United States
            Environmental Protection
            Agency
             Environmental Research
             Laboratory
             Athens GA30613
EPA-600/3-84-068
June 1984
            Research and Development
xvEPA
Leaching Evaluation of
Agricultural Chemicals
(LEACH) Handbook

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                                        EPA-600/3-84-068
                                        June  1984
      LEACHING EVALUATION OF AGRICULTURAL
          CHEMICALS  (LEACH) HANDBOOK

                      by

J.D. Dean, P.P. Jowise, and A.S. Donigian, Jr,
         Anderson-Nichols & Co., Inc.
             Palo Alto, CA  94303
             Contract No. 68-03-3116
                Project Officer

                 Lee A. Mulkey
 Technology Development and Applications Branch
       Environmental Research Laboratory
               Athens, GA  30613
       ENVIRONMENTAL RESEARCH LABORATORY
       OFFICE OF RESEARCH AND DEVELOPMENT
      U.S. ENVIRONMENTAL PROTECTION AGENCY
               ATHENS, GA  30613

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                           DISCLAIMER

The information in this document has been funded wholly or in
part by the United States Environmental Protection Agency under
Contract No. 68-03-3116 to Anderson-Nichols & Co., Inc.  It has
been subject to the Agency's peer and administrative review, and
it has been approved for publication as an EPA document.  Mention
of trade names or commercial products does not constitute endorse-
ment or recommendation for use by the U.S. Environmental Protec-
tion Agency.
                               11

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                           FOREWORD

As environmental controls become more costly to implement and
the penalties of judgment errors become more severe, environ-
mental quality management  requires  more efficient analytical
tools based on a greater knowledge of the environmental phenom-
ena to be managed.   As  part  of  this Laboratory's  research on
the occurrence, movement, transformation, impact,  and control
of environmental contaminants, the Technology Development and
Applications Branch develops management  or  engineering tools
to help pollution control officials achieve water quality goals
through watershed management.

As concern  about  potential contamination of  groundwater has
increased, efforts to  evaluate  the use  of  a pesticide  in  a
given agricultural  situation  have  become  more  important.
These evaluations to identify significant environmental  risk
have involved both model simulations and field or plot studies.
To augment  field  or  modeling studies,  a  screening technique
was developed by which agricultural chemicals can be evaluated
to determine whether they will move in significant quantities
past the crop  root  zone.  The procedure  accommodates  a  wide
range of basic  pesticide properties,  soils,  and  crops.   The
leaching evaluation technique is quickly  and  easily used and
requires minimal background  and  training on  the  part  of its
users.
                               William T. Donaldson
                               Acting Director
                               Environmental Research Laboratory
                               Athens, Georgia
                             111

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                           ABSTRACT
A methodology has  been  developed to assess  potential  pesti-
cide leaching from the  crop  root zones in major  (corn,  soy-
bean, wheat  and  cotton)  crop growing  areas  of the  United
States.  Use  of  the  Leaching  Evaluation  of  Agricultural
Chemicals (LEACH) methodology  provides  an indication  of  the
presence or absence  of  leaching  past the  rooting depth  and,
if such  leaching  is indicated,  its  severity.   LEACH  was
developed through the use  of  long term simulation  (i.e.,  25
years) of  annual pesticide leaching  time  series using  the
Pesticide Root  Zone   Model.    The  user  must  evaluate  key
parameters for a pesticide-site-crop-management  scenario  to
locate pesticide leaching cumulative frequency distributions.
Each scenario has  a  unique distribution associated with  it.
The distribution  functions  indicate  the  chance  that  the
annual quantity  of pesticide  leached past the crop  rooting
depth will exceed  a  given value.   The  distributions  can  be
used as an integral part  of a framework for decisions concern-
ing the use of the pesticide.

This report was  submi.tted  in fulfillment  of  Contract  No.  68-
03-3116 by Anderson-Nichols & Co., Inc., under the sponsorship
of the  U.S.   Environmental Protection  Agency.   The  report
covers a period  from June 1983 to  April  1984, and work  was
completed as of April 1984.
                              IV

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                           CONTENTS
                                                            Page
Foreword	 iii
Abstract	  iv
Figures	  vi
Tables	viii
Acknowledgments	  ix

1.   Introduction	 1
    1.1  Methodology	 2
    1.2  Scope	 2
    1.3  Required User Background, Training and Preparation. 4
    1. 4  Format of the Manual	 5
2.   Overview of the Methodology	 6
    2 .1  Methodology Development	 6
    2. 2  Assessment of Significant Leaching	 7
    2.3  Interpretation and Use of Results	 10
    2 . 4  Conclusions	 12
3.   Application Procedure for Assessment of Leaching
    Potential	 15
    3 .1  Introduction	 15
    3.2  Site Selection	 16
    3.3  Parameter Estimation	 20
    3 . 4  Frequency Distributions	 30
    3 . 5  Application Assumptions and Limitations	 33
4 .   Methodology Development	 38
    4 .1  Introduction	 38
    4.2  Model Selection	 38
    4.3  Sensitivity Analysis	 40
    4 . 4  Representative Site Selection	 50
    4 . 5  Frequency Distribution Production	 57
5 .   Example Applications	 59
    5.1  Example #1	 59
    5.2  Example #2	 63
6 .   References	 69
Appendices
    Appendix A:  Address Matrices and Cumulative
                 Frequency Curves	 72
    Appendix B:  Supplemental Chemical Data	 395
                               v

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                           FIGURES

                                                       Page

2.1   Schematic for Pesticide Leaching Evaluation	 9

2.2   Schematic of Agricultural Chemical Contamination
      of Ground Water Under a Scenario of Direct
      Recharge	„ . . 11

3.1   Schematic of Information Flow to Calculate
      Methodology Key Parameters	 . 16

3.2   Representative Sites #1 - #6 Associated with
      Wheat Farms	 . 18

3.3   Representative Sites #7 - #13 Associated with
      Corn Farms	 . 18

3.4   Representative Sites #14 - #17 Associated with
      Soybean Farms	 19

3.5   Representative Sites #18 - #19 Associated with
      Cotton Farms	 . 19

3.6   Percentage Nitrogen  (N) in the Surface Foot of
      Soil	 . 23

3.7   Relationship Between the Class Name of a Soil
      and  Its Particle Size Distribution	 . 24

3.8   Analysis of Bulk Density and Field Capacity
      Relationships	 . 26

3.9a  Example Address Matrix	 . 32

3. 9b  Example Frequency Curve Plot	 . 32

3.10  Comparison of Effects of Single and Multiple
      Pesticide Applications for Various Decay Rates....  35

4.1   Effect of Kd and  ps on Quantity of Pesticide
      Leached	• •  ^ 5

4.2   Effect of SCS Curve Number on Annual  Pesticide
      Leached  (Kd =  . 06)	 .  46

                            vi

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4.3   Effect of SCS Curve Number on Annual Pesticide
      Leached (Kd = 0.6)	  4V

4.4   Effect of SCS Curve Number on Annual Pesticide
      Leached (Kd = 1.5)	  47

4.5   Effect of Kd on Time to Reach Steady-State Mean
      Annual Output	  4 °

4.6   Effect of Half-Life on Time to Reach Steady-State
      Mean Annual Output	  4 9

4.7   Major Wheat Farming Regions of the U.S	  52

4.8   Major Corn Farming Regions of the U.S	  52

4.9   Major Soybean Farming Regions of the U.S	  53

4.10  Major Cotton Farming Regions of the U.S	  53

4.11  Average Annual Distribution of Precipitation	  54

4.12  Generalized Hydrologic Soil Groups for  the U.S....  55

4.13  Major Regions of  Insecticide Usage in  the U.S	  .56

4.14  Major Regions of  Herbicide Usage in the U.S	  56

5.1   Frequency Curves  for Example tl	  62
                            VII

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                            TABLES

                                                            Page

3.1  Meteorologic and Soils Characteristics of Represen-
     tative Sites	 17
3.2  Regression Equations for the Estimation of Koc	 22
3.3  Estimated Soil Bulk Density for Five Soil Types	 25
3.4  Soil Hydrologic Properties by Soil Texture	 27
3.5  Hydrologic Soil Classifications	 28
3.6  Runoff Curve Numbers for Hydrologic Soil-Cover
     Complexes	 29
4.1  Parameters, and Their Ranges, Chosen for Sensitivity
     Tests	 42
4.2  Soils, Crop, and Meteorologic Characteristics of
     Representative Sites	 58
B-l  Summary of Adsorption Partition Coefficients Compiled
     from Published Literature for Several Pesticides and
     Related Organic Compounds	 395
B-2  Measured Values of Koc for Selected Chemicals	 397
B-3  Summary of Octanol-Water Partition Coefficients for
     Pesticides Compiled from Literature--!	 398
B-4  Summary of Octanol-Water Partition Coefficients for
     Pesticides Compiled from the Literature—II	 399
B-5  Values of Ks for Dissipation of Pesticides from Soil
     Surfaces	 401
B-6  Values of Ks for Dissipation of Pesticides in Soil	 403
                              Vlll

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                        ACKNOWLEGMENT S
The  authors would like to gratefully acknowledge the support,
technical  advice and intuition of Mr. Lee A. Mulkey, the  EPA
Project  Officer,  and  Mr.  Robert F. Carsel  of  the  Athens
Environmental  Research  Laboratory.   In  addition,   various
personnel  of  the  U.S. Soil  Conservation  Service  provided
helpful  discussions  and  information.   The  manuscript  was
typed  by Ms. Mary Maffei and Ms. Meredith Mason and  graphics
were  adeptly  rendered  by Ms. Lisa Jowise and  Ms.  Virginia
Rombach.
                              IX

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                          SECTION I
                         INTRODUCTION
The  growing  threat to ground-water supplies  from  chemicals
introduced  into  the  environment  is an  issue  of  national
concern.   This  concern is reflected by the current  proposed
guidelines  for  the  registration of  pesticides  (U.S.  EPA,
1978),  which requires that a pesticide's mobility in soils be
established.   The potential for a pesticide to leach  through
the  soil  profile  can indeed be measured  in  laboratory  or
column  studies.   In general, however, such studies can  only
establish  the tendency for a compound to leach relative to  a
reference   compound/soil   combination.    Determination   of
whether  the compound will actually leach significantly  under
field conditions requires quite a different approach.

The   movement   of  a  pesticide  through  the  soil  in   an
agricultural  application  is a function of soil physical  and
chemical  properties,  pesticide properties,  local  watershed
and  meteorological conditions, and, additionally, soil, water
and  pesticide  management variables.  Field or  plot  studies
unfortunately  require  a  great deal of time and  expense  to
answer  the important questions concerning pesticide mobility.
The key question is:

          •  Does the use of a pesticide in a given
             agricultural situation constitute a
             significant environmental risk with
             regard to ground-water contamination?

Fortunately,  computer  models which simulate the movement  of
pesticide  in  soils  have been developed and can  be  applied
with  less  expense and in far less time than field  programs.
Even  so, with the extremely large number of chemical products
and  multiple  soils, watershed and  management  combinations,
the   application   of  models  in  every  case  may  not   be
justifiable.

In  many  cases, detailed analyses can be  eliminated  through
the  use of prior knowledge of the system involved.  In  other
words,  the  pesticide/soil/crop/management  variable  combin-
ation  could  be screened to determine if leaching to  ground-

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water  occurs, and, if it does,  to determine its frequency and
severity.   Such  information would be extremely important  in
eliminating  unnecessary  use  of  resources  for  laboratory,
field  and modeling studies.  To be useful,  the procedure must
be quickly and easily performed.
1.1  METHODOLOGY
This handbook presents the Leaching  Evaluation of Agricultural
Chemicals  (LEACH) methodology by which new agricultural chemi-
cals or chemicals being considered for alternative  uses can be
readily screened to determine whether they will  move in signi-
ficant quantities  past the crop  root zone.   If  significant
leaching is predicted,  the methodology provides a measure of
its frequency and severity and/or indicates the necessity for
more detailed analyses.  LEACH integrates the effects of cli-
mate, soil  properties, pesticide  properties  and  management
practices  on  pesticide leaching.   It  is quickly  and easily
used and requires minimal  background and training on the part
of its users.
1.2  SCOPE
This project  was  designed  to  identify  pesticide/soil/crop/
management combinations  conducive  to pesticide leachate pro-
duction and  quantify  their  effects on a  national  scale.  At
this level   it  is  entirely  possible,  even  probable,  that
certain critical  areas  which may  be identifiable on  a more
localized scale have not been considered.

Because of the  diversity  of pesticide,  soils,  crop,  climatic
and managerial  scenarios possible,   some  combinations  were
eliminated at the outset of the project in order to develop a
feasible project scope.  To delineate the scope with greatest
facility, the  following  sections  discuss  the  project scope
limitations  in  each of  our categories:   pesticides,  soils,
management variables and crops.
1.2.1  Pesticide Properties

The methodology is  limited  to those pesticides which are hy-
drophobically adsorbed to soil  materials  according to linear
and coincident  adsorption/desorption  isotherms.   Pesticides
which undergo  significant  (  >95%) deprotonation (ionization)
at normal soil pH  levels should not be screened.  This percent-
age can  be  easily  determined  (Mills,  et  al.,  1982).   The
methods are  designed  only  for a  single pesticide  in a soil/

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water (two)  phase  system.   They   are   not  appropriate  to
pesticides dissolved  in  more than  one solvent  (e.g.,  water
and toluene).   Also,   interactive  effects  (if   such  effects
exist) of more than one pesticide are not  considered.  Neither
are the  methods  generally  applicable to  pesticides  which
degrade to toxic  daughter products  with  different decay and
sorption characteristics from the parent.


1.2.2  Soil  Properties

The   soils   are   assumed  to  be  relatively homogeneous   in  the
horizontal   and   vertical  directions.  Soils  should  not  have
clay  or  plow  pans,  or  any low permeability layers   in  the
profile  which would  cause perched saturated  conditions and/or
lateral  flow.    Soils should be well  dispersed, well   drained
and   not    subject   to   fracturing,   cracking,  swelling    or
shrinking.


1.2.3  Management Practices

The   effect   of management practices  can  only be evaluated   to
the   extent  that  appropriate model parameter  adjustments  that
describe   their   impacts    can  be   estimated.    In  this
methodology,  the SCS runoff curve number technique is  used  to
link  management   practices   to  infiltration,  which   is  the
driving  force for pesticide leaching.  Therefore,  management
scenarios  capable of  being treated  are those which   can   be
evaluated  by this technique.   These  are, in  general,   limited
to  agricultural   land  use,  tillage   techniques   and   residue
management.

Effects   of non-structural management  practices  such    as
pesticide  formulation, application  mode  and  timing, or foliar
versus  soil application  are not directly  considered     (See
Section 3.5,  Application  Assumptions  and  Limitations).


1.2.4  Crops

The   crops specifically considered in  this  handbook are corn,
soybeans,  wheat,  and  cotton.   Thus,  the  geographic areas
covered  are where   these   crops  are grown in  significant
quantities     (See Section 4.4).  Use  of  this methodology  for
other   crops  outside   these   geographical  areas   is   not
recommended.   Because  the   effects  of   irrigation  were  not
considered   in  developing the  methodology, it does  not  take
into  account this agricultural  practice.  Irrigation  may   or
may   not  result in greater   quantities  being  transported
through  the soil profile.  Artificially drained  land  may   be
considered   as  long   as   drains  are   placed  below  the  crop

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rooting  depth and rooting depths correspond to those used  in
model simulations (See Section 3.5.1).

Considering  the  above  caveats it should  be  apparent  that
there  has been considerable idealization of the real  systems
involved  in order to provide a tractable scope.  The  serious
user  should be aware of the above and, in addition, carefully
consider  the  assumptions  and  limitations  at  the  end  of
Chapter  3.  Even then, the results should be interpreted with
caution   and   results  indicating   marginal  or   ambiguous
environmental risks should be subjected to further  analysis.
1.3  REQUIRED USER BACKGROUND, TRAINING AND PREPARATION
The  mechanics  of using this handbook are quite simple;   that
is,  it is easy to generate the parameters necessary  to  locate
the  cumulative frequency distributions  for a given   scenario.
The  only practical requirement is that  the user be acquainted
with   the  Soil  Conservation  Service  Runoff  Curve   Number
technique.   Even  here  the user is only asked to  provide   a
curve  number,  not  to carry the calculations  through   to   a
determination  of  runoff depth.  The estimation of   only one
parameter   requires  the  use  of  a  hand  calculator;   the
retardation  factor,   'R'.  A general knowledge of  hydrology,
soil  physics  and theory of contaminant migration  in   porous
media is helpful.

Although    the   mechanical   use   of   the   handboo-k    is
straight-forward,  effective  application of  the  methodology
requires  good  judgment  and common sense by the  user.   The
information  inputs  to obtain actual values  for  methodology
parameters     (e.g.,   pesticide   decay  rates,     partition
coefficients)   are   reported   under   a  wide   variety   of
non-standard   conditions.  Exact values  are therefore  subject
to   interpretation.  Likewise, methodology outputs are  subject
to   the  assumptions and limitations of  both  its  development
and  application.   The user is urged to read and study  these
caveats  and   keep  them in mind when making  decisions  which
utilize  methodology results.  Reading of the entire  handbook
is   recommended   as  a  preparation  for  methodology  use  and
especially  the   example  applications   in  Section   5.    Most
information  needed to perform analyses  with  this  methodology
are  contained in  this handbook.  An exception  is   SCS  soil
infiltration   information.  In order to  use the   curve   number
technique,  the   Hydrologic Group must be known  for  the  soils
under   consideration.   Most soils  in the U.S.  are   classified
by   the  Soil  Conservation  Service  by their   potential  to
produce  runoff.  These classes  (A,  B, C, or  D)  are  listed  by
the  soil  name   in  the  SCS  National  Engineering  Handbook
 (U.S.D.A.,   1971),   in  County  Soil  Surveys,  or  in   Carsel  et
al.,  (1984).

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1.4  FORMAT OF THE MANUAL
This  handbook,  is divided into five sections,  inclusive  of
this  introduction.   Section two (2) contains an overview  of
the  methodology  for determining the potential for  pesticide
leaching.    Section   three  (3)  contains   a   step-by-step
discussion   of   the  application  procedure  and   parameter
estimation  techniques  used to complete an assessment.   This
section  concludes  with  specific  inherent  assumptions  and
limitations  of the  methodology.  Section four (4)  describes
the  development  of this methodology and the  specific  steps
and    assumptions    taken   to   generate   the    frequency
distributions.    Section   five  (5)  contains  two   example
applications.    This  is  followed  by  references  and   two
appendices  as  noted  in  the Table of  Contents.   The  most
important,   Appendix   A,  contains  address   matrices   and
cumulative   frequency  distributions  for  determination   of
pesticide leaching probabilities.

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                          SECTION 2
                 OVERVIEW OF THE METHODOLOGY
The  purpose of this methodology is to provide the user with a
means   of   evaluating  the  probability   of    "significant
leaching"   of   a  pesticide  applied  in   an   agricultural
situation.   The  purpose of this section is to give  a  brief
overview  of how this is accomplished.  First, however, it  is
of  great  importance to provide a definition of  "significant
leaching" as used here.

In  this methodology, "significant leaching" is considered  to
be  any  movement   (>0.05% of the  annually  applied  chemical
mass)  of non-degraded pesticide (i.e., parent compound)  pas"t
the  rooting  depth of the crop.     This definition  is  both
necessary  and convenient in terms of methodology  development
because  it  allows  the methods to  ignore  the  complicating
effects  of  local ground-water conditions on the fate of  the
pesticide.   In  order  to perform a screening  assessment  of
this  type  on a national scale, such local effects could  not
be  feasibly  considered.  The rooting depth is  a  convenient
datum  because  many pesticide attenuation processes, such  as
plant  uptake,  cease  below this point and  others,  such  as
microbial   degradation  and  soil  adsorption  may  be   less
prominent.   Therefore, below this depth, the pesticide can be
considered,  in  a  truer sense, more "conservative"  than  at
some   merely  arbitrary  datum  in  the  soil  profile.   The
adoption  of this definition is inconvenient, however, to  the
user.   The results alone cannot and should not be interpreted
as  an assessment of exposure to biological species or as data
to  compare  to  water quality criteria because the  fate  and
transportprocesses in ground and/or adjacent surface  waters
have not been considered.
2.1  METHODOLOGY DEVELOPMENT
The  approach was to use a pesticide  leaching model  that  could
integrate  pesticide, climate, crop and  soil  characteristics,
and  management practices in order to generate  time  series   of
pesticide    leached   below  the  crop   root  zone.     Several

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candidate  models  were investigated and PRZM  (the  Pesticide
Root   Zone   Model),  developed  by  the  U.S.  EPA,   Athens
Environmental  Research  Laboratory, was chosen.   Sensitivity
analyses  were  performed  using  the model  to  evaluate  and
quantify  the  impact  of  parameters most  important  to  the
leaching  process.   Then,  using climatic, crop,  soils,  and
pesticide  usage  information, nineteen  (19) sites across  the
U.S.  were  delineated which are relatively  homogeneous  with
respect   to  these  characteristics.    Twenty-five  years  of
meteorologic   data  were  then  used  to  generate  pesticide
leaching  sequences  at important levels  of the key   parameters
as  determined  by model  sensitivity analyses.   This  resulted
in  over  47,000 model simulation years.   In order to  display
this  information  in  a  usable fashion, the   annual  leaching
time   series   were  organized  into    cumulative   frequency
distributions.   With 1900 potential curves, a matrix  indexing
system  was  derived  to  facilitate  their  organization  and
access.   These matrices  not only serve  as tools to  locate the
frequency  curves but also as screening  tools  for  determining
cutoff   values   of  the  key  parameters,  below   which   no
significant  leaching  exists.  An  in-depth discussion of  the
methodology  development  and  important  assumptions  can  be
found in Section 4.
2.2  ASSESSMENT OF SIGNIFICANT LEACHING
The  procedure  for assessing  significant  pesticide  leaching  is
shown   schematically   in   Figure  2.1.   The  first   step   is   to
select  a  crop  and  site  targeted  for  use   of  the  pesticide
(Section   3.3).   There  are  nineteen   crop/site   combinations
possible.   Once  this has been done, data  can  be  gathered   to
evaluate   the    three  key   parameters,  essential  to   the
methodology   (Section 3.4);   the pesticide  decay  rate,   the
retardation   factor for the pesticide/soil  combination  and  the
runoff  curve  number for  the soil/crop  management  scenario.
Eachsitenumber(T through 19) corresponds to  a  matrix   in
Appendix   A   (Al  through  A19).   These  matrices  are used   to
determine  if  significant leaching occurs  and  to  locate   the
cumulative   frequency  curves which   indicate  severity    as
follows.

The  pesticide  decay rate and retardation  factor are used   to
enter   the matrix. The matrix entries are figure  numbers which
correspond to  figures located in Appendix  A.   If there is  no
figure   number  corresponding   to the  nearest  values    of
retardation   factor   and decay  rate,  then   there  is    no
significant   leaching associated  with the use of  the pesticide
in  this circumstance.  Quantitatively, this means that there
was  no  year  during the  simulation period in  which  greater
than  0.05 percent of the annually applied pesticide  leached
below the  crop  rooting depth.

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If  the  retardation  factor and decay rate  fall  within  the
portion  of the matrix containing figure number entries,  then
the  user  may  proceed to find the  corresponding  figure  or
figures.    Each  of  these  figures  contains  a  family   of
cumulative  frequency   distributions.   Each member  of  this
family  is  associated with a runoff curve number.   The  user
selects  the curve having a runoff curve number closest to the
estimated  value for the site/scenario conditions.  Thus,  the
user  has  located a frequency distribution which  corresponds
to   a  region  of  the  United  States  with  a    crop/soil/
pesticide/management   scenario  described  by  the   computed
values  of pesticide decay rate, retardation factor and runoff
curve   number.    These  figures  have  on  the  x-axis   the
percentage  of  annually  applied pesticide leached  peist  the
crop  rooting depth, and on the y-axis, the percentage of time
that  the value on the x-axis will be exceeded.   (See Sections
2.3 and 3.4).

These   cumulative  frequency  distributions,  which  will  be
referred  to  as CDFs (cumulative density functions),  can  be
used in subsequent analyses, as discussed in Section 2.3.


2.2.1  Sensitivity Analysis

It  is  likely  that there will be enough uncertainty  in  the
input  information  that single values of the  key  parameters
derived  from  this  information  may  seem  inadequate.   For
instance,   it  is  possible  that  the  adsorption  partition
coefficient  for the compound has reported or  computed  values
which  range  from  1 to 6 giving values  of   the  retardation
factor  from  approximately 7 to 37, although  the most  likely
value  of retardation might be 10.  It is informative to  know
how  the methodology results change as the retardation  factor
goes  from  7  to 10 to 37.  If the  frequency curves- change
greatly  over  these retardation values or the result  changes
from  a  significant leaching to  a no leaching situation,  or
vice   versa,   then  the  user  would  naturally  have    less
confidence  in  the  result  (arising from the  uncertainty  in
this   parameter)  than  if changing   the  retardation  factor
produces  little  effect.   Such sensitivity analysis  can  be
performed  using  any or all of the three key  parameters.   In
general  the  user will find that sensitivity  to  runoff   curve
number  will decrease as decay rate increases  and  retardation
increases.    Sensitivity  to  decay   rate  will   increase  as
retardation    decreases    and   curve   number     decreases.
Sensitivity  to retardation factor will increase  as  decay rate
and  curve  number decrease.  Example  application number  two
 (Section   5)  contains  an  example   of  such a sensitivity
analysis.

-------
     c
     o
     5
     (0
     h»
     o
     M
     C
     O
    (0
                    Select site,
                  crop, pesticide
                        i
                    Gather data
                     Estimate
                  key parameters
                        i
     Find
address  matrix
    requenc
  distribution
    available
                       Find
                 frequency curves
                     Pesticide
                potentially unsafe
   No
significant
 leaching
                     Quantity
                  leached unac-
                    ceptable ?
                            Pesticide
                              safe
Figure  2.1  Schematic  for Pesticide  Leaching Evaluation

-------
2.3  INTERPRETATION AND USE OF RESULTS
The  methodology  gives  the user  very  specific  information
about  the  leaching  of a pesticide under a  certain  climate
/soil/crop/management  situation.  First, if the  retardation/
decay  rate  combination for a given site falls in  the  blank
lower  right  hand  corner  of the  address  matrix,  then  no
significant  leaching  (<0.05% of annually  applied  quantity)
was   observed   in  the  25  year  simulation   period.    If
sensitivity  iterations give combinations which all fall  into
this  region, then it is a good assumption that no significant
leaching  would  occur from the use of the compound.  It  must
be  remembered, however that pesticide toxicity plays a  major
role  in  determining  if  the presence of a  given  level  of
pesticide  in  water  or soil is safe or  unsafe.   The  0.05%
level  was somewhat arbitarily chosen but probably  represents
a  level  that  is safe given the toxicity of  most  currently
available  pesticides.  Levels of this magnitude in  pesticide
runoff studies are generally considered to be safe.

For  borderline  cases in which sensitivity analyses  indicate
some  significant  leaching situations or for cases  in  which
large  amounts  of  pesticide  would appear  to  be  leaching,
subsequent  "post screening" analyses are warranted.  The CDFs
in Appendix A will be useful in such cases.

The  CDFs  give an estimate of the probability that a  certain
annual  pesticide  loading  rate will result  below  the  crop
rooting  depth.   The next logical step in determining if  the
use  of  the pesticide constitutes a substantial risk  to  the
environment is to develop concentrations in ground water.

To  arrive  at  these  concentrations the  mass  of  pesticide
leached  must  be mixed in some volume of water.  This can  be
done  fairly  simply  but  there are  several  cautions  worth
mentioning  in  this  regard  which are  brought  out  in  the
following discussion, and are underscored.

A  simplified scenario of pesticide loading to ground water is
shown  in  Figure  2.2.  The source area  discharges  leachate
directly  to the aquifer in this case.  Notice that the bottom
of  the  root  zone  does not necessarily  coincide  with  the
ground-water table.

A  concentration  for  our purposes is defined as   a  mass  of
chemical  in  a volume of water.  The CDFs which  result  from
application   of  this  methodology  give  the  mass  loadings
exiting  the root zone a certain percentage of the  time for   a
unit  portion  of  the source area.  Unless  the  ground-water
table  is  very  close  to the bottom of the  root  zone,  the
quantity  of  leachate reaching ground water may be  different

                               10

-------
                       BOTTOM OF ROOT ZONE
                                        ,
                                       -»»*»!? ZONE
                                                  /
                                                 /
                                                            /
                                                          /
 Figure  2.2
Schematic of agricultural chemical contamination
of groundwater under a scenario of direct recharge,
from  that  exiting the root zone due to decay.  Of  course  a
"worst  case"  scenario  would assume that  no  decay  occurs;
Jience,  the  bottom  of  the foot zone  loadings  would  equal
ground-water  loadings  under "steady-state" conditions.   The
chemical  will  then  be mixed in ground water  to  a  certain
depth  (the mixing depth).  By knowing the area of the  source
(assuming  that this area and the contact area of ground water
by  leachate are equivalent) and the mixing depth, a volume of
contaminated   ground  water  can  be  determined  immediately
beneath  the source  and a concentration can be obtained.   An
inherent  assumption  in  this approach is that  the  recharge
volume  from  the  source area is negligible compared  to  the
mixing  volume.   This contaminated water will be advected  in
the  direction  of  ground-water flow.  Normally,  the  mixing
depth  becomes greater in this direction because of  diffusion
and  dispersion  of the chemical.  The plume also will  spread
horizontally.   Due to this dispersion, decay of chemical  and
possible  additional recharge the concentrations will decrease
as  the  chemical  moves downgradient in  the  aquifer.
concentrations
in the aquifer
    depend   crucially  at  where  and  when  one
Further   complications  arise  when  confining  layers
between the root zone and the ground-water aquifer.
 Thus
looks
                                             occur
This  type  of' analysis can only be done by  knowing  specific
localized  information,  such  as depth to  ground  water  and

                              11

-------
transport   and   fate   characteristics   in   the   aquifer.
Concentrations  can  be calculated at the screening  level  by
using  simplified  techniques e.g., Donigian, et al.,   (1983);
Brown   (1983)f or  at  a  higher  level  by  using  numerical
ground-water   simulation  models of which there are  many.   In
either  case,  the CDF is used as a  "loading  function,"  and
the  ground-water  model  as a transfer function  to generate
frequency  distributions  of concentrations at  various  times
and points in  the aquifer.

-Notice  also   that in the preceeding discussion it was   stated
that   the  CDFs  give  an  estimate  of  pesticide   leaching
probability.   Only  a  single 25 year  historical   sample  of
meteorological  data was used to generate these estimates.  To
get  a  better estimate, many 25 year sequences might be run.
This  would  result  in a "distribution about the   CDFs" from
which  confidence  limits  could be estimated.  This has  not
been  done  because of the obvious time and expense  required.
Another  way   of  "improving" this estimate would   be   to  fit
probability  density functions (e.g., log normal or  gamma)  to
each  sample   density function   This would result  in   "tails"
to  the  right on each CDF instead of the oblique   angles  at
which  some  curves  intersect the x-axis on  the   plots  (See
Appendix  A).   Because no one PDF could be found to fit  all
the  samples and because of the  small sample  size  (25   points)
this  was  not done.  Tail probabilites based on such a  small
sample  might  be grossly misleading.  Therefore  the   samples
were  simply fit with 4th-order  polynomials and the resulting
curves  appear in Appendix A.  The percent of time  that annual
pesticide  leaching  exceeds  a  given  level   should   not  be
strictly interpreted as a probability.
 2.4  CONCLUSIONS
 The   production  and  subsequent  use  of  a  methodology  to
 establish  the leaching potential of pesticides applied  under
 different  soil/crop/management scenarios has brought out some
 important  issues concerning the understanding of  contaminant
 migration in soils.

      •  First, through the use of model sensitivity
         analysis it has been shown that three parameters
         have major impact upon contaminant movement.
         These are the pesticide decay rate,  the
         pesticide/soil retardation factor and the
         infiltrated quantity of water through the
         profile.  Because the retardation factor and decay
         rate can vary so widely (over several orders of
         magnitude) they are the most important parameters.
                               12

-------
In general, it is shown that at values of
retardation factor greater than 50 no indica-
tions of leaching of the compound past the crop
rooting depth were found (<0.05% of applied
quantity) during the 25 year simulation period,
irrespective of quantity of infiltrated water or
pesticide decay rate.  This simple fact eliminates
the necessity of applying the methodology for most
pesticides with such large retardation factors.  At
values of retardation less than  50, the movement
of contaminants becomes more sensitive to other
factors.  As decay rate goes to zero (conservative
substance)  and the retardation factor goes to 1
(completely mobile, non-adsorbing chemical) the
amount of water infiltrated plays the greatest role
in determining the leached quantity.

It should be noted that soil and water conservation
management practices (as evidenced by curve number)
have only a minor impact on this quantity.
Climatic regime (i.e.,  precipitation quantity,
intensity and evaporation rates) have a much more
pronounced effect.  This is evidenced by the fact
that changing sites can have a greater impact on
the percentage leached than changing curve numbers
(at equal values of decay and retardation).  While
a curve number has maximum effect on pesticides
with low affinity for soils, i.e., low organic
carbon partition coefficient, Koc,  (as defined in
Section 3.3) the effects of factors such as soil
bulk density, organic carbon, and porosity are only
pronounced at intermediate values of Koc.  That is,
if Koc is extremely low the effect of the soil
characteristics on leaching is almost negligible.

Of course if the decay rate is very high, the
importance of adsorption in general is lessened.
Thus for short-lived, mobile chemicals, the key
management factor is the timing between pesticide
application and the next major rainfall event.

As half-life becomes longer and adsorption
coefficients higher, soil factors begin to play a
major role.  Thus management practices that
reduce infiltration, increase organic carbon or
increase bulk density would be effective in
reducing leaching.  Of course these factors are
not independent.  For instance, increasing organic
carbon will likely increase infiltration rates and
decrease bulk density.  At higher adsorption
and higher decay rates, as mentioned earlier,
leaching becomes an unimportant issue.

                     13

-------
The second major conclusion is that the parameter
inputs to the methodology are in general so inexact
that the use of quoted ranges give vastly different
results (See Section 5, Example Applications).
This indicates a need to develop more consistent
and meaningful techniques for measuring parameters
such as decay rates, Koc, etc.  There are two
primary sources of error involved in modeling;
error caused by inexact model inputs and  error
caused by inexact model descriptions of actual
processes by model algorithms.  Reducing either or
both of these results in diminished overall simula-
tion error.  Thus, if the variability in input.
parameters is reduced, the use of this handbook and
modeling in general as predictive tools is
enhanced.
                     14

-------
                          SECTION 3


  APPLICATION PROCEDURE FOR ASSESSMENT OF LEACHING POTENTIAL



3.1  INTRODUCTION
This   section   provides   a  step-by-step  guide   for   the
application  of  the methods contained in this handbook.   The
user's  inputs  (site selection and parameter estimation)  are
discussed   as   well  as  interpretation  of  the   frequency
distributions   used   to  make  decisions   about   potential
pesticide  leaching.  Finally, the.assumptions and limitations
of the application are discussed.

As  described  in earlier sections, the user  inputs  required
are  few,  but must be determined  from a variety  of  sources.
Much  of the information can be found in tables located in the
appendices  of  this  report or in referenced  documents.   To
apply   the  methodology  the  user  must  locate  and  choose
representative values for:

          •  site characteristics
             - hydrologic soil group
             - soil organic carbon and bulk density

          •  crop type

          •  pesticide properties
             - organic carbon partition coefficient,
               Koc  (or Kow)
             - decay rate, Ks, and
          •  management practices
             - used to determine the SCS curve number

A  chart  showing this flow of information appears  in  Figure
3.1.   The  following  sections  explore  site  selection  and
parameter estimation in detail.

Once  a  set of input values has been developed, they  can  be
used  to find the cumulative frequency distributions,  located
in  Appendix  A.  The method for choosing  appropriate  curves
and  their interpretation is provided in Sections 3.3  through
3.5.
                              15

-------
Input Information
 Intermediate
   Results
Management
 practice
 Key Parameters
     Figure 3.1  Schematic of Information  Flow  to  Calculate
                 Methodology Key Parameters
 3.2  SITE SELECTION

 The  site  selection  process  is  required  as  part  of   the
 application   procedure   to  find  a   representative   site,
 delineated  in this handbook, that corresponds to the are:a   or
 region   where   an  assessment  is  needed.   The   selection
 procedure  requires  only knowledge of  geographical  location
 and  crop type of concern.  Nineteen representative sites were
 delineated    by    combining   meteorologic,   soils,    crop
 distribution,    and  pesticide  usage  data.   Each  site    is
 characterized  by  a  unique set of descriptors  displayed   in
 Table   3.1.   Development  of  the  criteria  used  in   site
 delineation  can  be  found  in  the  Methodology  Development
 section  of  this  report  (Section  4.4).   Maps  of  the   19
 representative  sites  are  displayed by crop in  Figures   3.2
 through 3.5.

 The  first step in choosing a representative site is to  locate
 the  user's  site or region of interest on the maps  provided.
 The  four  maps are distinguished by their  associated   crops;
 wheat,  corn,   soybeans, and cotton.  Some sites overlap each
 other  (especially  in the corn and soybean regions).    It   is
 necessary  to  use  the map that corresponds to  the  crop   of
 interest.   Although representative sites cover the major crop
                                16

-------
 TABLE 3.1  METEOROLOGIC AND SOILS CHARACTERISTICS OF REPRESENTATIVE SITES

Site No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Meteorologic
State
WA
ID
MX
ND
NE
KS
CO
NE
IL
MI
OH
MD
SC
IA
MS
IN
SC
LS
AL
Station Location
Station No.
8926
10
3110
5479
4455
1383
834
3395
8179
7690
1466
700
1726
205
1707
6506
1726
1411
5140
Annual
Precip(in)
20-30
10-20
20
20
10-20
20-30
10-20
20-30
40
30-40
40
40
50-60
30-40
50
40
50-60
50-60
50
Soil
Group
C
B-C
A-C
B-C
A-C
A
C
D
C
B
B
C
C-D
B
B-C
B
C-D
C-D
B-C
Crop
Wheat
Wheat
Wheat
Wheat
Wheat
Wheat
Corn
Corn
Corn
Corn
Corn
Corn
Corn
Soy
Soy
Soy
Soy
Cotton
Cotton
% Organic
Carbon
0.5-2.0
0.5-1.0
1.0-2.0
1.0-2.0(+;
1.0-2.0
1.0-2.0
1.0-2.0
i.o-2.o(+;
i.o-2.o(+;
i.o-2.o(+;
1.0-2.0
1.0-2.0
0.0(+)-l .(
i.o-2.o(+;
0.0(+)-l.(
1.0-2.0
0.0 (+)-!.(
0.0(+)-l.(
0.5-1.0
      (1" = 2.54 cm)
growing  areas,  these   crops   are  often  grown  outside  the
regions  shown.    If  the area  of interest does lie outside  of
the  representative site boundaries,  the user should be  aware
of  the precipitation and soils characteristics in the area of
interest  and  choose  a site  with the same crop  and  similar
characteristics.    (See  Section 4.4 and Example  #2,  Section
5).   The  characteristics  that should  conform  are  average
annual  precipitation,   hydrologic  soil group,  soil  texture
(sand/silt/clay),  soil organic carbon, and suitability of the
soil for growing the  crop of interest.

An example of the  site  location procedure follows:

     •  Example crop  of interest - soybeans
     •  Example site  of interest - southern tip of Illinois

                               17

-------
Figure 3.2  Representative Sites No. 1 - No. 6 Associated
            with Wheat Farms
Figure 3.3  Representative Sites No. 7 - No. 13 Associated
            with Corn Farms
                              18

-------
Figure 3.4  Representative Sites No. 14 - No. 17 Associated
            with Soybean Farms
Figure  3.5   Representative  Sites  No.  18  -  No.  19  Associated
             with  Cotton  Farms
                             19

-------
     •  Turn to Figure 3.4 (sites associated with soybeans)
     •  Locate site of interest (southern Illinois) on
        map - site lies within representative site #16
Note:   The meteorologic station that provided weather data
        for development of the cumulative frequency distri-
        butions (Appendix A) for site #16 is station number
        6506 in Indiana  (Table 3.1).

If  the  example site of interest had been southern  Missouri,
the  user  would choose between representative sites  14,  15,
and   16.   This   would  require   investigation   into   the
meteorologic  regime, and the soils characteristics  (outlined
earlier)  for southern Missouri.  These characteristics  would
be  compared to those of representative sites 14, 15, and  16.
Basic  information used to delineate each representative   site
is listed in Table 4.1 of the next section.
 3.3   PARAMETER ESTIMATION
 The   use   of  this   handbook  requires  calculation  of  three
 parameters;   the  retardation factor (R)  for the pesticide/soil
 combination,   the  pesticide decay rate  (Ks),  and a  soil/crop
 management  curve  number (CN)  determined by the  SCS  method.
 These  parameters are  shown at  the bottom of Figure 3.1.   All
 of  the information required for the computations are outlined
 below  with   supplementary material to be found in Appendix  B
 (chemical  data  for  some currently used  pesticides).   Note
 that  the  pesticide of interest may not be found in  Appendix
 B.    In this case the  user will need to  locate an  appropriate
 data  source.   Additional pertinent soils data can be found in
 SCS  county soil surveys.
 3.3.1   Retardation Factor,  R

 The  retardation factor requires determination of the chemical
 partition  coefficient (Kd),  the soil bulk density (  ps),  and
 the  average soil water content at field capacity (  0fc).  The
 equation is


                        = ,  ,  (Kd) (ps)
                               0fc                         3.1
 where  R  is dimensionless,  Kd has units of cc/g-soil, ps  has
 units of g-soil/cc and 0fc has units of cc/cc.
                              20

-------
                     Kd = Koc  (OC)/100
where  Koc is the organic carbon partition coefficient  having
units  of  cc/g-organic  carbon  and OC is  the  percent  soil
organic  carbon.  If Kow (octanol-water partition coefficient)
or   S  (solubility)  is  reported  instead  of  Koc,  several
empirical  functions can be used to convert to Koc.  These can
be  found  in Table 3.2.  Compilations of Kow, Koc and Kd  for
some  pesticides  can  be found in Tables Bl through  B4.   An
excellent   source  of  solubility  data  for  herbicides   is
available  (Weed  Science  Society of America,  1983)  if  the
compound's Kow or Koc cannot be found in the literature.

No  compilations  of soil organic carbon on a  national  scale
have  been  found in the literature.  In the absence  of  this
specific  data,  soil  nitrogen  content  can  be  used  as  a
surrogate.   The organic carbon to nitrogen ratio in the  soil
is  approximately   11  to 1  (Brady, 1974).   Thus  OC  =  UN,
where  N is % nitrogen or g-N/100g-soil.  Nitrogen content  of
surface  soils in the United States can be estimated from  the
map,(Figure 3.6).

Once  the  soil  type is known,  the  textural  classification
(Figure  3.7) provides sufficient information to estimate bulk
density  (  ps)  (Table  3.3) and  the  field  capacity  water
content  (  0fc), which can be determined from the  regression
equation  in  Figure 3.8 or  from Table  3.4.  If the  user  has
access  to  profile information for the soil of  interest,  it
may  be  desireable to either use weighted mean values of  OC,
 p s  and   0fc for  the profile, or introduce this  variability
into sensitivity analysis of the retardation factor.
3.3.2  Decay Rate, Ks

Values  of  Ks   for many  agricultural  chemicals  are   shown  in
Tables  B5 and B6.  Extreme  care  should be  taken in  using   the
decay  rates reported  in  Appendix B.   There are  two   important
potential  problems.    First,  these  rates have   been  measured
under   a  wide   range of   environmental   conditions,   (e.g.,
varying  soils,   soil  pH, soil organic matter, soil   moisture,
soil  temperature,  etc.) which can  have dramatic effects  on
reported   values.     The user   should  check   the    original
references  to   ascertain what these conditions  were  and   the
applicability  of these  conditions to his  or   her  specific
situation.   These  references can  be found in   Nash  (1980).
Secondly,  in  this methodology,  the decay  rate  is applied  to
both  dissolved   and   adsorbed chemical.  The user   should  be
satisfied  that   this  condition was  met in  the measurement  of
                              21

-------
    TABLE 3.2  REGRESSION  EQUATIONS FOR THE ESTIMATION OF KQC



    £q. No.            Equation             No.b   r           Chemical Classes Represented

     1     log KQC = -0.55 log S + 3.61 (S in mg/L)  106   0.71    Wide variety, mostly preaticides
     2     log KQC = -0.51 log S t 0.11           10   0.91    Mostly aromatic or polynuclear aromatics;
           (S in mole fraction)                          tw° Chlorinated
r log
Koc
(S In
1 log
5 log

6 log

7 log
8 log

9d log

iOd,r log

11 log
12 log
Koc
Koc

K
oc
K
oc
K
oc
Koc

Koc

"oc
K
oc
= -0
mole
= 0.
= 0.

= 1.

= 0.
= 1.

= 0.

= 0.

= 0.
= 0.
.557
s/L)
511
937

log S + 1

log KQw +
log Kow -

00 log Knu -0.


91 log Kow «. 0
029

521

0067

681
681
Io8 Kow -

log KQH +

(P - 15N)

log BCF(f)
log BCF(t)
.277

1.377
0.006

21

.02
0.18

0.855

+ 0.237

+ 1.963
+ 1.886
15

15
19

10

9
13

30

29

13
22
0.

0.
0.

1,


0.

0.

0,

0,
0,
,99

,71
,95

.00

e
.91

,81

.69

.76
.83
Chlorinated hydrocarbons

Wide variety, mostly pesticides
Aromatics, polynuclear aromatics, tria-
zines and dinitroanillne herbicides
Mostly aromatic or polynuclear airomatics
two chlorinated







s-Triazines and dinitroaniline herbicides
Variety of insecticides, herbicides and
fungicides


Substituted phenyluraes and alkyl-N-phenyl-
carbamates

Aromatic compounds: ureas, 1 , 3,5-triazines,
carbamates, and uracils
Wide variety, mostly pesticides
Wide variety, mostly pesticides



     a. KQ  = soil (or sediment) adsorption coefficient; S = water solubility,KQw = octanol-water partition coef-
       ficient; BCF(f) s bioconcentration factor from flowing water tests, BCF(t) = bioconcentration factor from
       model ecosystems; P - parachor; N = number of sites in molecule which can participate in the formation'of
       a hydrogen bond.
     b. No. = number of chemicals used to obtain regression equation.

     c. r = correlation coefficient for regression equation.

     d. Equation originally given in terms of KQffl.  The relationship KQm = KQC/1.721 was used to rewrite

       the equation in

     e. Not available.

     f. Specific chemicals used to obtain regression equation not specified.
     Source:  Lyman, et al., 1982
the  decay   rate or find another  source of  information   about
degradation of  the  compound.

For    compounds   not   listed   in  the  tables,     registration
documents   for   the specific pesticide available   through  the
U.S.   EPA or the chemical  manufacturer may contain appropriate
values.    For   use  in  this methodology, the units  for Ks   must
be  per day.


3.3.3   Curve Number, CN

To   incorporate both soil  characteristics  and land   management
practices   into  the   system,  the  Soil   Conservation  Service
curve   number  method is used.   To estimate these  numbers, soil
infiltration    characteristics,   watershed  hydrologic   condi-
tions, and  conservation management practice  should be known.

                                      22

-------
                                                     •rH
                                                      O
                                                     to
                                                      -P
                                                       O
                                                       O
                                                      t-l

                                                       0)
                                                       O
                                                       (0
                                                       CO

                                                        c
                                                       •H
                                                        0)   0)
                                                        CP
                                                        O    -
                                                        !-l   M
                                                        •P   OJ
                                                        •H   ^
                                                        Z  i-l
                                                             rd
                                                         0)  &
                                                         (0
                                                         -P
                                                         C
                                                              OJ
                                                          CU   U
                                                          O   M
                                                          H   3
                                                          0)   O
                                                          P^   CO
                                                          ro

                                                           0)
23

-------
                                        Adopted from "Supplement to Soil Classification

                                             System (7th Approximation)," SCS,

                                             USDA, Second Printing, March, 1967
                                TV
.„ xyV\A/Yy\AA7
  /Y\AAA/Y\.AAA. .
 /yV\AAA/\AAAAA
/y v\	
/YW
                                    A/VYVSAA
                                    VvyVyVSA
                                     vvWVW
                                  Percent sand
        COMPARISON   OF  P A RT I C L E - S I Z E  SCALES

      Sieve Openings in Inches      U. S. Standard Sieve Numbers

        3  2 I'/? 1 3/4 Vs   4    10    20   40  60      200
      ii  ii  ii   n    i    i  11 i  i   i  MI  ii i
USDA

UNIFIED

AASHO
GRAVEL

SAND
Co*rse|Coor" Mtdium

GRAVEL
Coone | Fine

GRAVEL OR
Coarse 1 Medium
r 1 Very — — •
Fme 1 fine

SAND
Coarse

STONE
Fine
Medium

Fine

SAND
Coarse

Fine
CLAY

SILT OR CLAY

SILT -CLAY
Silt | Clay
      100  50        10    5     2   1  0.5X0 42 0 25   01 \ 0 05  002  001  0005  O.OQ2 0.001

                               Grain Size in Millimeters  0.074             u««»ciNt«m»mi •• •«.
Figure  3.7   Relationship Between the Class  Name of a  Soil and

               its  Particle Size Distribution
                                       24

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      TABLE 3.3  ESTIMATED SOIL BULK DENSITY FOR FIVE SOIL TYPES
       Soil type
   No.
observed
Mean
Observed
 range
       Silt loams

       Clay & clay
        loams

       Sandy loams

       Gravelly silt
        loams

       Loams

       All soils
  99

  49


  37

  15


  22

 222
1.32

1.30



1.49

1.22


1.42

1.35
0.86 to 1.67

0.94 to 1.54


1.25 to 1.76

1.02 to 1.42


1.16 to 1.58

0.86 to 1.76
     Source:  Baes and Sharp, 1983
Each  soil  belongs  to a hydrologic soil  group,  designated  A,
B,   C,   or  D.   These  index  letters   define  the   soil's
infiltration  capacity.   A  description   of  hydrologic  soil
classifications   is   found  in Table  3.5.   Most  soils  of  the
U.S.  have  been  classified by the U.S.D.A.  (1971) using  this
system.

Cropping  practices   as  well as soil  type affect  the  soil's
ability  to transmit water.  The hydrologic condition reflects
the  amount   of   dense  vegetation  in each  rotation.   Poor
hydrologic  conditions  result from repeated rotations of  the
same  crop  in  the  same field.   Good hydrologic  conditions
result  from  incorporation of legumes  or  grasses in  rotations
to improve the  infiltration characteristics of the soil.

Land  use  management  information also affects   curve  number
selection.    Variation  in  curve  numbers  reflects   runoff
potential   associated   with  straight   row,   contoured,   or
contoured and terraced lands.

Knowing  the  above  information a runoff  curve number  can  be
located  in   Table 3.6.  The user's CN should lie  within  the
                               25

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                                                         26

-------
  TABLE 3.4  SOIL HYDROLOGIC PROPERTIES BY SOIL TEXTURE
  Texture
   Class
       Range of
 Textural Properties
                      Percent

                 Sand    Silt
                 Clay
Water Retained at
-0.33 bar tension
    cm /cnP
SAND
LOAMY SAND
SANDY LOAM
LOAM
SILT LOAM
SANDY CLAY
  LOAM

CLAY LOAM
SILTY CLAY
  LOAM

SANDY CLAY
SILTY CLAY
CLAY
85-100   0-15   0-10
70-90    0-30   0-15
45-85    0-50   0-20
25-50   28-50   8-28
 0-50   50-100  0-28
45-80    0-28   20-35
20-45   15-55   28-50
 0-20   40-73   28-40
45-65    0-20   35-55
 0-20   40-60   40-60
 0-45    0-40   40-100
    0.0911
(0.018 - 0.164)2
    0.125
(0.060 - 0.190)

    0.207
(0.126 - 0.288)

    0.270
(0.195 - 0.345)

    0.330
(0.258 - 0.402)

    0.257
(0.186 - 0.324)

    0.318
(0.250 - 0.386)

    0.366
(0.304 - 0.428)

    0.339
(0.245 - 0.433)

    0.387
(0.332 - 0.442)

    0.396
(0.326 - 0.466)
  mean value
2
  one standard deviation about the mean
Source:  Rawls,  et al., 1982
                             27

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              TABLE 3-5  HYDROLOGIC SOIL CLASSIFICATIONS
Group/Runoff Potential

Group A.  Low Runoff Potential
Group B.  Moderately Low Runoff
               Potential
Group C.  Moderately High Runoff
               Potential
Group D.  High Runoff Potential
          Description

Soils having high infiltration
rates even when thoroughly wetted
and consisting chiefly of deep,
well-to excessively-drained sands
or gravels.  These soils have a
high rate of water transmission.

Soils having moderate infiltration
rates when thoroughly wetted and
consisting chiefly of moderately
deep to deep, moderately well to
well-drained soils with moderately
fine to moderately coarse textures.
These soils have a moderate rate of
water transmission.

Soils having slow infiltration
rates when thoroughly wetted and
consisting chiefly of soils with a
layer that impedes downward movement
of water, or soils with moderately
fine to fine texture.  These soils
have a slow rate of water transmission.

Soils having very slow infiltration
rates when thoroughly wetted and
consisting chiefly of clay soils
with a high swelling potential, soils
with a permanent high water table,
soils with a claypan or clay layer at
or near the surface, and shallow
soils over nearly impervious
material.  These soils have a very
slow rate of water transmission.
Source:  USDA (1971)
                                      28

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        TABLE 3.6  RUNOFF CURVE NUMBERS FOR HYDROLOGIC SOIL-COVER COMPLEXES


Land use
Fallow
Row crops





Small
grain




Close-
seeded
legumes
or rota-
tion
meadow
Pasture
or range




Meadow
Woods


Farmsteads
Roads
(dirtr
(ANTECEDENT MOISTURE
Cover
Treatment
or practice
Straight row
Straight row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced
Straight row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced
Straight row
Straight row
Contoured
Contoured
Contoured and terraced
Contoured and terraced



Contoured
Contoured
Contoured






?
CONDITION II,

Hydrologic
condition
	
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Good
Poor
Fair
Good
Poor
Fair
Good
Good
Poor
Fair
Good
	
	
__ —
AND Ia
Hydrc
A
77
72
67
70
65
66
62
65
63
63
61
61
59
66
58
64
55
63
51
68
49
39
47
25
6
30
45
36
25
59
72
74
= 0.2
ilogic
B
86
78
78
79
75
74
71
76
75
74
73
72
70
77
72
75
69
73
67
79
69
61
67
59
35
58
66
60
55
74
82
84
S)
soil
C
91
85
85
84
82
80
78
84
83
82
81
79
78
85
81
83
78
80
76
86
79
74
81
75
70
71
77
73
70
82
87
90

group
D
94
91
89
88
86
82
81
88
87
85
84
82
81
89
85
85
83
83
80
89
84
80
88
83
79
78
83
79
77
86
89
92
(hard surface)^
1  Close-drilled or broadcast.
2  Including right-of-way.

Source:  Schwab, et al.,  1966
                                        29

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range  of plotted curve numbers for the chosen  representative
site.    Condition  II  curve  numbers  found  in  Table   3.6
correspond  to  the  numbers listed in the box  at  the  upper
right-hand corner of each CDF plot in Appendix A.

At  this  point,  all of the necessary input  data  have  been
assembled  and combined to choose a representative site number
and  to  estimate the three key parameters; R, Ks, and CN.  It
should  be  stressed  again that jthere is uncertainty	in  the
reporting   and/or  estimation  of  these  parameters 7    Mos~t
parameter  values are reported as lying within a range or  are
expressed  as  a mean with a standard deviation.  The  example
problems  worked  in Section 5 illustrate how  these  reported
ranges  ' can   be  utilized  to  enhance   understanding   and
interpretation of methodology results.
3.4  FREQUENCY DISTRIBUTIONS
Once  values of R, Ks, and CN are known, CDFs which  correspond
to  these values are found by using the matrices at  the   front
of  Appendix  A.   The matrices are ordered  by  repesentative
site  numbers  1  through  19.   Various  site  identification
characteristics  are  listed  first and the  "address"   matrix
follows.   This  two-dimensional matrix  includes  retardation
coefficient  (R) values along the vertical axis and  decay rate
(Ks)  values  along  the  horizontal axis.  The  body  of  the
matrix  contains  figure numbers which direct the  user to  the
appropriate  frequency distributions.   Blank locations  in the
matrix  are  indicative of parameter combinations  that   result
in no significant leaching.


The   frequency   distributions  located  immediately  after  the
matrices  in Appendix A contain leaching information  relating
a  single value of R, a single  value of Ks and  four  values  of
CN    on  each  plot.   The  horizontal  axis  represents   the
percentage   of  the annual pesticide application  that can  be
expected  to leach per year.  The vertical axis represents the
percentage  of  time  that the  specified amount  of  pesticide
leached  will  be exceeded.   The  following is   a   step-by-step
example of how to locate and  interpret  the correct curves.

     1.  Assume that we are interested in a site located  in
        northeastern Colorado planted in corn.  Figure 3.3
        shows that representative site  #7 corresponds  to
        this location.

     2.  We calculate input parameter values  for R, Ks, and
        CN  (e.g. R=l, Ks=.010,  CN=80-85).  Note that
        computed values of R, Ks  and CN may  not match
        exactly those found in  the  matrix.   In  this  case a

                              30

-------
        value appearing in the matrix closest to that
        calculated is used.

    3.  Look at Figure 3.9a which contains the example
        "address" matrix used in locating the appropriate
        CDF.  The matrices for all of the representative
        sites are located at the front of Appendix A.

    4.  According to the key parameter values from step 2,
        the  corresponding frequency distributions can be
        found in Figure A7-3.  Normally, a range of values
        for R and Ks would necessitate looking at several
        plots. The blank spaces in the matrix means that no
        significant leaching occurs for these combinations
        of R, Ks and CN values.

    5.  Figure 3.9b contains the example plot A7-3 with
        explanations of its format - the frequency curves
        are also found in the second part of Appendix A
        following the address matrices.  For R=l,  Ks=.010,
        and CN=85; there is, for example, a 10% chance that
        more than 40% of the applied pesticide will leach
        per year.  For the same conditions, there is a 50%
        chance that more than 10% of the applied pesticide
        will leach per year.

Since  the  percentage leached is independent  of  application
rate   (See Section 4.3), the leached load can be expressed  as
the  applied  load  times  the percent leached.   Thus,  if   5
Kg-pesticide/ha  were applied, there is a 50% chance that more
than  0.5  Kg/ha  will leach past the root zone in  any  given
year.


The  family  of  curves represented on each plot  defines  the
entire  range of curve numbers used for each site.  This range
of  curve  numbers  reflects  the  range  of  soil  types  and
management  practices reported in the SCS Engineering Handbook
(U.S.D.A.  1971).   In  the  above example,  site  7  displays
characteristic  curve number values ranging from 78-85.   This
was  deduced  from the site descriptions defining the soil  as
part  of Hydrologic Soil Group "C" and planted in a row  crop,.
corn.   Different  editions  of the SCS  Engineering  Handbook
report slightly different hydrologic soil group values.

Just  as  a single figure contains a range of four  SCS  Curve
Numbers,   a group of figures provides curves for a range of   R
or  Ks values.  For example, plots A7-1 through A7-4  describe
the   results  of  varying  Ks  values  from  .001  to    .050.
Likewise,  curves  found on plots A7-6 and A7-11 describe  the
results of varying R values from 3 to 5.
                              31

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SITE  NO. 7

CROP:   CORN

  CN:   78-85
                                 Ks

1
3
5
20
50
.001
.005

A7-1
A7-6
A7-11
A7-14
A7-2
A7-7
A7-12
A7-15
.010

A7-3
A7-8
A7-13

.050 .100

A7-4 A7-5
A7-9 A7-10
                Figure  3.9a   Example  Address Matrix
                OF APPLIED PESTICIDE LEACHED PER YEAR
                                                        Range of
                                                        SCS curve
                                                        numbers
                Appendix A
               figure number
Retardation Decay rate
 factor
             Figure  3.9b  Example  Frequency Curve  Plot
                               32

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3.5  APPLICATION ASSUMPTIONS AND LIMITATIONS
The  leaching  frequency distributions in this  handbook  were
created  by  applying  PRZM  under  19  idealized  application
scenarios.   Various  simplifying  assumptions  were  made  to
reduce  the  number of computer simulation runs  required   for
each   representative  site.   The  assumptions  listed  below
pertain  to PRZM input parameter selection.  These assumptions
are  important  to the interpretation of  methodology  results
and  may  also  affect applicability  in  certain  situations.
Limitations  of the methodology are also listed below.   These
limitations  are  concerned with the overall  approach  rather
than model specific limitations which are found in Chapter  4.
3.5.1  Assumptions

     Soils
     •  A uniform soil profile was assumed  from  the  surface
        to the crop rooting depth.
     •  A representative soil texture was chosen for each
        representative site - only the characteristics of
        a single soil type were  input for that site.

     Crops
     •  Representative crop rooting depths  were  chosen for
        each crop type and were  the same for each site in
        which the crop is grown.  The average values
        reported by Carsel, et al., (1984)  were  used.
        These values were for corn, soybeans, wheat  and
        cotton; 90, 45, 22 and 60 cm, respectively.

     Management
     •  One pesticide application per year  was made  each
        year of the simulations.  The application was made
        just prior to the time of planting.  There are many
        cropping patterns and pesticide application  choices
        available.  For instance, insect control in  cotton
        may require foliar application several times during
        a growing season while weed control may  be accom-
        plished with one surface application prior to
        planting.  The full range of possible combinations
        was too extensive to treat on a national scale.
        The majority of pesticides which would cause
        significant leaching problems are herbicides or
        fungicides applied prior to crop emergence,  so an
        appropriate application  date for each site
        was chosen.  To best simulate actual application,
        conditions, the pesticide was applied at least two
        days following a rain event as well as at least  two
        days prior to the next rainfall.  This emulates  a

                              33

-------
drying period following events sufficient to allow
farm equipment use and a post application period
reflecting the farmer's probable use of weather
forecasts.

To test the validity of assuming a single pesticide
application scenario, an analysis was performed on
the effect of making a single annual application as
opposed to 10 applications over a 100 day period.
The results are displayed in Figure 3.10.  It can
be seen that the greatest discrepancy in pesticide
remaining at the end of the 100 day period occurs
in the  middle of the range of probable decay
rates.  By applying pesticide continuously over
this period, there will always be more chemical in
the soil profile at the end of the 100 day period
than if it is applied only once.  This analysis
does not address the question of how deep into the
soil profile pesticide has traveled under the two
application scenarios.  This will depend on the
meteorologic regime over the 100 day period.
It can be assumed that the longer the pesticide
is present in the soil, the further it will travel.
In the cases where the decay rate is either very
large or very small, little difference will be seen
at the end of the 100 day period if application
occurs once or at several times during the
growing season.  However in all three cases there
is a discrepancy between pesticide available for
leaching at the beginning of the 100 day period
under the two different application scenarios.
This effect disappears most quickly at high decay
rates.  Therefore, if the single application
assumption is made and multiple applications are
in fact used, at low decay rates actual pesticide
leaching would be over-estimated while at high
decay rates, leaching would be under-estimated.

The assumption of the application of the pesticide
each year has no impact on the frequency distri-
butions for short lived chemicals.  However, for
chemicals that carryover from year-to-year,
leaching would be overestimated.  A single cutoff
value of half-life (or decay rate) cannot be given
because it changes with each representative site
and value of Kd.

One crop per year was simulated instead of using a
rotation scenario.

The same crop was simulated each, year at each
representative site.
                      34

-------
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                                            (0
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                                                    H  (0

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     •  The SCS  curve number set after harvest of each crop
        was set  at  "residue" conditions to provide the
        worst case  scenario (more infiltration occurring
        than under  "fallow" conditions).

     •  Pesticide was surface applied rather than incor-
        porated  into the soil in the model.  Pesticide
        incorporation would reduce those surface losses
        such as  volatilization photolysis, and runoff.
        Therefore,  lower overall model decay rates would be
        associated  with subsurface incorporation.

        For foliar  applications, the user should attempt to
        estimate the fraction of applied pesticide reaching
        the soil and multiply percentage leached results by
        this quantity.   Note, however, that the quantity
        leached  may be under-estimated due to washoff from
        the plant onto the soil.

     Meteorology
     •  Representative sites 13 and 17 were the only ones
        with pan evaporation records complete enough for
        use.  All other sites required synthesis of daily
        pan evaporation data by Ramon's formula, executed
        by PRZM.
3.5.2  Limitations

     •  The greatest limitation of this methodology
        concerns the application of single-valued
        characteristics to large representative sites.
        The handbook was devised as a screening tool for
        evaluating the potential for pesticide leaching to
        ground water.  If site-specific characteristics
        would indicate more or less leaching potential than
        the representative site characteristics,  further
        investigation may be necessary.

     •  Another major limitation is that available
        chemical data is both incomplete and imprecise.
        Reported values for Koc and Kow differ according
        to the reporting body.  In many cases the
        coefficient of variation is over 100% of the
        mean value reported.  The user may find that by
        taking the full range of reported values in
        estimating the three key parameters the entire
        range of parameters used in the methodology
        development will be spanned.  In this case,
        the user should strive to narrow the uncer-
        tainty in his input data and re-evaluate.
                            36

-------
•  Because PRZM utilizes the runoff curve number
   approach, little practicable sensitivity analysis
   can be performed by the user in terms of altering
   specific management practices.  Only major changes
   in management such as contouring to terracing can
   be examined.
                       37

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                          SECTION 4
                   METHODOLOGY DEVELOPMENT
4.1 INTRODUCTION
The  following section explains the four basic steps taken  to
compile  this handbook.  First, a suitable model was selected.
Second,   those   model  .parameters  most  important  to   the
production  of  pesticide  leachate were  determined.   Third,
representative   agricultural  sites  were  chosen.   Finally,
production  runs  were made to generate the distributions  for
each site.
4.2  MODEL SELECTION
Several  models  are  currently available which  simulate   the
migration  of  organic contaminants through porous media.    In
order  to  evaluate  the available models in  terms  of  their
suitability  for this project, the following objective   review
criteria were developed:

     •  The model should be capable of simulating the
        leaching of organic compounds  (with important loss
        mechanisms).

     •  Important chemical and soil parameters should be
        easily changed.

     •  A variety of structural and nonstructural
        management practices  should be capable of
        being simulated.

     •  The model should require  readily available data  as
        inputs and should have no parameters which require
        extensive calibration.

     •  The model must be a working computer code, fully
        documented.
                             38

-------
     •  The model should be based on acceptable theory and
        utilize sound solution techniques.

     •  The model should be one-dimensional or easily
        collapsed to one dimension.

A  survey of available models was used to start the  selection
process.   A  very  good  survey was done  recently  by  Oster
(1982).   In  that report he reviewed 55 different models  for
flow  and solute transport in the unsaturated zone.  Of  these
55,  ten flow and transport models were selected on the  basis
of  documentation, applications and availability of the  code.
Of  these  ten, three are purely flow models.   The  remaining
seven were:

     1)  SEGOL (Segol, 1976)
     2)  SUMATRA-1 (van Genuchten, 1978)
     3)  TARGET (Sharma, 1979)
     4)  FEMWATER/FEMWASTE (Yeh and Ward; 1979,
          1981)
     5)  TRUST/MLTRAN (Reisenauer, et al., 1981
          and 1982)

The  SEGOL  model  is two or three-dimensional and  cannot  be
collapsed  to  one-dimension.   As  such it  likely  would  be
overly   expensive  to  run  and  overly  detailed  for   this
application.

SUMATRA-1  is  a  one-dimensional finite  element  model  that
simulates   flow   and   solute  transport   under   transient
saturated/unsaturated   conditions.   The  strengths  of  this
model   are  its  apparently  stable  and  accurate   solution
technique,  its ability to simulate temporarily saturated  and
unsaturated  percolation  and abruptly changing  soil  profile
properties.   It  considers  linear  adsorption  and  zero  or
first-order decay of the solute.

TARGET  is  a  proprietary code owned by Dames and  Moore  and
performs  simultaneous  hydrodynamics,  solute  transport  and
heat  transfer.  It is a three-dimensional model and thus  its
use  was  not  justified given the scope and  intent  of  this
study.

FEMWATER/FEMWASTE   is a two-dimensional finite element model.
It  includes  dispersion, advection, adsorption and  decay  of
the  chemical species but does not consider surface hydrologic
processes or evapotranspiration.

TRUST/MLTRAN  is either a one or two-dimensional nondispersive
kinetic  transport model.  The output is highly dependent upon
graphic displays.
                             39

-------
None  of  the above models were developed with the  simulation
of  agricultural applications in mind.  Most require  boundary
conditions  at  the  soil  surface  which  would  have  to  be
obtained  from  a  surface hydrologic model.   Therefore  none
have   the  innate  capability  to  simulate  the  effects  of
management  practices  upon  leaching.  Of those  reviewed  by
Oster, SUMATRA-1 looks most applicable to the problem.

Two  models  available  but not reviewed by  Oster  are  PRZM,
developed  by  the  U.S. EPA at Athens, GA,  (Carsel  et  al. ,
1984)  and  PESTAN (Enfield et al., 1982), also  developed  by
the  U.S. EPA at the RSK Environmental Research Laboratory   in
Ada, OK.

PESTAN  is  a  steady flow model using a constant  pore  water
velocity  and  thus  cannot  be used  effectively  to  develop
frequency curves for pesticide leaching.

PRZM  is a one-dimensional finite difference model for  solute
transport   under  unsaturated  conditions.   It  includes    a
hydrologic  model  which utilizes the SCS runoff curve  number
approach  to  generate  infiltration rates for  the  transport
model.   Therefore,  the  effects of  agricultural  management
practices  can  be simulated by selecting curve  numbers,   and
little  or no calibration is required.  Because of assumptions
made  in the hydrologic model, transient saturated  conditions
cannot  be  simulated and, therefore,  the solution  technique
is  deemed  accurate only on soils that are well drained.    It
is  on  these types of  soils, however, that leaching  is  more
likely  to  be  a  problem.  As a whole, this  model  is  best
suited  to  the scope of this project primarily because it   is
linked  to the curve number approach  for surface hydrology   to
supply  infiltration  volumes, and can also be operated  quite
inexpensively for  long  simulation time periods.
4.3  SENSITIVITY ANALYSIS
Once    the   model  was   chosen,   sensitivity   analyses    were
performed with  PRZM for  three purposes:

     •   To  choose the key parameters  of  primary
         importance to pesticide  leaching,
     •   To  assign meaningful ranges  for  these  parameters,
         and,
     •   To  determine an  appropriate  length of  simulation
         period  (i.e., a  length of  time  sufficient to
         provide a stationary mean  for frequency
         distributions).
                              40

-------
Approximately  500  model  runs  were made,  some   of   25   year
durations.   Before  the tests were performed,  critical   test
parameters and associated ranges were established.

There  are two categories of parameters that affect the amount
of  pesticide that will leach below the crop root  zone.    They
will  be  referred  to  as  transport  parameters  and supply
parameters.   Transport  parameters  affect   the   movement  of
contaminants  in the soil, while supply parameters govern   the
quantity   of  contaminant  present.   Table  4.1  lists    the
transport  and supply parameters  important to PRZM,  along  with
their associated ranges of values.

The  range of values for  each parameter  is indicative of  upper
and  lower   limits  found  for agricultural soils and   chemicals
in  current  production.  The 500  model  test runs  allowed   for
comparison   of  the  relative importance   of  each  parameter,
within  its established range, to pesticide leaching.

The  relative effect of each transport and supply  parameter is
governed  by the mass balance/transport  equation used in   PRZM
(Carsel,  et  al., 1984):

             ^Cw m   ^(Cw/R)   <9(CwV/R)                     4.1
                m           _          _
             fit       O Z         OZ

where  the  transport  parameters  are:

     •  R,   the retardation  factor
     •  D,   the dispersion/diffusion coefficient
     •  V,   the pore water velocity

and  the  supply parameters  are:

     •  Ks,  the pesticide  decay rate,  and
     •  U,   the plant uptake rate.

Cw,  is the dissolved pesticide  concentration,  t is time and
     z is  depth in the soil  profile.

The  retardation factor,  R, is defined as:

                                                 •        4-2
 Since  Kd  =  Koc(OC)/100,  then,
                           Koc (PC) ps                      4.3
                        1   1000
Koc   is   a   property  of the pesticide while OC  and   ps  are
properties   of the  soil.  Koc for many compounds are given  by

                              41

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Rao  and  Davidson  (1980).   These values  range  from  2  to
2xl0**6  with  coefficients  of variation ranging from  10  to
130%.   Brady (1974) gives ranges of organic matter from   0.1
to  15.1%.   Using  a conversion factor of 1.7 for  %  organic
matter  to  % organic carbon, OC, this range becomes  0.05  to
9.0%.   Bulk density varies with a number of factors including
depth  in the profile.  Baes and Sharp (1983) report values of
roughly  0.9  to  1.8 g/cc.  The field  capacity  and  wilting
point  moisture  contents  reported by Rawls (1982)  for  each
soil textural class were used as limits on #fc and  0wp.

Molecular  diffusion is only important at low water velocities
(Freeze  and  Cherry,  1979)  and so it  was  ignored  in  the
sensitivity  analyses.   According to the developers of  PRZM,
with  a  compartment  (i.e.,  soil layer) depth of  5  cm  and
temporal  timestep  of  one day, the numerical  dispersion  in
PRZM  approximates  actual dispersion observed in  the  field.
Therefore,  the diffusion/ dispersion parameter, D, was set to
zero.   The  pore  water  velocity is  affected  by  the  same
parameters  that  affect 6 and,  in fact, is a function  of  0.
Therefore,  no  sensitivity  analyses were performed  using   V
directly.

Of  course,  V is affected by relative rates of  rainfall  and
evapotranspiration.   For  the   analysis, 25  years  of  daily
rainfall  and  evaporation data  were obtained for  Macon,  GA.
It  was  felt that this site, having high intensity  rainfalls
and  sandy  soils  would show more pronounced  sensitivity  to
other   parameters.    Infiltration  depths  were  varied   by
manipulating  runoff curve numbers from 51 to 94, encompassing
the  complete  range of A to D soils planted in row  crops  or
small grains (Schwab, et al., 1966).

Notwithstanding  the  usually small quantities lost in  runoff
and   erosion  and  the  amounts  transported  from  the  soil
profile,  the amount of pesticide in the profile depends  upon
the  application  rate,  and  Ks and U  in  the  mass  balance
equation.   Pesticide application rates generally vary between
0.25  to 20.0 Kg/ha according to Nash  (1980).    Because U and
Ks  both  multiply  Cw in the  pesticide  advection/dispersion
equation,   the   sensitivity  of  the  output  to   each   is
equivalent.   Therefore, only Ks was varied in the sensitivity
tests.   Ks   is  the  lumped  first-order  decay  rate  which
represents  the decay process.   Typical rates vary from  about
.0001 to 2.9/day (Nash, 1980).

The  transport and supply parameters chosen above for  further
investigation   were   varied    both   individually   and   in
combinations  to find their  individual and synergistic effects
on  pesticide  movement.   Through sensitivity  analyses,  the
most  influential  parameters  were  found and  the  range  of
values  most important to frequency curve production  defined,

                              43

-------
The  following  are  the  major  qualitative  results  of  the
analyses:

     •  One of the major findings of the sensitivity tests
        was that climatic factors and pesticide properties
        seem to be more important factors in determining
        leaching losses than properties of the soils.

        Other specific conclusions are:

     •  The chemical specific organic carbon (or
        octanol/water) partition coefficient, Koc
        (or Kow) is the most important parameter
        that determines the mobility of the compound.
        This is primarily due to the extremely large
        range of possible values for the coefficient.

     •  Remaining variables that are important include:
        -  Rainfall amount and timing,
           Decay rate,
        -  Soil organic carbon content,
        -  Bulk density, and
        -  Runoff curve number.

        However, the sensitivity of these parameters can
        change significantly at various levels of the
        partition coefficient.  For instance, when Koc is
        large, the sensitivities (partial derivatives) of
        the model output (% of applied pesticide leached
        below the root zone) to PS, %OC, and curve number
        go to zero.  When Koc -*  0.0, the sensitivity of ps
        and OC% also go to zero, but curve number attciins
        its maximum sensitivity.  Thus,  ps and OC% are
        sensitive only at intermediate values of Koc.  This
        is illustrated for the case of bulk density in
        Figure 4.1.  The fact that the sensitivity to
        runoff curve number goes to zero as Koc becomes
        large is demonstrated in Figures 4.2 through
        4.4.  Notice that the amount of pesticide leached
        is lower and that the random behavior of the
        output is damped out as Kd (or Koc) increases.  The
        effect of runoff curve number also decreases cis
        decay rate goes to zero, i.e., half-life becomes
        long.

     •  The time required for a chemical to attain a
         "steady-state" mean soil concentration is a
         function primarily of Koc  (or Kd).  In these
        analyses, even at half-lives of two years, a
         steady-state mean was achieved in about seven
        years.  Figure 4.5 shows how increases in Kd
         resulted in lower mean output and time to achieve


                             44

-------
      steady-state was  lengthened.   Figure 4.6 shows that
      increasing  decay  rate also lowers the mean output,
      but shortens the  time to steady state.  The mean
      annual precipitation rate over this 23 year period
      was 115 cm.  For  long half-lives and high Kd,
      carryover effects must be considered.  Non-zero
      pesticide concentrations can be used as initial
      conditions  for  the simulations.

      The percentage  of applied pesticide leached below
      the root zone is  independent of the quantity
      applied in  PRZM.   In our tests, 100, 1000, and
      10,000 g/ha were  applied.  No differences were
      observed in the percentage leached.  Conse-
      quently, a  unit application rate was used in
      developing  the  methodology.  In reality,
      adsorption/desorption isotherms for pesticides in
      soils can be non-linear.  If this happens, then the
                    DECAY RATE =  .00I/day
           200    300     400     500    600    700
                   PESTICIDE LEACHED OUT OF ROOT ZONE  (g)
800    900
                                                            1000
Figure 4.1  Effect of Kd  and  PS  on Quantity of Pesticide
            Leached
                            45

-------
      1000 r-
                                                    "A* soil
                                                     O'soil
                        8      12     16

                        Time elapsed (years)
20
24
Figure 4.2   Effect of  SCS Curve Number on Annual  Pesticide
             Leached  (Kd  = .06)
                            46

-------
             500 r-
                            8      12     16

                           Tim* elapsed (years)
20
                                                    A* soil
                                                    '0' soil
                                                    24
Figure  4.3  Effect of SCS Curve Number on  Annual Pesticide
             Leached (Kd  = 0.6)
             500
         «•*   400
          g
          2   300
         o

         «  200
         u
         o   100 -
         «•
         •
         0.


              0
                            8     12     16

                            Time elapsed (years)
20
      24
Figure 4.4  Effect of  SCS Curve  Number on Annual  Pesticide
             Leached  (Kd  = 1.5)
                               47

-------
        percentage leached will not be  independent  of  the
        load.  However, if concentrations  in  the  soil  are
        low  (as they would be in agricultural applications)
        linearity can usually be assumed without  intro-
        ducing substantial error.

The  major problem with producing  frequency distributions  for
each  region  was to determine the least number of   parameters
that  could be used to describe the leaching  phenomenon  and to
select  ranges  of these parameters,  and levels   within   these
ranges,  to characterize the system.  From the results of  the
sensitivity  analysis  it was learned that, at a  minimum,  Ks,
ps, OC,  Koc,  dfc, and CN must be considered. The  remaining
important  parameters, precipitation, evapotranspiration,  and
depth  of  root zone were fixed by selection  of   location  and
crop  type.   Variations  in rainfall amount  and   timing  were
                               K8 = .001/day
                                         Kd -e.o
                         a    12   16

                         Tim* ilapud (yeiri)
                                     20
           Figure 4.5  Effect of Kd (cm3/g) on
                       Time to Reach Steady-
                       State Mean Annual Output
                              48

-------
factored  in  by  using  a  long  period of record at  each  site
chosen  for analysis.  Fortunately,   ps,  OC,  Kd and  0fc,  can
be  combined  into  R, the retardation  factor,  leaving  only
three parameters to vary for  each site.

Although  the format and presentation of the frequency  curves
are  simplified  by the  use of  3  parameters,  all of the  input
factors  listed  earlier  are important.    The  production  of
leachate  is  most  sensitive however to fluctuations  in  the
retardation  factor,   curve  number  and decay  rate  and  the
consideration  of  these parameters is adequate for  screening
purposes  at  a particular site.   The actual ranges  of  model
parameter  values chosen to produce the frequency curves  were
distilled  from  those  ranges  reported in  Table  4.1.   Five
values  of R; 1, 3, 5, 10  and 50  and five values of Ks; 0.001,
0.005,  0.01, 0.05, 0.1  were  chosen to represent the  spectrum
                                  = 0.6cm°/g
                                        Ka= 0.001
                                         = 0.01
                                         = 0.06
                         8   12    16

                         Time elapsed (years)
          Figure 4.6  Effect of  Decay  Rate  (/day)
                      on Time  to Reach Steady-
                      State Mean Annual Output
                               49

-------
of   problematic  compounds.   Four  values  of  curve  number
appropriate  to  the  soil/crop/management scenarios  at  each
site were also used.

For  the  majority of cases, leaching of pesticide  was  shown
not  to  be  a  potential problem.  Only  small  values  of  R
resulted  in significant leachate production, while the entire
range   of  observed  Ks  and  CN  values  affected   Leaching
potential.   Regardless  of  hydrologic regime  or  half-life,
values  of R greater than 50 produced no significant  leaching
over  a 25 year period.  This eliminates concern over leaching
for a majority of pesticides on the market today.

The  final  purpose of performing sensitivity analyses was  to
determine  an  appropriate  length of  simulation  period.   A
period of 25 years was chosen for three reasons;

     •  steady-state mean output  is achieved  for most
        compounds within this time  frame,

     •  known meteorologic periodicities are  likely
        to  fully cycle  during that  period,  and

     •  few  stations have concurrent precipitation and
        evaporation  data for more  than  25 years.

During  the production of frequency curves,  if  the  leaching of
pesticide   was  significantly different  in  the  first  five  years
than  in   the  last  20  years, an  initial pesticide level   equal
to  the remaining pesticide level  after the  25  year  simulation
was  input  and  the  entire simulation was rerun.  This was  done
to  provide  a  stationary mean and variance  in   the   frequency
distributions.   Thus,  the  variation  in  annual   percentage
leached   in  any individual distribution is  solely  a   function
of  natural  variations  in climate.

Note  that  the  intention here is  not to make a  statement  about
the  total  quantity   of pesticide leached over  the   25   year
period,   but to provide a probabilistic estimate of  the  amount
that  may  move past the root zone  once  a quasi  "steady   state"
concentration  profile  is reached  in the soil.
 4.4   REPRESENTATIVE  SITE  SELECTION
 The   four   most  economically important  crops  in the U.S.   were
 chosen   to   define   areas   of  potential   pesticide  leaching.
 These  crops  -  wheat,  corn,  soybeans,  and cotton - are  grown
 extensively  throughout the country under a wide range of soil
 characteristics,     meteorologic    regimes,    and    pesticide
 application   scenarios.     The  most  widespread   pesticide

                              50

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leaching  problems  would  likely arise  in  conjunction  with
usage on one of these crops.

The  method  chosen for delineation of the  19  representative
sites  involved  the transfer of  agricultural,  meteorologic,
soils,  and  pesticide  usage information  to  national  maps.
Each  map  contains the boundaries of a single  characteristic
of  interest  and  was overlayed with other maps  to  discover
naturally  occurring  spatial relationships.  The  major  crop
growing  areas  of the U.S. are delineated in the 1978  Census
of   Agriculture   (U.S.  Dept.  of  Commerce,   1982).    The
boundaries  of  the  major  growing  areas  for  wheat,  corn,
soybeans,  and 'cotton were transferred to four national  maps
(Figures  4.7 - 4.10).  Areas in which irrigation is essential
for    crop   production   were   eliminated   from    further
consideration.   Precipitation  isopleths  in  10"   (25.4  cm)
increments  were also drawn on a map (Figure 4.11).  Likewise,
maps  of  the  hydrologic   soil  groups  and  major  areas   of
herbicide  and insecticide  usage were produced  (Figures 4.12 -
4.14).   The  final representative site delineations  were drawn
onto  four  maps,  each one corresponding to  a  single  crop.
These were previously displayed in Figures  3.2 to 3.5.

Nineteen  sites were delineated in this fashion.   After  they
were  chosen,  site-specific data were gathered  for  each  site
to  satisfy   input  requirements for PRZM.   Table   4.2  lists
meteorologic,   soils,  and  crop  data  used  which  describe
individual  sites.  Information from Tables 3.1, and 4.2,  was
combined to produce input parameter values  for PRZM.

The  meteorologic stations  chosen to represent   precipitation,
evaporation,  and  temperature  conditions  at each   site  were
selected  from  a  data base developed  by   EPA-Athens  staff
using  data   provided by the Nationral Center  for  Atmospheric
Research.   The  station  records were required  to   contain   a
minimum  of 25 years of continuous daily values, with  as  few
missing  entries as possible.  Average annual rainfall  for the
chosen   25  year period was compared to the  site  description
chosen   earlier  to check for  compatability  (Table   3.1).    If
more  than  25%  of the evaporation .data  were   missing,  PRZM
internally  calculated  daily  values  using  Hamon's   method.
This  method  was used in all cases except one   -(Station  1726,
South  Carolina), primarily because during  the   winter  months
few   stations   make   evaporation   measurements.     Missing
temperature   data were  generated by averaging other  values   on
that  day over the 25 year  period.  This method  was  chosen,  as
opposed  to   interpolation, because many gaps extended  longer
than  just  a few days  (sometimes a month  or more   data  were
missing).   The meteorologic station numbers, the chosen  time
periods,  and average  annual precipitation  and  temperature
values for each site are also  listed in Table 4.2.
                             51

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Figure 4.7  Major Wheat Farming Regions of the U.S.
            Source:  U.S. Dept. of Commerce, 1982
Figure 4.8  Major Corn Farming Regions of the U.S.
            Source:  U.S. Dept. of Commerce, 1982
                        52

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Figure 4.9  Major Soybean Farming Regions of  the U.S,
            Source:  U.S. Dept. of Commerce,  1982
Figure 4.10 .Major Cotton Farming Regions of the U.S,
             Source:  U.S". Dept. .of Commerce, 1982

                            53

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Figure 4.13  Major Regions of Insecticide Usage in the U.S
             Source:  U.S. Dept. of Commerce, 1982
Figure 4.14  Major Regions of Herbicide Usage in the U.S
             Source:   U.S. Dept.  of Commerce, 1982
                            56

-------
SCS  county  soil  surveys were used to further  define  soils
characteristics  at  each site.  For each site three  or  four
surveys  were randomly selected; the soil textures listed, and
their  respective  areas summed.  In most cases a single  soil
type  was consistently prevalent within a site.  If more  than
one  soil type was prevalent the soil having the largest areal
coverage  was chosen.  Soil textural classifications for  each
site  are ^Iso listed in Table 4.2.  These textures were  used
to  determine  field capacity  (0fc) wilting point ( 0wp),  and
bulk density ( ps) input values for PRZM.

Crop  information  was also determined by consulting SCS  soil
surveys,  Carsel  et al.,(1984), and Agricultural  Handbook   #
283  (U.S.D.A. 1972).  The planting and harvest dates for  the
major  crop  of each site are also listed in Table  4.2.   The
plant  rooting  depth  was taken as the average of  the  range
reported by Carsel et al., (1984).
4.5  FREQUENCY  DISTRIBUTION  PRODUCTION
After   selecting  a model,  choosing  the  appropriate  range  of
input   parameter  values,   and   choosing   the   representative
agricultural   application   sites,  the  final step was   to  make
the  simulation runs  required  to  produce the frequency  curves
in  Appendix   A.  One hundred  runs of  25 years   duration  were
made   for   each site, resulting  in 2500  simulation years  per
site.   The  frequency   distributions   in   Appendix  A  are  a
compilation  of  nineteen   multiples of  these runs,  or  47500
simulation  years.    The simulations were  made  on  an  HP-1000
micro-computer,  requiring   approximately  seventeen  hours  of
run-time per  site.
                              57

-------








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                          SECTION 5
                     EXAMPLE APPLICATIONS
This   section  contains  two  example  problems.   They   are
presented  to  help  guide the user through  test  cases   from
start  to  finish.   The  first  example  presents  a   simple,
straight-forward   case  without  complications.   The  second
example  explains the use of the handbook when available   data
does  not conform exactly to the format presented.  These  two
examples  strive to cover most situations where this  handbook
procedure  is  applicable and offer insightful  discussion of
the results, assumptions and limitations.
5.1  EXAMPLE #1
In  this example application we will assume that the pesticide
aldicarb  is  hypothetically being applied to  soybeans  in   the
delta  area of Mississippi.  We wish to know if the  continued
annual  application of the pesticide to this crop will   result
in leaching of aldicarb below the crop rooting depth.
Step 1;  Choose site:

         Crop        :
         Pesticide   :
Columbus, MS
long .  88° 30 '
soybeans
aldicarb
lat.
33° 30
Step 2;  Calculate input parameters  for  locating  frequency
         distributions  (R, Ks, CN)
Retardation Factor  (R)

a)  Look up Koc, Kow, or S value  for Aldicarb:
    B-3 in Appendix B, Kow =  5.00
                   from Table
b)  If Koc is not reported,  it must be  computed  from
    Kow or S:
         from Table 3.2 eq.  4.
         log Koc =  .544 (log Kow) + 1.377
         log Koc =  .544 (log (5.00))  +  1.377  = 1.7572
         Koc =57.2
                              59

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c)  Determine organic carbon  (OC) in  soil  from  reported
    values or calculate as follows:

         From national soil nitrogen  survey,  Figure  3.6
                     N = 0. to  .05% and
                    OC = 11*N
          therefore OC = 0. to  .55%

d)  Determine bulk density (  ps)  from the soil textural
    classification, if known.   If texture  is  not known use
    that listed for the representative site;   Columbus,
    MS is within site #15 and the predominant soil texture
    is silt loam (from Table  4.2)

       p silt loam = 0.86 to  1.67  (from  Table 3.3)

e)  Determine field capacity  (  0fc)  from field values,
    reported values, or regression  equations.

          0fc, silt loam =  .258  to  .402 (from Table 3.4)

f)  Calculate R  (equation  3.1):


                 _     57.2 (0.0)  (1.17)     _
             Rlow ~  l +      0.402          " 1>0

                       57.2 (0.0055)  (1.67)  _
             \igh  i        0.258         " J'U


          Note:   a  single  value of Kow was reported for
          aldicarb.   There is uncertainty about  this value
          that  is  not  reported in Appendix B.   Further
          investigation is warranted if a more  repre-
          sentative range  for R is desired.

 Decay Rate (Ks)

 Ks must  be found in the literature.   Some values  are
 reported in Appendix B.

      Ks  for aldicarb = .03695 with a  coefficient  of
      variation of 103.8% (Table B-6,  mean of 5 values)
      therefore,    Ks =  0.0 to 0.0753

 This  spans  nearly the entire range  of values in the  address
 matrices   for   Ks.   The  user  may want   to  gather   more
 information specific to the  soils in  question.
                                60

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Curve Number (CN)

In  most cases the curve numbers span a range having far  less
impact  on  the  system than either R or Ks.  If the  user  is
working  with  a  wide range of values for R and/or  Ks  there
will  be little need to calculate an exact CN.  In this  case,
there  is so much uncertainty in Ks that calculating an  exact
CN is not essential.

Were the user to have well defined R and Ks values, CN
can  be found by noting that, in this region,  'B'  and  'C' type
soils    predominate    (Table   3.1)   and   the   predominant
conservation   practice  is  contour  plowing.   Soybeans,  of
course,  is  a   row crop.  Using a range  of  hydrologic  soil
conditions   (poor to good) values of 75 to  84 can  be   selected
from Table 3.6.

Step 3:  Find the corresponding frequency distributions.
Use  the  address matrices  at  the  beginning  of Appendix A.
For  representative  site #15  the  matrix  looks as  follows:

Site #15
Crop:  soybeans
  CN:  71-85
1
3
5
20
50
.001
A15-1
A15-6
A15-11
A15-15
A15-18
.005
A15-2
A15-7
A15-12
A15-16
A15-19
Ks
.010
A15-3
A15-8
A15-13
A15-17
A15-20
.050
A15-4
A15-9
A15-14
.100
A15-5.
A15-10

 The  calculations  above  have provided values of

            R  =1-3
            Ks  = 0.0-.075
            CN  = 75-84

 The   relevant  frequency distributions can be found in  Figures
 A15-1  through  A15-10.

 Step 4;   Interpret  the  frequency curves.

 The   worst   case   scenario is exemplified by plot  A15-1,   the
 other  end  of  the spectrum by A15-10.  Both plots are shown in
 Figure 5.1.
                              61

-------
Q
UJ 80
D
UJ
UJ
u 7D
X
UJ
h
z
Ul 30
u
a
UJ
Q. 20
        % OF  APPLIED  PESTICIDE LEACHED PER  YEAR

        FIGURE  A15-1       R=].    KS = . GDI
D
UJ BO
a
LU
X
III
z
111 30 -
CJ
a
UJ
a. 2
                                                    i 75

                                                    D so

                                                    O 85
            - - I -     I     t-    I     t      I - - --t	H	1
    Q     10    2O    3O    <4O    5O     GO    7O    BO     9O   1 HO

        %  OF APPLIED PESTICIDE LEACHED PER  YEAR

        FIGURE  A15-1G       R = 3.     KS = . 1 DO



    Figure 5.1   Frequency Curves  for Example #1


                              62

-------
For  the worst case under all management scenarios   (CN=71-85)
some  leaching can be expected.  More specifically,  for  CN=85
there  is  a 98% chance that greater than 10% of  the  applied
pesticide  will  be leached when the decay rate is   very  low.
Regardless  of  management  practice, there is an  18%  chance
that  all  of  the  applied pesticide will  be  leached.   The
reason  that  plot  A15-1  shows that more than  100%  of  the
applied  pesticide could leach beyond the root zone  is due  to
carry-over.   The  soil profile may retain pesticide  in  some
years  due to low precipitation and low pesticide decay  rate.
In  relatively  wet  years all of the pesticide  applied  plus
stored pesticide may migrate through.

Plot  A15-10 (Figure 5.1) shows that under the best  conditions
very little pesticide is expected to leach.

The  plots  shown in Figure 5.1 reveal a large  difference  in
outcome  due to the range of expected decay rates.   If a  more
precise  indication  of  leaching potential is  required,  the
range  of Ks values used to address the plots must be reduced.
The  relative  shapes  and positions of the  curves  shown  in
Figure  5.1  indicate that more precise decay  rate  estimates
can  provide  a  more accurate assessment.  If, on   the  other
hand,  the  two  plots had looked very similar,  finding  more
detailed  information  about  decay rates would not  gain  the
user   any  more  useful  information.   By  looking at   the
orientation  of  curves  at both extremes,  indicated  by  the
range  of  input parameters used, the user can identify  which
input parameters warrant further refinement.
5.2  EXAMPLE #2
The  second example provides a case study of a  site that   lies
just  outside the boundaries of three representative  sites,   a
few  miles northeast of Rapid City, S.D.  The pesticide chosen
for  this example is diuron, which is currently used  on spring
wheat  in  Oregon,  Washington, and Idaho.  This  example  was
chosen  to present an application in which complications arise
due to insufficient or conflicting data.
Step 1;  Choose site:

         crop        :
         pesticide   :
Rapid City, S.D.

Spring Wheat
diuron
long.  44 10'
lat.  103 05'
         WSSA  (1983) indicates that diuron  is a pre- or
         postemergence herbicide which makes the analysis
         directly applicable.
                               63

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Step 2:   Calculate input parameters for locating frequency
         curves (R, Ks, CN)

Retardation factor (R)

a)  Look up Koc, Kow, or S value for diuron: Tables B-l and
    B-3 give:
      Kd  = 8.9      CV = 150.8%
      Koc = 382.6    CV =  72.4%
      Kow = 650

    In this case a range of Koc values is reported.  Other
    references  should be consulted to compare  reported
    values.  If, in  this case, only a Kow value  could  be
    obtained, the  following procedure would  be followed;:

    The structural formula for diuron can be described as
    aromatic
                       ci^
                               H o
                               I  II
                               N-C-N(CH3h
    with two chlorinated positions.  According  to Table 3.2
    the best conversion equation  from  Kow to Koc for a
    chemical of  this nature  is  equation  6.   Substitution of
    the Kow value reported above  into  the equation yields

      log Koc =1.00  (log 650)  -  0.21
          Koc =401

    This value lies within the  range of  values  for Koc by
    Rao and Davidson  (1980).  Unfortunately,  Kow was
    reported as  a single value  with no associated range.
    Because this parameter is most  influential  in
    determining  the potential for a compound to leach, more
    investigation is appropriate.  The wide range of Koc
    values recorded in Appendix B for  each pesticide
    indicates a  large amount of uncertainty in  its
    determination.  For this example the range  reported by
    Rao and Davidson will be used:

      Koc = 382.6   %CV =72.4           range = 106 - 660

    Note:  A value  for Kd was also  supplied by  Rao and
    Davidson.  Kd values may be used  in  the calculation of
    R, but they  introduce an element of  uncertainty beyond
    reported values of Koc and  Kow.  Kd  is. determined
    according to the  amount  of  OC present in the soil and
    thus  is a function of both  the  chemical and soil
    properties combined.  Unless  the  user is aware of the
    soil  used in its  determination,  reported Kd values may

                               64

-------
    be misleading.  In this case the range of Kd  values
    reported is 0. to 13.4.  The range of Kd values
    calculated from the reported values of Koc  and OC  for
    this site is  .6 to 19.8.  In this case the  values  are
    similar, but  if the OC content had been higher,  the
    reported range of Kd would have been too low.

b)  Determine organic carbon  (OC) in soil from  reported
    values or calculation.  Figure 3.6 indicates  that
    this site lies in an area having a range of
    soil nitrogen values from 0.10 to 0.19%.  Thus the %
    organic carbon ranges  from 1.1 to 2.1.

 c)  Determine  bulk  density (  ps)  from the  soil" texture
    classification.   This  example  site  lies just outside of
    three  representative  sites  (nos.  3,  4,  and 5).   In this
    case the Mead Co.,  South Dakota  soil survey reports
    primarily  loams  and silt loams  present.   The range of
    ps  values  covering  these two textural  classifications
    is  0.86 to 1.67.  Note in addition  that sites 3, 4, and
    5 are  characterized by predominantly loam,  and sandy
    loam soils,  respectively,  which  have a  ps range of
    1.16 to 1.76.  Therefore,  little uncertainty is
    introduced into  the bulk density parameter by using
    site 3, 4,  or 5  as  representative.

 d)  Determine  field  capacity (  0fc)  from soil textural
    classification.   Like  bulk density,  field capacity
    values span a relatively small  range.   Field capacity
    can also be determined from soil textural information.
    Using  the  same  technique as for  bulk density determin-
    ation,  Qfc for  loams  and silt  loams covers the range
    from 0.195 to 0.402 (Table 3.4).   Figure 3.8 could also
    have been  used.   Using the range of reported ps, the
    range  of   0fc is predicted as  0.12  to 0.65.  The
    narrower range  from Table 3.4 will  be used here.

 e)  Calculate  R:   (equation 3.1)

             R    =     106  (0.011) (0.86)    = 3.5
             low   1        0.402
              „     _  , ^ 660 (0.021) (1.67)   _ ._.
              \igh ~  l   ~   0.195          ~ 12°

     This range of values for R is quite large.  The cases
     presented in this handbook cover the range 1 to 50.
     For this case there is a fair amount of uncertainty in
     all of the components of R.  This results in a situ-
     ation where a clear-cut decision cannot be made about
     the leaching potential of diuron in this application.

                              65

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    This will be discussed further following the deter-
    mination of Ks and CN.

Decay Rate (Ks)

Values  for Ks must be found in the chemical literature.  Some
values are reported in Appendix B.  From Tables B-5 and B-6,

     Ks for diuron = 0.136 and 0.214 (soil surface) and
                     0.0064 and 0.0072 (soil)

Since  these  values are so disparate alternative  information
should  be  sought  to  narrow the range.   Rao  and  Davidson
(1980)  report  values  of  Ks for diuron  at  0.0031  with   a
coefficient   of variation  of  58.1%.  This  gives  a  one  standard
deviation  range   of 0.0013 to 0.0049.   The  upper  end  of   this
range  has  values similar to those reported  in   Appendix   B.
This   range will be used  in this  example.  For the present  the
very high values in Table  B-5 will be  ignored.

Curve  Number  (CN)

In  most cases the curve  numbers  span  a  range  having  far   less
impact on  the system than either R or  Ks.  Again,  when  the
user   is  working  with a  wide range of values  for  R  and/or   Ks
there  will be little need to calculate  an exact CN.   We   will
assume in  this example  that the curve  numbers  for  wheat   in
the area cover the entire  range  found  in the address matrix.

In  this  case  the calculated value for R does  cover   a   wide
range  of possibilities.   Because the  range  of values   extends
into   those   indicating a  potential leaching  hazard,   further
investigation is  warranted.  This will require the   user   to
obtain more  accurate values  for  the component variables  in R,
especially  Koc.   The  range of  values  reported   by   Rao  and
Davidson  for Koc  were determined on 84  soils.   Although   this
is  a  large sample size,  a more  detailed look  at the  84 values
and   soil  types is in order.  A few values  could  be   causing
the  wide  spread.  By narrowing this range a  more   accurate
estimate  of   a  potential leaching hazard  is  possible.   In
another  circumstance,  a  wide   range of  R  values   may  not
require  further   refinement.  If the  entire range had  fallen
outside  those  values  used  for this  handbook  development
 (e.g.,  R  =   100  to 500), no significant  leaching  would  be
predicted for the  25 year  period.

Step  3;  Find the  corresponding  frequency  curve(s):

This   test site lies outside, but close  to,  the  boundaries   of
three  representative  sites.    Although  bulk  densities  are
roughly  equivalent between the  Meade  Co., South Dakota  soils
and   the site 3, 4, or 5  representative  soils, the  indication


                             66

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is  that  the  loams and silt loams  (Meade  Co.)  have  higher
values  of  field  capacity than the loams of  site  5  (Table
3.4).   Therefore  site 5 can probably be eliminated.   Higher
field  capacities  would give lower  values of R and  therefore
site  3  or  4  frequency curves  would  slightly  overpredict
leaching,  all  other  factors being constant.   Figure  4.11,
however,  shows  that  precipitation in Rapid City is  in  the
neighborhood  of  15  to 20 inches  (38 to 51 cm).   Table  4.2
reveals  that the meteorological stations used for sites 3 and
4  had annual normal totals of 11.6  and 15.6 inches  (29 and  40
cm)   respectively.   Thus  site  3  would  be  eliminated   in
preference  to site 4 based on rainfall considerations.    The
appropriate  curves  are determined  by looking at the  address
matrix  for  representative  site 4.  The  address  matrix   is
shown below:
 Site  #4
 Crop:   Wheat
   CN:   70-84
R

1
3
5
20
50
.001
A4-1
A4-6
A4-11
A4-15
A4-16
.005
A4-2
A4-7
A4-12

Ks
.010
A4-3
A4-8
A4-13
.050
A4-4
A4-9
A4-14
.100
A4-5
A4-10
 Figure   A4-1,  represents the wo.rst case at site #4.   Using the
 full  range  of values calculated for R and Ks  Figures  A4-1,
 A4-2,   A4-6,   A4-7,   A4-11,   A4-12,  A4-15 and  A4-16  are  all
 pertinent.

 Step  4;   Interpret the curves

 Inspection   of Figure A4-1 (Appendix A) reveals that  leaching
 of diuron  is potentially quite high.  Under worst case (CN  =
 70)   there  is a 100% chance  that at least 48% of the  compound
 will  leach  below  the crop rooting  depth.   Increasing  the
 decay  rate from 0.001/day to 0.005/day results in a  decrease
 of about one-third in amount leached (Figure A4-2).  One  can
 see   that  as  R  and Ks get incrementally  larger,   that  the
 amount   leached  decreases  drastically  .(Figure  A4-2,  A4-6,
 A4-7,   A4-11,   A4-12,   A4-15).   Under the  best  circumstances
 shown  (Figure A4-16), there is no case in which more than  10
 or 12%  of the annually applied quantity is leached.   It  is
 obvious  that  to  get  a  more  definative  answer  in  this
 circumstance  that  uncertainty  in the value of  Kd  must  be
 reduced.
                               67

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Finally,  application  rates  of diuron vary from  0.7  to  18
Kg/ha  for  most agricultural applications (WSSA,  1983).   Ag
Consultants,  (1983) indicate rates from 0.9 to 1.35 Kg/ha  in
spring  wheat.  Multiplication of the percentages from Figures
A4-1,  etc.  by  a  typical application rate  would  give  the
actual load of the pesticide below the crop root zone.
                              68

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                          SECTION 6
                          REFERENCES
Ag Consultants.  1983.  1983 Weed Control Manual, Berg,
     G.L. ed.  Meister Pub. Co., Willoughby, OH.

Baes, C.F. III. and R.D. Sharp.  1983.  A Proposal  for
     Estimation of Soil Leaching Constants  for Use  in
     Assessment Models.  Journal of Environmental Quality
     12(1): 17-28.

Battelle Pacific Northwest Laboratory,  1982.  Section 3.3,
     Module Four — Failure Prediction, in:  Post-Closure
     Liability Trust Fund Simulation Model,  Draft Report by
     ICF Inc., Washington, B.C.  Report to  U.S.  EPA Office
     of Solid Waste.

Brady, N.C. 1974.  The Nature and Properties of  Soils.  8th
     Edition, McMillan Publishing Co.,  Inc.  New York.

Brown, S.M.  1983.  Simplified Methods  for  the Evaluation
     of Subsurface and Waste Control Remedial Action
     Technologies at Uncontrolled Hazardous Waste Sites.
     Draft Report, U.S. Environmental Protection Agency.

Carsel, R.F., C.N. Smith, J.D. Dean, P.P. Jowise, L.A.
     Mulkey and *1.N. Lorber.  1984.  Pesticide Root Zone
     Model (PRZM): Version II. User's Guide.  U.S.
     Environmental Protection Agency, Environmental
     Research Laboratory, Athens, Georgia.

Donigian, A.S., Jr., T.Y.R. Lo, and E.W. Shanahan.  1983.
     Rapid Assessment of Potential Ground-Water
     Contamination Under Emergency Response Conditions.
     EPA-600/8-83-030, U.S. Environmental Protection
     Agency.

Enfield, G.G., R.F. Carsel. S.E. Cohen, T.  Phan  and M.F.
     Walters.  1982.  Approximating Pollutant Transport to
     Ground Water.  Ground Water 20(6): 711.

Freeze, R.A., and J.A. Cherry.  1979.   Ground Water,
     Prentice Hall, Englewood Cliffs, N.J.

                             69

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Geraghty, J.J., D.W. Miller, F. Van Der Leeden and F.L.
     Troise.  1973.  Water Atlas of the United Stats
     Water Information  Center Publication.  Port
     Washington, New York.

Lyman, W.J.', W.F. Reehl and D.H. Rosenblatt.  1982.
     Handbook of Chemical Property Estimation Methods:
     Environmental Behavior of Organic Compounds.
     McGraw-Hill Book Company.

Mills, W.B., J.D. Dean, D.B. Porcella, S.A. Gherini,  R.J.M.
     Hudson, W.E. Frick, G.L. Rupp and G.L. Bowie.   1982.
     Water Quality Assessment: A Screening Procedure  for
     Toxic and Conventional Pollutants.  Part 1.
     EPA-600/6-82-004a.

Nash, R.G.  1980.  Dissipation Rate of Pesticides From
     Soils.  Chapter 17 _In CREAMS: A Field Scale Model  For
     Chemicals, Runoff, and Erosion from Agricultural
     Management Systems.  W.G. Knisel, ed.  USDA
     Conservation Research Report No. 26.  640 pp.

Oster, C.A.  1982  Review of Ground Water Flow and
     Transport Models  in the Unsaturated Zone, U.S.N.R.C.,
     NUREG/CR-2917

Parker, C.A., et. al. ,'  1946.  Fertilizers and Lime in the
     United States.  USDA Misc. Pub. No. 586.

Rao, P.S.C, and J.M. Davidson.  1980.  Estimation of
     Pesticide Retention and Transformation Parameters
     Required in Nonpoint Source Pollution Models.   In
     Environmental Impact of Nonpoint Source Pollution.
     M.R. Overcash and  J.M. Davidson, eds.  Ann Arbor
     Science Publishers, Inc.  Ann Arbor, Michigan

Rawls, W.J., D.L. Brakenseik and K.E. Saxton.  1982.
     Estimation of Soil Water Properties.  ASAE Paper No.
     81-2510, pp 1316-1320.

Reisenauer, A.E., et al.  1981.  Advective Radionuclide
     Transport with  Soil Interaction Under Variably
     Saturated Flow  Conditions.  PNL-3994, Pacific
     Northwest Laboratory, Richland, Washington.

Reisenauer, A.E., et al.  1982.  TRUST: A Computer Program
     for Variably Saturated Flow in Multi-Dimensional,
     Deformable Media,  NUREG/CR-2360, PNL/3975, Prepared
     for the Division  of Health, Siting, and Waste
     Management, Office of Nuclear Research, U.S. Nuclear
     Regulatory Commision by Battelle Pacific Northwest
     Laboratory, Richland, WA.

                             70

-------
Schwab,  G.O., R.K. Frevert, T.W. Edminster and K.K. Barnes.
     1966.  Soil and Water Conservation Engineering.  John
     Wiley and Sons.

Segol, G. 1976.  A Three-Dimensional Galerkin Finite-
     Element Model for the Analysis of Contaminant
     Transport in Variably Saturated Porous Media, User's
     Guide.  Dept. of Earth Sciences, Univ. of Waterloo,
     Waterloo, Ontario, Canada.

Sharma,  D.  1979.  A Comprehensive Mathematical Model
     Capable of Predicting Flow, Heat Transfer as Well as
     Chemical - Species Transport in Porous Media, Dames
     and Moore, Advanced Technology Group, Rep. TN-DN-42.

U.S. Department of Agriculture, Soil Conservation Service.
     1972.  National Engineering Handbook.  Section 4,
     Hydrology.

U.S. Department of Agriculture, Statistical Reporting
     Service.  1972.  Usual Planting and Harvesting Dates:
     Field and Seed Crops.  Agricultural Handbook No. 283.

U.S. Department of Commerce.   1982.  1978 Census of
     Agriculture, Vol. 5 pt 1, AC78-SR-1.

U.S. Environmental Protection  Agency.  1978.  Registration
     of Pesticides in the United States; Proposed
     Guidelines.  Federal Register, Monday, July 10,  1978,
     Part II.

van Genuchten, M.Th.  1978.  Mass Transport in Saturated-
     Unsaturated Media; One-Dimensional Solutions, Research
     Report  78-WR-ll, Water Resources Program,  Princeton
     University, Princeton, NJ.

Weed Science Society of America.  1983.  Herbicide
     Handbook.  5th ed.  Weed  Science Society of America,
     Champaigne, II.

Yeh, G.T. and D.S. Ward.   1979.  FEMWATER: A Finite-Element
     Model of Water Flow Through Saturated-Unsaturated
     Porous  Media, Oak Ridge National Lab. Report
     ORNL-5567.

Yeh, G.T. and D.S. Ward.   1981.  FEMWASTE: A Finite-Element
     Model of Waste Transport  Through Saturated-Unsaturated
     Porous  Media, Oak Ridge National Lab., Report
     ORNL-5601.
                           71

-------
APPENDIX A
SITE
CROP:
CN:




R


SITE
CROP:
CN:




R


NO. 1
WHEAT
78-84
Ks
.001 .005 .010 .050 .100
1
3
5
20
50
Al-1 Al-2 Al-3 Al-4 Al-5
Al-6 Al-7 Al-8 Al-9
Al-10 Al-11 Al-12 Al-13
Al-14 Al-15
Al-16
NO. 2
WHEAT
70-84
Ks
.001 .005 .010 .050 .100
1
3
5
20
50
A2-1 A2-2 A2-3 A2-4 A2-5
A2-6 A2-7 A2-8 A2-9
A2-10 A2-11 A2-12
A2-13

     72

-------
SITE NO. 3
CROP:  WHEAT
  CN:  59-84
           .001
SITE NO. 4
CROP:  WHEAT
  CN:  70-84
           .001
.005
                             Ks
.010
.050
.100
1
3
5
20
50
A3-1
A3-6
A3-11
A3-15

A3-2
A3-7
A3-12


A3-3 A3-4
A3-8 A3-9
A3-13 A3-rl4


A3-5
A3-10



.005
                             Ks
.010
.050
.100
1
3
5
20
50
A4-1
A4-6
A4-11
A4-15
A4-16
A4-2
A4-7
A4-12


A4-3
A4-8
A4-13


A4-4
A4-9
A4-14


A4-5
A4-10



                          73

-------
SITE NO. 5
CROP : WHEAT
CN: 59-84
Ks
.001 .005 .010 .050 .100
1
3
R 5
20
50
A5-1 A5-2 A5-3 A5-4 A5-5
A5-6 A5-7 A5-8 A5-9 A5-10
A5-11 A5-12 A5-13 A5-14
A5-15 A5-16
A5-17
SITE NO. 6
CROP : WHEAT
CN: 59-65
Ks
.001 .005 .010 .050 .100
1
3
R 5
20
50
A6-1 A6-2 A6-3 A6-4 A6-5
A6-6 A6-7 A6-8 A6-9 A6-10
A6-11 A6-12 A6-13 A6-14 A6-15
A6-16 A6-17 A6-18
A6-19 A6-20
74

-------
SITE NO. 7
CROP:  CORN
  CN:  78-85
            .001
SITE NO. 8
CROP:   CORN
  CN:   71-91
                             Ks
.005
.010
.050
.100
1
3
5
20
50
A7-1
A7-6
A7-11
A7-14

A7-2
A7-7
A7-12
A7-15

A7-3 A7-4
A7-8 A7-9
A7-13


A7-5
A7-10



                             KS
  R
            .001
.005
.010
.050
.100
1
3
5
20
50
A8-1
A8-6
A8-9
A8-11

A8-2 A8-3
A8-7 A8-8
A8-10


A8-4 A8-5




                         75

-------
SITE NO. 9
CROP:   CORN
  CN:   78-85
            .001
SITE NO. 10
CROP:  CORN
  CN:  71-78
             .001
                             Ks
.005
.010
.050
.100
1
3
5
20
50
A9-1
A9-6
A9-10
A9-13

A9-2
A9-7
A9-11
A9-14

A9-3 A9-4
A9-8 A9-9
A9-12


A9-5




                             Ks
.005
.010
.050
.100
1
3
5
20
50
A10-1
A10-6
A10-10
A10-14
A10-16
A10-2
A10-7
A10-11
A10-15
A10-17
A10-3
A10-8
A10-12


A10-4
A10-9
A10-13


A10-5




                          76

-------
SITE NO. 11
CROP:  CORN
  CN:  71-78
                             Ks
            .001
.005
.010
.050
.100
1
3
5
20
50
All-1
All-6
All-10
All-14
All-16
All-2
All-7
All-11
All-15
All-17
All-3
All-8
All-12


All-4
All-9
All-13


All-5




SITE NO. 12
CROP:  CORN
  CN:  78-85
                             KS
            .001
.005
.010
.050
.100
1
3
5
20
50
A12-1
A12-6
A12-10
A12-14
A12-16
A12-2
A12-7
A12-11
A12-15
A12-17
A12-3
A12-8
A12-12


A12-4
A12-9
A12-13


A12-5




                         77

-------
SITE NO. 13
CROP:   CORN
  CN:   78-91
                             Ks
  R
            .001
.005
.010
.050
.100
1
3
5
20
50
A13-1
A13-6
A13-10
A13-13
A13-15
A13-2
A13-7
A13-11
A13-14

A13-3 A13-4
A13-8 A13-9
A13-12


A13-5




SITE NO. 14
CROP:  SOYBEANS
  CN:  71-85
                             Ks
  R
            .001
.005
.010
.050
.100
1
3
5
20
50
A14-1
A14-6
A14-11
A14-15
A14-17
A14-2
A14-7
A14-12
A14-16
A14-18
A14-3
A14-8
A14-13


A14-4
A14-9
A14-14


A14-5
A14-10



                          78

-------
SITE NO. 15
CROP:  SOYBEANS
  CN:  71-85
                             Ks
  R
            .001
.005
.010
.050
.100
1
3
5
20
50
A15-1
A15-6
A15-11
A15-15
A15-18
A15-2
A15-7
A15-12
A15-16
A15-19
A15-3
A15-8
A15-13
A15-17
A15-20
A15-4
A15-9
A15-14


A15-5
A15-10



SITE NO. 16
CROP:  SOYBEANS
  CN:  71-85
                             Ks
  R
            .001
.005
.010
.050
.100
1
3
5
20
50
A16-1
A16-6
A16-11
A16-15
A16-18
A16-2
A16-7
A16-12
A16-16
A16-19
A16-3
A16-8
A16-13
A16-17
A16-20
A16-4
A16-9
A16-14


A16-5
A16-10



                         79

-------
SITE NO.  17
CROP:   SOYBEANS
  CN:   78-91
                              Ks
             .001
.005
.010
                                        .050
                            .100
1
3
5
20
50
A17-1
A17-6
A17-11
A17-15
A17-18
A17-2
A17-7
A17-12
A17-16
A17-19
A17-3
A17-8
A17-13
A17-17

A17-4
A17-9
A17-14


A17-5
A17-10



SITE NO. 18
CROP:   COTTON
  CN:   71-91
                             Ks
  R
            .001
.005
.010
                                        .050
                           .100
1
3
5
20
50
A18-1
A18-6
A18-10
A18-13
A18-15
A18-2
A18-7
A18-11
A18-14

A18-3 A18-4
A18-8 A18-9
A18-12


A18-5




                         80

-------
SITE NO. 19
CROP:  COTTON
  CN:  71-85
            .001
                             Ks
.005
.010
.050
.100
1
3
5
20
50
A19-1
A19-6
A19-11
A19-15
A19-17
A19-2
A19-7
A19-12
A19-16
A19-18
A19-3
A19-8
A19-13


A19-4
A19-9
A19-14


A19-5
A19-10



                            81

-------
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-------
                        APPENDIX B
               Supplemental  Chemical Data
TABLE B-1.  SUMMARY OF ADSORPTION  PARTITION COEFFICIENT  VALUES
COMPILED  FROM PUBLISHED LITERATURE FOR SEVERAL PESTICIDES AND
RELATED ORGANIC COMPOUNDS

Pesticide
AMETRYNE
AMIBEN
ATRAZINE
BROMACIL
CARBOFURAN
CHLOROBROMURON
CHLORONEB
CHLOROXURON
CHLOROPROPHAM
CHLORTHIAMID
CIODRIN
DDT
DICAMBA
DICHLOBENIL
DIMETHYL AMINE
DIPROPETRYNE
DISULFOTON
DIURON
FENURON
LINDANE
LINURON
Number
of
Soils
32
12
56
2
5
5
1
5
36
6
3
2
5
34
5
5
20
84
10
3
33
Kd
Mean (%CV)
6.16(65.1)
1.40(145.2)
3.20(89.8)

1.05(111.8)
37.22(121.2)

234.0(71.1)

5-57(39.7)


0.11(103.9)
3-0(70.9)
14.0(77.3)
13.5(91.5)
32.3(91.7)
8.9(150.8)
2.11(120.8)
20.1(13.5)
21.2(100.2)
Koc
Mean (%CV)
388.4(57.1)
189.6(149.7)
163-0(49.1)
72.0 (102.1)
29.4(30.0)
995.6(55.1)
1652. 9( — )
4343.3(28.8)
816. 3(~)
98.3(27.5)
74.8(59.1)
243118.0(65.0)
2.2(73.5)
224.4(77-4)
434.9(19.8)
1180.8(74.9)
1603.0(144.2)
382.6(72.4)
42.2(84.7)
1080.9(13-0)
862.8(72.3)
                                          (continued)
                           395

-------
                          TABLE B-1 (continued)

Pesticide
MALATHION
METHYL PARATHION
METHYL UREA
METOBROMURON
MONOLINURON
MONURON
NEBURON
p-CHLOROANILINE
PARATHION
PHENYL UREA
PICLORAM
PROMETONE
PROMETRYNE
PROPAZINE
SIMAZINE
TELONE (cis)
TELONE (trans)
TERBACIL
THIMET
TRITHION
2,4-D
2,2-D AMINE
2,4,5-T
Number
of
Soils
20
7
5
4
10
18
5
5
it
5
26
29
38
36
147
6
6
4
4
4
9
3
4
Kd
Mean (% CV)
34. 1(67.1)
12.7(67.2)
3.5(80.1)
6.7(62.8)
12.3(83.6)
7.6(122.5)
166.8(68.3)
16.8(79.4)
21.9(63-7)
4.9(91.9)
0.63(150.2)
7.2(147.5)
10.8(123.8)
3.K135.8)
2.3(158.5)
54.8(78.6)
93-2(72.0)
0.78(145.0)
8.8(77.7)
74.6(26.6)
0.78(128.6)
2.0(112.5)
1.6(87.3)
K
oc
Mean (% CV )
1796.9(65.9)
5101.5(113.6)
58.8(15.1)
271.5(37.1)
284.3(55.2)
183-5(60.8)
3110.5(23.5)
561.5(33-6)
10650.3(74.6)
76.3(12.3)
25.5(138.5)
524.3(143.6)
614.3(99.1)
153.5(37.0)
138.4(12.6)
798.1(44.3)
1379.0(45.4)
41.2(42.2)
3255.2(49.5)
46579-7(80.2)
19.6(72.4)
109-1(30.2)
80.1(45-3)
Source:  Rao and Davidson,  1980
                                   396

-------
    TABLE B-2.
MEASURED VALUES OF KQC FOR SELECTED CHEMICALS
            Chemical
                           Koc

                      Mean  (% CV)
            Dicamba
            2,4-D
            Picloram
            Carbofuran
            Acetophenone
            Ethylene dibromide
            Benzene
            Chlorthiamid
            Simazine
            Atrazine
            Fluometrom
            Carboryl
            p-Cresol
            Quinoline
            Linuron
            Nitralin
            Lindane
            Disulfoton
            Malathion
            Diallate
            Neburon
            Hexachlorobenzene
            Parathion
            Dibenzothiophene
            Trifluralin
            2,2',4,5,5'-Penta-
              chlorobiphenyl
            Methoxychlor
            DDT
            7,12-Dimethylbenz[a]-
              anthracene
            Benz[a]anthracene
            Mirex
                                 2.2(74)
                                20(72)
                                26(140)
                                29(30)
                                43
                                44
                                83
                                98(28)
                               140(13)
                               160(49)
                               175
                               230
                             <500
                               570
                               860(72)
                               960
                             1,080(13)
                             1,600(140)
                             1,800(66)
                             1,900
                             3,100(24)
                             3,900
                            10,600(75)
                            11,200
                            13,700
                            42,500

                            80,000
                           243,000(65)
                           476,000

                         1,380,000
                        24,000,000
    Values given are often the mean of measurements on a variety of
    soils and/or sediments.  All values may be assumed to be uncer-
    tain by at least 10%.  Units of KQC are cm**3/gram.   In several
    cases the values of K   have been  rounded off from those given
                         oc
    in the original references.

    % CV = % Coefficient of variation = (standard deviation/mean) x
    100.  Unlisted values are not available.
Source:  Lyman, et al.,  1982
                              397

-------
          TABLE  B-3.   SUMMARY  OF  OCTANOL-WATEP  PARTITION COEFFICIENTS
          (K   )  FOR  PESTICIDES COMPILED  FROM LITERATURE-I
Pesticide
A. INSECTICIDES

ALDICARB
ALTOSID
CARBARYL
CARBOFURAN
CHLORDANE
CHLORPYRIFOS
CHLORPYRIFOS
CHLORPYRIFOS
CHLORPYRIFOS, METHYL
CHLORPYRIFOS, METHYL
DDD
DDE
DDE p,p
DDT
DDT p,p
DDVP
DIALIFOR
DIAZINON
DICHLOFENTHION
DICIFOL
DIELDRIN
DINOSEB
ENDRIN
ETHOXYCHLOR
FENITROTHION
HCB
HEPTACHLOR
LEPTOFOS
LEPTOPHOS
LINDANE
MALATHION
MALATHION
METHOMYL
METHOXYCHLOR
METHOXYCHLOR
METHYL PARATHION
PARATHION
PERMETHRIN
PHORATE
PHOSALONE
PHOSMET
K
ow


5.00000E+00
1.76000E+02
6.51000E+02
2.07000E+02
2.10800E+03
2.05900E+03
6.60000E+04
1 .28825E+05
1 .97000E+03
2.04170E+04
1.15000E+05
7.34450E+04
4.89779E+05
3.70000E+05
1.54882E+06
1 .95000E+02
4.89780E+04
1.05200E+03
1.38038E+05
3.46100E+03
4.93000E+03
1 .98000E+02
1.61900E+03
1.18000E+03
2.29900E+03
1 .66000E+06
7-36600E+03
4.12200E+03
2.04174E+06
6.43000E+02
2.30000E+02
7.76000E+02
1.20000E+01
2.05000E+03
1 .20000E+05
2.07600E+03
6.45500E+03
7.53000E+02
8.23000E+02
1 .99530E+04
6.76000E+02
Pesticide
PROPOXUR
RONNEL
TERBUFOS
TOXAPHENE

B. HERBICIDES

ALACHLOR
ATRAZINE
ATRAZINE
BIFENOX
BROMACIL
CHLORAMBEN
CHLOROPROPHAM
DALAPON
DALAPON, NA SALT
DICAMBA
DICHLOBENIL
DIURON
MONURON
MSMA
NITROFEN
PARAQUAT .2HCL
PICLORAM
PROPACHLOR
PROPANIL
SIMAZINE
TERBACIL
TRIFLURALIN
2,4-D
2,4-D
2,4-D
2,4,5-T
2,4,5-T, BUTYL ESTER
2,4,5-T OCTYL ESTER

C. FUNGICIDES

BENOMYL
CAPTAN
PCP


K
ow
2.80000E+01
7.58580E+Qi|
1.67000E+02
1.69500E+03



4.34000E+02
2.12000E+02
2.26000E+02
1 .74000E+02
1.04000E+02
1 -30000E+01
1 .16000E+03
5.70000E+00
1 .OOOOOE+00
3.00000E+00
7.87000E+02
6.50000E+02
1.33000E+02
8.00000E-04
1 .24500E+03
1 .OOOOOE+00
2. OOOOOE+00
4.10000E+01
1 .06000E+02
8.80000E+01
7.80000E+01
1 . 15000E+03
4.16000E+02
4.43000E+02
6.46000E+02
7. OOOOOE+00
6.40000E+04
9.09000E+02



2.64000E+02
3.30000E+01
1.42900E+04


Source:  Rao and Davidson,  1980
                                   398

-------
          TABLE B-4.  SUMMARY OF OCTANOL-WATER PARTITION COEFFICIENTS
          (K  ) FOR PESTICIDES COMPILED FROM THE LITERATURE-II
            ow
          Compound
  Observed
     Kow
Methylacetylene
Fluoroform
Isobutylene
Ethanol
Dimethyl ether
Cyclohexane
Propane
2-Propanol
tert-Butylamine
2-Phenylethylamine
N-Phenylacetamide
Halothane
Benzimidazole
/j-Nitrophenol
Cyclohexene
1,2-Dichlorotetrafluoroethane
Hexachlorophene
1,2-Methylenedioxybenzene
2-Phenyl-1,3-indandione
Carbon tetrachloride
Dioxane
2-Bromoacetic acid
2-Chloroethanol
Indene
Fluorene
Anthracene
Pyrene
Quinoxaline
Carbozole
Menadione
Chloramphenicol
2-Hydroxy-1,4-naphthoquinone
2-Methyl-3-hydroxyl-
    1,4-naphthoquinone
2-Methoxy-1,4-naphthoquinone
Benzothiazole
o-Phenanthroline
Thiazole
Piperazine
Morpholine
Salicylic acid
    8.71
    4.37
  218.8
    0.49
    1.26
 2754-0
  229.1
    1
    2
12
51
   25.7
   14.
  199,
   21.9
   81.3
  724.4
  660.7
 8511.0
  120.2
  794.3
  676.1
    0.38
    2.57
    1.07
  831.8
13183.0
28184.0
75858.0
   12.0
 3236.0
  158.5
   13-8
   28.8

   15.8
   22.4
  102.3
   67.6
    2.75
    0.07
    0.08
  173-8
                                             (continued)
                                       399

-------
                            TABLE B-4 (continued
Compound
Imidazole
Cylcohexanol
o-Phenyleneurea
Tripropylamine
Di-T7-propylamine
Coumarin
Trifluoromethylbenzene
Trifluoromethylsulfonanilide
1 ,3-Indandione
9-Fluorenone
Phenazine
Morphine
2,2, 2-Trif luoroethanol
2,2,2-Trifluoroacetamide
2,2, 2-Trichloroethanol
2,2, 2-Trichloroacetamide
Pyrimidine
Glucose
Cyclohexylamine
Neopentane
2-Methylpropane
Crotonic acid
Cinnamonitrile
Cinnamic acid
Cinnamamide
Methyl cinnamate
Phenyl vinyl ketone
Styrene
1 -Phenyl-3-h"ydroxypropane
Methyl styryl ketone
1 , 1 ,2-Trichloroethylene
2-Methoxyanisole
Ethyl vinyl ether
Pyrazole
1 , 1-Difluoroethylene
1 ,2,3,4-Tetrahydroquinoline
Observed
Kow
0.83
17.0
13.2
616.6
41.7
24.5
794.3
1122.0
4.07
3802.0
691.8
6.76
2.57
1.32
22.4
11.0
0.40
0.001
30.9
1288.0
575.4
5.25
91.2
134.9
25.7
416.9
75.9
891.3
89.1
117.5
195.0
120.2
11.0
1.35
17.4
195.0
Source:   Lyman,  et  al.,1982
                                    400

-------
TABLE B-5.  VALUES OF K  FOR DISSIPATION
OF PESTICIDES FROM SOILSSURFACES

Pesticide
HERBICIDES
Alulam

Benefin
Butralin
2,4-D isooctyl
2,4-D isooctyl
2,4-D
2,4-D isooctyl
2,4-D amine
2,4-D araine
2,4-D amine
2,4-D
2,4-D
2,4-D amine
2,4-D isooctyl
Dicamba
Dif lubenzuron
Dinitramine
Diuron
Diuron
Fluchloralin
Fluometuron
Fluometuron
Isopropalin
Nitralin
Oryzalin
Oxmyl
Oxmyl
Oxmyl
Pendimethalin
Picloram
Picloram
Profluralin
Prometryne
Pronamide

Pronamide
Propham
Propham
Silvex (spray)
Silvex (granules)
Simazine
k
s

0.0141
.1174
.3349
.1077
.0923
.0183
.0788
.0486
.0522
.0139
.0108
.1634
.1036
.0352
.2603
.0151
.0040
.0856
.136
.214
.0169
.043
.077
.1948
.1042
.0284
.0646
.0354
.0448
.1695
.2689
.0712
.2434
.0127
.0203
.0603
.0173
.025
.279
.0213
.0346
.0089
Pesticide
INSECTICIDES
Aldrin
Aldrin
(+dieldrin)
(granules )
Azinphosmethyl
(spray)
Azinphosmethyl
(spray)
Carbofuran
Carbofuran
Carbofuran
Carbofuran
Carbofuran
Carbofuran
Carbofuran
Carbofuran
Carbofuran
Chlordane
Chlordane
DDT
DDT
DDT
DDT
DDT
DDT
DDT
DDT
DDT
DDT
DDT
Diazinon
Endrin
Endrin
Ethion
Ethion
Ethyl
parathion
Ethyl
parathion
Ethyl
parathion

k
s

0.2406

.0045

.0486

.0434

.0075
.0690
.0180
.0048
.034
.086
.0040
.0059
.0132
.0101
.007
.004
.053
.0060
.0049
.0060
.00015
.000023
.00040
.00014
.00024
.00044
.1422
.0110
.2436
.0647
.0702

.0332

.0328

.0282

                                 (continued)
                   401

-------
                         TABLE B-5  (continued)
     Pesticide
                  Pesticide
     HERBICIDES

2,4,5-T (isooctyl)

2,4,5-T
2,4,5-T
Trifluralin

Trifluralin
Trifluralin
Trifluralin
Trifluralin
Trifluralin
.0266
.075
.0674
.1323
.0748
.0681
.0299
.0599
.1729
.0071
.0956
   INSECTICIDES

Hexachlorobenzene   0.050
Methyl parathion     .0165
Methyl parathion     .0153
Methyl parathion     .0147
Parathion            .0058
Toxaphene            .0046
Source:  Nash, 1980
                                    402

-------
             TABLE B-6.   VALUES OF K  FOR DISSIPATION
             OF PESTICIDES IN SOIL  S
Pesticide
Pesticide
HERBICIDES
Alachlor
Amitrole
Arsenic acid
Asulam
Asulam
Asulam
Atrazine
Atrazine
Atrazine
Atrazine
Atrazine
Benefin

Benefin

Bifenox
Butralin
Butralin
Cyanazine
Di-Allate
Di-Allate
Di-Allate
Di-Allate
Dicamba
Dicamba
Dicamba
Dicamba
Dicamba
Dicamba
Dicamba
Dicamba
2,4-D
2,4-D acid
2,4-D
2,4-D salt
2,4-D
2,4-D ester
2,4-D isooctyl
ester
2,4-D
2,4-D amine
2, 4-D amine
0 .0384
.0768
<.0064
.0986
.0519
.0310
.0131
.0063
.0064
.0133
.0149
.0053
.0077
.0077
.0070
.142
.0128
.0077
.0064
.0138
.0248
.0180
.0110
.0197
.2140
>.2140
.0486
.0902
.0217
.0407
.0267
.1733
>.0768
.1386
.0768
.1733
>.0768
.2546

.2731
.1457
.1008
2,4-D isooctyl
ester a amine
2,4-D isooctyl
ester a amine
2,4-D isooctyl
ester a amine
2,4-D isooctyl
ester a amine
Dichlorprop
Dichlorprop
Dichlorprop
Dinitramine
Dinitramine
Diuron
Diuron
EPTC
EPTC
Fluchloralin
Fluchloralin
Isopropalin
Isopropalin


Isopropalin
Isopropalin
Isopropalin
Karbutilate

Karbutilate
Linuron

Linuron
Linuron
Linuron
Linuron
MCPA
MCPA
Metribuzin
Metobromuron
Metobromuron
Monolinuron
Monuron
0.0951

.0555

.0852

.0257

.0578
.0866
.0693
.0193
.0193
.0064
.0072
.0220
.0248
.0070
.0045
.0023
.0036
.0054
.0040
.0304
.0214
.0275
.0057
.0282
.0118
.0104
.0231
.0047
.0280
.0039
.0061
.1221
.1070
.0298
.0231
.0248
.00216
.0060
                              403
                                            (continued)

-------
                    TABLE  B-6   (continued)
Pesticide
Pesticide
HERBICIDES
Monuron
Neburon
Neburon
Nitralin

Nitralin

Nitralin
Nitralin
Nitralin
Nitralin
Nitralin
Nitralin
Oryzalin
Oryzalin
Pebulate
Pebulate
Picloram


Pebulate
Pebulate
Picloram
Picloram
Picloram
Picloram
Picloram
Picloram
Picloram
Picloram
Picloram
Picloram
Picloram
Picloram
Profluralin
Profluralin
Pometryne
Propazine
Propazine
Propyzamide
0.0075
.0073
.0059
.0062
.0086
.0096
.0086
.0110
.0079
.0090
.0024
.0155
.0091
.0054-. 0083
.0144-. 0056
.0396
.0396
.0025
.0083
.0056
.0396
.0396
.0025
.00772
.0044
.0050
.0354
.0258
.0268
.0269
.004
.0019
.0048
.0028
.0047
.0051
.0238
.0108
.0056
.0061-. 0158
Silvex
Silvex
Silvex
Simazine
Simazine
Simazine
Simazine
Simazine
Simazine
Simazine
Tebuthiuron
Tebuthiuron
Tebuthiuron
Tebuthiuron
Tebuthiuron
Tebuthiuron
Tebuthiuron
Triallate
Triallate
Triallate
Triallate
Triallate
Triallate
2,4,5-T
2,4,5-T
2,4,5-T
2,4,5-T
2,4,5-T
2,4,5-T
2,4,5-T
Trifluralin
Trifluralin
Trifluralin
Trifluralin
Trifluralin
Trifluralin
Trifluralin
Trifluralin
Trifluralin
Trifluralin
0.0330
.0495
.0462
.0074
.0083
.0116
.0082
.0539
.062
.0187
.0024
.0060
.0427
.0201
.0517
.0624
.0069
.0090
.01 10
.0144
.0067
.0088
.0053
.0289
.0330
.0330
.0508
.0495
.0416
.0414
.0037
.0047
.0051
.0044
.0175
.0956
.0189
.0145
.0117
.0104
                                           (continued)
                                404

-------
                    TABLE  B-6  (continued)
Pesticide
Pesticide
HERBICIDES
Trifluralin
Trifluralin
Trifluralin
Vernolate
Vernolate

INSECTICIDES

Aldicarb
Aldicarb
Aldicarb
Aldicarb
Aldicarb
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
Aldrin
(Dieldrin)
Aldrin
(Dieldrin)
Aldrin
(Dieldrin)
Aldrin
(Dieldrin)
Akton
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl

0.0026
.0155
.0091
.0396
.0396



0.00273
.008?
.0991
.0420
.0322
<.0032
.0264
.0259
.0014
.0136
.0256
.0258
.0066
.0101
.0136
.0149 x of 19
.0165 x of 19
.0061
.0096
.0038

.0006

.0008

.0012

.0017
.0032
.0239
.0026
.0014
.0533
.0273
INSECTICIDES
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
Azinphosmethyl
BHC
BHC

BHC
BHC alpha
BHC beta
BHC gamma
BHC delta
Bromophos
Carbaryl
Carbaryl
Carbaryl
Carbofuran
Carbofuran
CGA-12223
CGA-12223
Chlordane
Chlordane
Chlorfenvinphos
Diazinon
Diazinon
Diazinon
Diazinon
Diazinon
Diazinon
Diazinon
Diazinon
Diazinon
Diazinon
Diazinon
Dieldrin
Dieldrin
Dieldrin

0.0516
.0086
.0119
.0235
.0074
.0101
.0458
.0505
.0211
.0021
.0140
X of 19
0098 x of 19
.0006
.00015
.00042
.00036
.0198
.0768
1196 x of 8
0969 x of 8
.0768
.0079
.0385
.0693
.00072
.0020
.0055
.0330
.0151
.0067
.0242
.0239
.0239
.0248
.0189
.0260
.0166
.0171
.0142
.0187
.0003
                                            (continued)
                               405

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                    TABLE B-6   (continued)
Pesticide
Pesticide
INSECTIDES
Dieldrin
Dieldrin
Dieldrin
Dioxacarb
Dioxacarb
Dioxathion
Dioxathion
Dioxathion
Dioxathion
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
p,p'-DDT
Dimethoate
Disulfoton
Ednosulfan
Ethion
Ethion
Ethion
Ethion
Ethion
Ethion
Ethion
Ethion
Ethion
Ethion
Fenitrcthion
Fenitrothion
Fonofos

0.0002
.0001
.0008
.0248
.3465
.0156
.0128
.0141
.0229
.0008
.0005
.0021
.0014
.0009
.0004
.0009
.0037
.0024
.0048
.0003
.0002
.0011
.0029
.00016
.0007
.00029
.0990
.1604
.0162
.0014
.0012
.0009
.0015
.0014
.0015
.0009
.0022
.0032
.0025
.0578
.1155
.0158

Heptachlor
Heptachlor
Heptachlor
Hexachlorobenzene
Isobenzan
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Lindane
Malathion
Malathion
Malathion
Malathion
Malathion
Malathion
Mecarbam
Methidathion
Methidathion
Methoxychlor
Methoxychlor
Methyl Parathion
Mevinphos
Parathion
Parathion
Parathion
Parathion

Parathion

Parathion
Parathion
Parathion

.0021
.0025
.0028
.0006
.0050
.0022
.0026
.0017
.0046
.0011
.0014
.0048
.0147
.0264
.0074
.0263
.0264
.0139
.0059
2.9173
2.4618
1.2681
.4152
1 .9832
1 .9026
.0495
.0108
.0495
.0046
.0033
.2207
.2936
.0248
.056
.0046
.1239
x of 8
.0727
x of 7
.1371
.1306
.0944
                                           (continued]
                            406

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    Pesticide
                         TABLE B-6  (continued)
    INSECTIDES
Parathion
Parathion
Parathion
Parathion
Parathion
Parathion
Phenthoate
Phenthoate
Phenthoate
Phorate
Phorate
Phorate
Phorate
Phorate
Phorate
Zinophos
Zinothos
Zinophos
Zinophos
Zinophos
Zinophos
Zinophos
Zinophos
Zinophos
0.1150
 .0866
 .0654
 .0891
 .2962
 .2614
 .2865
 .0156
 .0141
 .0229
 .0040
 .0043
 .0051
 .0363
 .0078
 .0277
 .0223
 .0164
 .0144
 .0244
 .0096
 .0133
 .0206
 .0075
Source:  Nash (1980)
                                  407
                                                    • US GOVERNMENT PRINTING OFFICE 1984 - 759-102/0986

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