-------
Vertical distributions of sediment and alachlor concentrations near
the mouth of Four Mile Creek are shown in Figures 32, 33 and 34. In these
figures, elevations of water and bed surfaces are 272.56 m and 271.77 m,
respectively. Figure 32 indicates that all suspended sediments have higher
concentrations near the river bed, but cohesive sediments show relatively
more uniform vertical distributions. This is due to the smaller fall
velocities of silt and clay as compared to the fall velocity of sand.
Figure 33 presents vertical distributions of particulate alachlor concen-
trations per unit weight of sand, silt and clay. This figure reveals that
except those attached to sand, particulate alachlor concentrations are
almost vertically uniform. Vertical distributions of average particulate
(weighted average of particulate alachlor associated with sand, silt and
clay), dissolved and total alachlor concentrations are shown in Figure 34.
In this figure, dissolved alachlor concentration is also shown to be almost
vertically uniform. However, the particulate alachlor concentration per
unit volume of water indicates higher concentration near the river bed due
to higher sediment concentrations near the bed. The total alachlor concen-
tration (sum of dissolved and particulate alachlor concentrations) reflects
patterns of dissolved and particulate alachlor distributions.
Following model calibration, EXPIORE-I and SERATRA were used to simu-
late pesticide migration for the three-year duration between June 1971 and
May 1974. For this case, the entire study reach was divided into seven
equal-distance segments and a 30 minute time step was used to reduce the
required computational time. Some of the simulation results near the mouth
of Wolf Creek (River Kilometer 5) are shown in Figures 35 through 46. Fig-
ure 35 shows the time variation of flow rate calculated near the mouth of
the Wolf Creek. The figure indicates that all 10 high flows occurred dur-
ing the three-year simulation period happened to occur in summer and fall.
Snow melt did not produce significant runoff to receiving waters.
Time variation of total sediment concentration near the Wolf Creek
mouth is shown in Figure 36, which clearly indicates a small number of
sharp peaks associated with high flows shown in Figure 35.
Figures 37 and 38 indicate time variations of total particulate pesti-
cide concentrations associated with sediments per unit weight of sediment
and per unit volume of water, respectively. There are two large peaks of
particulate alachlor concentrations per unit weight of sediment (Figure 37)
but only one large peak of particulate pesticide concentrations per unit
volume of water (Figure 38). This is due to the fact that although
alachlor concentrations attached to sediment per unit weight of sediment
during the summer of 1972 is high, the sediment concentration during the
same period is relatively low, so that the particulate alachlor concentra-
tion per unit volume of water becomes low.
Dissolved and total (sum of dissolved and particulate) alachlor con-
centrations near the mouth of Wolf Creek are shown in Figures 39 and 40,
respectively. There are two high peaks of dissolved and total alachlor
during the three-year simulation period. Figures 35 through 40 clearly
67
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indicate a small number of sharp peaks of water discharge, sediment con-
centration, and concentrations of particulate, dissolved and total alachlor
occurred during the three-year period. Comparison of Figures 38, 39 and
40 reveals that most of alachlor transported during May 1972 and May 1973
was in a dissolved form.
There simulation results revealed that high pesticide concentrations
in the streams do not directly correlate with peak runoff or soil erosion
events, but rather with the time between pesticide application and the
first storm event after the application. In other words, pesticide con-
centrations in the stream have strong seasonal patterns (high peaks in
May and June) corresponding to pesticide applications that were closely
followed by precipitation causing runoff and soil erosion.
The results imply that the amount of pesticide being transport is con-
trolled by the supply of pesticide on the land surface. Since alachlor
degrades very rapidly after application to farm land, significant improve-
ments in water quality can be obtained through the control and curtailment
of both runoff and erosion shortly after pesticide application.
As shown in Figure 40, the highest predicted alachlor concentration
during the three-year period occurred around the end of May to early
June 1973. Detailed simulated flow rate, sediment concentration and
concentrations of particulate, dissolved and total alachlor during this
period are shown in Figures 41, 42, 43 and 44. As indicated by Figure 44,
on June 5, 1973, when the maximum alachlor concentration occurred,
sediment carries up to approximately 35% of the total alachlor being
transported, while 65% was in a dissolved form, according to the model
prediction.
Simulated longitudinal distributions of sediment concentration and
concentrations of dissolved, particulate and total alachlor occurring on
June 4, 1973, are presented in Figures 45 and 46, respectively.
Instream modeling demonstrates some important effects of sediment
transport on pesticide migration:
1. Through adsorption of dissolved pesticides by sediment, immediate
biological availability of the pesticide may be reduced.
2. Through deposition of contaminated sediment, pesticide concentrations
in a water column will be reduced.
3. However, the contaminated bed sediment then becomes a long-term
source of pollution through resuspension and desorption.
The effect of adsorption as described above is demonstrated in Figures 44
and 46. When the maximum alachlor concentration occurred near the mouth
of Wolf Creek on June 5, 1973, approximately 35% of the total alachlor was
being carried by sediment, while 65% was in a dissolved form that is
generally subject to more immediate uptake by aquatic biota (Figure 44).
Near the Four Mile Creek Mouth, dissolved and particulate alachlor
83
-------
consisted of 22% and 78% of the total, respectively. Since most of
alachlor at the stream edge was in a dissolved form, sediment uptake in
the receiving water reduced the dissolved pesticide amount by 78%
(Figure 46).
The effect of contaminated sediment deposition is clearly seen in the
variation of longitudinal alachlor concentrations in Wolf Creek
(Figure 46). The percentage of particulate alachlor to total alachlor
steadily decreased from 59% to 8% with the downstream distance in Wolf
Creek. The reduction of particulate concentration with the downstream
distance was due to the deposition of contaminated sediment before it
reached the mouth of Wolf Creek. Another possible cause of particulate
contaminant reduction is the dilution of pesticide by clean Wolf Creek
water inducing desorption of pesticide from sediment to water. Reduction
of total alachlor concentration with distance reflects the particulate
alachlor trend.
The effect of sediment migration on contaminant distribution is
demonstrated in Figure 47, showing longitudinal particulate alachlor
distributions associated with the three sediment-size fractions and their
weighted average in the top bed layer after the 3-year simulation. Since
it was assumed that initially there was no alachlor in the stream bed, the
accumulation of alachlor in the bed must have occurred during the 3-year
period through deposition and resuspension of contaminated sediment and
direct adsorption and desorption with overlying water. Consequently, even
if the use of alachlor is terminated, already contaminated bed sediment
will continue to introduce alachlor back to the water column.
Although predicted concentrations of sediment and pesticide obtained
by SERATRA seem reasonable, no detailed examination of these results was
not possible due to no measured data available.
The dissolved pesticide distribution near the mouth of Wolf Creek
River Kilometer 5 shown in Figure 39 was then statistically analyzed by
FRANCO for the pesticide risk assessment.
STATISTICAL ANALYSIS AND RISK ASSESSMENT
Case Study of Alachlor
Alachlor was assessed for its known lethal and sublethal effects to
fish. The following toxicity information was obtained for Lasso® MCB/C-9,
Lasso® E.C., and for alachlor (100% active ingredient). Lasso®(MCB/C-9)
was used for the risk assessment process. . The Lasso® E.G. formulation was
included to help formulate information on channel catfish. The increased
toxicity of Lasso® over alachlor demonstrates the probable effects of sur-
factant synergism.
With the exception of fathead minnow data which are from flow-through
measured experimentation, the results below are from static bioassays of
nominal (calculated) concentrations. Optional standardization to flow-
through, measured concentration for LC50 values is shown below:
84
-------
ixicr6 pr
en
LU
CJ
to
LU
Q.
IxlO'7
1 1*10
-8
1x10"
1
MEAN PARTI CULATE ALACHLOR
PARTI CULATE ALACHLOR WITH SAND
PARTICULATE ALACHLOR WITH
SILT
PARTICULATE ALACHLOR WITH
CLAY
r
0 10 20 30 40 50 60
DISTANCE ABOVE THE MOUTH OF WOLF CREEK, km
Figure 47. Variations of simulated particulate alachlor in the top bed
layer accumulated during the three-year simulation period.
85
-------
Static value x 0.71 approximates flow through concentration
Nominal value x 0.77 approximates measured concentration
Bluegill 96-hr LC50: 6.2 x 0.71 (static) x 0.77 (nominal) = 3.39
Rainbow 96-hr LC50: 3.7 x 0.71 (static) x 0.77 (nominal) = 2.02
Thus, the acute numbers standardize to roughly half their original
concentration values. For lack of direct evidence indicating that these
specific values are more correct than the original concentration levels,
the original set was employed to continue the risk assessment. However,
values obtained by standardization may more closely approximate actual
toxic concentration. This emphasizes the necessity of accurate testing
techniques and recommends the use of flow-through bioassays of measured
concentration for best results.
LC50 in mg/1
24-hr 48-hr 96-hr
Source
Lasso® (MCB/C-9)
bluegill
rainbow
Lasso® E.G.
channel
catfish
bluegill
rainbow
Alachlor
bluegill
rainbow
fathead
minnow
16
9.6
10
7.8
13
9.2
8.7
6.4
3.5
5.8
6.2
3.7
6.5
13.4
2.3
2.8
1.8
Monsanto Agricultural Product Co.
Monsanto Agricultural Product Co.
Monsanto Agricultural Product Co.
Weed Science Society of America
1979
Weed Science Society of America
1979
Monsanto Agricultural Product Co.
Monsanto Agricultural Product Co.
4.4 (192-hr LC50-2.5) Call et al. 1979
The objective is to evaluate La'sso's® (MCB/C-9) toxicity for
bluegill, rainbow, and channel catfish. Sufficient information is
provided for only the first two species, so results from Lasso® E.G. and
alachlor were used to derive a 96-hr LC50 value for catfish. One would
expect the ratios of (MCB/C-9) to Lasso®E.G. data to be similar for
rainbow and bluegill. This ratio could then be used to define a value for
catfish. Actually, however, there is a large difference in these ratios,
possibly due to the different effects of the emulsion formation on the two
fish species.
Bluegill
= 0.46
Rainbow
= 1.61
Use was made of both ratios to calculate a range which may include
the true value for catfish.
Catfish 6.5 x 0.46 = 3.0 and 6.5 x 1.61 = 10.5
86
-------
Assuming the 96-hr LC50 lies between 3.0 and 10.5 mg/1, a geometric
mean, based on this range, for convenient handling is calculated as:
Catfish 96-hr LC50: so x 10.5 = 5.6
The next step is the calculation of 24- and 48-hr LC50 values for
catfish to Lasso® (MCB/C-9). As catfish are not closely related to blue-
gill or rainbow, the slopes from 24-, 48-, and 96-hr LC50 data of bluegill
and rainbow were used to derive concentration ranges for catfish.
Slopes
Bluegill Rainbow
6-2 - 16 mg/1 = 13g 3.7 - 9.6 = _
96 - 24 hr u' JD 96 - 24
6.2 - 10 _ 0 07g 3.7 - 7.8 _ n
96 - 48 U'079 96 - 48 °'
The ranges generated will be used to approximate the 24 and 48-hr
LC50 values for catfish.
96-hr LC50 = 5.6 mg/1
°"
95.24
= -0-082 to -0.136 ' = -0.082 to -0.136
Solving the above equations for X yields
24-hr LC50 = 8.9 to 12.8 mg/1
24-hr LC50 = 16.4 to 20.3 mg/1
Combining these values leads
24-hr LC50 range = 8.9 to 20 mg/1
= -0.079 to -0.085 °'5 7X = 0.079 to 0.085
Solving the above equations for X yields
48-hr LC50 = 6.8 to 7.1 mg/1
48-hr LC50 = 14.3 to 14.6 mg/1
Combining these results leads
48-hr LC50 range = 6.8 to 14.6 mg/1
87
-------
The geometric mean converts these ranges to points.
24-hr LC50: V8.9 x 20.3 = 13.4 mg/1
48-hr LC50: \6.8 x 14.6 = 10.0 mg/1
Standardization factors based on the 96-hr LC50 geometric mean of
5.6 mg/1 would give these results. Slopes associated with these values
are dissimilar to those experimentally derived for bluegill and rainbow.
5.6 x 1.52 = 24-hr LC50 of 8.5
5.6 x 1.23 = 48-hr LC50 of 6.9
The acute data considered most appropriate from the steps above are
included here in mg/1. The symbol (~) above a number differentiates it as
an estimated (nonexperimental) value.
24-hr LC50 48-hr LC50 96-hr LC50
Lasso (MCB/C9)
bluegill 16 10 6.2
rainbow 9.6 7.8 3.7
catfish 13?4 10^0 5?6
No chronic toxicity values were currently available for Lasso®
(MCB/C-9). However, recent results of fathead exposure to alachlor show
the no-effect level between 0.52 and 1.0 mg/1 (Call et al. 1979). Its
application factor range is 0.12 to 0.23. Assuming the application factor
for Lasso® (MCB/C-9) is the same as for alachlor, the MATC factors can be
calculated for the other fish species.
96-hr LC50 MATC Range
bluegill 6.2 x 0.12 to 0.23 = 0.74 to 1.43
rainbow 3.7 x 0.12 to 0.12 = 0.74 to 0.85
catfish 5.6 x 0.12 to 0.23 = 0.67 to 1.29
Had an experimental MATC value for alachlor been unavailable, selec-
tion of an arbitrary no-effect level would have been necessary,, This pro-
cedure is also given, and comparisons between the results of both methods
can be examined. Since Lasso® is not known to be either persistent or
cumulative in its effects under normal conditions, the selected arbitrary
application factors are 0.05 for a 24-hour average and 0.1 for a temporary
level. Use of the 24-hour average should give a conservative estimate of
the MATC value:
96 hr-LC50 x AF = MATC
bluegill 6.2 x 0.05 = 0.31
rainbow 3.7 x 0.05 = 0.19
catfish 5.6 x 0.05 = 0.28
88
-------
In this case the arbitrary MATC values produce a lower, more conservative
measurement of the no-effect level.
The rainbow trout species (Salmo gairdneri) was chosen for the
methodology illustration with the following curves using LC50 data and the
MATC value.
1. The SERATRA cutoff.
2. The MATC line (see Figure 48).
3. The LC50-MATC curve (see Figure 49).
The six pairs of points used for each curve are listed below. Con-
centrations are given as kg/m3 (= 1000 ppm). While six points were used
to define such curves, some could have been defined with fewer points.
1. Curve 1 uses the concentration of 1.0 x 10~9 at each of the time
steps which include 0.5, 24, 48, 96, 168, and 336 hours.
2. Curve 2 uses the concentration of 1.9 x 10~^ at each time step
including 0.5, 24, 48, 96, 168, and 336 hours.
3. Curve 3 uses the following (duration, concentration) pairs: (0.5,
0.0096), (24, 0.0096), (48, 0.0078), (96, 0.0037), (96, 0.00019),
(336, 0.00019).
The mouth of Wolf Creek has been used for modeling the concentration-
duration aspects of the FRANCO code.
The Exceedance Summary for alachlor includes several parameters for
each curve. In this case the summary shows that the duration-concentra-
tion levels of alachlor were insufficient to register as an event. No
duration exceeded even the MATC function, and therefore, global exceedance
is 0%. Investigation of the data generated under each category confirms
that the minimum given value for alachlor, its estimated MATC concentration
of 1.9 x 10"4 kg/m3 (or 0.19 ppm), is not exceeded at any time (see
Figure 39).
Thus, it is concluded that dissolved concentrations of alachlor near
the mouth of Wolf Creek is less than the MATC for the three years modeled
and should present no direct threat of toxicity to the rainbow trout or
other species with a MATC greater than 0.19 ppm, assuming no unusual cir-
cumstances increasing the effective concentration or effective toxic level.
Case Study of Tpxaphene
Alachlor was chosen in the modeling study because of its available
field data. The computer evaluation demonstrated that alachlor should not
have caused damage to rainbow trout. To more clearly illustrate possible
applications of the model, toxaphene was chosen as a second pesticide for
the risk assessment.
89
-------
ppm
.6
.4
ppm
10
24 48 72
96
Hours
2668
Figure 48. MATC line for alachlor.
I
24 48 72 96
Hours
Figure 49. LC50 - MATC curve for Lasso®.
90
2668
-------
Toxaphene, a chlorinated hydrocarbon, is very persistent in soil and
water and may be bioconcentrated to a high degree. Its addition to lakes
has been shown to cause toxicity for ten years. Its persistence in soils
has been cited as 4 to 16 years. Its solubility of 1.5 ppm and its esti-
mated Kd value of 5 x 10^ differ considerably from those of alachlor.
One would expect toxaphene to be strongly sorbed to the sediments, raising
interest in modeling toxaphene in sediments as well as the dissolved state.
The risk assessment procedure for toxaphene is based on the same
stream concentrations as generated for alachlor. This is done only as an
example and is not meant to imply actual concentration levels for toxa-
phene. The physical and chemical properties of toxaphene are considerably
different from alachlor.
Toxicity data used here are from pages B-27 and B-28 in the Appen-
dix B. The following species have been selected to illustrate the assess-
ment procedure for toxaphene. Again, the values are expressed as mg/1.
Without knowledge of the technique of each bioassay, one cannot make
a logical decision on the necessity for standardization of the values.
For the sake of this example, it was assumed that the following numbers
represent actual toxicity values.
24-hr LC50 48-hr LC50 96-hr LC50
Coho Salmon 0.0130 0.0105 0.0094
Cffinook Salmon 0.0079 0.0033 0.0025
Rainbow Trout 0.0115 0.0084 0.0084
Brown Trout 0.003
Carp 0.004
Fathead Minnow 0.014
Channel Catfish 0.013
Guppy 0.020
Bluegill Sunfish 0.0035
Largemouth Bass 0.002
The minimum three data points are available in Appendix B for only
three species. The rest must be derived from these or from toxicity data
for related pesticides.
The brown trout is related by genus to the rainbow trout so the slope
of the 24-96 and 48-96 hour segments from the rainbow were used to esti-
mate the 24- and 48-hr LC50 values for the brown trout.
rainbow "•«% \ °j0115 . -4.3 x IP'5
91
-------
0.0084 - 0.0084 _ n
96 - 48U
brown 0.0.03 -4X B .4.3 x 10-5
24-hr LC50 = 0.0061 mg/1
0.003 - X _ n
96-48
48-hr LC50 = 0.003 mg/1
None of the other species other than the salmon are related to the
trout except by class, so other steps are used to estimate the 24- and
48-hr LC50 values where they are missing.
The use of the geometric mean of the slopes will be demonstrated to
approximate the unknowns. There is no way of knowing whether values cal-
culated in this manner fall within the range provided by the other slopes.
However, the chances are improved with a larger number of experimental
values already established for each time interval. The use of estimated
values from previous steps introduces more probability for error and should
be avoided. In this example, the experimental values obtained for coho,
Chinook, and rainbow are used.
24 - 96-hr Slope 48 - 96-hr Slope
coho salmon -5.0 x 10"5 -2.3 x 10"5
Chinook salmon -7.5 x 10" -1.7 x 10~
rainbow trout -4.3 x 10"5 0
Range -7.5 x 10"5 -2.3 x 10"5
to -4.3 x 10"5 to 0.0
Geometric mean -5.44 x 10"5 -1.58 x 10"5
(as 3V-2.3 x 1.7 x 10"10)
Applying these values to the fish with incomplete data sets results
in the following ranges and geometric means:
Example:
(
96 - 24
carp °^04 :,X = -7.5 x 10"5 to -4.3 x 10"5
Range Geometric Mean
24-hr LC50 = 0.0071 to 0.0094 0.0079
92
-------
Estimated Geometric Means as mg/1:
24-hr LC50 48-hr LC50 96-hr LC50
carp 0.008 0.005
fathead minnow 0.018 0.015
channel catfish 0.017 0.014
guppy 0.024 0.021
bluegill sunfish 0.0077 0.0040
largemouth bass 0.006 0.002
0.004
0.014
0.013
0.020
0.0035
0.002
The values above can be contrasted with those calculated using 24,
48, and 96-hr standardization factors:
96-hr LC50 x 1.52 = 24-hr LC50
96-hr LC50 x 1.23 = 48-hr LC50
24-hr LC50 48-hr LC50
carp 0.006 0.005
fathead minnow 0.021 0.017
channel catfish 0.020 0.016
guppy 0.030 0.025
bluegill sunfish 0.0053 0.0043
largemouth bass 0.003 0.0025
96-hr LC50
0.004
0.014
0.013
0.020
0.0035
0.002
In this case the two methods produced similar results. Because there
are some experimental data sets from which slopes can be derived, the
estimated geometric mean values were used as the most likely to represent
the actual values. This yields the following toxaphene acute toxicity
data as mg/1 for use with the probability model:
24-hr LC50 48-hr LC50
coho salmon 0.0130 0.0105
Chinook salmon 0.0079 0.0033
rainbow trout 0.0115 0.0084
brown trout 0.006 0.003
carp 0.008 0.005
fathead minnow 0.018 0.015
channel catfish 0.017 O.ol4
guppy 0.024 0.021
bluegill sunfish 0.0077 O.ot)40
largemouth bass 0.006 0.002
96-hr LC50
0.0094
0.0025
0.0084
0.003
0.004
0.014
0.013
0.020
0.0035
0.002
The symbol (~) indicates estimated values.
93
-------
Chronic toxicity values for toxaphene stated as MATC ranges have been
located for the fathead minnow and channel catfish.
fathead minnow 0.000025 < MATC < 0.000054
channel catfish 0.000049 < MATC < 0.000072
Application factor ranges are calculated below from MATC arid 96-hr
LC50 data.
MATC range/96-hr LC50 = AF range
fathead minnow 0.000025 to 0.000054/0.014 = AF
AF = 0.0018 - 0.0038
channel catfish 0.000049 to 0.000072/0.013 = AF
AF = 0.0038 - 0.0055
Carp and1 fathead minnows are related at the family level so the appli-
cation factor derived for the fathead minnow will be applied to the 96-hour
LC50 point for carp.
0.004 x 0.0018 to 0.0038 = MATC
0.0000072 < MATC < 0.0000152
For the unrelated species the high and low values from fathead minnow
and channel catfish can be used to set a range. The geometric mean can be
used to convert the ranges to points.
AF range = 0.0018 - 0.0055
Toxaphene is known to be very persistent and would require the use of
the more conservative arbitrary application factor of 0.01 for a 24 hour
average if no experimental data were available.
Geometric means, as well as MATC experimental and derived ranges, have
been included in this list of chronic toxaphene toxicity as mg/1.
< MATC Range < Geometric Mean
coho salmon
chinook salmon
rainbow trout
brown trout
carp
fathead minnow
channel catfish
guppy
bluegill
94
1.7 x 10"5
0.45 x 10"5
1.5 x 10"5
0.54 x 10'5
0.72 x 10"5
2.5 x 10"5
4.9 x 10"5
3.6 x 10"5
6.3 x 10"5
3.6 x 10"5
5.2 x 10"5
1.4 x 10"5
4.6 x 10"5
1.7 x 10"5
1.5 x 10"5
5.4 x 10"5
7.2 x 10"5
11.1 x 10"5
1.9 x 10'5
1.1 x 10"5
3.0 x 10"5
0.79 x 10"5
2.6 x 10"5
0.96 x 10"5
1.3 x 10 "5
3.7 x 10"5
5.9 x 10 "5
6.3 x 10"5
3.5 x 10"5
2.0 x 10"5
-------
Lethal and sublethal data was used to assess environmental damage to
rainbow trout (Salmo gairdneri) from toxaphene exposure. Because of toxa-
phene's extreme toxicity to fish, seven curves were chosen, in addition to
the SERATRA cutoff, to demonstrate the kind of additional information
which can be gained. All are illustrated in Figures 50 through 57.
1. Curve A is the SERATRA cutoff value.
2. Curve B is the MATC for rainbow trout.
3. Curve C is the least conservative lethality curve.
4. Curve D is the most conservative curve designated as LC50-MATC.
5. Curve E is intermediate between curves C and D.
6. Curve F considered the results of the LC50-MATC line segments after
24 hours.
7. Curve G generates results exceeding the LC50-MATC lines beginning at
48 hours.
8. Curve H describes exceedance of the MATC starting at 96 hours.
The six pairs of points used for each curve are listed with duration
in hours and concentrations as Kg/nH.
1. Curve A uses the concentration of 1.0 x 10~9 Kg/m3 at each of the
following durations: 0.5, 24, 48, 96, 168, and 336 hours.
2. Curve B uses the most conservative MATC endpoint for rainbow trout of
1.5 x lO'8 Kg/m3 . Time durations are again 0.5, 24, 48, 96, 168,
and 336 hours.
3. Curve C includes the following sets of points:
(24, 1.15 x 10-4), (24, 1.15 x 10-5),
(48, 8.4 x 10-6), (96, 8.4 x 10-6),
(168, 8.4 x.10-6), and (336, 8.4 x lO'6).
4. Curve D is described by the following points:
(0.5, 1.15 x 10-5), (24, 1.15 x 1Q-5),
(48, 8.4 x 10-6), (96, 8.4 x 10-6),
(96, 1.5 x 10-8), and (336, 1.5 x lO'8).
5. Curve E has points at (0.5, 1.15 x 10'5),
(24, 1.15 x 10-5), (48> 8>4 x 10-6)f
(96, 8.4 x 10-6), (168, 8.4 x 10'6), and
(336, 8.4 x ID"6).
6. Curve F includes the following points: (24, 1.15 x 10"4),
(24, 1.15 x ID'5), (48> 9.4 x 10-6)f (95, 9.4 x uj-6)
(96, 1.15 x 10-8), and (336, 1.5 x 10"8).
95
-------
Kg/nf
1x10
-9
I I
I
24 48 72 96
Hours
2668
Figure 50. Curve A - SERATRA Cutoff value.
Kg/rrT
2x10~£
1x10~£
I
I
24 48 72 96
Hours
Figure 51. Curve B - MATC line.
2668
96
-------
Kg/nr
~6
12x10
10x10
~6
8xlO
"6
6x1 O
"6
.<
24 48 72 96
Hours
2668
Figure 52. Curve C - Lethality curve least likely to
indiciate mortality.
Kg/m°
-6
12x10
10x10
-6
8x10
-6
1.5x10
-8
j i
2668
24 48 72 96
Hours
Figure 53. Curve D - LC50 - MATC conservative curve.
97
-------
Kg/nT
-6
12x10
10x10
-6
8x10
-6
6x10
-6
I
I
I
I
24
96
2668
48 72
Hours
Figure 54. Curve E - LC50 - alternative curve.
Kg/m3
12xlO"6
lOxlO"6
8x1 O"6
1.5xlO"8
-
- \
:
i i i i . ,
24 48 72 96 26
Hours
Figure 55. Curve F - LC50 - MATC curve for durations
exceeding 24 hours.
98
-------
Kg/m3
10xlO~6
8xlO"6
1.5xlO"8
0
-
I
i
1
1
i
I
1
i
i i / / l
24 48 72 96 26
Hours
Figure 56. Curve G - LC50 - MATC curve for durations
exceeding 48 hours.
Kg/m°
8x1 O"6
**
5xlO~8
n
-
^
i i i i //
24 48 72 96
Hours
2668
Figure 57. Curve H - MATC exceedance for durations greater
than 96 hours.
99
-------
7. Curve G has the following points: (48, 1.15 x 10'4),
(48, 1.15 x 10-5), (48, 8.4 x 10'6), (96, 8.4 x lO'6),
(96, 1.5 x 10-8), and (336, 1.5 x lO'8).
8. Curve H is described by the following points:
(96, 1.15 x ID"4), (96, 1.5 x lO'5), (96, 8.4 x 1CT6),
(96, 1.5 x ID'8), and (336, 1.5 x lO'8).
The exceedance summary inumerates the following curve results for the
mouth of Wolf Creek.
Curve Events Total Duration Global Exceedance
A 9 4160 hr 16.23%
B 12 3828 14.93
C 2 314 1.23
D 6 3461.5 13.50
E 2 314 1.23
F 6 3461.5 13.50
G 6 3461.5 13.50
H 6 3461.5 13.50
1. Curve A - 16.2% of total time divided into nine events exceeded the
SERATRA cutoff value.
2. Curve B - Twelve events, comprising 14.9% of the modeling time,
exceeded the MATC designated line of 1.5 x lO'5 ppm. Had the high
MATC endpoint of 4.6 x 10~5 ppm or the geometric mean value of 2.6
x 10~5 been selected to represent the MATC, the global exceedance
value would have been reduced to 14.0% and 14.5%, respectively.
Of the twelve events, six lasted for more than 96 hours and accounted
for 90.4% of the time exceeding the MATC. The longest event of
1257.5 hours began at time-step 16496 and lasted through time-step
19011. Clearly, 1257.5 hours at concentrations above the MATC sug-
gests concern for sublethal effects. Damage may largely depend on
life stages present during such long-term events.
3. Curve C - The lethal curve based on known LC50 values was exceeded in
two events lasting a total of 314 hours, which was 1.2% of the total
modeling time. A breakdown of the event durations shows 196.5 hours
for the first event and 117.5 hours for the second. The assumption
of no lethality prior to D suggests that, in spite of values exceed-
ing GI at DI, no median fish kill occurs.
4. Curve D - The MATC incorporated with the LC50 curve is represented by
six events lasting 3461.5 total hours for a global exceedance of
13.5%. Those events, represented by curve B (lasting more than
96 hours) and by curve C, are included in this percentage.
100
-------
5. Curve E - This curve is equivalent to curve C in this example. This
may seem surprising since two events had concentrations greater than
1.5 x 10~5. However, these concentrations lasted for 96 and
168 hours, which is long enough to be counted in curve C. The assump-
tion that C0 = GI indicates that most rainbow trout would not survive
the first 24 hours of exposure at concentration levels greater than
GI- Had concentrations greater than 1.15 x 10~5 been evident for
durations less than 24 hours, the global exceedance for curve E would
be greater than curve C by the area represented by C > C\ and D > 0]_.
6. Curve F - Results of curve F are identical to curve D because concen-
trations greater than GI at durations less than DI exceed the
24 hour minimum function time.
7. Curve G - This curve again shows the same results as curve D. Both
events exceeding curve G are also exceeded by curves 0 and F.
8. Curve H - Once more, the function represented by C >. 1.5 x 10~5 ppm
at D .>94 hours is exceeded six times for a total of 13.5%. Had any
of the six events noted in curves F or G above lasted for less than
96 hours, the exceedance of curve H would be reduced proportionally.
A summary of the curves is shown in Figure 58. Concentrations are
within safe limits 85.1% of the total modeling time. Concentration-
duration values fell within the .potential lethal/sublethal (
-------
Kg/nr
-6
12x10
10x10
-6
8x10
-6
6x10"
4x10
-6
1.5x10
-8
1.2%
13.7%
I I
85.1%
MATC
24 48 72 96 120
Hours
Figure 58. Summary of toxaphene curves.
2668
102
-------
SECTION 6
EVALUATION OF THE CMRA METHODOLOGY
The CMRA Methodology was developed and was applied to the Four Mile
and Wolf Creek area in Iowa to examine the applicability and limitation of
this methodology. The methodology is general enough to evaluate the migra-
tion and risk assessment as applied to a wide range of pesticides, agri-
cultural lands, receiving streams and aquatic biota. For example, this
methodology could be used to select optimum pesticide application practices
including timing and doses.
The application of the methodology to to Four Mile and Wolf Creeks
revealed that in general, the CMRA Methodology is capable of simulating
migration and fate of a pesticide and its associated risk to aquatic biota.
However, accuracy of predicted results depends largely on the availability
of data. The application exercise also revealed that it requires a rather
extensive effort to evaluate pesticide movements and its risks. Further-
more, due to a general lack of data, calibration and verification of the
models, especially portions concerning pesticide transport, adsorption/
desorption, and degradation were not fully performed and that further test-
ing of the methodology is required. Also a FRANCO output of probability
of acute and chronic damages to aquatic biota must be supplemented by
other pertinent information on pesticide characteristics, environmental
stress, life stages of biota, etc., to obtain more comprehensive risk
assessment. Applicability and limitations of each of the four components
of the CMRA Methodology are discussed below.
OVERLAND PESTICIDE MODELING
The ARM model was specifically developed as a tool to evaluate the
quantity and water quality of runoff coming from agricultural areas, as
well as the impacts of alternative management practices. The model's
capability to continuously simulate nonpoint source pollution processes
makes it useful for evaluating both short- and long-term migration of
pesticides overland. The application of ARM is limited primarily by the
availability of data for model calibration and by the size of an area
which can be accurately modeled in a single simulation. Given the state-
of-the-art of modeling nonpoint source pollution from agriculture, ARM is
the most appropriate pesticide loading technique for this methodology.
INSTREAM PESTICIDE MODELING
The unsteady, two-dimensional model, SERATRA, which is used for the
instream pesticide modeling, includes all important mechanisms of both
103
-------
dissolved and particulate pesticide transport phenomena, such as convec-
tion and dispersion of pesticides, interaction of pesticides with sediment
(adsorption and desorption of pesticides with sediment, and transport,
deposition and resuspension of particulate pesticides associated with
sediment), and chemical (oxidation, hydrolysis, and photolyses) and
biological degradation of pesticides and volatilization. Since SERATRA is
one of the few tested models capable of simulating both dissolved and par-
ticulate contaminants with sediment-contaminant interactions, it is well
suited to this methodo"1ogy. SERATRA is applicable to the nonticlal rivers
and narrow lakes where vertical and longitudinal distributions are of
interest, but is not suitable to estuaries and coastal areas where lateral
and longitudinal distribution may be of concern. Another important aspect
of this portion of the methodology is that SERATRA provides vertical and
longitudinal distributions of dissolved and particulate pesticides, as well
as the pesticide accumulation in the stream bed. However, because of the
rather limited knowledge of pesticide toxicity, only cross-sectionally
averaged dissolved pesticide concentrations are used for the risk assess-
ment. Additional data collection for pesticide toxicity may make it pos-
sible to take the full advantage of detailed simulation results of SERATRA
in the future. Lack of field data needed for the model calibration is the
major difficulty apparent when SERATRA is applied to an actual study area.
Another limitation is the limited applicability of EXPLORE-I to very small
streams. Furthermore, since SERATRA is a two-dimensional (longitudinal and
vertical) model and EXPLORE-I is one-dimensional (possibly used as longitu-
dinal and vertical two-dimensional model), compatibility between SERATRA
and EXPLORE-I is less than ideal.
STATISTICAL ANALYSIS AND RISK ASSESSMENT
The risk assessment procedure uses a finite number of parameters to
summarize the pesticide concentration within the watershed of interest and
to predict the probability of toxic effects to indigenous fish species. Of
necessity, the risk assessment procedure must be simplistic compared to the
environmental complexities found within the actual system. The acceptance,
or at least acknowledgement, of certain assumptions used in this procedure
allows for the prediction of environmental hazards to fish based on those
assumptions.
Several important limitations exist in the risk assessment portion of
the methodology. Some of the major ones are these:
1. Laboratory bioassays are assumed applicable to the prediction of
field toxicities.
2. Synergistic effects of pesticides with river water quality parameters
are ignored for lack of information.
3. Bioconcentration and biomagnification properties with predator-prey
relationships were not assessed.
4. Interpretation of sublethal effects based on exceedance of the MATC
may overestimate hazards.
104
-------
5. Effects of ingesting pesticides from the suspended fraction and from
bottom sediments have not been addressed.
6. Avoidance behavior and migration resulting from pesticide concentra-
tion in the water needs investigation.
7. Assessment for resistant or susceptible strains requires identifica-
tion of such properties for modification of risk assessment input
data.
The FRANCO program is very flexible in its risk assessment aspects.
The output describes pesticide concentration as the number of times a given
value (Ci,dj) is exceeded, the percentage of total time (C-j,dj) exceeded,
and individual and total time durations when the concentration for a par-
ticular river segment exceeds (C-j,dj). It also computes the percentage
of total time the designated function has been exceeded. The model has
been demonstrated using only the dissolved portion of pesticide. The pro-
gram is also capable of assessing the pesticide concentration in the sus-
pended particulate and bed sediment fractions.
The CMRA Methodology was used specifically with fish species to graph
the LC50 curve and the MATC and measure its exceedance. However, any other
measure of effects may be used instead, such as LC10 or LC90 values. Con-
fidence limits are especially good for indicating a probability range of
toxicity. The curve may be partitioned into acute and chronic sections or
described as short-term and long-term effects with time boundaries indi-
cated by the methodology user to best fit the situation. The selection of
input values may be changed in accordance with the needs. However, the
user must be cautioned to make appropriate interpretations based on the
assumptions used for data input.
In spite of the limitations inherent in FRANCO, there are real bene-
fits associated with the modeling procedure for predicting the extent of
environmental hazards. It has defined some of the problems which may be
resolved in the future by one of at least three possible methods. Addi-
tion of new mathematical relationships may allow for modeling parameters
which more closely fit field conditions. Improvement of data collection
systems to fit modeling needs may reduce present model uncertainties. The
use of more appropriate hazard indicators and resultant interpretations
will improve the predictive ability of the modeling system.
MODELING FLEXIBILITY
Although, as discussed above, the CMRA Methodology consists of four
components, the models can be used in different combinations depending on
the problems. However, the statistical analysis and risk assessment based
on FRANCO must be used together. Examples of combined uses are as
follows:
1. If the migration and risk assessment of a toxic contaminant are of
concern only at a stream edge, FRANCO can be used to summarize ARM
results.
105
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2. If a toxic contaminant is directly discharged to a receiving water
body, only EXPLORE-I, SERATRA, and FRANCO are required to evaluate
transport and risk assessment.
3. If there are continuous measurements of toxic chemical concentrations
available and only the risk assessment is needed, FRANCO alone can be
used.
4. If the receiving water bodies are estuaries or coastal waters,
SERATRA can be replaced by other appropriate sediment-contaminant
models such as FLESCOT and FETRA (Onishi and Wise 1978).
106
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SECTION 7
SIMPLIFIED STATISTICAL METHODOLOGY
Overland and instream pesticide concentrations were statistically
examined to study whether pesticide concentration patterns can be expressed
by statistical distributions. In this case, gamma, normal and log-normal
distributions were selected for testing. This task was conducted to deter-
mine if a simpler pesticide assessment methodology which relies only on
the statistical nature on precipitation, watershed characteristics, and
receiving water body characteristics, could be developed to replace the
more detailed computer-model-based CMRA Methodology, as described in
Section 4.
The principal steps in the development of any simplified statistical
methodology are the statistical summarization of the system input (precip-
itation), the statistical summarization of the system output (e.g., dis-
solved, particulate, and total pesticide concentrations) and a simple pro-
cedure for predicting the output from the input. The development of these
steps begins by first determining the information desired. The stated
objective is the frequency of occurrence and duration of given pesticide
concentrations. In Subsection 4.2 seven specific summary measures are
given to meet this objective. One particular measure, Pj[C >.C-j], gives
the relative frequency of occurrence of simulated concentrations greater
than or equal to specified concentration levels. This measure was chosen
for the simplified statistical methodology, because it is one of the
simplest summaries of a concentration time series.
The FRANCO program summarizes the time series of instream dissolved,
particulate and total (sum of dissolved and particulate pesticides) pesti-
cide concentrations. The series is characterized by periods when pesti-
cide concentration is lower than the chosen SERATRA cutoff value, and other
periods when runoff events cause the concentration to increase above this
value. The cutoff value is selected to represent the point below which
the simulated concentration values are due to limitations on the numerical
accuracy of the model and the computer. Hence, time periods below this
concentration are considered to be zero. As a result the relative fre-
quency of pesticide concentration consists of two parts: A discrete part
at the SERATRA cutoff value with an associated frequency that estimates
the percent of the time the concentration is considered to be zero and a
continuous part that summarizes the relative frequency of concentrations
above the cutoff value.
Let GI equal zero, C2 equal the cutoff value and the remaining C-j
chosen such that C3 < C4 < . . . < Cm. Using the notation introduced in
107
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Section 4.2, this choice results in Pj[C >C]J = 1 and Pj [C ^62! equal
to the relative frequency the simulated concentra- tion was greater than
or equal to the cutoff value.
Define
6(C) = pF(C) + (l-p)I(C)
where p = Pj [C >_ 63] is the relative frequency of concentrations
occurring above the cutoff value, I(C) is a discrete cumulative
frequency function with probability one at the cuttoff value,
and F(C) is a continuous distribution function.
The investigation of the feasibility of a simplified statistical
methodology is based on analyzing the cumulative distribution function
F(x) resulting from the dissolved, particulate and total pesticide concen-
tration time series from SERATRA for instream and ARM for stream-edge.
The same model can also be applied to the stream discharge and overland
runoff. The latter tow can then be compared to an analysis of precipita-
tion data. These analyses form the basis for discussing the feasibility
of a simplified statistical methodology. Each of these analyses are indi-
vidually discussed, followed by a discussion of their interrelationships.
The dissolved, particulate and total pesticide concentrations (in kg/rtH)
simulated by SERATRA near the mouth of Wolf Creek (River Kilometer 5) is
statistically summarized below.
During the three-year period starting June 1971 the dissolved pesti-
cide concentration was greater than the cutoff value of 1.0 x 1Q~9 for
16.23 percent of the time. Hence p is estimated as 16.23. The continuous
part F(C) of the cumulative distribution function 6(C) is well represented
by the log-normal distribution. A probability plot construction from the
cumulative frequency concentration data illustrates this fit in Figure 59.
Similar probability plots using the normal and gamma distributions as
alternative choices did not result in the expected straight line plot
characteristic when the data fits a particular probability distribution.
The log-normal probability density function is:
, - i (In X - u)2/a2
f (X) = - i— e * X > o.
Xa
Stated in this form, the parameters u and o^ are estimated by:
,2 ^ (In X -u)2
a = J 1 '
n - 1
108
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-8.1
-11
-------
The statistical distribution summary for the dissolved pesticide con-
centration is completed by the program FRANCO. The distribution parameter
estimates are obtained from all concentrations greater than or equal to
the SERATRA cutoff value. Because of the number of time steps involved,
these estimates are computed by summarizing the data with a flow through
technique that does not require all the data to be present at once. In
constructing the probability plots ,a modification of the standard proce-
dure, such as described by Hann (1977), was necessary. It was riot feasi-
ble for all data points to be plotted individually for the probability
plot. To overcome this difficulty the data were accumulated as an empiri-
cal cumulative distribution function with up to 50 intervals used for the
concentration. The probability plot was constructed using those values.
The effect on the plot is minimal if these concentration values are appro-
priately selected to result in approximately an equal number of time steps
in each interval. The interpretation of the plot is not affected.
The dissolved pesticide concentration at Wolf Creek River Kilometer 5
is adequately summarized by the mixed distribution:
G(C) = 0.1623LN(C;-14.83,2.26) + .8377I(C)
where
nn _ f 1 C < 1.0 x IQ'l
i((j) 10 C > 1.0 x 10"y
and LN(C;u,a) denotes the log-normal distribution with parameters u and a.
A severe limitation of this summary is the total loss of all information
concerning the actual time sequence of the concentrations.
A similar analyses of computed results of SERATRA simulation at Four
Mile Creek River Kilometer 5 was completed. A mixed distribution using the
log-normal distribution for the continuous part again provided the best fit
when compared to the normal and gamma distributions as alternatives. The
probability plot in Figure 60 illustrates the general conformance to a
straight line. The corresponding plot for the gamma distribution (Fig-
ure 61) is not a straight line, indicating the gamma is not as good a fit
for this data set. The distribution with estimated parameters is:
G(C) = 0.1472 LN(C;-13.55,2.42) + 0.8528I(C)
where the terms are as previously defined. These parameter values result
in an estimated mean value of 2.44 x 10~5 with a standard deviation of
4.55 x ID'4.
The summary statistics at Four Mile Creek River Kilometer 5 and Wolf
Creek River Kilometer 5 show the effect of the instream model embodied in
SERATRA. First, the pesticide concentration is above the cutoff value
longer near the Wolf Creek mouth (16.23%) than near the mouth of Four Mile
Creek (14.72%). Second, the average concentration downstream in Wolf Creek
is approximately five times smaller than that in Four Mile Creek. If the
110
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-7.8
-10
CO
o
I
<
o
o
-16
-18
-21
-20
-18
-15 -12
LOG (concentration)
-10
-7.5
Figure 60.
Log-normal probability plot for dissolved pesticide
concentration at Four Mile Creek River kilometer 5
(u = -13.55, a = 2.42)
comparison is made in log concentrations where the estimated distribution
is normal, then it appears that the difference is mainly a shift in the
mean value with the log variability approximately the same between these
two locations.
The pesticide attached to particulates was also summarized using the
mixed distribution analysis just described. The distributions of the simu-
lated results for particulate pesticides (in kg/m3) from ARM, at Four
Mile Creek River Kilometer 5 and at Wolf Creek River Kilometer 5 were again
a mixed distribution with the log-normal distribution as the continuous
form. At stream-edge,
6(C) = 0.0017 LN (C;-17.27,1.92) + 0.9983 I(C)
at Four Mile Creek River Kilometer 5
G(C) = 0.1367 LN (C;-17.34,2.88) + 0.8633 I(C)
111
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10x10
8x10
-5
c/o
= 6x10
a
-------
The distribution analysis was also applied to the dissolved pesticide
concentration at stream-edge as simulated by the ARM model. The mixed dis-
tribution model with the log-normal as the continuous part again was an
adequate fit. In this case the gamma distribution was a close competitor.
The log-normal was selected to facilitiate the comparison with the SERATRA
results. The estimated distribution is:
G(C) = 0.0707 LN(C; -10.66, 2.45) + 0.9293 I(C)
T/n I I C < 2 x TO'9
ULJ 1 0 C > 2 x TO'9.
In comparison with the instream results, the stream edge concentrations
above the cutoff value were less than half as long (7.07%) and the concen-
tration distribution was shifted to higher concentrations with no differ-
ence in the variability in log units. The average concentration based on
the log-normal distribution was 4.72 x 10~4 with a standard deviation of
9.48 x 10'3. The general effect of SERATRA on the pesticide distribu-
tion is to lengthen the time the concentration is above the cutoff value
and to reduce the average concentration level.
The statistical summary of the pesticide concentrations from ARM and
SERATRA has demonstrated that it is possible to describe the distribution
with a mixed probability distribution with the continuous part being a
log-normal distribution. The estimated parameters for this mixed distri-
bution reflect the anticipated pesticide concentration changes expected
when going from stream-edge, to instream, and instream further down-
stream. A severe limitation of this type of summary is the loss of all
the time sequence history of the concentrations. In the present work no
method was found to include duration of events in a distribution analysis.
The distribution analysis illustrates the feasibility of summarizing
the pesticide concentration distribution at stream-edge and at selected
instream locations. The summaries were shown to have the same mixed dis-
tribution form with only the values of the parameters in the distribution
changing at each location. To simplify the methodology, the relationship
among the parameters across locations must be determined. The case study
illustrated the parameters changed in the direction intuitively expected.
However, it is not possible to determine a quantitative relationship. A
large number of case studies at different sites, each simulated under a
variety of conditions, would be necessary to determine a quantitative rela-
tionship. The computational time required for each simulation may become
excessive when a large number of simulations must be completed. A second
limitation is related to the calibration procedures presently required for
both the ARM and SERATRA models. If the calibration is unique to each site
of application, then the simplified statistical methodology must be able to
account for this. The severity of the problem can not be determined until
more case studies are completed.
Additional study on a simplified statistical methodology must con-
sider another question. What is the minimal information required concern-
ing pesticide occurrence and duration for a useful risk assessment? An
113
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answer to the question along with a specific statistical summary procedure
must be available before detailed studies of a simplified approach can be
useful. Experience in applying the present CMRA Methodology will be use-
ful in determining the information necessary for a risk assessment and in
formulating an effective statistical summary for the information.
114
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Crawford, N. H. and A. S. Donigian. 1973. Pesticide Transport and Runoff
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Donigian, A. S. and H. HJ. Davis. 1978. Agricultural Runoff Management
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Fread, D. L. 1973. "Technique for Implicit Dynamic Routing in Rivers with
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Jordan, G. L. 1978. "Environmental FActors and Soil Relationships
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Laflen, J. M., J. L. Baker, R. 0. Hartwig, W. F. Buchele and H. P. Johnson.
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* O •
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Sediment and Contaminant Transport in Rivers and its Application to
Pesticide Transport in Four Mile and Wolf Creeks in Iowa. Submitted to the
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Contaminant Transport Model. SERATRA. Submitted to the U.S. Environmental
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Quality Model (Hydraulic Module Only).Submitted to the U.S. Environmental
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Toxicology and Hazard Evaluation Proceedings of the Annual Symposium on
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APPENDIX A
PROCEDURE FOR CMRA METHODOLOGY APPLICATION
Step-by-step instructions to use the CMRA Methodology will be
described.
OVERLAND PESTICIDE MODELING
The first step in the CMRA Methodology is the application of ARM to
the selected study area. The procedure to be followed is essentially the
same as the five major steps outlined by Donigian and Davis (1977). These
steps are discussed briefly below and have been slightly modified for use
in this study.
Step 1: Data Collection and Analysis
Once the pesticide, application practice and study area have been
selected, the input data for ARM must be collected and analyzed. These
input or execution data basically consist of meteorologic data, such as
precipitation, potential evapotranspiration, maximum-minimum air tempera-
ture, wind movement, dewpoint temperature and solar radiation. If snow-
melt calculations are not required, only the first two types of meteoro-
logic data are needed. Because ARM is a continuous simulation model, these
data are required for the entire duration of the simulation time period.
The length of this time period is primarily determined by statistical
considerations to be discussed later as well as data availability and the
required computation time.
Step 2: Preparation of Meteorologic Data and Model Input Sequence
This step cpnsists of the construction of input data files for each
type of meteorologic data. The procedures for constructing these files
and their input formats have been described in detail by Donigian and
Davis (1977).
Step 3: Parameter Evaluation
Several of the ARM model parameters can be evaluated directly without
going through the calibration process. The types of information required
to evaluate these parameters include topographic maps, soil maps, hydro-
logic/meteorologic studies, water quality studies, cropping pattern sur-
veys, and data on pesticide application rates and modes. The specific
parameters which can be evaluated in this manner and methods of evaluation
have also been described by Donigian and Davis (1977).
119
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Step 4: Model Calibration and Verification
Calibration is an iterative process of making computer runs and coef-
ficient adjustments until simulated results match observed results. It is
required for parameters which cannot be determined directly. The calibra-
tion process for the ARM model has been refined over several years of
effort and is discussed in great detail by Donigian and Davis (1977).
Step 5: Generation of Runoff and Edge-of-Stream Sediment and Pesticide
Loadings
Upon completion of the previous steps, the ARM model can be used to
generate the runoff and sediment and pesticide loading information required
as input to the instream modeling component of the methodology. This step
basically involves obtaining historical meteorological data for a given
time period or the selection of data which represent a specific sequence of
storm events. Pesticide application times can be based on local conditions
or selected arbitrarily. In the latter case, pesticide removal by runoff
and erosion can be maximized by selecting pesticide application times sev-
eral days to several weeks before major storm events. Pesticide applica-
tion rates and modes and cropping patterns can be obtained from local
information; if the pesticide being evaluated is new, application rates
and modes can be obtained from the manufacturer.
The information generated in this step of the methodology is a time
history of each of the following: runoff, sediment loading, pesticide
dissolved in the runoff and pesticide attached to the sediment. Depending
on the size and configuration of the watershed, this information is gener-
ated for each catchment individually or for the total watershed. The
in-stream sediment and contaminant transport model, SERATRA (Onishi and
Wise 1979a,b), requires the pesticide attached to each size fraction of
sediment being modeled in the river system. However, ARM only simulates
total sediment loading and total pesticide adsorbed to the sediment.
Therefore, the ARM simulation results have to be modified prior to their
input to SERATRA.
INSTREAM PESTICIDE MODELING
Instream pesticide modeling requires two computer models. EXPLORE-I
(Baca et al. 1973; Onishi 1979) is used to obtain one-dimensional, unsteady
distributions of discharge (or velocity) and depth in a receiving water
body. Computed runoff is input to EXPLORE-I as a tributary contribution.
SERATRA is used to simulate pesticide migration by solving both sediment
and pesticide transport. SERATRA uses EXPLORE-I results and computed sedi-
ment and pesticide loading from overland as input data. Simulation results
of pesticide distributions in a receiving stream are then statistically
analyzed to be used for the pesticide risk assessment. Because of the lack
of toxicological knowledge, only dissolved pesticide distributions are used
for the risk assessment.
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Step 1: Hydrodynamic Data Collection and Analysis:
Once the study area is selected, the input data for EXPLORE-I must
be collected and analyzed. These data consist of channel geometry and
the Manning's coefficient of the stream. If there is a dam in the study
area, obtain the operational characteristics of the dam to be inputed to
EXPLORE-I. Boundary conditions at the upstream and downstream ends and at
tributary confluences must be specified. These boundary conditions may be
time varying inflows or water surface elevations during the entire simula-
tion period. Initial flow conditions must also be specified. The simula-
tion period is the same as that used in the overland modeling component.
Step 2: Preparation of EXPLORE Input:
Assemble the input data outlined in Step 1 along with the runoff com-
puted with ARM.
Step 3: EXPLORE-I Calibration and Verification:
EXPLORE-I must then be calibrated to match measured depth and velocity
distributions by adjusting the Manning coefficient. If more than one set
of measured data are available for different discharges, use the first set
of observed data for calibration and the other set(s) of data for model
verification.
Step 4: Production Run for Hydrodynamic Simulation:
Upon completion of above steps, run the EXPLORE-I model to obtain
depth and velocity (or discharge) distributions.
Step 5: Sediment and Pesticide Data Collection and Analysis
Collect necessary input data of sediment and pesticide characteris-
tics, as well as initial and boundary values of sediment and pesticide con-
centrations. Detailed information of SERATRA formulation, data need and
modeling procedure is described in Onishi and Wise (1979a,b). Based on the
analysis of data availability, decide whether to treat sediment as one bulk
sediment, consequently one bulk particulate pesticide, or to divide the
sediment into three sediment size fractions (or sediment types), resulting
in three groups of particulate pesticide associated with each size frac-
tions (or type) of sediment. The SERATRA user must realize that the dis-
crepancy between actual and simulated sediment transport rates, hence also
particulate pesticide transport rates will probably be large if only a
single size fraction (or single type) of sediment is considered. The use
of three size fractions (or types) is itself a compromise. If sediments
are divided into three size fractions (or sediment types), then bulk sedi-
ment and pesticide loading computed by ARM must be separated into each size
fraction (or sediment type), as described in Subsection 4.3.2. Care must
be taken for selection of pesticide distribution coefficients.
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Step 6: Preparation of SERATRA Input
Using data prepared under Step 5, and results obtained by ARM and
EXPLORE-I, assemble the input data for SERATRA.
Step 7: SERATRA Calibration and Verification
With available field data, adjust model parameters, especially those
associated with erosion and deposition of cohesive sediments (i.e., criti-
cal shear stresses and the erodibility coefficient) and dispersion coeffi-
cient, as model calibration. If there are additional field data for other
flow conditions, use the sets of field data for model verification.
Step 8: Production Run for Sediment-Pesticide Transport Simulation
Upon completion of the previous steps, run SERATRA to obtain unsteady,
vertical and longitudinal distributions of sediment and pesticide (both
dissolved and particulate) concentrations. Sediment and pesticide condi-
tions in the river bed will also be computed. Cross-sectionally averaged
dissolved pesticide concentrations will then be used for the statistical
analysis.
STATISTICAL ANALYSIS
The CMRA Methodology procedures require the summary of SERATRA output
results of dissolved pesticide concentrations. Each of these areas are
described in the order of their occurrence in the methodology.
Step 1: FRANCO (Olsen and Wise 1979) Analysis of SERATRA Instream
Pesticide Concentration
The FRANCO program requires particular information to be available
for a meaningful analysis (Olsen and Wise, 1979). The SERATRA model cut-
off pesticide concentration value and the maximum pesticide concentration
are needed. A time series plot of the data is helpful. An array of user
specified concentration values, a similar array of duration values and a
number of LC (lethal concentration) functions are needed to control the
summary of the simulated pesticide concentrations. The LC functions are
specified to provide the information needed for the risk assessment. The
selection of these functions is discussed in the following risk assessment
steps. The concentration and duration values are selected with considera-
tion for both the risk assessment needs and the statistical requirements
for providing detail on the frequency of occurrence and duration of spe-
cific pesticide concentrations.
RISK ASSESSMENT
Step 1: Lethality
Data must be located for pertinent aquatic species in response to
the pesticide under investigation. These values should be stated as LC50
122
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(median lethal concentration) or TLm (median tolerance limit) for a spe-
cific time interval such as 24-hr LC50. The minimum number of points
required include the 24-, 48-, and 96-hr LC50 numbers, for each pesticide
and species. Include the confidence limits around each point and any
experimental conditions specifically indicated. Incipient LC50 values,
when given, should also be recorded. Sources for such information include
toxicological literature, the Office of Pesticide Programs, EPA files, and
pesticide manufacturers. A reference list is included in the Appendix B
and may be useful in obtaining these data.
If all values 24, 48, and 96-hr LC50 for the specific pesticide and
aquatic species are available and there are no conflicting values, use the
data as it is. Where conflicting data occur and can be traced to the con-
ditions under which the tests were performed, use the results most similar
to the field conditions being studied. For example, results may be given
for temperatures of 7 and 20°C. The temperature most closely approximat-
ing an actual average stream temperature should be used.
(Optional) Standardize values from static bioassays of calculated con-
centrations to flow-through bioassays of measured concentrations by the
following factors:
Static value x 0.71 approximates Flow-through values
Calculated concentrations x 0.77 approximates Measured values.
Frequently, data lists do not provide all necessary LC50 points. Use
one of the following methods to extrapolate missing values in the data.
1. Find the slope between concentrations at the 24, 48, and 96-hr
LC50 intervals for a genus or family related aquatic species
subjected to the same pesticide to approximate the missing point
or points. See Appendix B for a partial phylogenetic chart.
Example:(from dieldrin) 24-hr 48-hr 96-hr LC50
Given: Lepomis macrochirus 5.5 3.4 2.8 ppb
Lepomis gibbosus - - 6.7 ppb
Lepomis macrochirus Lepomis gibbosus
= - = -°125 = "°125
C48 = 7.3
^ A _ R R -71 '^ ~ Co*
48 - 24 = -VT - -0875 48 - 24 = -0875
Co, = 9.4
123
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= -0375 rr = -0375
C24 = 9.4
Now concentration values for 24 and 48 hours have been generated
and may be used to approximate experimental numbers.
2. Use the geometric mean of the toxicity slopes for other species
of the same order exposed to the same pesticide. Calculate the
missing values using these geometric mean values for 24, 48, and
96 hours.
3. Use the standardization factors suggested in the Federal Regis-
ter (1978) to estimate 96-hr LC50 values. The reciprocals of
these number can be used to derive data points for 24,, 48, and
72 hours.
Given: 24-hr LC50: multiply by 0.66 to estimate 96-hr LC50
48-hr LC50: multiply by 0.81 to estimate 96-hr LC50
72-hr LC50: multiply by 0.92 to estimate 96-hr LC50
96-hr LC50: multiply by 1.52 to estimate 24-hr LC50
96-hr LC50: multiply by 1.23 to estimate 48-hr LC50
96-hr LC50: multiply by 1.09 to estimate 72-hr LC50
If the pesticide of interest has no toxicity data for the species,
several alternatives remain to find data indicative of the probable toxic-
ity trend. However, without experimental results, the accuracy of these
estimates cannot be assumed.
1. Find the toxicity range of genus related species and use this
range as the most likely to include the actual values. A geo-
metric mean may be calculated, if desired, for computer handling.
2. For no genus related species, use family, then order, then class
if necessary to obtain enough data to describe a toxicity range.
3. Use toxicity data from a related pesticide for the same species
or at least the same genus to approximate toxicity if the two
pesticides are known to cause similar biological effects.
Step 2: Sublethal Toxicity
Find the MATC (maximum acceptable toxicant concentration) range for
the pesticide and aquatic species in question. If the range is specifi-
cally given in the literature, use it. Where chronic data is given and
includes no effect levels, use the highest no effect level and the lowest
chronic effects concentration to determine the MATC range.
Example: Highest concentration with no effect - 50 ppb
Lowest concentration with effects - 100 ppb
124
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50 < MATC < 100 ppb
A MATC range may be found for another species for the same pesticide.
The next two steps describe the procedure to use this MATC value for deriv-
ing one for the species in question.
1. Calculate an application factor (AF) from the known MATC of the
other species.
MATC (other species)/96-hr LC50 (other species)
= Application Factor Range
2. Use the AF to calculate the new MATC.
AF range (from above) x 96-hr LC50 (of desired species)
= MATC range (for desired species)
Alternatives may be used when no MATC ranges or chronic data can be
obtained. These include the following:
1. Use an arbitrary AF based on the pesticide's known persistence
and cumulative effects.
a. If the pesticide is neither persistent nor cumulative in
toxicity, use 0.05 as the AF for a 24 hr average or 0.1 for
a temporary concentration level.
b. If the chemical is known to have persistent and/or cumula-
tive toxicity properties, use 0.01 for the 24 hr average or
0.05' for a temporary AF.
2. Where a MATC range is given for the species for a related
pesticide, the use of its derived AF may be a better esti-
mate than an arbitrary AF.
The MATC range may be converted to a single concentration value for
convenient computer manipulation. Either endpoint or a geometric mean are
possible choices. The lowest value in the range (the highest no effect
level) is preferred as the most conservative choice.
Step 3. Input to the FRANCO Model
The choice of curve formation depends on its intended use. FRANCO
will determine the number of times a curve is exceeded, the duration of
each exceedance event, the total time of exceedance, and the percentage of
total time that the curve is exceeded as the fraction over SERATRA cutoff,
i.e., total event time. Suggested curves are included below for each
aquatic species being studied.
1. A SERATRA cutoff. This one is discussed in Chapter 4.
125
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2. The MATC as one of the endpoints or the geometric mean. Use of
both endpoints requires two curves (see Figure 13a).
3. The LC50-MATC conservative curve indicating the greatest chance
for lethality (see Figure 13b).
4. The lethal curve least likely to indicate lethality (see
Figure 13c).
5. The intermediate curve (see Figure 13d).
6. The MATC line from 96 hours to the end of the test period.
7. The LC50 or LC50-MATC line beyond 48 or 96 hours.
FRANCO accepts a maximum of six duration-concentration pairs of points
to define each curve (function). Time is in hours and concentrations are
expressed as Kg/m3 (1 x 103 mg/1). Pair point choices are dictated by
the selection of each curve. Each line segment must be defined by two
points. For straight line sections such as the MATC, designation of two
separate durations at the same concentration is required.
Step 4. Interpretation of Results
The Exceedance Summary produced by FRANCO lists the following values
for each curve:
1. The number of times curve concentration levels were exceeded.
2. The total number of time steps.
3. The total percent of modeling time in which the curve was
exceeded.
Additional information obtained by FRANCO is described in Section 4.2.
Particular points of interest may include the highest concentration level
reached, the time steps in which an event occurred, and the percentage of
time steps lasting longer than some time dx at Concentration Cx.
Results may be briefly summarized as illustrated in Figure A.I.
Section A, below the MATC line, is presumed safe and is calculated as the
percentage exceeding the MATC (sample curve 2 in Figure 13a) subtracted
from 100$. Potential lethal and sublethal Section B, which includes
lethality less than 50%, is calculated by subtracting the global exceed-
ance determined from sample curve 4 (Figure 13c) from the MATC curve
(sample curve 2). Section C measures lethality as exceedance of the LC50
curve. Sample curve 4 is used in this example. Sample curve 3 (Fig-
ure 13b) may be used instead if desired.
126
-------
ppm
12
LC50
10
MATC
I I I
24 48 72 96 120 144 168
Hours
Figure A.I. Summary of global exceedance results,
REFERENCES
Baca, R. G., W. W. Waddel, C. R. Cole, A. Brandstetter and D. B. Cearlock.
1973. EXPLORE-I: A River Basin Water Quality Model. Submitted to U.S.
Environmenta1 Protection Agency.Battelle, Pacific Northwest Laboratories,
Rich land, WA 99352
Oonigian Jr., A. S., and H. H. Davis, Jr. 1977. Agricultural Runoff Manage-
ment (ARM) Model User's Manual: Versions I and II. Submitted to U.S. Envi-
ronmental Protection Agency, Environmental Research Laboratory, Athens GA,
Hydrcomp, Inc., Palo Alto, CA.
Olsen, A. R., and S. E. Wise. 1979.
Concentrations for Risk Assessment.
Frequency Analysis of Pesticide
Submitted to U.S. Environmental Pr o -
tection Agency.Battelle, Pacific Northwest Laboratories, Richland, WA.
Onishi, Y. 1979. 1979 User's Manual for EXPLORE-I:
Quality Model (Hydraulic Module Only
Protection Agency.Battelle, Pacific Northwest Laboratories, Richland, WA.
A River Basin Water
Submitted to U.S. Environmental
127
-------
Onishi, Y. and S. E. Wise. 1979a. Mathematical Model, SERATRA, for Sediment
and Contaminant Transport in Rivers and its Application to Pesticide Transport
in Four Mile and Wolf Creeks in lowaTSubmitted to U.S. Environmental
Protection Agency.Battelle, Pacific Northwest Laboratories, Richland, WA.
Onishi, Y. and S. E. Wise. 1979b. User's Manual for the Instream Sediment-
Contaminant Transport Model, SERATRA. Submitted to U.S. Environmental
Protection Agency, Battelle, Pacific Northwest Laboratories, Richland, WA.
128
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APPENDIX B
TOXICOL06ICAL PROPERTIES OF PESTICIDES
The following tables are divided into two sections. The first is a
compilation of toxicity data on individual pesticides and the second is a
listing of groups of related pesticides along with properties of persis-
tence, solubility, and Kd values.
Table B.I lists pesticides by their common generic names and func-
tions. Several trade names are included to help identify the pesticide,
but the use of such trade names is not intended as an endorsement of the
product. The table has been divided into the taxonomic classes of fish,
crustaceans, and insects. While the risk assessment procedure of the CMRA
Methodology is not sufficiently sophisticated to assess the toxicological
risk to invertebrates, they have been included for toxicity data compari-
son. A phylogenetic chart shown in Figure B.I will help identify the
relationships between common fish species and may be referred to during
the assessment process. The list in Table B.2 identifies the scientific
names of common fish.
The individual pesticide tables include information on lethality as
24-, 48-, and 96-hr LC50 concentrations, MATC ranges, and other acute or
chronic effects. Occasionally, experimental conditions will be noted.
All values are given in mg/1 (ppm), and no attempt has been made to judge
them for accuracy or standardize them to flow-though bioassays of measured
concentration.
The reference bibliography is located at the end of this appendix.
Not all citations are from the original articles. Further search for the
original article is recommended for a better perception of the bioassay
techniques and water conditions used.
Table B.3 divides many pesticides into chemical classes and lists
solubility, Kd, soil and water persistence for them. Soil persistence is
frequently listed as a half-life value (T/2). Water persistence values
listed as percentages should be interpreted as percent remaining effective
after the given length of time. The chemical family names used here
include organochlorines, organophosphates, carbamates, phenylamides,
phenoxyalkanoates, triazines, and a miscellaneous class. While chemically
related compounds usually exhibit similar properties, individual members
may display wide variation. Therefore, the generalizations stated below
may not be applicable to all pesticides in the group.
129
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Organochlorlnes
Chlorinated hydrocarbons, cyclodienes, and other oxygenated chlorine
compounds are grouped in this category. These insecticides are persistent
and most are rapidly assimilated by organisms in contact with them. Most
are only very slightly soluble in water; typically less than 1 ppm will
dissolve. Their high affinity for and solubility in lipids is the basis
for their bioconcentration in the fats of an organism. Because of their
low water solubility, they are highly immobile in soils where they are
held and inactivated mostly by the soil organic component.
Most of the organochlorines are very persistent with half-lives of
several years. Their degradation in soils takes place at a slow rate but
can occur by microbial metabolism, photodecomposition, and by chemical
reaction. Degradation products of some of these are also insecticidal,
prolonging their effect in the soil. Volatilization may be a signficant
method of removal from both soil and water. These pesticides should leach
only a very short distance through the soil profile.
Entrance into the aquatic system from agricultural lands occurs
almost entirely through soil erosion and watershed runoff. Once in the
stream most will settle to the bottom and be associated with the sediment
where it will equilibrate with the small dissolved fraction. Persistence
in static water bodies is very long and toxaphene has been shown to be
active after ten years.
Organochlorines as a group are highly toxic to aquatic animals and
manifest an acute median lethal concentration less than 0.5 ppm for almost
all of them. Biomagnification by uptake through the food chain or direct
adsorption from water is not likely to occur in moving river communities
(Macek, 1977). However, where biomagnification and transfer does occur,
resistances to a pesticide by a species may have drastic effects on the
next trophic level.
Organophosphates
Organophosphates have demonstrated toxic effects to a variety of
organisms and have been used not only as insecticides, but also as herbi-
cides and fungicides. Their advantage over organochlorines lies in their
transience in the soil with residues disappearing between crop seasons.
The properties of Organophosphates often cover a wide range. As a
group they are slightly soluble in water but can vary from less than 1 ppm
(Dioxathion) to 13% (Trichlorfon) or more. Movement in soils is related
to their solubilities with the most mobile usually being the most soluble.
However, they tend to adsorb strongly to soils and are seldom leached
below the surface.
130
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Vapor pressures for this group exceed the organochlorines and they
are readily susceptible to microbial and chemical degradation. The rate
of degradation rises with increases in soil moisture, temperature, and
acidity; with hydrolysis; oxidation; and volatility contributing to the
low stability. Under moderate conditions organophosphates remain in soil
no more than several months though parathion has been detected in sandy
loam for up to 16 years (Guenzi, 1974).
Most aquatic contamination will result from runoff entering the
watershed. Organophosphates tend to hydrolyze readily and have a compara-
tively short half-life in water. Prior to their degradation they are
highly toxic to aquatic animals, several compounds are responsible for
lethal effects to crustaceans around the 0.001 ppm level.
Carbamates
Carbamate compounds have been divided into the methyl carbamates, a
thio- and dithio- group and the carbanilates and are effective as insecti-
cides, fungicides, and herbicides. Insecticides act by contact or inges-
tion as competitive inhibitors of cholinesterase. Fungicides are useful
in controlling foliar disease in agricultural crops. Herbicides are
effective in preemergence application. Most methyl carbamates have insec-
ticidal activity and most phenyl carbamates are herbicides. Thio- and
dithiocarbamates are used as herbicides and fungicides. Carbamates have a
relatively short residual life in soils and are readily degraded by most
nontarget organism.
Carbamate compounds are somewhat volatile and most are broken down
rather quickly by microbial and chemical degradation. They do not sorb
strongly to soils and are easily leached. The carbanilates, however, are
relatively immobile. Once in the water, they are quickly broken down with
a significant reduction within one week. Most are moderately toxic to
fish and very toxic to crustaceans.
Phenylamides
Amines and an-i lines, the nitroanilines, and the ureas comprise this
group. Alternative names for these chemicals are the acetamides,
acylanilides, toludines, and phenylureas, among others. Nearly all of
these have herbicidal action and are metabolized similarly.
These pesticides show moderate soil persistence with significant
microbial degradation. None are very soluble, and the nitroanilines are
particularly insoluble (1 ppm or less in water). Several are somewhat
volatile and their adsorption is directly related to the amount of organic
matter. The nitroanilines are fairly immobile in the soils but the ureas,
amides, and anilines do not sorb strongly and desorb rapidly. Toxicity of
this category to aquatic animals is low.
131
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Phenoxyalkanoates
This group of herbicides also exhibits low toxicity to aquatic ani-
mals. The phenoxy compounds are not readily adsorbed to clay minerals but
will sorb in limited amounts to organic matter. They are slightly soluble
and are easily leached. Their persistence in soils depends on soil water
content usually ranging from one to six months.
Triazines
Triazines and triazoles are largely nonvolatile herbicides. Their
wide range in persistence from two weeks to more than a year depends on
soil type, soil moisture, and application amount. Adsorption, which is
readily reversible, depends on soil composition, moisture, pH, and temper-
ature. Some are moderately mobile while others demonstrate little leach-
ing or lateral movement. Toxicity of these pesticides is low for fish and
may be moderate to invertebrates.
Miscellaneous Pesticides
Included among the miscellaneous are such groups as the aliphatic and
benzoic acids, the dipyridyls, nitrophenols, phthalimides, pyridines, and
the uracils.
132
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TABLE B.2. SCIENTIFIC AND COMMON NAMES OF FISH USED IN THIS REPORT.
Scientific Name Common Name
Oncorhynchus kisutch
Oncorhynchus tschawytscha
Salmo clarki
Salmo gairdneri
Salmo trutta
Salvelinus fontinalis
Salvelinus namaycush
Esox lucius
Carassius auratus
Cyprinus carpio
Notemigonus crysoleucas
Notropis atherinoides
Notropis lutrensis
Notropis umbratilus
Pimephales notatus
Pimephales promelas
Rasbora heteromorpha
Catostomus commersoni
Ictalurus melas
Ictalurus natal is
Ictalurus nebulosus
Ictalurus punctatus
Gambusia affinis
Poecilia latipinna
Poecilia reticulata
Gasterosteus aculeatus
Lepomis cyanellus
Leponns gibbosus
Lepomis macrochirus
Lepomis microlophus
Micropterus dolomieui
Micropterus salmoides
Perca flavescens
Stizostedion vitreutn
Mugil cephalus
Morone saxatilis
coho salmon
chinook
cutthroat trout
rainbow trout
brown trout
brook trout
lake trout
northern pike
goldfish
carp
golden shiner
emerald shiner
red shiner
redfin shiner
bluntnose minnow
fathead minnow
harlequin
white sucker
black bullhead
yellow bullhead
brown bullhead
channel catfish
mosquito fish
sailfin molly
guppy
threespine stickleback
green sunfish
pumpkinseed fish
bluefill sunfish
redear sunfish
smallmouth bass
largemouth bass
yellow perch
walleye
striped mullet
striped bass
160
-------
ORGANOCHLORINES
CHLORINATED HYDROCARBONS
Aldrin
Chlordane
DDT
Heptachlor
Isodrin
Lindane
Mirex
TDE (ODD)
Toxaphene
.011-. 2
Very low
.01-. 037
.056
10
.001
ins
1.5
OXYGENATED-CHLORINATED HYDROCARBON
Chlordecone (Kepone®)
Dichlone
Dicofol (Kel thane®)
Dieldrin
Endosulfan
Endrin
Methoxychlor
Tetradif Ion
ORGANOPHOSPHATES
ALIPHATIC DERIVATIVES
Acephate
Demeton
Dichlorovos
Dicrotophos
Dimethoate
Dioxathion
Disulfoton
Ethion
Ethoprop
Malathion
Methamidophos
Mevinophos
Monocrotophos
Naled
Oxydemeton -Met hy 1
Phorate
Phosphamidon
TEPP
Trichlorfon
HETEROCYCLIC DERIVATIVES
Azinphos-Ethyl
Azinphos-Methyl
Chlorofenvinphos
Chlorpyrifos
Coumaphos
Diazinon
Methidathion
Phosalone
Phosmet
Thionazin
1.5 to 2.0
.1
ins
.186
<1
.23
.62
200 at 50°C
very
6.6
1.0%
misc
2-3%
ins
25
1
ins
145
9%
misc
misc
slightly
300
50
misc
misc
13%
ins
33
145
20
1.5
40
240
ins
25
1140
TABLE B.3. PESTICIDE CLASSES
Solubility (mq/1) Kd
5 x 103
5 x 103
104
104
103
1 x
1 x
5 x
1 x
1 x
5 x
104
104
104
25
5 x 102
50
50
25 9
1 x 102
1 x 102
5 x 102
50
1 x 102
10
50
25
50
1 x 102
5 x 102
50
50
10
1 x
1 x
5 x
50
102
102
102
50
50
5 x 102
5 x 102
Soil
Persistence
very stable
T/2 :.-7 yr
T/2 4-8 yr
T/2 200 d
T/2 122 d
T/2 290 d
1 wk
T/2 26 d
2 wk
T/2 140 d
T/2 20 d
T/2 29 d
12 wks
Water
Persistence
7
5
1
1
7
1
5
5
5
x
X
X
X
X
X
X
X
X
10*
104
105
104
104
103
104
104
104
T/2 1-4 yr
T/2 2-4 yr
T/2 3-10 yr
T/2 7-10 yr
T/2 4-8 yr
1 yr
very stable
4-16 yr
40%
20%
85%
100%
25%
9-10
- 4 wks
- 8 wks
- 8 wks
- 4 wks
- 2 wks
yr
high at 1 ppb
or less
high
100% - 8 wks
30% - 2 wks
5% - 4 wks
100% - 8 wks
20 - 38 wks
62 d at 20°C
85% - 4 wks
10-25% - 2 wks
0-10% - 4 wks
100% - 4 wks
T/2 7-8 hr
detected at 256 d
T/2 30 d at pH 9
50% - 1 wk
161
-------
PHENYL DERIVATIVES
Carbophenothion
Chlorothion®
Crotoxyphos
Crufomate
Dicapthon
EPN
Famphor
Fenthion
Fonofos
Parathion
Parathion-Methyl
Ronnel.
Stirofos
Temephos (Abate®)
PHENOALKANOATES
TABLE B
Solubility (mq/ll
2
40
110
ins
35
100 - 11%
slightly
55
13
20-24
50-60
.3. (contd)
Soil
1 Kd Persistence
1 x 103
2 x 102 T/2 - 36 d
1 x 102
1 x 102
5 x 102
1 x 103
2 x 102
5 x 102
2 x 102
5 x 102 7-20 d
3 x 102 2-60 d
Water
Persistence
50% - 2 wks
10% - 4 wks
50% - 2 wks
303! - 4 wks
11% - 2 wks
-0% - 4 wks
40
11
ins
ALIPHATIC ACIDS AND ESTERS
Dalapon 50%
Glyphosate 1120 mg/1
Trichloracetic acid (TCA)83%
AROMATIC ACIDS AND ESTERS
Bifenox .35
Chloramben 700
Chlorthal-Dimethyl (DCPA).5
Dicatnba 4500-7918
Endothall 21%
Fenac
Naptalam
Picloram
Propargite
PHENOXY COMPOUNDS
2,4-D
Dinoseb
Erbon
MCPA
Si 1 vex
2,4,5-7
PHENYLAMIDES
AMINES AND ANILINES
Alachlor
Bensulide
CDAA
Diphenamid
Pronamide
Propachlor
Propanil
NITROANILINES
Benfluralin
Butralin
Dinitramine
Fluchloralin
200
200
430
ins
620 at 25°C
52
ins
27%
140
140
240
25-50
2%
260
15
700
500
.5
1
-70
2 x 102
2 x 102
0.2
0.2
0.2
0.5-1
0.5-1
0.5-1
0.5-1
0.2
0.5-1
0.5-1
0.5-1
0.5-1
1.0
10 ,
1 x 102
1.0
2.0
2.0
50
5
102
1 x
50
50
10
5 x 102
5 x 102
50
1 x 102
15-30 d
150 d
20-70 d
40-60 d
40-60 d
400 d
2 months
350-700 d
20-60 d
550 d
10-30 d
15-30 d
30-180 d
5 months
40-70 d
10 months
20-40 d
90-180 d
60-270 d
30-50 d
1-3 d
120-150 d
90-120 d
90-120 d
2-3 d
50%
10%
1 wk
2 wks
1% in 30 d
162
-------
TABLE B.3. (contd)
Nitralin
Phenoxalin
Prof1ura1 in
Trifluralin
UREAS
Chlorbrorouron
Chloroxuron
Diuron
Fenuron
Fluometuron
Linuron
Monuron
CARBAMATES
Aminocarb
Benomyl
Bufencarb
Carbaryl
Carbofuran
Chlorpropham
Karbutilate
Methiocarb
Methomyl
Mexacarbate
Propham
Propoxur
THIOCARBAMATES
Butyl ate
EPIC
Molinate
Thiram
Vernolate
TRIAZINES AND TRIAZOLES
Ametryne
Amitrole
Atrazine
Cyanazine
Metribuzin
Prometon
Propazine
Simazine
MISCELLANEOUS
Solubility (mg/1) Kd
.6 50
.5 1 x
.1 5 x
1-24 5 x
103
102
102
2 x
50
3.7
42
3850
90 20
75 1 x
230 50
102
5 x 102
1 x 102
10
102
slightly 1 x 102
ins
100
40-99
700
88-108
325
ins
5.8%
100
250
2000
15-300 5 x 102
370 1 x 102
800 50
30 5 x 102
90 1 x 102
185 8
28% at 25'C 1
33 at 27°C 5
160-171 3
1200 1
677-750 8
8.6 1
5 6
Soil
Persistence
moderate
320-640 d
120-180 m
300-400 d
200-500 d
30-270 d
120 d
1250-350 d
Water
Persistence
40-80 d
30 d
80 d
50 d
30-90 d
15-30 d
300-500 d
150-200 d
>400 d
200-400 d
200-400 d
3 months
60* - 2 wks
20% - 4 wks
20* - 4 wks
none - 8 wks
40* - 2 wks
30* - 4 wks
60% - 2 wks
10% - 4 wks
10
5 x 102
5 x 102
5 x 102
1 x 102
2
1 x 102
5
1 x 102
50
1 x 102
16 wks
T/2 8 days
120-260 d
20-60 d
5% -
0% -
15%
0% -
50%
30%
2 wks
4 wks
- 2 wks
4 wks
- 2 wks
- 4 wks
BOTANICALS
Allethrin ins
Pyrethrum/ ins
Pyrethrin (synthetic)
Rotenone slight
1 x ID*
1 x 104
1 x 103
moderate
decomposes readily
163
-------
ARSENICALS
CMA
DSMA
MSMA
DIAZINES-URACILS
Bentagon
Bromacil
Pyrazon
Terbacil
DIPYRIOINUMS
Diquat
Paraquat
CYANATES
Chlorothalonil
Dichlobenil
METALOIDS
Copper Napthalate
Mancozeb
OTHER
Acrolein
Captan
Carboxin
Difolatan
Dodine
Methazole
Methyl Bromide
Norflurazon
TABLE B.
Solubility (mg/1)
2%
2.8%
3. (contd)
Soil
Kd Persistence
0.2
0.2
0.2
Water
Persistence
500
815
300-400
710
7%
completely
0.2
5
30
50
5 x
5 x
103
103
T/2 5-6 m
30-60 d
700 d
>500 d rapid
inactivation
>500 d
>2 yr in mud
7-27 d
7-14 d
.6
1.8
ins
moderate
>.5
170
;ns
6300
1.5
1.75*
28
50
20
5 x 102
5
0.2
30
10
50
0.5
5
10
50
60-180 d
2-3 m
164
-------
REFERENCES
American Chemical Society. 1969. Cleaning Our Environment: The Chemical
Basis for Action. Washington, DC.
American Fisheries Society. 1970. A List of Common and Scientific Names
of Fishes from the United States and Canada. R. M. Bailey (Chairman),
Third Edition, Special Publications, No. 6, Washington, DC.
Alabaster, J. S. 1969. "Survival of Fish in 164 Herbicides, Insecti-
cides, Fungicides, Wetting Agents and Miscellaneous Substances." Interna-
tional Pest Control, March/April, pp 29-35.
Allison, D. T., and R. 0. Hermanutz. 1977. Toxicity of Diazinon to Brook
Trout and Fathead Minnows. EPA-600/3-77-060, U.S. Environmental Protec-
tion Agenty, Duluth, MN.
Battelle's Columbus Laboratories. 1971. Water Quality Criteria Data Book
- Volume 3. 18050 GWV05/71. U.S. Environmental Protection Agency,
Washington DC.
Bond, C. E., R. H. Lewis and J. L. Fryer. 1960. Toxicity of Various
Herbicidal Materials to Fish. R. A. Taft. San. Eng. Cen. Tech. Rept.
W60-3:96-l-l.
Bridges, W. R. and 0. B. Cope. 1965. "The Relative Toxicities of Similar
Formulations of Pyrethrum and Rotenone to Fish and Immature Stoneflies."
Pyrethrum Post, 8:3-5.
Brungs, W. A., R. W. Carlson, W. B. Horning II, 0. H. McCormick, R. L.
Spehar and J. D. Yount. 1978. "Effects of Pollution on Freshwater Fish."
Journal of Water Control Federation, 50(6) -.1582-1637.
Burdick, G. E., H. J. Dean, and E. 0. Harris. 1964. "Toxicity of Aqualin
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APPENDIX C
DISTRIBUTION COEFFICIENTS OF ORGANIC PESTICIDES IN AQUATIC ECOSYSTEMS*
INTRODUCTION
This report considers certain aspects of the distribution of organic
pesticides between water and solid abiotic phases in natural aquatic (fresh
water) ecosystems. This study was performed for Battelle Pacific Northwest
Laboratory in support of a larger study of the mobility and transport of the
pesticides in natural riverine ecosystems.
The report considers three main points:
1. A discussion of the molecular and environmental parameters that
affect the distribution of the pesticides in natural systems,
2. A discussion of mathematical expressions useful in describing the
distribution (or partitioning) of the pesticides between the
aqueous and solid phases, and
3. The presentation of estimated distribution coefficients for the
specific pesticides of interest in this study.
The first two points are considered in Part 1 of this report, while the
estimated distribution coefficients are presented in tabular form in Part 2.
*Submitted to Battelle, Pacific Northwest Laboratories, by R.N. Dexter, URS
Company, as final report of the services provided under consultant agree-
ment B-62522-B-L.
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PART 1: ADSORPTIVE EQUILIBRIA
The discussion below is based both on a consideration of general partitioning
theory and a review of pertinent literature concerned with adsorption in
natural systems. Many of these points considered can be found in reviews by
Hamaker and Thompson, 1972; Zettlemoyer and Micale, 1971; and Bailey and
White, 1970.
ADSORPTION
For the discussion below, equilibrium between the adsorbed and solution
phases is assumed. At this point a balance of forces is established and
the chemical potential or activity of the sorbate (pesticide) must be
the same in the solution and on the surface of the solid matrix. A con-
sideration of the intermolecular interactions which give rise to these
chemical potentials is then informative in defining the behavior to be
expected for different types of sorbates and in determining the parameters
of natural systems which can be expected to affect the chemical potentials
and thus the adsorptive equilibria.
Aqueous Solution
It is well recognized that liquid water is anomalous in its behavior com-
pared to similar chemical species. This results primarily from the high
polarity of the water molecules which produces strong internal hydrogen
bonding. The dipole also generates strong electrostatic attractive forces
between water molecules and ionizable and polar moieties of organic and
inorganic compounds.
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At the same time, the hydrogen bonding among water molecules leads to con-
siderable internal structure in liquid water. As a result, the introduction
of solute fprces the rearrangement of the normal structure of liquid water in
the vicinity of the solute molecules. This restructuring generally requires
an energy contribution to compensate for the entropy change. In a practical
sense, this acts as a repulsive force opposing the accommodation of the
solute. This apparent repulsion from water can force strong associations
between certain molecules and is referred to as hydrophobic interaction (or
hydrophobic bonding). As a general rule, the entropic contribution and thus
the strength of the hydrophobic interaction is a function of the effective
size .of the solute molecule in solutions (Franks, 1975).
The activity, then, is a function of both the concentration and the balance
between the attractive electrostatic forces and the entropy-generated repul-
sion. Solute compounds can thus he ranked based on their size and their
ability to participate in electrostatic interactions. For example, totally
ionized inorganic solutes are small species with high charge densities
leading to low aqueous activity coefficients as reflected in their relatively
high solubilities. On the other end of the scale, non-polar hydrocarbons
interact only through relatively weak van der Waals interactions. For these
compounds, the hydrophobic interaction is strong resulting in high activity
coefficients and low solubilities. The solubilities and activity coeffi-
cients of hydrocarbons correlate well with the size of these molecules
(Tsonopolus and Prausnitz, 1971; Frank and Evans, 1945).
The majority of organic pesticides fall somewhere between these extremes.
Most have some charge localization arising from heteroatoms in the structure,
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particularly oxygen, and some have ionizable hydrogens. For these compounds,
the interactions with water are complex functions of both the electrostatic
and hydrophobic interactions. Within limits, the relative activities of a
group of compounds with the same polar moiety will correlate with the size of
the non-polar substituents. Conversely, from such correlations,, the absolute
contribution of the polar moiety can be estimated to generate empirical
additivity rules for ranking other similar compounds (Tsonopolus and Prausnitz,
1971).
These activity relationships are complicated by two factors. First, substi-
tution of various moieties on the parent molecule can have secondary impacts,
primarily by withdrawing or contributing electrons to polar sites or sites
with active hydrogens. For example, monosubstituted nitrophenols as a group
are much less soluble than phenol (solubility = 93 g/1). However, j3- nitro-
phenol (solubility = 2 g/1) is nearly an order of magnitude less soluble
than the m- or £- substituted compounds (m- nitrophenol, solubility - 14 g/1;
£- nitrophenol, solubility - 17 g/1) (Morrison and Boyd, 1966, pp. 790-792).
Second, natural waters are not pure, but are rather complex and variable
solutions. The principal parameters affecting the activity of dissolved
species (particularly organics) are pH, ionic strength and type of ions, the
quantity and nature of dissolved (and colloidal) organic matter, and tempera-
ture.
pH
The pH controls the speciation of ionizable acid and base groups; the ionized
forms interact more strongly with water, e.g., are more soluble.
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Ionic Strength
The ions of natural dissolved salts generally tend to increase the normal
ordering within the liquid such that the ability to accommodate organic
compounds in reduced ("salting out") and hydrophobic interactions are
increased.
Dissolved Organic Matter
Natural dissolved organic matter (DOM) consists principally of refractory
polyelectrolytes resulting from the degradation of biological materials
(Christman and Minear, 1971). The DOM form stable solutions which can
scavenge and suspend pesticides either through electrostatic (ion-ion,
ion-dipole, or ligand) interactions or, for less polar materials, through
hydrophobic interactions with non-polar sites in the DOM matrix. The net
result is to reduce the dissolved concentration (and thus the activity) of
the pesticides in solution without necessarily decreasing the (analytically
determined) concentration. The interactions between DOM and pesticides may
in turn be altered by changes in either pH or ion content, which will affect
the degree of ionization, the effective charge density, and the three dimen-
sional structure of the DOM molecules.
Temperature
In all cases, interactions are affected by temperature changes. In general,
as the temperature increases the activity decreases for polar species while
it may increase for non-polar compounds. Over the normal range of temperature
fluctuations exhibited by natural systems, the effect is small for most
compounds.
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Surface Interactions
Interactions between the sorbate pesticide compounds and sites on the
absorbent solid matrix are entirely electrostatic (excluding chemisorption)
and primarily a function of the polarity of thf sorbate molecule,, ranging
from weak van der Waals to ion-ion bonding. Natural soil and and sediment
matrices are either inorganic mineral grains, usually coated or aggregated
with organic polyelectrolytes, or detrital organic particles (Kononova,
1966). In either case, numerous sites are available which carry weak to
strong charge localizations, usually with a net negative charge exhibited by
the whole particle (Neihof and Loeb, 1972). To the extent that adsorption is
a surface phenomena, smaller particles will show higher mass-normalized
concentrations of pesticide due to their greater specific surface area
(Leland, et^L, 1973).
In addition, many inorganic particles in natural environments contain pores
and interstices between crystallization planes which allow sorbate molecules
to diffuse into the interior of the particle. (Knight and Tomlinson, 1970.)
Similarly, pesticides may be capable of diffusing into the interior of
detrital organic particles in a fashion similar to passive diffusion through
biological membranes. In both cases, quantitative differences may exist in
the interactions between the sorbate and the readily accessible surfaces of
the particles and the interior sites. Further, exchange rates with the
interior would be expected to be much slower than with surface sites.
Both pH and ionic strength will affect the characteristics of the surfaces.
The pH effectively gives a measure of the H or OH" ions available to
satisfy specific acidic or basic moieties on the surface, while counterions
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act less specifically to satisfy residual charges sites. Due to these
facto.s, rather complex adsorption trends may develop resulting from compet-
itive ion exchange, ligand, and dipole Interactions between surface sites,
natural ions, and polar pesticides. Non-polar pesticides would not be
significantly affected by these changes.
One factor which must be considered, but is not often recognized, is that in
adsorption from aqueous solutions the water molecules themselves interact
with the surface and are in competition for polar sites with all other
adsorbates. Even in cases where the relative binding strengths of water
molecules may be weak, the predominance of water molecules in natural systems
(i.e., dilute solutions) makes them important contributors to the overall
process.
The factors discussed above will affect the adsorption are summarized in
Table 1. Based on a consideration of the dominant types of interactions
(hydrophobic or electrostatic).
Table 1
SUMMARY OF ADSORPTION INTERACTIONS IN NATURAL AQUEOUS ENVIRONMENTS
TYPE OF
INTERACTION EFFECTOR VARIABLES
ENVIRONMENTAL
MOLECULAR AQUEOUS PHASE SOLID PHASE
Hydrophobic Molecular Size Ionic Strength, Surface Chemistry
DOM* Temp Temp
Electorstatic Charge Density, Ionic Strength, Surface Chemistry,
Polarizability, pH, DOM* Temp Ionic strenght,
Acid/Basic Moieties pH, Temp
*DOM acts to reduce the dissolved concentration.
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EQUILIBRIUM RELATIONSHIPS
Theoretical Basis
To provide useful data for predicting the distribution behavior it is desire-
able to establish adsorption equilibrium relationships between the concentra-
tions in the aqueous and sorbed phases, i.e.,, adsorption isotherms. The
primary intent is to obtain mathematical expressions, hopefully not overly
complex, for the isotherms.
A number of isotherm equations are available which have been examined as
to their applicability to adsorption from solution in natural systems.
These include the Freundlich, Langmuir and Brunauer-Emmett-Teller (BET)
isotherms. Of these only the last two have any coherent theoretical basis
(Adamson, 1976). Each is based on different assumptions as to the processes
leading to adsorption and each finds use in explaining different adsorption
systems. It should be noted that most of the theoretical studies in develop-
ing and justifying these expressions has relied on well characterized,
vapor phase systems (Adamson, 1976).
Due to the strong and specific binding arising from ion-ion interactions,
it would be anticipated that metal salt pesticides would be the most likely
to follow a Langmuir type isotherm. Conversely, adsorption of non-polar
organics, for which hydrophobic interactions perdominate, would be expected
to include multilayer formation at higher concentrations and thus more
likely to be represented by a BET type isotherm. Similarly, BET would
be a likely model isotherm for organic pesticides of intermediate polarity
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since electrostatic behavior could predominate at low concentrations, but
hydrophobia interactions would undoubtedly be important as the aqueous
solution approaches saturation.
At the same time, neither Langmuir nor BET isotherm expressions have not been
utilized to any great extent with natural systems. The primary argument has
been that natural adsorbents do not present a homogenous surface (constant
adsorption energies independent of surface coverage) which is required for
these equations to be applicable (Adamson, 1976). As a result, the most fre-
quently encountered expression is the Freundlich isotherm, which while having
no theoretical basis, is a semi-logarithmic type expression with empirically
determined parameters and has been used to fit many observed adsorption
relationships.
However, the present body of data which has popularized the Freundlich
isotherm is not free from criticism. First, the perponderance of studies
have been performed by soils chemists and engineers primarily concerned with
the factors controlling the biocidal activity of agricultural pesticides. In
the majority of these studies, the aqueous concentrations of pesticides
utilized were often much higher than normally encountered in natural systems,
the experimental levels often approaching or even exceeding the solubilities
of the test compounds (Hamaker and Thompson, 1972). In some cases this was
done to maintain detectable levels of the pesticides in all phases; in other
cases, levels approximating field application levels were used.
In addition most studies have examined a relatively limited range of aqueous
concentrations compare to what is encountered in natural systems. With
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this limited experimental base, it is difficult to insure that the data can
be extrapolated to either higher or lower concentrations or that the isotherm
expression is truly representative of the tdsorption behavior.
At higher concentrations, particularly as the solubility is approached,
a number of effects which can give rise to non-linear isotherms ean occur.
1) At higher concentrations, solute-solute intermolecular interactions
may increase particularly for more hydrophobic compounds. This results in
a decrease in the activity coefficient and thus a non-linear relationship
between the aqueous activity and the concentration such that adsorption
would be relatively diminished (other factors being invariant)j i.e, l/nl.
All of these effects are greatly diminished at reduced concentrations when
the opportunity for solute-solute interactions either in solution or on the
surface and the number of occupied surface sites are all minimal.
It can be argued convincingly that these conditions are most likely in
natural systems (except, of course, agricultural land receiving direct
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applications), both on the basis of measured residue levels which are rou-
tinely observed at very low concentrations and by considering that the
residue undoubtedly undergo a number of adsorption-desorption steps during
mobilization from the site of application. Under these conditions, it would
seem apparent that, since the probably sources of non-linear adsorption are
virtually eliminated, 1/n should approach unity for those systems where the
Freundlich isotherm is applicable. Note also that at low concentrations both
the Langmuir and BET isotherms reduce to linear forms.
A further criticism of the available data is concerned with the interpreta-
tion of the kinetics of the experimental results. Where rate information is
available, the usual behavior observed is rapid initial uptake (<24 hours)
followed by slow uptake which increases the adsorbed concentration by about
10% over a period of one to two months. Desorption experiments performed
immediately after adsorption generally yield reversible adsorption isotherms
(at least when the aqueous concentrations of the solute are below saturation).
However, desorption and residue recovery experiments performed after long
equilibration periods have often, but not always, shown a portion of the
adsorbed residues to be more strongly retained in the solid matrix than would
be indicated from the adsorption isotherm (Hamaker and Thompson, 1972, pp.
92-97).
Two alternatives have been considered to explain this behavior; 1) chemisorp-
tion (Huangand Liao, 1970) and 2) migration of the residues either to stronger
binding sites not entirely occupied during the initial adsorption, or to the
interior of the particle. (Saha, et al., 1969). Chemisoi*ption seems unlikely
181
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since the residues can be recovered in unaltered form, and considering the
energy requirements which would be required to produce chemical binding
without destroying the pesticide molecule. Simple migration to strongly
binding surface sites would not seem to require such long periods before
their effect would be observed. Obviously, the simplest and most reasonable
explanation is the physical migration of the pesticide molecules into the
interior of the particle, either through pores and between the crystalliza-
tion planes of laminar clays (e.g. montmorillonites), or below the surface of
organic detrital particles. With organically coated particles, it is possible
that the migration of polar and ionic pesticides may also yield inherently
stronger binding to the inorganic matrix (Burns and Andrus, 1970). In most
cases, however, it appears likely that the slow rates of intraparticle
diffusion which would be sufficient to explain the slow desorption and
apparent incomplete recoveries.
Of major concern at this point is the implication of these results in esti-
mating the mobility of the pesticide residues in natural systems. The
major conclusions that can be drawn are 1) that the majority of experimental
K,values underestimate the actual distribution coefficient applicable
in natural systems, and 2) that many of the available desorption studies
do not realistically represent the behavior of the residues 1ri natural
systems.
In further support of these conclusions is the observation that most studies
examining the migration of pesticides in actual field situations indicate
that the movement of even relatively soluble herbicides is generally limited
and much less than what would be predicted on the basis of laboratory
equilibrium or soil leaching experiments.
182
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Natural Systems
The information discussed above should be considered within the framework
of natural systems to discern the implication of equilibrium distribution
coefficients for predicting environmental mobility associated with the
movement of water.
The first situation is the movement of pesticides from the site of Initial
application, e.g., agricultural land. Two major processes can be defined
1) leaching via percolation through the soil in the groundwater, and 2)
direct overland runoff associated with heavy rains or excess irrigation
water. The great predominance of soil mass compared to water in groundwater
flow indicates that movement of even slightly adsorptive compounds should be
very slow. On the other hand, runoff events are rapid with relatively
short contact times between the runoff water and the soils in the fields
compared to the probable rates of desorption of most compounds. In addition
contact with the soil-incorporated residues is minimal, except for contact
with that portion of the soil which is itself mobilized by the runoff. In
many situations, this latter soil component probably contributes the largest
fraction of mobile pesticides, independent of the strength of adsorption
of the residues.
As a result of these effects, neither the mass moved nor the distribution
between particulate and water fractions of the residues could be expected
to be strong functions of distribution coefficients. Rather, both the
mass movement and the total concentration of the residues should correlate
with the corresponding parameters for suspended soil particles. Such behavior
has been noted for storm-generated runoff from experimental plots (Donigian,
et__a]_L, 1977).
183
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In permanent water bodies (rivers, lakes, etc.), however, the situation
is markedly different. Since the residues have already undergone numerous
adsorption/desorption steps in traveling to the Mater body the aqueous
concentrations are much reduced compared to levels in agricultural usage.
Further dilution normally occurs soon after introduction into the system.
The effect should be reduced the pesticide concentrations to the range
of linear activity-concentration relationships for the residues in both
the aqueous and solid phases, and thus linear distribution coefficients, K.,
should be applicable.
Further, the solid particles are always completely hydrated. Meiny of
the physical effects which can alter the mobility of pesticides in soils
as a result of wet/dry cycles are eliminated, e.g., collapse of pores
and voids in organic coatings on drying the slow rehydration of interior
binding sites, and the swelling of the inter-laminar spaces of clays.
It is reasonable that this stability of the solid matrix would tend to
eliminate differences in the long term adsorption/desorption behavior
and, by keeping intraparticle voids and pores open, facilitate exchange
between the solid and aqueous phases. These points would further argue
for linear, equilibrium adsorption.
Summary
From the foregoing discussions the following conclusions can be made:
1. The majority of the available adsorption data underestimates the actual
strength of the adsorption on solid matrices.
2. The majority of the available data also oversimplifies the equilibrium
adsorption relationships.
184
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3. In natural runoff from agricultural land receiving direct application of
pesticides, the mobility of any pesticide and its relative distribution
in the mobile aqueous and $ottd ph&ses ftrt greftoffltnantly functions of
the physical processes taking place, with adsorption-desorption consi-
derations of lesser importance. It is probably not reasonable to apply
distribution coefficients or even more complex adsorption isotherms to
these situations.
4. In permenant water bodies receiving indirect pesticide inputs, linear
adsorption isotherms would be applicable, and the relative distribution
should be adequately characterized by a single distribution coefficient,
PART 2: VALUES OF THE DISTRIBUTION COEFFICIENT, Kd
The estimation of reasonable K . values for the pesticides of interest in
this study is difficult for three reasons. (1) Fundamental physical
chemical properties, e.g., solubility, have been determined for only a
few of the compounds and even the data which have been reported are not
always reliable. (2) Even empirical parameters, such as the octanol-water
partition coefficient, which would be suitable for ranking the adsorptability
and estimating approximate values for K., are not readily available.
(3) The adsorption data which have been reported in the literature often
suffer from the limitations discussed in Part 1. In addition, the data for a
single compound often vary by more than an order of magnitude, reflecting
both artifacts of different experimental techniques and real variability
185
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resulting from differences in the adsorptive matrix, particularly when
comparisons are made between pure clays, natural loams,and natural muck or
peat soils.
The Kj values presented below were estimated for an "average" or "normal"
freshwater system. The solid matrix is assumed to consist of silty-sand of
about l%.to 3% organic matter. The aqueous phase is assumed to have low
total solids, pH of between 7 and 8 and to be unpolluted by large quantities
of detrital organic matter.
K. values for some non-polar (organochlorine) pesticides and related
compounds have been predicted previously, based on fundamental physical
chemical properties (Dexter and Pavlou, 1978). Such an approach was not
viable for this study, however, due in part to the lack of reading available
fundamental data for all of the compounds, and in part to the time restraints
encountered.
The values presented are, of necessity, fairly rough estimates based on
careful consideration of the adsorption coefficients and relative adsorption
data available in the literature, and on a consideration of the relative
molecular structural contributions to the adsorptive interactions, e.g.,
molecular size, polarity of heteroatom moieties, number of polar groups,
etc. As a first step, the molecular structures were compared and the
pesticides within each group ranked according to probable relative adsorp-
tion strength. From the previous calculations and from selected literature
references, Kd values for some of the pesticides could be estimated with
some certainty. By further comparisons of the molecular structures of
186
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these "marker" compounds with the ranked pesticides, appropriate K ,
values were estimated for natural systems.
The values are presented in tables for each pesticide group with an approxi-
mate ranking of the compounds within each group beginning with the highest
Kd value.
Each table is followed by a short discussion of the rationale for the esti-
mated K values and any available supporting literature data.
ORGANOCHLORINE PESTICIDES
A. Aromatic K,
DDT 1 X 105
TDE (ODD) 5 X 104
Tetradiflon 5 X 104
Methoxychlor 1 X 104
Kelthane 1 X 104
Dichlone 5 X 10*
B.
Aliphatic
Aldrin
Isodrin
Chlordane
Toxphene
Mi rex
Heptachlor
Endrin
Dieldrin
Chlodecone
Endosulfan
Lindane
BHC
7 X
7 X
5 X
5 X
5 X
1 X
1 X
1 X
5 X
5 X
1 X
1 X
104
104
104
104
104
104
104
104
103
103
103
103
187
-------
Rationale: These compounds by and large are the most easily considered since
they contain relatively few polar moieties. As a result adsorption is due
primarily to hydrophobic interaction and van der Walls forces. For this
reason relative adsorption will be correlated with the molecular size with
corrections for polar groups and non-conjugated double bonds. Further,
values of K . can be approximated from theoretical calculations (Dexter
and Pavlou, 1978).
Some literature support for the values can be obtained from the literature.
Values for Kd for DDT of approximately 1 X 10 have been reported in
soils by Shien et al. (1974), and Pavlou et al. (1974). The value for
lindane was reported by Lotse et al. (1968) and Boucher and Lee (1972).
Values of distribution coefficients for some of the other compounds have
been reported, but most appear to be far from a reasonable range (Hamaker
and Thompson, 1972).
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ORGANOPHOSPHATE PESTICIDES
A. Aliphatic Deriatives Krf
Ethion 5 X 102
Disulfoton 5 X 102
Counter 5 X 102
?
Demeton 5 X 10
Phorate 5 X 102
Dioxathion 1 X 102
Malthion 1 X 102
Oxymeton methyl 1 X 102
Dichlorvos 50
Ethoprop 50
Phosphamidon 50
Dicrotophos 50
Mevinphos 50
Naled 50
TEPP 50
Dimethoate 25
Monocrotophos 25
Acephate 25
Trichlorphon 10
Methamidophos 10
B. Phenyl Derivatives
EPN 1 X 103
Carbophenothion 1 X 10
189
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Kd
Dicapthon 5 X 102
Fenthion 5 X 102
Parathion ethyl 5 X 102
2
Parathion, methyl 3 X 10
Ronnel 2 X 102
Stirofos 2 X 102
Chlorothion 2 X 102
Famphur 2 X 102
Dyphonate 2 X 102
Ciodrin 1 X 102
Crufomate 1 X 102
C. Heterocyclic Derivatives
2
Chlorofenvinphos 5 X 10
Phosalone 5 X 102
Imidan 5 X 102
Azinophosethyl 1 X 102
Azinophosmethyl 1 X 102
Diazinon 50
Methidathion 50
Chlorpyrifos 50
Rationale: The phosphate-based pesticides range from large, complex mole-
cules to relatively small. All have polar moieties ranging in activity
from simply electron-rich heteroatoms to reasonably strong acidic hydrogens
190
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and basic moieties (e.g., heterocyclic nitrogens). As a result, the pesti-
cides themselves show a wide range adsorption strength.
Limited literature references are available. Strong binding to soils was
reported for chlorofenvinphos, carbophenothion, dioxathion and dichlofen-
thion (Inch, et al., 1972), azinophosmethyl (Helling, 1971), and for malthion
(Konrad, et al., 1969). K. values observed for parathion include 76
(Saltzman, £t a]_._, 1972), about 120 (Bowman and Sans, 1977), and 500 (Leen-
heer and Ahlrichs, 1971).
CARBONATE PESTICIDES
A. Methyl carbonates K.
Bux (metalkamate) 5 X 102
Carbonfuran 5 x 102
Carbaryl 5 X 102
Propoxur 1 X 102
Methiocarb 1 X 102
2
Mexacarbate 1 X 10
2
Aminocarb 1 X 10
Chlorpropham 1 X 102
Propham 50
Benomyl 10
Karbutilate 2
191
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B. Thiocarbamates
Thiram 5 X 102
CDEC 5 X 102
Butyl ate 5 X 102
^
2
Vernolate 1 X 102
EPIC 1 X 10'
Molinate 50
Rationale: The carbonate (urethane) moiety is relatively polar and con-
tributes markedly to the greater solubility and reduced adsorption of
these compounds compared to the organochlorine pesticides. Unfortunately,
few fundamental parameters are readily available for these compounds, nor
have extensive adsorption data been reported.
Octanol-water partition coefficients for a number of simple analogs
are all about four orders-of-magnitude less than DDT (Leo et _al_._, 1971),
indicating their low adsorption potential. Values of K. for carbaryl
(= 125; Leenheer and Adhlrichs, 1971), benomyl (= 4.5; Austin and Briggs,
1976), and propham (= 51; Briggs, 1969) have been reported. The relative
adsorption of some thiocarbamates has been reported by Gray and Weienrich,
(1968).
192
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AMINE AND ANILINE PESTICIDES
Pesticide Kd
Diphenamide 1 X 102
Pronamide 50
Alachlor 50
Propachlor 50
Propanil 10
Carboxin 10
Bensultde 5
Rationale: The amide pesticides are chemically similar to the carbonates
and should exhibit similar adsorption characteristics. There are generally
smaller molecules than the carbonates but this factor should be compensated
by the reduced ftumoer of polar consttutents. The less adsorptive amides
have N-H 'groups wtiteh iShrotrtd hydrogen borrd with water molecules.
193
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NTIROANILINE PESTICIDES
Pesticide Krf
Phenoxalin 1 X 103
Butralin 5 X 102
Profluralin 5 X 102
Trifluralin 5 X 102
Benefin 5 X 102
Fluchloralin 1 X 102
Dinitramine 50
Nitralin 50
Rationale: The nitroanilines contain relatively few non-conjugated polar
groups to contribute to electrostatic interactions. Being quite large
molecules, hydrophobic interactions should be strong, resulting in large
KJ'S which are primarily dependent on the size of the substitution.
The ranking presented is supported by adsorption studies on soils (Harvey,
1974) The high K, values are indicated in soil adsorption studies
by Helling (1971), Majka and Lavy (1977), and Grover (1974). Krf values
of 1.6 X 10 and 2.8 X 10 have been reported for profluralin and
butralin, respectively (Carringer, et al., 1975); but these values were
based on adsorption on pure soil organic matter and thus are probably
higher than would be representative of "average" soils and sediments.
194
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TRIAZINE PESTICIDES
Pesticide Kd
Prometome 8
Ametryne 8
Simazine 6
Atrazine 5
Cyanazine 3
Propazine 1
Metribuzin 1
Aminotriazole 1
Rationale: The triazine herbicides are all compact molecules containing
at least one polar-ionizable group, usually amino-hydrogens, available
to hydrogen bond with water. As a result, these herbicides are quite
soluble and exhibit low adsorption on soils.
The ranking presented above has been observed in soil adsorption studies
by Helling (1971) and Rogers (1968). Hurle and Freed (1972) reported Kd
values of 2.2. to 4.3 and 4.1 to 8.2 for atrazine and simazine, respectively,
on silt loam. Average Kd values for adsorption by 25 soils were reported
to be: propazine, Kd = 2.0; atrazine, Kd = 2.7; simazine, Kd = 3.7;
and prometone, Kd = 7.8 (Talbert and Fletchall, 1965). Kd values of
approximately 4 were reported for both cyanozine and atrazine (Majka and
Lavy, 1977), while K, for atrazine have also been noted at about 2.8
(Dao and Lavy, 1978). Liu et jiK, (1970) observed Krf values for ametryne
ranging from 2 to 10, depending primarily on the amount of organic matter
in the soils. The latter authors reported a Kd for ametryne of approxi-
mately 150 for a muck (very high organic content) soil.
195
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ORGANIC ACID PESTICIDES
A. Aliphatic Acids and Esters K.
Glyphosate 0.2
Trichloroacetic Acid 0.2
Dalapon 0.2
B. Aromatic Acids and Esters
Bifenox 0.5 - 1
Chloramben 0.5 - 1
Dicamba 0.5 - 1
DCPA 0.5-1
Fenac 0.5 - 1
Naptalam 0.5-1
Picloram 0.5 - 1
propargite 0.5-1
C. Phenoxy Compounds
Erbon 1 X 102
2,4-D 1.0
MCPA 1.0
2,4,5-T 2.0
Silvex 2.0
Dinoseb 10
Endothall 0.2
196
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Rationale: The organic acid pesticides are treated together since they
share the common feature that, with few exceptions, the activity of the
carbonic acid moiety is sufficient to make these compounds readily soluble
and to exhibit minimal adsorption. The two exceptions to this generaliza-
tion are dinoseb, a weakly-acidic phenol, and erbon, an ester. Both of
these latter compounds should show increased adsorption due to strong
hydrophic interactions not countered by solibilizing hydrogen or ionic
bonding.
The low adsorption of these compounds have been reported for dicamba, pic-
loram, fenac and 2,4-D (Helling, 1971); picloram (Farmer and Aochi, 1974;
Gaynor and Volk, 1976; Grover, 1971; Davidson and McDougal, 1973); 2,4,5-T
(O'Connor and Anderson, 1974); 2,4-D (Grover, 1973); picloram and 2,4-D
(Khan, 1973); picloram and 2,4,5-T (Majka and Lavy, 1977); dicamba, picloram
and 2,4-D (Grover, 1977); and dicamba (Garringer, et al., 1975).
197
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UREA PESTICIDES
Pesticide K.
2
Chloroxuron 5 X 10
Chlorbromuron 2 X 10
Linuron 1 X 102
Diuron 1 X 102
Monuron 50
Fluometuron 20
Fenuron 10
Rationale: The urea pesticides are chemically similar to the carbamate and
amide pesticides and show the same range of K. values. The molecules are
not small, but polar and hydrogen bonding moieties (N-H and C=0) decrease the
aqueous activity.
A relatively large number of studies have been reported for these compounds.
The results are summarized below as a table of observed Kd values in soils.
The references are indicated by the numbers in parentheses and are noted at
the end of the table.
198
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Pesticide Kd
Chloroxuron 40-110(2^ 650^
Chlorbromuron 217^
Linuron 50-250(2); 10.2-15(3>; 210(4); 154(5)
(1) Geissbuhler, et al., 1963
(2) Hance, 1971
(3) Hurle and Freed, 1972
(4) Lambert, 1967
(5) Briggs, 1969
Diuron 85-12; 70; 94^
Monuron 33(4); 29(5)
Fluometuron 22
Fenuron 15
199
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Miscellaneous Pesticides
The miscellaneous pesticides are presented in the order they were provided
by Battelle. The name of the compound is followed by the estimated K.
values. Any supporting literature references are indicated by numbers which
refer to the list following the table.
Pesticide Kd
Allethrin 1 X 104
Pyrethrum 1 X 104
Rotenone 1 X 103
CMA 0.2
DSMA 0.2
MSMA 0.2
Bentazon OJL(I)'
Bromacil 5(2)
Pyrazon 30(3,4)
Terbacil 50(2)
Diquat 5 X 103(5)
Paraquat 5 X 103(6,7)
Chlorothalonil 50
Dichlobenil 20 (8)
Copper Napthalate 5 X 102
Fenbutalin Oxide
Mancozeb 5
Acrolein 0.2
200
-------
Pesticide K.
Captan 30
Difol atari 50
Dinitrobutyl Phenol 50
Dodine 0.5
Methazole 5
Methomyl 5
Methyl Bromide 10
Norflura/on 50
1. Abernathy and Wax, 1973
2. Rhodes, et _al^, 1970
3. Fusi, et AL» 1976
4. Jamet et Marie - Andree Piedallu, 1975
5. Helling, 1971
6. Damanakis, et al., 1970
7. Burns, jet jfL_, 1973
8. Furmidge and Ogersby, 1967
201
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