Preliminary Interpretation of the Ecological Significance of Atrazine
Stream-Water Concentrations Using a Statistically-Designed Monitoring
Program

In Support of an

Interim Reregistration Eligibility Decision

on Atrazine

Submitted to the FIFRA Scientific Advisory Panel

for Review and Comment

December 4 – 7, 2007

Prepared by the

Office of Prevention, Pesticides, and Toxic Substances

Office of Pesticide Programs

Environmental Fate and Effects Division

Washington, D. C.

Office of Research and Development

National Health and Environmental Effects Research Laboratory (NHEERL)

Mid-Continent Ecology Division

Duluth, MN

and

Western Ecology Division,

Corvalis, OR

November 15, 2007

Contents

  TOC \o "1-5" \h \z \u    HYPERLINK \l "_Toc182871016"  I. Introduction
  PAGEREF _Toc182871016 \h  8

  HYPERLINK \l "_Toc182871017"  A.	Purpose of the Scientific Advisory
Panel Meeting	  PAGEREF _Toc182871017 \h  8

  HYPERLINK \l "_Toc182871018"  B.	Background	  PAGEREF _Toc182871018 \h
 8

  HYPERLINK \l "_Toc182871019"  C.	Monitoring Study Objectives	  PAGEREF
_Toc182871019 \h  9

  HYPERLINK \l "_Toc182871020"  D.	Ecological Endpoint and Level of
Concern for Atrazine	  PAGEREF _Toc182871020 \h  10

  HYPERLINK \l "_Toc182871021"  1.	Freshwater Microcosm/Mesocosm Studies
on Atrazine	  PAGEREF _Toc182871021 \h  10

  HYPERLINK \l "_Toc182871022"  2.	Determining a Level of Concern Based
on Microcosm/Mesocosm Studies	  PAGEREF _Toc182871022 \h  11

  HYPERLINK \l "_Toc182871023"  E.	Atrazine Ecological Monitoring
Program	  PAGEREF _Toc182871023 \h  13

  HYPERLINK \l "_Toc182871024"  F.	Key Study Documents	  PAGEREF
_Toc182871024 \h  14

  HYPERLINK \l "_Toc182871025"  1.	CASM Model Development	  PAGEREF
_Toc182871025 \h  14

  HYPERLINK \l "_Toc182871026"  2.	Watershed Selection Process	  PAGEREF
_Toc182871026 \h  15

  HYPERLINK \l "_Toc182871027"  3.	Overview of Atrazine Monitoring Study
Results	  PAGEREF _Toc182871027 \h  15

  HYPERLINK \l "_Toc182871028"  4.	Sampling Frequency Analyses	  PAGEREF
_Toc182871028 \h  15

  HYPERLINK \l "_Toc182871029"  5.	GIS Approaches to Assessing Watershed
Vulnerability	  PAGEREF _Toc182871029 \h  16

  HYPERLINK \l "_Toc182871030"  G.	Issues To Be Addressed in Future SAP
Consultations	  PAGEREF _Toc182871030 \h  16

  HYPERLINK \l "_Toc182871031"  II. Use of a Community Simulation Model
for Extrapolation of Atrazine Levels of Concern for Exposure Time-Series
  PAGEREF _Toc182871031 \h  18

  HYPERLINK \l "_Toc182871032"  A.	Problem Definition and General
Strategy	  PAGEREF _Toc182871032 \h  18

  HYPERLINK \l "_Toc182871033"  1.	Nature of Assessment Problem
PAGEREF _Toc182871033 \h  18

  HYPERLINK \l "_Toc182871034"  2.	General Strategy for Assessment
Methodology	  PAGEREF _Toc182871034 \h  20

  HYPERLINK \l "_Toc182871035"  B.	Model Formulation and
Parameterization	  PAGEREF _Toc182871035 \h  22

  HYPERLINK \l "_Toc182871036"  1.	General Model Formulation and
Parameterization for Reference Conditions	  PAGEREF _Toc182871036 \h  22

  HYPERLINK \l "_Toc182871037"  2.	Incorporation of Toxicity Information
into the Model	  PAGEREF _Toc182871037 \h  23

  HYPERLINK \l "_Toc182871038"  C.	Model Implementation and Example
Application	  PAGEREF _Toc182871038 \h  27

  HYPERLINK \l "_Toc182871039"  1.	Model Effects Index Selection
PAGEREF _Toc182871039 \h  27

  HYPERLINK \l "_Toc182871040"  2.	LOC Determination for Base Model
Configuration	  PAGEREF _Toc182871040 \h  28

  HYPERLINK \l "_Toc182871041"  3.	Example Applications	  PAGEREF
_Toc182871041 \h  30

  HYPERLINK \l "_Toc182871042"  D.	Sensitivity Analysis	  PAGEREF
_Toc182871042 \h  31

  HYPERLINK \l "_Toc182871043"  1.	Overview	  PAGEREF _Toc182871043 \h
31

  HYPERLINK \l "_Toc182871044"  2.	Model Effects Index	  PAGEREF
_Toc182871044 \h  32

  HYPERLINK \l "_Toc182871045"  3.	Exposure Start Date	  PAGEREF
_Toc182871045 \h  33

  HYPERLINK \l "_Toc182871046"  4.	Environmental Parameters	  PAGEREF
_Toc182871046 \h  34

  HYPERLINK \l "_Toc182871047"  5.	Plant Toxicity EC50s	  PAGEREF
_Toc182871047 \h  34

  HYPERLINK \l "_Toc182871048"  6.	Chronic, Low-Level Exposures
PAGEREF _Toc182871048 \h  37

  HYPERLINK \l "_Toc182871049"  7.	Slope of the Toxicity Curve	  PAGEREF
_Toc182871049 \h  37

  HYPERLINK \l "_Toc182871050"  E.	Summary and Next Steps	  PAGEREF
_Toc182871050 \h  37

  HYPERLINK \l "_Toc182871051"  F.	SAP Charge Questions on the Use of
the CASM Model	  PAGEREF _Toc182871051 \h  37

  HYPERLINK \l "_Toc182871052"  III. Atrazine Monitoring Program For
Ecological Effects: Determining The Extent Of Waters Exceeding
Effects-Based Thresholds For Atrazine	  PAGEREF _Toc182871052 \h  39

  HYPERLINK \l "_Toc182871053"  A.	Monitoring Study Objectives	  PAGEREF
_Toc182871053 \h  39

  HYPERLINK \l "_Toc182871054"  B.	Study Design	  PAGEREF _Toc182871054
\h  40

  HYPERLINK \l "_Toc182871055"  1.	Site Selection	  PAGEREF
_Toc182871055 \h  40

  HYPERLINK \l "_Toc182871056"  a)	Watershed Vulnerability Assessment
PAGEREF _Toc182871056 \h  40

  HYPERLINK \l "_Toc182871057"  b)	Representative Sampling of Vulnerable
Watersheds	  PAGEREF _Toc182871057 \h  45

  HYPERLINK \l "_Toc182871058"  (1)	Target Population	  PAGEREF
_Toc182871058 \h  45

  HYPERLINK \l "_Toc182871059"  (2)	Survey Design	  PAGEREF
_Toc182871059 \h  46

  HYPERLINK \l "_Toc182871060"  (3)	Sample Size And Analysis Weights
PAGEREF _Toc182871060 \h  47

  HYPERLINK \l "_Toc182871061"  (4)	Sampled Population	  PAGEREF
_Toc182871061 \h  48

  HYPERLINK \l "_Toc182871062"  (5)	Monitoring Question	  PAGEREF
_Toc182871062 \h  50

  HYPERLINK \l "_Toc182871063"  c)	Selection Of Monitoring Site Within
The Watershed	  PAGEREF _Toc182871063 \h  50

  HYPERLINK \l "_Toc182871064"  2.	Sampling/Monitoring Design	  PAGEREF
_Toc182871064 \h  51

  HYPERLINK \l "_Toc182871065"  C.	Monitoring Results	  PAGEREF
_Toc182871065 \h  52

  HYPERLINK \l "_Toc182871066"  1.	Atrazine Chemographs	  PAGEREF
_Toc182871066 \h  52

  HYPERLINK \l "_Toc182871067"  2.	Analyzing Atrazine Chemographs: LOC
Exceedances	  PAGEREF _Toc182871067 \h  54

  HYPERLINK \l "_Toc182871068"  D.	Percentage Of Watersheds Exceeding
The LOC Threshold	  PAGEREF _Toc182871068 \h  57

  HYPERLINK \l "_Toc182871069"  1.	Statistical Analysis Based On GRTS
PAGEREF _Toc182871069 \h  57

  HYPERLINK \l "_Toc182871070"  a)	Methods	  PAGEREF _Toc182871070 \h
57

  HYPERLINK \l "_Toc182871071"  b)	Assumptions	  PAGEREF _Toc182871071
\h  58

  HYPERLINK \l "_Toc182871072"  c)	Results	  PAGEREF _Toc182871072 \h
59

  HYPERLINK \l "_Toc182871073"  2.	Population Estimates	  PAGEREF
_Toc182871073 \h  59

  HYPERLINK \l "_Toc182871074"  a)	Excluded Sites	  PAGEREF
_Toc182871074 \h  59

  HYPERLINK \l "_Toc182871075"  b)	Sites That Did Not Exceet the LOC
PAGEREF _Toc182871075 \h  60

  HYPERLINK \l "_Toc182871076"  c)	Sites That Exceeded the LOC in At
Least 2 Years	  PAGEREF _Toc182871076 \h  60

  HYPERLINK \l "_Toc182871077"  d)	Sites That Exceeded the LOC in 1 Year
  PAGEREF _Toc182871077 \h  60

  HYPERLINK \l "_Toc182871078"  e)	Uncertain Sites	  PAGEREF
_Toc182871078 \h  60

  HYPERLINK \l "_Toc182871079"  f)	Reliability of WARP estimates for
HUCs and Sub-watersheds	  PAGEREF _Toc182871079 \h  62

  HYPERLINK \l "_Toc182871080"  g)	Comparison of Alternative WARP
estimates for HUCs	  PAGEREF _Toc182871080 \h  65

  HYPERLINK \l "_Toc182871081"  E.	Sensitivity / Uncertainty Analyses
PAGEREF _Toc182871081 \h  70

  HYPERLINK \l "_Toc182871082"  1.	Weather	  PAGEREF _Toc182871082 \h
71

  HYPERLINK \l "_Toc182871083"  2.	Flow rates / Low flow sites	  PAGEREF
_Toc182871083 \h  72

  HYPERLINK \l "_Toc182871084"  a)	Low Flow Sites	  PAGEREF
_Toc182871084 \h  72

  HYPERLINK \l "_Toc182871085"  b)	Flow Measured at the Monitoring Sites
  PAGEREF _Toc182871085 \h  73

  HYPERLINK \l "_Toc182871086"  3.	Sampling Frequency / Auto-samples
PAGEREF _Toc182871086 \h  77

  HYPERLINK \l "_Toc182871087"  a)	Evaluation of Sampling Frequency
Using AEMP Data	  PAGEREF _Toc182871087 \h  78

  HYPERLINK \l "_Toc182871088"  b)	Auto Sample Results	  PAGEREF
_Toc182871088 \h  80

  HYPERLINK \l "_Toc182871089"  c)	Syngenta PRZM Augmentation	  PAGEREF
_Toc182871089 \h  82

  HYPERLINK \l "_Toc182871090"  d)	Evaluation of Alternate Sampling
Frequency Strategies	  PAGEREF _Toc182871090 \h  86

  HYPERLINK \l "_Toc182871091"  e)	Conclusions	  PAGEREF _Toc182871091
\h  91

  HYPERLINK \l "_Toc182871092"  F.	SAP Charge Questions on the Atrazine
Monitoring Results	  PAGEREF _Toc182871092 \h  91

  HYPERLINK \l "_Toc182871093"  IV. Approaches To Address The Question
“Where Are The Waters That Are Exceeding Effects-Based Atrazine
Thresholds?”	  PAGEREF _Toc182871093 \h  93

  HYPERLINK \l "_Toc182871094"  A.	From Watersheds To Waterbodies
PAGEREF _Toc182871094 \h  93

  HYPERLINK \l "_Toc182871095"  B.	From The 40 Sampled Watersheds To The
Larger Population Of Vulnerable Watersheds	  PAGEREF _Toc182871095 \h
95

  HYPERLINK \l "_Toc182871096"  1.	Evaluation of WARP Parameters
PAGEREF _Toc182871096 \h  95

  HYPERLINK \l "_Toc182871097"  2.	Evaluation Of Other Soil- And
Hydrology-Related Parameters	  PAGEREF _Toc182871097 \h  97

  HYPERLINK \l "_Toc182871098"  C.	SAP Charge Questions Relating to
Identifying Where Atrazine Exceedances Are Likely to Occur	  PAGEREF
_Toc182871098 \h  102

  HYPERLINK \l "_Toc182871099"  V. References	  PAGEREF _Toc182871099 \h
 103

  HYPERLINK \l "_Toc182871100"  VI. Appendix 1: Microcosm and Mesocosm
Studies Used in CASM_Atrazine	  PAGEREF _Toc182871100 \h  114

  HYPERLINK \l "_Toc182871101"  VII. Appendix 2: Summary of Atrazine
Monitoring Data	  PAGEREF _Toc182871101 \h  126

  HYPERLINK \l "_Toc182871102"  VIII. Appendix 3: Comparisons of
precipitation during monitoring years to historical averages	  PAGEREF
_Toc182871102 \h  135

  HYPERLINK \l "_Toc182871103"  1.	IA-01, 2004-05	  PAGEREF
_Toc182871103 \h  136

  HYPERLINK \l "_Toc182871104"  2.	IA-02, 2004-05	  PAGEREF
_Toc182871104 \h  138

  HYPERLINK \l "_Toc182871105"  3.	IL-01, 2004-05	  PAGEREF
_Toc182871105 \h  140

  HYPERLINK \l "_Toc182871106"  4.	IL-02, 2004-05	  PAGEREF
_Toc182871106 \h  142

  HYPERLINK \l "_Toc182871107"  5.	IL-03, 2005-06	  PAGEREF
_Toc182871107 \h  144

  HYPERLINK \l "_Toc182871108"  6.	IL-04, 2005-06	  PAGEREF
_Toc182871108 \h  146

  HYPERLINK \l "_Toc182871109"  7.	IL-05, 2004-05	  PAGEREF
_Toc182871109 \h  148

  HYPERLINK \l "_Toc182871110"  8.	IL-06, 2004-05	  PAGEREF
_Toc182871110 \h  150

  HYPERLINK \l "_Toc182871111"  9.	IL-07, 2004-05	  PAGEREF
_Toc182871111 \h  152

  HYPERLINK \l "_Toc182871112"  10.	IL-08, 2005-06	  PAGEREF
_Toc182871112 \h  154

  HYPERLINK \l "_Toc182871113"  11.	IL-09, 2004-05	  PAGEREF
_Toc182871113 \h  156

  HYPERLINK \l "_Toc182871114"  12.	IN-01, 2004-05	  PAGEREF
_Toc182871114 \h  158

  HYPERLINK \l "_Toc182871115"  13.	IN-02, 2004-05	  PAGEREF
_Toc182871115 \h  160

  HYPERLINK \l "_Toc182871116"  14.	IN-03, 2005-06	  PAGEREF
_Toc182871116 \h  162

  HYPERLINK \l "_Toc182871117"  15.	IN-04, 2004-06	  PAGEREF
_Toc182871117 \h  164

  HYPERLINK \l "_Toc182871118"  16.	IN-05, 2004-06	  PAGEREF
_Toc182871118 \h  167

  HYPERLINK \l "_Toc182871119"  17.	IN-06, 2005-06	  PAGEREF
_Toc182871119 \h  170

  HYPERLINK \l "_Toc182871120"  18.	IN-07 2005-06	  PAGEREF
_Toc182871120 \h  172

  HYPERLINK \l "_Toc182871121"  19.	IN-08 2005-06	  PAGEREF
_Toc182871121 \h  174

  HYPERLINK \l "_Toc182871122"  20.	IN-09 2005-06	  PAGEREF
_Toc182871122 \h  176

  HYPERLINK \l "_Toc182871123"  21.	IN-10 2005-06	  PAGEREF
_Toc182871123 \h  178

  HYPERLINK \l "_Toc182871124"  22.	IN-11 2005-06	  PAGEREF
_Toc182871124 \h  180

  HYPERLINK \l "_Toc182871125"  23.	KY-01 2005-06	  PAGEREF
_Toc182871125 \h  182

  HYPERLINK \l "_Toc182871126"  24.	KY-02 2005-06	  PAGEREF
_Toc182871126 \h  184

  HYPERLINK \l "_Toc182871127"  25.	MN-01 2005-06	  PAGEREF
_Toc182871127 \h  186

  HYPERLINK \l "_Toc182871128"  26.	MO-01, 2004-06	  PAGEREF
_Toc182871128 \h  188

  HYPERLINK \l "_Toc182871129"  27.	MO-02, 2004-06	  PAGEREF
_Toc182871129 \h  191

  HYPERLINK \l "_Toc182871130"  28.	MO-03, 2004-06	  PAGEREF
_Toc182871130 \h  194

  HYPERLINK \l "_Toc182871131"  29.	NE-01, 2004-2005	  PAGEREF
_Toc182871131 \h  197

  HYPERLINK \l "_Toc182871132"  30.	NE-02 2005-06	  PAGEREF
_Toc182871132 \h  199

  HYPERLINK \l "_Toc182871133"  31.	NE-03, 2004-2005	  PAGEREF
_Toc182871133 \h  201

  HYPERLINK \l "_Toc182871134"  32.	NE-04 2005-06	  PAGEREF
_Toc182871134 \h  203

  HYPERLINK \l "_Toc182871135"  33.	NE-05 2005-06	  PAGEREF
_Toc182871135 \h  205

  HYPERLINK \l "_Toc182871136"  34.	NE-06 2004-06	  PAGEREF
_Toc182871136 \h  207

  HYPERLINK \l "_Toc182871137"  35.	NE-07 2005-06	  PAGEREF
_Toc182871137 \h  210

  HYPERLINK \l "_Toc182871138"  36.	OH-01 2004-05	  PAGEREF
_Toc182871138 \h  212

  HYPERLINK \l "_Toc182871139"  37.	OH-02 2005-06	  PAGEREF
_Toc182871139 \h  214

  HYPERLINK \l "_Toc182871140"  38.	OH-03 2004-05	  PAGEREF
_Toc182871140 \h  216

  HYPERLINK \l "_Toc182871141"  39.	OH-04 2005-06	  PAGEREF
_Toc182871141 \h  218

  HYPERLINK \l "_Toc182871142"  40.	TN-01 2005-06	  PAGEREF
_Toc182871142 \h  220

List of Tables

  TOC \h \z \c "Table"    HYPERLINK \l "_Toc182871143"  Table II-1
Atrazine plant toxicity tests used in assessment methodology.	  PAGEREF
_Toc182871143 \h  24

  HYPERLINK \l "_Toc182871144"  Table II-2 Ten sets of randomly selected
EC50s (ug/L atrazine) for sensitivity analysis.	  PAGEREF _Toc182871144
\h  36

  HYPERLINK \l "_Toc182871145"  Table III-1 Evaluation of selected
watershed vulnerability approaches for atrazine monitoring, based on
Williams et al, 2004a	  PAGEREF _Toc182871145 \h  43

  HYPERLINK \l "_Toc182871146"  Table III-2 Target Population and Survey
Design Stratification	  PAGEREF _Toc182871146 \h  46

  HYPERLINK \l "_Toc182871147"  Table III-3 Watersheds selected for
monitoring using the GRTS approach.	  PAGEREF _Toc182871147 \h  49

  HYPERLINK \l "_Toc182871148"  Table III-4 Summary of chemograph
shapes, numbers and magnitudes of atrazine peaks for the 40 monitoring
sites	  PAGEREF _Toc182871148 \h  52

  HYPERLINK \l "_Toc182871149"  Table III-5 Summary of monitoring
results and LOC exceedances for the 40 watersheds.	  PAGEREF
_Toc182871149 \h  55

  HYPERLINK \l "_Toc182871150"  Table III-6 Population Estimates for the
1,172 Vulnerable Watersheds Based on  the 40 Monitoring Sites	  PAGEREF
_Toc182871150 \h  61

  HYPERLINK \l "_Toc182871151"  Table III-7 Population Estimates
Assuming Non-Monitored HUCs Missing at Random	  PAGEREF _Toc182871151 \h
 61

  HYPERLINK \l "_Toc182871152"  Table III-8 Atrazine Use Data and Model
Inputs Used for Alternative WARP Estimates	  PAGEREF _Toc182871152 \h
66

  HYPERLINK \l "_Toc182871153"  Table III-9 Correlations of Alternative
WARP Predictions for 1172 vulnerable HUCs	  PAGEREF _Toc182871153 \h  66

  HYPERLINK \l "_Toc182871154"  Table III-10 Correlations of Alternative
WARP Predictions for 5860 High Atrazine Use HUCs	  PAGEREF _Toc182871154
\h  68

  HYPERLINK \l "_Toc182871155"  Table III-11 Comparison of HUCs in Top
20% WARP Predictions	  PAGEREF _Toc182871155 \h  68

  HYPERLINK \l "_Toc182871156"  Table III-12 Summary of annual average
flow rate for the 40 monitoring sites by year.	  PAGEREF _Toc182871156
\h  73

  HYPERLINK \l "_Toc182871157"  Table III-13 Comparison of estimated
atrazine concentrations between 4-day and 12-day grab samples, IN-04,
2004	  PAGEREF _Toc182871157 \h  80

  HYPERLINK \l "_Toc182871158"  Table III-14 4-day Grab Samples
Augmented with Auto Samples - Rolling Averages (ppb)	  PAGEREF
_Toc182871158 \h  81

  HYPERLINK \l "_Toc182871159"  Table III-15 Percent Difference Between
Grab Averages and Auto Sample Adjusted Averages using CASM SSI%
PAGEREF _Toc182871159 \h  82

  HYPERLINK \l "_Toc182871160"  Table III-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)	  PAGEREF _Toc182871160 \h  84

  HYPERLINK \l "_Toc182871161"  Table III-17 Percentile of Ranked
Distribution of Percent Differences from Table III-16	  PAGEREF
_Toc182871161 \h  86

  HYPERLINK \l "_Toc182871162"  Table III-18 Sampling frequency
strategies used by Crawford (2004)	  PAGEREF _Toc182871162 \h  87

  HYPERLINK \l "_Toc182871163"  Table III-19 Error Distribution for
Various Sample Strategies of Crawford (2004) for Different Concentration
Profiles	  PAGEREF _Toc182871163 \h  87

  HYPERLINK \l "_Toc182871164"  Table III-20 Variability of estimated
concentrations based on different sample frequencies (Crawford, 2004)
PAGEREF _Toc182871164 \h  89

  HYPERLINK \l "_Toc182871165"  Table VI-1 Micro- and mesocosm studies
table with Brock scores and estimated average % change in community
structure (Steinhaus similarity) of primary producers	  PAGEREF
_Toc182871165 \h  114

  HYPERLINK \l "_Toc182871166"  Table VII-1 Summary of Detection
Frequencies by Monitoring Site and Year	  PAGEREF _Toc182871166 \h  126

  HYPERLINK \l "_Toc182871167"  Table VII-2 Summary of Rolling Averages
From Ecological Watershed Monitoring Data for Comparison with CASM
Thresholds	  PAGEREF _Toc182871167 \h  128

  HYPERLINK \l "_Toc182871168"  Table VII-3 Sites Ranked from Lowest to
Highest Based on Annual Average Flow	  PAGEREF _Toc182871168 \h  130

  HYPERLINK \l "_Toc182871169"  Table VII-4 Summary of Rolling Average
Concentrations (ppb) and SSI% for PRZM Augmented Chemographs from
Snyder, et al (2007)	  PAGEREF _Toc182871169 \h  133

 List of Figures

  TOC \h \z \c "Figure"    HYPERLINK \l "_Toc182871170"  Figure I-1
Species sensitivity distributions of various atrazine-induced toxicity
endpoints.	  PAGEREF _Toc182871170 \h  10

  HYPERLINK \l "_Toc182871171"  Figure II-1 Examples of atrazine
exposure time series (chemographs) in natural systems.	  PAGEREF
_Toc182871171 \h  19

  HYPERLINK \l "_Toc182871172"  Figure II-2 Score for the severity of
effects in microcosm and mesocosm atrazine exposures versus exposure
duration and average concentration.	  PAGEREF _Toc182871172 \h  20

  HYPERLINK \l "_Toc182871173"  Figure II-3 LOC=4.0 based on correlation
of micro/mesocosm effect scores with model effect index values (upper
panel), and comparison of model LOC to micro/ mesocosm effect scores at
different exposure concentration/ durations (lower panel)	  PAGEREF
_Toc182871173 \h  29

  HYPERLINK \l "_Toc182871174"  Figure II-4 Model effect index values
and multiplication factors for sixteen sample chemographs, using base
model configuration	  PAGEREF _Toc182871174 \h  31

  HYPERLINK \l "_Toc182871175"  Figure II-5 Comparison of multiplication
factors for the base model configuration to alternatives with different
model effect indices	  PAGEREF _Toc182871175 \h  33

  HYPERLINK \l "_Toc182871176"  Figure II-6 Comparison of multiplication
factors for base model configuration to alternative configurations with
different start dates	  PAGEREF _Toc182871176 \h  34

  HYPERLINK \l "_Toc182871177"  Figure II-7 Comparison of multiplication
factors for the base model configuration to alternative configurations
with lower and higher nutrients.	  PAGEREF _Toc182871177 \h  34

  HYPERLINK \l "_Toc182871178"  Figure II-8 Comparison of multiplication
factors for the base model configuration to alternative configurations
with lower and higher temperature	  PAGEREF _Toc182871178 \h  35

  HYPERLINK \l "_Toc182871179"  Figure II-9 Comparison of multiplication
factors for the base model configuration to alternative configurations
with lower and higher light.	  PAGEREF _Toc182871179 \h  35

  HYPERLINK \l "_Toc182871180"  Figure II-10 Comparison of
multiplication factors for the base model configuration (bold solid
line) to the mean (bold dashed line) and mean +/- one standard deviation
(narrow dashed lines) of the multiplication factors for ten alternative
plant EC50 sets.	  PAGEREF _Toc182871180 \h  36

  HYPERLINK \l "_Toc182871181"  Figure III-1 Ranking of watersheds in
quintiles using the USGS WAtershed Regression on Pesticides model
PAGEREF _Toc182871181 \h  42

  HYPERLINK \l "_Toc182871182"  Figure III-2 Location of atrazine use
area (gray), areas of use > 0.25 lb/ac (green), most vulnerable 20th
percentile of watersheds (yellow), and sampled watersheds (red).
PAGEREF _Toc182871182 \h  44

  HYPERLINK \l "_Toc182871183"  Figure III-3 Location of the 40
watersheds (red) sampled for the atrazine eco-monitoring study.
PAGEREF _Toc182871183 \h  48

  HYPERLINK \l "_Toc182871184"  Figure III-4 Estimated Cumulative
Distribution for CASM Score.	  PAGEREF _Toc182871184 \h  62

  HYPERLINK \l "_Toc182871185"  Figure III-5 Comparing WARP scores for
HUC-based watersheds vs. sub-watersheds	  PAGEREF _Toc182871185 \h  63

  HYPERLINK \l "_Toc182871186"  Figure III-6 Comparison of WARP scores
for sub-watershed and HUCs	  PAGEREF _Toc182871186 \h  63

  HYPERLINK \l "_Toc182871187"  Figure III-7 Illustrating WARP’s
capabilities to accurately predict moderately high vs. high
vulnerability to atrazine by relating it to resulting CASM scores.
PAGEREF _Toc182871187 \h  64

  HYPERLINK \l "_Toc182871188"  Figure III-8 Comparing how well WARP
predicts maximum 14 day rolling averages	  PAGEREF _Toc182871188 \h  65

  HYPERLINK \l "_Toc182871189"  Figure III-9 Scatterplots of Alternative
WARP Predictions for 1172 vulnerable HUCs	  PAGEREF _Toc182871189 \h  67

  HYPERLINK \l "_Toc182871190"  Figure III-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.	  PAGEREF _Toc182871190 \h  69

  HYPERLINK \l "_Toc182871191"  Figure III-11 Comparison of original
vulnerable watersheds identified using 1998-2002 use data with
vulnerable watersheds identified using 2001-03 use data.	  PAGEREF
_Toc182871191 \h  70

  HYPERLINK \l "_Toc182871192"  Figure III-12 Atrazine concentrations
and measured stream flow, NE-04, 2005	  PAGEREF _Toc182871192 \h  75

  HYPERLINK \l "_Toc182871193"  Figure III-13 Atrazine concentrations
and measured stream flow, NE-05, 2005	  PAGEREF _Toc182871193 \h  76

  HYPERLINK \l "_Toc182871194"  Figure III-14 Atrazine concentrations
and measured stream flow, NE-07, 2005	  PAGEREF _Toc182871194 \h  76

  HYPERLINK \l "_Toc182871195"  Figure III-15 Atrazine concentrations
and measured stream flow, MO-01, 2005	  PAGEREF _Toc182871195 \h  77

  HYPERLINK \l "_Toc182871196"  Figure III-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)	  PAGEREF _Toc182871196 \h  83

  HYPERLINK \l "_Toc182871197"  Figure IV-1 Extent of stream segments in
the MO-02 HUC that met the sample selection criteria (from Harbourt et
al, 2004).	  PAGEREF _Toc182871197 \h  94

  HYPERLINK \l "_Toc182871198"  Figure IV-2 Comparison of WARP values to
%SSI deviation for the 1,172 vulnerable watersheds (sites exceeding the
4% LOC trigger are labeled)	  PAGEREF _Toc182871198 \h  96

  HYPERLINK \l "_Toc182871199"  Figure IV-3 Comparison of original WARP
values based on 1998-2002 use data with new WARP values based on
2001-2003 use data	  PAGEREF _Toc182871199 \h  97

  HYPERLINK \l "_Toc182871200"  Figure IV-4 Depth to restrictive soil
layers in MO-01 and MO-02, located in the Central Claypan MLRA.
PAGEREF _Toc182871200 \h  99

  HYPERLINK \l "_Toc182871201"  Figure IV-5 Distribution of Hydrologic
Group C and D Soils in MO-01 and MO-02, located in the Central Claypan
MLRA.	  PAGEREF _Toc182871201 \h  100

  HYPERLINK \l "_Toc182871202"  Figure IV-6 Distribution of Hydrologic
Group C and D Soils in the Atrazine Monitoring Sites in Nebraska,
Missouri, Iowa, and western Illinois	  PAGEREF _Toc182871202 \h  101

Preliminary Interpretation of the Ecological Significance of Atrazine
Stream-Water Concentrations Using a Statistically-Designed Monitoring
Program

 Introduction

Purpose of the Scientific Advisory Panel Meeting

In January, 2003, the US Environmental Protection Agency (US EPA) issued
an ecological risk assessment as part of the Interim Registration
Eligibility Decision (IRED) for atrazine (US EPA, 2003a).  As a
condition of re-registration, the atrazine registrants were required to
develop a monitoring program to determine whether atrazine
concentrations in streams associated with corn and sorghum production
were exceeding a designated effects-based threshold. This threshold was
based on aquatic plant community effects. If this threshold is exceeded,
then a watershed-based mitigation program would be required.

This document summarizes the Agency’s preliminary review and
interpretation of the results of a three-year atrazine monitoring
program conducted in flowing water bodies associated with corn and
sorghum production. The Agency is consulting with this Scientific
Advisory Panel (SAP) on scientific issues related to the interpretation
of the results of the monitoring program and the extent to which the
methods used by the Agency could be used or adapted in any future
atrazine aquatic assessments or monitoring efforts to determine the
extent to which water bodies exceed atrazine thresholds of concern for
aquatic community effects.

In its review and sensitivity analysis of the aquatic community model
used to relate atrazine monitoring concentrations to effects found in
microcosm and mesocosm studies, the Agency identified some issues
related to the model’s response to low concentrations and to slope
responses. The Agency has posed questions for the SAP on these issues
and, once they are resolved, will return to the SAP with an update.

Background

Atrazine, a triazine herbicide currently registered for use against
broadleaf and some grassy weeds, was first registered for use in 1958,
and is estimated to be one of the most widely used herbicides in the
United States.  Atrazine inhibits primary production by reversibly
blocking photosynthesis. It is both mobile and persistent in the
environment.

The atrazine ecological risk assessment for the January 2003 IRED
identified ecological risk concerns from the use of atrazine based on
the potential for community- and population-level risks to aquatic
ecosystems at prolonged concentrations of atrazine ranging from 10 to 20
µg/L. The Agency required the atrazine registrants, in consultation
with the US EPA, to develop a program under which the registrants
monitor for atrazine concentrations and mitigate environmental exposures
if the US EPA determines that mitigation is necessary. This program was
to focus on stream systems within a watershed context (US EPA, 2003a).

The scope of the monitoring program included identification of an
appropriate ecological level of concern (LOC) based on the IRED and
development of a protocol for monitoring that specifies the frequency,
location, and timing of sampling. The program also identified atrazine
exposures that would trigger mitigation measures and described resultant
mitigation measures. This monitoring and mitigation program was
designed, conducted and implemented on a tiered watershed level basis
consistent with existing state and federal water quality programs (US
EPA, 2003a).

On October 31, 2003, the US EPA issued an addendum to update the January
2003 IRED. This addendum specified the key questions the US EPA wanted
the monitoring study to address, outlined the methodology for
determining the LOC trigger, and briefly described the monitoring study
design and proposed protocol submitted by Syngenta Crop Protection,
Inc., the principal atrazine registrant. The LOC trigger was based on
results of microcosm and mesocosm studies. An existing, previously
published aquatic community model was used to compare atrazine atrazine
monitoring data to exposure profiles observed in the microcosm and
mesocosm studies to ascertain if the level of concern derived from these
studies was exceeded. The monitoring protocol described a
watershed-based approach that identified 40 monitoring sites
representing watersheds associated with corn and sorghum production that
are the most vulnerable watersheds for atrazine contamination and
specified a minimum of two years of monitoring to determine if LOC
triggers were exceeded. The 40 watersheds are statistically
representative of 1,172 potentially vulnerable watersheds. Results of
the monitoring study will be used to determine if further monitoring or
remedial efforts are needed (US EPA, 2003b).

Mitigation actions in the watershed were triggered if atrazine
concentrations from the monitoring site exceeded the LOC trigger for two
years. If the site exceeded the LOC trigger in any one year, then a
third year of monitoring would be required before mitigation actions
would be undertaken (US EPA, 2003b).

Monitoring Study Objectives

Based on the 2003 ecological risk assessment for atrazine, the Agency
identified several key questions to be addressed in the monitoring study
(US EPA, 2003b). These questions formed the study objectives required to
meet the US EPA’s needs under the Federal Insecticide, Fungicide, and
Rodenticide Act (FIFRA) and support the development of an atrazine
aquatic life criterion under the Clean Water Act.

Identify aquatic community level thresholds of concern based on
available microcosm and mesocosm studies and develop a method that
relates these aquatic community responses to atrazine exposure profiles
in a reasonable and transparent manner.

Design a monitoring program to estimate the extent of watersheds in corn
and sorghum producing areas that have flowing waters which exceed
atrazine LOC triggers for aquatic community effects.

Based on results of the monitoring study, identify watershed attributes
that can be used to identify where these higher atrazine exposure areas
are likely to occur.

Syngenta designed the study to address these objectives. This document
focuses on the first two objectives. While initial approaches concerning
the third objective are presented, a subsequent document and SAP
consultation will focus on the final objective.

Ecological Endpoint and Level of Concern for Atrazine

Freshwater Microcosm/Mesocosm Studies on Atrazine

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EC50s for aquatic plant growth (●), LC50s/EC50s for aquatic animal
acute lethality/immobiliation (□) and various chronic/ sublethal
animal toxicity endpoints (■), from Tables 5.1 and 5.2 of Giddings

Numerous toxicity tests with a variety of plant and animal species have
shown aquatic plant growth to generally be much more sensitive to
atrazine than are various effects on aquatic animals.  Figure I-1 shows
species sensitivity distributions of EC50s for plant growth, EC50s for
acute lethality/immobilization to aquatic animals, and various
chronic/sublethal endpoints for aquatic animals, based on the Table 5.1
and 5.2 summaries of Giddings et al. (2000).  At comparable percentages
in these cumulative distributions, plant growth is circa 3-10 times more
sensitive than chronic endpoints for animals and circa 20-100 times more
sensitive than acute toxicity to animals.  Within each distribution, the
data used for Figure I-1 vary with respect to the duration of exposure
and nature of effects, so this figure is intended to only illustrate
general differences in taxa sensitivity, not rigorously define these
distributions.  Because of the plant sensitivity illustrated in Figure
I-1, the problem definition here focused on effects to the aquatic plant
community.

Applying individual toxicity tests such as those summarized in Figure
I-1 to plant community risk assessments is made difficult by issues
related to the rapid, reversible nature of small to moderate effects of
atrazine on plants, the influences of competition and compensation
within the community, recovery rates of community perturbations, and the
seasonality of community sensitivity.  In contrast, microcosm and
mesocosm studies with atrazine address the aggregate responses of
multiple species in aquatic plant communities. Mesocosm and microcosm
studies also allow observation of population and community recovery from
atrazine effects and of indirect effects on higher trophic levels.

Atrazine has been the subject of many mesocosm and microcosm studies.
The durations of these studies have ranged from a few weeks to several
years at exposure concentrations ranging from 0.1 to 10,000 µg/L.  Most
of the studies have focused on atrazine effects on phytoplankton,
periphyton, and macrophytes; however, some also included measurements on
animals.

Based on these studies, as described in the 2003 IRED for atrazine (U.S.
EPA, 2003a), potential adverse effects on sensitive aquatic plants and
non-target aquatic organisms, including populations and communities, are
likely to be greatest when atrazine concentrations in water equal or
exceed approximately 10 to 20 µg/L on a recurrent basis or over a
prolonged period of time.  Appendix 1 summarizes the freshwater aquatic
microcosm, mesocosm, and field studies that were reviewed as part of the
2003 IRED.  An open literature search for studies not included in the
2003 IRED was completed in May 2007.  It found that the available
studies all showed effects levels to freshwater fish, invertebrates, and
aquatic plants at concentrations greater than 10 µg/L.

Determining a Level of Concern Based on Microcosm/Mesocosm Studies

Based on the results of available micro- and mesocosm studies for
atrazine, the US EPA identified changes in the aquatic plant community
structure as the endpoint of concern.  This appeared to be the most
sensitive endpoint affecting aquatic plants. Further, the effect of
atrazine on aquatic plants, whether direct or indirect, appeared to be
more sensitive than effects on other organisms in the aquatic ecosystem,
e.g., aquatic invertebrates or fish. Thus, by focusing on aquatic plant
community structural changes, the Agency would, in effect, protect
against adverse effects on the rest of the aquatic community.

The degradates of atrazine were not included in the determination of the
endpoint of concern since their toxicity to aquatic plants, freshwater
fish, and aquatic invertebrates is much lower than that of the parent
molecule (US EPA, 2007, 2003a). Given the lesser toxicity of the
degradates compared to the parent, and the relatively small proportion
of the degradates expected to be in the environment and available for
exposure relative to atrazine, the focus of this assessment is parent
atrazine.

The ecological level of concern (LOC) was based on 77 results from 25
micro- and mesocosm studies on atrazine (Appendix 1). The analysis
evaluated the change in aquatic community structure and function of
primary producers. The studies measured growth (rate) and biomass in the
laboratory and reduction in primary production and changes in structure
of primary producer communities in the field.

Establishing the LOC required quantifying the results of the microcosm
and mesocosm studies using a comparable measure of effects on aquatic
plant productivity and community structure. An analysis of the reported
effect(s) and the atrazine exposure profile (i.e., the magnitude and
duration of atrazine concentrations) revealed a wide range of study
designs and quality and also indicated that a wide range of atrazine
exposure profiles could result in significant change in aquatic
community productivity and structure. A method was developed to separate
the reported results on plant community productivity and structure
observed in these studies into those that were significant versus those
with slight to no effects.

First, the severity of effects of atrazine on the aquatic plant
community were quantified. Because there was not a single, consistent,
quantitative effects measure that could be compared among these studies,
Brock et al. (2000) analyzed many of these microcosm/mesocosm studies
using an effects score summarized below:

Effect Scores:

1   = 	no effect

2   = 	slight effect

3   = 	significant effect followed by return to control levels within 56
d

4   = 	significant effect without return to control levels during an
observation period of

less than 56 d

5   = 	significant effect without return to control levels for more than
56 d

The US EPA subjected those studies not included in the Brock analysis to
the same scoring system.  For the 77 results from the microcosm/mesocosm
studies, 15 had a Brock score of 1, 12 had a score of 2, 12 had a score
of 3, 23 had a score of 4, and 15 had a score of 5.

Since atrazine exposure profiles in natural systems, in this case
streams, are typically complex, a method was needed to analyze
monitoring data to determine when monitored exposure profiles are
functionally equivalent to those profiles observed in mesocosm and
microcosm studies that showed significant changes or, conversely, that
showed no significant effects.  A model that predicts changes in aquatic
communities in streams, the Comprehensive Aquatic Systems Model (CASM)
(DeAngelis et al. 1989; Bartell et al. 1999, 2000; Bartell 2003), was
used to assess whether an atrazine exposure profile from a monitoring
study would likely be associated with a significant effect on aquatic
communities.  CASM is an ecological food web model that can indicate
changes in the aquatic community structure and function of primary
producers due to the addition of a chemical to the system.  CASM can
include a large number of species in the model structure and readily
accepts complicated chemical exposure profile inputs.

Syngenta commissioned the CASM model developer to provide the Agency
with a version of the model specifically tailored to atrazine,
containing information on how atrazine affects different species.  This
model, the Copmprehensive Aquatic Systems Model for Atrazine
(CASM_Atrazine), was developed to simulate complex ecological production
dynamics of a 2nd or 3rd order Midwestern stream. (Volz, et al., 2007)
This Agency is using this model version to interpret the data generated
by the ecological monitoring program.  This current version has a shell
which will allow users to input their data. Once current development and
testing are concluded, it will be made publicly available

One output from the CASM_Atrazine model, the Steinhaus Similarity Index
(SSI) deviation, represents the relative magnitude of change in aquatic
community structure and function of primary producers between results of
the simulated atrazine concentration profile and results of a control
simulation, i.e., the same community over the same time period with no
atrazine input.  SSI deviation is expressed as a percentage, with higher
percentages indicating greater deviation of the atrazine-exposed aquatic
community from the control.

With model simulations of the exposure profiles in the microcosm and
mesocosm studies, a value for the SSI can be selected to segregate those
studies that exhibited significant effects (Brock scores of 3-5) from
those that did not (Brock scores of 1-2).   An initial assessment of
such an LOC was provided in the IRED addendum (US EPA 2003b) and is
updated in Section II of this document.

CASM simulations can be used to test whether the LOC was exceeded for
any exposure time-series (e.g., a chemograph from a monitoring study).
Section II also provides examples of this approach and describes a
sensitivity analysis of the CASM model that addresses possible sources
of uncertainty in the approach.

Atrazine Ecological Monitoring Program

The next step in the assessment involved designing a monitoring program
that generated chemographs sufficient to assess the magnitude, duration,
and frequency of atrazine exposures, given the potentially flashy nature
of atrazine exposure in stream systems. Because the LOC trigger reflects
both magnitude and duration of exposure, the chemograph must reflect
sampling at a sufficient frequency to characterize atrazine exposure in
flowing waters. The monitoring program needed to determine the extent to
which waters in vulnerable watersheds exceed the effects-based
thresholds for atrazine. In addition, Syngenta was asked to collect
watershed and sub-watershed data that could be used to identify other
areas where waters exceeding the LOC thresholds are located.

Syngenta monitored 40 sites for at least 2 years based on a
probability-based survey design that sampled 1,172 vulnerable
watersheds. The study used a watershed-based approach to focus on those
water bodies that would likely be most vulnerable to atrazine loadings.
Surveying vulnerable watersheds increases the probability of finding
waters with atrazine exposures above the levels of concern. Conversely,
if no problems were found in these watersheds, the probability is low
that problems would exist in less vulnerable watersheds. HUC-10/11 scale
watersheds (typically 40,000 to 250,000 acres in size) provided a
workable scale for evaluating the vulnerability of watersheds and
establishing the watershed population of interest. A tiered approach
based first on atrazine use intensity and then on the USGS’ Watershed
Regression for Pesticides (WARP) model (Larson et al, 2004) defined the
most potentially vulnerable tier of watersheds based on a ranking of
WARP values. From this tier, a spatially-balanced survey design was used
to select a representative sample of 40 watersheds. Williams et al.
(2004a) describe the watershed vulnerability approach and site selection
process. Section III of this document provides the US EPA review of the
approach and interpretation in relation to the monitoring study
objectives.

Section III also presents the Agency’s review and interpretation of
Syngenta’s monitoring study for the 40 watersheds (Hampton et al.,
2007a). Both Syngenta (Hampton et al., 2007b) and the US EPA are
continuing to evaluate the results to address the third study objective
relating to identifying where the watersheds that exceed the LOC might
occur. With regard to this latter issue, Section IV of this document
describes some approaches the US EPA is considering and requests comment
from the SAP. A future SAP consultation will focus on interpretations of
the study results with a particular focus on applying the results to
identify watershed attributes and watersheds with corn and sorghum
production most likely to have streams with the highest atrazine
concentrations.

Key Study Documents

Syngenta has submitted a number of documents to the US EPA related to
the development of a method of determining the threshold trigger for
atrazine impacts on aquatic communities and to monitoring for atrazine
in watersheds representative of the most vulnerable watersheds. The
Agency is reviewing these studies as a part of its assessment of the
monitoring program and is presenting its preliminary assessment to the
SAP.  The following documents sufficiently capture the details and
results of the study and have been provided to the SAP for additional
background.

CASM Model Development

Volz, D.C., S.M. Bartell, S.K. Nair, and P. Hendley. 2007. Modeling the
Potential for Atrazine-Induced Changes in Midwestern Stream Ecosystems
using the Comprehensive Aquatic Systems Model (CASM). Final Report. MRID
47174103.

This report contains background on the CASM model, documentation of the
development of the model specifically for atrazine (CASM_Atrazine),
development of rolling average triggers, and an uncertainty analysis
conducted by the model developer. The report concludes that the model
provides a flexible tool for predicting community-level impacts from
atrazine exposure in low-order Midwestern streams using monitoring data.

As a result of the US EPA evaluation, some model parameters have been
revised, resulting in a revised LOC trigger. The US EPA evaluation and
sensitivity analyses are reported in Section II.

Watershed Selection Process

Williams, W. M., C.M. Harbourt, M.K. Matella, M.H. Ball, and J.R. Trask.
2004a. Atrazine Ecological Exposure Flowing Water Chemical Monitoring
Study in Vulnerable Watersheds Interim Report: Watershed Selection
Process.  Prepared by Waterborne Environmental, Inc., Leesburg, VA for
Syngenta Crop Protection, Inc., Greensboro, NC.

Williams, W. M., C.M. Harbourt, M.H. Ball, M.K. Matella, J.R. Trask, and
N.J. Snyder. 2004b.  Atrazine Ecological Exposure Monitoring Program
Interim Report: Supporting Spatial Data.  Prepared by Waterborne
Environmental, Inc., Leesburg, VA for Syngenta Crop Protection, Inc.,
Greensboro, NC.

Williams et al (2004a) describes the steps Syngenta used to identify the
most vulnerable watersheds based on atrazine use intensity and factors
affecting the potential for atrazine runoff using the USGS Watershed
Regression on Pesticides (WARP) model. It also describes the criteria
used for selecting a monitoring site within the watersheds. Williams et
al (2004b) provides detailed documentation on the spatial and monitoring
data used in the watershed vulnerability assessment and site selection
process.

Section III describes US EPA’s evaluation of the watershed
vulnerability and selection approach.

Overview of Atrazine Monitoring Study Results

Hampton, M., Burnett, G., Carver, L.S., Harbourt, C.M., Hendley, P.,
Johnston, E.A., Perez, S., Snyder, N.J., and Trask, J.R., 2007a.  2007
Interim Report - 2004 - 2006 Data Overview - Atrazine Ecological
Exposure Flowing Water Chemical Monitoring Study in Vulnerable
Watersheds Interim Report.  Prepared by Waterborne Environmental, Inc.,
Leesburg, VA for Syngenta Crop Protection, Inc., Greensboro, NC.  MRID
47174102.

This report includes a description of the sampling instrumentation and
methods and provides the monitoring results for the 40 sites.

The US EPA used the data in this report for its analysis of the
monitoring results in Section III. In addition, the US EPA has
interpreted the results based on a revised version of the CASM-Atrazine
model described in Section II. The results, compared against a revised
LOC, are discussed in Section III.

Sampling Frequency Analyses

Snyder, N.J., Harbourt, C.M., Miller, P.S., Trask, J.R., Prenger, J.J.,
Hendley, P., and Johnston, E.A., 2007.  Atrazine Ecological Exposure
Flowing Water Chemical Monitoring Study in Vulnerable Watersheds:
Analysis of Chemograph Behavior between Grab Samples - Measurement and
Hybrid PRZM Approaches.  Prepared by Waterborne Environmental, Inc.,
Leesburg, VA for Syngenta Crop Protection, Inc., Greensboro, NC.

Snyder et al (2007) compared the results of the 4-day grab samples to
both autosampler data triggered by flow events at selected sites and to
data where the Pesticide Root Zone Model (PRZM) version 3.12.2 (Carsel
et al, 1998) was used to estimate atrazine concentrations between
sampling events. While the CASM SSI deviation values generally increased
with both the autosample data and the PRZM-estimated concentrations,
neither resulted in chemographs that triggered the threshold LOC.

The USEPA also evaluated sampling frequency uncertainties and various
approaches for describing this uncertainty in Section III.

GIS Approaches to Assessing Watershed Vulnerability

Hampton, M. Prenger, J.J., Harbourt, C.M., Hendley, P., and Miller,
P.S., 2007b.  Atrazine Ecological Exposure Flowing Water Chemical
Monitoring Study in Vulnerable Watersheds: Approaches to Assessing
Potential Watershed Scale Vulnerability for Atrazine Runoff.  Prepared
by Waterborne Environmental, Inc., Leesburg, VA for Syngenta Crop
Protection, Inc., Greensboro, NC.  MRID 47174101.

This report provides a brief summary of the original approach for
identifying watersheds vulnerable to atrazine runoff (see Williams et
al, 2004a) but focuses primarily on additional analyses with spatial
data that was not as readily available at the time of the original study
design.

The USEPA has completed preliminary analyses of this study, as well as
its own assessments. Those analyses are discussed briefly in Section IV
in terms of future directions, but will be the subject of a future SAP.

Issues To Be Addressed in Future SAP Consultations

As noted in the purpose section, the Agency is consulting with this SAP
on the use of a community simulation model as a means of extrapolating
the results of microcosm/mesocosm studies to stream monitoring exposure
data and on the preliminary interpretation of the results of a
watershed-based monitoring study in the corn and sorghum growing region.
Several issues noted in this document will be brought to a future SAP.
These are highlighted below:

Once issues related to the way in which the CASM model appears to both
overestimate the effects of extended-duration, low-level exposures and
underestimate the effects of short-term, high-level exposures are
resolved (see Section II.D), the Agency will return to the SAP with a
revised interpretation of the monitoring study.

Monitoring continues in 2007 for a number of sites. The results of this
monitoring may change the current interpretation of some of the
monitoring sites (see Section III.D.2). These updates will be folded
into the revised interpretation.

A separate pilot monitoring study is underway for water bodies in
sugarcane areas. Additionally, the Agency is evaluating existing
monitoring of reservoirs to determine the potential impact of atrazine
on aquatic communities in static water bodies. The interpretation of
these results may require an aquatic community model specific for the
sugarcane areas. To the extent that such a model departs from the
current CASM approach, the Agency may determine that a consultation with
the SAP is warranted.

Based on input from this SAP, the Agency will continue to work on
approaches to (a) identify streams that exceed the LOC and (b) identify
additional watersheds and/or streams beyond the 40 monitoring sites that
exceed the LOC. The Agency plans a future SAP consultation on the
results of this effort.

 Use of a Community Simulation Model for Extrapolation of Atrazine
Levels of Concern for Exposure Time-Series

This section addresses procedures for assessing the ecological level of
concern (LOC) for atrazine exposures in freshwater systems.  It first
defines the problem and presents a general strategy for the assessment
methodology. Second, it describes the formulation and parameterization
of a community simulation model to be used in the methodology. After a
discussion of the implementation of the model with example applications,
a sensitivity analysis is presented to address the uncertainties
introduced into assessments due to various choices for model formulation
and parameterization.

Problem Definition and General Strategy

Nature of Assessment Problem

The problem being addressed here is a common one in aquatic risk
assessments, namely that exposure time-series in natural ecosystems for
which risks need to be assessed are markedly different from exposure
time-series in experimental systems for which effects have been
determined. Experimental system exposures usually involve a relatively
constant concentration over a pre-determined duration, sometimes
followed by assessment of recovery after exposure termination.  Field
exposures tend to be more variable and open-ended, without a fixed
duration.  This is particularly true for atrazine, which enters aquatic
systems largely in rainfall-driven runoff, resulting in highly variable
and episodic exposures that depend on rainfall distribution, atrazine
application patterns, topography, and soil properties.  The assessment
methodology therefore needs to address effects under exposure scenarios
such as those in   REF _Ref180376001 \h  Figure II-1 , which includes a
variety of exposure time-series derived from atrazine monitoring
efforts.

As summarized in Section   REF _Ref182025133 \w \h  I.D , LOCs are based
on plant community effects observed in micro/mesocosm studies,
discriminating between no or slight effects (Brock Scores 1-2) and
significant effects (Brock Scores 3-5).  A major assumption here is that
these micro/mesocosm data collectively describe a relationship of
effects to exposure that is applicable to the field sites of interest.
Although concentrations during the exposure periods in these studies are
not absolutely constant, exposures can be approximately characterized by
a concentration and duration, which are related to the effect scores in
 REF _Ref181755648 \h  Figure II-2 .  The two groups of scores to be
discriminated in setting an LOC (1-2 versus 3-5) are largely separated (
 REF _Ref181755648 \h  Figure II-2 ); however, some overlap exists,
indicative of variable effects among the different types of systems
and/or uncertainty in evaluation methods, the causes of which are
uncertain.  The assessment methodology must therefore address how LOCs
should be defined relative to the presence of false negatives
(micro/mesocosm scores of 3-5 which lie below the LOC) and false
positives (scores of 1-2 which lie above the LOC).

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  1  Examples of
atrazine exposure time series (chemographs) in natural systems.

Chemograph numbers 7 and 9 are not shown because they are identical to
numbers 6 and 8, respectively, except shifted to two weeks earlier.

Figur–呓䱙剅䙅ㄠ尠⁳䤔ᕉጞ匠充䘠杩牵⩜䄠䅒䥂
⁃獜ㄠᐠᔲ匠潣敲映牯琠敨猠癥牥瑩⁹景攠晦捥獴椠
業牣捯獯湡敭潳潣浳愠牴穡湩硥潰畳敲⁳敶獲獵
攠灸獯牵畤慲楴湯愠摮愠敶慲敧挠湯散瑮慲楴湯

 denotes score 2, and ∆ denotes score 1.

General Strategy for Assessment Methodology

Although the use of the micro/mesocosm data better addresses integrated
and cumulative aquatic community effects than would application of
toxicity test results for individual plant species, it still presents
the challenge of extrapolating to highly variable field exposures (  REF
_Ref180376001 \h  Figure II-1 ) from the more regular exposures in the
experimental systems.  Thus a method is needed to provide a consistent,
quantitative index of effect for the entire range of exposures in both
the experimental systems and the field systems of interest.  This
methodology does not need to provide any absolute predictions of effects
in any system, but rather must be able to provide a measure of the
relative severity of effects resulting from different exposure patterns.
 Conceptually, this would allow LOCs defined based on micro/mesocosm
data to be extrapolated to different exposure time-series in field
systems.  It should be emphasized that the purpose of this methodology
is only to address extrapolations across exposure time-series, and not
to address other factors which might make atrazine effects vary between
systems (such as might be partly responsible for the overlap of the
score on Figure II-2) and which are not predictable based on current
knowledge.

Just as it was decided to base LOCs on plant community effects observed
in experimental ecosystems rather than individual species toxicity
tests, it was decided to use a simulation model that addresses an entire
aquatic community for the desired extrapolations among exposure
time-series.  By incorporating representations of major aquatic
community processes and the effects of atrazine on these processes, this
model is expected to provide useful assessments of the relative impacts
of different exposures, and thereby support extrapolations among
exposure time-series for different experimental and field ecosystems.
For example, if 100 ug/L atrazine was the LOC for a 60 day constant
exposure, what would be the LOC for a 30-day constant exposure?  Or what
would be the LOC based on the highest peak (or some average
concentration) in a chemograph with a complex shape, such as any of
those in   REF _Ref180376001 \h  Figure II-1 ?

The general steps in the assessment methodology are as follows:

(1)  A single version of an aquatic community model is formulated and
parameterized. This model formulation should include a sufficient number
of plant species to accommodate assigning a range of atrazine
sensitivities reflective of available single-species toxicity tests (
REF _Ref181755648 \h  Figure II-2 ).  Because relative effects of
different exposures are of concern, rather than absolute effects on a
specific system, this formulation and parameterization will not address
any specific system of interest, but still should have some general
relevance to the types of experimental and natural systems of interest.
Just as it was assumed that the micro/mesocosm results have generic
relevance to field assessments, it is assumed that exposure
extrapolations based on this single model version would have generic
applicability to the experimental and field ecosystems.  The sensitivity
analysis in Section   REF _Ref180376659 \w \h  II.D  will evaluate this
assumption and some uncertainties associated with it.

(2)  A model simulation is conducted for each of the atrazine
concentration/duration combinations in the micro/mesocosm data set, as
well as for the absence of atrazine exposure (control).  The duration of
the model simulations is not the exposure duration, but rather a fixed
length long enough to assess effects over an entire growing season (with
the exposure period being a subset of the total simulation). The model
simulation also should be timed to start at a typical expected onset
date of significant atrazine exposure in natural systems.  A measure of
the change of the model output variables between control and exposed
conditions that provides a consistent measure of effects independent of
exposure duration is designated as a model effects index.

(3)  The observed effects scores (1-5) for each micro/mesocosm treatment
are then correlated to the model effects index values computed by the
model for the exposure concentrations/durations of each treatment.  An
LOC for the model effects index is selected that provides the best
discrimination between the effects score groups (i.e., 1-2 vs 3-5).  The
model output is thus "calibrated" to the micro/mesocosm data so that the
LOC for the model effects index is related to effects of concern
identified in the micro/mesocosm experiments.  It is important to
emphasize that the model effects index value selected as the LOC does
not define the level of ecological protection being provided.  Rather,
the significance of the model LOC is in (a) its correlation to the
effects in the micro/mesocosm experiments that do define the desired
level of ecological protection and (b) its utility as a reference value
for assessing the relative impact of different exposure time-series.

(4)  A model simulation is then conducted for each of the field exposure
time-series of interest, and the model effects indices for these
exposures are examined to determine if the LOC is exceeded.  In
addition, the model could iteratively estimate the factor
(multiplication factor) by which the time-series would need to be
increased or decreased for the model effects index to exactly equal the
LOC, thereby providing further information useful for risk assessment
and management.

Model Formulation and Parameterization

General Model Formulation and Parameterization for Reference Conditions

The aquatic community simulation model selected to provide the desired
extrapolation tool was the "Comprehensive Aquatic Systems Model" (CASM).
 This selection was based on the extensive history of this model (and it
predecessors and derivatives) in aquatic risk assessments (O'Neill et
al. 1981, 1983; DeAngelis et al. 1989; Bartell et al. 1992, 1999, 2000;
Hanratty and Stay 1994; Hanratty and Liber 1996; Naito et al. 2002,
2003; Bartell 2003) and its ready adaptability for use in the atrazine
assessment methodology.  Other conceptually similar models (e.g.
AQUATOX) could have been used and would likely have provided similar
extrapolations if comparably formulated and parameterized.  General
information regarding the CASM model in the aforementioned references
and its specific adaptation for atrazine is described in Volz et al.
(2007).  Only a summary of the general nature of the model and its basic
parameterization for reference conditions will be given here.

The state variables for CASM are (a) the biomasses for various species
defining a simplified aquatic community and (b) concentrations for
dissolved oxygen, dissolved and particular organic matter, and certain
nutrients.  The state equation for each biological species is a
bioenergetics equation that includes terms, as appropriate to each
species, which define gains/losses of biomass from photosynthesis,
respiration, food consumption, export/import, mortality, etc.  For
example, for phytoplankton species i, the basic bioenergetics equation
is:

 	(1)

where Bi is the biomass of the species, t is time, pi is the gross
photosynthesis rate, ri is the respiration rate, si is a sinking rate,
mi is a natural mortality rate, and gij is the grazing rate by a
consumer species j.  Each of these rates requires specification of (a) a
functional form that relates the rate to various state and input
variables and (b) one to several parameters required for function; for
example, pi is a function of light, temperature, and nutrients which
includes parameters for maximum photosynthesis rate, light saturation,
optimal temperature, and nutrient half-saturation concentrations
(DeAngelis et al. 1989; Bartell et al. 1999, 2000; Bartell 2003).
Input variables to CASM include light, temperature, nutrient imports,
water flow, and depth.

For atrazine assessment needs, a version of CASM was formulated and
parameterized to represent a second- to third-order midwestern U.S.
stream that would be a typical recipient of runoff from atrazine corn
applications (Volz et al. 2007).  This model aquatic community includes
ten species of phytoplankton, ten species of periphyton, six species of
macrophytes, two species of zooplankton, five species of benthic
invertebrates, seven species of fish, and bacteria in both the water
column and sediment.  Physicochemical environmental input variables were
based on data for an Ohio stream, and parameters for the bioenergetics
equations were obtained from peer-reviewed technical literature (Volz et
al. 2007).  The simulation is for a calendar year and, in the absence of
atrazine exposures, the model shows seasonal trends for the populations
of different taxa that made the model appropriate for assessing stressor
effects on community dynamics (Volz et al. 2007).

Incorporation of Toxicity Information into the Model

One challenge in applying a bioenergetics-based community model such as
CASM to toxicity assessments is that toxicity tests do not directly
address the effects of the chemical on the specific parameters within
the bioenergetics state equation (e.g., Equation 1).  For example, a
plant growth test will provide information on the net growth rate, but
not on how specific parameters within the photosynthesis and respiration
functions are being affected.  There is just not enough information in
the toxicity tests to independently adjust all the model parameters.

To address this problem, CASM uses the bioenergetics equations to
simulate the concentration/effects curve of toxicity tests, and
estimates a "toxic effects factor" (TEF) for each concentration that,
when used to adjust several model parameters, will reproduce the effect
level at that concentration (DeAngelis et al. 1989; Bartell et al. 1999,
2000; Bartell 2003; Volz et al. 2007).  For plants, the version of CASM
developed for this atrazine assessment offers two alternatives regarding
what model parameters are altered by the TEF.  For the "General Stress
Syndrome" (GSS), the TEF is used to (a) reduce photosynthesis by
decreasing the maximum photosynthesis and increasing light and nutrient
saturation parameters and (b) increase the various loss terms, such as
respiration, in the bioenergetics equation.  For the "Photosynthesis
Stress Syndrome" (PSS), only the maximum photosynthesis parameter is
modified.

To model the response of each plant species, CASM therefore requires the
specification of a function for the fraction reduction in plant growth
versus atrazine concentration over a specified exposure duration.  The
assumed functional shape of percent growth inhibition versus
log10(concentration) is the cumulative Gaussian distribution, so that
the probit-transform of the percent growth reduction versus
log10(concentration) is linear.  This function can be specified by
inputting either (1) the slope and intercept of this linear transform or
(2) an exposure concentration showint 50% effect (EC50) and No Observed
Adverse Effect (NOEC) which defines the function under an assumption
that the NOEC is five probit units below the EC50.  For the exposure
concentration on each day of a simulation, CASM estimates a TEF that
causes the bioenergetics equation to produce the growth reduction
specified by the toxicity function over the specified test duration
under optimal nutrient, light, and temperature conditions.

To provide the needed plant toxicity information, individual plant
species toxicity tests collated by Giddings et al. (2000) and identified
from other sources were reviewed. A subset of these were identified for
use in model parameterization based on a suitably-defined growth EC50
over a specified duration (  REF _Ref180376742 \h  Table II-1 ).  Next,
the geometric mean EC50s for each species were computed, and assigned to
the various plant species within CASM based on bioenergetics similarity
of the test species to the modeled species (  REF _Ref180376742 \h
Table II-1 ).  The probit versus log10(concentration) slope for each
species was set to 3.3 based on the mean of the slopes reported in the
U.S.EPA Office of Pesticide Programs one-liner database (Montague 1998).
 This slope and the mean EC50s for each species were used to calculate
the probit line intercepts for input into CASM.

The assignment of EC50s to the modelled plant species is a possible
major source of uncertainty given the variability of results within
species (  REF _Ref180376742 \h  Table II-1 ).  The EC50s in Table II-1
were subject to an analysis of variance to compare between-species and
within-species variability.  For log10-transformed EC50s, this analysis
indicated an overall mean of 2.01 (corresponding to an untransformed
EC50 of 103), a within species mean squared error of 0.481 (df=45), a
between species mean squared error of 0.486 (df=36), and a
non-significant F-statistic (1.02). This lack of a demonstrated
difference between the within-species and between-species variability
indicates a source of variability which makes any single EC50 for a
species subject to considerable uncertainty.  Additionally, it may also
make mean EC50s based on a large number of tests subject to some amount
of uncertainty in any particular situation.  This is not to say that
sensitivity to atrazine does not vary among species, but rather that
various sources of variability make the selection of specific EC50s
uncertain.  Alternative sets of plant EC50s should therefore be examined
(see Section   REF _Ref180376659 \w \h  II.D ).

Toxicity values were also assigned to the animal species (Volz et al.
2007).  Because the lowest EC50 assigned to an animal species was much
greater than the EC50s for most of the plant species, substantial
effects on the plant community would occur at concentrations below any
toxic effects on animals in these simulations.  Thus, assessment results
are not sensitive to the animal toxicity value selection, which will not
be further discussed or evaluated here.

Table   STYLEREF 1 \s  II -  SEQ Table \* ARABIC \s 1  1  Atrazine plant
toxicity tests used in assessment methodology.

Species	EC50 (ppb)	Dura-tion (d)	Reference	Species Geometric Mean EC50
CASM

Population

Assignment

GREEN ALGAE

Ankistrodesmus braunii	60	11	Burrell et al. 1985	61	#5 – Phytoplankton

#15 - Periphyton

	61	24	Larsen et al. 1986

Chlamydomonas nostigana	330	3	Kallqvist and Romstad 1994	330

	Chlamydomonas reinhardi	10	10	Schafer et al. 1993	59	#4 - Phytoplankton

	19	1	Larsen et al. 1986

	176	4	Fairchild et al. 1998

	350	3	Schafer et al. 1994

Chlorella fusca	15	1	Faust et al. 1993	15

	Chlorella pyrenoidosa	125	1	Stratton and Giles 1990	280

175	5	Gramlich and Frans 1964

	282	5	Montague 1998

	1000	14	Stratton 1984

Chlorella vulgaris	94	4	Fairchild et al. 1998	88	#6 – Phytoplankton

#20 - Periphyton

	25	11	Burrell et al. 1985

	293	1	Larsen et al. 1986

Scenedesmus obliquus	38	1	Larsen et al. 1986	38	#3 – Phytoplankton

#14 - Periphyton

Scenedesmus quadricauda	169	4	Fairchild et al. 1998	169

	Scenedesmus subspicatus	21	4	Kirby and Sheahan 1994	76

72	3	Schafer et al. 1994

	110	4	Geyer et al. 1985

	200	3	Zagorc-Koncan 1996

Selenastrum capricornutum	4	4	University of Mississippi 1991	84	#16 -
Periphyton

	26	4	Caux et al. 1996

	50	4	Versteeg 1990

	53	5	Montague 1998

	55	5	Hoberg 1993c

	70	1	Turbak et al. 1986

	95	5	Roberts et al. 1990

	117	4	Fairchild et al. 1998

	118	3	Radetski et al. 1995

	128	4	Gala and Giesy 1990

	130	4	Hoberg 1993b

	158	3	Mayer et al. 1998

	200	3	Kallqvist and Romstad 1994

	214	7	Abou-Waly et al. 1991

	235	4	Fairchild et al. 1997, 1999

	359	3	van der Heever and Grobbelaar 1996

	42	1	Larsen et al. 1986

CRYPTOMONADS

Cryptomonas pyrinoidifera	500	6	Kallqvist and Romstad 1994	500	#17 -
Periphyton

BLUEGREEN ALGAE

Anabaena cylindrica	178	1	Stratton 1984	178	#19 - Periphyton

Anabaena flos-aquae	58	3	Abou-Waly et al. 1991	342	#10 - Phytoplankton

	230	5	Hughes et al. 1988

	3000	4	Fairchild et al. 1998

Anabaena inaequalis	100	14	Stratton 1984	100	#8 - Phytoplankton

Microcystis aeruginosa	129	5	Parrish 1978; Montague 1998	285

630	6	Kallqvist and Romstad 1994

Microcystis sp.	90	4	Fairchild et al. 1998	90	#7 – Phytoplankton

#18 - Periphyton

Synechococcus leopoliensis	130	3	Kallqvist and Romstad 1994	130	#9 -
Phytoplankton

DIATOMS

Cyclotella sp.	430	6	Kallqvist and Romstad 1994	430	#13 - Periphyton

Navicula pelliculosa	60	5	Hughes et al. 1988	60	#1 – Phytoplankton

#11 - Periphyton

Skeletonema costatum	265	2	Walsh 1983	69	#2 – Phytoplanton

#12 - Periphyton

	260	2	Mayer 1987

	50	2	Walsh et al. 1988

	24	5	Parrish 1978; Montague 1998

	55	5	Hoberg 1993c

	24	4	Montague 1998

MACROPHYTES

Ceratophyllum demersum	22	14	Fairchild et al. 1998	22	#23 - Macrophyte

Elodea canadensis	1200	10	University of Mississippi 1991	159	#24 -
Macrophyte

	21	14	Fairchild et al. 1998

Hydrilla verticillata	110	14	Hinman 1989	110

	Lemna gibba	22	14	Hoberg 1993a	159

(Genus Mean)	#25 – Macrophyte

	180	7	Hoberg 1991

	170	5	Montague 1998

Lemna minor	8700	14	University of Mississippi 1991

	92	4	Fairchild et al. 1998

	56	10	Kirby and Sheehan 1994

	153	4	Fairchild et al. 1997

Myriophyllum heterophyllum	132	14	Fairchild et al. 1998	132	#22 -
Macrophyte

Najas sp.	24	14	Fairchild et al. 1998	24

	Thallassia testudinum	320	1.7	University of Mississippi 1991	320

	Potamogeton perfoliatus	474	21	Forney and Davis 1981	170	#26 -
Macrophyte

	130	28	Kemp et al. 1985

	80	0.1	Jones et al. 1986

Vallisneria americana	163	42	Forney and Davis 1981	163	#21 - Macrophyte

Model Implementation and Example Application

Model Effects Index Selection

The basic output of CASM is a time series of daily biomass values for
each modeled species over the simulation year.  Various possibilities
exist for synthesizing this information into a model effects index for
the primary producer community.  The index might address how just total
plant biomass or production is perturbed in an exposed system compared
to an unexposed system, or might also reflect perturbations in the
relative biomasses of each species.  The index might indicate the
maximum perturbation across the entire simulation, the average
perturbation over the entire simulation, an average perturbation during
a fixed time window during the simulation, or a maximum running average
of the perturbations.

The model effects index selected is based on the average, over the
entire simulation, of the daily Steinhaus Similarity Indices (SSI).  The
SSI quantifies the similarity between two communities (in this case,
exposed and control model-calculated communities).  For each day of the
simulation, the SSI is calculated as:

where BR,i is the daily biomass of the ith species in the reference
(control) simulation and BE,i is the daily biomass of the ith species in
the atrazine-exposed simulation.  SSI values thus reflect changes in
both absolute and relative population sizes, ranging from 1.0 for
identical community structures to 0.0 for completely disjoint
communities.  The model effects index will be expressed as the percent
reduction of this average SSI from the maximum value of 1.0 (i.e., a
model effects index of 10 represents an average SSI of 0.90).

This type of measure was selected in order to include changes in
relative species abundance as part of the risk assessment, rather than
just some measure of total primary productivity.  The entire simulation
period was chosen as the averaging period to provide a consistent
measure appropriate to a variety of exposure time series shapes. For
example, two exposure time series might have the same initial atrazine
peak that produces the same maximum perturbation of the SSI, but one of
the time series might have additional peaks that cause more effects on
average than the other time series.  Although a year-long average for
the model effects index will be reduced by including pre-exposure
periods in which both the control and exposed communities are identical
(daily SSI=1.0), this will not alter risk assessments significantly
because both the LOC and model effects indices for systems of interest
will be reduced to similar degrees.

Although the rationale for choosing the average SSI over the entire
simulation as the model effects index has been described above, it is
not certain a priori how well this choice will support discrimination of
micro/mesocosm effects scores or their extrapolation to field exposures.
 Therefore, the sensitivity analysis below will address what level of
uncertainty in risk assessments might exist because of other possible
choices for the model effects index.

LOC Determination for Base Model Configuration

A "base model configuration" was selected for conducting assessments
that was (a) the Midwestern U.S. stream community structure discussed
above and described in Volz et al. 2007, (b) the Ohio stream
physicochemical parameters also discussed above and in Volz et al. 2007,
(c) the use of the GSS for toxicity calculations, and (d) the toxicity
relationships summarized in   REF _Ref180376742 \h  Table II-1 .  In
addition, for model simulations of the exposures used in the
micro/mesocosm data base, exposures were started on Julian day 105,
selected as a typical average starting date for atrazine applications in
the corn belt.

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  3  LOC=4.0
based on correlation of micro/mesocosm effect scores with model effect
index values (upper panel), and comparison of model LOC to micro/
mesocosm effect scores at different exposure concentration/ durations
(lower panel)

Simulations were conducted without atrazine exposure (control) and with
each atrazine exposure concentration/duration from the micro/mesocosm
database, and used to calculate a year-long average SSI for each
atrazine exposure.  For each micro/mesocosm exposure, the upper panel of
  REF _Ref180377373 \h  \* MERGEFORMAT  Figure II-3  compares the
micro/mesocosm effects score to the model effect index (percent
reduction in the average modeled SSI from its maximum value of 1.0).
The LOC for the model effects index for this consultation was selected
simply by determining the midpoint of the range of values for which
false negatives (i.e., micro/mesocosm scores of 3-5 for exposures with a
model effect index below the LOC) equaled false positives (i.e.,
micro/mesocosm scores of 1-2 for exposures with a model effect index
above the LOC). In a similar fashion, the LOC could also be identified
in order to minimize the number of false negatives.   REF _Ref180377373
\h  \* MERGEFORMAT  Figure II-3  illustrates the LOC determination of
4.0, with seven false positives and seven false negatives, all of which
are within a factor of 3 of the LOC.

The lower panel of   REF _Ref180377373 \h  \* MERGEFORMAT  Figure II-3
illustrates the discrimination of the micro/mesocosm 1-2 and 3-5 score
groups with the model LOC=4.0. This figure shows the
concentration/duration dependence of micro/mesocosm scores and includes
a model-calculated line showing the concentration/duration dependence of
the LOC. It also shows how close the false negatives and false positives
are to the LOC on an exposure concentration scale, differing again by no
more than a factor of 3.  Better performance than this would not be
expected given the overlap between the score groups.  The model does
show more time dependence than is evident in the micro/mesocosm data,
because the model effects index reflects effects over an entire season,
and not just during the exposure period.  This results in some tendency
for the false positives to be at longer durations than the false
negatives.

The base model described here differs somewhat from that described in
the October 2003 IRED addendum (US EPA, 2003b) or by Volz et al (2007),
most notably that the LOC is 4.0 rather than 5. Several changes are
responsible for this change in the LOC.  Most importantly, the model
effects index previously had only been averaged from Julian Day 105 to
the end of the year, but is now averaged over the entire year to include
any effects before this date.  Because the early parts of the year
typically have low enough exposures to have no effects, this longer
averaging period will include significant stretches of time when the SSI
deviation is zero, thus reducing the average deviation (by a factor of
1.4).  This has little impact on actual assessments because the model
effects index for the field chemographs of interest will be reduced by
the same factor as the LOC (i.e., this change will result in a greater
likelihood of exceeding the LOC only for those field chemographs with
early exposures).  To a lesser degree, the LOC has also been affected by
other changes from the earlier version, including improvements in how
toxicity test information is translated into bioenergetics equations,
applying the results of just one simulation using the best estimate for
toxicity factors rather than the median results of multiple simulations
based on random selections from a distribution of toxicity factors, and
direct simulations of each exposure in the micro/mesocosm dataset rather
than interpolations from a matrix of different exposure/duration
combinations.  Again, however, higher or lower values for the LOC are
largely compensated for by higher or lower effects in the simulations
for the field chemographs of interest.  The previous CASM implementation
also differed in having average screening concentrations that needed to
be exceeded before CASM was even run; the purpose of this was to avoid
unnecessary use of the substantial computing resources this earlier CASM
implementation required. In the current version, this is no longer an
issue due to faster computing resources and the elimination of multiple
simulations .

Example Applications

Once an LOC is determined based on comparing micro/mesocosm effect
scores with the model effects indices, application of the model simply
involves conducting a simulation for an exposure time series of interest
and determining whether the model effects index from this simulation
exceeds the LOC.  The upper panel of   REF _Ref181757713 \h  \*
MERGEFORMAT  Figure II-4  shows the model effects indices for sixteen
such simulations, using the example chemographs from   REF _Ref180376001
\h  Figure II-1 .  Only two of the chemographs exceeded the LOC, but a
few others have model effects index values close to the LOC.

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  4  Model effect
index values and multiplication factors for sixteen sample chemographs,
using base model configuration

In addition to simply determining whether the model effect index exceeds
the LOC, the model can also evaluate the multiplication factor by which
the exposure time-series must be changed so that the model effects index
equals the LOC. If the LOC is exceeded, this factor indicates the
reduction in exposure necessary to be below the LOC. If the LOC is not
exceeded, this factor indicates the amount exposure could be increased
and still not exceed the LOC.  Such information can benefit remediation
efforts, evaluations of "margins of safety", and assessments of possible
risks from future activities and conditions.  The lower panel of   REF
_Ref181757713 \h  \* MERGEFORMAT  Figure II-4  provides the
multiplication factors for the sixteen example chemographs.  The
multiplication factors for the two chemographs that exceeded the LOC
trigger are less than 1, indicating the amount of reduction needed to
reduce effects to the LOC.  The other multiplication factors range from
barely over 1 to about 5, indicating the margin of safety present in
these exposure time series.

Sensitivity Analysis

Overview

The proposed methodology depends on whether the predicted relative
effects in different exposure time-series from a single model
configuration are useful approximations for extrapolations among a range
of experimental and natural ecosystems.  Although suitable data from
experimental and natural ecosystems are not available to directly
validate the utility of such extrapolations, this methodology can be
validated to some degree by evaluating how sensitive results compare to
alternative model configurations.  Such a sensitivity analysis has three
potential benefits.  First, if relative effects are sufficiently similar
across a range of possible model configurations, the need to
independently justify specific options for model configuration is
reduced.  Second, a lack of sensitivity of results to a range of
possibilities in a simulated system increases confidence that
extrapolations among natural systems also would not be highly sensitive
to system properties.  Third, a sensitivity analysis will provide some
quantitative information on certain sources of uncertainty, which can
inform risk management decisions for assessments with exposures near the
LOC.

A variety of decisions went into formulating the base model
configuration used in Section   REF _Ref180377931 \w \h  II.C  and
warrant consideration in this sensitivity analysis.  The community
structure and bioenergetics equations of the base model configuration
were intended to describe a second- to third-order Midwestern stream.
Alternative community structures currently under development will be
part of an expanded sensitivity analysis in the future and thus are not
considered here.  Areas of method development that are part of the
sensitivity analysis include (a) the selection of the model effects
index, (b) the start date for model simulations of the micro/mesocosm
exposures, (c) the environmental driving variables (nutrients,
temperature, and light), and (d) the EC50 selection.  A comparison was
also made regarding use of the GSS versus the PSS, but no significant
differences in results were found.

Because the LOC is dependent on the model configuration and
parameterization, the LOC for each model configuration addressed in the
sensitivity analysis must first be determined.  Each model configuration
requires new simulations of an unexposed system and of each exposure
concentration/duration in the micro/mesocosm data set, after which a new
LOC is selected for discriminating the micro/mesocosm scores with this
model configuration.   Simulations are then done again for the 16
example time series in   REF _Ref180376001 \h  Figure II-1 , determining
the model effects index to compare to the LOC and also the
multiplication factors that specify how much the exposure must be
changed to equal the LOC.  Because each model configuration has its own
LOC, the absolute model effects index values cannot be compared across
model configurations to assess their similarity.  However, the
multiplication factors for each configuration can be compared, because
they each address changing the exposure to equal the respective LOCs,
and thus address how similar the risk assessments are for each
configuration.  Therefore, the basic criterion for assessing similarity
in the subsequent sections will be to compare the multiplication factors
for the alternative model configurations to the base model
configuration.

Model Effects Index

As already noted, alternatives to the selected model effects index (the
percent reduction in the annual average of the daily SSI values from the
maximum value of 1.0) could include (a) a measure of total plant
community biomass/production, not considering relative changes in
different plant species, and (b) a different averaging period.  For this
sensitivity analysis, two alternatives were examined.  One alternative
(AVP) used the difference between the total plant biomass in the control
and exposure simulations, but still averaging over the entire
simulation.  The other alternative (MXS) still used the daily SSI
values, but used the maximum percentage deviation from 1.0 anytime
during the simulation as the effects index, to contrast the smallest
averaging period (one day) with the average over the entire simulation.

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  5  Comparison
of multiplication factors for the base model configuration to
alternatives with different model effect indices

The upper panel of   REF _Ref180378699 \h  \* MERGEFORMAT  Figure II-5
contrasts the multiplication factors for these two alternative effects
indices with the effects index for the base model configuration (solid
line).  To better show the magnitude of deviations, the lower panel of
REF _Ref180378699 \h  \* MERGEFORMAT  Figure II-5  show the relative
multiplication factors (the multiplication for the alternatives divided
by that for the base model configuration) on a finer scale, with
horizontal lines denoting deviations of a factor of 1.2, 1.5, and 2.0.

Although the AVP endpoint had a markedly lower LOC (2.70) and the MXS
endpoint a markedly higher LOC (7.45), than the base case (4.00), the
multiplication factors for these different model configurations are all
approximately the same.  This is because a change in the LOC arises from
an alternative model configuration producing generally higher or lower
effects than the base case for all exposures, so that a higher LOC is
being compared to a similarly higher effects index for any simulation,
and a lower LOC is being compared to lower model effects indices.
Therefore, there is little change in the multiplication factors.  In
REF _Ref180378699 \h  \* MERGEFORMAT  Figure II-5 , alternative model
effects indices indicate exposures of concern within 20% of that of the
base model effects index for all the example exposures, with average
deviations of less than 10%.  This close agreement indicates that the
choice of model effects index does not result in any substantial changes
to risk assessments using this methodology, so that the index selected
for the base model has general applicability within reasonable
uncertainty.

Exposure Start Date

The simulations of the exposures of the micro/mesocosm data set to
determine an LOC for the model effects index assume a start date for the
model exposure on Julian day 105 (April 15) for the base model
configuration.  In natural systems, the first significant atrazine
exposure will vary depending on application practices and rainfall, and
so different options for this start date are of obvious interest.  Start
dates 15 days before and after the base choice were therefore tested.
This shift only affects simulations of the constant exposure/fixed
duration combinations used in the LOC determination, and does not affect
the example field time series to which the model is then applied.  Thus
any change in LOC will lower or raise the multiplication factor by a
fairly uniform amount.

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  6  Comparison
of multiplication factors for base model configuration to alternative
configurations with different start dates

  REF _Ref180380098 \h  \* MERGEFORMAT  Figure II-6  shows the results
for these alternative start dates.  An earlier start slightly increases
model effect index values, leading to a higher LOC (4.25).  Because
model simulations for the example time series are not affected by the
changed start date, multiplication factors are slightly elevated, but by
less than 10%.  In contrast, a later start slightly decreases model
effect index values, lead to a lower LOC (3.60) and slightly lower
multiplication factors (range from 10-18% lower (average 14%).  As was
true for the choice of the model effects index, these limited effects
indicate little sensitivity of results to the choice of start date and
good applicability of the start date choice for the base model
configuration.

Environmental Parameters

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  7  Comparison
of multiplication factors for the base model configuration to
alternative configurations with lower and higher nutrients.

The base model configuration employs environmental data (nutrients,
light, temperature) based on an Ohio stream (Volz et al. 2007).    REF
_Ref180380641 \h  \* MERGEFORMAT  Figure II-7  shows the effect of
altering nutrients (nitrogen, phosphorus, silicon) up or down, together,
by a factor of 2.    REF _Ref180380907 \h  Figure II-8  shows effects of
changing temperature up or down by 5o C.    REF _Ref180381121 \h  Figure
II-9  shows the effects of halving or doubling light intensity.

For all these variables, the multiplication factors were never more than
20% different from those for the base model configuration, and the mean
deviation was less than 10%.  As for the model effects index and the
starting date, risk assessments using this methodology are insensitive
to the choices for these environmental variables.

Plant Toxicity EC50s

As discussed in Section   REF _Ref180833304 \w \h  II.B.2 , there is
considerable uncertainty in the assignment of EC50s to the modeled plant
species, especially because of the large variation of values within a
species.  Based on the analysis of variance of the plant toxicity data
discussed in Section   REF _Ref180833318 \r \h  B.2 , ten alternative
sets of plant EC50s were randomly selected from a log-normal
distribution with median 100 ug/L and log standard deviation of 0.4 (
REF _Ref180381356 \h  Table II-2 ).  The methodology was applied using
each of these toxicity data sets, and the mean and standard deviation of
the log multiplication factors was computed for each sample chemograph.

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  8  Comparison
of multiplication factors for the base model configuration to
alternative configurations with lower and higher temperature

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  9  Comparison
of multiplication factors for the base model configuration to
alternative configurations with lower and higher light.

  REF _Ref180381585 \h  Figure II-10  compares the mean and standard
deviation of the multiplication factors for the alternative plant EC50
sets to the multiplication factors for the base model configuration.
Compared to the other factors in this sensitivity analysis, this
variation of EC50s resulted in greater deviations of multiplication
factors from that of the base case, and thus represents the greatest
source of uncertainty.  However, the base model configuration is still
within one standard deviation of the mean of the alternative toxicity
data sets and within 20% of the mean except for chemograph #1.
Furthermore, even if the range of the alternative toxicity values were
extended to two standard deviations, this differs by no more than a
factor of two, except for the upper tail of chemograph #1, for which the
base configuration is more conservative.  This justifies the use of the
base model configuration as being within this uncertainty of toxicity
values, and documents the magnitude of an important uncertainty.

Table   STYLEREF 1 \s  II -  SEQ Table \* ARABIC \s 1  2  Ten sets of
randomly selected EC50s (ug/L atrazine) for sensitivity analysis.

Mode Species	Set 1	Set 2	Set 3	Set 4	Set 5	Set 6	Set 7	Set 8	Set 9	Set
10

Phytoplankton 1	48	241	34	200	225	179	109	102	1196	292

Phytoplankton 2	125	96	89	65	84	112	11	42	150	160

Phytoplankton 3	54	42	65	55	168	167	641	63	162	89

Phytoplankton 4	149	185	438	41	122	148	31	164	351	788

Phytoplankton 5	198	53	241	56	13	283	67	22	133	324

Phytoplankton 6	66	23	44	121	325	384	208	205	90	12

Phytoplankton 7	1077	282	411	116	107	16	148	78	31	167

Phytoplankton 8	37	39	141	410	154	109	131	220	76	836

Phytoplankton 9	142	16	137	252	29	203	176	108	528	58

Phytoplankton 10	365	64	102	58	188	234	248	62	94	130

Periphyton 1	62	561	57	14	164	111	74	25	94	63

Periphyton 2	24	336	112	113	66	82	231	106	26	23

Periphyton 3	204	204	102	125	819	107	140	112	388	51

Periphyton 4	73	121	74	80	329	158	112	245	124	128

Periphyton 5	41	341	31	23	48	47	97	187	63	241

Periphyton 6	289	151	59	143	94	161	207	326	30	50

Periphyton 7	163	58	29	232	420	83	55	33	160	97

Periphyton 8	47	156	300	87	220	128	67	308	69	84

Periphyton 9	187	142	68	140	118	16	96	152	128	107

Periphyton 10	317	54	20	84	59	99	200	852	53	823

Macrophyte 1	269	62	285	407	86	105	85	136	44	90

Macrophyte 2	70	246	101	92	59	19	51	30	86	334

Macrophyte 3	288	50	61	51	214	116	76	123	27	677

Macrophyte 4	88	6	46	477	192	51	50	55	130	59

Macrophyte 5	55	55	30	38	136	58	485	230	248	16

Macrophyte 6	47	419	708	81	47	49	129	416	176	116

Figure   STYLEREF 1 \s  II -  SEQ Figure \* ARABIC \s 1  10  Comparison
of multiplication factors for the base model configuration (bold solid
line) to the mean (bold dashed line) and mean +/- one standard deviation
(narrow dashed lines) of the multiplication factors for ten alternative
plant EC50 sets.

Chronic, Low-Level Exposures

Although this sensitivity analysis was oriented to choices for model
configuration and inputs, it required testing of a broad set of
representative chemographs (Figure II-1) to define a universe of
exposures to which conclusions about sensitivity apply.  These
chemographs all lacked any significant atrazine exposure during the
first few months of the year, but other recent model simulations with
extended exposures of a few ug/L during this period indicated
significant model responses which could have significance to risk
assessments.  Further evaluations of these responses have identified
possible problems in CASM regarding (a) translation of the lower tails
of the toxicity dose/response curves into the bioenergetics equations
and (b) the sensitivity of some components of the modeled community to
small, prolonged effects during the first few months of the year.  These
issues are currently being addressed, and will entail the development of
alternative model configurations which more appropriately address low
concentration and early-year exposures.  Once these changes are made,
they will support revisions to the analyses reported in this paper.

Slope of the Toxicity Curve

The current CASM_Atrazine configuration used a default probit slope of
3.31. This sensitivity analysis did not assess the performance of the
model with varying the slope of the toxicity curve or the EC50 within
the bounds of uncertainty. The slope of the toxicity curve might impact
whether certain types of chemographs – in particular, short-term, high
concentration exposures characteristic of Midwestern streams – exceed
the LOC trigger. While the Agency plans to include this in future
sensitivity analyses, it is also seeking SAP input on this issue.

Summary and Next Steps

The methodology described here addresses the need to assess the likely
ecological effects of diverse, highly time-variable atrazine exposures.
This was accomplished by using a community simulation model to estimate
the relative effects of different exposure time series, with the
absolute level of concern for model-predicted effects based on
correlations with effects observed in microcosm and mesocosm studies.
The feasibility of this methodology has been established for
well-defined exposure time-series and the model-defined level of concern
has been demonstrated to discriminate well between acceptable and
unacceptable effects in the microcosm and mesocosm studies.  A
sensitivity analysis demonstrated that results are similar across a wide
range of options for model formulation, parameterization, and inputs.
Additional work in progress will expand the sensitivity analysis,
further refine estimates for uncertainty associated with the base model
configuration, address further changes to CASM algorithms for
incorporating toxicity information, and quality assure the software
being used.

SAP Charge Questions on the Use of the CASM Model

(1) 	Please comment on the use of a community simulation model for
assessing the relative effects of different exposure time series. Please
provide any recommendations for a community response model other than,
or along with, CASM that could be used for assessing the effects of
atrazine. What are the strengths and weaknesses associated with the
other model(s). Please comment on approaches that do not require an
aquatic community response model and discuss the advantages and
disadvantages of any alternative non-modeling approaches for
extrapolating the effects seen in micro/mesocosm data to the effects
resulting from field exposure.

(2) 	The general methodology employed in this analysis consists of (a)
correlating model outputs to micro/mesocosm data to determine a model
LOC and (b) applying the model to chemographs of interest to determine
whether the LOC is exceeded.  Please comment on the scientific strengths
and limitations of this approach.

(3) 	Please comment on the reasonableness of the general CASM_Atrazine
model formulation and parameterization, and the various options selected
for the base model configuration.

(4) 	Please comment on whether the described sensitivity analyses are
suitable for characterizing uncertainties associated with the choice of
options for configuration of the base model and the input variables.
What additional sources of uncertainty alternatives should be examined
in this analysis?  Please comment on whether the sensitivity of results
to the slope of the toxicity curve, as well as the EC50, should be
examined to address possible effects on responses to short pulses.

(5)	During its review of the CASM_Atrazine model, the Agency found that
the model appears to overestimate the effects of low, chronic
concentrations possibly due to the way the model simulates population
levels and decline of macrophytes early in the year.

The Agency sees two approaches for addressing this issue: (1) exclude
early season atrazine exposures from the chemograph inputs, or (2)
modify the model to better account for the impacts of early-season
exposures. Please comment on the strengths and weaknesses of the
Agency’s approaches and provide recommendations for any alternatives.

Given that the Agency identified this issue during the exposure
evaluation, please provide recommendations on additional steps the
Agency could take for quality assurance for the model and methodology.

 Atrazine Monitoring Program For Ecological Effects: Determining The
Extent Of Waters Exceeding Effects-Based Thresholds For Atrazine

Over the years, atrazine surface water monitoring data have been
collected by numerous programs and sources, including the U.S.
Geological Survey (Goolsby and Battaglin, 2003; USGS, 2006b; USGS,
2007), registrants, states, and universities (see Williams et al, 2004b
for a listing and description of atrazine monitoring studies). However,
these monitoring studies were conducted for different purposes and were
not intended to evaluate the extent to which atrazine concentrations in
water bodies would exceed the LOC or to identify the areas/conditions
under which the LOCs might be exceeded.

This section describes the objectives of the monitoring study, the
watershed selection process, and the design and conduct of the
monitoring study. The subsequent analysis of the monitoring results
includes an evaluation of whether exposure exceeds the LOC, sensitivity
analyses of the impacts of sampling and environmental factors, and
extrapolation of the results for the monitoring sites to the larger
population of vulnerable watersheds. The Agency is soliciting feedback
from the SAP related to the interpretation of the results and
sampling/study design issues critical to the application of this
approach for assessing potential atrazine impacts to other water bodies.

Monitoring Study Objectives

The purpose of the monitoring program in flowing waters is to measure
the magnitude and extent of atrazine concentrations in water bodies with
the greatest potential vulnerability to atrazine exposure from use in
corn and sorghum production (based on atrazine use and runoff potential)
and to estimate percentage that exceed the LOC. The study focused on
water bodies in the most vulnerable watersheds because this is where
elevated concentrations of atrazine were most likely to occur,
increasing the likelihood of identifying water bodies that might exceed
the ecological thresholds of concern. If no exceedances occurred in
these watersheds, the likelihood of finding atrazine exposures of
concern would be lower in less vulnerable watersheds.

The 2003 addendum to the Atrazine IRED specified that the monitoring
program initially focus on flowing waters associated with corn and
sorghum production (US EPA, 2003b). A separate pilot monitoring study is
underway for water bodies in sugarcane areas. Additionally, the Agency
is evaluating existing monitoring of reservoirs to determine the
potential impact of atrazine on aquatic communities in static water
bodies. The sugarcane and reservoir efforts are not the subject of this
SAP.

The atrazine monitoring program was intended to identify watersheds in
corn and sorghum areas that contained water bodies exceeding the
effects-based LOC trigger described in Section II. The monitoring
program also gathered additional information on atrazine use and
practices, watershed and water body characteristics, and other factors
to facilitate identifying water bodies beyond the initial sampling pool
that have the potential for atrazine loadings that exceed effects-based
thresholds.

The atrazine ecological monitoring program was designed to:

Determine the extent to which waters in vulnerable watersheds exceed
effects-based thresholds for atrazine, and

Collect information that will help identify where the waters that exceed
the effects-based atrazine thresholds occur.

This section focuses on the first objective. Section   REF _Ref180988777
\r \h  IV  describes the approach the USEPA proposes to take for
addressing the second objective.

Study Design

The study design relied on the wealth of information already known about
the fate and transport properties of atrazine, its use, and its history
of occurrence in water. The following sections briefly describe the site
selection process, from identifying vulnerable watersheds to selecting
monitoring sites, and the sampling/monitoring methods. Williams et al
(2004a) provide details of the study design.

Site Selection

The site selection process used existing knowledge about atrazine
occurrence in water to maximize the potential for finding waters that
exceed the LOC. Key factors were:

Use: amount and intensity of atrazine use on corn and sorghum

Vulnerability based on use/runoff: integrating both atrazine use and
surface runoff vulnerability

Representative sampling: 40 sites selected from most vulnerable
watersheds, with probability of selection proportional to use

This section summarizes the steps used to identify those watersheds that
are expected to be most potentially vulnerable to atrazine loading. From
this upper tier of vulnerable watersheds, a spatially-balanced survey
design was used to select a representative sample of 40 watersheds.
Sub-watershed land use and flow data were used to identify candidate
stream segments for monitoring within those watersheds.  Details and
documentation are available in Williams et al (2004a).

Watershed Vulnerability Assessment

The monitoring program used a watershed-based approach to target areas
for monitoring. Because of the widespread use of atrazine on corn and
sorghum, the initial approach focused on nationally-available data.
Watershed, or hydrologic unit, boundaries define the extent of surface
water drainage to a stream point and provide a framework for organizing
and processing spatial data pertinent to the vulnerability of watersheds
and receiving water bodies to atrazine loading.

Hydrologic unit maps defined by the USGS (Seaber et al, 1987; USGS,
2006a) provide a hierarchical system of mapping watersheds. This system
includes six levels, with national maps available for the first four
levels at the time the study was being designed. The assessment used the
fifth, HUC-10 or HUC-11, level, representing watersheds that are
typically 40,000 to 250,000 acres in size. Because mapping at this level
was not available for all states, Syngenta collected the best available
coverages for the 37-state area that encompassed the extent of
significant atrazine use on corn and sorghum (Williams et al, 2004a,
2004b).

HUC-10/11 watershed boundaries were used to analyze a number of spatial
data layers that might serve as indicators of the potential for
pesticide (i.e., atrazine) movement from the fields of application to
water bodies. Williams et al. (2004a, 2004b) provide details of those
watershed vulnerability analyses and the spatial data sources used for
the analysis, as well as documentation of additional data manipulation.

Syngenta evaluated a number of potential factors affecting pesticide
runoff and watershed vulnerability, including atrazine use intensity,
land use types, flow/drainage under row crops, precipitation, and soil
characteristics (Williams et al., 2004a). At the same time, the US EPA
looked at the potential for using the Watershed Regression for
Pesticides (WARP) model, developed by the USGS for atrazine (Larson et
al, 2004), as a tool for identifying the watersheds potentially most
vulnerable to atrazine loading in flowing water bodies.

The WARP model integrates use intensity, watershed area, soil
susceptibility to runoff and rainfall intensity with available water
monitoring data (Larson et al., 2004). WARP estimates various
percentiles of the annual distribution of atrazine concentrations using
separate regression equations based on the following parameters:

Atrazine use intensity: amount of atrazine applied per watershed area

Area: Total watershed area

Rainfall intensity: R factor from USDA/NRCS

Soil erodibility: K factor from USDA/NRCS

Dunne Overland Flow: fraction of runoff from saturated soils

The atrazine watershed vulnerability assessment is based on
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WARP = 10^ (− 4.60 + (0.67U0.25 )+ (1.12LogR)+ (3.59K)+ (0.0006A0.5
)−(0.11*D))

where,

U = watershed cropped area use intensity in kg/km2

R = the USLE rainfall erosivity factor

K = the USLE soil erodibility factor

A = watershed area (km2)

D = Dunne overland flow

The WARP model calculations required collecting and re-aggregating
values for atrazine use intensity (market research data on atrazine use
data by county, averaged across the years 1998-2002), R factor (USDA,
2001, Natural Resource Inventory), K factor (average for the STATSGO
mapping units, USDA, 1994), and Dunne overland flow (Wolock, 2003) at
the HUC-10/11 watershed-scale. Details are provided in Williams et al
(2004a and 2004b).   REF _Ref180382416 \h  Figure III-1  shows the
results of the WARP assessment for those watersheds that intersected
counties with atrazine use intensities of 0.25 lb ai/ac or greater
(5,860 watersheds). The watersheds are ranked in five quintiles based on
the estimated 95th percentile WARP concentrations.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  1  Ranking of
watersheds in quintiles using the USGS WAtershed Regression on
Pesticides model

Williams et al. (2004a) also considered watershed vulnerability
approaches based on flow accumulation under row crops (derived from the
National Elevation Dataset and the 1992 National Land Cover Dataset) and
a soil surface runoff potential rating based on intrinsic soil
properties only (Pierce and Anderson, 1992).

Syngenta evaluated the various watershed vulnerability approaches using
atrazine monitoring data from a number of sources, including USGS, their
own monitoring programs, and various state and university programs
(Williams et al, 2004a, 2004b). Williams et al (2004a) grouped
monitoring stations with no reported atrazine concentration greater than
0.1 ppb into a “low” detection group and monitoring stations with at
least one detection of atrazine greater than 3.0 ppb into a “high”
group.

The low detection group included 526 monitoring stations that
represented the lower 21st percentile of the atrazine monitoring data.
The high detection group included 487 monitoring stations, representing
the upper 18th percentile of atrazine monitoring data. Syngenta used GIS
tools to evaluate how well the various watershed vulnerability
approaches separated sites with detections >3 ppb from those with
detections <0.1 ppb. The highest 20% of vulnerable watersheds identified
using WARP provided the most effective distinction between high and low
detection sites (  REF _Ref181161804 \h  Table III-1 ).

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  1  Evaluation of
selected watershed vulnerability approaches for atrazine monitoring,
based on Williams et al, 2004a

Watershed Vulnerability Approaches1	Total HUCs included	HUCs with sample
stations2	Stations with atrazine detects >3 ppb	Stations with atrazine
detects <0.1 ppb

	No.	Pct	No.	Pct

Initial Pool of 3,440 HUCs in 11 states

WARP upper 90th %ile	339	51	45	88%	0	0%

WARP upper 80th %ile	632	102	85	83%	0	0%

Flow Acc. Upper 90th %ile 	287	32	15	47%	7	22%

Sev. Soil Runoff upper 90th %ile	304	43	17	40%	10	23%

Combined upper 90th%ile WARP + Flow Acc	626	83	60	72%	7	8%

Combined upper 90th%ile WARP + Flow Acc + Sev. Soil Runoff	930	126	77
61%	17	13%

Combined upper 95th%ile WARP + Flow Acc + Sev. Soil Runoff	493	59	39	66%
8	14%

Final Pool of 5,860 HUCs in 37 states

WARP upper 80th %ile	1172	195	156	80%	2	1%

Flow Acc. Upper 90th %ile not included in WARP upper 80th %ile	263	26	5
19%	16	62%

Combined WARP upper 80th and Flow Acc. Upper 90th %ile	1435	221	161	73%
18	8%

1 Watershed vulnerability approaches:

WARP = Watershed Regression for Pesticides 95th percentile annual
residue prediction algorithm

Flow Acc = Flow Accumulation under Row Crops (from NED, NLCD)

Sev. Soil Runoff = Soils rated as having a severe potential for surface
runoff of pesticides

2 Pool of sampled HUC’s for the 11 state group was 574 (295 in the
“upper” group and 87 in the “lower” group). For the 37 state
group was 797 (333 in the “upper” group and 198 in the “lower”
group).

The upper 10th and upper 20th percentiles of WARP effectively grouped
the highest percentile of monitoring stations with atrazine detections
>3 ppb (80-88% of the stations included in these tiers of vulnerable
watersheds had at least one reported detection of atrazine >3 ppb) with
the fewest numbers of stations that never had detects >0.1 ppb (  REF
_Ref181161804 \h  Table III-1 ). Vulnerability approaches based on flow
accumulation under row crops and percentage of soils rated as having a
severe potential for surface runoff included more monitoring stations
with low or no atrazine detections. Most likely these approaches did not
account for atrazine use and, thus, could include areas that, while
vulnerable to pesticide runoff, did not have any atrazine use in the
watershed.

Using the watershed vulnerability approach, the atrazine use area in the
corn and sorghum-growing regions of the U.S. was divided into three
tiers (  REF _Ref180382390 \h  Figure III-2 ):

Approximately 10,000 HUC-10/11 watersheds in which atrazine was used on
corn and sorghum.

5,860 HUC-10/11 watersheds that intersected counties with use
intensities of 0.25 lb ai/county acre or higher, based on atrazine use
between 1998 and 2002.

High vulnerability tier of 1,172 HUC-10/11 watersheds that represent the
upper 20th percentile of the second tier of watersheds, based on USGS’
WARP model.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  2  Location of
atrazine use area (gray), areas of use > 0.25 lb/ac (green), most
vulnerable 20th percentile of watersheds (yellow), and sampled
watersheds (red).

Several assumptions are implicit in the watershed vulnerability
approach:

The WARP estimates reflect watershed vulnerability over time. In
particular, the approach assumes that year-to-year variability in
atrazine use intensity, one of the major driving factors in the
regression model estimates, will not result in drastic changes in the
relative ranking of the watersheds. This assumption may not be
unreasonable since atrazine use has remained relatively steady over
time. However, if shifts in relative usage of atrazine on corn and
sorghum or if shifts in corn-sorghum cropping patterns occur, this
assumption may not hold.

The WARP estimate of the 95th percentile atrazine concentration in a
year captures the major factors affecting the relative vulnerability of
watersheds to atrazine loading in water bodies. Since WARP was based on
nationally-available datasets, it does not necessarily capture locally
important characteristics that may influence atrazine movement to water
bodies.

The distributional approach to atrazine concentrations (i.e., WARP
estimates of the 95th percentile of atrazine concentrations in a year)
are a reasonable surrogate for the magnitude-duration chemographs that
impact aquatic communities.

A watershed-based vulnerability is reflective of the relative
vulnerability of the water bodies within that watershed.

Representative Sampling of Vulnerable Watersheds

The goal of the atrazine monitoring program was to employ a
probability-based, spatial survey design to select sampling locations so
that the extent to which waters are exceeding effect-based atrazine
thresholds could be defensibly estimated via the use of the appropriate
statistical methods. For clarity, the components of the population and
sample are first summarized.

Target Population

The critical question for the monitoring program was “To what extent
are waters exceeding effects-based thresholds for atrazine?”  One
interpretation of the target population is then defined as the
collection of all watersheds within the United States (the 48
conterminous states in this instance).  Since watersheds are defined for
all locations on streams and rivers, the monitoring question of interest
can be stated as “How many kilometers of all streams and rivers within
the United States exceed the established level of concern (LOC)?” In
actuality, to focus the field monitoring on areas considered to be most
vulnerable and to address limitations on where field monitoring could be
implemented, a restricted set of watersheds, based on HUCs, was defined
for the target population (  REF _Ref181271383 \h  Table III-2 ).  It is
important to understand this restriction and how it impacts the survey
design and its interpretation.

  REF _Ref181271383 \h  Table III-2  distinguishes two alternative
target populations: (1) Stream and river based and (2) HUC-based.  The
HUC-based target population is a subset of the stream and river based
target population since HUC pour points are a subset of all possible
locations on the stream and river network.  The HUC-based target
population was used because of uncertainties associated with
county-level atrazine use estimates and because, at the time, the
framework was not available to compute WARP scores for all stream and
river locations within the corn/sorghum use areas (see Section   REF
_Ref181761340 \w \h  IV.A  for some potential future approaches).
Consequently, rather than defining the target population as watersheds
defined by all locations on stream and river network, the target
population is defined in terms of HUCs where it is feasible to compute
WARP scores for the 5,860 HUCs that intersect counties with atrazine use
>0.25 lb ai/county acre.  This is Stratum B for the HUC target
population in   REF _Ref181271383 \h  Table III-2 .  This stratum was
then split into two additional strata based on the WARP scores.  Stratum
B WARP > 80 Percentile includes the set of 1,172 HUCs in areas where
corn and sorghum are grown that were predicted by the USGS WARP model to
be the 20% most vulnerable watersheds to atrazine out of the 5,860
watersheds.  This stratum is further split into the those HUCs predicted
by USGS WARP model to be between the 80th and 95th percentile (874 HUCs)
and those above the 95th percentile (298 HUCs).

Because the intent of the monitoring study was to focus on the most
vulnerable watersheds, Stratum A and Stratum B WARP 0-80 Percentile are
explicitly excluded from the survey design.  HUCs in these two strata
are not included in the atrazine monitoring program.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  2  Target
Population and Survey Design Stratification

Survey Design Strata	Stream and River Target Population	Hydrologic Unit
(HUC-10) Target Population

Stratum A	All streams and rivers in HUC watersheds that do not intersect
counties with atrazine use >0.25 lb atrazine/county acre	All HUC
watersheds that do not intersect counties with atrazine use >0.25 lb
atrazine/county acre

Stratum B	All streams and rivers in HUC watersheds that intersect
counties with atrazine use >0.25 lb atrazine/county acre	All HUC
watersheds that intersect counties with atrazine use >0.25 lb
atrazine/county acre (5,860 HUCs)

Stratum B WARP 0-80 Percentile	All streams and rivers in Stratum B with
WARP scores less than or equal to 80th percentile of all WARP scores in
Stratum B	All HUC watersheds in Stratum B with WARP scores less than or
equal to 80th percentile of all WARP Scores in Stratum B (4,688 HUCs)

Stratum B WARP > 80 Percentile	All streams and rivers in Stratum B with
WARP scores greater than 80th percentile of all WARP scores in Stratum B
All HUC watersheds in Stratum B with WARP scores greater than 80th
percentile of all WARP Scores in Stratum B (1,172 HUCs)

Stratum B WARP (2,4]

All HUC watersheds in Stratum B WARP >80 with WARP scores between 2 and
4 (874 HUCs)

Stratum B WARP (4,14]

All HUC watersheds in Stratum B WARP >80 with WARP scores > 4 (298 HUCs)

Survey Design

The probability-based survey design is stratified based on the
categories described for the HUC-10 target population.  Stratum A and
stratum “B WARP 0-80 Percentile” were not sampled.  Only Stratum
“B WARP (2,4]” and stratum “B WARP (4,14]” were sampled.  Given
the available resources and the desire to focus the sampling on HUCs in
areas where corn and sorghum are grown that are the most vulnerable to
exposures to atrazine, samples were selected only from HUCs within the
top 20% of USGS WARP scores.  That is the sampling units (i.e. the units
actually sampled) were the HUCs, with the sampling frame (i.e. the list
of sampling units) being a GIS coverage of the centroids of each of the
HUCs.

Within each of the two strata sampled, the HUCs were selected with
probability proportional to the value of atrazine use estimated for the
HUC from the county-level use data.  Consequently, HUCs with higher
estimated atrazine use were more likely to be selected than those with
lower estimated use within each stratum. The rationale was to focus
sampling in HUCs more likely to result in an exceedance of the LOC.

Sample Size And Analysis Weights

The total sample size was 40 HUCs with 20 allocated to each of the two
strata WARP (2,4] and WARP (4,14].  In addition, 5 additional HUCs were
selected in each stratum to be used as backup replacement HUCs in the
event that one or more of the original 20 HUCs could not be monitored
due to field operation limitations.

One stratum, where N1=874, represents “moderately high” WARP scores
of (2,4].  The other stratum represents watersheds with the overall
highest WARP scores, which fall in the range of (4, 14] where N2=298.
Since the survey design includes stratification and unequal probability
of selection proportional to atrazine use (kg/km2), the statistical
analyses must incorporate this information through the use of weights
associated with each HUC.  The weights are inversely proportional to the
probability of inclusion; therefore, each of the selected watersheds in
the WARP (2,4] stratum (N1=874) represent 20 to 93 watersheds, where a
HUC with higher atrazine use would represent fewer HUCs and a HUC with
lower atrazine use would represent more HUCs (since more HUCs have lower
atrazine use than high atrazine use within the stratum.  Similarly, in
the WARP (4,14] stratum (N2=298) the selected watersheds represent
approximately 9 to 38 watersheds.

The survey design selection also incorporated a process that ensures
that every potential sample selection would be spatially-balanced.  That
is, the algorithm guarantees that the HUCs will always be spatially
representative over the extent of the population of 1,172 watersheds.
The method is termed a generalized random tessellation stratified (GRTS)
design (Stevens and Olsen, 2004; Stevens and Olsen, 1999).  The GRTS
method was developed as part of the USEPA Office of Research and
Development (ORD) Environmental Mapping and Assessment Program (EMAP)
(McDonald et al, 2002) and has been used extensively by EPA and many
state programs.  Actual selection of HUCs was completed using the
spsurvey library (ARM 2007) developed by EMAP for use with the R
Statistical Software program (R Development Core Team, 2006).

  REF _Ref181163437 \h  Figure III-3  shows the location of the 1,172
HUC-10s and the subset of 40 HUC-10s that were actually selected and
monitored.

Figure   STYLEREF 1 \s  III -  SEQ Figure \* ARABIC \s 1  3  Location of
the 40 watersheds (red) sampled for the atrazine eco-monitoring study.

Sampled Population

The survey design selected 40 HUCs to be sampled (  REF _Ref182036577 \h
 Table III-3 ). Each of the selected HUCs was evaluated to determine if
it was feasible to locate and maintain a stream monitoring site within
the HUC.  In seven of the initial 40 HUCs a suitable stream sampling
site could not be determined (4 from WARP (2,4] stratum and 3 from WARP
(4,14] stratum).  Following procedures for replacing HUCs for GRTS
sampling these were replaced by other HUCs within each stratum.
Although only 40 HUC-10s were monitored, the actual sample size is 47
with missing data for the 7 non-sampleable HUCs.  It is critical that
the seven non-monitored HUCs be included since they represent the
collection of HUCs within the 1,172 HUCs where it would not have been
possible to locate and maintain a monitoring site.  Unless additional
assumptions are made it is not possible to know whether the 40 monitored
HUCs are representative of this collection of non-monitored HUCs.  One
potential assumption would be that this collection is “missing at
random” and hence 40 monitored HUCs would represent all 1,172 HUCs.
Note that the number of HUCs in this collection can, and will, be
estimated.

Table   STYLEREF 1 \s  III -  SEQ Table \* ARABIC \s 1  3  Watersheds
selected for monitoring using the GRTS approach.

Site	Watershed_Name	HUC11 Code	Stratum WARP	Years Monitored	Auto-samples

IA-01	Wolf Creek<PHONE_NUMBER>0	(2,4]	2004-05

	IA-02	Nishnabotana River<PHONE_NUMBER>0	(2,4]	2004-05	2004-05

IL-01	Pecatonica River<PHONE_NUMBER>0	(2,4]	2004-05

	IL-02	Pine Creek<PHONE_NUMBER>0	(2,4]	2004-05

	IL-03	Spring Creek<PHONE_NUMBER>0	(4,14]	2005-07 1

	IL-04	Iroquois River<PHONE_NUMBER>0	(2,4]	2005-07 1

	IL-05	Panther Creek<PHONE_NUMBER>0	(2,4]	2004-05

	IL-06	Sugar Creek West Fork<PHONE_NUMBER>0	(2,4]	2004-05	2004-05

IL-07	Grindstone Creek<PHONE_NUMBER>0	(2,4]	2004-05

	IL-08	Horse Creek<PHONE_NUMBER>0	(4,14]	2005-07 1	2005-06

IL-09	Muddy Creek, Illinois<PHONE_NUMBER>0	(4,14]	2004-05

	IL-NS

07140202090	(2,4]	Not sampled

	IN-01	Mill Creek<PHONE_NUMBER>0	(2,4]	2004-05

	IN-02	Eel River<PHONE_NUMBER>0	(2,4]	2004-05

	IN-03	Eightmile Creek<PHONE_NUMBER>0	(4,14]	2005-06

	IN-04	Rock Creek<PHONE_NUMBER>0	(4,14]	2004-06

	IN-05	Limber Lost Creek<PHONE_NUMBER>0	(4,14]	2004-06

	IN-06	Vermilion River, North Fork<PHONE_NUMBER>0	(4,14]	2005-07 1	2005-07

IN-07	White River<PHONE_NUMBER>0	(4,14]	2005-06

	IN-08	Whitewater, Nolans Fork<PHONE_NUMBER>0	(4,14]	2005-06

	IN-09	Raccoon Creek<PHONE_NUMBER>0	(4,14]	2005-06	2005-06

IN-10	Brandywine Creek<PHONE_NUMBER>0	(4,14]	2005-06

	IN-11	Little Pigeon Creek<PHONE_NUMBER>4	(2,4]	2005-07 1	2005-07

IN-NS1

05120111170	(2,4]	Not sampled

	IN-NS2

05120111090	(4,14]	Not sampled

	KY-01	Brashears Creek<PHONE_NUMBER>0	(4,14]	2005-06

	KY-02	Twomile Creek<PHONE_NUMBER>0	(4,14]	2005-06

	KY-NS

05110004110	(2,4]	Not sampled

	LA_NS

01114020891	(4,14]	Not sampled

	MN-01	Whitewater North Fork<PHONE_NUMBER>0	(2,4]	2005-06	2005-06

MO-01	South Fabius River<PHONE_NUMBER>0	(2,4]	2004-07 1	2004-07

MO-02	Youngs Creek<PHONE_NUMBER>0	(4,14]	2004-07 1

	MO-03	Little Sni-a-Bar Creek<PHONE_NUMBER>0	(2,4]	2004-06	2006

MO-NS

10240011060	(2,4]	Not sampled

	NE-01	Wahoo Creek<PHONE_NUMBER>0	(2,4]	2004-05

	NE-02	Middle Loup River<PHONE_NUMBER>0	(2,4]	2005-06

	NE-03	Platte River<PHONE_NUMBER>0	(2,4]	2004-05

	NE-04	Big Blue River, Upper Gage<PHONE_NUMBER>0	(4,14]	2005-06	2006

NE-05	Muddy Creek, NE<PHONE_NUMBER>1	(4,14]	2005-06	2006

NE-06	Crooked Creek<PHONE_NUMBER>1	(2,4]	2004-06	2004-06

NE-07	Big Blue River, Lower Gage<PHONE_NUMBER>1	(4,14]	2005-06

	NE-NS

10270203060	(4,14]	Not sampled

	OH-01	Kokosing River<PHONE_NUMBER>0	(2,4]	2004-05

	OH-02	Licking River, North Fork<PHONE_NUMBER>0	(4,14]	2005-07 1

	OH-03	Mad River<PHONE_NUMBER>0	(2,4]	2004-05	2004-05

OH-04	Deer Creek<PHONE_NUMBER>0	(4,14]	2005-06

	TN-01	Obion Middle Fork<PHONE_NUMBER>3	(4,14]	2005-06

	1 – Monitoring data for 2007 are still undergoing QA/QC and are not
included in this assessment.

Monitoring Question

The monitoring question initially posed was “To what extent are waters
exceeding effects-based thresholds for atrazine? The extent of
exceedances will be quantified in terms of X% of watersheds having
flowing water bodies that exceed the trigger with Y% confidence.”  The
actual monitoring question that can be answered by the survey design is
“How many (%) HUCs in corn and sorghum growing regions in the United