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.
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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
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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
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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
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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
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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
-------
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
-------
10
o
II
CM
£t
©
in
O
CO
UI
o
X
_l
D
m
4-1
•H
O
rC
d,
nj
U
'd
rH
0)
•H
tn
c
rtj
(1)
Q
to
CX
M-l -H
O X!
o:
(0 C
•H O
(/} -H
>i -P
ITS
5
CO
ro
0)
0)
o
CM
( UIO/ UIO)
B c
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
-------
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
-------
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
-------
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
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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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• 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
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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
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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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42
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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
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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
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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
-------
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
-------
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
-------
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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54
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55
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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
-------
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
-------
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
-------
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
-------
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
-------
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.
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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.
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for Variably Saturated Flow in Multi-Dimensional,
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for the Division of Health, Siting, and Waste
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Capable of Predicting Flow, Heat Transfer as Well as
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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
-------
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
-------
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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