computational  mode per time step was 0.0023  cp second on  *ne
COC-7&00 computer.
t/aer Support
    The  user's  manual  and system documentation  are  st''i unae-go'-n; ' -«v'«w  a
       and  are  ngt   yet  available   for   puol.'.cation.    >e   "£*5A
operational,  has  Seen  Implemented  in selected  applications,  ana  '*
to the puolu.                     .


    Node! information  can De cPta'ned  &y contact'ng:

    Yasuo Onishl
    Sattelle - Pacific Northwest 'Laboratories                      ,
    aicfi'and. Washington  99352
    FTS 4*4-8202  CCM  509-376-8202
Onlshl  Y.   1981.   Sediment-contaminant  transport  -nodel.    Jour^a"   3?   t-.e
Hydraulics Division, ASCE, Vol. 107, No. MY9.   Proc .  !>aper  16505.
pp. 1089-1107.

On1sh1 Y, Mayer. OW.  Argo.  RS.   1982.   Sediment and toxic contaminant  transscr:
modeling  in  coastal  waters.   In:   Finite  Clement  Flow  Analysis,   '-ac.
pp 733-740.
On1sh1  Y,  et  al.   1981.   Critical  review:   Radibnuclide  Transport, S
Transport,   and   Water   Quality  Mathematical   Modeling;   anc  Sad'ongcl 'c?
Adsorption/desorptlon  Mechanisms.   R*. en Una,  Wash'.ngtorc.    'acific
Laboratory, 3atte11e Memorial Institute.  NURES/CR-1322', PNi.  2901 .

USE PA.   1982.   EPA   Environmental   Modeling   Catalogue.     Abstracts  of
Environmental Models,  pp. 363-366.
                                     11-47

-------
                 Sediment-Contaminant Transport Model (SERATRA)
                                                 Capsule Suwiwry;
    The    Sediment-Contaminant    Transport
Model (StRATRA)  (Onlshi  and  wise I982a) is
a  time-varying  2-dlmenslonal  (longitudinal
and  vertical   resolution  in   tne   water
co'umn  and bed)  sediment and  contaminant
transport  -nodel   developed   by  9atte11e-
3ac'Mc  Northwest  Laooratorles.  The  model
predicts  distributions  of' sediments-  and
tax'c  contaminants   in   rivers  and    some
'^counanefits.   The model  consists of  'the
) advection  and  dispersion  of  dissolved  and" ^articulate contaminants;
chemical "  resulting   from   hydrolysis,   oxidation,   pno'tolysis.   aio'.og
activities, and  radionucUde decay where  applicable; (3)  volatilization;
adsorption/desorptlon;  and   (5)   deposition   and   scouring  of   particu
contaminants,    SERATRA  also  computes   changes   in  Mver&ed  ;canditiorts
sediment and contaminant distributions.
                                                                            '.2]
                                                                            cal
                                                                            (<)
                                                                            ate
                                                                            *or
    Required   input   includes   channel   and   sediment   characteristics   and
adsorptlon/desorptlon  properties  of  the  contaminants.    In  addition,  SERATRA
"equires  discharge  and  depth  distributions  wnlcn  can  be  obtained  by  a
nydrodynamic  model  sucn  as  EXPlORE-I.   EXPLORE  applications  for use  with
SERAT8A   do   not   require   reprogrammlng;  however.   some  reformatting  and
recalculation  of  the  Input parameters  may be  required.   EXPLORE is discussed
in further detail  1n the FETRA summary.
                                     IMS

-------
    SERATRA 1s  similar  to  FETRA  in  that'Both consist of the same three coupled
submodels;  and  provide  time-varying. 2-d1men$lonal  transport  simulation using
comprehensive   second-order   decay   kinetics.    Both  provide   longitudinal
resolution,  whereas  the  other  dimension  for FETRA  is  lateral  rather  than
vertical (as in SERATRA).
f/ipa
    SERAT8A consists  of  the same three coupled  submodels  that  csmoMses >"
and therefore- requires  identical Input data.   Sefer to  the  FETRA summary
discussion of SERATRA's input data requirements.
          A  provides  output   identical   to   the  Ft'SA  outsut  e*cest
long4tucMna' and  vertical,  rather  than long', tudina'  ano late»a', c'sf's
anc -esolut^on are provided.
    lUe ^t'^A,  SESA'RA  provides  the  capaoil.ity  of simulating the :smc'e«
mechan'. ins  '.nvolved  In  contaminant   migration   ay  cogp^ng  contam'nart
transport and  degradation with sediment  transport.   SE3AT3A aiso' lane'es
tlme-var'.aole analysis of stratified (2-dlmenslona1) water iodes.


    Ad.sorption/desorptlon  mechanisms   are  expressed  by  a  distribution
coefficient  and  a  transfer  rate  wnich  descrioes  the  rate  at  -*nicn
dissolved   and   particulate   contaminant'  concentrations   reach   their
equilibrium condition.   Unlike most Other models,  SERATRA 'uses  different
distribution  coefficients  for  adsorption  and  desorptlon  and  treats
adsorptlon/desorptlon mechanisms as not being fully reversible.


    SERATSA   requires   extensive   Input   data,    which   may   l^mlt   Us
applicability.   It  also  requires  rather  extensive computer time, In which
long-tern, continuous simulations can be expensive.
                                    11-49

-------
    The model  cannot be  applied  to estuary  systems  because longitudinal
diffusion  1s neglected  and  lateral  sediment concentrations are assumed to
be  uniform.   However,   the  model   does   handle   vertical   variations  of
longitudinal velocity to cause some  longitudinal dispersal of sediment.


    SF.RATRA  requires an  exterior  hydrodynamlc  model  to  supply required
hydrodynamic data.    EXPLORE  applications  require adjustment  of several
parameters for use with SERATRA.
node 1
    SERATRA  has   been   applied   under  both  steady  and  unsteady  flow
conditions and  has  also  undergone field applications with calibration and
verification  data  (Qnlshl  et al.  1982).   Also,  SERATRA has  been field
tested as an  Integral  component  of the Chemical Migration Disk Assessment
(C."RA) »ethodology  (Onlshl et al.  1981).


    under  steady .flow  conditions,  SCRATRA was  applied  to  t.ie  ColumoU
River  1n Washington  and  the Clinch  River In  Tennessee (Onlsnl  et  al.
1982).    The   Columbia   River  application  simulated   trie   transport  of
sediments,  radioactive  65Zn.  and  a  heavy  metal.    The   CUncn  River
application   simulated    instantaneous    and.   continuous    releases   of
radioactive  13?Cs and 90Sr.  Reasonably  good  agreement between predicted
and measured results was obtained  1n both  applications.
    Under  unsteady  flow  conditions,  SERATRA  was  applied  to  two
streams  with   rapidly  changing   flows   (Onlshl   et  al.  1982).   This
application  simulated  migration  and  fate  of  a  pesticide   and   stream
sediments.   No measured  field  data are  available  for  comparison  of  the
model's predicted  results.-


    The calibration  and verification application of  SCRATRA simulated  the
transport  of  sediment  and  four  radlonuclldes  in  the  Cattaraugus  Creek
watershed  In  New  York  (Onlshl  et  al.  1982).   Although  there were some
discrepancies  between  predicted  and  measured  values,  considering  the
complexity  of  the  mode Ling  system  and  field data  accuracy,  agreement
between predicted  and measured  results were judged  to be reasonable.


    SERATRA.   as   part  of  CMRA,  was  applied  to  the   Four   Nile  Creek
watershed  in Iowa for  a three-year field  study (Onlshl and wise  1V82&).
Migration  and  fate  of  a  herbicide were  simulated  In  this application.
"lir nation   r«;»lt.  revealed   a   strong   seasonal   pattern  of  herbicide
transport.
                                     11-50

-------
Resource
    The  computer  program  for  SERATRA is written  1n  FORTRAN preprocessor
language,  FiECS.    A  standard   FORTRAN   IV  version  of  SERATRA  ts  also
available. SERATRA  can  be  Implemented in a  batch mode on VAX or POP 11/70
computers..    Execution   time  and  run   costs   vary,   depending  on  the
characteristics  of  the  system   to   be  modeled.   One  cost estimate  is
JO. 0088 per  time  step  per  segment.   As  part of the. CMRA Methodology, four
man-months were estimated  to be  required For  the  SERATRA component to be
Implemented  at a  cost  of approximately  J100  to  1200  per run per one year
simulation (for all  four of the  CNRA components}.   This time estimate 's
based on  the following  assumptions:  (i) all data necessary  te ueet tie
input  requirements  are  available;  and  (2)  qualified  personnel  are
available to  Implement the model.
User Suppor;
    Copies  of  the  user's  manual  are  available  *pom  0*0  Pyb' 'cat -on$ .
Center  for  Environmental  Research  Information.  USEPA.  Cine '.^nat ' .  Cn'o
45268 (telepnone 513/684/7562; ask for publication EPA-&OC/3-3Z-CS5 »
    User assistance can oe obtained by contactlnq;

    Robert Ambrose
    USEPA
    EPA Athens Environmental Research Laboratory
    Center for Hater Quality Modeling
    Athens. Georgia  30613
    (404) 546-3546
    Model Information can be obtained by contacting:

    rasuo Onlshl
    Sattelle Pacific Northwest Laboratories
    P.O. Box 999
    Rlchland. Washington  99352
    (509) 376-8302 .

    The  SERATRA  model  Is  operational, has  been implemented  In  selected
applications, and is available to the public.
                                  11-51

-------
flgference*
       r, wise SE.  1982a.  User's Manual for tne Instream
Sediment-Contaminant   Transport   Model   SERATRA.     EPA   600/3-82-055.
Environmental  Researcn Laboratory.  Office  of  Research  ana  Development.
USEPA, Athens, Georgia.
Onishl *.  Brown  SK, Olsen  A«.  Parknurst MA.   1981.   "Chemical
and  8Uk  Assessment  Methodology."   Proceedings   of   the  Conference
Environmental Engineering.  Proc. Paper, ap. 'bS-HJ.
OnUni Y. rafiusakt SB, tOncaiC CT.  1982.  'Performance Testing of tne
Sediment-Contaminant  Transport   *odel,   SESAT!?A.*    P^oceedin^t   s^  t^e
                           > to H
PO. 623-U2.
          wise SE.  19825.  mathematical "ode'. StaATaA. for
          ontaminant Transport  in  9^ve"$  ane  : :s _AppM;at ^sn _;s
           "i  rpur  "t^e  anc  no 1 f  Creecs  ^  !ova.   £3ft  sCC-3 32-0*5.
                                   U-52

-------
       Transient' One-Dimensional Degradation and Migration Model
    The    Transient    One-D1mens1onal
Degradation    and    Migration    Model
(TOQAM)  1s  a  time-varying,  1-d1men-
slonal  (longitudinal)  transport  model
developed     by     BatteHe    Pacific
Northwest   Laboratories  . 1n  Rlchland,
Washington.   TODAM  Includes  the  long-
itudinal   dispersion    term  and   can
handle       river/stream       systems
estuaries,  and dry  bed conditions.   The  model
where vertical stratification is not a concern.
                                            CapsuJe  Summary:   TCOA/1
                                                          l-dlater.slar.dj.
                                                         snt  cra/isport
Tifl»-vary 1.19.
Complex sedi.n
apt* 111 ties.
           vf second-order kl.ieties
      a/Jd escu-ary ,ay3terns
                                                  Is  suitable  for  many
    TOOAM 1$ a  modified  and simplified version  of  tne Z-dimens^ona'
model,  Sediment  Contaminant  Transport  Model   (S£SA'RA).   a'sc  '
Sattelle.   700AM  is  composed   of  the  following three  sucmooe's  camo
descrioe sediment-contaminant interaction anc migration:

    o  Sediment transport

    o  Dissolved contaminant transport

    o  Soroed contaminant (contaminants adsoraed Dy sed'ment) transport
                                                                         nec
These  submodels  solve an  advectlon-dlf fusion equation using  a  finite
solution  technique  with decay  and sink/source terms  with  appropriate
and Boundary conditions.
    "he  sediment  transport  submodel  simulates  transport,  deposition,  and
erosion  of  three sediment  size  fractions  (or  sediment  types)  of cohesive ana
none ones Ive  sediments.   The dissolved contaminant  transport submodel  includes
mechanisms of  contaminant  adsorptlon/desorption.  as  well as radlonucllde decay
and contaminant  degradation resulting from  hydrolysis,  oxidation, photolysis.
volatilization,   and   biological   activity.    The   particulate  contaminant
transport   submodel   simulates   transport,   deposition,   and   erosion    of
contaminants associated with each sediment size fraction.
    TO DAM  includes  the  mechanisms  of  advectlon  and  dlffuslon/dispersiun
sorbed contaminants; adsorption (uptake) of dissolved contaminants by
                                                                             of
                                     11-53

-------
sediments  or  desorption  from  sediments;  radlonucllde decay;  deposition  of
sorped contaminants  to  the river  bed  or resuspension from fe  river  bed;  and
contributions of sorped  contaminants from point  and  non-point  sources  into  tne
system.   TCOAM  also computes changes  In river  bed  conditions,  including  bed
elevation,  sediment  size  distribution,  and  sorbed  contaminant. distribution
within the bed.


    An  exterior  hydrodynamlc  model,   such   as   EXPLORE,  or  tne  Distributed
K'nematic wave Model for Channel  Flows  (OKWAV),  is  required  to provide channel
f'ow. cross-sectional area,  depth, jhear stress, and wetted  perimeter for  y$e
sy TQOA«.   deprogramming ts  not  required  if either EXPLORE or  OKWAV  is used.
£X?'.3RE  appHcations  require  some  reformatting  and  recalculation   of  input
parameters;  «nereas. OKWAV  applications can be directly  integrated  far
    OKWAV.  also   developed   by  Battelle.   1s   an   unsteady,   l-dimensiona*.
secono-or3er.    explicit.   finite-difference  'model   wnich   simulates   the
•wC'odynamics   '•"  dendritic  river systems to  obtain  time  varying  dlstnsgt'ons
or' 3es:.i  and  ve'odty in a  channel.   The model, which can  be  easily combined
«?tn overland  f'ow models, routes  flows  through  arbitrarily  shaped channels  *n
•men  the channel  reach is  divided  into  sections   bounded  by  points  ca-ed
"nodes",  ~'ow  routing  is  performed frsm node to  node  by a  marcntng solution.
**e ecuations  of  motion  with the kinematic wave  approximation  are numerically
           via  a   modified   version   of   the    cax-wendroff.   second-order,
           erence  scheme.    Numerical   stability  1s based   upon  the  Courant
              'oint  inflow  or continuous  (or  both)  lateral  inflow is
'i  -n^.i so'nt  inflow  occurs  at  nodes  or  continuous   lateral  inflow
set'-eei  icdes.   associated   with   each  channel   section   is   Us  own  seepage
*e'oc*ty.   A   cross-sectional  area  versus  discharge  relationship  exists  for
eacn  segment  between  nodes.   Based  on  this  relationship,  other characteristics
parameters  (flow  depth,  wetted  perimeter,  and  so forth) of each  section can
also   be  obtained.    Each   channel   section   retains   Its-  own   individual
characteristics,  which  include a roughness parameter,  lateral  or point  inflow
rates,  slope,  seepage velocity, and natural cross-sectional shape.
"n'te-d***
    The EXPLORE model  Is discussed  in  further detail  in  the FETRA  summary.
    The  following  Items are  Input data  requirements of  TOO AM:

    t  Channel  geometry
                                      11-54

-------
•  Flow characteristic*
   -  Depth and velocity distributions
t  Sediment characteristics
   -  Sediment size distribution
   -  Density
   -  Critical shear stresses and credibility cae'^'c'ent
      sediment
•  Contaminant characteristics
   -  Distribution coefficients
   -  Transfer rates
   -  Decay and degradation' rates or associated o
   -  Initial conditions
   -  Boundary candU'ons
                                                                  ccnes've
CugpuC
    .with the input data described above, TODAM provides tne 'o'.'s^'^q autsut.
    1.  Sediment, simulation and  distributions  of  total sed'.ment. sec'ment s':e
        fractions, and changes in bed elevation.
    2.  Contaminant  simulation  and  distributions  of  dissolved  contaminants.
        and concentrations adsorbed by eacn sediment  size and witmn trie oed,
               Lioii saclona
    The major  strength of  the  TODAH  model  is that  H*e  FtTRA  and SE9ATRA,  't
has   very   sophisticated   sediment   resuspenslon  and   bed   load  predictive
capabilities.   Its  1 -dimensional  framework  makes TODAM more  tailored  to river
applications.  TODAM '$ kinetics are comprehensive second-order.
Also,  TOOAM  can handle  reversible flow and  dry bed conditions.   TODAM,  as a
simplified v,.-rsl-»n  of  S£RATRA. can  be substituted  for  SERATRA  in estuarlne
applications.
                                   11-55
                                                                                   (

-------
           requires  extensive input  data
used only  in 1-dlmen$1onal applications,
                                          and computer  time.
model can
»odfl
    TOOAM was  applied to *ortandad  and South  Mortandad  Canyons  in  New  «e«ica
to estimate  1n-stream flow,  sediment  transport,  and radionuclide. transport  in
intermittent  streams.   Transport  of  seven  substances  -as  simulated:   sand.
silt.  day.  d.tssolve-d  *^9  pu>  an.  at  JS£3»
    are  *>ot yet  avai'Uo'le for  puolication.   The  TGDAM mode'  is  in  ope'at'on.
    2een imo'emented  in  selected -appi icat ions.  and  «s  ava''aD'e to tr^e 5-3''c
         information on  this model  can  oe  obtained  oy  contacting:
    irasuo Onisni
    Battelle Pacific .Northwest  Laboratories
    P.O. Box 999
    Ricnland." Washington  99352
    (509) 376-8302
References

Onishi  Y.  Whelan  G,   and  Skaggs   RL.   1982.   OeveVooment  of  a  ••ui
^ad'ongclide  exposure  Assessment  Methodology  for  low-Level  waste  management.
PML-3370. BatteUe-Padfic Northwest Laboratory.  JMcnland.  Washington.
                                     11-56
                                                                    202-566-0556

-------
                                                                                  i
                 Hydrologual  Simulation Program-"OR'SAN -;HS?n
    The     Hydrological     Simulation
Program-fQRTSAN   (HSPF)   (Jonanson  et
al.   1980)   is   a   series   of   fully
integrated  computer  codes capable  of
simulating -atershed  hycrology  and the |
benaV.or  of  convent'onal  and  organic :
poi'utants  in  land surface  runoff and i
receiving   .aters.     Simulations   are '
per'arraeC;   on   a   time-varying,   1-
dmens'or.a" sas's and  can  ae  performed
far  streams  and  non-tidal rivers  and
*or  «e'l  .fl'xed.  non-stratl.fled  reser-
voirs.
                                                capsule Summary.   HSPF  '
                                                           l-di.inensisr.Al  ncdcl-
                                             oe
                                                       .•ncdul