Sethoxydim Risk Assessment



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Worksheet A04 [HK]Estimated pesticide residues on various types of vegetation shortly after an 
application of 1 lb/acre. 
Concentration (mg chemical/kg vegetation) 
Type of Vegetation 
Typical 
Upper Limit 
ID 
Value 
ID 
Value 
The following values are from Hoerger and Kenaga (1972). 
Range grass 
RGT 
125 
RGU 
240 
Grass 
GST 
92 
GSU 
110 
Leaves and leafy crops 
LVT 
35 
LVU 
125 
Forage crops 
FCT 
33 
FCU 
58 
Pods containing seeds 
PDT 

PDU 
12 
Grain 
GNT 

GNU 
10 
Fruit 
FRT 
1.5 
FRU 

The following values are from Fletcher et al. (1994) 
Short grass 
SGT 
85 
SGU 
240 
Tall grass 
TGT 
36 
TGU 
110 
Broadleaf/forage plants and 
BLT 
45 
BLU 
135 
small insects 
Fruits, pods, seeds, and large 
FRT2 

FRU2 
15 
insects 
Worksheet A05 [FRUIT]: Concentration of a chemical on spheres of various sizes at an application rate of 1 
lb/acre. 
Diameter (cm) 
Planar Surface 
Area (cm
2
)

Amount deposited 
(mg)

Weight of sphere 
( kg)

Concentration 
(mg/kg)


0.78540 
0.00880 
0.00052 
16.8 

19.63495 
0.21991 
0.06545 
3.36 
10 
78.53982 
0.87965 
0.52360 
1.68 
Application rate 
1 lb/acre = 
0.0112 
mg/cm





Planar surface area of a sphere = 
B
 r

where r is the radius in cm. 
Amount deposited is calculated as the application rate in mg/cm
2
 multiplied by the planar 
surface area. 
Assumes a density of 1 g/cm
3
 for the fruit. The volume of a sphere is(1÷6)× 
B
 × d
3
 where 
d is the diameter in cm.  Assuming a density of 1 g/cm
3
, the weight of the sphere in kg is 
equal to:
 kg= (1÷6)× 
B
 × d
3
 ÷ 1000 
Amount of chemical in mg divided by the weight of the sphere in kg. 
WS-8  


Worksheet A06 [OFFSITE]: Central estimates of off-site drift (expressed as fraction of application 
rate) associated with ground applications of pesticides 
1
 (from AgDRIFT Version 1.16, Teske et al. 
2001) 
Distance Down Wind (feet) 
Low Boom 
High Boom 
Orchard Airblast 
(Normal) 
25 
0.0187 
0.1034 
0.0057 
50 
0.0101 
0.0515 
0.0029 
100 
0.0058 
0.0262 
0.0007 
300 
0.0024 
0.0078 
0.0001 
500 
0.0015 
0.0038 
0.0000403 
900 
0.0008 
0.0015 
0.000013 
990 
0.0007 
0.0013 
<0.0000108 
1
 Estimates based on very fine to fine spray.  This will over-estimate drift for applications involving 
larger droplets. 
WS-9  


Worksheet A07a [KAMODEL]: Estimate of first-order absorption rate (k
a
 in hour
-1
) and 95% 
confidence intervals (from SERA 1997). 
Model parameters 
ID 
Value 
Coefficient for k
o/w 
C_KOW 
0.233255 
Coefficient for MW 
C_MW 
0.005657 
Model Constant 

1.49615 
Number of data points 
DP 
29 
Degrees of Freedom (d.f.) 
DF 
26 
Critical value of 
0.025
 with 26 d.f.

CRIT 
2.056 
Standard error of the estimate 
SEE 
16.1125 
Mean square error or model variance 
MDLV 
0.619712 
Standard deviation of model (s) 
MSD 
0.787218 
MDLV
0.5 
X
N
X, cross products matrix 
0.307537 
-0.00103089 
0.00822769 
-0.00103089 
0.000004377 
-0.0000944359 
0.0082 
-0.0000944359 
0.0085286 
1
 Mendenhall and Scheaffer 1973, Appendix 3, 4, p. A31. 
Central (maximum likelihood ) estimate: 
log
10
 k
a
 =  0.233255 log
10
(k
o/w
) - 0.005657 MW - 1.49615 
95% Confidence intervals for log
10
 k

log
10
 k
a
 ± t
0.025
 ×  ×  (a
NN
X
NN
X a)
0.5 
where a is a column vector of {1, MW, log
10
(k
o/w
)}. 
NB: Although the equation for the central estimate is presented with k
o/w
 appearing before MW to be consistent 
with the way a similar equation is presented by EPA, MW must appear first in column vector a because of the way 
the statistical analysis was conducted to derive X
N
X . 
See following page for details of calculating a
NN
X
NN
X a without using matrix arithmetic. 
WS-10  


Worksheet Worksheet A07a (continued) 
Details of calculating a
NN
X
NN
X a 
The term a'
A
(X'X)
-1
A
a requires matrix multiplication.  While this is most easily accomplished using a program that 
does matrix arithmetic, the calculation can be done with a standard calculator. 
Letting 
a = {a_1, a_2, a_3} 
and
 (X'X)
-1
 = 

{b_1, b_2, b_3}, 
{c_1, c_2, c_3}, 
{d_1, d_2, d_3} 
}, 
a'
A
(X'X)
-1
A
a is equal to 
Term 1: {a_1 ×([a_1×b_1] + [a_2×c_1] + [a_3×d_1])} + 
Term 2: {a_2 ×([a_1×b_2] + [a_2×c_2] + [a_3×d_2])} + 
Term 3: {a_3 ×([a_1×b_3] + [a_2×c_3] + [a_3×d_3])}. 
WS-11  


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