Creating New Scenarios for Use in Pesticide Surface Water Exposure
Assessments
U.S. Environmental Protection Agency
Office of Pesticide Programs
12/31/2019

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Table of Contents
1.	Executive Summary	3
2.	Introduction	3
2.1.	Regulatory Context	3
2.2.	Purpose Statement	4
2.3.	Document Organization	4
3.	Overview of OPP Surface Water Modeling	4
3.1. Scenario Background	5
4.	Methods	6
Methods Step 1: Generate combinations of soil, land-cover, and weather	6
Methods Step 2: Create tables of input parameters	7
Methods Step 3: Group Scenarios into HUCs and Crop	8
Methods Step 4: Subsample Scenarios	10
Methods Step 5: Select Chemical Parameters for PWC Simulations	10
Methods Step 6. Perform PWC Runs	11
Methods Step 7: Sort the scenarios from high to low concentrations	12
5.	Preliminary Results	13
5.1.	Corn and Wheat Distributions	13
5.2.	Sensitivity of the Scenario Distributions	14
5.3.	Scenario Differences for Acute, Chronic and Cancer	16
5.4.	Comparison with current scenarios	16
6.	Summary	18
7.	References	18
2

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1.	Executive Summary
OPP developed methods to select realistic input parameters for field scenarios used in the
Pesticide in Water Calculator (PWC), which is OPP's main tool to estimate aquatic
concentrations of pesticides. While PWC estimates aquatic concentrations for both surface
water and groundwater, this effort focuses on field scenarios used for estimating surface-water
concentrations. These input parameters (or field scenarios) are spatially comprehensive such
that the PWC output can reflect aquatic concentrations across the landscape. The field
scenarios are ranked here by the concentrations they produce. These rankings may facilitate
policy makers' decisions on selecting the appropriate level of scenario vulnerability. OPP
performed preliminary analyses of the scenarios by running the PWC and using the output
concentration endpoints of acute, chronic, and cancer concentrations as ranking criteria.
Additionally, since chemicals are transported in solution by runoff and sorbed to eroded soil,
OPP used organic carbon sorption coefficients of 10, 1000, and 10000 mL/g which should allow
capture of both mechanisms of transport in PWC simulations. Preliminary results suggest that
the difference between a 90th percentile scenario and a 50th percentile scenario is usually less
than a factor of 2, regardless of the endpoint or the dominant transport mechanism (i.e., runoff
or erosion).
2.	Introduction
2.1. Regulatory Context
Pesticides are regulated in the United States under both the Federal Insecticide, Fungicide, and
Rodenticide Act (FIFRA) and the Federal Food, Drug and Cosmetics Act (FFDCA). Through these
statutes, the United States Environmental Protection Agency Office of Pesticide Programs (OPP)
must determine that aggregate exposure to the pesticide residues is safe, i.e., that "there is a
reasonable certainty of no harm" from aggregate exposure to the pesticide, before issuing a
tolerance.
OPP's Environmental Fate and Effects Division's (EFED) scientists are responsible for conducting
Drinking Water Assessments (DWA), which include an analysis of the potential for and
magnitude of pesticide occurrence in both surface water and groundwater sources. OPP
estimates Drinking Water Concentrations (EDWCs) in surface waters that supply Community
Surface Water (CWS) intakes and compares them to benchmark values called Drinking Water
Level of Comparison (DWLOC) to determine if pesticide concentrations have the potential to
cause adverse effects to human health (USEPA OPP, 2019a, p. 13). EFED employs a robust,
tiered DWA process that is designed to efficiently screen out pesticides that do not pose a
potential risk to human health from those requiring more highly refined analyses to better
understand potential risks (e.g., in terms of when and where there may be concerns). Lower
tier assessments are intended to be conservative so that the assessor can confidently screen
out chemicals that represent a low risk. Higher tiers successively incorporate refinements that
3

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draw on more focused chemical, spatial, temporal, and agronomic information, including
consideration of available monitoring data to inform risk management decisions. For additional
information on OPP's tiered approach to DWAs, including a detailed description of individual
tiers, see the Draft Framework for Conducting Pesticide Drinking Water Assessments for Surface
Water (USEPA OPP, 2019a).
2.2.	Purpose Statement
OPP estimates pesticide exposure concentrations for DWA primarily using the Pesticide in
Water Calculator (PWC). The PWC requires specification of environmental conditions, or
scenarios, that include both water body and field (or watershed) components, in order to
calculate surface water concentrations. This effort focuses on the field scenario components.
Currently, OPP uses scenarios it developed largely using best professional judgement. With
recent advances in automation and improvements in data quality, OPP is proposing a step
toward improving the field scenario-development process. OPP's goal for this current effort is
to build new scenarios for surface water risk assessments that better reflect environmental
characteristics and to facilitate policy maker's decision on selecting the appropriate level of
scenario vulnerability.
2.3.	Document Organization
	Section 3 (Overview of OPP Surface Water Modeling) provides an overview of the OPP
model used to calculate surface water concentrations and gives background on previous
scenario developments.
	Section 4 (Methods) details the methods for creating the new scenarios.
	Section 5 (Preliminary Results) gives preliminary results and indicates how the new
scenarios will compare with OPP's previous scenarios.
	Section 6 (Summary) summarizes the methods and preliminary results and discusses
potential impact to risk assessments.
3. Overview of OPP Surface Water Modeling
Surface waters potentially impacted by pesticides are widespread across the U.S, and efficient
assessment requires the assistance of models which simplify the task. To this end, the OPP uses
a simplified conceptualization of the pesticide use area as depicted in Figure 1. In this
conceptualization, runoff and erosion move the pesticide from an agricultural field into an
adjacent water body where it mixes with the water column and sediment. A computer model
called the Pesticide in Water Calculator (PWC) performs the necessary calculations (Young,
2019), taking into consideration the chemical properties of the pesticide along with the
characteristics of the soil, weather, hydrology, and agricultural management conditions.
Ultimately, the PWC estimates the pesticide concentrations in the waterbody that OPP uses for
regulatory decisions.
4

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Field
Water Body
Volatilization
Volatilization
Runoff &
Erosion
Degradation
Degradation,
Degradation/^^^
Uptake to Sediment
Washout
Leaching
Figure 1. The conceptual model for calculating waterbody concentrations of pesticides due to
runoff and erosion from an adjacent field. (Drift is also a transport mechanism, but OPP
calculates drift independently of the PWC and is independent of location or scenario.)
The PWC requires inputs for pesticide properties (e.g., degradation rate, application rate) and
inputs for the environmental properties (field and waterbody characteristics). The set of PWC
inputs that characterize the field (e.g., soil organic matter, runoff characteristics, crop/land
cover, rainfall amounts) and waterbody (surface area, depth, benthic carbon) makes up a
scenario, while the subset of parameters that describe only the field properties is a field
scenario. In this current effort, OPP is only addressing improvements to field scenarios, while
improvements to water body characterization and watershed-size characterization are being
addressed in a concurrent project (USEPA OPP, 2015).
3.1. Scenario Background
For surface water exposure assessments, OPP currently has 125 field scenarios developed over
the previous two decades using guidance (USEPA OPP, 2007), professional judgment, and the
best available data at the time. In developing these scenarios, OPP's goal was to produce
reasonably conservative scenarios, or those with higher-than-average runoff, erosion, and
chemical transport. In the past, OPP sometimes referred to these protective scenarios as "90th
percentile" scenarios, but the actual percentile was unknown, and the scale upon which
percentile comparisons were made also remained undefined (i.e., 90th percentile of what?).
With acknowledgement of this uncertainty, OPP subsequently characterized these scenarios
simply as "high-end."
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4. Methods
OPP proposes the following steps to develop scenarios for use with the PWC in a reproducible
manner. In a preliminary effort, OPP has followed these steps to develop example scenarios for
corn and wheat in order to evaluate the method's performance.
Methods Step 1: Generate combinations of soil, land-cover, and weather
OPP generated combinations of soil, land cover, and weather using GIS data. To accomplish
this, OPP applied a new guidance Estimating Field and Watershed Parameters Used in USEPA's
Office of Pesticide Programs Aquatic Exposure Models - The Pesticide Water Calculator
(PWC)/Pesticide Root Zone Model (PRZM) and Spatial Aquatic Model (SAM) hereafter referred
to as USEPA OPP (2019b). This guidance specifies parameters that are representative of the
area of interest. The previous OPP guidance specified parameters with an upward bias with
regard to pesticide transport. The details of the computerized methods used for finding and
consolidating these parameters sets are given in Methods for Automated Field-Scenario
Generation for Use in the Pesticide Water Calculator and the Spatial Aquatic Model (USEPA
OPP, 2019c).
To build the parameters sets, OPP first obtained the following data layers:
	Soil map units from USDA's Soil Survey Geographic (SSURGO) database (USDA NRCS SSS,
2018)
	the latest five years of land cover/crop groups from the USDA's Cropland Data Layer
(CDL) (USDA NASS, 2014-2018)
	meteorological data generated from the National Oceanic and Atmospheric
Administration (NOAA) data for the years 1961 to 2014 (Fry et a I, 2016)
OPP then combined these layers as shown in Figure 2. The three data sets are on the left of the
figure; the resulting combination of the overlay is on the right of the figure. Each color on the
right represents a combination of parameters that are identical. In this example, there are 7
different parameter combinations (7 different colors), and each of these combinations has a
different pixel count (as seen by the different sizes of the colors).
6

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NOAA
Climate/
Rain Gauge
Analysis
USDA Soil
Survey
Geographic
Database
(SSURGO)
USDA
Cropland
Data Layer
(CDL)
Weather
grid
(Met)
Corn
Wheat
Combinations
Soil A, Met 1, Corn
Soil A, Met 2, Corn
Soil A, Met 2, Wheat
Soil B, Met 1, Corn
Soil B, Met 1, Wheat
Soil B, Met 2, Corn
Soil B, Met 2, Wheat
Figure 2. Systematic creation of field scenarios. Scenario are built from the overlap of spatial
data layers - weather grids (NOAA), soils (SSURGO), and land cover (CDL).
Methods Step 2: Create tables of input parameters
With the overlays from Step 1, OPP created a Field Scenario Input Table as depicted in Figure 3
(Note: partial table shown with sample values). OPP first created a scenario ID (on the left of
figure) that identified the pixel location and parameters (soil, weather, crop) from Step 1. OPP
combined the parameters from each data set (middle of figure) into the Field Input Table (on
the right figure). The Input Table is used later as an input to the PWC in Step 5.
7

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Field Scenarios
Parameters
Field Scenario Input Table
ScenariolD
Soil
Met
Land Use
sAwlluCorn
A
1
Corn
sAw2luCorn
A
2
Corn
sAwlluWheat
A
2
Wheat
sBwlluCorn
B
1
Corn
sBwlluWheat
B
1
Wheat
sBw2luCorn
B
2
Corn
sBw2luWheat
B
2
Wheat

Water
Water
Soil
_max
_min
A
0.3
0.2
B
0.4
0.1
+
Met
ireg
1
4
2
3
4
Soil
Land Use
curve_number
A
Corn
64
A
Wheat
60
B
Corn
74
B
Wheat
72

Water
Water


ScenariolD
max
_min
Ireg
curve_number
sAwlluCorn
0.3
0.2
4
64
sAw2luCorn
0.3
0.2
3
64
sAwlluWheat
0.3
0.2
4
60
sBwlluCorn
0.4
0.1
4
74
sBwlluWheat
0.4
0.1
4
72
sBw2lucorn
0.4
0.1
3
74
sBw2luWheat
0.4
0.1
3
72
Figure 3. Process to create parameters for field scenarios. Note: Partial table shown with
sample values. Parameters are defined in USEPA OPP (2019b)
Methods Step 3: Group Scenarios into HUCs and Crop
To facilitate PWC runs, OPP organized the Field Scenario Input Table by the USGS Hydrologic
unit code 2 (HUC2); thus, each scenario table contained scenario data for only one HUC2
region. HUC2 regions divide the conterminous United States into 18 units based on
topographic, hydrologic, and other relevant landscape characteristics, as shown in Figure 4. OPP
used the National Hydrography Dataset (NHDplus, version 2) processing regions, which further
subdivides Regions 3 (southeast US) and 10 (Missouri River), resulting in 21 regions (Figure 4).
OPP also organized the Field Scenario Input Table by crop, and thus the scenario tables used for
PWC batch runs (Step 5) contained parameters for only one crop (as well as for only one HUC2).
OPP plans to develop a full set of field input parameters for crops/crop groups representing the
top 16 annual cultivated crops listed in Table 1. To test the methods on a more limited scale,
OPP developed field scenarios only for corn and winter wheat in the examples that follow. Corn
and wheat are good test examples because they are among the most cultivated crops in the
U.S., and they occur in each of the HUC2 regions. When the methods are fully implemented
after review and revisions, OPP plans to develop scenarios for the other relevant crops in Table
land for other important crops such as vegetables, orchards, vineyards, and perennial legume
and grass pasture/hay/forage crops.
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tZi
PACIFIC
NORTHWEST
17
SOURIS-RED-RAINY
09
NORTHEAS
01
|
flfi
UPPER MISSOURI
10U
GREAT BASIN
16
CALIFORNIA
18
UPPER
COLORADO
LOWER
COLORADO
LOWER MISSOURI
10L
UPPER
MISSISSIPPI
07
GREAT LAKES
04
MID-
ATLANTIC
02
ARK-RED-WHITE
11
OHIO
05
TENNESSEE
LOWER
MISSISSIPPI
08
RIO
GRANDE
13
TEXAS
12
E3
SOUTH
SOUTH
ATLANTIC
WEST
SOUTH
ATLANTIC
SOUTH
Figure 4. HUC2 Processing Regions for the National Hydrography Dataset (NHDplus, version 2)
used for scenario selections.
Table 1. Top 16 major crops listed in order of acreage in the U.S.
Rank
Crop
2012 Census
of Ag Acres
Major Crop/Crop
Groups
1
Corn
94,609,673
Corn
2
Soybeans
76,104,780
Soybeans
3
Winter Wheat
34,723,361
Wheat
4
Spring Wheat
12,177,715
Wheat
5
Cotton
9,384,080
Cotton
6
Sorghum
5,628,744
Other Grains
7
Barley
3,283,905
Other Grains
8
Rice
2,693,759
Rice
9
Durum Wheat
2,139,150
Wheat
10
Sunflower
1,877,145
Row Crops
11
Canola
1,736,409
Row Crops
12
Dry Beans
1,642,797
Vegetables
13
Peanuts
1,621,631
Row Crops
14
Sugar Beets
1,249,481
Row Crops
15
Potatoes
1,168,199
Vegetables
16
Oats
1,078,698
Other Grains
9

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Methods Step 4: Subsample Scenarios
Using this methodology, the number of scenarios OPP generated for corn and wheat for each
HUC2 was quite large (USEPA OPP, 2019c), on the order of 100,000 within a HUC2 region in
most cases. With PWC simulation times of about 10 to 20 scenarios per minute, the total run
time for the full scenario set for an entire region would be too long for practical purposes.
Therefore, OPP took random samples of 25% or a minimum of 1,000 random samples,
whichever was greater, from the full Field Scenario Input Table sets generated within each
HUC2 region from Step 3. For those regions with less than 1,000 scenarios, OPP processed the
full set of field scenarios.
Methods Step 5: Select Chemical Parameters for PWC Simulations
OPP, through years of experience with the PWC, is aware that field parameters are not the only
factor in determining the pesticide transport potential. Some chemical properties, most
importantly the sorption coefficient and persistence (i.e., degradation rate), can have important
effects on pesticide transport as described below, and thus the values used in modeling can
have important implications on scenario results, as described below.
As modeled in the PWC, a pesticide is transported to surface waters in dissolved form by water
runoff and in sorbed form by eroded solids. Depending on the chemical's sorption properties
(typically characterized by Koc or the organic-carbon-normalized sorption coefficient), the
dominant means of transport will vary between these two mechanisms. Experience within OPP
(and as shown later in Section 5: Preliminary Results) suggests that peak transport to surface
water occurs somewhere between a Koc of 500 and 1000 mL/g. This indicates that the pesticide
concentration of a scenario is not monotonically dependent on the chemical's Koc. Pesticide
aquatic concentration may increase or decrease as Koc increases. This is due to the tradeoffs
with transport by the two processes of erosion and runoff. To address the effect of Koc, OPP
assessed scenarios using Koc values of 10,1000, and 10000 ml/g.
Although persistence of a chemical may also impact pesticide concentration, it is much less
straightforward to evaluate. Effects of persistence will be heavily influenced by application
timing because the date of pesticide application will determine whether the pesticide is on a
crop canopy or in the soil and how long it will remain before rainfall moves the pesticide into
another environmental compartment. This is an important aspect of pesticide risk assessments,
but it is beyond the scope of this project. Instead, this current effort focuses on pesticide
transport potential due to the physical processes of runoff and erosion regardless of application
timing.
To decrease the effect of application timing on pesticide transport potential, OPP (1) used a
chemical half-life that is relatively long, or persistent, (180 days) and (2) spread out the
10

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pesticide application over a long period (50 days, starting from emergence). Experience in OPP
with PWC has shown that the EDWCs for any one scenario become insensitive to soil half-lives
above 60 days. This is because the pesticide's typical residence time in the top few centimeters
of the soil (where runoff and erosion occur) is on this order. Thus, for many scenarios, the
pesticide will dissipate from the top soil by the time the next seasonal pesticide application
occurs. For a few scenarios (mostly dry regions), the residence time may be quite a bit longer
and thus the pesticide could accumulate in the top layer. A continuously increasing baseline
pesticide mass (amount of pesticide accumulating in the soil) in the top soil could increase to
levels where concentration variations due to year-to-year weather changes may become
difficult to detect in the PWC output. Thus, OPP used the 180-day soil half-life to prevent
excessive accumulation that may have occurred in some scenarios. OPP may explore optimizing
this soil degradation value in the future to enhance the relevant response after OPP gains some
experience with the initial phase of this work. The second action OPP took to decrease the
effect of application timing was to evenly distribute the pesticide applications in our PWC
simulations over a 50-day period within the growing season. Daily pesticide applications start
on the planting date and end 50 days later. In this way, the variations caused by timing are
damped, thereby increasing likelihood that concentration variations are due strictly to runoff
and erosion variations.
Methods Step 6. Perform PWC Runs
In this preliminary work, OPP ran separate scenario batches for each of the three Koc values (10,
1000, 10000 mL/g) for corn and wheat and each HUC2. Figure 5 shows a typical partial batch
input file and output from the PWC. For this effort, OPP is interested in the outputs for acute,
chronic, and cancer concentrations which are revealed in the bottom left of the PWC output
page. The PWC records the acute (1-day), chronic (365-day), and cancer (overall, 53+ years)
concentrations for each completed scenario in a text file (Young, 2019). When the batch run is
complete, the text file contains a distribution of concentrations for use in the next step analysis.
11

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Field Scenario Input Table

Water
Water


ScenariolD
_max
_mln
Ireg
rurs/fnuni bfr
sAwlluOorn
0.3
0.2
3
82
sAw2luCorn
0.3
0.2
3
82
$A3PLuCorn
0.2
0.1
3
77
sBwlluCorn
0.4
0.1
3
73
sBw2lucom
0.4
0.1
3
73
PWC Output
| Pii*c4i MMar CjfcuMcr (PKt I nn IJi
fit Scbicid Hrip
| Crp | Puk* |	| 6** fun | WtovOpkra Cu *4 &"* P
m | Otf.GW |
tkxr*J. KSCr&i Parert
 WiHtCifer*
' lt!H
4 0 8 10 12 U 16 16 3) 22 24 20 26 30
y
M> - 1*5
** GdJHt Iwt lO pmt Cmw fa
nhrtUc Wiriw
HtfbmWorl
M**g[U7
KS^ |" J 2Hfrtaf 1M
ban Nun (434 S&d*j<*j |lJ
p* i in
21 d* Avp pST
(v l4 Yr Cxnc
Was lj(.a>r See
|MB
jfin'j
0 | &.W1
n I C tBta
ft pftto:
radian o	Ih
li_r czipMwl  uoa^tnj ITTI&AII
Wwtniu DiuJiiip "*ddfafrW/Oadfkr2t
U Nn FVW>rt(W:*f*5
Figure 5. The batch input file and the output from the PWC (for one scenario) showing surface
water concentrations for acute, chronic and cancer. Note: Partial table shown with sample
values. Parameters are defined in USEPA OPP (2019b)
Methods Step 7: Sort the scenarios from high to low concentrations
With the PWC output from Step 6, OPP sorted the scenarios by concentration. Details are
described in USEPA OPP (2019c). As shown in Figure 6, this sorted list results in a plottable,
cumulative distribution of scenarios.
12

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Text file with Sorted Scenarios
Graphical Distribution of Sorted Scenarios
Scenario ID	EDWC Rank (%)
sAwlluCorn	50	99.9
sBw2luCorn	49	99.8
sAelluCorn	40	98
sSklluCorn	35	96
sTwllgCorn	32	90
sAnlljCom	30	80
sAwllhCorn	25	60
sAwllyCorn	20	50
xAv2uhCorn	15	25
xAi2uhCorn	10	5
xAo2uhCorn	5	2

o.
 75
u
<1)
100
25
50
sAwllyCorn sTvyllgCorn
0
0
20
40
60
Chronic EDWC (ug/L)
Figure 6. Sorting the scenarios by concentration and plotting them as a distribution.
Hypothetical distribution showing the sorting of the original data and its graphical
distribution. Also indicated are the positions of the 50th and 90th percentile scenarios. (Table is
severely culled and truncated and shown for demonstration purposes only.)
5. Preliminary Results
5.1. Corn and Wheat Distributions
Figure 7 shows example distributions for corn and winter wheat for the chronic concentrations
(in ug/L) from each scenario. Each x-axis concentration results from one scenario. In this regard,
the absolute concentration values on the x axis are not determinative because they are
dependent on the values that OPP chose for the application rate; OPP could have chosen any
application rate for the simulations as long as the same rate was used in every simulation. This
is because pesticide concentrations in the waterbody are proportional to the amount of
pesticide application rate. The estimated concentrations are important only for determining the
relative order of the scenarios.
13

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0	20	40	60	0	10	20	30
Chronic EDWC (ng/L)	Chronic EDWC (ng/L)
Figure 7. Distribution of chronic concentrations for Region 7 for corn and wheat, respectively,
with Koc of 10 mL/g.
5.2. Sensitivity of the Scenario Distributions
To explore the sensitivity of the results (as indicated by the slope of the curves), OPP calculated
the ratio of the 90th percentile EDWC (EDWC90) to the 50th percentile EDWC (EDWC50) for the
HUC2 regions for corn, as shown in Table 2. This ratio (EDWC90/EDWC50) has a practical
meaning to a risk assessment as it represents how much the EDWCs could vary between
scenarios. In other words, the primary purpose of these 90/50 ratios is to give risk assessors a
quick idea of how much the EDWCs would vary if they chose a 90th percentile scenario instead
of a 50th percentile scenario.
Table 2. Ratios of the 90th EDWC to the 50th EDWC for Corn in all cases examined
HUC2
Koc (ml7g)
Acute 90th: 50th
Chronic 90th: 50th
Cancer 90th: 50th


EDWC
EDWC
EDWC
rOl
10
2.04
1.64
1.73

1000
1.35
1.25
1.21

10000
1.19
1.20
1.20
r02
10
2.47
1.90
2.20

1000
1.56
1.29
1.35

10000
1.45
1.51
1.51
r03N
10
2.39
1.75
2.02

1000
1.60
1.33
1.35

10000
1.48
1.38
1.38
r03S
10
2.71
1.87
1.97

1000
1.70
1.37
1.64

10000
1.47
1.29
1.27
r03W
10
2.89
2.26
2.58

1000
1.64
1.34
1.38

10000
1.50
1.54
1.46
r04
10
1.72
1.54
1.61

1000
1.54
1.39
1.42

10000
1.38
1.31
1.40
r05
10
1.60
1.54
1.63

1000
1.42
1.29
1.34
14

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HUC2
Koc (miyg)
Acute 90th: 50th
Chronic 90th: 50th
Cancer 90th: 50th


EDWC
EDWC
EDWC

10000
1.34
1.18
1.20
r06
10
2.11
1.59
1.79

1000
1.78
1.40
1.44

10000
1.46
1.48
1.60
r07
10
1.55
1.59
1.62

1000
1.31
1.20
1.20

10000
1.53
1.51
1.52
r08
10
1.89
1.69
1.84

1000
1.40
1.25
1.35

10000
1.36
1.23
1.22
r09
10
1.41
1.41
1.35

1000
1.22
1.19
1.16

10000
1.37
1.33
1.38
rlOL
10
1.43
1.39
1.40

1000
1.26
1.33
1.38

10000
1.25
1.29
1.29
rlOU
10
1.45
1.39
1.37

1000
1.27
1.23
1.21

10000
1.50
1.55
1.59
rll
10
1.86
1.59
1.47

1000
1.29
1.52
1.53

10000
1.28
1.30
1.25
rl2
10
1.82
1.63
1.73

1000
1.31
1.34
1.37

10000
1.31
1.50
1.37
rl3
10
1.81
1.67
1.83

1000
1.55
1.53
1.55

10000
2.03
1.58
1.93
rl4
10
2.97
2.80
2.50

1000
1.92
1.89
1.66

10000
2.50
2.41
2.54
rl5
10
2.79
2.83
3.05

1000
1.36
1.36
1.36

10000
1.65
1.58
1.57
rl6
10
3.58
3.19
2.80

1000
1.56
1.59
1.52

10000
1.46
1.48
1.51
rl7
10
3.91
3.86
4.30

1000
1.58
1.65
1.81

10000
2.53
2.77
2.98
rl8
10
2.16
1.97
2.23

1000
1.40
1.37
1.38

10000
1.68
1.69
1.78
As a general trend, 90:50 ratios were higher and more variable for a Koc of 10 mL/g across all
regions and all exposure endpoints (acute, chronic, and cancer concentrations). The reason for
the variation is not clear, but it could imply that runoff is more variable than erosion (since low-
Koc chemical are transported by runoff and high-K0C chemicals are transported by erosion).
15

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Generally, the higher Koc classes (i.e., 1000 and 10000 mL/g) produced lower ratio values. HUC2
Region 17 consistently returns higher ratios across Koc classes and exposure endpoints than
most other regions. Further, Region 17 returns 90th percentile EDWC values for the 10 mL/g Koc
class that are approximately 4 times greater than those estimated at the 50th percentile for all
exposure endpoints, suggesting this region has greater variation in scenario characteristics.
5.3. Scenario Differences for Acute, Chronic and Cancer
Another consideration is whether there will be a need for separate scenarios to address acute,
chronic and cancer assessments, or if scenarios for a single exposure endpoint would suffice.
OPP performed an initial assessment of this with the results given in Table 3. In this example,
OPP used the 90th percentile EDWC as the target scenario ranking. The first row gives the
scenario with the closest ranking to the 90th percentile acute value; its corresponding ranking
for chronic and cancer is slightly lower at 89th and 85th percentile, respectively. In a similar way,
row 2 provides information for the scenario with the closest ranking to the 90th percentile
chronic value and row 3 for the cancer value. The table demonstrates that the values are within
10% of each other regardless of the exposure endpoint. It is possible that such differences may
not be determinative for risk assessments. This may ultimately reduce the number of necessary
scenarios, but OPP needs to explore this further.
Table 3. Consideration for separate Acute, Chronic and Cancer Scenarios, for Region 7, Koc =
10 mL/g
Target
Scenario
Acute Percentile
Chronic Percentile
Cancer
Percentile
Acute
539W21130LC1
90
89
85
Chronic
542W22075LC1
95
90
99
Cancer
402164W20187LC1
96
90
90
5.4. Comparison with current scenarios
OPP currently has 13 field scenarios representing corn across the U.S. As previously mentioned,
OPP constructed these using best professional judgement to produce "high-end" scenarios for a
variety of assessment types (aquatic species, human health, cumulative risk assessments, etc.),
but their actual vulnerability is unknown. OPP can use the same ranking scheme that we used
for the new scenarios to obtain a quantitative ranking for the old scenarios. In this way, OPP
can better assess the impact that the new scenarios would have on a risk assessment by
comparing the old and new ranks.
For the comparison, OPP ran new and existing scenarios in the same HUC2 region with identical
chemical inputs. OPP then compared the existing and new scenario concentrations for acute (1-
day), chronic (1-year), and cancer endpoints. The rankings appear in Table 4.
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Results show that the concentration for the existing PA corn scenario (first row) would rank as a
98.5th percentile scenario with the new ranking system (for acute concentrations and a Koc of 10
mL/g). The PA corn scenario would rank higher than a 99.9th percentile scenario for acute
concentrations and a chemical with a Koc of 10000 mL/g. Further investigation is required, but
these initial findings indicate the existing scenarios may in fact be "high-end scenarios"
according to the new proposed ranking criteria.
At the low Koc (10 mL/g), acute concentrations for 10 of the 13 existing PWC scenarios fell
above the 95th percentile rankings for their specific regions. Only one scenario - Iowa Corn - fell
below the 90th percentile ranking. For chronic and cancer, 7 of the 13 existing scenarios fell
above the 95th percentile rankings for their specific regions. The percentile rankings for the
existing scenarios ranged from the 69th to >99.9th percentile for chronic concentrations and
from the 60th to >99.9th percentile for cancer concentrations. At the intermediate Koc (1000
mL/g), the existing scenarios showed a wider range in percentile rankings (from 57th to >99.9th
percentile) for acute concentrations than at the other two Koc classes. For longer-duration
exposures, the existing scenario rankings fall within the 83rd to >99.9th percentile in regional
rankings. At the highest Koc (10,000 ml/g), the estimated concentrations from all the old
scenarios were around the 97th percentile ranking within their respective regions.
Table 4. Estimated percentile rankings for existing PWC scenarios by Koc

Koc
= 10 mL/g
II
O
1,000 mL/g
II
u
O
10,000 mL/g
Current PWC
Scenario
NHD
+
Reg
Acut
e
Chro
n
Cane
Acute
Chro
n
Cane
Acute
Chro
n
Cane
PA Corn
R02
98.5
96.2
94.1
99.2
>99.9
>99.9
>99.9
>99.9
>99.9
NC (east)
Corn
R03N
95.6
97.7
99.2
99.3
>99.9
>99.9
>99.9
>99.9
>99.9
IN Corn
R05
90.0
68.7
82.1
99.7
96.7
96.8
>99.9
>99.9
>99.9
OH Corn
R05
>99.9
99.8
99.8
98.1
>99.9
>99.9
>99.9
>99.9
>99.9
IA Corn
R07
85.0
76.6
59.6
78.8
87.6
83.2
99.9
99.9
99.9
IL Corn
R07
99.2
76.2
84.0
93.1
98.9
>99.9
>99.9
99.9
>99.9
MN Corn
R07
99.3
84.5
95.5
98.5
>99.9
99.9
99.5
99.7
99.2
MS Corn
R08
99.9
99.7
99.0
57.9
>99.9
>99.9
>99.9
>99.9
>99.9
ND Corn
R09
97.9
93.5
99.1
96.7
93.4
96.9
99.4
99.2
99.4
KS Corn
R10L
>99.9
98.4
99.5
>99.9
88.9
>99.9
99.9
>99.9
>99.9
NE Corn
R10L
99.0
73.7
91.5
99.9
92.0
90.9
>99.9
>99.9
>99.9
TX (south)
Corn
R12
>99.9
>99.9
>99.
9
>99.9
99.8
99.4
96.8
>99.9
>99.9
TX Corn
R12
93.2
99.7
76.6
57.0
92.7
94.7
>99.9
>99.9
>99.9
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6.	Summary
OPP developed methods to create a comprehensive set of new PWC scenarios and
quantitatively ranked them by their resulting surface water concentrations (EDWCs).
Importantly, and unlike previous OPP scenario-creation efforts, these new scenarios comprise
parameters that are biased neither up nor down and are consistent and transparent. OPP then
ranked these scenarios according to their resultant surface water concentrations.
A scenario's ranking depends not only on the environmental properties captured in the field
scenario but also on the chemical applied (namely the Koc value) as well as the endpoint desired
(acute, chronic, cancer). The PWC's dual mechanisms of transport, in which runoff and erosion
compete to carry dissolved and sorbed pesticide, causes a scenario's rank to be dependent on
chemical sorption. Rank dependence on endpoint is likely due in part to application timing
issues and weather variations, although this is not fully understood. OPP intends to explore
these issues further after review of additional results.
Using this new approach, OPP developed scenario distributions for corn and wheat. These
demonstrate that corn and wheat have similar scenario distributions with a relatively small
range in EDWCs within percentile ranks in the central region of the distribution. Preliminary
review indicates that EDWCs at the 90th percentile are rarely greater than 2 times the 50th
percentile value for any Koc or endpoint examined for corn and wheat. The relatively small
difference in concentration change for a relatively large change in rank (percentile) means that
EDWCs estimated from the PWC in any one HUC2 region will generally vary within a factor of 2
or less in most cases regardless of the percentile rank of the scenario.
7.	References
Fry, M.M., Rothman, G., Young, D.F., and Thurman, N., 2016. Daily gridded weather for
exposure modeling, Environmental Modelling & Software, 82,167-173,
doi. org/10.1016/j.envsoft.2016.04.008
USDA National Agricultural Statistics Service (USDA NASS) Cropland Data Layer. 2014-2018.
Published crop-specific data layer [Online]. Available at
https://nassgeodata.gmu.edu/CropScape/ (accessed Feb 2019). USDA-NASS, Washington, DC.
USDA Natural Resources Conservation Service Soil Survey Staff (USDA NRCS SSS). 2018. Gridded
Soil Survey Geographic (gSSURGO) Database. United States Department of Agriculture, Natural
Resources Conservation Service. Available online at http://datagateway.nrcs.usda.gov/.
October 11-12, 2018 (FY2018 official release).
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USEPA Office of Pesticide Programs (USEPA OPP), 2007. Pesticide Root Zone Model (PRZM)
Field and Orchard Crop Scenarios: Guidance for Selecting Field Crop and Orchard Scenario Input
Parameters, February 2007 in Memo of May 9, 2007
USEPA Office of Pesticide Programs (USEPA OPP), 2015. U.S. Environmental Protection Agency
Office of Pesticide Programs (Development of a Spatial Aquatic Model (SAM) for Pesticide Risk
Assessments. Presented to the FIFRA Scientific Advisory Panel, September 15-17, 2015.
Available in the public e-docket, Docket No. EPA-HQ-OPP-2015-0424, accessible through the
docket portal: http://wv.rw.reeulations.gov.
USEPA Office of Pesticide Programs (USEPA OPP), 2019a. Framework for Conducting Pesticide
Drinking Water Assessments for Surface Water. Office of Pesticides Programs, U.S.
Environmental Protection Agency. August 22, 2019. Available at
https://www. regulations.gov/document?D=EPA~HQ~OPP~2019~0417~0QQ6
USEPA Office of Pesticide Programs (USEPA OPP), 2019b. Estimating Field and Watershed
Parameters Used in USEPA's Office of Pesticide Programs Aquatic Exposure Models - The
Pesticide Water Calculator (PWC)/Pesticide Root Zone Model (PRZM) and Spatial Aquatic Model
(SAM). See attachments package.
USEPA Office of Pesticide Programs (USEPA OPP), 2019c. Methods for Automated Field-
Scenario Generation for Use in the Pesticide Water Calculator and the Spatial Aquatic Model.
See attachments package.
Young, D.F. 2019. The USEPA Model for Estimating Pesticides in Surface Water, in Pesticides in
Surface Water: Monitoring, Modeling, Risk Assessment, and Management. American Chemical
Society, editors Goh, Kean, and Young, American Chemical Society, Washington DC
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