States where predicted exposure to atrazine is greatest are estimated to
have atrazine concentrations that exceed the LOC in at least one
sub-watershed?”  The latter phase “in at least one sub-watershed”
is necessary since only one potential monitoring site on the stream and
river network within the HUC was monitored.  The next section describes
the process for selecting monitoring sites within a HUC.

Selection Of Monitoring Site Within The Watershed

Williams et al (2004a) details the criteria used to select monitoring
sites within the targeted watersheds (described in Section   REF
_Ref181170489 \w \h  III.B.1.b) . The criteria for selecting stream
segments for monitoring focused on stream segments relevant to study
goals, e.g., sub-watersheds with higher row-crop densities (and, thus,
higher likelihood of atrazine use) and sub-watersheds with minimal urban
influences. The selection process also avoided sub-watersheds that may
be subject to major annual variation in atrazine load as a result of
crop rotation or highly “flashy” hydrology which may minimize longer
atrazine exposures.

Syngenta identified stream segments with maximum drainage areas that
would

exclude major river stems running through the interbasin HUC10/11
watersheds,

avoid larger streams/rivers (5th and 6th order) within larger HUCs, and

allow for a sufficient watershed size to minimize the likelihood of
monitoring data distortion due to annual crop rotation changes.

The following criteria were used to identify eligible stream segments
using GIS data and tools:

Minimum drainage area of 9 square miles

Maximum drainage area of no more than one-half of the HUC11 watershed,
unless total watershed area is less than 50 square miles. In such
instances, tributaries of larger streams will be manually identified

Percent flow accumulation under urban land use is less than 10%

Percent flow accumulation under cropland is in the upper 50th percentile

Syngenta randomly numbered all stream segments that met the criteria for
field evaluation. Beginning with the most downstream point of the first
randomly-ordered segment and working upstream, the field crew looked for
a suitable sampling site. The field crews verified that corn and/or
sorghum agriculture was present and that the streams were accessible for
monitoring. Conditions that would exclude a stream segment included
areas with few acres in corn and sorghum, a prevalence of
herbicide-tolerant corn or use of herbicides other than atrazine, point
sources such as pesticide distributors, or other anomalies. If no
suitable location was found for the first segment, the process was
repeated for the next randomly selected segment until a suitable site
was located.

Sampling/Monitoring Design

Williams et al (2004a) and Hampton et al (2007a) provide details of the
monitoring design and conduct. Half (20) of the monitoring sties were
sampled in 2004-05 and the remaining 20 sites were sampled in 2005-06 (
REF _Ref181271383 \h  Table III-2 ). Each site was sampled for at least
two years. Monitoring extended into a third year for some sites in which
atrazine concentrations exceeded the LOC in one or more years, elevated
levels of atrazine below the LOC occurred in lower-than-normal rainfall
years, or low flow conditions affected sample collection.

Sampling began in early April, before planting, and continued for
approximately 120 days after an estimated 50% of the local corn acres
were planted (Hampton et al, 2007a). The sampling period typically ran
from April through August at most sites.

At every site, water samples were collected every 4 days during sampling
period. In addition, flow-triggered automatic samplers were installed at
10 of the sites to provide a comparison between the 4-day grab samples
and autosamples. The automatic samplers were triggered to start and stop
at specified changes in stream flow, determined specifically for each
site (Hampton et al, 2007a). In addition, all sites were instrumented to
measure stream flow and to collect weather data (e.g., meteorological
conditions, rainfall, soil moisture).

All water samples were analyzed for atrazine using immuno-assay (limit
of detection of 0.05 ug/L), with detections confirmed using gas
chromatography/mass spectrometry (GC/MS). After May 2, 2005, all samples
were analyzed using liquid chromatography/ mass spectrometry/mass
spectrometry (LC/MS/MS) (Hampton et al, 2007a).

Monitoring Results

Hampton et al (2007a) and Volz et al (2007) provide results of the
monitoring study. This section provides the US EPA’s analysis of the
data.

Atrazine Chemographs

μg/L from the IN-11 site in 2005.  The mean annual concentrations
ranged from a maximum of 9.5 μg/Ll from the MO-01 site in 2004 to a low
of 0.1 μg/L for the NE-06 site in 2006, while the median values ranged
from 4.2 μg/L for the MO-02 site in 2004 to 0.08 μg/L for the OH-03
site in 2005.  For all 40 sites, peak concentrations ranged from 0.13 to
208.76 μg/L, 14-day average concentrations from 0.1 to 80 μg/L, 30-day
average concentrations from 0.1 to 45 μg/L, 60-day average
concentrations from 0.1 to 26 μg/L, and 90-day average concentrations
from 0.1 to 18 μg/L.

μg/L. Most of the sites had multiple atrazine peaks (defined as
detections greater than 1 μg/L), but these were generally of short
duration, usually no more than 1 to 3 sample points (  REF _Ref181177077
\h  Table III-4 , Appendix 3). The exceptions are MO-01 and MO-02, both
of which had extended periods of elevated atrazine concentrations. Forty
of the site-years had atrazine peaks greater than10 μg/L; 25 had peaks
greater than 20 μg/L; and 10 had peaks greater than 50 μg/L, with 4 of
those peaks associated with the same monitoring site (MO-01).

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  4  Summary of
chemograph shapes, numbers and magnitudes of atrazine peaks for the 40
monitoring sites

Site	Year	Chemograph Shape	# Peaks	Max. Peak (μg/L)	Range in
consecutive  days >1 μg/L2

Peaks >1 μg/L	Pattern 1	10-20 μg/L	20-50 μg/L	>50 μg/L

IA-01	2004	2	M-L	1

	10.0	8-12

IA-01	2005	2	L-L

1.2	4

IA-02	2004	5	L-L-L-L-L

1.8	4-16

IA-02	2005	2	L-L

5.5	4-12

IL-01	2004	4	L-M-L-L	1

	13.2	4-16

IL-01	2005	0

	0.0	na

IL-02	2004	3	L-L-L

4.9	4-12

IL-02	2005	1	L

2.9	12

IL-03	2005	1	L

5.6	4

IL-03	2006	1	L

2.5	4

IL-04	2005	1	L

2.8	12

IL-04	2006	4	M-L-L-L	1

	11.5	4-12

IL-05	2004	3	L-L-H

1

22.1	4

IL-05	2005	1	L

1.8	4

IL-06	2004	3	L-L-L

2.2	4-8

IL-06	2005	0

	0.0	na

IL-07	2004	3	L-H-L

1

21.8	4-12

IL-07	2005	2	L-L

2.3	1-8

IL-08	2005	3	L-L-L

5.6	4-20

IL-08	2006	2	H-M	1	1

33.1	8

IL-09	2004	4	M-M-M-L	3

	13.3	4-20

IL-09	2005	2	M-L	1

	16.0	8-12

IN-01	2004	3	L-L-L

8.6	8-24

IN-01	2005	2	L-L

4.4	4-12

IN-02	2004	5	L-L-L-L-L

9.3	12-20

IN-02	2005	3	L-H-L

1

20.3	4-12

IN-03	2005	3	L-L-L

7.6	8-12

IN-03	2006	6	M-L-L-L-L-L	1

	16.9	4-12

IN-04	2004	3	VH-L-L

	1	78.1	4-8

IN-04	2005	3	L-L-L

8.7	4-8

IN-04	2006	3	M-L-L	1

	10.2	4-8

IN-05	2004	4	L-H-H-L

2

28.9	4-12

IN-05	2005	3+	M-L-M	2

	17.3	8-36

IN-05	2006	4	L-H-H-L

2

41.0	4-12

IN-06	2005	2	L-L

7.2	4-8

IN-06	2006	5	L-L-L-L-L

9.4	8-20

IN-07	2005	3	H-M-L	1

	22.6	4-11

IN-07	2006	3	M-L-L	1

	10.5	4-16

IN-08	2005	4	L-H-L-L

1

21.1	4-8

IN-08	2006	5	L-H-H-L-L

2

20.7	4-12

IN-09	2005	4	L-L-L-L

9.4	4-12

IN-09	2006	2	L-L

8.3	4-8

IN-10	2005	3	M-L-L	1

	12.4	4-20

IN-10	2006	3	M-M-L	2

	16.4	4-12

IN-11	2005	1	VH

	1	208.0	24

IN-11	2006	3	L-L-L

9.8	4-12

KY-01	2005	2	L-L

2.2	12-20

KY-01	2006	3	L-H-L

1

22.4	4

KY-02	2005	5	L-M-M-L-L	2

	19.3	8-12

KY-02	2006	2	M-L	1

	14.3	8-12

MN-01	2005	1	L

5.9	16

MN-01	2006	0

	0.0	na

MO-01	2004	4	VH-VH-M-L	1

2	66.0	12-24

MO-01	2005	3	H-VH-H

2	1	182.0	24-32

MO-01	2006	3	VH-L-L

	1	82.8	8-32

MO-02	2004	5	H-VH-H-L-L

2	1	54.0	20-24

MO-02	2005	5	M-M-H-L-L	2	1

28.1	12-76

MO-02	2006	3	H-L-L

1

43.2	8-60

MO-03	2004	2	L-VH

	1	59.0	13-79

MO-03	2005	4	L-M-L-L	1

	12.3	20-56

MO-03	2006	1	L

3.9	52

NE-01	2004	2	L-M	1

	19.3	16-36

NE-01	2005	3	L-M-M 	2

	16.7	8-16

NE-02	2005	5	M-H-M-M-L	3	1

20.7	8-12

NE-02	2006	2	VH-L

	1	82.0	4-16

NE-03	2004	3	L-L-L

2.3	4-8

NE-03	2005	2	L-M	1

	11.3	4

NE-04	2005	3	H-L-L

1

49.9	4-60 3

NE-04	2006	1	L

4.1	28

NE-05	2005	3	L-H-H

2

49.9	8-44 3

NE-05	2006	1	L

6.8	52

NE-06	2004	5	L-L-L-L-L

7.8	4-8

NE-06	2005	2	M-H 	1	1

33.1	8-12

NE-06	2006	0

	0.0	na

NE-07	2005	3	L-M-VH	1

1	112.2	8-24 3

NE-07	2006	1	L

1.9	37

OH-01	2004	3	M-M-L	2

	18.3	8-12

OH-01	2005	3	L-L-L

3.0	4

OH-02	2005	4	M-L-L-L	1

	18.2	8-24

OH-02	2006	2	L-M	1

	14.0	8-20

OH-03	2004	4	M-M-H-L	2	1

21.5	8-20

OH-03	2005	2	L-L

8.2	4-39

OH-04	2005	4	H-L-L-L

1

20.2	4-16

OH-04	2006	2	L-L

6.3	4

TN-01	2005	5	L-L-L-L-L

7.6	8-20

TN-01	2006	4	L-L-L-M	1

	10.7	4-8

1 – Pattern refers to the relative size of the peak in sequence. L =
1-10 ug/l; M = 10-20 ug/l; H = 20-50 ug/l; VH = >50 ug/l

2 – the range in consecutive days >1 ug/l is based on 4-day sample
intervals, except where autosample data were available. For the 4-day
samples, the measured concentration applied to a 4-day period.

3 – the span of consecutive days with concentrations >1 ug/L for
NE-04, NE-05, and NE-07 in 2005 include sample gaps (see text for
discussion).

Analyzing Atrazine Chemographs: LOC Exceedances

Ultimately, the site chemographs must be interpreted against the LOC,
i.e., relating the magnitude and duration of atrazine concentrations to
results of the microcosm/mesocosm studies using CASM.   REF
_Ref181177887 \h  Table III-5  summarizes results of the CASM analysis
for the site-year chemographs. Each site/year of data was expanded to a
365 day time series. Preliminary data interpolation used a stair-step
method where the grab sample concentration was extended (imputed) to the
subsequent 3 days between sampling events. Samples prior to the first
sample date were given the same concentration as the first sample date
from that year, and a similar approach was taken for the dates after the
last sampling event. Sample results from each date that were reported as
a non-detection were conservatively assigned an assumed value of the
detection limit. Because of the low concentrations, this assumption did
not impact the LOC determination.  Finally, dates where no sample was
collected or analyzed were assumed to be equal to the nearest previous
sample with a result.  This final assumption resulted in significant
uncertainty for three sites in Nebraska, where dry or low-flow
conditions resulted in fewer samples being collected. It is not clear
how much the lack of samples are due to dry stream/no flow conditions
and how much are due to low flow with stream bed morphologies that
precluded the collection of samples. The uncertainties arising from the
approach the Agency took in extrapolating concentrations across periods
when no samples are taken are addressed in Section   REF _Ref182290615
\w \h  III.E.2 .

μg/L)	%SSI Dev.	Mult. Factor	LOC Status

Peak	14-day	30-day	60-day

	IA-01	2004	10.0	3.7	2.1	1.2	0.3	12.1	Below LOC 2 or more years

	2005	1.2	0.5	0.3	0.3	0.0	13.2

	IA-02	2004	1.8	1.1	0.8	0.6	0.4	12.2	Below LOC 2 or more years

	2005	5.5	2.1	1.4	0.8	0.3	14.1

	IL-01	2004	13.2	6.6	4.1	2.5	0.8	5.5	Below LOC 2 or more years

	2005	0.6	0.3	0.3	0.3	0.0	7.2

	IL-02	2004	4.9	2.9	2.3	1.5	0.5	8.6	Below LOC 2 or more years

	2005	2.9	1.8	1.1	0.6	0.2	8.5

	IL-03	2005	5.6	1.8	1.0	0.6	0.1	23.1	Below LOC 2 or more years

	2006	2.5	0.9	0.5	0.3	0.1	18.4

	IL-04	2005	2.8	1.4	0.8	0.5	0.1	16.9	Below LOC 2 or more years

	2006	11.5	3.4	1.8	1.8	0.6	7.5

	IL-05	2004	22.1	7.2	3.6	2.0	0.7	7.9	Below LOC 2 or more years

	2005	1.8	0.7	0.4	0.3	0.1	19.1

	IL-06	2004	2.2	1.1	0.7	0.5	0.3	15.3	Below LOC 2 or more years

	2005	0.2	0.2	0.2	0.2	0.0	13.3

	IL-07	2004	21.8	7.0	4.2	2.4	0.9	6.6	Below LOC 2 or more years

	2005	2.3	0.9	0.6	0.5	0.1	14.3

	IL-08	2005	5.6	4.4	2.8	1.8	1.1	3.6	Below LOC 2 or more years

	2006	33.1	11.0	8.1	4.4	1.9	2.9

	IL-09	2004	13.2	8.1	6.3	4.6	1.4	2.9	Below LOC 2 or more years

	2005	16.0	6.2	3.4	2.3	1.0	3.2

	IN-01	2004	8.6	4.0	3.5	2.4	0.7	5.9	Below LOC 2 or more years

	2005	4.4	1.4	1.0	0.7	0.3	5.5

	IN-02	2004	9.3	6.3	4.5	2.8	0.8	4.6	Below LOC 2 or more years

	2005	20.3	6.3	4.3	3.0	1.0	4.5

	IN-03	2005	7.6	4.3	3.3	2.3	0.7	5.1	Below LOC 2 or more years

	2006	16.9	10.6	6.2	3.9	1.3	3.7

	IN-04	2004	78.1	23.8	12.0	6.4	1.7	2.7	Below LOC 2 or more years

	2005	8.8	3.6	2.1	1.4	0.4	7.2

2006	10.2	5.6	3.7	2.2	0.6	6.0

	IN-05	2004	28.9	14.9	11.9	7.0	2.2	2.2	Below LOC 2 or more years

	2005	17.3	7.8	4.5	4.1	1.5	2.8

2006	41.3	17.9	13.1	7.4	2.4	2.2

	IN-06	2005	7.2	2.9	1.8	1.0	0.4	11.5	Below LOC 2 or more years

	2006	9.4	4.0	2.7	1.9	0.9	5.4

	IN-07	2005	22.6	9.6	6.4	3.9	0.6	7.6	Below LOC 2 or more years

	2006	10.5	5.4	3.6	2.0	0.6	7.4

	IN-08	2005	21.1	6.9	4.9	2.8	1.0	5.5	Below LOC 2 or more years

	2006	20.7	8.9	7.7	4.4	1.5	3.8

	IN-09	2005	9.4	3.7	2.4	1.7	1.2	4.2	Below LOC 2 or more years

	2006	8.3	3.0	1.8	1.2	0.6	7.1

	IN-10	2005	12.4	6.1	4.0	2.8	0.9	4.8	Below LOC 2 or more years

	2006	16.4	7.5	6.3	3.6	1.2	4.4

	IN-11	2005	208.8	65.1	31.5	16.2	5.6	0.7	Exceeds LOC in 1 year

	2006	9.8	5.9	3.3	1.9	1.3	3.5

	KY-01	2005	2.2	1.5	1.2	0.9	0.3	14.2	Below LOC 2 or more years

	2006	22.4	6.9	3.6	1.9	0.7	8.5

	KY-02	2005	19.3	8.7	7.1	4.5	1.7	2.0	Exposures within 2x of LOC in 1
year

	2006	14.3	4.7	3.9	2.3	0.6	3.9

	MN-01	2005	5.8	2.6	1.4	0.8	1.2	3.8	Below LOC 2 or more years

	2006	0.2	0.2	0.1	0.1	0.0	20.4

	MO-01	2004	65.9	39.6	28.6	19.4	4.4	0.9	Exceeds LOC in 2 or more years

	2005	182.8	78.1	42.5	25.7	6.8	0.5

2006	82.8	48.2	31.6	17.5	4.4	0.9

	MO-02	2004	53.8	33.0	25.9	16.8	4.7	0.8	Exceeds LOC in 2 or more years

	2005	28.1	18.7	14.6	11.5	5.2	0.8

2006	43.2	34.7	27.4	15.4	4.5	0.8

	MO-03	2004	59.0	23.3	13.1	8.1	3.0	1.3	Exposures within 2x of LOC in 2
years

	2005	12.3	8.7	6.9	5.5	1.9	2.0

2006	3.9	2.3	1.9	1.5	1.0	4.8

	NE-01	2004	19.2	13.0	7.5	4.3	1.3	3.8	Below LOC 2 or more years

	2005	16.7	6.6	5.6	3.6	1.2	4.4

	NE-02	2005	20.7	11.4	10.7	6.3	2.0	2.4	Below LOC 2 or more years

	2006	82.0	28.6	14.1	7.3	1.8	2.3

	NE-03	2004	2.3	1.1	1.0	0.6	0.1	19.0	Below LOC 2 or more years

	2005	11.9	3.7	2.1	1.2	0.3	10.0

	NE-04	2005	36.0	36.0	27.3	17.4	4.5	0.8	Low flow/ exceeds 1 year

	2006	4.1	4.1	3.1	1.7	2.1	2.1

	NE-05	2005	49.9	23.8	19.9	16.5	4.6	0.8	Low flow/ exceeds 1 year

	2006	6.8	6.8	6.8	5.1	0.8	4.5

	NE-06	2004	7.7	2.8	2.1	1.7	0.5	7.3	Below LOC 2 or more years

	2005	33.1	20.6	11.4	6.0	1.9	2.8

2006	0.1	0.1	0.1	0.1	0.0	20.5

	NE-07	2005	112.2	80.0	45.2	22.7	5.2	0.7	Low flow/ exceeds 1 year

	2006	na	na	na	na	0.6	8.5

	OH-01	2004	18.3	8.8	5.7	3.2	1.0	5.3	Below LOC 2 or more years

	2005	3.0	1.0	0.9	0.6	0.2	12.0

	OH-02	2005	18.1	7.1	4.0	2.9	1.0	5.1	Below LOC 2 or more years

	2006	14.0	5.9	5.2	2.9	0.8	5.3

	OH-03	2004	21.5	9.1	7.3	4.5	1.4	3.9	Below LOC 2 or more years

	2005	8.2	3.0	1.6	0.9	0.5	11.5

	OH-04	2005	20.2	8.0	4.7	2.7	1.0	5.5	Below LOC 2 or more years

	2006	6.3	2.6	1.7	1.0	0.3	12.9

	TN-01	2005	7.6	5.6	4.0	2.9	1.0	3.9	Below LOC 2 or more years

	2006	10.7	3.6	2.4	2.0	0.7	3.5

The level of concern is triggered when the Steinhaus Similarity Index
(SSI) exceeds 4 percent. Only two sites – MO-01 and MO-02 exceeded the
4% SSI trigger in multiple years. Those sites exceeded in all three
years of monitoring. IN-11 exceeded the 4% SSI in 2005 but not in 2006.
Monitoring continued at this site in 2007, but the results are not
available at this time. Three sites in Nebraska – NE-04, NE-05, and
NE-07 – exceeded the 4% SSI in one year (2005), but not in subsequent
years when there was sufficient flow to allow for sampling on a 4-day
interval. The chemographs associated with the LOC exceedances in these
sites include periods in which no samples were taken because of low
stream flow (Hampton et al, 2007a). Two sites – KY-02 and MO-03 did
not exceed the 4% SSI trigger but had exposures that were within a
factor of 2 of exceeding the trigger based on the multiplication factor.
Because of uncertainties identified in the way the model may
underestimate the effects of short-duration high exposures and in the
extent to which the sample frequencies may capture peak concentrations,
these sites are flagged as uncertain. These conditions are grouped
separately in the statistical assessment below.

Percentage Of Watersheds Exceeding The LOC Threshold

Statistical Analysis Based On GRTS

Methods

This monitoring study was specifically designed to answer the question,
“How many watersheds in corn and sorghum growing regions in the United
States where predicted exposure to atrazine is greatest are estimated to
have atrazine concentrations that exceed the LOC in at least one
subwatershed?” This section explains how the statistical analysis
performed directly corresponds with the survey design that was used, in
order to accurately address the question.

The primary objective of the analysis was to determine how many
watersheds out of the 1,172 are estimated to fall within each of three
basic categories: 1) considered an excluded site 2) are not likely to
contain a water body with atrazine concentrations that exceed the LOC,
and 3) are likely to contain a water body with atrazine concentrations
that exceed the LOC.   The analysis provides the estimated proportion
(expressed as a percent) of watersheds as well as the number of
watersheds, along with 95% confidence intervals for each.

This monitoring study required 47 watersheds be visited in order to
successfully attain an overall sample size of n=40 (i.e. 7 sites had to
be excluded because of logistical constraints to sampling).  Although it
may seem intuitive to completely disregard these 7 excluded sites and
only focus on the 40 in which monitoring was actually performed, doing
so would result in the loss of key information describing the
characteristics of the watersheds within the population and potentially
result in a bias in the estimates.  Therefore, in order to accurately
represent the status of the population of 1,172 watersheds, the
estimated number of excluded sites in the population is calculated,
where n=24 for stratum (2,4], and n=23 for stratum (4,14], totaling an
overall n=47.

Proportion (expressed as a percent) and unit estimates are calculated
using the Horvitz-Thompson ratio estimator (Horvitz and Thompson, 1952).
 For each stratum, the numerator of the ratio is calculated by summing
the design weights for each sample response within a category and then
dividing by the sum of the design weights over the sample size for the
stratum.  The number of watersheds for a response category is then
estimated simply by multiplying the estimated proportion of watersheds
by the number of watersheds within that stratum.  Estimates and standard
errors are then calculated for all strata combined by weighting each
stratum by its respective population size, with N1=874 and N2=298
representing approximately 75 and 25 percent, respectively, of the
population of 1,172.

Variance estimates are calculated using the local mean variance (Stevens
and Olsen, 2003), which utilizes the x- and y-coordinates for each
watershed, and confidence intervals are calculated using a Normal
distribution multiplier value of 1.96, multiplied by the standard error.
 Simulation studies reported by Stevens and Olsen (2003) have shown that
this variance estimator performs better than the Horvitz-Thompson
variance estimator.

Assumptions

The following assumptions drive the accuracy of the estimates presented
in the results.

The 1172 HUCs accurately represent the 20% most vulnerable watersheds
within the true population.

All other HUCs within the United States are assumed not to exceed the
LOC.

If the monitored site within each HUC shows an exceedance, then any
other similar site within that HUC would have exceeded the LOC if it had
been selected to be monitored.

The atrazine chemograph reported for each site is independent of whether
that site was sampled during the period of 2004-2005 or from 2005-2006.
This validates the data being grouped together and generalized as a
single 2004-2006 sampling period.

The true atrazine chemographs that occurred throughout the sampling
period were in fact captured by the data.

Data responses have been correctly categorized.

Results

The use of the phrase “based upon this data” at the beginning of
each statement refers to the fact that the estimates and their
confidence intervals are completely dependent upon the sample size of
data used, the circumstances under which the data was collected, and the
way that the data was manipulated and/or used prior to the calculation
of the estimates themselves. A level of concern (LOC) exceedance is
based on CASM SSI % deviation calculation for each sub-watershed.

Population Estimates

Population estimates have been generated for 47 HUCs representing the
1172 watersheds.  These estimates have been broken into four categories:

(1) 	Excluded sites - 7 HUCs

(2)	Sites that did not exceed the LOC in either year - 32 HUCs

(3)	Sites that exceed the LOC in multiple years - 2 HUCs

(4)	Sites that exceed the LOC in one year with additional monitoring
pending – 1 HUC

(5)	Sites where the interpretation is uncertain - 5 HUCs

This last category includes one site where the LOC was exceeded 1 year
and a third year of data is pending, three sites in Nebraska where
low/no flow conditions limited sample collection, and two sites where
application of an uncertainty bound based on model sensitivity analysis
(see Section II) suggests that exceedances could occur.  The following
sections summarize the population estimates for each category separately
however the Agency is only certain about exceedances of the LOC for the
two sites with more than one year of exceedances.  All other categories
are subject to interpretation based on the uncertainty analysis
discussed in Section II (model sensitivity) and Section III (monitoring
data uncertainty).

Excluded Sites

Based upon the data, an estimated 255 (22%) HUCs out of the population
of 1172 HUCs during 2004-2006 would have had to be “excluded,”
whereby monitoring activities could not have been successfully performed
(  REF _Ref181764879 \h  Table III-6 ). The 95% confidence interval for
the estimate is from 119 to 390 HUCs (i.e. 10%-33%).  No inference is
possible whether these HUC-10s would have had one or more subwatersheds
with LOC exceedances, without making additional assumptions.  The fact
that a monitoring site could not be located within these HUCs may result
in having different characteristics with respect to atrazine chemographs
than HUCs where a monitoring site could be located.

Sites That Did Not Exceet the LOC

Out of the 47 HUC-10s in the sample, 32 of the 40 monitored
sub-watersheds within the HUC-10s reported atrazine concentrations
resulting in an assessment that they were below the level of concern.
Based upon the data, it is estimated that 669 HUC-10s (57% of 1172)
would have atrazine concentrations resulting in an assessment that they
were below the established level of concern.  The 95% confidence
intervals for the estimates are 47-67% in terms of percents and 551-787
in terms of HUC-10s.

Sites That Exceeded the LOC in At Least 2 Years

Two watersheds (MO 01 and MO 02) have experienced exposures that exceed
the LOC in two or more years (LOC Exceeds 2YR).  The LOC exceeds 2YR
category includes an estimated 101 (9% of 1172) HUCs that would contain
at least one sub-watershed having atrazine concentrations resulting in
an exceedance of the designated level of concern in at least 2 years,
with 95% confidence limits of 0 and 218 HUCs, or 0% to 19% of 1172 HUCs.
These are sites that triggered follow-up mitigation actions as a result
of 2 years of LOC exceedances.

Sites That Exceeded the LOC in 1 Year

One watershed (IN-11) exceeded the LOC in one year of monitoring but not
in a second year. The LOC exceeds 1YR category includes an estimated 46
(4% of 1172) HUCs that would contain at least one sub-watershed having
atrazine concentrations resulting in an exceedance of the designated
level of concern in 1 of 2 monitoring years, with 95% confidence limits
of 0 and 125 HUCs, or 0% to 11% of 1172 HUCs. Monitoring has been
extended for a third year at this site.

Uncertain Sites

Two categories of watersheds are considered uncertain in the
interpretation of the LOC exceedance for the years that were monitored:
1) Sites in which samples were not taken during low flow but exceeded
the LOC in one year based on extrapolated monitoring (Low Flow Exceeds),
and 2) Sites with exposures below the 4% SSI but within 2 times of
exceeding (LOC within 2X).  These estimates are shown in Table III-6.
These categories are uncertain and final determination of whether they
should be considered as exceeding the LOC will depend on the outcome of
this SAP meeting.  The population estimates are simply provided to give
context to the estimates.

The Low Flow Exceeds category includes an estimated 34 (3% of 1172) HUCs
that would contain at least one sub-watershed having atrazine
concentrations resulting in an exceedance of the designated level of
concern due to low flow conditions, with 95% confidence limits of 15 and
54 HUCs, or 1% to 5% of 1172 HUCs.

The LOC within 2X category includes an estimated 67 (6% of 1172) HUCs
that would contain at least one sub-watershed having atrazine
concentrations that are within 2X of an exceedance of the designated
level of concern, with 95% confidence limits of 0 and 154 HUCs, or 0% to
13% of 1172 HUCs.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  6  Population
Estimates for the 1,172 Vulnerable Watersheds Based on  the 40
Monitoring Sites

Category	Fre-quency	Estimated Population Percent 	95% Lower Confidence
Bound	95% Upper Confidence Bound	Estimated Number of Watersheds	95%
Lower Confidence Bound	95% Upper Confidence Bound

Excluded	7	22%	10%	33%	255	120	390

LOC Below	32	57%	47%	67%	669	551	787

LOC Exceeds 2YR	2	9%	0%	19%	101	0	218

LOC Exceeds 1YR	1	4%	0%	11%	46	0	125

Low Flow Exceeds	3	3%	1%	5%	34	15	54

LOC within 2X	2	6%	0%	13%	67	0	154

Total	47	100%	 	 	1172

Previously, the question was raised about what assumptions could be made
about the seven HUCs initially selected that could not be monitored.
One assumption is that they are missing at random, i.e., that the HUCs
that could not be monitored have the same atrazine chemograph
characteristics as HUCs that could be monitored.    REF _Ref181765022 \h
 Table III-7  summarizes population estimates for HUCs exceeding the LOC
when this assumption is made.  Typically, the estimates under this
assumption are 1-2% greater for LOC exceedances while the estimated
number of HUCs that are below the LOC exceedance changes from 57% to 73%
(669 to 855 HUCs).

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  7  Population
Estimates Assuming Non-Monitored HUCs Missing at Random

Category	Fre-quency	Estimated Population Percent 	95% Lower Confidence
Bound	95% Upper Confidence Bound	Estimated Number of Watersheds	95%
Lower Confidence Bound	95% Upper Confidence Bound

LOC Below	32	73%	59%	87%	855	695	1016

LOC Exceeds 2YR	2	11%	0%	24%	130	0	283

LOC Exceeds 1YR	1	5%	0%	13%	61	0	150

Low Flow Exceeds	3	4%	2%	5%	40	19	61

LOC within 2X	2	7%	0%	17%	86	0	199

Total	40	100%

	1172

Another way to summarize the population estimates is to estimate the
cumulative distribution for CASM SSI deviation (%) scores.    REF
_Ref181272450 \h  Figure III-4  shows the estimated cumulative
distribution and 95% confidence limits.  The flat portion of the plot
between approximately 2 and 4 is potentially indicative that two
populations of HUC-10s may exist with respect to CASM scores.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  4  Estimated
Cumulative Distribution for CASM Score.

 Reliability of WARP estimates for HUCs and Sub-watersheds

The WARP predictions for the sampled HUCs closely correlated (r= 0.874)
with those predicted for the monitored sub-watersheds within the
selected HUCs (  REF _Ref181765305 \h  Figure III-5 ). Sub-watershed
WARP scores tend to be greater than those predicted for HUCs (  REF
_Ref181272652 \h  Figure III-6 ).  Given that the data was collected at
the sub-watershed level, WARP predictions for sub-watershed, rather than
HUCs will be used in subsequent plot comparisons.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  5  Comparing
WARP scores for HUC-based watersheds vs. sub-watersheds

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  6  Comparison
of WARP scores for sub-watershed and HUCs

The monitoring results indicate that WARP is not able to successfully
distinguish “moderately high” vulnerability from “high to very
high” vulnerability predictions.  If WARP were in fact able to
distinguish between the two, we would expect to see a positive
relationship between CASM SSI deviations and WARP scores (  REF
_Ref181272792 \h  Figure III-7 ).  It might also be expected that all
WARP predictions between (2,4] would have values falling in Quadrant 4,
indicating that they also received a low CASM score.  Likewise, it would
be expected that all WARP predictions with values greater than 4 would
appear in Quadrant 2, having CASM scores greater than 4%.  It is notable
for these for sub-watersheds that no CASM scores occur between
approximately 2 and 4.  It is yet to be determined whether or not the 6
points with CASM SSI scores >4% share a common factor that could
potentially be added to the WARP model in order to improve prediction
capabilities within the most vulnerable sites.  For CASM scores less
than or equal to 2%, however, an overall direct linear relationship can
be seen in WARP model, although some discrepancies appear to be present
around 2%.  A similar plot is shown in   REF _Ref181765560 \h  Figure
III-8 , plotting WARP scores against the maximum 14-day rolling average
across year per site.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  7
Illustrating WARP’s capabilities to accurately predict moderately high
vs. high vulnerability to atrazine by relating it to resulting CASM
scores.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  8  Comparing
how well WARP predicts maximum 14 day rolling averages

The WARP predictions in this analysis were used as a surrogate for the
%SSI to identify vulnerable watersheds that were likely to exceed LOC
when the study was designed.  This analysis asked the question "What
evidence does the monitoring results give to indicates that WARP was a
reasonable surrogate for SSI and consequently that the study focused on
the appropriate set of watersheds?"  Unfortunately the only data
available for comparison is the 40 monitored watersheds which are at the
upper end of WARP predictions.  At this upper end, the Agency sees no
evidence of a relationship.  Potentially this suggests that WARP may be
a "weak" surrogate for %SSI. The analysis is limited because %SSI is not
available for any watersheds below the 80th percentile.  A relationship
may exist but is not evident at the upper end of the plot.

 Comparison of Alternative WARP estimates for HUCs

WARP model predictions depend on the values of the input variables. The
initial WARP estimates used to define vulnerable watersheds used
atrazine use data averaged for the years 1998 to 2002 and did not
include Dunne overland flow as a variable (WARPDOFZ). Subsequently,
Syngenta calculated two updated WARP predictions (WARP and WARP_NewUse)
where WARP used same atrazine use data as WARPDOFZ and WARP_NewUse used
atrazine use data updated for 2001 to 2003 (Hampton et al, 2007b).  The
US EPA used atrazine data from 1999 to 2001 in its original watershed
vulnerability assessment (WARP_EFED_NoPrecip) and considered a version
of the WARP model that included May-June Precipitation as one of the
predictive parameters (WARP_EFED_Precip). The question is whether these
alternatives would identify the same set of vulnerable watersheds. The
various WARP estimates are listed in   REF _Ref182294339 \h  Table III-8
.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  8  Atrazine Use
Data and Model Inputs Used for Alternative WARP Estimates

WARP Estimate	Atrazine Use Data	WARP Model Parameters (1)

WARPDOFZ	1998-2002	U, R, K, A

WARP	1998-2002	U, R, K, A, D

WARP_NewUse	2001-2003	U, R, K, A, D

WARP_EFED_NoPrecip	1999-2001	U, R, K, A, D

WARP_EFED_Precip	1999-2001	U, R, K, A, D, P

1 U = Use Intensity; R = R Factor; K = K Factor; A = Watershed Area; D =
Dunne Overland Flow; P = May-June Precipitation

  REF _Ref181766115 \h  Table III-9  gives the correlations for the five
WARP predictions while   REF _Ref181766154 \h  Figure III-9  shows the
relationship for the 1172 vulnerable HUCs identified by WARPDOFZ.  In
general correlations are higher when the same atrazine use data is used.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  9  Correlations
of Alternative WARP Predictions for 1172 vulnerable HUCs

	WARPDOFZ  	WARP	WARP_NewUs	NOPRECIP	PRECIP95

WARPDOFZ  	1	0.895	0.616	0.620	0.583

WARP	0.895	1	0.563	0.597	0.564

WARP_NewU 	0.616	0.563	1	0.777	0.704

NOPRECIP	0.620	0.597	0.777	1	0.965

PRECIP95	0.583	0.564	0.704	0.965	1

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  9
Scatterplots of Alternative WARP Predictions for 1172 vulnerable HUCs

The above comparison is based only on the 1172 vulnerable HUCs.  To
determine whether the five alternatives would identify the same top 20%
vulnerable HUCs requires the comparison to be made on the 5860 HUCs with
high atrazine use.  This information is available for all alternatives,
although only the actual 1172 WARP predictions are available for
WARPDOFZ.  First, the correlations for the five WARP predictions based
on 5860 HUCs are stronger, as would be expected, than when only the 1172
HUCs are considered (  REF _Ref181766255 \h  Table III-10 ). Note the
correlations with WARPDOFZ are the same since only the 1172 HUCs are
available for it.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  10  Correlations
of Alternative WARP Predictions for 5860 High Atrazine Use HUCs

	WARPDOFZ  	WARP	WARP_NewUse	NOPRECIP	PRECIP95

WARPDOFZ  	1	0.895	0.616	0.620	0.583

WARP	0.895	1	0.824	0.818	0.811

WARP_NewUse 	0.616	0.824	1	0.777	0.704

NOPRECIP	0.620	0.818	0.919	1	0.986

PRECIP95	0.583	0.811	0.900	0.986	1

The top 20% of the WARP predictions were identified for each alternative
assuming 5860 HUCs.  Then all HUCs that were common across all
alternatives were identified (Top20_All) and all HUCs that were in the
top 20% for at least one alternative were identified (Top20_Any). Across
the alternatives, 819 HUCs are identified by all alternatives and 1585
are in the top 20% for at least one alternative.    REF _Ref181766547 \h
 Table III-11  compares how the alternatives classify vulnerability of
HUCs compared to the initial classification based on WARPDOFZ.  Each
small table would have no HUCs in the off diagonals of the table similar
to the WARPDOFZ first table. Each of the four new alternatives
classifies from 87 to 218 HUCs as being in the top 20th percentile
compared to the original WARPDOFZ classification.  Note that since
NOPRECIP and PRECIP WARP predictions were available for all 9510 HUCs
where atrazine was used, 34 and 41 of the top 20th percentile HUCs
occurred outside the high atrazine use study region.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  11  Comparison
of HUCs in Top 20% WARP Predictions

Alternative WARP Prediction Vulnerable HUCs	WARPDOFZ Vulnerable HUCs
Total

	<80%	80-95%	>95%	Outside 5860 HUCs

	WARPDOFZ

	<80%	4688	0	0

4688

	80-95%	0	874	0

874

	>95%	0	0	298

298

	Total	4688	874	298

5860

WARP

	<80%	4601	87	2

4690

	80-95%	71	774	28

873

	>95%	16	13	268

297

	Total	4688	874	298

5860

WARP_NewUse

	<80%	4518	172	1

4691

	80-95%	166	572	134

872

	>95%	4	130	163

297

	Total	4688	874	298

5860

NOPRECIP

	<80%	4501	225	14	3599	8339

	80-95%	178	537	115	43	873

	>95%	9	112	169	8	298

	Total	4688	874	298	3650	9510

PRECIP

	<80%	4470	235	17	3616	8338

	80-95%	210	520	119	27	876

	>95%	8	119	162	7	296

	Total	4688	874	298	3650	9510

The Agency then looked at the spatial distribution of the vulnerable
watersheds identified by the different WARP estimates.   REF
_Ref181767332 \h  Figure III-10  compares the locations of the 1,172
vulnerable watersheds identified using WARPDOFZ (WARP estimates with
Dunne Overland Flow set to 0) and the watersheds identified using WARP
with Dunne Overland Flow included. The green areas show where the HUCs
overlap; watersheds in yellow are in the original 1172 but not in the
revised WARP set; and watersheds in blue are in the revised group only.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  10  Comparison
of the spatial distribution of the original vulnerable watersheds used
to select the monitoring sites with watersheds identified with the full
WARP equation using Dunne Overland Flow.

The extensive blue area is misleading because it includes larger HUC-8
watersheds where no HUC-10 watersheds were available for TX, OK, or AR.
All 40 of the watersheds selected for monitoring are included in both
sets of vulnerable watersheds. That does not preclude the possibilty
that the GRTS process would possibly have selected a different suite of
watersheds based on the revised WARP group.

  REF _Ref181767664 \h  Figure III-11  contrasts the locations of the
original 1172 HUCs selected with WARPDOFZ (yellow) with the upper 20th
%ile set of HUCs that would have been identified using the newer
atrazine use (WARP_NewUse), shown in red. The orange represents the
areas of overlap. In this case, 3 of the monitoring sites would not have
been in the most vulnerable tier with the new use data (1 in MN, 1 in
KY, 1 in NE).

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  11  Comparison
of original vulnerable watersheds identified using 1998-2002 use data
with vulnerable watersheds identified using 2001-03 use data.

Sensitivity / Uncertainty Analyses

This section focuses on factors that might impact how the results could
be interpreted or how future monitoring studies and data could be used
to assess the impacts of atrazine on aquatic communities.

Weather

One of the assumptions of the population estimates (Section   REF
_Ref181719026 \w \h  III.D ) is that the chemographs are independent of
the year of sampling. Sources of variability in monitoring data
collected over multiple years include:

variability in weather patterns, particularly extreme events

timing of rainfall in  relation to application period

amount of flow in the water body prior to runoff events

stark differences in rainfall amounts, timing, and intensity that make
comparisons among sites or among years at the same site difficult.

In the 2003 IRED addendum (US EPA, 2003b), the Agency specified that
monitoring might be extended for a third year at a site if either of the
following occurred:

atrazine concentrations in water triggered an LOC exceedance in any of
the sampling years, or

unusual meteorological conditions (e.g., high or low rainfall) occurred
during the monitoring period.

The US EPA analyzed the rainfall data two ways (see Appendix 3):

Rainfall totals by month were compared against historical totals, with
special emphasis placed on rainfall amounts from April to June, which is
considered the principal atrazine use period and coincides with the bulk
of the analytical sampling;

Daily precipitation data was plotted against the monitoring data and
overlaid the planting season for a more detailed evaluation.

In the first analysis, the site-specific precipitation data were summed
by month and compared with historical monthly totals. A site year with
precipitation less than the 25th percentile for one or more of the
critical months is considered below normal precipitation and the results
from that year would be considered not representative of a normal, or
above normal climatic conditions.  Site years where data was between the
25 percentile and the 75th percentile were considered normal, and above
the 75th percentile were considered above normal.

In general, more sites had months with lower-than-normal than
higher-than-normal precipitation. In particular, 2005 was a low rainfall
year (based on the monthly rainfall comparisons for the Apr-Aug sampling
period) for a number of sampling locations, including nine sites in
Illinois, three in Indiana (IN-02, -06, -11), two in Ohio (OH-02, -03),
and one each in Kentucky (KY-02) and Minnesota (MN-01). Such rainfall
discrepancies may make it difficult to draw definitive conclusions
regarding the relative vulnerabilities of the water bodies to atrazine
runoff.

However, lower-than-normal precipitation during the likely application
months does not necessarily mean that atrazine concentrations will be
correspondingly low. The timing of the precipitation is critical. If
precipitation is low in the time leading up to planting, then low-flow
conditions may exist in the streams and a rainfall event after
application could result in high concentrations because the relative
amount of dilution (runoff water into stream water) would be lower than
in a “normal” rainfall year with “normal” stream flow. The high
concentration (208 ppb) at IN-11 in 2005 is an example of this. In 2006,
when monthly rainfall totals at IN-11 were higher than normal, measured
atrazine concentrations were much lower than in 2005.

A comparison of total precipitation against yearly or monthly averages
may not be a sufficient assessment of the potential for high atrazine
concentrations to occur in water body without an additional evaluation
of the timing of rainfall events and of stream flow at the time of the
runoff event. The US EPA has requested that Syngenta continue monitoring
for a third year in IL-03, IL-04, IL-08, IN-06, IN-11, MO-01, MO-02, and
OH-02, collecting data on stream flow as well as rainfall during the
monitoring period.

Flow rates / Low flow sites

Low Flow Sites

Sampling frequency is a critical issue for understanding uncertainty in
monitoring data and by extension the interpretation of the SSI scores
from CASM.  Although robust in both number of samples and geographic
diversity the AEMP data does include years where samples could not be
collected at some sites.  Of particular interest were sites monitored in
Nebraska.  Three of the seven sites in Nebraska experienced a higher
frequency of missed samples compared to other sites both within Nebraska
and across the entire AEMP geographic range.  According to the
registrant (Hampton, et al, 2007) three sites in Nebraska – NE-04,
NE-05, and NE-07 – experienced low to no-flow conditions that resulted
in significant number of missed grab samples in 2005.

Because of the missing samples, Syngenta did not use CASM to evaluate
the three NE sites (Hampton, 2007a), suggesting that a dry stream would
experience “significant stressors” other than atrazine runoff. They
implied that these low- to no-flow events would make exposure to
atrazine irrelevant because the stressors on aquatic communities due to
low flow conditions are significant and would overwhelm these
communities (Hampton et al, 2007a, Hampton et al, 2007b).

The issue raises several important questions.  First, if sites exhibit
low- or no-flow conditions such as those which may have been encountered
in the three Nebraska sites, does this preclude the analysis of these
data for evaluating aquatic community impacts?  Meyer et al (2007) have
demonstrated that headwater streams, including both low flow and
intermittent streams, can have significant biodiversity.  They note that
these types of streams are critical to the integrity of the entire river
network (Meyer, et al 2007).  Given this, the Agency believes the
question which should be asked is whether the type of streams
represented by the three Nebraska sites with missing samples due to
low/no flow conditions can be evaluated in the context of the microcosm
and mesocosm studies that are the foundation of the LOC (4% SSI response
from CASM).

The second question is, if the microcosm/mesocosm studies are
representative of low/no flow sites (intermittent streams), what is the
impact of missing samples on interpreting CASM output?  If sites have a
“significant” number of missing samples, is it appropriate to use
these data in CASM?  A logical follow up question would be that if it is
inappropriate to use sites with missing data, then how many samples must
be missing to exclude the data from use?

Finally, the Agency evaluated a series of landscape and watershed
metrics for any obvious explanations for the uniqueness of the three
Nebraska sites. These three sites with low flow conditions are in
southeastern Nebraska while other sites in western Nebraska did not
experience similar conditions.  Metrics evaluated included flow
information (both from AEMP and USGS Gages), stream order,
precipitation, base flow conditions, recharge potential, soils, geology,
irrigation use, and presence of tile drains.  Generally, there does not
appear to be an obvious explanation from any of these metrics accounting
for why these three sites should have experienced conditions unique from
the other 37 sites and, in particular, the other four sites in Nebraska
and nearby areas of Iowa and Missouri.  Interestingly, it did become
apparent during this evaluation that sites exceeding the LOC tended to
be lower flow sites.

Given this uncertainty the Agency decided to conduct stair step
interpolation for the three sites and run each chemograph through CASM.
The sites exceeded the CASM LOC, principally due to the fact that
several of the sample concentrations were high and interpolation using
the stair step approach extended these exposures well beyond the
standard 4 day gap, possibly overestimating the importance of individual
sample results.  For now, the three Nebraska sites are grouped
separately in the population estimates (see Section   REF _Ref181719026
\w \h  \* MERGEFORMAT  III.D ) in order to evaluate what these sites
would represent if they were considered to be exceeding.  Given the
uncertainty in the appropriateness of the use of these data, these
population estimates could be useful for determining how many sites
might be expected to experience similar conditions whether they be
interpreted as simply low/no flow or exceeding the LOC.

Flow Measured at the Monitoring Sites

Stream flow was the most obvious factor to compare between the
monitoring sites.Flow data for each year was summarized for each site by
calculating the annual average flow rate for each site by year (  REF
_Ref181697430 \h  Table III-12 ).

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  12  Summary of
annual average flow rate for the 40 monitoring sites by year.

Site	2004 flow (cfs)	2005 flow (cfs)	2006 flow (cfs)

IA 01	50.20	69.95	37.70

IA 02	20.81	21.09	97.78

IL 01	102.25	15.05

	IL 02	71.71	56.08

	IL 03

46.34	37.70

IL 04

21.45	97.78

IL 05	102.89	83.12

	IL 06	30.02	20.49

	IL 07	29.71	20.82

	IL 08

146.39	125.51

IL 09	32.95	27.57

	IN 01	63.86	145.85

	IN 02	30.57	14.22

	IN 03

19.49	36.36

IN 04	42.96	14.77	12.55

IN 05	8.28	7.22	9.26

IN 06

132.89	213.52

IN 07

199.77	202.98

IN 08

27.60	42.37

IN 09

56.13	79.52

IN 10

15.23	29.35

IN 11

11.43	36.79

KY 01

6.55	14.30

KY 02

46.46	63.70

MN 01

28.33	16.46

MO 01	2.73	3.13	2.30

MO 02	28.30	25.86	29.69

MO 03	85.10	69.73	24.78

NE 01	42.85	57.50

	NE 02

84.83	37.90

NE 03	2.73	4.82

	NE 04

9.50	6.59

NE 05

35.22	22.35

NE 06	3.62	1.04	4.40

NE 07

15.37	8.56

OH 01	34.63	26.97

	OH 02

27.41	36.24

OH 03	21.14	21.42

	OH 04

192.55	208.70

TN 01

17.22	11.97

In addition, the 86 years of flow data were ranked relative to each
other (  REF _Ref182217040 \h  Table VII-3  in Appendix 2  REF
_Ref181697520 \h  ).  The three NE sites did not have the lowest average
flow: NE-04 in 2005 had the 15th lowest flow out of 86 site years, NE-07
in 2005 was 24th, and NE-05 was 51st.  Interestingly, three of the five
lowest flow site years were the three sample years forMO-01, which
exceeded the LOC each year.

  REF _Ref180382723 \h  Figure III-12  through   REF _Ref180382745 \h
Figure III-15  plot atrazine concentrations against measured stream flow
for NE-04, NE-05, NE-07, and MO-01 in 2005 to illustrate the
relationship between atrazine concentrations and flow conditions. In
these figures the missing samples are shown as 0. While   REF
_Ref180382723 \h  Figure III-12  and   REF _Ref180382745 \h  Figure
III-15  suggest a relationship between atrazine concentrations and flow
events, even it is a delayed response, the relationship is not as
evident in   REF _Ref181772715 \h  Figure III-13  and   REF
_Ref181076461 \h  Figure III-14 . One possible explanation is that
atrazine concentrations may be higher in some low-flow streams because
there is less receiving waters to dilute the atrazine load in runoff
waters. It is also possible that some unexplained feature of streambed
morphology could influence the ability to collect samples.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  12  Atrazine
concentrations and measured stream flow, NE-04, 2005

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  13  Atrazine
concentrations and measured stream flow, NE-05, 2005

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  14  Atrazine
concentrations and measured stream flow, NE-07, 2005

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  15  Atrazine
concentrations and measured stream flow, MO-01, 2005

Sampling Frequency / Auto-samples

The AEMP data set represents a robust and targeted monitoring data set
available for atrazine with 4-day grab samples augmented with more
frequent flow triggered auto-samples.  The AEMP site averaged 36 samples
per year for 37 of the 40 sites with an average of 17 samples per year
for the three sites (NE-04, NE-05, and NE-07) that experienced more
missing samples than the others.  On average, this equates to roughly 7
samples per month during the sample season (generally from April to
September) while the sites with missing samples averaged 3 samples per
month during the sample season.  The AEMP has been input into CASM in
order to determine whether community level effects can be expected given
the duration and magnitude of atrazine exposures in each chemograph.
Even with the robust nature of this data, CASM requires a complete 365
day chemograph for estimating effects levels.  Thus each AEMP chemograph
is augmented by interpolation between sample events and extrapolation to
estimate atrazine concentrations prior to the first sample and after the
last sample.

Sampling frequency is a critical component of uncertainty analysis for
this evaluation because atrazine exposure and interpretation of
potential community responses relative to the microcosm and mesocosm
studies is driven by the magnitude and duration of exposure.  In this
construct the means of “filling in” (e.g. interpolation or
imputation) missing data is critical and thus the more data that is
missing (e.g. less frequent sampling) the greater uncertainty there will
be in determining if the LOC has been exceeded based on increasing
uncertainty in the magnitude and duration of atrazine exposure.  Thus an
understanding of the inherent uncertainty in the AEMP data is critical
to evaluating the risk conclusions drawn from CASM.  Sensitivity
analysis of the CASM model informed model uncertainty, however, this
analysis did not address the potential uncertainty in the AEMP data.
Understanding uncertainty associated with the AEMP data due to
interpolation, missing samples, and less frequent sampling is critical
and can assist in interpreting uncertainty in the model estimates and
the amount of conservativeness of overall risk conclusions.  Also, a
better understanding of how these factors might influence model output
for the AEMP data can inform how other monitoring data not collected as
part of the AEMP might, or might not be reasonably interpreted when run
in CASM

For the AEMP, the method chosen to interpolate or impute a ‘missing’
value is the stair step approach whereby a concentration at a given
sampling event is carried forward across the four day window to the next
sampling date.  Estimating atrazine concentrations pre- and
post-sampling occurs by extrapolation of the first sample backwards to
the beginning of the year and the last sample forward to the end of the
year except where a chemograph represents a second or third year at a
site, in which case the last sample result is carried beyond the end of
the year to the first sampling event of the following year.  Thus even
though this represents a robust data set, there is uncertainty
associated with the means of estimating the chemographs to obtain a 365
day profile.

The impact of the infilling process was evaluated to determine how
important the assumptions used to infill are on CASM output (e.g. risk
conclusions for each site).  First, sampling frequency was evaluated by
simulating a less frequent sampling design using the AEMP data.  The
ramifications of this are then compared with selected sites within the
AEMP where sampling frequency was less (e.g. the three Nebraska sites
with low flow conditions and missing data) and how missing samples or
less robust data might influence uncertainty.  Second, an alternative
approach to assessing uncertainty in different sampling frequencies was
considered.  Third, alternative approaches to infilling are considered
in the context of uncertainty.

Evaluation of Sampling Frequency Using AEMP Data

As noted in the analysis of the three Nebraska sites there will clearly
be instances where less robust monitoring data can be encountered.  The
following evaluations were completed to determine how important sampling
frequency is in the use of the CASM model relative to AEMP data, to
determine if uncertainty with missing samples can be characterized, and
by extension if monitoring data that is less robust can and/or should be
evaluated using CASM.

The CASM model uses 365 day chemographs as the exposure profile input
and thus even for the robust AEMP data some level of interpolation, or
infilling of missing data points, is required.  The method chosen for
interpolation in CASM is a stair-step approach by which each sample
result is carried forward across the un-sampled days until the next
sample result is reached.  The infilling then proceeds across the
profile until the last sample is reached.  How dates prior to the first
sample event and after the final sample event in a given year are
imputed raises an issue of uncertainty.  In their analysis, Syngenta
assumed that all days prior to the first event and after the last event
were 0.  The Agency’s approach is predicated on an assumption
(supported by other data sets such as the Heidelberg College data; Volz
et al, 2007) that there will be quantifiable exposure year round
including these early and late time frames.  Thus, the Agency has
estimated the last days of the 365 day profile with the results from the
last day sampled.  For the days prior to the first sample we have
estimated using two methods.  First, if no sampling has occurred in the
previous year, the result of the first sample date was assigned to every
day extending backwards to January 1.  If a previous year’s sampling
has occurred, the last value from the previous year is carried forward
to the first date of the following year.  Comparison of rolling average
estimation and CASM risk conclusions indicate that the Agency’s
approach increases the atrazine exposure compared to that used by the
registrant and increases the overall exposure profile slightly but does
not change the ultimate conclusion for the AEMP sites (the same sites
are identified to exceed the LOC using both approaches).

The second issue associated with interpretation of the AEMP data in CASM
is interpolation between sampling events and the sampling frequency of
individual chemographs.  CASM requires a 365 day profile and will
“interpolate” between sampling events where data is lacking.
Options in CASM for interpolation include linear and stair-step
interpolation between sampling events.  In both instances, there is a
question of whether peak concentrations are being missed by 4 day grab
samples and what impact potentially missed pulses of atrazine exposure
might be having on model output based on a magnitude and duration of
exposure.

In order to test the importance of sampling frequency the Agency
conducted an analysis where data from one of the AEMP sites was reduced
to a 12-day grab sample profile.  First, the first sampling date was
intentionally skewed to miss the peak concentration from the 4 day grab
sample data.  Then an alternative 12 day profile was created which
included the peak concentration.  14-day, 30-day, 60-day, and 90-day
rolling average concentrations for each of the three profiles were then
calculated.

Comparisons for 2004 chemograph for IN-04 show a wide range in peak and
average concentrations depending on the length and timing of sampling
events (  REF _Ref182191164 \h  Table III-13 ). Missing the peak
concentration of 78 ppb resulted in much lower longer-term average
concentrations than those found with the 4-day grab samples while
hitting the peak concentration resulted in higher longer-term average
concentrations.  This analysis suggests that sampling frequency and when
the samples are collected can have a significant impact on how the
results are interpreted.  In this case, neither of the alternative 12
day grab sample profiles exceeded the LOC although the 12 day profile
that captured the peak was 3.8% compared to the 4% LOC and was well
within the 2x multiplication factor uncertainty bound for CASM.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  13  Comparison
of estimated atrazine concentrations between 4-day and 12-day grab
samples, IN-04, 2004

	Atrazine concentration (ug/L) and CASM SSI% for various average periods

	Peak	14-da	21-da	30-da	60-da	90-da	Annual Avg.	SSI%

4-day grab samples w/stair step interpolation	78.08	23.81	16.33	12.05
6.35	4.37	1.20	1.7

12-day grab samples w/peak EEC missed	2.26	2.04	1.60	1.48	1.06	0.88	0.34
0.3

12-day grab samples w/peak EEC captured	78.08	67.38	45.97	33.20	17.29
11.68	3.03	3.8

Auto Sample Results

In addition to 4-day grab samples, 13 sites were instrumented with
auto-samplers.  The auto-samplers were installed to collect 8-hour
composite samples triggered by increased flow events within a stream
site.  A total of 25 site years of data was collected using the auto
samplers.  These data were used to augment the 4-day grab time series to
investigate the impact of grab samples on uncertainty in the CASM runs
using 4-day samples. Both the 4-day grab sample chemographs and the
chemograph created from 4-day grab samples augmented with autosample
results were interpolated/extrapolated to create 365 day profiles.

In order to conduct this analysis the 4-day time series was compared
with the auto-sample results for each site-year of data and the
auto-sample results were added to the time series.  For days with more
than one auto-sample result the highest value from that day was used as
though that value was the grab sample for that day.  The auto-sample
results were compared against the 4-day grab samples and where an
auto-sample event occurred on the same day as a 4-day grab sample the
highest of the two sample types was used in the distribution.  If the
auto-sample event occurred on a day when no 4-day grab sample was
collected then the auto-sample result was placed in the time series on
the day it was collected.  Data interpolation then proceeded as with the
4-day samples with the exception being that if there were un-sampled
days after an auto-sample event these days were assumed to have the same
value as the auto-sample result.  In essence, the auto-sample results
were used to fill exposure gaps between the 4-day grabs and then used as
part of the stair-step interpolation process in the same manner as the
4-day grab sample interpolation.

In order to test the robustness of the 4 day samples an analysis was
conducted whereby various rolling average concentrations (  REF
_Ref182191200 \h  Table III-14 ) were calculated for each site by year
for the 4 day grab samples augmented with auto-samples (the 4 day grab
sample rolling averages and SSI% were summarized previously in   REF
_Ref182368984 \h  Table III-5 .

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  14  4-day Grab
Samples Augmented with Auto Samples - Rolling Averages (ppb)

	Maximum atrazine concentration, ug/L (ppb) over averaging periods of

Site	Year	Peak	14 days	21 days	30 days	60 days	90 days	365 days

IA 02	2004	5.37	2.41	1.83	1.84	1.26	0.92	0.17

IA 02	2005	5.53	2.14	1.53	1.39	0.85	0.63	0.58

IL 06	2004	5.26	1.07	0.92	0.74	0.53	0.42	0.19

IL 06	2005	0.23	0.15	0.14	0.13	0.12	0.11	0.09

IL 08	2005	24.67	9.22	7.11	5.82	3.53	2.61	0.55

IL 08	2006	50.70	11.74	8.06	10.06	5.54	3.78	0.84

IN 06	2005	7.23	3.85	2.93	2.31	1.22	0.88	0.24

IN 06	2006	24.30	5.73	4.79	3.87	2.39	1.79	0.47

IN 09	2005	34.49	8.81	6.08	5.40	3.45	2.46	0.40

IN 09	2006	13.80	4.27	3.13	2.85	1.93	1.37	0.31

IN 11	2005	237.50	65.13	44.41	31.51	16.18	11.32	3.80

IN 11	2006	15.89	5.90	4.36	3.34	1.92	1.37	0.83

MN 01	2005	15.03	5.10	3.62	2.61	1.41	1.04	0.33

MN 01	2006	0.22	0.16	0.14	0.13	0.12	0.11	0.10

MO 01	2004	65.94	28.83	21.92	22.08	15.43	11.10	3.20

MO 01	2005	182.75	78.06	54.19	45.66	26.98	19.18	4.98

MO 01	2006	106.00	45.11	39.06	30.20	17.01	11.69	3.09

MO 03	2006	20.31	5.00	3.82	3.12	2.10	1.68	0.86

NE 04	2006	125.00	21.08	14.10	13.98	8.00	5.47	1.45

NE 05	2006	6.76	5.29	4.49	3.94	3.18	2.22	0.63

NE 06	2004	10.99	3.08	2.75	2.20	1.70	1.56	0.45

NE 06	2005	36.13	19.29	13.01	10.85	5.79	3.95	1.05

NE 06	2006	0.13	0.11	0.11	0.10	0.10	0.10	0.10

OH 03	2004	21.50	8.14	7.60	6.97	4.25	2.86	0.74

OH 03	2005	15.84	4.06	2.78	2.43	1.29	0.90	0.28

Once the chemographs were augmented with auto-sample results and
interpolated/extrapolated to a 365 day profile using the stair-step
approach each data set was input into CASM where a SSI% and
multiplication factor (MF) was estimated.  The SSI% and MF for the
auto-sample augmented chemographs were then compared with the original
chemograph SSI% and MF to understand what influence the incorporation of
continuous auto-sample results into the profile had on model behavior (
REF _Ref182191236 \h  Table III-15 ).  While none of the auto-sample
augmented profiles exceeded the 4% SSI LOC (except where the original
chemograph exceeded) the overall influence on model response was an
average 64% increase in SSI% and a 18% decrease in MF across all sites
with auto-samplers.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  15  Percent
Difference Between Grab Averages and Auto Sample Adjusted Averages using
CASM SSI%

Site Name	Auto Sampled Sites	Four Day Sites	Percent Difference

	AD365	MF	AD365	MF	AD365	MF

IA02 2004	0.35	12.22	0.14	18.75	150	-35

IA02 2005	0.33	14.06	0.28	16.13	18	-13

IL06 2004	0.27	15.25	0.14	21.00	93	-27

IL06 2005	0.00	13.32	0.00	27.16	0	-51

IL08 2005	1.13	3.63	0.61	5.70	85	-36

IL08 2006	1.91	2.87	1.62	3.72	18	-23

IN06 2005	0.37	11.47	0.28	14.45	32	-21

IN06 2006	0.87	5.44	0.68	6.50	28	-16

IN09 2005	1.19	4.16	0.56	7.72	113	-46

IN09 2006	0.64	7.09	0.43	9.80	49	-28

IN11 2005	5.64	0.71	5.09	0.78	11	-9

IN11 2006	1.32	3.50	0.53	7.81	149	-55

MN01 2006	0.00	20.40	0.00	20.40	0	0

MN01 2005	1.15	3.84	0.22	13.75	423	-72

MO01 2004	4.44	0.85	5.04	0.70	-12	22

MO01 2005	6.80	0.53	6.31	0.57	8	-7

MO01 2006	4.43	0.87	4.48	0.84	-1	3

MO03 2006	0.96	4.78	0.78	5.41	23	-12

NE04 2006	2.14	2.10	0.44	7.97	386	-74

NE05 2006	0.78	4.54	1.05	2.77	-26	64

NE06 2004	0.54	7.31	0.53	7.13	2	3

NE06 2005	1.94	2.85	2.04	2.75	-5	4

NE06 2006	0.00	20.49	0.00	20.49	0	0

NE07 2006	0.55	8.46	na	na	na	na

OH03 2004	1.41	3.91	1.46	3.66	-3	7

OH03 2005	0.54	11.47	0.36	15.38	50	-25

Average	64	-18

In general, overall rolling average concentrations increased across all
sites and the overall change in CASM SSI% was 64% across all sites.  The
analysis indicates that incorporation of continuous sampling is likely
to increase the overall exposure profile vis. the profile presented by
grab samples, and that use of less frequent [grab] samples is likely to
underestimate exposure. However, none of the revised chemographs
exceeded the LOC (4% SSI) except for sites already above the LOC (e.g.
MO 01).

Syngenta PRZM Augmentation

In Snyder et al (2007) Syngenta presented additional analysis on the
importance of data interpolation.  This analysis involved augmentation
of the 4-day grab sample profile using the Pesticide Root Zone Model
(PRZM) (Carsel et al, 1998) to predict edge of field concentrations with
site-specific climate data and application information.   Predicted peak
concentrations were matched to 4-day grab sample results to establish
dilution and dissipation factors.  The remaining PRZM concentrations not
corresponding with 4-day grab samples were then “augmented” to the
time series profile.  Syngenta completed this analysis for all 40 sites
and years monitored (80 site years in all) available at the time of
Snyder (2007).  Each PRZM augmented chemograph was then run through CASM
to generate rolling average concentrations for 14-day, 30-day, 60-day,
and 90-day durations as well as calculated SSI%.  More detail on the
methodology and assumptions employed in this evaluation are presented in
Snyder (2007).  A graphical representation of how the analysis was
performed is presented in   REF _Ref182215828 \h  \* MERGEFORMAT  Figure
III-16 .

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  16  Graphical
Representation of PRZM Augmentation of a 4-Day Grab Sample Time series
(taken from Syngenta Presentation Titled “Atrazine Ecological
Monitoring Program review” dated December 14, 2006)

Additional analysis of this evaluation was conducted by the Agency and
consisted of comparing the rolling average concentrations and SSI% from
the original un-adjusted chemographs and the PRZM augmented chemographs.
 The SSI% are presented for completeness but not used for further
analysis because the SSI% were created using a different version of CASM
than currently being evaluated and because the majority of the 80
chemographs remained unchanged at 0% SSI (24 or 30% of all site years)
or increased but the original SSI% was 0 (36 or 45% of all site years)
and no percent difference could be calculated.  For all 80 chemographs
the percent change in rolling average concentrations and SSI% (where
values greater than 0 were available for both original and augmented
chemographs) were calculated.  The PRZM augmented rolling average
concentrations and SSI% is presented in   REF _Ref182215973 \h  Table
VII-4  in Appendix 2 while the percent change from the original rolling
average and SSI is summarized   REF _Ref182215941 \h  Table III-16 .
The original rolling average and SSI% are presented in   REF
_Ref181177887 \h  Table III-5 .

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  16  Summary of
Percent Differences Between Original Rolling Average Concentrations and
SSI% and revised Rolling Average Concentrations (ppb) and SSI% for PRZM
Augmented Chemographs from Snyder, et al (2007)

Site	Year	% Difference in the

14-day rolling average	30-day rolling average	60-day rolling average
90-day rolling average	SSI%

IA 01	2004	180	273	323	284	a

IA 01	2005	842	767	587	387	b

IA 02	2004	604	535	485	402	a

IA 02	2005	100	135	235	210	a

IL 01	2004	22	43	56	48	b

IL 01	2005	940	1180	1320	900	a

IL 02	2004	367	251	252	235	a

IL 02	2005	32	9	23	62	b

IL 03	2005	384	437	368	443	a

IL 03	2006	287	292	273	435	a

IL 04	2005	103	163	122	113	b

IL 04	2006	105	222	166	144	a

IL 05	2004	66	153	173	182	43

IL05	2005	459	430	267	300	b

IL 06	2004	524	763	652	560	a

IL 06	2005	1095	1040	710	460	a

IL 07	2004	78	120	129	122	a

IL 07	2005	653	610	302	348	a

IL 08	2005	61	94	137	129	a

IL 08	2006	58	40	50	78	140

IL 09	2004	54	75	72	70	a

IL 09	2005	16	57	61	95	600

IN 01	2004	232	158	223	217	a

IN 01	2005	226	220	281	276	a

IN 02	2004	32	60	65	55	a

IN 02	2005	-7	-8	-3	1	b

IN 03	2005	-2	-1	0	11	b

IN 03	2006	7	22	66	64	a

IN 04	2004	26	49	68	64	24

IN 04	2005	388	338	443	437	a

IN 04	2006	101	80	83	78	a

IN 05	2004	2	5	17	20	-44

IN 05	2005	2	25	-3	15	a

IN 05	2006	-31	-19	-6	-1	-68

IN 06	2005	19	18	12	27	b

IN 06	2006	24	27	15	31	b

IN 07	2005	-29	-16	-8	-4	-69

IN 07	2006	65	57	121	121	a

IN 08	2005	0	0	0	10	b

IN 08	2006	25	19	25	37	a

IN 09	2005	213	184	245	225	a

IN 09	2006	63	70	89	107	b

IN 10	2005	-1	30	43	40	b

IN 10	2006	49	42	42	49	a

IN 11	2005	-30	-29	-23	-21	-15

IN 11	2006	24	58	71	66	b

MO 01	2004	-29	-24	-14	-14	-15

MO 01	2005	-16	-13	-10	-33	-41

MO 01	2006	-8	-2	10	10	-5

MO 02	2004	-5	-1	1	2	-2

MO 02	2005	5	17	25	21	144

MO 02	2006	-13	-8	0	-2	-4

MO 03	2004	-18	-12	-6	-4	-6

MO 03	2005	20	24	21	18	b

MO 03	2006	125	69	44	80	b

NE 01	2004	3	4	3	6	b

NE 01	2005	26	30	42	40	a

NE 02	2005	-5	-9	-1	0	a

NE 02	2006	-2	13	14	14	6

NE 03	2004	557	301	227	220	a

NE 03	2005	5	12	21	19	b

NE 06	2004	92	49	67	44	a

NE 06	2005	-29	-19	-16	-16	-71

NE 06	2006	1440	1310	640	420	a

KY 01	2005	25	27	21	33	b

KY 01	2006	7	38	67	79	-71

KY 02	2005	23	17	17	16	a

KY 02	2006	60	82	87	82	b

MN 01	2005	97	172	186	185	a

MN 01	2006	1170	1460	950	770	a

OH 01	2004	-9	6	16	19	b

OH 01	2005	108	86	130	82	b

OH 02	2005	8	13	50	74	300

OH 02	2006	23	26	44	43	b

OH 03	2004	-1	5	45	41	a

OH 03	2005	42	115	102	105	a

OH 04	2005	-2	35	83	107	1600

OH 04	2006	123	171	230	233	a

TN 01	2005	-12	-6	5	5	b

TN 01	2006	1	11	-8	1	b

a – 0% SSI in both original and augmented chemographs

b – 0% SSI in original chemograph

Finally, the percent differences were ranked and percentile generated
for each duration and the SSI% separately.  In addition, a single
percentile distribution was created for all four rolling averages.  The
result of this analysis is presented in   REF _Ref182216184 \h  Table
III-17 .

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  17  Percentile
of Ranked Distribution of Percent Differences from Table III-16

	14-day rolling average	30-day rolling average	60-day rolling average
90-day rolling average	SSI%1	All rolling averages

average	151	162	145	132	122	148

median	26	42	61	66	-4	49

99th	1224	1340	1024	796	1410	1280

95th	842	767	640	443	650	653

90th	524	437	368	402	330	436

75th	108	163	186	185	67	171

50th	26	42	61	66	-4	49

25th	1	11	15	18	-42	10

10th	-12	-8	-3	0	-69	-6

5th	-29	-16	-8	-4	-71	-15

1st	-31	-25	-17	-24	-71	-29

1 – SSI% percent difference distributions are suspect due to limited
number of suitable data.

This overall analysis suggests that augmentation of the original 4-day
grab sample chemograph using a modified PRZM approach can increase
thresholds by roughly 50% at the median and as much as 150% for the
average.  This analysis is consistent with the comparison of grab sample
only chemographs versus grab sample augmented with autosample results
chemographs that suggests that 4 day grab sample profiles by themselves
are likely to miss peak exposures and thus underestimate overall
exposure. This does not suggest that all sites will increase by this
amount using this type of approach to augment a time series but it does
provide context to the uncertainty in using a stair step interpolation
method for the 4-day grab samples.

Evaluation of Alternate Sampling Frequency Strategies

Application of the CASM model beyond the AEMP will be highly influenced
by the type of sample program implemented as well as type of streams
being assessed.  Setting aside stream type which is addressed elsewhere,
the analysis presented above clearly suggests that sample programs less
robust than the AEMP are likely to miss peak concentrations and may
result in an under-estimation of exposure.  Many monitoring programs are
being conducted for the implementation of TMDL programs and for the
evaluation of the establishment of aquatic life criteria.  It is highly
unlikely that these sample designs will include the level of monitoring
captured in the AEMP.

One possible solution to the uncertainty associated with differing
sample frequencies may be found in the work of Crawford (2004).
Crawford (2004) used a robust data set of 10 years of atrazine data
collected by Heidelberg College Water Quality Laboratory (WQL) from four
streams in Ohio was linearly interpolated to provide a synthetic
chemograph of hourly observations.  Crawford (2004) conducted a Monte
Carlo assessment using the Heidelberg data to approximate different
sampling strategies ranging from a limited sampling consisting of one
sample per quarter to a more robust program of 10 samples per month.
The probabilistic assessment ran 1,000 iterations and error
distributions based on percent difference between computed and actual
mean, 90th, 95th, and 99th percentile concentrations.  As expected, the
analysis shows that as sampling frequency increases the difference
between actual and sampled values decreases.  A summary of the various
monitoring programs evaluated by Crawford (2004) and the +/- differences
at the 10th and 90th percentiles are presented in   REF _Ref181071264 \h
 Table III-18  and   REF _Ref181071320 \h  Table III-19 .

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  18  Sampling
frequency strategies used by Crawford (2004)

Abbreviation1	Samples per Year	Samples per Month during Runoff Period

Q	4	1

W7	7	1

W10	10	2

M	12	1

W14	14	2

W22	22	4

M2	24	2

W462	46	10

M4	48	4

M10	120	10

		1 – Detailed descriptions of sample strategies may be found in
Crawford, 2004

		2 – Sample strategy W46 most closely matches AEMP

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  19  Error
Distribution for Various Sample Strategies of Crawford (2004) for
Different Concentration Profiles

	Mean Concentration	90th % Concentration	95th % Concentration	99th %
Concentration

Site	Method	10th %	90th %	10th %	90th %	10th %	90th %	10th %	90th %

Honey Creek	Q	-77.18	98.70	-79.54	213.79	-91.46	48.56	-97.72	-36.24

	W7	-48.70	66.14	-70.15	65.97	-54.91	215.90	-84.40	0.12

	W10	-37.47	49.94	-36.07	42.27	-49.99	40.44	-74.46	14.50

	M	-49.68	69.82	-59.79	35.26	-50.32	214.69	-84.96	3.60

	W14	-38.71	35.94	-35.26	34.61	-50.16	33.22	-74.70	10.83

	W22	-21.74	20.06	-19.90	28.96	-30.36	21.97	-48.23	69.39

	M2	-38.80	47.36	-35.60	39.11	-50.12	33.49	-74.33	29.90

	W46	-9.02	10.85	-13.16	10.43	-17.01	19.05	-32.34	7.61

	M4	-23.29	15.22	-15.77	27.96	-28.89	19.00	-52.02	58.51

	M10	-12.82	7.34	-12.66	15.33	-14.31	18.32	-41.83	6.31

Maumee River	Q	-68.50	83.64	-76.56	105.18	-86.91	22.96	-93.66	-16.57

	W7	-29.16	26.37	-59.24	48.51	-33.64	52.33	-69.42	-0.69

	W10	-14.73	26.17	-31.84	26.35	-37.52	11.80	-43.41	5.09

	M	-27.13	26.67	-56.22	15.50	-31.51	63.34	-62.37	0.41

	W14	-17.66	16.70	-32.15	15.26	-36.16	8.66	-43.57	4.59

	W22	-9.18	8.72	-16.64	25.50	-17.12	9.12	-22.01	15.55

	M2	-17.55	17.23	-29.31	19.41	-38.95	12.69	-43.20	7.37

	W46	-3.42	6.30	-7.41	5.77	-8.18	6.37	-18.27	2.75

	M4	-7.33	6.56	-14.23	29.85	-14.12	8.31	-22.66	16.60

	M10	-2.99	1.30	-8.99	4.47	-6.51	6.40	-18.72	2.75

Rock Creek	Q	-79.62	138.39	-82.18	327.73	-92.57	71.38	-97.59	-38.85

	W7	-59.24	65.53	-72.11	176.81	-61.16	198.07	-87.08	-10.84

	W10	-41.47	44.17	-48.40	66.81	-56.96	48.68	-73.12	17.40

	M	-58.29	84.30	-68.55	70.73	-63.04	249.73	-89.30	4.42

	W14	-42.95	32.08	-41.81	45.60	-59.16	41.70	-73.23	6.54

	W22	-28.33	28.20	-27.45	41.90	-31.46	39.77	-54.50	66.09

	M2	-44.46	47.89	-38.90	60.55	-58.63	54.55	-72.53	33.67

	W46	-11.35	13.34	-18.34	17.04	-18.20	20.10	-29.03	9.24

	M4	-27.61	20.80	-26.37	34.03	-34.32	42.30	-55.79	58.42

	M10	-6.20	13.20	-5.64	17.09	-16.44	30.79	-32.79	5.09

Sandusky River	Q	-73.39	104.09	-78.24	173.84	-89.14	38.23	-95.61	-27.29

	W7	-44.29	54.28	-68.72	63.49	-46.41	94.62	-80.06	3.32

	W10	-26.11	34.14	-44.31	78.33	-47.57	31.43	-52.67	12.70

	M	-40.06	44.07	-64.72	33.93	-43.37	110.12	-76.09	3.76

	W14	-27.50	25.15	-44.05	40.15	-47.26	26.84	-54.63	10.40

	W22	-15.51	18.96	-17.63	41.40	-23.50	30.03	-34.11	31.54

	M2	-28.93	26.86	-39.14	44.16	-48.33	30.17	-53.83	15.93

	W46	-6.10	10.70	-8.57	13.21	-10.93	16.72	-21.08	9.50

	M4	-15.34	15.12	-17.87	41.97	-21.32	30.19	-33.32	28.75

	M10	-6.99	6.45	-8.54	12.56	-9.10	20.99	-22.11	11.39

The data and analysis in Crawford (2004) were evaluated with two
objectives.  First, can the error estimates provide additional
information on the uncertainty associated with a sampling frequency
comparable to the AEMP relative to a continuous 365 day exposure profile
(e.g. an analysis similar to the grab versus autosample analysis above)?
 In this case, it is assumed that sampling strategy W46 from Crawford
(2004) is the closest approximation of the AEMP.  It is then assumed
that the error estimate for this strategy at the 99th percentile
concentration can provide context to the autosample analysis above.  In
essence the Heidelberg data approximates the autosample augmented
profile described above.  The error estimates for W46 at the 99th
percentile range from 18 to 32%.  This comparison is limited because the
autosample analysis above includes interpolation between events while
the Crawford (2004) data does not estimate the intermittent peak
concentration.

In the summary above the potential for underestimation of exposure in a
sampling design such as W46 can vary by as much as 30% depending on
target concentration (e.g. 99th percentile of the yearly distribution)
keeping in mind that the error estimate represents the uncertainty in
the ability of a sampling strategy to capture a certain percentile of a
365 day time series.  This analysis suggests that it may be possible to
estimate how likely a given sampling strategy will capture a given
target exposure.  Interestingly, the error estimates for W46 are similar
to the estimated uncertainty seen when comparing 4-day grab sample
chemograph with autosample augmented chemographs.

An additional value in the Crawford (2004) data is the ability to
compare uncertainty in different sampling frequencies (e.g. monitoring
other than the AEMP).  As an example of how this approach might work the
following table (  REF _Ref181698327 \h  Table III-20 ) was created to
show the difference in error estimates for the various sampling
strategies for the annual mean concentration relative to W46.  The W46
sampling strategy evaluated by Crawford (2004) best represents the AEMP
and thus could form the foundation for comparison.  It should be noted
that the error estimates provided below do not include the influence of
interpolation/extrapolation used on the AEMP.  Thus the analysis is
useful for comparing uncertainty in raw data (e.g. data that has not
been interpolated to a 365 day profile) from sampling scheme similar to
the AEMP with other less frequent sample strategies that could
potentially be input in CASM.  This table provides a clearer picture of
the trend summarized above suggesting that as sampling frequency
decreases the error differential increases.  In this example, it could
be interpreted that a sampling strategy approximated by the monthly
sampling strategy (i.e. method M below) would be expected to
under-predict exposures relative to W46 by 40%.

It is suggested that the different error distributions surrounding the
various sampling strategies will allow for an estimation of the
uncertainty in less robust data.  For example, the percent difference
for one watershed (Sandusky River) in Crawford (2004) for the annual
time weighted mean concentration when sampling 10 times per month ranged
from -13% at the 5th percentile up to roughly +7% at the 95th percentile
while quarterly sampling yielded a range of -77% to 152%.  Additionally,
it should be possible to provide uncertainty bounds on less robust
monitoring data based on the differences seen in the Crawford data.  It
is speculated that these data can allow for an uncertainty factor to be
put in place which, when monitoring yields exposure duration profiles
within a certain bound of the LOC established by CASM, would be similar
to the MF approach based on the model sensitivity presented in Section
II.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  20  Variability
of estimated concentrations based on different sample frequencies
(Crawford, 2004)

Sample Site Name	Sample Method Code

	% Difference from W46

p10	p90	p10	p90

Honey Creek	Q	-77.18	98.70	-68	88

Honey Creek	W7	-48.70	66.14	-40	55

Honey Creek	W10	-37.47	49.94	-28	39

Honey Creek	M	-49.68	69.82	-41	59

Honey Creek	W14	-38.71	35.94	-30	25

Honey Creek	W22	-21.74	20.06	-13	9

Honey Creek	M2	-38.80	47.36	-30	37

Honey Creek	W46	-9.02	10.85	0	0

Honey Creek	M4	-23.29	15.22	-14	4

Honey Creek	M10	-12.82	7.34	-4	-4

Maumee River	Q	-68.50	83.64	-65	77

Maumee River	W7	-29.16	26.37	-26	20

Maumee River	W10	-14.73	26.17	-11	20

Maumee River	M	-27.13	26.67	-24	20

Maumee River	W14	-17.66	16.70	-14	10

Maumee River	W22	-9.18	8.72	-6	2

Maumee River	M2	-17.55	17.23	-14	11

Maumee River	W46	-3.42	6.30	0	0

Maumee River	M4	-7.33	6.56	-4	0

Maumee River	M10	-2.99	1.30	0	-5

Rock Creek	Q	-79.62	138.39	-68	125

Rock Creek	W7	-59.24	65.53	-48	52

Rock Creek	W10	-41.47	44.17	-30	31

Rock Creek	M	-58.29	84.30	-47	71

Rock Creek	W14	-42.95	32.08	-32	19

Rock Creek	W22	-28.33	28.20	-17	15

Rock Creek	M2	-44.46	47.89	-33	35

Rock Creek	W46	-11.35	13.34	0	0

Rock Creek	M4	-27.61	20.80	-16	7

Rock Creek	M10	-6.20	13.20	5	0

Sandusky River	Q	-73.39	104.09	-67	93

Sandusky River	W7	-44.29	54.28	-38	44

Sandusky River	W10	-26.11	34.14	-20	23

Sandusky River	M	-40.06	44.07	-34	33

Sandusky River	W14	-27.50	25.15	-21	14

Sandusky River	W22	-15.51	18.96	-9	8

Sandusky River	M2	-28.93	26.86	-23	16

Sandusky River	W46	-6.10	10.70	0	0

Sandusky River	M4	-15.34	15.12	-9	4

Sandusky River	M10	-6.99	6.45	-1	-4

At this point, this analysis is not proposed specifically as a metric
for others implementing lower frequency sampling to use but as an
outline of a potential approach.  Additional tools for interpolation of
daily concentrations from temporal monitoring may include kriging,
conditional simulation (stochastic sequential simulation), and  time
series interpolation techniques (Deutsch and Journel, 1998; Isaaks and
Srivastava, 1989).   As Crawford (2004) suggests, use of these tools
requires an analysis of data stationarity (constant mean and variance)
and development of correlation functions using semi-variagrams and
correlograms.  None of these approaches have been evaluated as an
alternative to the stair step method of interpolation at this time.

Conclusions

The AEMP represents one of the most robust and targeted monitoring data
sets for atrazine submitted to the Agency.  However, no large-scale
monitoring can provide continuous exposure data and the analysis above
suggests that there is still uncertainty with even a robust data set
such as the AEMP.  The comparison of sampling frequency results suggests
that sampling design is critical to incorporation of a chemograph (i.e.
monitoring data profile) into the LOC evaluation.  Monitoring programs
that are less robust than AEMP will have greater uncertainty in
capturing maximum peak and even some longer-term exposures.  Analysis of
grab versus auto sample results suggest that auto-samplers provide an
excellent choice for replacing or augmenting a grab sample program but
resources may limit this approach.  The auto sample analysis conducted
above suggests that some uncertainty is expected even with a robust data
set like the AEMP but the analysis also suggests that this type of
uncertainty can be quantified.  In addition, Syngenta has evaluated an
approach of augmenting monitoring data with PRZM estimates that also
suggests that even data as robust as the AEMP can underestimate
continuous exposure.  Finally, the analysis of sampling frequency using
the data from Crawford (2004) suggests that error bounds on a sampling
design may be appropriate for less robust monitoring data.  This could
be a key element to allowing application of uncertainty criteria to
monitoring results.

SAP Charge Questions on the Atrazine Monitoring Results

(1)	The monitoring program used a tool (WARP) designed to assess the
vulnerability of watersheds and stream segments to (1) identify
watersheds within the corn/sorghum growing region that are likely to be
most vulnerable to atrazine exposure and, (2) select sampling sites
within the watersheds that are likely to be more susceptible to atrazine
runoff.

Please comment on the use of WARP predictions for hydrologic units (HUC
10/11) to restrict the survey design to those HUCs in the upper 20th
percentile and then (1) to stratify by WARP predictions between 80th –
95th percentiles and above 95th percentile and (2) to select HUCs with
probability proportional to higher atrazine use rates.

Comment on the use of survey design population estimation approach for
estimating the number (and %) of HUCs that may have LOC exceedances.

(2)	Once the vulnerable HUC 10/11 watersheds were selected for
monitoring, specific monitoring sites were selected within each
watershed using criteria that were designed to maximize the potential
for selecting the streams most vulnerable to atrazine exposure.
However, with only a single point monitored per watershed, estimates of
within-HUC variability for detections of atrazine could not be
calculated. The resulting population estimates reflect variability
across watersheds but not within the monitored watersheds. Please
comment on this approach and identify and discuss any alternative
approaches to extend the results of the monitoring sites.

(3)	Three monitoring sites in NE experienced low- or no-flow conditions
that precluded sampling. While Hampton et al. (2007a) suggest that these
sites with intermittent or low flow are already stressed by other
factors, Meyer et al. (2007) indicate that such aquatic communities are
rich in diversity. The Agency has generated statistics for these three
sites as a separate stratum, however the meaning of these separate
population estimates is uncertain.

Please comment on whether the Agency should consider the low flow sites
and/or intermittent streams as a part of the population estimates or
treat them separately.

Please comment on whether the aquatic systems and exposure conditions of
the existing microcosm and mesocosm studies adequately represent these
low flow and/or intermittent stream communities.  If not, how could EPA
determine an LOC for low flow conditions?

(4)	The monitoring study sampled for atrazine concentrations at 4-day
intervals to characterize the atrazine chemograph in these low-order
Midwestern streams. The CASM_Atrazine model used these chemographs with
a stair-step interpolation between samples dates to relate atrazine
exposures in the streams to microcosm/mesocosm studies in order to
determine whether the exposures triggered LOC thresholds.

What other approaches for interpolation should be considered?  Given the
concentration-duration endpoint, how frequently must sampling occur to
appropriately capture the magnitude and durations of exposure associated
with atrazine?

Sensitivity analysis of CASM_Atrazine model inputs suggests that some
uncertainty bound on model results is appropriate.  The Agency used a 2x
multiplication factor from the model sensitivity analysis  to estimate
uncertainty in model output.  The sample frequency analysis indicates
that there is uncertainty associated with monitoring data that may not
be accounted for by the model uncertainty factor of 2x.  Given the
importance of sample frequency and interpolation, please comment on
whether consideration should be given to placing additional uncertainty
bounds on monitoring data to account for uncertainty in the ability of
the sampling strategy to capture the magnitude and duration of atrazine
exposures. Please provide any suggestions for how to proceed with this
approach.

 Approaches To Address The Question “Where Are The Waters That Are
Exceeding Effects-Based Atrazine Thresholds?”

From Watersheds To Waterbodies

The monitoring study design was based on a watershed vulnerability
assessment. As noted earlier, it addresses the question regarding how
many vulnerable watersheds in corn and sorghum growing regions in the
United States exceed the LOC for atrazine in at least one of its
subwatersheds.  However, ultimately, the value in the results of the
study is in identifying water bodies (stream miles) where atrazine
concentrations exceed the LOC.  While the initial results of the study
indicate that, for example, 9% (0 to 19%, based on a 95th percent
confidence bound) of the HUC-10 watersheds in this vulnerable tier had
streams that triggered the LOC for primary producers in multiple years,
that does not necessarily imply that all of the flowing waters in those
watersheds exceed the LOC. The results imply that conditions exist
within those HUC-10 watersheds that trigger an LOC in at least some of
the waters.

Since watershed-based approaches make sense for addressing nonpoint
sources of impact, that is where the follow-up actions should be
focused. Part of the follow-up action specified in the atrazine IRED
addendum was to identify other areas in the larger HUC-10 watershed that
might have similar conditions and concerns. This work is ongoing and
includes more detailed watershed study, discussions with local soil and
water quality experts, and additional monitoring.

The US EPA is exploring several options for addressing the question,
“How many stream miles/kilometers exceed the established LOC?” These
options are briefly summarized here and will be explored in more detail
in a future SAP. For now, the Agency is seeking feedback and
recommendations from the SAP on these or other viable options.

A first approach to identifying stream segments within the HUC-10
watersheds that exceed the LOC for atrazine would be to map out the
stream segments that met the initial selection criteria for monitoring
site locations (described in Section   REF _Ref181163359 \w \h
III.B.1.c)  and assume that, as a first cut, this represents the extent
of stream segments that similarly exceed the LOC.

  REF _Ref181414541 \h  Figure IV-1  (taken from Harbourt et al, 2004)
shows the extent of stream segments that met the sample selection
criteria with a breakdown of stream segment miles that met the criteria.

Figure   STYLEREF 1 \s  IV -  SEQ Figure \* ARABIC \s 1  1  Extent of
stream segments in the MO-02 HUC that met the sample selection criteria
(from Harbourt et al, 2004).

This approach has several limitations:

The stream segments were generated using digital elevation models
(Harbourt et al, 2004) and, thus, may not exactly correspond to actual
streams in the watershed. In addition, such an approach will be
dependent not only on the source of the data, but also on the scale.

This approach assumes that the specific criteria used for site selection
are valid predictors of atrazine loading in streams within a larger
HUC-10 watershed.

Limiting the focus to stream segments that meet the criteria fails to
address the extent to which atrazine loads that exceed the LOC continue
downstream at concentrations sufficient to exceed the LOC. It also
assumes that concentrations upstream of the identified segments are less
than those that would trigger the LOC.

Localized subwatershed characteristics are assumed to be captured by the
watershed-scale WARP vulnerability assessment approach.

The limitation regarding the synthetic nature of the stream segments and
data source/scale issues can be addressed by using a consistent
hydrography database. Since the initiation of this atrazine monitoring
study, an improved version of the National Hydrography Dataset (NHD),
called NHDPlus, has been released (HSC, 2006). NHDPlus includes an
increased set of value-added attributes and tools that allow for better
catchment characterization, stream network and flow evaluations, and
user-added capabilities (HSC, 2006). The US EPA plans to take advantage
of the catchment characteristics, flow network capabilities, and tool
development options to

Link the USGS WARP model to the NHDPlus stream segments and map WARP
values to stream segments (thus the vulnerability approach could be
applied ot the NHDPlus stream segments)

Accumulate and map stream catchment characteristics, such as land use,
to the stream segments

Evaluate subwatershed characteristics that may have an impact on
atrazine loading and identify stream segments with characteristics
similar to those monitored in the atrazine study (this is addressed in
more detail in the following section).

From The 40 Sampled Watersheds To The Larger Population Of Vulnerable
Watersheds

A challenge in addressing the third objective of the monitoring study
– identifying areas where higher atrazine exposures are likely to
occur – is to determine whether the watersheds that exceeded the LOC
in multiple years are randomly distributed within the 1,172 vulnerable
watersheds or represent a unique subset of conditions that may be
applied to other areas. This section presents two approaches the Agency
is taking to address this question.

Evaluation of WARP Parameters

A plot of the WARP values estimated for the most vulnerable 1,172 HUC-10
watersheds against the percent changes in the Steinhaus Similarity Index
(  REF _Ref181435840 \h  Figure IV-2 ) shows that the estimated 95th
percentile atrazine concentration from WARP does not distinguish between
the 10 site years that exceeded the LOC and the remaining 76 site years
that fell below the 4% SSI deviation. In fact, the only two sites (MO-01
and MO-02) that exceeded the LOC in multiple years of monitoring
represented the high and low end of WARP values. As noted in Section
REF _Ref182299701 \w \h  III.D.2 , no apparent trend was seen between
WARP values and %SSI for the 40 monitored watersheds. However, the
relationship may not be evident because only the upper end of the plot
(the watersheds represent the upper 80th percentile of watersheds) is
available.

Figure   STYLEREF 1 \s  IV -  SEQ Figure \* ARABIC \s 1  2  Comparison
of WARP values to %SSI deviation for the 1,172 vulnerable watersheds
(sites exceeding the 4% LOC trigger are labeled)

The WARP estimates used for the watershed vulnerability assessment
represent an average of atrazine use over the years 1998 through 2002
while the monitoring data represent more recent atrazine usage (for the
years 2004 through 2006). A plot of WARP based on 1998-2002 usage data
and WARP estimates based on more recent usage data (2001-2003) shows a
fair bit of scatter around the 1-to-1 line (  REF _Ref181437888 \h
Figure IV-3 ).

Figure   STYLEREF 1 \s  IV -  SEQ Figure \* ARABIC \s 1  3  Comparison
of original WARP values based on 1998-2002 use data with new WARP values
based on 2001-2003 use data

The variations in WARP estimates based on changes in usage data could be
expected because atrazine use is the major explanatory factor in the
regression models, accounting for 53 to 64 percent of the variability in
the monitoring data used for WARP development (Crawford, 2004). The
results illustrate the importance of atrazine use as a factor affecting
the vulnerability of water bodies to atrazine exposure. Changes in use
patterns both locally and at a larger watershed scale, could be expected
to result the variations in the WARP vulnerability estimates seen here.

The Agency plans to explore the use of the WARP model with atrazine use
zeroed out to evaluate the intrinsic watershed vulnerability independent
of use. In additition, coupling the WARP parameters with updated
atrazine use intensities using the NHDPlus might provide a useful means
of identifying potential vulnerable areas based on both current use and
on projections of changing use (for instance, increasing acreage planted
in corn because of demands for ethanol). This tool, coupled with the
results of the monitoring study, could serve as a useful tool to
identify areas to target future monitoring efforts for atrazine.

Evaluation Of Other Soil- And Hydrology-Related Parameters

Results of the monitoring program suggest that the chemographs that
triggered the LOC had relatively high concentrations with a prolonged
period of elevated exposures (see MO-01 and MO-01 in Appendix 2). In
their preliminary evaluation of these two Missouri sites that triggered
the LOC in multiple years, Syngenta noted that the prolonged periods of
elevated exposure might be attributable to the presence of ‘claypan’
soils in the region. This was supported by additional research conducted
by the USDA in the Central Claypan Major Land Resource Area (MLRA),
where the watersheds are located (Blanchard and Lerch, 2000; Lerch and
Blanchard, 2003).

The US EPA is exploring the potential for the presence of a shallow
restrictive layer, such as but not limited to the claypan soils in the
Central Claypan MLRA, to be a driving factor that results in high
atrazine loads to water bodies for prolonged periods of time. The impact
of such drainage-restrictive layers is two-fold:

A shallow depth to a drainage-restrictive layer reduces the water
storage capacity of the soil. During rainfall events, the soil overlying
the restrictive layer becomes saturated quickly, increasing the
frequency and volume of runoff events in comparison to deeper soils with
no restrictive layer in otherwise similar conditions. If the rainfall
occurs after atrazine has been applied to the field, the runoff could
include sufficient quantities of atrazine in the runoff to result in
high concentrations.

Subsurface drainage laterally over the restrictive layer would result in
a delayed baseflow, contributing additional loadings of atrazine over
time, prolonging the exposure period in the receiving water bodies

Syngenta proposes that the MO-01 and MO-02 are unique because of their
location in the Central Claypan MLRA and, as such, should be treated as
a separate stratum. The US EPA believes that MO-01 and MO-02 potentially
represent conditions where a shallow restrictive layer may result in
atrazine exposures that exceed the LOC. To test this, the Agency plans
to compare the extent, type, and depth of drainage-restrictive layers in
these 2 watersheds with the other 38 monitored watersheds to determine
whether distinctions are present. Where distinctions between the
watersheds are found, the US EPA plans to identify other areas where
conditions similar to those found in MO-01 and MO-02 may exist.

The USDA NRCS defines a restrictive layer as a “nearly continuous
layer that has one or more physical, chemical, or thermal properties
that significantly reduce the movement of water and air through the soil
or that otherwise provide an unfavorable root environment. Cemented
layers, dense layers, frozen layers, abrupt or stratified layers,
strongly contrasting textures, and dispersed layers are examples of soil
layers that are restrictions.” (USDA NRCS, 2007).

A number of specific restrictive layers are defined by the USDA NRCS.
The Agency is evaluating county-level spatial soil survey data (SSURGO)
to determine the extent, type, and depth of the following kinds of
layers that can restrict water movement vertically through the soil
profile:

Abrupt textural change

Strongly contrasting textural stratification

Dense material

Duripan/ Fragipan

Cemented horizon

Petroferric/ Petrocalcic/ Petrogypsic/ Placic

Plinthite/ Ortstein

Bedrock (lithic or paralithic)

A preliminary evaluation of SSURGO data in the Claypan MLRA indicate
that soils with an abrupt textural change located at shallow depths (<50
cm below the surface) are more predominant in and around MO-01 and MO-02
than in the other monitoring sites (  REF _Ref181460376 \h  Figure IV-4
). Other drainage restrictive layers, particularly dense material,
bedrock, and fragipans, are found in some of the other monitoring sites,
but are typically deeper than what occurs in and around MO-01 and MO-02.
  REF _Ref181460376 \h  Figure IV-4  illustrates that the soil factors
that may influence atrazine loadings to receiving water bodies are
likely to be distinguishable at subwatershed scales. Additional
distinctions, such as the extent of shallow restrictive layers under
cropland, will also be considered.

Figure   STYLEREF 1 \s  IV -  SEQ Figure \* ARABIC \s 1  4  Depth to
restrictive soil layers in MO-01 and MO-02, located in the Central
Claypan MLRA.

In addition to the depth to restrictive layer, the US EPA plans to use
SSURGO data to evaluate the relative percentage of hydrologic soil
groups (particularly D or C and D soils combined), soil drainage classes
(somewhat poorly and poorly drained soils), low hydraulic conductivity
soils, and soils with a high surface runoff potential. A preliminary
evaluation of SSURGO data for the counties that intersect the monitoring
sites shows that not only are the MO-01 and MO-02 watersheds dominated
by soils in the hydrologic soil group D class (  REF _Ref181461414 \h
Figure IV-5 ), but that the three Nebraska sites that exceeded the LOC
in one year as a result of low flow conditions also had higher
percentages of hydrologic group D soils than did the other four Nebraska
sites or other monitored sites (  REF _Ref181461424 \h  Figure IV-6 ).

Figure   STYLEREF 1 \s  IV -  SEQ Figure \* ARABIC \s 1  5  Distribution
of Hydrologic Group C and D Soils in MO-01 and MO-02, located in the
Central Claypan MLRA.

Figure   STYLEREF 1 \s  IV -  SEQ Figure \* ARABIC \s 1  6  Distribution
of Hydrologic Group C and D Soils in the Atrazine Monitoring Sites in
Nebraska, Missouri, Iowa, and western Illinois

While the preliminary evaluations suggest that potential differences in
soil and hydrology may exist between the two MO monitoring sites that
exceeded the LOC in multiple years, as well as between the three NE
monitoring sites that exceeded the LOC in one year as a result of low
flow, more extensive comparisons will require a substantial amount of
data processing and analysis. The US EPA plans a three step approach:

Compare soil/hydrologic properties among the 40 monitored watersheds,
both at the HUC-10 level and at the specific subwatershed level;

Extend the soil/hydrologic analysis to the 1,172 watersheds the
represented the most vulnerable tier of watersheds based on WARP

Because the soil/hydrologic parameters being investigated were not
explicitly part of the WARP parameters, further extend the evaluation to
the larger extent of atrazine use, in particular, the 5,860 watersheds
that intersected a county with atrazine use of greater than 0.25 lb
ai/county acre

These approaches are data and resource intensive. While SSURGO data are
now available for most of the counties in the US, they have to be
processed and compiled from individual county coverages to state and
regional coverages. Currently, the US EPA has only processed SSURGO for
the counties that encompass each of the 40 watersheds included in the
monitoring study. Before proceeding, the US EPA first intends to consult
with this SAP on the feasibility of such an assessment and to solicit
recommendations and suggestions on other parameters and/or approaches
that might be useful in extending the results of the monitoring study
beyond the 40 monitored watersheds in a manner that would be useful to
the US EPA for identifying other water bodies exceeding the LOC.

SAP Charge Questions Relating to Identifying Where Atrazine Exceedances
Are Likely to Occur

(1) 	While the monitoring study was based on a watershed vulnerability
assessment, the ultimate value is in identifying water bodies where
atrazine concentrations exceed the LOC. One approach is to use the
updated version of the National Hydrography Database (NHDPlus) and apply
the criteria used to select the monitoring locations to identify streams
that appear to have the potential to exceed the LOC.

Please comment on the strengths and weaknesses of the Agency’s
proposed approach for identifying streams within watersheds that
exceeded the LOC.

In what ways can the preliminary approach be improved?

Please recommend alternative approaches, if any, that may be better
suited to apply the watershed-based assessment to streams?

(2) 	In order to identify areas beyond the 40 study sites where higher
atrazine exposures are likely to occur, the Agency must determine
whether the watersheds that exceeded the LOC in multiple years are
randomly distributed within the 1,172 vulnerable watersheds or represent
a unique subset of conditions. If the latter and the conditions can be
identified, monitoring could be focused only in watersheds where those
conditions exist.The Agency has proposed evaluating WARP parameters and
other sub-watershed soil and hydrologic properties to determine the
extent to which the monitoring results can be used to identify other
water bodies exceeding the LOC.

To what extent can WARP be used to identify other watersheds of concern?
Given the influence of atrazine use on vulnerability and exposure,
please comment on whether the extrapolation should be limited to the
original 1,172 watersheds or include a broader atrazine use area?

Please comment on the soil and hydrology parameters the Agency is
evaluating for extrapolation to vulnerable watersheds. What additional
soil and hydrologic parameters should the Agency consider?

What additional approaches to the identification of watersheds that may
have atrazine levels that exceed the LOC should the Agency consider?

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