### Page 1 of 96 

# APPENDIX D

### Page 2 of 96 TABLE OF CONTENTS

APPENDIX D.1. MONITO RING DATA SETS USED IN EVALUATING THE DR INKING WATER MONITOR ING

FREQUENCY, EXPOSURE ESTIMATIONS, AND UNC ERTAINTY ANALYSES

................................ ................................ . 3

APPENDIX D.2. GEOSTA TISTICAL ANALYSIS AN D CONDITIONAL STOCHA STIC SIMULATION APPL ICATION

........... 10

APPENDIX D.3



### .......... EXPLORATION OF USING A WATERSHED MASS BAL ANCE APPROACH TO EST IMATING DAILY

ATRAZINE CONCENTRATI ONS IN MONITORED AGR ICULTURAL WATERSHED EFFLUENT

................................ ...... 57

### Page 3 of 96 APPENDIX D.1. MONITO RING DATA SETS USED IN EVALUATING THE DR INKING

WATER MONITORING FRE QUENCY, EXPOSURE EST IMATIONS, AND

UNCERTAINTY ANALYSES

D.1.1 NCWQR 1995 Maumee River Data Set

Sour ce: NCWQR (2010)

Date Julian Day Flow (cfs) Atrazine (ug/L) Acetochlor (ug/L) Chlorpyrifos (ug/L)

01/03/95 2 362.2 0.331 0.000 0.000

01/06/95 5 2859 0.388 0.000 0.000

01/09/95 8 2861.6 0.182 0.000 0.000

01/13/95 12 896.6 0.351 0.000 0.000

01/15/95 14 3924.7 0.334 0.000 0.000

01/16/95 15 6035.7 0.318 0.000 0.000

01/20/95 19 4460.6 0.286 0.000 0.000

01/20/95 19 5113 0.396 0.000 0.000

01/20/95 19 8955.8 0.298 0.000 0.000

01/21/95 20 14609 0.300 0.000 0.000

01/21/95 20 20612 0.302 0.000 0.000

01/21/95 20 24674 0.288 0.497 0.000

01/22/95 21 25640 0.316 0.000 0.000

01/22/95 21 23795 0.334 0.000 0.000

01/22/95 21 21372 0.351 0.000 0.000

01/23/95 22 19071 0.351 0.000 0.000

01/23/95 22 17348.9 0.317 0.000 0.000

01/25/95 24 7953.8 0.300 0.505 0.000

01/27/95 26 4914.9 0.337 0.000 0.176

01/30/95 29 3749.2 0.188 0.000 0.000

02/03/95 33 2189.8 0.246 0.000 0.000

02/06/95 36 1392.3 0.179 0.000 0.000

02/10/95 40 1245.8 0.249 0.000 0.000

02/13/95 43 1208.4 0.147 0.000 0.000

02/17/95 47 1028.2 0.192 0.000 0.000

02/24/95 54 3924.7 0.224 0.288 0.000

02/27/95 57 3461.4 0.149 0.000 0.000

02/28/95 58 5935.9 0.221 0.000 0.000

03/01/95 59 10332 0.236 0.000 0.000

03/02/95 60 9564 0.238 0.000 0.000

03/03/95 61 6741 0.238 0.000 0.000

03/04/95 62 5177.1 0.167 0.000 0.095

03/04/95 62 4345.2 0.237 0.000 0.000

03/05/95 63 3411.9 0.234 0.000 0.000

### Page 4 of 96 Date Julian Day Flow (cfs) Atrazine (ug/L) Acetochlor (ug/L) Chlorpyrifos (ug/L)

03/06/95 64 2901.1 0.232 0.000 0.000

03/07/95 65 3837.4 0.252 0.000 0.000

03/08/95 66 27764 0.285 0.000 0.000

03/09/95 67 34670 0.298 0.000 0.000

03/10/95 68 27476 0.284 0.000 0.000

03/11/95 69 21372 0.313 0.000 0.000

03/12/95 70 16034 0.253 0.000 0.000

03/13/95 71 13063.3 0.302 0.000 0.000

03/14/95 72 9831.6 0.219 0.000 0.000

03/16/95 74 5971 0.000 0.000 0.000

03/18/95 76 4042.2 0.265 0.000 0.000

03/20/95 78 3101.5 0.000 0.000 0.000

03/24/95 82 2332.7 0.235 0.000 0.000

03/27/95 85 1314.8 0.198 0.000 0.000

03/31/95 89 1207.4 0.203 0.281 0.000

04/09/95 98 3793.1 0.318 0.000 0.000

04/09/95 98 6120.7 0.189 0.000 0.000

04/09/95 98 8448.2 0.657 0.897 0.000

04/10/95 99 23302.1 0.700 0.798 0.000

04/11/95 100 46754 0.484 0.438 0.000

04/12/95 101 51240 0.578 0.484 0.000

04/13/95 102 0 0.524 0.429 0.000

04/15/95 104 24198 0.406 0.453 0.000

04/17/95 106 11302.2 0.000 0.346 0.103

04/20/95 109 6517.5 0.552 0.000 0.000

04/21/95 110 6741 0.460 0.413 0.000

04/22/95 111 11074 0.380 0.364 0.000

04/24/95 113 11968.7 0.442 0.000 0.000

04/28/95 117 4376.4 0.430 0.364 0.000

05/01/95 120 2399.2 0.404 0.306 0.000

05/05/95 124 1818.1 0.645 0.409 0.000

05/08/95 127 1410.9 0.388 0.000 0.000

05/12/95 131 3195.1 0.000 1.372 0.158

05/15/95 134 2201.6 0.205 0.000 0.000

05/17/95 136 1866.3 0.337 0.000 0.000

05/19/95 138 2484.8 0.833 0.577 0.000

05/21/95 140 7007 1.154 0.529 0.000

05/22/95 141 6332 1.032 1.085 0.000

05/23/95 142 4193.2 3.828 2.459 0.000

05/24/95 143 3924.7 6.042 2.704 0.000

05/25/95 144 11506 6.716 2.288 0.000

### Page 5 of 96 Date Julian Day Flow (cfs) Atrazine (ug/L) Acetochlor (ug/L) Chlorpyrifos (ug/L)

05/26/95 145 13764 4.984 3.414 0.000

05/27/95 146 10700 8.510 3.958 0.000

05/28/95 147 7712.4 10.740 3.891 0.000

05/29/95 148 9301.8 14.058 3.866 0.000

05/29/95 148 8771 12.306 3.790 0.000

05/30/95 149 8240.1 11.863 3.344 0.000

05/30/95 149 7473 11.122 3.141 0.000

05/30/95 149 7450.2 10.597 3.276 0.000

05/31/95 150 6817 9.857 3.079 0.000

06/01/95 151 5483.6 8.850 2.883 0.000

06/02/95 152 4163 6.336 2.390 0.000

06/03/95 153 3664.6 5.665 2.737 0.000

06/04/95 154 3357.7 4.701 0.314 0.000

06/06/95 156 3439 4.786 0.627 0.000

06/07/95 157 2742.2 4.448 0.552 0.000

06/08/95 158 2512.2 4.322 0.521 0.000

06/09/95 159 1818.1 3.891 0.469 0.000

06/10/95 160 3779.2 3.429 0.413 0.000

06/11/95 161 4345.2 5.094 0.519 0.000

06/12/95 162 5113 4.612 0.481 0.000

06/19/95 169 1730.2 7.417 0.344 0.000

06/23/95 173 817.8 7.930 0.544 0.000

06/26/95 176 692.3 7.606 0.576 0.000

06/28/95 178 14874 3.631 0.323 0.000

06/29/95 179 16650 4.062 0.385 0.000

06/29/95 179 16766 3.985 0.354 0.000

06/29/95 179 16708 2.699 0.000 0.000

06/30/95 180 16940 2.776 0.000 0.000

06/30/95 180 17172 3.401 0.419 0.000

07/01/95 181 15922 3.215 0.000 0.000

07/02/95 182 11890 3.160 0.337 0.000

07/03/95 183 9258.1 3.720 0.000 0.000

07/04/95 184 7632.6 4.463 0.368 0.000

07/06/95 186 4253.6 4.427 0.356 0.000

07/08/95 188 1794 4.141 0.328 0.125

07/14/95 194 745.1 1.250 0.378 0.000

07/17/95 197 554.7 3.874 0.000 0.000

07/21/95 201 516 2.992 0.000 0.000

07/24/95 204 408.3 2.912 0.000 0.000

07/29/95 209 1133.8 1.993 0.000 0.000

07/31/95 211 1009.7 1.262 0.000 0.000

### Page 6 of 96 Date Julian Day Flow (cfs) Atrazine (ug/L) Acetochlor (ug/L) Chlorpyrifos (ug/L)

08/04/95 215 832.3 1.944 0.000 0.000

08/07/95 218 2554.4 1.892 0.000 0.000

08/12/95 223 2539.6 1.462 0.000 0.000

08/14/95 225 1463 0.952 0.000 0.000

08/18/95 229 1486.4 0.970 0.000 0.000

08/21/95 232 3350.1 0.831 0.000 0.000

08/25/95 236 912.7 0.802 0.000 0.000

08/28/95 239 498.3 0.852 0.000 0.000

09/01/95 243 328.6 0.900 0.000 0.000

09/04/95 246 261.8 0.738 0.000 0.000

09/08/95 250 160.5 0.978 0.848 0.000

09/11/95 253 515.7 0.872 0.000 0.000

09/15/95 257 149.5 1.068 0.000 0.000

09/18/95 260 165.4 0.850 0.000 0.000

09/22/95 264 287.9 0.707 0.000 0.000

09/25/95 267 118.4 0.777 0.000 0.000

10/07/95 279 2889.9 0.515 0.000 0.000

10/09/95 281 3075.3 0.493 0.000 0.000

10/13/95 285 1063.4 0.399 0.000 0.000

10/16/95 288 730.6 0.303 0.000 0.000

10/20/95 292 327.7 0.350 0.000 0.000

10/23/95 295 136.5 0.334 0.000 0.000

10/27/95 299 354.3 0.387 0.000 0.000

10/30/95 302 594.1 0.388 0.000 0.000

10/30/95 302 603.1 0.289 0.000 0.000

11/03/95 306 2347.8 0.303 0.000 0.000

11/06/95 309 2573.1 0.265 0.000 0.000

11/11/95 314 4132.8 0.311 0.000 0.000

11/11/95 314 7045 0.417 0.000 0.000

11/12/95 315 7632.6 0.339 0.000 0.000

11/12/95 315 8035.6 0.333 0.000 0.000

11/13/95 316 9874.3 0.317 0.000 0.000

11/13/95 316 9831.6 0.172 0.000 0.000

11/14/95 317 7994.7 0.151 0.000 0.000

11/15/95 318 6115.4 0.167 0.000 0.000

11/16/95 319 5013.4 0.169 0.000 0.000

11/17/95 320 3357.7 0.168 0.000 0.000

11/20/95 323 3579.1 0.143 0.000 0.000

11/27/95 330 1638.6 0.176 0.000 0.000

12/04/95 337 912.7 0.338 0.000 0.000

### Page 7 of 96 D.2.2 AEEMP 2007 MO

- 01 Source: See USEPA (2007 and 2009a) for site/study description and data set.

DATE Atrazine

Grab Sample, ug/L Atrazine

Auto

Sample, ug/L Atrazine

Combined Samples, ug/L Daily Precipitation (cm) Avg Daily Flow (m3/s)

4/1/2007 0.051

4/2/2007 <0.05 <0.05 0.05 0.102 0.00086

4/3/2007 <0.05 0.05 1.727 1.08462

4/4/2007 <0.05 0.05 0.000 0.06207

4/5/2007 <0.05 0.05 0.000 0.00039

4/6/2007 <0.05 0.05 0.05 0.000 0.00027

4/7/2007 0.05 0.000 0.00024

4/8/2007 0.05 0.000 0.00024

4/9/2007 0.05 0.000 0.00023

4/10/2007 <0.05 <0.05 0.05 1.092 0.00023

4/11/2007 <0.05 0.05 1.448 0.41993

4/12/2007 <0.05 0.05 0.000 0.02044

4/13/2007 <0.05 0.05 0.000 0.00030

4/14/2007 <0.05 <0.05 0.05 0.356 0.00027

4/15/2007 0.05 0.000 0.00026

4/16/2007 0.05 0.000 0.00026

4/17/2007 0.05 0.000 0.00024

4/18/2007 <0.05 <0.05 0.05 0.000 0.00023

4/19/2007 0.05 0.000 0.00022

4/20/2007 0.05 0.000 0.00021

4/21/2007 <0.05 0.05 0.000 0.00021

4/22/2007 <0.05 0.05 0.152 0.00020

4/23/2007 0.05 0.000 0.00020

4/24/2007 0.05 1.956 0.00020

4/25/2007 0.05 0.279 0.07109

4/26/2007 7.18 29.72 29.72 0.965 0.47783

4/27/2007 23.01 23.01 0.000 0.00456

4/28/2007 22.00 22.00 0.000 0.00026

4/29/2007 23.80 23.80 0.000 0.00025

4/30/2007 23.21 15.23 23.21 0.000 0.00024

5/1/2007 23.21 0.254 0.00022

5/2/2007 23.21 0.229 0.00021

5/3/2007 23.21 0.813 0.00022

5/4/2007 0.79 16.46 16.46 1.702 0.56913

5/5/2007 14.26 14.26 0.000 0.00378

5/6/2007 8.14 8.14 1.829 0.43706

### Page 8 of 96 DATE Atrazine

Grab Sample, ug/L Atrazine

Auto

Sample, ug/L Atrazine

Combined Samples, ug/L Daily Precipitation (cm) Avg Daily Flow (m3/s)

5/7/2007 6.23 6.23 0.000 0.47543

5/8/2007 4.34 4.42 4.42 0.000 0.00049

5/9/2007 4.58 4.58 0.000 0.00027

5/10/2007 3.66 3.66 0.203 0.00024

5/11/2007 2.71 2.71 0.000 0.00022

5/12/2007 2.20 2.30 2.30 0.000 0.00021

5/13/2007 2.30 0.000 0.00019

5/14/2007 2.30 0.000 0.00019

5/15/2007 2.30 1.753 0.00019

5/16/2007 1.57 1.33 1.57 0.000 0.00018

5/17/2007 1.57 0.000 0.00018

5/18/2007 1.57 0.000 0.00017

5/19/2007 1.57 0.000 0.00017

5/20/2007 1.17 1.07 1.17 0.000 0.00017

5/21/2007 1.17 0.000 0.00016

5/22/2007 1.17 0.000 0.00016

5/23/2007 1.17 0.000 0.00016

5/24/2007 0.90 0.97 0.97 0.254 0.00016

5/25/2007 0.97 0.051 0.00016

5/26/2007 0.97 0.051 0.00017

5/27/2007 0.97 0.483 0.00017

5/28/2007 0.89 1.07 1.07 0.025 0.00017

5/29/2007 1.07 0.000 0.00016

5/30/2007 1.08 1.08 0.127 0.00016

5/31/2007 1.31 1.31 0.889 0.00018

6/1/2007 2.75 9.62 9.62 0.025 0.00023

6/2/2007 91.60 91.60 0.178 0.00017

6/3/2007 71.80 71.80 0.000 0.00016

6/4/2007 42.00 42.00 0.000 0.00016

6/5/2007 33.29 27.00 33.29 0.000 0.00015

6/6/2007 26.20 26.20 0.000 0.00015

6/7/2007 18.96 18.96 0.051 0.00014

6/8/2007 17.22 17.22 0.051 0.00014

6/9/2007 17.56 15.16 17.56 0.000 0.00014

6/10/2007 12.12 12.12 0.381 0.00014

6/11/2007 11.80 11.80 0.000 0.00015

6/12/2007 9.97 9.97 0.000 0.00014

6/13/2007 7.90 5.64 7.90 0.000 0.00014

6/14/2007 7.90 0.000 0.00014

6/15/2007 7.90 0.000 0.00014

### Page 9 of 96 DATE Atrazine

Grab Sample, ug/L Atrazine

Auto

Sample, ug/L Atrazine

Combined Samples, ug/L Daily Precipitation (cm) Avg Daily Flow (m3/s)

6/16/2007 7.90 0.000 0.00013

6/17/2007 2.98 2.42 2.98 0.000 0.00013

6/18/2007 2.98 1.549 0.00013

6/19/2007 2.98 0.051 0.00016

6/20/2007 2.98 0.000 0.00016

6/21/2007 2.58 2.28 2.58 0.000 0.00014

6/22/2007 2.58 0.356 0.00014

6/23/2007 2.58 0.559 0.00015

6/24/2007 2.58 0.000 0.00014

6/25/2007 1.54 2.35 2.35 0.203 0.00014

6/26/2007 2.35 0.000 0.00016

6/27/2007 1.31 1.31 0.483 0.00015

6/28/2007 1.31 1.245 0.00016

6/29/2007 1.48 1.85 1.85 0.000 0.00016

6/30/2007 1.52 1.52 0.000 0.00015

7/1/2007 1.04 1.04 0.000 0.00015

7/2/2007 1.18 1.18 0.000 0.00014

7/3/2007 1.33 0.72 1.33 0.000 0.00013

7/4/2007 1.33 0.000 0.00013

7/5/2007 1.33 0.000 0.00013

7/6/2007 1.33 0.000 0.00012

7/7/2007 0.70 0.72 0.72 0.000 0.00011

7/8/2007 0.72 0.000 0.00010

7/9/2007 1.04 1.04 0.000 0.00008

7/10/2007 1.04 2.108 0.00010

7/11/2007 0.50 0.67 0.67 0.000 0.00014

7/12/2007 0.67 0.000 0.00012

7/13/2007 0.67 0.000 0.00012

7/14/2007 0.67 1.092 0.00011

7/15/2007 0.48 0.57 0.57 0.000 0.00015

7/16/2007 0.57 0.203 0.00013

7/17/2007 0.57 0.000 0.00013

7/18/2007 0.57 0.000 0.00012

7/19/2007 ns 0.78 0.78 0.025 0.00011

7/20/2007 0.78 0.000 0.00010

7/21/2007 0.78 0.000 0.00008

7/22/2007 0.78 0.000 0.00006

7/23/2007 ns 0.78 0.000 0.00004

7/24/2007 0.78 0.000 0.00001

7/25/2007 0.78 0.000 0.00002

### Page 10 of 96 DATE Atrazine

Grab Sample, ug/L Atrazine

Auto

Sample, ug/L Atrazine

Combined Samples, ug/L Daily Precipitation (cm) Avg Daily Flow (m3/s)

7/26/2007 0.78 0.000 0.00002

7/27/2007 ns 0.78 0.686 0.00005

7/28/2007 0.78 0.000 0.00002

7/29/2007 0.78 0.000 0.00001

7/30/2007 0.78 0.000 0.00002

7/31/2007 ns 0.78 0.000 0.00002

8/1/2007 0.78 0.000 0.00002

8/2/2007 0.78 0.000 0.00002

8/3/2007 0.78 0.000 0.00002

8/4/2007 ns 0.78 0.000 0.00002

8/5/2007 0.78 0.000 0.00002

8/6/2007 0.78 0.102 0.00001

8/7/2007 0.78 0.102 0.00001

8/8/2007 ns 0.78 2.489 0.00007

8/9/2007 0.78 0.127 0.00006

8/10/2007 0.78 0.000 0.00003

8/11/2007 0.78 0.000 0.00001

8/12/2007 ns 0.78 0.432 0.00001

8/13/2007 0.78 1.372 0.00006

8/14/2007 0.27 0.27 0.000 0.00010

8/15/2007 0.27 0.000 0.00008

8/16/2007 ns 0.16 0.16 0.610 0.00008

8/17/2007 0.16 0.000 0.00008

8/18/2007 0.16 0.000 0.00006

8/19/2007 0.16 0.025 0.00002

8/20/2007 ns 0.16 0.178 0.00001

8/21/2007 0.38 0.38 3.327 0.00018

8/22/2007 0.38 0.889 0.00017

8/23/2007 0.38 0.000 0.00015

8/24/2007 0.21 0.26 0.26 2.896 0.00017

8/25/2007 0.26 0.152 0.00019

8/26/2007 0.26 0.000 0.00015

8/27/2007 0.26 0.000 0.00014

8/28/2007 0.19 0.22 0.22 0.000 0.00013



### Appendix D.2. Geostatistical Analysis and Conditional Stochastic Simulation

Application

### Page 11 of 96 D.2.1 Introduction

Pesticide concentrations in surface water change over space and time as a result of
numerous factors, including hydrology, meteorology, agronomic practices, pesticide
application rates, application timing, application method, and environmental fate

prope rties of the pesticide. The quantitative analysis of pesticide monitoring data is an
important aspect in assessing exposure to humans and wildlife and needs to consider
the influence of space and time. The analysis of changes in pesticide concentrations i n surface water monitoring data with time requires an integration of factors noted above
plus an understanding of the sampling strategy employed. There are few monitoring
programs with daily monitoring of concentrations of pesticides and even fewer that ha ve been conducted over several years. Heidelberg University’s National Center for Water
Quality Research (NCWQR) surface water monitoring program is an example of an
intensive monitoring program for pesticides (NCWQR, 2010). Surface water monitoring

progra ms, however, generally have sampling frequencies ranging from biweekly to
monthly (USGS NAWQA and Blomquist et a l., 2001). The ability to predict a complete
time series of pesticide concentrations in surface water from surface monitoring data

with differe nt sampling frequencies is especially important when trying to understand
pesticide exposure where monitoring is infrequent.

Historically, in the pesticide registration process, deterministic models such as PRZM
(Suárez, 2005) and EXAMS (B urns, 2002) have been used to estimate pesticide
concentrations in surface water. More recently, the USGS Watershed Regression on
Pesticides (WARP) model has been developed for estimating a distribution of atrazine

concentrations in specific watersheds. This model requir es watershed specific data on
the atrazine use intensity in the watershed, meteorology (May and June precipitation
and rainfall intensity factor), soil properties (soil erodibility factor), hydrology (percent of
runoff resulting from Dunne overland flow), and watershed size (Stone and Gilliom,
2009). Although the WARP model has been used for assessing vulnerability of
watersheds (USEPA 2007, 2009a and 2009b), it has not been used in the pesticide
registration process to predict concentrations of atrazine i n untreated drinking water.
The success of the use of modeling data for assessing monitoring data is dependent on
the availability of temporally and spatially explicit watershed specific data ( USEPA,
2009a, 2010a and 2010b)

.

Another approach recommend ed by FIFRA SAP is the use of statistical modeling
approaches for predicting pesticide time series (FIFRA SAP, 2010b). Geostatistical
procedures such as kriging have been recommended as an approach for interpolating
pesticide monitoring data in a time ser ies. This modeling approach is based on
selection of a stationary random function model to describe the relationship of variance

### Page 12 of 96 as a function of distance or time (Isaaks and Srivastava, 1989). This relationship can be
quantified using a variogram, correl ogram, or a covariance function. This type of

nearest

- neighbor modeling approach assumes that properties ( e.g. , pesticide
concentration) in close proximity in space or time are more similar than properties with

wide separations in distance or time. Appli cation of a random function model to
pesticide time series analysis generally requires a condition of stationarity (Isaaks and
Srivastava, 1989), which assumes that an estimate has an equal probability of occurring
regardless of location in space and time. Although this condition may not be applicable

for an annual time

- series of pesticide concentration due to seasonal trends in
meteorology, pesticide persistence, pesticide application timing, and hydrology, it may
be applicable to specific time periods du ring the pesticide application period.

Time point estimation of pesticide concentrations in a time series can be accomplished
through nearest neighbor approaches, including local sample mean, polygons,
triangulation, and inverse distance squared methods . Another point estimation
approach is ordinary kriging (Isaaks and Srivastava, 1989). This technique provides
point estimates based on the best linear unbiased estimator (B.L.U.E ).

Uncertainty analysis of predicted pesticide concentrations in the time s eries can be
evaluated using the absolute error estimate, standard deviation from kriging, and
conditional stochastic simulation (Isaaks and Srivastava, 1989; Deutsch and Journel,
1998). Errors in prediction can be associated with the number of samples, p roximity of
available samples and their temporal or spatial arrangement, and the nature of the

phenomena.

The objective of this appendix is to assess the application of ordinary kriging on the
interpolation of pesticide concentrations in various times series with different sampling
frequencies. Although this exercise is directed toward atrazine, the analysis will also
consider the applicability of this approach to other pesticides (chlorpyrifos and
acetochlor) with different time series profiles.

D.2. 2 Material and Methods

The time series data considered in this analysis was taken from the Atrazine Ecological
Exposure Monitoring Program (AEEMP) and the Heidelberg University National Center
for Water Quality Research (NCWQR) surface water monitoring pr ogram ( Table D

- 1 ).
The data were selected to provide an evaluation of the technique for different years and
hydrology. Although both monitoring programs have high sampling frequencies, they
generally differ in watersheds, stream flow characteristics, and sample collection
methodology. Grab samples were collected a the Missouri site (MO

- 01

- 2007) every

### Page 13 of 96 four days from April 5, 2007 to July 15, 2007 (Julian Days 93 to 197). Additionally,
samples were taken with an autosampler to fill in days between the grab sample events.
The case study analysis will focus on both grab samples and a combined time series
from grab and autosamples. In contrast, high intensity sampling in the NCWQR was
focused during the high runoff period from April to July (Julian Days 100 to 200).

Table D

- 1 : Surface Water Monitoring Data For Case Studies

Program Station

Name Year Sampling Frequency Detection

Limit (ug/L) Watershed

Size (km 2 

### ) Average Daily

Flow

m 3/ 

### sec

AEEMP MO

- 01

- 2007 2007 4

- day and daily autosamplers 0.05 30.1 0.02

NCWQR Maumee

River 1995 Daily 0 16,995 106.143 1



### NCWQR Sandusky

River 1995 Daily 0 3,678 29 1

1

- Annual average flow



### Descriptive statistics for the available monitoring data are shown in Table D

- 2.

Table D

- 2 : Descriptive Statistics of Heidelberg University NCWQR (Maumee 1995) and Atrazine Ecological Exposure Monitoring Data (MO

- 01

- 2007).

Statistic Maumee 1995 MO

- 01

- 2007

Atrazine Acetochlor Chlorpyrifos Atrazine

Mean 1.89 0.43 0.00421 5.02



### Standard Error 0.23 0.07 0.00191 0.96



### Median 0.40 0.00 0.00000 0.78



### Standard Deviation 2.89 0.94 0.02389 11.76



### Sample Variance 8.34 0.88 0.00057 138.22



### Kurtosis 4.38 5.85 34.16 26.42



### Skewness 2.16 2.63 5.84 4.58



### Minimum 0.00 0.00 0.00000 0.05



### Maximum 14.06 3.96 0.17600 91.60



### To address the stationarity issue associated with seasonality of pesticide monitoring
data, a subsection of the monitoring data was selected to encompass the earliest corn
planting date to approximately 100 days post corn planting date. This time series p eriod
represents the period of time with the highest magnitude and frequency of atrazine
detections. The time series sections from Julian Day 0 to the earliest corn planting date
(approximately Day 101) and from Day 200 to Day 365 are expected to represen t base

flow concentrations of atrazine. Figure D

- 1 illustrates two distinct populations of
atrazine pesticide concentrations dominated by either runoff or base

- flow conditions. In
contrast, the random nature of the chlorpyrifos data does not represent a clear

### Page 14 of 96 difference in runoff and base

- flow concentrations within an annual time series (Figure
1).

Figure D

- 1 : Times Series Analysis of Base

- Flow and Runoff Dominated Processes for Atrazine (left) and Chlorpyrifos (right) Concentrations in Surface Water (Maumee, 1995).



### Additionally, the missing monitoring data for the Maumee River was estimated using
kriging. The MO

- 01

- 2007 data set did not require data estimation because a complete
daily time series could be constructed from the grab and autosampler samples. The
descriptive statistics of the actual time series used in the variogram analysis are shown

in Table D

- 3

.

Table D

- 3 : Descriptive Statistics of Atrazine Kriged Data for Maumee River 1995 (1 00

- 200 days) and Actual Monitoring Data for MO

- 01

- 2007 (91

- 190 days)

Statistics Maumee River MO

- 01

- 2007

Mean 3.85 7.13



### Standard Error 0.46 1.37



### Median 3.83 1.57



### Standard Deviation 3.38 13.81 

### Sample Variance 11.45 190.64



### Kurtosis 0.60 17.76



### Skewness 0.93 3.76



### Minimum 0 0.05



### Maximum 14.06 91.60



### Some adjustments to standard geostatistical analysis is needed to address limitations of
the tools used to make the analysis appropriate for modeling time series of pesticide
concentrations ( Figure D

- 2 ). An important consideration is the number of samples in
the time series. In preliminary analyses, variogram modeling and kriging were difficult
for data sets with less than 15 data points as there were poor fits to the variograms.

Additionally, the st andard deviations from kriging for predicted daily pesticide

### Page 15 of 96 concentrations were relatively large. In general, sampling frequencies of 14

- day and

28

- day intervals provided the poorest variogram models. In the absence of more
extensive monitoring data, a data infilling process is needed. Soft data may be used to

allow additional refinement in prediction of pesticide concentrations in the time series.
This infilling process needs to consider the descriptive statistics of the underlying data.

It should als o consider the temporal correlation of atrazine concentrations in the time
series. The soft data, such as flow or acetochlor concentrations, can be used to predict

atrazine concentrations using established regression equations. Additionally,
regression mo dels such as WARP can be used to provide an underlying distribution of
percentiles in the time series, provided the underlying pesticide use and watershed
characteristics are available. Merging the WARP results with existing monitoring data

allows the est imation of a new distribution of values. One option is to fit the merged

WARP and available monitoring data to a known distribution. Another option is to use
the merged WARP percentiles and the available monitoring data without additional

estimation. The merged data need to be assembled in the proper sequence in the time
series. Matching percentiles of the merged data with percentiles of flow data may
provide a reliable estimate for the sequence in the time series. Matching percentiles

requires an a pri ori understanding of the relationship between flow and pesticide
concentration. The autocorrelation of daily pesticide concentrations can be described

using the variogram. This variogram is used to generate a kriged time series.
Uncertainty bounds for es timated daily atrazine concentrations can then be generated
using sequential stochastic simulation.

### Page 16 of 96 Figure D

- 2 : Proposed Analysis Strategy for Time Series Modeling of Pesticide Concentrations from Monitoring Data.



### The software programs used in this analysis include GEOEAS (USEPA Version 1.2.1),
EXCEL(2007), SIGMAPLOT(Version 10), SGSIM (SGSIM Version 2, Deutsch and
Journel, 1998), and WARP (Stone and Gilliom, 2009). Below is a description of the

procedure used to gene rate time series from monitoring data.

Construct a GEOEAS input file ( ADDENDUM ).

Use GEOEAS (STATS) to evaluate the descriptive statistics. Evaluate the need
for log transformation to produce a normal distribution.

Use GEOEAS (PREVAR) to produce a covariance matrix of temporal lag pairs.

Use GEOEAS (VARIO) to model variogram. Fit the best variogram to predict the

model, nugget, range, and sill.

Use GEOEAS (KRIGE) to generate infill the time series using the BLUE. Kriging parameters included typ e o f kriging, grid parameter (x,y,z), and search parameters. We used ordinary point kriging and the variogram model. Also, the

### Page 17 of 96 grid cell has been modified to allow a one dimensional kriging (APPENDIX). Output files for kriged time series were saved as * .grd file.

The kriged time series is then prepared for conditional, Gaussian sequential
stochastic simulation (SGSIM). This procedure allows generation of equally

probable realizations with same covariance temporal structure and descriptive

statistics as the kriged time series The stochastic simulation was conditioned on
the actual monitoring data used for variogram modeling and kriging. For our
purposes, 1,000 realizations were generated for each time series. An example

input and parameter file is show n the ADDENDUM.

D.2.3 Variogram Modeling and Kriging

Variogram analysis and kriging were conducted on the Maumee River 1995 and
Sandusky 1995 data from the NCWQR and MO

- 01

- 2007 data from the AEEMP. These
data were selected to evaluate the capability of the geostatistical tools for different
pesticides and watersheds. Analyses for other sites and pesticides are shown in the
tables and figures at the end of this document. In order to provide a base

- line time
series for error analysis as well as a complet e time series for assessing sampling
frequency, standard time series were adopted from the available monitoring data. The
standard time series were developed by kriging (Maumee River 1995) or actual
monitoring data (MO

- 01

- 2007). These standard time series are assumed to be
representative of the true daily time series.

Table D

- 4 shows the parameters for the fitted variogram models for the 1995 Maumee

and MO

- 01

- 2007 data for different sampling frequencies. The variogram models were
spherical or gaussian. Nugget models were identified for most sampling frequencies.

The variance of the nugget ranged from 0.0 to 0.5 μg/L 2 

### , which corresponds to standard
deviations of 0 to 0.7 μg/L. A nugget indicates a random process without temporal

correlation . It may be associated with sampling error, sampling technique, or inherent

variation i n daily pesticide concentrations. The variance of the sill ranged from 2.9 to
6.0 μg/L 2 

### , which corresponds to standard deviations of 1.73 to 2.44 μg/L. Although the
extent of autocorrelation, as depicted by the range, was highly variable among the case

s tudy data sets, longer ranges (52

- 80 days) were found in the 1995 Maumee River
data. This phenomenon may be associated with the larger watershed size and daily flow

characteristics. Figure D

- 3 shows variograms for the 4

- day and 7

- day sampling
frequency fo r the Maumee and MO

- 01 2007 data sets.

### Page 18 of 96 Table D

- 4 : Variogram Models using the Maumee River 1995 Time Series (100

- 200 days) and Missouri Ecological Monitoring Site (MO

- 01 2007).

Data Sampling Frequency N Nugget (μg/L 2 

### ) Model 1 

### Sill (μg/L 2 

### ) Range (Days)

Maumee95 Actual Data 100 0.10 Spherical 0.97 16

4

- day 25 0.00 Spherical 3.70 52

7

- day 15 0.00 Spherical 3.20 61

14

- day 8 0.30 Gaussian 3.20 80

28

- day 4 2.2 no temporal correlation

MO

- 01 2007 Complete

Time Series 4

- day 25 0.00 Spherical 3.60 27

7

– day 15 0.00 Spherical 3.80 27

14

– day 8 0.50 Spherical 3.80 32

28

- day 4 3.80 no temporal correlation

MO

- 01 2007

Grab

samples 4

- day 2 

### 27 0.2 Spherical 4.0 27

7

– day 3 

### 15 0.2 Gaussian 2.9 25

8

– day 2 

### 15 0.5 Spherical 3.5 33

14

– day 3 

### 8 0.3 Spherical 6.0 30

16

- day 2 

### 7 0.1 Linear 6.0 41

16

- day 2,4 

### 6 0.2 Gaussian 3.6 45

1 Grab samples collected every 4 days from 04/01/07 to 07/19/07; to additional samples were collected on

8/24/07 and 8/28/07 and are not included.

2 Grab samples: 4, 8, and 16 day sampling interval.

3 Estimated from kriged samples.

4 Highest data value removed to assess impact on variogram model

1 Variogram models provide a description of variance as a function of lag distance or lag time intervals. The models used in this analysis include Gaussian and Spherical. The Spherical model is defined as the variance at lag(h)=C*(1.5*h/a

- 0.58(h/a) 3 )+nugget. The Gaussian model is defined as the variance at lag(h)=C*(1

- exp(

- (3h) 2 /a 2 )+nugget. This model does not represent the Gaussian distribution model. The nu gget model represents inherent variance or “white noise” at lag distances or lag times shorter than described in the variogram model. Parameters in the variogram model are h=lag time or lag distance, C= sill= variance for randon process (ug/L 2 ),

a=range = in days or distance for correlation of variance.

### Page 19 of 96 Lag Time (Days) 0 10 20 30 40 50 60 70Variance (ug/L2)

0 1 2 3 4

Spherical
Nugget=0 ug/L 2

Sill=3.7 ug/L 2

Range=52 days a.)

Lag Time (Days) 0 20 40 60 80 100Variance (ug/L2)

0 1 2 3 4

Spherical
Nugget=0.00 ug/L 2

Sill=3.20 ug/L 2

Range=61 days b.)

Lag Time (Days) 0 10 20 30 40 50 60Variance (ug/L2)

0 1 2 3 4 5 6 7

Spherical
Nugget=0.20 ug/L 2

Sill=4.0 ug/L 2

Range=27 days c.)

Lag Time (Days) 0 20 40 60 80 100 0 1 2 3 4 5 6Variance (ug/L2) Gaussain Nugget=0.20 ug/L 2

Sill=2.9 ug/L 2

Range=25 days d.)



### Figure D

- 3 : Representative Variograms for 4

- day and 7

- day Sampling Frequency for the

Maumee and MO

- 01 2007 data: a.) 4

- day 1995 Maumee; b.)7

- day 1995 Maumee; c.) 4

- day

Grab MO

- 01 2007; and d.) 7

- day Grab MO

- 01 2007.



### The kriged time series for the Maumee River for the actual data, 4

- day, and 7

- day
sampling frequencies exhibit similar descriptive statistics (mean, median, variance) for
atrazine concentrations ( Table D

- 5 ). One notable difference is the estimated maximum

atrazine concentration. The 4

- day sampling frequency captured the maximum daily
atrazine concentration of 14.06 μg/L. In contrast, the 7

- day and 14

- day sampling

interval missed the peak atrazine concentration. The 14

- day and 28

- day kriged time
series were not predicted because of the inability t o estimate a reliable variogram.

### Page 20 of 96 Table D

- 5 : Descriptive Statistics from Kriged 1995 Maumee River Time Series (100

- 200 days).

Statistics Actual 4

- day 7

- day

Mean 3.85 3.46 3.55

Median 3.88 2.86 3.41

Minimum 0.00 0.03 0.23

Maximum 14.06 14.06 11.86

Standard Deviation 3.88 3.27 3.04

Sample Variance 11.45 10.72 9.26

Kurtosis 0.60 0.39

- 0.50

Skewness 0.93 0.94 0.57



### The estimation of missing data in the 4

- day and 7

- day kriged time series provide
reasonable prediction of missing data in the time series ( Figures D

- 4 and D

- 5 ). There

is a clear linear relationship between the predicted atrazine concentration and actual
atrazine concentrations. Additionally, the kriging standard deviations for the missing
data points are low, which further suggest a reasonable prediction of estimated daily

atrazine concentrations.

### Page 21 of 96 Actual Atrazine Concentration (ug/L) 0 2 4 6 8 10 12 14 16Predicted Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14 16

Julian Date (Days)

80 100 120 140 160 180 200 220Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14 16



### Figure D

- 4 : Prediction Error in the Kriged Atrazine Time Series from 4

- day Sampling of the Maumee River 1995 (Days 100

- 200).

### Page 22 of 96 0 2 4 6 8 10 12 14 16Predicted Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14 16

Actual Atrazine Concentration (ug/L)

Julian Date (Days) 80 100 120 140 160 180 200 220Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14



### Figure D

- 5 : Prediction Error in the Kriged Atrazine Time Series from 7

- day Sampling of the Maumee River 1995 (Days 100

- 200).



### The kriged time series for MO

- 0

- 2007 based on daily samples had higher mean,

maximum, and variance when compared to the kriged time series for MO

- 01

- 2007

based on the 4

- day grab samples ( Table D

- 6 ). This difference can be attributed to
capturing the higher maximum concentration from the daily series. Unlike the Maumee
kriged time series, the maximum atrazine concentration was highest for the 7

- day
sampling interval. The 4

- day sampling frequency had a maximum daily atrazine
concentration of 33.34 μg/L. In contrast, the 7

- day and 14

- day grab samples had a
maximum daily atrazine concentration of 23 μg/L. The actual peak, however, was 91.6
μg/L. This difference illustrates the difficulty of capturing th e peak without continuous
monitoring.

### Page 23 of 96 Table D

- 6 : Descriptive Statistics from Kriged MO

- 01

- 2007 Time Series (91

- 190 days)

Statistics 

### Daily Samples Grab Samples

Actual

4

- day 7

- day 4

- day 7

- day 14

- day

Mean 6.46 

### 6.63 4.59 4.34 5.21 7.13

Median 

### 2.09 1.98 1.58 2.18 2.93 1.57

Minimum 

### 0.05 0.54 0.05

- 0.10

- 1.25 0.05

Maximum 

### 33.34 42.00 33.29 23.59 23.21 91.60

Standard Deviation 

### 8.90 9.69 6.81 5.59 6.16 13.81

Sample Variance 

### 79.26 93.80 46.41 31.28 37.93 190.64

Kurtosis 

### 0.97 2.97 4.38 2.01 0.11 17.76

Skewness 

### 1.50 1.88 2.17 1.75 1.01 3.76



### Unlike the Maumee time series, the MO

- 01

- 2007 time series had numerous distinct
atrazine peaks. Although the time series data show two prominent atrazine peaks, the
magnitude and duration of peaks varied according to sampling frequency ( Figures D

- 6

– D

- 10 ). Regardless of sampling design, the kriged 4

- day time series had similar
statistics to the meaured daily time series. Although the 7

- day time series captured

both atrazine peak s, there was a lower magnitude in atrazine concentrations for the
grab samples. This phenomenon may illustrate the importance of formulating the time
series on data rather than kriged results. The kriged time series for the14

- day sampling
frequency did no t adequately capture the second atrazine peak. In all cases, the kriging
standard deviations for estimate daily atrazine concentrations were low.

Julian Day 100 120 140 160 180Atrazine Concentration (ug/L)

0 5 10 15 20 25 30 35



### Figure D

- 6 : Prediction Error in the Atrazine Time Series from 4

- day sampling of

the MO

- 01

- 2007 (Days 91

- 190).

### Page 24 of 96 Figure D

- 7 : Prediction Error in the Kriged Atrazine Time Series from 4

- day Sampling of

the MO

- 01

- 2007 (Days 91

- 190).

Julian Day 100 120 140 160 180Atrazine Concentration (ug/L)

0 10 20 30 40 50



### Figure D

- 8 : Prediction Error in the Atrazine Time Series from 7

- day Sampling of the MO

01

- 2007 (Days 91

- 190) Julian Date (Days) 80 100 120 140 160 180 200 -5 0 5 10 15 20 25 30 35

### Page 25 of 96 Julian Date (Days) 80 100 120 140 160 180 200Atrazine Concentration (ug/L)

-5 0 5 10 15 20 25



### Figure D

- 9 : Prediction Error in the Kriged Atrazine Tim e Series from 7

- day Sampling of

the MO

- 01

- 2007 (Days 91

- 190)

Figure D

- 10 : Prediction Error in the Kriged Atrazine Time Series from 14

- day Sampling of

the MO

- 01

- 2007 (Days 91

- 190)



### D.2.4 Conditional Stochastic Simulations

Conditional stochastic simulations were conducted to assess the uncertainty associated
with estimated daily concentrations. This procedure allows generation of equally

probable realizations of time series with the same temporal autocorrelation and Julian Date (Days) 80 100 120 140 160 180 200 -5 0 5 10 15 20 25

### Page 26 of 96 descript ive statistics as the kriged time series. In our analysis, 1,000 realizations were
generated for each time series. A subsampling of 1000 realizations of 4

- day and 7

- day
sampling derived time series provide a reasonable description of temporal behavior an d
descriptive statistics of atrazine concentration for the Maumee 1995 site. ( Tables D

- 7

– D

- 12 and Figures D

- 11

– D

- 16 ). The conditional simulation for the MO

- 01

- 2007 site
provides reasonable approximation of temporal occurrence of atrazine concentratio ns. The peak concentration, however, is vastly underestimated from the actual data.

Table D

- 7 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulations of the 4

- day Kriged Atrazine Time Series (Days 100

- 200) from the Maumee River (1995).

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 3.47 3.59 3.42 3.35 3.51 3.39 3.56 3.37 3.33 3.35 3.85

SD 3.18 3.31 3.11 3.16 3.29 2.96 3.19 3.05 3.15 3.09 3.38

Max 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.00 14.06

Median 3.40 3.41 3.05 3.26 3.13 3.37 3.41 3.14 3.23 3.18 3.88

Min 0.03 0.03 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.00

Variance 10.02 10.85 9.57 9.90 10.69 8.65 10.05 9.20 9.84 9.47 11.45

Kurtosis

- 0.45 0.20

- 0.38 0.10 0.57 0.51 0.27 0.08 0.71 0.00 0.60

Julian Day 100 120 140 160 180 200 220Atrazine Conc (ug/L)

0 2 4 6 8 10 12 14 16

Estimate Mean (SD)

Actual Data



### Figure D

- 11 : Representative Realizations of Kriged Atrazine Time Series from 4

- day Sampling of the Maumee River 1995 (Days 100

- 200).

### Page 27 of 96 Table D

- 8 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the Actual 4

- day Data in Kriged Atrazine Time Series (Days 91

- 190) from the MO

- 01

- 2007.

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 7.23 8.38 6.66 5.87 6.65 7.10 7.10 6.19 6.43 6.45 7.13

SD 10.29 10.96 10.30 9.07 9.82 10.17 10.17 10.61 9.68 9.37 13.81

Max 33.29 46.50 42.14 33.29 33.29 38.47 38.47 67.39 42.56 33.29 91.60

Median 2.24 2.35 1.50 1.76 2.19 1.49 1.49 1.90 1.78 1.84 1.57

Min 0.01 0.05 0.04 0.02 0.02 0.02 0.02 0.02 0.02 0.04 0.05

Variance 105.87 120.17 106.12 82.21 96.46 103.39 103.39 112.65 93.79 87.76 190.64

Kurtosis 0.76 0.70 1.88 2.33 0.95 0.86 0.86 11.11 2.41 1.21 17.76

Julian Day 100 120 140 160 180Atrazine Concentration (ug/L)

0 20 40 60 80 100

Estimated Mean (SD)

Actual Data



### Figure D

- 12 : Representative Realizations of Atrazine Time Series from 4

- day sampling of

the M0

- 01

- 2007 (Days 91

- 190).

### Page 28 of 96 Table D

- 9 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the Grab 4

- day Data in Kriged Atrazine Time Series (Days 91

- 190) from the MO

- 01

- 2007.

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 5.24 6.64 4.74 3.58 4.26 3.88 4.77 4.24 4.07 4.36 7.13

SD 8.43 9.65 8.64 6.08 7.08 6.09 7.71 7.87 7.40 7.36 13.81

Max 35.3 34.7 54.9 33.2 33.2 33.2 33.2 50.2 41.06 37.26 91.6

Median 1.55 2.25 1.49 1.55 1.53 1.53 1.49 1.40 1.36 1.51 1.57

Min 0.01 0.04 0.04 0.01 0.01 0.01 0.02 0.02 0.01 0.03 0.05

Variance 71.0 93.1 74.6 36.9 50.1 37.1 59.3 61.9 54.7 54.2 190.6

Kurtosis 3.38 1.87 12.5

1 9.10 6.07 7.25 4.94 13.4

2 10.16 6.19 17.76

Julian Date (Days) 80 100 120 140 160 180 200Atrazine Concentration (ug/L)

0 20 40 60 80 100

Estimated Mean (SD)

Actual Data



### Figure D

- 13 : Representative Realizations of Kriged Atrazine Time Series from 4

- day Grab Sampling of the M0

- 01

- 2007 (Days 91

- 190).

### Page 29 of 96 Table D

- 10 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the 7

- day Kriged Atrazine Time Series (Days 100

- 200) from the Maumee River (1995).

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 3.64 3.76 3.67 3.34 3.57 3.31 3.02 3.53 3.40 3.69 3.85

SD 3.13 3.05 3.30 2.79 3.03 2.99 2.75 3.09 3.12 3.37 3.38

Max 11.06 11.06 11.06 11.06 11.06 11.06 11.06 11.06 11.06 11.06 14.06

Median 4.06 4.30 3.82 4.12 4.20 3.92 2.54 3.94 3.96 3.98 3.88

Min 0.19 0.21 0.20 0.23 0.23 0.23 0.23 0.23 0.21 0.20 0.00

Variance 9.73 9.18 10.78 7.68 9.09 8.88 4.74 9.45 9.66 11.24 11.45

Kurtosis

- 1.12

- 0.43

- 0.54

- 0.54

- 0.69

- 1.00

- 0.39

- 0.94

- 0.37

- 0.81 0.60

Julian Day 100 120 140 160 180 200 220Atrazine Conc (ug/L)

0 2 4 6 8 10 12 14 16

Estimate Mean (SD)

Actual Data



### Figure D

- 14 : Representative Realizations of Kriged Atrazine Time Series from 7

- Day Sampling of the Maumee River 1995 (Days 100

- 200).

### Page 30 of 96 Table D

- 11 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the 7

- Day Sample Data in Atrazine Time Series (Days 91

- 190)

from the MO

- 01

- 2007.

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 

### 7.79 7.98 7.27 6.47 7.93 7.47 5.94 8.31 7.33 7.72 7.13

SD 

### 11.69 12.60 10.63 10.99 12.75 13.15 10.08 12.19 13.34 13.02 13.81

Max 

### 56.67 65.57 45.77 50.79 72.39 67.50 42.00 57.35 65.08 58.18 91.60

Median 

### 1.92 2.34 1.41 2.31 2.35 1.28 1.18 2.33 2.30 2.23 1.57

Min 

### 0.01 0.02 0.04 0.03 0.01 0.02 0.05 0.01 0.00 0.02 0.05

Variance 136.6

4 158.8

6 113.0

4 120.7

2 162.5

1 172.8

5 101.5

5 148.6

3 177.9

8 169.5

8 190.64

Kurtosis 

### 3.84 5.12 1.94 5.44 7.33 5.85 4.21 3.84 5.66 3.46 17.76

Julian Day 100 120 140 160 180Atrazine Concentration (ug/L)

0 20 40 60 80 100

Estimated Mean (SD)

Actual Data



### Figure D

- 15 : Representative Realizations of Kriged Atrazine Time Series from 7

- Day Sampling of the M0

- 01

- 2007 (Days 91

- 190).

### Page 31 of 96 Table D

- 12 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the 7

- Day Kriged Atrazine Time Series (Days 91

- 190) from the

MO

- 01

- 2007 (1995).

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 5.98 6.53 4.48 5.68 6.84 8.14 4.72 6.31 5.20 6.72 7.13

SD 10.95 12.07 5.59 14.60 14.73 20.88 9.68 13.21 9.27 14.40 13.81

Max 73.70 72.39 23.59 105.89 128.75 126.89 73.70 101.91 63.37 70.87 91.60

Median 1.87 2.21 2.25 1.99 1.84 1.87 1.73 1.99 1.91 1.73 1.57

Min 0.01 0.03 0.04 0.05 0.05 0.02 0.04 0.01 0.02 0.04 0.05

Variance 119.88 145.57 31.29 213.12 216.90 436.17 93.78 174.51 85.94 207.37 190.64

Kurtosis 21.70 15.71 2.82 30.07 48.11 19.25 29.89 29.98 17.13 12.20 17.76

Julian Date (Days) 80 100 120 140 160 180 200Atrazine Concentration (ug/L)

0 20 40 60 80 100

Estimated Mean (SD)

Actual Data



### Figure D

- 16 : Representative Realizations of Kriged Atrazine Time Series from 7

- Day Sampling of the M0

- 01

- 2007 (Days 91

- 190).



### The impact of missing data is more pronounced in the 14

- day and 28

- day sampling
intervals ( Tables D

- 13

– D

- 17 and Figures D

- 17

– D

- 21 ). As exp ected, uncertainty
bounds are higher for 14

- day and 28

- day sampling intervals when compared to the 4

- day and 7

- day sampling intervals. Additionally, descriptive statistics and temporal
patterns of daily atrazine concentrations were variable for the case s tudy data.
Although the temporal pattern for the 14

- day sampling interval for MO

- 01

- 2007 did not
exactly match the data, the conditional simulations were capable of identifying the
temporal occurrence of the peaks. In contrast, the Maumee time series rea lizations for

the 14

- day and 28

- day sampling frequencies for MO

- 01

- 2007 were not capable of

### Page 32 of 96 identifying the temporal occurrence of the atrazine peaks. This illustrates that the
temporal correlation is no longer observable with the 28

- day sampling frequen cy.

Table D

- 13 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the 14

- day Kriged Atrazine Time Series (Days 100

- 200) from the Maumee River (1995).

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 3.28 4.96 2.74 2.67 3.06 3.11 3.76 2.06 3.36 4.24 3.85

SD 3.01 3.78 2.10 2.65 3.43 1.97 1.56 1.78 1.88 2.36 3.38

Max 12.75 13.86 6.68 11.18 12.55 6.52 6.81 4.70 9.80 10.07 14.06

Median 3.81 4.25 3.95 0.64 0.48 4.24 4.23 1.48 4.02 4.44 3.88

Min 0.02 0.21 0.15 0.15 0.02 0.08 0.42 0.06 0.37 0.34 0.00

Variance 9.00 14.17 4.36 6.97 11.62 3.86 2.41 3.12 3.50 5.53 11.45

Kurtosis 0.79

- 0.04

- 1.76

- 0.09

- 0.24

- 1.43 0.37

- 1.78 0.20

- 0.36 0.60

Julian Day 100 120 140 160 180 200 220Atrazine Conc (ug/L)

0 2 4 6 8 10 12 14 16

Estimate Mean (SD)

Actual Data



### Figure D

- 17 : Representative Realizations Kriged Atrazine Time Series from 14

- day sampling of the Maumee River 1995 (Days 100

- 200).

### Page 33 of 96 Table D

- 14 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the 14

- Day Kriged Atrazine Time Series (Days 91

- 190) from the

MO

- 01

- 2007.

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 

### 9.65 6.90 5.97 11.31 14.20 3.33 8.87 3.82 6.81 7.83 7.13

SD 

### 16.95 11.02 12.82 19.20 22.94 5.33 16.48 6.15 9.83 12.29 13.81

Max 

### 83.62 66.83 84.78 85.07 89.57 29.67 83.42 23.21 56.70 77.04 91.60

Median 

### 2.31 2.30 1.21 2.30 2.34 1.13 2.12 1.06 2.31 2.33 1.57

Min 

### 0.01 0.05 0.01 0.00 0.00 0.00 0.05 0.01 0.04 0.04 0.05

Variance 287.4

3 121.4

1 164.2

3 368.7

0 526.2

6 28.37 271.5

7 37.81 96.64 150.9

8 190.64

Kurtosis 

### 7.39 13.68 21.03 4.03 3.77 8.64 9.37 2.78 6.99 12.54 17.76

Julian Day 100 120 140 160 180Atrazine Concentration (ug/L)

0 20 40 60 80 100

Estimated Mean (SD)

Actual Data



### Figure D

- 18 : Representative Realizations of Atrazine Time Series from 14

- day Sampling

of the M0

- 01

- 2007 (Days 91

- 190).

### Page 34 of 96 Table D

- 15 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the 14

- Day Kriged Atrazine Time Series (Days 91

- 190) fro m the

MO

- 01

- 2007.

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 7.51 6.90 7.73 14.94 16.71 3.31 9.70 3.98 7.77 9.38 7.13

SD 13.04 10.44 21.31 31.86 35.19 5.19 21.76 6.76 13.69 19.13 13.81

Max 102.38 69.17 138.32 140.64 149.09 23.21 124.85 31.90 95.88 127.05 91.60

Median 1.80 1.78 1.22 1.65 1.88 1.07 1.44 0.95 1.62 1.76 1.57

Min 0.00 0.03 0.01 0.01 0.00 0.00 0.04 0.01 0.04 0.03 0.05

Variance 170.14 109.0

0 453.91 1015.0

1 1238.0

3 26.99 473.40 45.75 187.39 365.92 190.64

Kurtosis 28.14 14.20 24.28 5.68 7.75 4.05 16.68 3.36 19.91 18.94 17.76

Julian Date (Days) 80 100 120 140 160 180 200Atrazine Concentration (ug/L)

0 20 40 60 80 100 120 140

Estimated Mean (SD)

Actual Data



### Figure D

- 19 : Representative Realizations of Kriged Atrazine Time Series from 14

- day Sampling of the M0

- 01

- 2007 (Days 91

- 190).

### Page 35 of 96 Table D

- 16 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the 28

- Day Kriged Atrazine Time Series (Days 100

- 200) from the Maumee River (1995).

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 4.84 4.94 5.11 5.58 5.87 5.65 6.04 6.23 6.17 6.16 3.85

SD 3.96 4.06 3.58 3.34 3.75 3.45 3.80 4.10 4.14 4.50 3,38

Max 13.23 13.84 13.44 13.19 13.13 12.84 13.43 13.77 13.61 13.98 14.06

Median 3.50 3.80 4.18 4.41 4.43 4.57 4.48 4.72 4.60 4.18 3.88

Min 0.05 0.02 0.07 0.13 0.31 0.33 0.04 0.28 0.04 0.08 0.00

Variance 15.51 16.32 12.72 11.07 13.95 11.77 14.31 16.66 16.94 20.06 11.45

Kurtosis

- 0.74

- 0.48

- 0.19

- 0.61

- 0.88

- 0.77

- 1.01

- 1.28

- 1.21

- 1.39 0.60

Julian Day 100 120 140 160 180 200 220Atrazine Conc (ug/L)

0 2 4 6 8 10 12 14 16

Estimate Mean (SD)

Actual Data



### Figure D

- 20 : Representative Realizations Kriged Atrazine Time Series from 28

- day Sampling of the Maumee River 1995 (Days 100

- 200) [Variogram from flow data was used in conditional simulation].

### Page 36 of 96 Table D

- 17 : Descriptive Statistics of 10 Realizations from Conditional Stochastic Sequential Simulation of the 28

- Day Kriged Atrazine Time Series (Days 90

- 190) from the

MO

- 01

- 2007 (Days 90

- 190).

Statistic Realization

Actual 1 2 3 4 5 6 7 8 9 10

Mean 10.73 18.11 12.10 9.34 14.08 14.16 10.32 4.98 7.35 8.91 7.13

SD 22.13 27.75 23.15 20.52 27.36 23.82 22.08 13.08 19.84 19.22 13.81

Max 89.64 89.77 88.87 88.88 89.90 88.37 88.49 79.59 89.68 83.09 91.60

Median 1.21 1.73 0.94 1.06 1.10 1.54 0.78 0.39 0.58 0.90 1.57

Min 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.05

Variance 489.92 770.28 535.93 421.12 748.33 567.20 487.74 171.18 393.64 369.48 190.64

Kurtosis 5.15 0.55 3.69 6.33 2.27 2.83 4.94 16.51 9.71 6.08 17.76

Julian Day 100 120 140 160 180Atrazine Concentration (ug/L)

0 20 40 60 80 100

Estimated Mean (SD)

Actual Data



### Figure D

- 21 : Representative Realizations Atrazine Time Series from 28

- day sampling of

the MO

- 01

- 2007 (Days 90

- 190).



### D.2.5 Estimation of Time Series Data Using Soft Data and Available

Monitorin g Data

An analysis of the 14

- day and 28

- day sampling frequencies illustrate a high uncertainty
in predicting daily atrazine concentrations in an annual time series. This result is not
unexpected because of the low number of samples used in estimation of t emporal

correlation of atrazine concentrations. In the absence of measured data, the
incorporation of additional information (soft data) for predicting a time series of pesticide

### Page 37 of 96 concentrations is another approach for estimating daily atrazine pesticide

c oncentrations. Potential approaches for estimating daily atrazine concentrations in a

time series are as follows:

**•** Regression Approach

- Use of a covariate to estimate atrazine concentrations.

**•** WARP Modeling/Regression Approach

- Merging available monitorin g data
with model estimates ( i.e, WARP) to estimate a distribution of concentration in a
time series followed by a regression approach for assigning WARP percentiles

into the appropriate sequence in a time series.

Either method can be easily applied to al low integration into the proposed geostatistical
tools, including kriging and sequential stochastic simulation.

D.2.5.1 Regression Approach

One possible regression approach is the use of a covariate to estimate daily atrazine
concentrations. Covariates such as nitrate, flow, or another pesticide may provide
available independent variable for estimating daily atrazine concentrations. Potentia l

covariates in the MO

- 01

- 2007 and Maumee 1995 data

- sets are daily flow. Additionally,

acetochlor and rainfall data are available in the Maumee 1995 and MO

- 01

- 2007 data

sets, respectively.

Linear regression equations were developed to explore the rela tionship of daily atrazine
concentration and mean daily flow and acetochlor concentrations. Table D

- 18 provides
linear regression statistics of the relationship between atrazine and acetochlor
concentrations at the Maumee River sampling station in 1995. The most notable result
is that the regression equations for the 14

- day and 28

- day samplings are not
significant. In contrast, the 4

- day and 7

- day sampling frequencies yielded significant
regression equations (p<0.05). This analysis illustrates the loss in power in analyzing
pesticide monitoring data with infrequent sampling (>14 days). The coefficient of
determination (R 2 

### ) indicates that between 35 to 49% of the variability in atrazine
concentrations at the Maumee River 1995 sampling site can be account ed for using
acetochlor concentrations. This is not a surprising result because acetochlor and
atrazine are corn herbicides with comparable application dates. The NCWQR dataset,
however, is unique because it provides high frequency sampling of multiple pe sticides.
Unfortunately, most pesticide monitoring programs, such as NAWQA, take samples at
biweekly or monthly intervals. One potential option is to explore the use of nutrient
concentrations such as nitrate (NO 3 - 

### ) or flow as a covariate in estimating a trazine

concentrations.

### Page 38 of 96 Table D

- 18 : Linear Regression Equation of Atrazine Dependence on Acetochlor Concentration in the Maumee River 1995 University of Heidelberg Dataset. 

### Sampling Frequency N Intercept Slope R 2 

### F Test Probability
Complete

Time Series 100 1.90 (0.32) 2.03 (0.28) 0.3555 5.94E

- 11*

4

- day 25 1.46 (0.66) 2.85 (0.60) 0.4906 9.66E

- 5*

7

- day 15 1.87 (0.91) 2.26 (0.80) 0.3785 0.0146*

14

- day 8 3.11 (1.36) 0.25 (1.42) 0.0051 0.866

28

- day 4 2.50 (7.06)

- 0.12 (13.66) 3.83E

- 5 0.99
( ) = Standard error

*

- Significant (P<0.05)

Julian Day 100 120 140 160 180 200Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14 16

Daily Samples
4-day Samples
7-day samples

Actual



### Figure D

- 22 : Estimated Atrazine Concentration Time Series Using Acetochlor

Concentration as a Covariate at the Maumee River (1995).



### Flow data may also be useful for estimating atrazine concentrations in flowing water
bodies. An analysis was conducted to explore the relationship of flow and atrazine
concentrations at the 1995 Maumee River sampling site and the AEEMP MO

- O1

- 200 7
site ( Tables D

- 19 and D

- 20; Figures D

- 23 and D

- 24 ). Although both watersheds are
dominated by agricultural crops, they represent different hydrological systems because
of differences in average daily flow rate and watershed size. Although there is no t a
strong dependence of atrazine concentration on flow at various time intervals from 100
to 200 days at the Maumee River site in 1995, there was a significant regression
(P<0.05) for the sampling interval of 120

- 200 days.

### Page 39 of 96 Table D

- 19 : Linear Regression Equation of Atrazine Dependence on Flow in the Maumee River 1995 University of Heidelberg Data.



### Time Interval (days) N Intercept Slope R 2 

### F Test Probability

100

- 200 100 3.74

- 6E

- 5 0.0303 0.0828

110

- 200 90 3.205 9.24E

- 5 0.0138 0.2662

120

- 200 80 3.122 2.18E

- 3 0.0741 0.0139*

130

- 200 70 3.821 1.59E

- 4 0.0462 0.0717

140

- 200 60 4.871 7.52E

- 5 0.0148 0.3487

150

- 200 50 5.191

- 1.6E

- 5 0.0009 0.8295

160

- 200 40 5.140

- 8.9E

- 5 0.0439 0.1883

170

- 200 30 4.946

- 9.3E

- 5 0.0539 0.2087

180

- 200 20 3.046 5.04E

- 5 0.0855 0.1981

*

- Significant (P<0.05)

100-200 days

Flow 0 10000 20000 30000 40000 50000 60000Atrazine Conc (ug/L)

0 2 4 6 8 10 12 14 16

120-200 days

Flow (cfs) 0 2000 4000

6000

8000

10000

12000

14000

16000

18000

20000Atrazine Conc (ug/L)

-4 -2 0 2 4 6 8 10 12 14 16

160-200 days

Flow (cfs) 0 2000 4000

6000

8000

10000

12000

14000

16000

18000

20000Atrazine (ug/L)

0 2 4 6 8 10



### Figure D

- 23 : Regression Equations for the Dependence of Atrazine Concentration on Average Daily Flow at the 1995 Maumee River Sampling Site.

### Page 40 of 96 The MO

- O1

- 2007 site shows a similar trend in atrazine dependence of atrazine
concentration on flow ( Table D

- 20; Figure D

- 24 ). The regression equation was only
significant at the 160

- 200 and 170

- 200 day time intervals.

Table D

- 20 : Linear Regression Equation of Atrazine Dependence on Flow in the Atrazine Ecological Monitoring Site (MO

- 01

- 2007).



### Time Interval (days) N Intercept Slope R 2 

### F Test Probability

100

- 200 100

- 0.6024

- 0.1100 0.0150 0.2233

110

- 200 90 0.0083

- 0.0684 0.0061 0.4617

120

- 200 80 1.3516 0.0416 0.0042 0.5626

130

- 200 70

- 8.3079

- 0.0563 0.0013 0.7643

140

- 200 60 6.3615 0.6193 0.0099 0.4447

150

- 200 50 14.3448 1.5113 0.0418 0.1498

160

- 200 40 27.7944 2.9575 0.1712 0.0072*

170

- 200 30 26.2956 2.8679 0.2452 0.0046*

180

- 200 20

- 0.6024

- 0.1100 0.0150 0.2233

*

- Significant (P<0.05)

100-200 days

Ln Flow -10 -8 -6 -4 -2 0 2ln Atrazine Conc

-8 -6 -4 -2 0 2 4

160-200 days

ln Flow -9.6 -9.4 -9.2 -9.0 -8.8 -8.6 -8.4 -8.2ln Atrazine Conc (ug/L)

-4 -2 0 2 4 6



### Figure D

- 24 : Regression Equations for the Dependence of Atrazine Concentration on Average Daily Flow at the Atrazine Ecological Site (MO

- 01

- 2007).



### The fact that the dependence of the atrazine concentration on flow applies only for
certain periods is not surprising because atrazine application timing is a critical piece of
information in determining the correct time interval. Both PRZM/EXAMS modeling and

### Page 41 of 96 monitoring indicate that the highest concentrations in a time series are generally found
in runoff immediately after pesticide application (USEPA, 2007, 2009a). Additionally,
high average daily flow events in the absence of atrazine, such as prior to atrazine
application or after extensive degradation of atrazine, are expected to impact the slope
of the regression equation. Unfortunately, exploiting any relationship of flow and

atrazine concentration requires knowledge of the atrazine application dates within a
watershed. Since atrazine application dates should parallel corn planting dates in most
areas, any variable influencing corn planting dates, such as air temperature, soil

temperature, or soil moisture conditions, may be an appropriate predictor for estimating
atrazine application timing.

Another option is to approximate the end date where there is a significant relationship
between flow and atrazine concentration. The end date may be appro ximated by
using the soil aerobic metabolism half

- life of the pesticide. The soil metabolism half

- life
for atrazine is approximately 60 days. In our analysis, we used a 100

- day interval to
represent runoff dominated atrazine concentrations in the time series. Selection of the
appropriate time interval may be complicated by pesticide use on multiple crops ( i.e

., sorghum and corn) in a watershed.

D.2.5.2 WARP Modeling and Monitoring Data using Flow as a Covariate

The use of WARP predicted atrazine c oncentrations in a time series requires a
comprehensive data integration process. The WARP model has been extensively
reviewed (FIFRA SAP, 2007, 2009, Stone and Gilliom,2009). Sequencing the model

predicted atrazine concentrations into a time series is a critical aspect in the time series
prediction. For our analysis, we merged WARP predicted atrazine concentrations and

the 28

- day interval monitoring data from the Mamuee River data (1995) into a new
distribution of atrazine concentrations. This process w as conducted to provide ample
data for variogram analysis and conditional simulation. Although the WARP model

cannot discriminate runoff

- dominated or base

- flow dominated atrazine concentrations,
the annual time series distribution from WARP is expected t o capture a range of

atrazine concentrations in the runoff

- dominated period ( Figure D

- 25 ). More importantly,
the explanatory variables in the WARP regression model, with the exception of pesticide

application use rate and treated area in the watershed, pr ovide a description of the
runoff potential for atrazine. Therefore, the WARP model predictions of atrazine
concentrations are expected to predominately represent the runoff dominated atrazine

concentrations.

### Page 42 of 96 Julian Day 0 100 200 300Rank Percentile of Atrazine Concentration

0.0 0.2 0.4 0.6 0.8 1.0



### Figure D

- 25 : Time Series of Percentiles of Atrazine Concentrations for 1995 Maumee

River.



### As a proof

- of

- concept evaluation, WARP modeling was conducted using available
watershed properties reported for the Ohio sites in the AEEMP as surrogates for the

Maumee River watershed. Two AEEMP Ohio sites were selected to bracket WARP
predictions for the Maumee River watershed. For the CWS included in the AMP, WARP
input parameters specific to the CWS watershed were used. The WARP regres sion

model parameters are shown in Table D

- 21

.

Table D

- 21 : WARP Model Parameters for Prediction of Percentiles in the 1995 Maumee

River Data.



### WARP Parameter OHIO 01

- 2004 OHIO 03

- 2005

Use Intensity (kg ai/km 2 

### watershed) 82 82

Precip May and June (mm) 311.66 93

K Factor 0.38 0.38

R Factor 150 150

Dunne Overflow (%) 2.00 2.00

The WARP predicted atrazine concentrations were merged with 28

- day monitoring data

from the Maumee River data to form a new distribution of atrazine concentrations. This
distribution of atrazine concentrations is expected to represent atrazine concentrati ons during the runoff

– dominated period. Descriptive statistics of the merged data and the

estimated data are shown in Table D

- 22

.

### Page 43 of 96 Table D

- 22 : Descriptive Statistics for the 28 day Merged WARP Atrazine Concentrations

for the Maumee River 1995.



### Statistic Merged WARP

OHIO 01

- 2004 Merged WARP

OHIO 03

- 2005

Mean 9.28 2.82

Median 1.38 0.60

Standard Deviation 20.43 5.04

Sample Variance 417.24 25.38

Kurtosis 11.40 9.87

Skewness 3.30 3.00

Minimum 0.27 0.15

Maximum 77.42 19.19

The next step in the time series simulation process is sequencing the predicted atrazine
concentrations. One procedure for time sequencing atrazine concentrations is relating
flow to atrazine concentrations. As previously discussed, a significant regression
equation exists for atrazine and flow in the 120

- 200 day samples in the Maumee River
data. The flow was weighted to ensure that flow events immediately after pesticide
application have more influence on atrazine runoff. The weightin g factor for flow was
derived using the first

- order degradation model [A=A 0 

### e

- k*time; where A 0 

### = 1 lb/A, k=
degradation rate (0.01152 days - 1 

### ) and time (days)]. The actual daily flow data from Day
120 to Day 200 were multipled by the daily flow weighting factor. A comparison of
actual flow and weighed flow is shown in Figure D

- 26

.

Julian Day 120 140 160 180 200Flow (cfs)

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Actual Flow
Weighted Flow



### Figure D

- 26 : Time Series of Actual Daily Flow and Weighed Daily Flow on First

- Order Atrazine Degradation.

### Page 44 of 96 Using this information, the flow and atrazine data were separately ranked according to
percentiles in the distribution. The weighted

- flow and atra zine concentrations were

paired to create a time series. This procedure assumes a 1:1 relationship in the relative
percentile of flow and atrazine concentration ( Table D

- 23 ). For example, the 93 rd



### percentile atrazine concentration was linked to the 93 rd 

### percentile weighted flow rate.
The matched atrazine concentrations and weighted

- flow rates were sequenced in the
chronological order of the weighted flow rates. These time series were used for

variogram analysis, kriging, and conditional stochastic simula tion ( Table D

- 24 ). The
variograms provided a similar description of the temporal correlation for the different
merged WARP data (OH

- 01

- 2004 and OH

- 03

- 2005) ( Figure D

- 27 ). This is not an
unexpected observation because the temporal structure of simulated t ime series was
dependent on the temporal correlation of weighed

- flow values in the time series.

Table D

- 23 : Matched Percentiles of Flow and Merged WARP Predictions and 28

- Day Atrazine Concentrations from Julian Day 120 to Day 200 for Maumee River (1995).



### Julian

Day Weighted Flow (cfs) Percentile Merged

WARP

OH

- 01

-

2004 Merged

WARP

OH

- 03

-

2005

131 1559.629 38% 0.48 0.29

132 2519.694 63% 4.18 1.75

134 2729.214 69% 4.78 2.31

136 1808.896 46% 0.56 0.48

143 4843.374 85% 10.84 4.79

147 10080.82 100% 77.42 19.19

158 2305.225 54% 2.19 0.71

169 1426.829 31% 0.35 0.28

173 454.9293 15% 0.3 0.18

178 358.9314 8% 0.29 0.16

180 7248.331 93% 20.26 4.83

186 3552.483 78% 6.92 4.18

190 786.0219 23% 0.33 0.21

198 237.2983 0% 0.27 0.15

Table D

- 24 : Variogram Model for Predicted Atrazine Concentration in the Maumee River 1995 Time Series (120

- 200 days).



### Data Time Series Samples N Nugget Model Sill Range (Days)

WARP OH

- 01

- 2004 Maumee 28

- day 14 0.9 Spherical 3.90 15

WARP OH

- 03

- Maumee 28

- day 14 0.9 Spherical 2.40 17

### Page 45 of 96 2005

Lag Time (Days) 0 10 20 30 40 50Variance (ug/L2)

0 1 2 3 4 5

Spherical
Nugget=0.9 ug/L 2

Sill=3.90 ug/L 2

Range=15 days

Lag Time (Days) 0 10 20 30 40 50Variance (ug/L2)

0 1 2 3 4

Spherical
Nugget=0.9 ug/L 2

Sill=2.40 ug/L 2

Range=17 days



### Figure D

- 27 : Variograms for the Merged 28

- day Monitoring Data with WARP Estimated

OH

- 01

- 2004 (top) and WARP Estimated OH

- 03

- 2005 (bottom) Time Series.



### Conditional simulations of the merged WARP data were highly dependent on the WARP

model predictions ( Figures D

- 27 and D

- 28; Tables D

- 25 and D

- 26 ). The merged
WARP data predicted for OH

- 01

- 2004 had a much higher predicted m aximum atrazine

concentration than OH

- 03

- 2005. This difference is mainly attributed to the difference in

May and June precipitation. However, the two simulated time series clearly illustrate
that the temporal occurrence of the atrazine peak mimics the ac tual time series. This
situation suggests that weighted flow may be a reasonable predictor of temporal

occurrence daily atrazine concentrations.

### Page 46 of 96 Table D

- 25 : Descriptive Statistics from 10 Realizations of Estimated Time s Series from Merged 28

- Day Monitoring Data and WARP Data (OHIO 01

- 2004 Watershed) for 1995 Maumee River (Days 120

- 200).



### Statisti

c Realization

1 2 3 4 5 6 7 8 9 10 Actu

al

Mean 10.7 9.9 9.3 14.6 9.9 11.4 14.1 12.5 15.4 10.4 3.85

Median 2.0 1.1 4.2 4.7 0.9 2.0 2.9 1.4 2.7 2.5 1.57

Min 0.0 0.2 0.2 0.3 0.3 0.0 0.3 0.1 0.1 0.3 0.0

Max 77.4 77.4 77.4 77.4 77.4 77.4 77.4 77.4 77.4 77.4 14.06

SD 21.8 19.3 15.9 22.7 20.3 21.9 24.5 24.2 24.7 19.9 3.38

Varianc

e 475.

1 370.

8 251.

7 514.

8 412.

8 480.

5 600.

5 585.

8 611.

9 394.

7 11.45

Julian Day 120 140 160 180 200Atrazine Concentration (ug/L)

0 20 40 60 80 100



### Figure D

- 28 : Error Analysis from 10 Realizations of Estimated Times Series from Merged 28 day Monitoring Data and WARP Modeling for the OHIO 01

- 2004 Watershed.

### Page 47 of 96 Table D

- 26 : Descriptive Statistics from 10 Realizations of the Estimated Time Series from

28

- Day Sampling Data and WARP Modeling (OHIO 03

- 2005 Watershed) for 1995 Maumee River (Days 120

- 200).



### Statisti

c Realization Actu

al 1 2 3 4 5 6 7 8 9 10

Mean 3.05 2.94 3.13 4.50 2.95 3.35 4.11 3.37 3.96 3.19 3.85

Median 0.71 0.60 1.86 2.21 0.59 0.71 1.38 0.53 1.02 1.12 1.57

Min 0.01 0.15 0.13 0.15 0.15 0.02 0.15 0.06 0.08 0.15 0.0

Max 19.1

9 19.1

9 19.1

9 19.1

9 19.1

9 19.1

9 19.1

9 19.1

9 19.1

9 19.1

9 14.06

SD 5.23 4.81 4.21 5.81 5.10 5.36 6.08 5.55 5.68 4.78 3.38

Varianc

e 27.3

4 23.1

8 17.7

0 33.7

4 25.9

9 28.7

4 36.9

2 30.8

2 32.2

9 22.8

2 11.45

Julian Day 120 140 160 180 200Atrazine Concentration (ug/L)

0 5 10 15 20 25



### Figure D

- 29 : Error Analysis from 10 Realizations of Estimated Time Series from 28

- day Monitoring Data and WARP Modeling for the OHIO 03

- 2005 Watershed.

### Page 48 of 96 D.2.6 Summary

Geostatistical techniques such as variography and kriging have provided a reasonable

approximation of interpolated atrazine concentrations for selected time series for
atrazine and acetochlor. These techniques failed for the analysis of chlorpyrifos

monit oring data. This failure is likely associated with the randon nature of occurrence
with short duration peaks. Because of the concern for stationarity in the time series
modeling , the application of geostatistical techniques was limited to the atrazine run off

dominated period (Julian Day 90

- 200). Generally, monitoring data with sampling
frequencies of 7 days or less were required to provide the best estimate of temporal
occurrence and magnitude of atrazine concentrations. Based on our analysis, a

minimum o f 15 samples of known atrazine concentrations in a time series were required
for reliable estimation of variogram (covariance structure) and kriging. Fewer samples in
the time series (<15 samples) caused more uncertainty in both variogram modeling and

krig ing estimation. This is not unexpected because the temporal correlation of atrazine
concentration may be shorter than the longer sampling frequencies. Additionally, there
are fewer data to provide a reliable covariance matrix. One consequence of this

estim ation approach is that geostatistical techniques are only as good as the monitoring
data. For example, the MO

- 01

- 2007 site had a daily peak atrazine concentration of
91.60 ug/L. This peak concentration was not captured in the monitoring data for 4, 7,

14, or 28

- day sampling frequencies. Therefore, the peak concentration was consistently
missed in the geostatistical analysis.

Conditional stochastic simulation provides a reasonable approach to address

uncertainty in atrazine concentrations in the time seri es. A critical issue in the
conditional simulation process is extrapolation limits for estimation of upper end

concentrations. A priori knowledge of reasonable upper limits on atrazine
concentrations is needed for estimating reliable peak concentrations. In our case, we
conditioned the stochastic simulation using atrazine monitoring data from 4, 7, 14, and
28 day sampling frequencies. We generated a 1000 realizations of the time series in
order provide a distribution of daily atrazine concentrations.

In the absence of extensive monitoring data, regression techniques may be useful in
predicting a time series of atrazine concentrations. Covariates such as flow and
acetochlor concentrations may provide useful information for predicting atrazine

conce ntrations. Another estimation approach may be the use of merging modeling
(WARP) and monitoring data to construct a base

- line time series for variography and

conditional stochastic simulation. These methods were shown to provide reasonable

approximation s of the temporal occurrence and magnitude of atrazine concentrations.

### Page 49 of 96 D.2.7 Additional Tables and Figures for Analyses of Other Pesticides and

Sites

D.2.7.1 Maumee River 1995

- Acetochlor

Table D

- 27 : Variogram Models using the Maumee River 1995 Time Series for Acetochlor with Different Sampling Frequencies



### Data Parameter N Nugget Model Sill Range (Days)

Mauumee95 Actual Data 0.05 Gaussian 0.97 12

4

- day 25 0.210 Linear 0.90 40

7

- day 15 0.6 Linear 1.80 49

14

- day 8 0.75 Gaussian 2.00 55

28

- day 4 NA NA NA NA

Table D

- 28 : Descriptive Statistics from Kriged Maumee River 1995 Time Series for

Acetochlor.



### Statistic Actual 4 day 7 day

Mean 0.71 0.69 0.73

Standard Error 0.09 0.07 0.06

Median 0.40 0.44 0.47

Standard Deviation 0.93 0.73 0.59 Sample Variance 0.86 0.53 0.35

Kurtosis 4.52 5.39 3.50

Skewness 2.36 2.43 1.79
Range 3.96 3.87 3.33

Minimum 0.00 0.00 0.01

Maximum 3.96 3.87 3.34

### Page 50 of 96 Julian Date (Days) 80 100 120 140 160 180 200 220Acetochlor Concentration (ug/L)

0 1 2 3 4 5 Maumy River 1995 Kriged Time Series 4-Day Sampling Frequency

Actual Acetochlor Concentration (ug/L) 0 1 2 3 4 5Predicted Acetochlor Concentration (ug/L)

0 1 2 3 4 5



### Figure D

- 30 : Prediction Error in the Kriged Acetochlor Time Series from 4

- day sampling of the Maumee River 1995 (Days 100

- 200).

### Page 51 of 96 Maumy River 1995 7-day sampling frequency

0 1 2 3 4 5Predicted Acetochlor Concentration (ug/L)

0 1 2 3 4

Julian Date

80 100 120 140 160 180 200 220Acetochlor Conc (ug/L)

-2 -1 0 1 2 3 4 Actual Acetochlor Concentration (ug/L)



### Figure D

- 31 : Prediction Error in the Kriged Acetochlor Time Series from 7

- day sampling of the Maumee River 1995 (Days 100

- 200).

### Page 52 of 96 D.2.7.2 Sandusky River 1995

- Atrazine

Table D

- 29 : Variogram Models using the Sandusky River 1995 Time Series (100

- 200 days) with Different Sampl ing Frequencies



### Data Parameter N Nugget Model Sill Range (Days)

San95 Actual Data 0.10 Gaussian 2.90 50

4

- day 25 0.05 Gaussian 2.50 46

7

- day 15 0.00 Gaussian 2.50 60

14

- day 8 0.00 Gaussian 2.50 60

Table D

- 30 : Descriptive Statistics from Kriged Sandusky River 1995 Time Series for Atrazine (100

- 200 days)



### Statistic Actual 4

- day 7

- day

Mean 4.380582 4.390118 4.031238

Standard Error 0.391665 0.370324 0.377063

Median 3.217868 3.251112 3.066691

Standard Deviation 3.916654 3.703244 3.770625
Sample Variance 15.34018 13.71402 14.21761

Kurtosis

- 0.05869

- 0.91427

- 0.24019

Skewness 0.930649 0.586317 0.982755
Range 15.464 14.7 12.81051

Minimum 0 0 0

Maximum 15.464 14.7 12.81051

### Page 53 of 96 Sandusky River 1995 Atrazine 4 day sampling frequency

Actual Atrazine Concentration (ug/L) 0 2 4 6 8 10 12 14 16 18Predicted Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14 16

Julian Date (Days)

80 100 120 140 160 180 200 220Atrazine Concentration (ug/L)

-2 0 2 4 6 8 10 12 14 16



### Figure D

- 32 : Prediction Error in the Kriged Acetochlor Time Series from 4

- day sampling of the Sandusky River 1995 (Days 100

- 200).

### Page 54 of 96 Sandusky River 1995 Atrazine 7 day sampling interval

0 2 4 6 8 10 12 14 16Predicted Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14

Julian Date (days) 80 100 120 140 160 180 200 220Atrazine Concentration (ug/L)

0 2 4 6 8 10 12 14 16

Actual Data

Predicted Data Actual Atrazine Concentration (ug/L)



### Figure D

- 33 : Prediction Error in the Kriged Acetochlor Time Series from 7

- day sampling of the Sandusky River 1995 (Days 100

- 200).

### Page 55 of 96 ADDENDUM

DATA INPUT FILE FOR GEOEASE

95MW28.dat



### 3

x

y

atrazin 

### e

131 10 0.48

132 10 4.18

134 10 4.78

136 10 0.56

143 10 10.84

147 10 77.42

158 10 2.19

169 10 0.35

173 10 0.3

178 10 0.29

180 10 20.26

186 10 6.92

190 10 0.33

198 10 0.27



### GRID MATRIX EXAMPLE FOR 1

- D KRIGING (VARIO in GEOEASE)

Variable X
Origin 120 10 Y

Spacing 1 1

Number 80 1



### DATA INPUT FILE FOR SGSIM

95mwA28.dat

5

x

y

atrazine

KSvalue

weight

11 1 0.29 0 1

12 1 1.75 0 1

14 1 2.31 0 1

16 1 0.48 0 1

23 1 4.79 0 1

27 1 19.19 0 1

38 1 0.71 0 1

49 1 0.28 0 1

### Page 56 of 96 53 1 0.18 0 1

58 1 0.16 0 1

60 1 4.83 0 1

66 1 4.18 0 1

70 1 0.21 0 1

78 1 0.15 0 1



### COMMAND FILE FOR SGSIM

Parameters for SGSIM

********************

START OF PARAMETERS:

mwa28.dat \ file with data

1 2 0 3 5 0 \ columns for X,Y,Z,vr,wt,sec.var.

- 1.0e21 1.0e21 \ trimming limits

1 \ transform the data (0=no, 1=yes)

X.trn \ file for output trans table

0 \ consider ref. dist (0=no, 1=yes)

histsmth.out \ file with ref. dist distribution

1 2 \ columns for vr and wt

0.0 19.19 \ zmin,zmax(tail extrapolation)

1 0.0 \ lower tail option, parameter

1 19.19 \ upper tail option, parameter

3 \ debugging level: 0,1,2,3
x.dbg \ file for debugging output

mwa28.out \ file for simulation output

1000 \ number of realizations to generate

80 1.0 1.0 \ nx,xmn,xsiz

1 1.0 1.0 \ ny,ymn,ysiz

1 1.0 1.0 \ nz,zmn,zsiz

69069 \ random number seed

0 16 \ min and max original data for sim

12 \ number of simulated nodes to use

0 \ assign data to nodes (0=no, 1=yes)

0 3 \ multiple grid search (0=no, 1=yes),num

0 \ maximum data per octant (0=not used)

10.0 10.0 10.0 \ maximum search radii (hmax,hmin,vert)

0.0 0.0 0.0 \ angles for search ellipsoid

1 0.60 1.0 \ ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COL C

ydata.dat \ file with LVM, EXDR, or COLC variable

4 \ column for secondary variable

1 0.3 \ nst, nugget effect

1 0.7 0.0 0.0 0.0 \ it,cc,ang1,ang2,ang3

17.0 17 .0 17.0 \ a_hmax, a_hmin, a_vert

### Page 57 of 96 APPENDIX D.3 EXPLORATION OF USING A WATERSHED MASS BAL ANCE

APPROACH TO ESTIMATI NG DAILY ATRAZINE CO NCENTRATIONS IN

MONITORED AGRICULTUR AL WATERSHED EFFLUEN T

D.3.1 Abstract

A simple watershed mass balance model was calibrated to stream

- water atrazine data
from small agricultural watersheds from the Midwestern U.S., an area believed to

produce high exposures to atrazine in water. These watersheds were instrumented to

monitor flow at the watershed outfall and rainfall within the watershed. Atrazine was
measured in the discharge from the watershed using four

- day interval grab samples
(and for selected watershed and year combinations, daily samples collected by auto

samplers) throughout the mid

- spring application timefram e through the growing season
and into the early fall for multiple years. The simple mass balance models estimate the
contribution of atrazine to the watershed from each individual storm event greater than

0.4 cm rainfall based on the load of atrazine disch arged from the watershed. Discharge
was calculated by iteratively fitting daily predictions of atrazine concentration to the less

frequently measured atrazine concentrations. Based on a set of six watershed and year

combination model calibrations that main tained mass

- balance conditions over the
monitoring period and fit atrazine discharge data with r 2 

### ≥ 0.85, the fraction of atrazine
discharged over the monitoring period was on the order of 1 to 3.5 % of atrazine
applied. However within this set of watershe ds, it was the watershed and year
combinations with the lowest percentage of applied atrazine discharged that produced
high atrazine concentrations for extended periods. Those watershed and year
combinations with the highest % of atrazine discharged expell ed the atrazine in high
flow, but short duration events, while the watershed and year combinations with the
lowest % of atrazine discharged expelled the atrazine in lower flow, but longer duration
events that tended to merge multiple discharge events into much longer periods of high

concentration. Many other watershed and year combination model calibrations were
unable to maintain mass balance over the period of monitoring. This was attributed to
the coarseness of the model temporal resolution (daily time

- s tep) and an apparent
problem with the magnitude of the measured flows for some watershed and year
combinations, at least in U.S. EPA’s compilation of the AEEMP data set.

D.3.2 Introduction

This paper explores the concept of using a mechanistic model to d escribe atrazine
concentrations in surface drinking water sources. This paper is part of a collection of
papers exploring different modeling approaches (simple statistical, geostatistical, and

### Page 58 of 96 mechanistic models) to describe atrazine concentrations. The in tent here is to use a
very simple mechanistic model to facilitate a discussion of the potential benefits and

problems associated with any mechanistic model rather than to advocate any particular
model or mechanistic modeling approach. The mechanistic model presented is
intentionally designed to be simple and relatively easy to calibrate in hopes of facilitating
a higher

- level, conceptual discussion of the benefits and concerns of a mechanistic
approach to monitoring atrazine concentrations in drinking water rather than focusing on

model specification and calibration details.

D.3.3 Methods

Several individual watershed and year combination data sets were selected for
modeling from the much larger Atrazine Ecological Exposure Monitoring Program
(AEEMP) data s et (see USEPA, 2007 and 2009a for more information on the monitoring
program). Many watershed and year combinations had incomplete flow monitoring data
sets. Because it would be difficult to interpret model results for portions of the growing

season, only watershed and year combination data sets with complete flow data records
for the monitoring season were selected for modeling. Additionally, several watershed
and year combination data sets were avoided because negative values appeared in

their flow data. In order to avoid these problematic data sets, the data sets to be
modeled were selected by the investigator’s subjective professional judgments and
limited in number by time/resource constraints.

The simple watershed atrazine mass

- balance model operates on a daily time step
because that is the temporal resolution of the data set that U.S. EPA was provided
(grab and auto

- sampler samples were only recorded to a date rather than a time of day
and watershed discharge was provided as average daily flow). It is implemented in
Microsoft Excel software and calibrated using the iterative fitting methods available in

the Solver routine of Excel.

The simple watershed atrazine mass

- balance model essentially describes variations in

atrazine concentration as a series of storm

- event associated peaks that taper off over

time. Figure D.3

- 1 depicts two pathways that might produce such peaks. For the runoff
pathway, atrazine is applied over the soil surface/crop and is transported over the soil

surface to the surface waterb ody (stream) draining the watershed. A peak would occur
when runoff enters the stream and diminish as that runoff water is discharged and/or

diluted by groundwater inputs to the stream. For the tile

- drainage pathway, atrazine is
applied over the soil surfa ce/crop and is transported through the near subsurface soil
(Region A in Figure D.3

- 1) by leaching with each rainfall event. Eventually (in days to
weeks), a storm event finally pushes (leaches) the atrazine into Region B (referred to in

### Page 59 of 96 this document as “ break

- through”), which represents the area around a tile drain
(pictured, C) or preferential flow pathway that discharges (D) the atrazine to the stream
draining the watershed. The peak would subside as the slug of water from Region B is
discharged and/or diluted by groundwater inputs to the stream.

Runoff Pathway Tile

- drainage Pathway

Figure D.3

- 34 . Cross

- sectional views of a stream channel in a tile

- drained agricultural
watershed depicting runoff and tile drainage pathways by which atrazine applied to crops
might reach a stream draining the watershed.



### A cursory examination of the atrazine data to be modeled indicates that atrazine
concentration peaks are almost always associated with storm events (major

precipitation events that precede an increase flow in the stream), but not all storm
events result in a large atrazine concentration peak. Data from some watershed and

year combinations show a single atrazine peak, while other watershed and year

combinations show multiple atr azine peaks. The model cannot attribute the peaks to
different pathways, but is general enough to describe both pathways. If it is assumed
that either or both pathways are responsible for the majority of atrazine occurring in

these streams, the model sho uld be able to describe it.

Because the simple watershed mass

- balance model is a relatively crude proof of
concept model, not all of the pathways depicted in Figure D.3

- 1 are fully described in
this model. Specifically, modeling the distribution of atrazi ne applications over time to
the crop/soil surface and the subsequent transport through Region A of Figure D.3

- 1
would have added considerable complexity and would have required assumptions
regarding the speed of atrazine leaching and depth/distance travel ed by the atrazine.

Therefore, the simple watershed atrazine mass

- balance model only follows the atrazine
from the time a runoff event begins or a slug of atrazine crosses from Region A to
Region B in Figure D.3

- 1 until it exits the watershed in the stre am discharge. This is
described mathematically as: Runoff

Stream Stream D A

B C

### Page 60 of 96 

### 0 and 1 if 0 and 1 if

0 1 if

1 1 1 .



### ≤ **+**

**− >**  **> +** 

**− >**  **+**

**− =** 

**=**

**− −**  **−**

t t t t t t t t t Init

t 

### AI AE WAB t AI AE WAB t AI AE WAB t

WAB **β**

where: WAB t 

### is the watershed atrazine balance at any time (day), t . If t =1 (the first day
of flow monitoring for that specific site), an initial watershed atrazine balance is

esti mated, β Init. 

### based on an iterative fitting of the atrazine discharge data. Or if t > 1
(any subsequent day), the watershed atrazine balance at the current time step (day) is
the watershed atrazine balance from the previous time step minus any atrazine

exp orted (discharged from the watershed), AE t 

### , plus any atrazine imported (added) to

the watershed, AI t



### .

Potentially the units of WAB t 

### could simply be expressed as mass ( e.g. , kg of atrazine).
However, using units of mass creates difficulties in comparing across different
watersheds sizes ( e.g. , larger watersheds would have larger watershed atrazine
balances, exports, and imports). Therefore, these values are expressed on a per
hectare basis, resulting in units of kg/ha for the watershed atrazine balances, exports,
and imports.

The atrazine exported, AE t 

### , only accounts for the discharge from the watershed outfall
(no degradation, volatilization, leaching to deep groundwater considered). It is modeled

as:

TS WAB ADFY AE

t t Exp t 

### × **× ×**  **=**

**−** 1

. 

### β



### where: β Exp. 

### is a fitted par ameter describing the fraction of WAB exported in units of kg

litre - 1 

### ; ADFY t 

### is average daily flow yield in units of litre day - 1 

### ha - 1 

### ; WAB t

- 1 

### is the
watershed atrazine balance (kg/ha) from the previous day, t ; and TS is the time step, 1

day.

The atrazine imported, AI t 

### , describes the runoff of atrazine or transfer of atrazine Region
A of the soil in Figure D.3

- 1 to Region B with each storm and/or subsequent leaching

event. Therefore, a series of fitted parameters, β i 

### , are used to describe the mass of

atrazi ne in runoff or entering Region B of Figure D.3

- 1 in units of kg/ha.



### cm RF cm RF AI

t i t

t 

### 4

. 0 if 4

. 0 if 0

**≥ <** 

**= β** 

### Page 61 of 96 Potentially, each storm event could be modeled with its own β i 

### parameter. This would
result in a large number of parameter estimates ( β Init. 

### , β Exp. 

### , and all the β i 

### terms) relative

to the number of atrazine concentration measurements. Therefore, in order to reduce

the number of β i 

### parameters an arbitrarily chosen threshold of 0.4 cm of rainfall was
selected as the minimum rainfall needed to cause runoff or leaching of atrazine. The
justification for a threshold is that small rainfall events would simply replace water lost
by evapotranspiration and therefore would be unlikely to promote much runoff or

leaching.

The daily predicted concentration, PC t 

### (μg/L), is calcul ated as:

9 

### 10 **× =** 

t t

t 

### ADFY AE PC

where: 10 9 

### is a conversion factor from kg to μg.

The model is calibrated to the observed atrazine concentrations by minimizing the mean
squared deviations (error) between observed and predicted atrazine concentrations,

MS E :



### t SampleCoun SE MSE t 

# ∑

**=**

where: SampleCount is the number of atrazine samples (does not include field or
laboratory duplicates) and the squared error on any day t ( SE t 

### ) is calculated as follows:



# ( **) (**  **) (**  **)**

**( )**  **( )**  **( )** 

t t t t t t t t t t

t n t n t n t n t



### DL OC DL PC DL OC DL PC DL OC

OC PC OC PC t

SE

**> >**  **< <**  **<**

**+**

**− +**  **+**

**− +**  **=**

if and if and if

1 log 1 log 1 log 1 log 0 day on sample no if 0

2 2



### This method of calculating error avoids biasing model parameter estimates that occurs
when particular values are assumed for less than detection limit ( DL ) values ( e.g. , 1⁄2 the
detection limit). This method indicates agreement (0 error) if both the observed and

predicted concentrations ar e below the detection limit (condition 2 in the equation
above), assigns error in proportion to the deviation between the detection limit and the

predicted concentration when the measured concentration is below the detection limit

and the predicted concent ration is above the detection limit (condition 3), or assigns

### Page 62 of 96 error in proportion to the deviation between the observed and predicted concentration
when the measured concentration is above the detection limit (condition 4).

D.3.4 Results and Discussion

S everal anomalies were noted in the flow records from the AEEMP atrazine monitoring
data set. Many watershed year combinations could not be modeled because of breaks
in the flow records. Additionally, some flow data from particular watershed and year

combin ations (mainly from Nebraska) contained long stretches of negative values which
could not be meaningfully interpreted.

It appeared that the quality of the flow data deteriorated as the length data record
increased. Figure D.3

- 2 depicts the changes in ave rage daily flow for the MO

- 02
watershed from the first year the site was monitored, 2004, to 2008. In years 2004
through 2006, the average daily flow values varied within a relatively narrow band

between 0.28 and 7.75 m 3 

### /s, compared to 0.00017 to 3.15 m 3 

### /s for years 2007 and
2008. Only 13 days from 2007 and 2008 (1 in 2007 and 12 in 2008) had measured
average daily flow values that fell within the total range of measured average daily flow
values from 2004 through 2006.

Figure D.3

- 35 . Average daily flow measurements from the MO

- 02 watershed from the
2004 to 2008 monitoring periods.



### The watershed atrazine mass

- balance model was fit to 11 watersheds that varied with
respect to watershed size (11.6 to 387.6 mi 2 

### ) and atrazine use int ensity (11.1 to 53.2 %
of land in corn and sorghum). Because monitoring does not occur year

- round, the
model was fit to each watershed and year combination’s data separately. To determine 0.0001

0.001

0.01

0.1

1 10

1/1/2004 1/1/2005 1/1/2006 1/1/2007 1/1/2008 1/1/2009Average Daily Flow (m3/s)

Date

### Page 63 of 96 if the model’s parameter estimates were similar for different years of data from the same
watershed, multiple years from four watersheds (IA

- 01, IL

- 04, MO

- 01, MO

- 02) were

modeled. To determine if the model’s parameter estimates were similar for grab and

auto

- sampler samples, models were fit separately for grab and auto

- sam pler samples
as well as together (combined) for four watershed and year combinations ( MO

- 01 2007,

MO

- 02 2007 and 2008, and MO

- 04b 2008 ). For four watershed and year combinations
(IA

- 02 2004, IL

- 04 2007, IN

- 11 2005, and MO

- 012004), there were too few auto

s ampler samples to perform a separate auto

- sampler calibration only. Therefore these
watershed and year combinations were calibrated as a combined (models calibrated

with grab and auto

- sampler samples together) fitting of both sample types.

Model Performa nce Criteria

Table D.3

- 1 summarizes the mass balance described by the calibrated models and
provides some measures of model performance. Two criteria were used to judge how

well the model worked with the available atrazine and flow data. The first criteri a is the
model fit to the atrazine concentration data as measured by r 2 

### (last two columns in

Table D.3

- 1). Calibrations achieving an r 2 

### of ≥0.85 were considered acceptable. This

criteria was chosen in order to select a small number of watersheds for which the

parameter estimates would be assured to be relatively accurate.

### Page 64 of 96 Table D.3

- 31 . Comparison of the watershed atrazine mass balance model performance on six of the atrazine ecological
monitoring watersheds.

Water

shed Year (Sample Type 1 ) Watershed Characteristics Watershed Atrazine Mass

- balance Summary in kg Model Performance

Area in

Mi 2 (ha) Fraction in

Corn

and/or Sorghum (%) Estimated

Atrazine Application (kg) Initial

mass in

Watershed (date) Atrazine Export Atrazine Discharge Final Mass

in

Watershed (date) Mass

Imbalance Fraction

of **WAB t**  Exported

(liter - 1

day - 1 ha 1 ) Export as

a Fraction

of App.

Estimate (%) Concentration Fit ( **r** 2 )

Grab Auto Sampler

IA

- 01 2004 G 21.3 (5,517) 42.6 3705 0.34 (4/5) 47.47 46.88 0.93 (8/27) 0 1.35E

- 6 1.28 0.98 NA

2005 G 50.4 3987 0.10 (4/2) 10.08 10.65 0.22 (8/31)

- 0.69 2.36E

- 6 0.253 0.42 NA

IA

- 02 2004 C 21.7 (5,623) 40.2 6114 0.029 (4/12) 4.108 4.137 0.00044 (8/20) 0 2.60E

- 5 0.067 0.47 0.82

IL

- 01 2004 G 21.8 (5,633) 23.8 3166 0.54 (4/2) 114.2 112.3 2.5 (8/27) 0 1.06E

- 6 3.61 0.95 NA

IL

- 02 2004 G 33.4 (8,644) 48.8 9974 0.23 (4/2) 23.26 22.00 1.5 (8/27) 0 9.08E

- 6 2.07 0.91 NA

IL

- 04 2006 G 20.6
(5,323) 45.9 4871 0.066 (4/2) 61.03 60.83 0.27 (8/30) 0 5.75E

- 6 1.25 0.74 NA

2007 C 49.6 6195 0.024 (4/3) 8.683 8.671 0.037 (9/6) 0 1.81E

- 5 0.140 0.77 0.60

IL

- 05 2004 G 54.2 (14,025) 53.2 17,606 1.1 (4/7) 51.12 51.59 0.59 (8/19) 0 2.33E

- 6 0.290 0.38 NA

IN

- 11 2005 C 9.03 (2339) 27.7 1708 0.89 (4/7) 21.50 24.50 0.014 (8/22) - 2.6 1.64E

- 5 1.26 0.98 0.99

MO

- 01 2004 C

11.6
(3010) 37.6 5021 0.013 (4/2) 7.362 7.290 0.086 (8/15) 0 3.39E

- 5 0.147 0.51 .02

2007 G

29.0 3874 0 (4/2) 0.1360 10.22 2E

- 5 (8/28)

- 10 2.37E

- 2 0.0035 0.81 NA

2007 A 1E

- 05 (4/2) 0.088 1.404 2E

- 5 (8/28)

- 1.3 2.40E

- 2 0.0023 NA 0.18

2007 C 9E

- 06 (4/2) 0.1645 0.8551 2E

- 5 (8/28)

- 0.69 2.57E

- 2 0.0042 0.84 0.19

MO

- 02 2004 G

28.2 (7294) 27.9 8394 0.40 (4/21) 79.63 78.67 1.4 (8/15) 0 1.30E

- 5 0.949 0.85 NA

2005 G 24.8 7490 1.6 (4/2) 65.11 65.37 1.3 (8/17) 0 6.24E

- 6 0.869 0.92 NA

2006 G 32.9 9631 0.49 (4/2) 88.10 87.85 0.83 (8/17) 0 7.77E

- 6 0.915 0.97 NA

2007 G

25.0 7512 0.030 (4/2) 1.602 1.458 0.17 (8/6) 0 1.20E

- 4 0.021 0.73 NA

2007 A 0.033 (4/2) 1.343 1.274 0.10 (8/6) 0 1.03E

- 4 0.018 NA 0.55

2007 C 0.034 (4/2) 1.347 1.278 0.18 (8/6) 0 1.04E

- 4 0.018 0.55 0.47

2008 G 16.8 5024 0.0068 (4/1) 5.668 5.749 0.016 (10/1) - 0.080 1.28E

- 4 0.11 0.78 NA

2008 A 0.0033 2.649 2.843 0.003

- 0.19 2.69E

- 4 0.053 NA 0.64

### Page 65 of 96 Water

shed Year (Sample Type 1 ) Watershed Characteristics Watershed Atrazine Mass

- balance Summary in kg Model Performance

Area in

Mi 2 (ha) Fraction in

Corn

and/or Sorghum (%) Estimated

Atrazine Application (kg) Initial

mass in

Watershed (date) Atrazine Export Atrazine Discharge Final Mass

in

Watershed (date) Mass

Imbalance Fraction

of **WAB t**  Exported

(liter - 1

day - 1 ha 1 ) Export as

a Fraction

of App.

Estimate (%) Concentration Fit ( **r** 2 )

Grab Auto Sampler (4/1) (10/1)

2008 C 0.0035 (4/1) 2.882 3.083 0.003 (10/1) - 0.20 2.90E

- 4 0.057 0.55 0.64

MO

- 04b 2008 G 387.6 (100,394) 11.1 49,531 0.32 (4/2) 596.8 599.7 0.039 (10/1) - 2.6 1.17E

- 5 1.20 0.93 NA

2008 A 0.16 (4/2) 475.3 486.5 0.16 (10/1)

- 11 3.14E

- 5 0.96 NA 0.75

2008 C 0.26 (4/2) 517.3 519.5 0.32 (10/1)

- 2.3 1.41E

- 5 1.04 0.88 0.79

NE

- 02 2005 G 65.0 (16,841) 31.70 14,012 0.44 (4/2) 180.1 180.0 0.49 (8/26) 0 5.79E

- 6 1.28 0.71 NA

1 

### Monitoring sample type (G = grab; A = auto sampler; or C = combined analysis of both grab and auto sampler samples).

### Page 66 of 96 The second criteria is mass imbalance (the 10 th 

### column in the table) which should be
zero indicating that mass balance was maintained over the course of the monitoring
period (initial mass + inputs

– outputs = final mass). Eleven of the 25 model calibrations

presented in Table D.3

- 1 resulted in negativ e mass imbalances (no positive mass
imbalances) which occurred when a time

- step removed more atrazine than was
available in the watershed (negative atrazine balance).

Model Time

- step Considerations

The imbalance problem is likely due primarily to the co arseness of the model temporal
resolution (time

- step). Between each 1 day time

- step, both the flow and concentration
can change dramatically in the same direction (higher flows often result in higher
concentrations). The predicted load (the product of pred icted concentration and flow) in

the stream

- flow is removed from the previous day’s watershed atrazine balance each

day to calculate the current day’s watershed atrazine balance. The rate at which the
atrazine is removed is some fraction of previous day’s watershed atrazine balance
( WAB t

- 1 

### ) per liter discharged from the watershed. Theoretically, earlier liters discharged

during the same day would remove more atrazine than subsequent liters because each

liter removes atrazine from the WAB t 

### making it unavaila ble for withdraw by subsequent

liters. Handled in this way, the WAB t 

### would never be depleted (it would asymptotically
approach zero). However, WAB t 

### is constant during a time

- step. Therefore, each liter
discharged contains the same amount of atrazine as any other liter discharged on that
day. So, when the flow changes greatly between time

- steps, it is possible for the WAB t 

### to be depleted. Converting the model to smaller time

- steps such as 15 minute intervals
(the flow monitoring interval normally used by USG S) would split each of the current

model’s time

- steps into 96 (24 hr × 60 min/hr / 15 min) 15 minute time

- steps. This would
greatly alleviate the potential for this problem because the previous time

- step’s WAB t 

### changes every 15 minutes, which better approx imates a continuously changing WAB t



### . Table D.3

- 2 provides some estimates of the magnitude of the time

- step issue by
providing the maximum monitored daily load compared to the model

- predicted total
atrazine discharged during the monitoring period. Notice sp ecifically, the IA

- 01 2004
calibration (a calibration that met both of the aforementioned criteria) discharged 36.4%
of the total atrazine discharged during the entire monitoring period on a single day.

Table D.3

- 32 . Comparison of maximum monitored daily load.

Watershed Year
(Sample

Type 1 ) Total Atrazine
Discharge (kg)
during Monitoring
Period (days) Maximum Monitored Daily Load

Concentration (ug/L) Average Daily Flow

(m3/sec) Maximum Load (kg of

Atrazine) % of Total

Monitored

Atrazine Discharge

IA

- 01 2004 G 46.9 (144) 10.03 19.7 17.1 36.4

2005 G 10.7 (151) 1.21 48.3 5.1 47.4

### Page 67 of 96 Watershed Year
(Sample

Type 1 ) Total Atrazine
Discharge (kg)
during Monitoring
Period (days) Maximum Monitored Daily Load

Concentration (ug/L) Average Daily Flow

(m3/sec) Maximum Load (kg of

Atrazine) % of Total

Monitored

Atrazine Discharge

IA

- 02 2004 C 4.1 (130) 5.37 2.3 1.0 25.3

IL

- 01 2004 G 112.3 (147) 4.29 30.9 11.4 10.2

IL

- 02 2004 C 22.0 (147) 3.66 6.0 1.9 8.6

IL

- 04 2006 G 60.8 (150) 9.49 9.06 7.4 12.2

2007 C 8.7 (156) 12.69 2.7 3.0 34.0

IL

- 05 2004 G 51.6 (134) 22.13 11.2 21.5 41.6

IN

- 11 2005 C 25.0 (137) 237.5 0.926 19.0 76.1

MO

- 01 2004 C 7.3 (135) 64.07 0.537 3.0 40.8

2007 G 10.1 (148) 7.18 0.48 0.3 12.0

2007 A 1.4 (148) 29.72 0.48 1.2 87.4

2007 C 0.86 (148) 29.72 0.48 1.2 143.5

MO

- 02 2004 G 78.7 (148) 23.93 1.67 3.5 4.4

2005 G 65.4 (154) 28.07 1.09 2.6 4.1

2006 G 87.9 (137) 43.17 0.83 3.1 3.5

2007 G 1.5 (126) 16.18 0.56 0.79 54.0

2007 A 1.3 (126) 13.80 0.56 0.67 52.7

2007 C 1.3 (126) 16.18 0.56 0.79 61.6

2008 G 5.7 (183) 56.6 0.27 1.3 22.7

2008 A 2.8 (183) 28.95 0.27 0.67 23.5

2008 C 3.1 (183) 56.6 0.27 1.3 42.4

MO

- 04b 2008 G 599.7 (182) 3.15 176.4 48.0 8.0

2008 A 486.5 (182) 17.26 26.3 39.2 8.1

2008 C 519.5 (182) 3.15 176.4 48.0 9.2

NE

- 02 2005 G 180.0 (146) 12.62 22.1 24.1 13.4

1 

### Monitoring sample type (G = grab; A = auto sampler; or C = combined analysis of both
grab and auto sampler samples).

Characteristics of Successful Model Calibrations

Only six watershed and year combinations (IA

- 01 2004, IL

- 01 2004, IL

- 02 2004, and

MO

- 02 2004, 2005 and 2006) met both criteria (indicated in bold in Tables D.3

- 1 and

D.3

- 2). All of these watershed and year combinations were from small (21.3 t o 33.4 mi 2 

### )
watersheds and were calibrated to grab samples collected from relatively early in the
sampling program. The Iowa and Illinois watershed and year combinations that met

both criteria were from 2004. The MO

- 02 watershed years that met both criteri a were from the early years (2004 through 2006) before the dramatic changes in average daily
flow at this watershed (depicted in Figure D.3

- 2). Auto

- samplers were used to a much
greater extent in the later sampling years and yielded some calibrations with relatively
good fits to the atrazine data (high r 2 

### s). Therefore, the lack of calibrations to auto

sampler data that met the model selection criteria is thought to be due to the

### Page 68 of 96 aforementioned flow issue, rather than an indication of any problem with the au to

sampler data.

The six watershed and year combinations that met both criteria tended to have rather

small β Exp. 

### parameter estimates (fraction of WAB t 

### Exported; column 11 of Table D.3

- 1)
of approximately 1 × 10 - 6 

### . This compares to a range from all calibr ations in Table D.3

- 1

of 1.06 × 10 - 6 

### to 2.37 × 10 - 2



### .

These six watersheds varied with respect to the amount of atrazine exported as a
fraction of the estimated atrazine applied to the watershed (column 12 of Table D.3

- 1)

The Iowa and Illinois watershed an d year combinations that met both criteria ranged
from 1.28 to 3.61 %, while the three years from MO

- 02 that met both criteria were tightly
clustered in a range from 0.869 to0.949 %. This measure is intended to indicate how

well the atrazine applied in a w atershed is retained within that watershed.

By this measure, the MO

- 02 watershed and year combinations might be expected to
yield generally low atrazine concentrations with infrequent peaks. However, the MO

- 02
watershed and year combinations yield high c oncentrations that persist for long periods.
This appears to be due to the Iowa and Illinois sites releasing their atrazine loads
quickly in flash

- flood events, while the Missouri watershed and year combinations
appear to release high atrazine concentratio n water slowly during and between less
“flashy” events.

Determination of Optimal Sampling Intervals

The original motivations for this research were to develop a mechanistic model,

calibrate it to a watershed with daily auto

- sampler data, and then subsample that daily
data to simulate less frequent sampling intervals to estimate an “optimal” sampling
interval (lowest sampling intensity that provides a reasonable approximation of the
original data set). Unfortunately, three of the four calibrations t o auto

- sampler data had

a non

- zero mass imbalance, while the fourth (MO

- 02 2007) had a poor model fit ( r 2


### = 0.55). Additionally, this fourth watershed and year combination is based on the 2007
flow data depicted in Figure D.3

- 2, which is suspected of being problematic. Therefore,
the effort to estimate optimal sampling intervals from this mechanistic modeling

approach was abandoned. Potentially, there are watershed and year combinations in
the AEEMP data set that have acceptable flow data, but this modeling effort ran out of

time and resources before these combinations were found.

### Page 69 of 96 Scaling Issues with Larger Drinking Water Watersheds

In order to calibrate a model similar to that presented here, sufficient data would be

needed to estimate each storm event pe ak (each β i 

### parameter). Potentially, the

sampling could be optimized based on when the storm occurs and collected by auto

samplers rather than with a field crew on a fixed schedule (every 4, 7, 14 days, etc ).

Notice that many samples in the attached graphs are collected at low flows long after

the last rain event has occurred. If a model were calibrated to a site based on an initial
year of intensive data collection, the model could be used to indicate when it is likely

that samples would and would not need to be collected. This would likely require that
data collection and model recalibration with model prediction availability occurring
rapidly throughout the season of concern.

Another consideration is spatial variability in storm occurrence. Most of the r elatively
small watersheds modeled in the AEEMP appear to show atrazine being discharged

over many storm events. While it may be possible that multiple individual peaks could
be caused by rain events that affect all portions of the watershed equally with t he low
interceding concentrations caused by dilution with less

- contaminated baseflow, it seems

more likely that the multiple peaks are caused by small

- scale intense storms causing

runoff or break

- through of the leaching atrazine in different places within the watershed.
Since some drinking water source watersheds are much larger than the watersheds
considered here, spatial variability in storm occurrence would be more of a problem.

Potentially, a good method for investigating this issue would be to acquire raster images

of rainfall totals from historical radar weather data. This rainfall data over the
watersheds of interest could be used to determine if the multiple peaks are generated
from small intense storms occurring over specific sections of the waters hed at different

times or whether the rainfall totals are more

- or

- less evenly distributed over the
watersheds just prior to each atrazine peak. Such information would be usefully for

evaluating what storm event conditions and agricultural practices led to high atrazine

concentrations and/or durations and which did not.

Finally, a word of caution about the general applicability of this type of model to other
existing or planned atrazine monitoring data sets. The watershed and year

combinations modeled here are quite intensively sampled (4 day interval for the grab
samples). Other data sets are typically sampled less intensively (weekly, monthly, or
quarterly). Less frequent sampling would often miss large peaks, which can transport
large fractions of the loa d of atrazine discharged during a season (e.g., 36% discharged
in single storm event for the IL

- 01 2004 data in Table D.3

- 2).

### Page 70 of 96 While this research did potentially provide some insights into what would likely be
needed to successfully use a mechanistic mod eling approach to aid in the interpretation
atrazine monitoring data, it was unable to estimate what sampling intensity was needed
to support such a mechanistic modeling and monitoring approach.

Graphs depicting individual watershed and year combination c alibrations analyzed for
the watershed mass balance approach are shown in Figures D.3

- 3 through D.3

- 54.

### Page 71 of 96 Figure D.3

- 36 . Comparison of observed and predicted atrazine concentrations for the IA

01 watershed in 2004.

Figure D.3

- 37 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the IA

- 01 watershed in 2004. 

### IA-01 2004

0.00 2.00 4.00 6.00 8.00 10.00 12.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

DateAtrazine (μg/L)

0 100000 200000 300000 400000 500000 600000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21Rain22

EventAtrazine Runoff/Break-through (kg/ha)

### Page 72 of 96 Figure D.3

- 38 . Comparison of observed and predicted atrazine concentrations for the IA

01 watershed in 2005. (Note that the predicted concentrations fall to zero in the late
June/early July.)

Figure D.3

- 39 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the IA

- 01 watershed in 2005. 

### IA-01 2005

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

3/9 3/29 4/18 5/8 5/28 6/17 7/7 7/27 8/16 9/5 9/25

DateAtrazine(μg/L)

0 100000 200000 300000 400000 500000 600000 700000 800000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21Rain22

EventAtrazine Runoff/Break-through (kg/ha)

### Page 73 of 96 Figure D.3

- 40 . Comparison of observed and predicted atrazine concentrations for the IA

02 watershed in 2004.

Figure D.3

- 41 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the IA

- 02 watershed in 2004. 

### IA-02 2004

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

DateAtrazine (ug/L)

0 5000 10000 15000 20000 25000 30000 35000 40000Daily Flow Yield (L/day/ha) Grab Samples

Auto Sampler

Predicted

Flow Yield

0 0.00005

0.0001 0.00015

0.0002 0.00025Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18

EventAtrazine Runoff/Break-through (kg/ha)

### Page 74 of 96 Figure D.3

- 42 . Comparison of observed and predicted atrazine concen trations for the IL

01 watershed in 2004.

Figure D.3

- 43 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the IL

- 01 watershed in 2004. 

### IL-01 2004

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

DateAtrazine(μg/L)

0 50000 100000 150000 200000 250000 300000 350000 400000 450000 500000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.002 0.004 0.006 0.008

0.01 0.012 0.014Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21Rain22

EventAtrazine Runoff/Break-through (kg/ha)

### Page 75 of 96 Figure D.3

- 44 . Comparison of observed and predicted atrazine concentrations for the IL

02 watershed in 2004.

Figure D.3

- 45 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the p eriod of monitoring for the IL

- 02 watershed in 2004. 

### IL-02 2004

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

DateAtrazine(μg/L)

0 10000 20000 30000 40000 50000 60000 70000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21Rain22

EventAtrazine Runoff/Break-through (kg/ha)

### Page 76 of 96 Figure D.3

- 46 . Comparison of observed and predicted atrazine concentrations for the IL

04 watershed in 2006.

Figure D.3

- 47 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the IL

- 04 watershed in 2006. IL-04 2006

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00

3/24 4/13 5/3 5/23 6/12 7/2 7/22 8/11 8/31 9/20

DateAtrazine(μg/L)

0 100000 200000 300000 400000 500000 600000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19

EventAtrazine Runoff/Break-through (kg/ha)

### Page 77 of 96 Figure D.3

- 48 . Comparison of observed and predicted atrazine concentrations for the IL

04 watershed i n 2007.

Figure D.3

- 49 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the IL

- 04 watershed in 2007. 

### IL-04 2007

0 5 10 15 20 25 30

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

DateAtrazine (ug/L)

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000Daily Flow Yield (L/day/ha) Grab Sample

Auto Sampler

Storm Events > 0.4 cm

Predicted Conc ug/L

Daily Flow Yield

0 0.0002 0.0004 0.0006 0.0008

0.001 0.0012 0.0014 0.0016Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14

EventAtrazine Runoff/Break-through (kg/ha)

### Page 78 of 96 Figure D.3

- 50 . Comparison of observed and predicted atrazine concentrations for the IL

05 watershed in 2004.

Figure D.3

- 51 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the IL

- 02 watershed in 2004. 

### IL-05 2004

0.00 5.00 10.00 15.00 20.00 25.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

DateAtrazine (ug/L)

0 50000 100000 150000 200000 250000 300000 350000Daily Flow Yield (L/day/ha) Grab Sample

Storm Events > 0.4 cm

Predicted Conc ug/L

Daily Flow Yield

0 0.0002 0.0004 0.0006 0.0008

0.001 0.0012 0.0014 0.0016 0.0018

0.002Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19

EventAtrazine Runoff/Break-through (kg/ha)

### Page 79 of 96 Figure D.3

- 52 . Comparison of observed and predicted atrazine concentrations for the IN

11 watershed in 2005.

Figure D.3

- 53 . Comparison of atrazine runoff or break

- through during r ain events in
excess of 0.4 cm over the period of monitoring for the IN

- 11 watershed in 2005. 

### IN-11 2005

0.00 50.00 100.00 150.00 200.00 250.00

3/9 3/29 4/18 5/8 5/28 6/17 7/7 7/27 8/16 9/5

DateAtrazine (ug/L)

0 20000 40000 60000 80000 100000 120000 140000Daily Flow Yield (L/day/ha) Grab Sample

Storm Events > 0.4 cm

Predicted Conc ug/L

Auto Sample Conc, ug/L

Daily Flow Yield

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009Rain1Rain3Rain5Rain7Rain9Rain11Rain13Rain15Rain17Rain19Rain21Rain23

EventAtrazine Runoff/Break-through (kg/ha)

### Page 80 of 96 Figure D.3

- 54 . Comparison of observed and predicted atrazine concentrations for the

MO

- 01 watershed in 2004.

Figure D.3

- 55 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 01 watershed in 2004. 

### MO-01 2004

0 10 20 30 40 50 60 70

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

DateAtrazine(μg/L)

0 2000 4000 6000 8000 10000 12000 14000 16000 18000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Grab Sample Conc, ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.0002 0.0004 0.0006 0.0008

0.001 0.0012 0.0014 0.0016 0.0018

0.002Rain1Rain3Rain5Rain7Rain9Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27

EventAtrazine Runoff/Break-through (kg/ha)

### Page 81 of 96 Figure D.3

- 56 . Comparison of observed and predicted atrazine concentrations for the

MO

- 01 watershed in 2007.

Figure D.3

- 57 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 01 watershed in 2007. 

### MO-01 2007

0 50 100 150 200 250

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

DateAtrazine(μg/L)

0 5000 10000 15000 20000 25000 30000 35000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.000005

0.00001 0.000015

0.00002 0.000025Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21

EventAtrazine Runoff/Break-through (kg/ha)

### Page 82 of 96 Figure D.3

- 58 . Comparison of observed and predicted atrazine concentrations for the

MO

- 01 watershed in 2007.

Figure D.3

- 59 . Comparison of atrazine runoff or break

- through during rain events in

excess of 0.4 cm ove r the period of monitoring for the MO

- 01 watershed in 2007. 

### MO-01 2007 Auto-samples

0 20 40 60 80 100 120 140 160 180 200

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

DateAtrazine(μg/L)

0 5000 10000 15000 20000 25000 30000 35000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.000002 0.000004 0.000006 0.000008

0.00001 0.000012 0.000014 0.000016 0.000018Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21

EventAtrazine Runoff/Break-through (kg/ha)

### Page 83 of 96 Figure D.3

- 60 . Comparison of observed and predicted atrazine concentrations for the IL

05 watershed in 2004.

Figure D.3

- 61 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the IL

- 02 watershed in 2004. 

### MO-01 2007 Combined

0 20 40 60 80 100 120 140 160 180 200

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

DateAtrazine(μg/L)

0 5000 10000 15000 20000 25000 30000 35000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Grab Sample Conc, ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.000005

0.00001 0.000015

0.00002 0.000025

0.00003Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20Rain21

EventAtrazine Runoff/Break-through (kg/ha)

### Page 84 of 96 Figure D.3

- 62 . Comparison of observed and predicted atrazine concentrations for the

MO

- 02 wat ershed in 2004.

Figure D.3

- 63 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 02 watershed in 2004. 

### MO-02 2004 Grab

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00

3/14 4/3 4/23 5/13 6/2 6/22 7/12 8/1 8/21 9/10

DateAtrazine(μg/L)

0 5000 10000 15000 20000 25000 30000 35000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.0005

0.001 0.0015

0.002 0.0025

0.003 0.0035

0.004 0.0045Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17

EventAtrazine Runoff/Break-through (kg/ha)

### Page 85 of 96 Figure D.3

- 64 . Comp arison of observed and predicted atrazine concentrations for the

MO

- 02 watershed in 2005.

Figure D.3

- 65 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for th e MO

- 02 watershed in 2005. 

### MO-02 2005 Grab

0.00 5.00 10.00 15.00 20.00 25.00 30.00

3/9 3/29 4/18 5/8 5/28 6/17 7/7 7/27 8/16 9/5 9/25

DateAtrazine(μg/L)

0 5000 10000 15000 20000 25000 30000 35000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.0005

0.001 0.0015

0.002 0.0025

0.003 0.0035

0.004Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14

EventAtrazine Runoff/Break-through (kg/ha)

### Page 86 of 96 Figure D.3

- 66 . Comparison of observed and predicted atrazine concentrations for the

MO

- 02 watershed in 2006.

Figure D.3

- 67 . Comparison of atrazine runoff or break

- through d uring rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 02 watershed in 2006. 

### MO-02 2006 Grab

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00

3/24 4/13 5/3 5/23 6/12 7/2 7/22 8/11 8/31

DateAtrazine(μg/L)

0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14

EventAtrazine Runoff/Break-through (kg/ha)

### Page 87 of 96 Figure D.3

- 68 . Comparison of observed and predicted atrazine concentrations for the

MO

- 02 watershed in 2007.

Figure D.3

- 69 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 02 watershed in 2007. 

### MO-02 2007 Grab

0 5 10 15 20 25

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

DateAtrazine(μg/L)

0 1000 2000 3000 4000 5000 6000 7000 8000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.00002 0.00004 0.00006 0.00008

0.0001 0.00012 0.00014 0.00016 0.00018Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15

EventAtrazine Runoff/Break-through (kg/ha)

### Page 88 of 96 Figure D.3

- 70 . Comparison of observed and predicte d atrazine concentrations for the

MO

- 02 watershed in 2007.

Figure D.3

- 71 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 02 watershed in 2007. 

### MO-02 2007 Auto-samples

0 2 4 6 8 10 12 14 16 18 20

3/19 4/8 4/28 5/18 6/7 6/27 7/17 8/6 8/26 9/15

DateAtrazine(μg/L)

0 1000 2000 3000 4000 5000 6000 7000 8000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.00002 0.00004 0.00006 0.00008

0.0001 0.00012 0.00014 0.00016 0.00018

0.0002Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15

EventAtrazine Runoff/Break-through (kg/ha)

### Page 89 of 96 Figure D.3

- 72 . Comparison of observed and predicted atrazine concentrations for the

MO

- 02 watershed in 2007.

Figure D.3

- 73 . Comparison of atrazine runoff or break

- through during rain events in

excess o f 0.4 cm over the period of monitoring for the MO

- 02 watershed in 2007. 

### MO-02 2007 Combined

0 2 4 6 8 10 12 14 16 18 20

3/9 4/28 6/17 8/6 9/25 11/14

DateAtrazine(μg/L)

0 1000 2000 3000 4000 5000 6000 7000 8000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Grab Sample Conc, ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.00002 0.00004 0.00006 0.00008

0.0001 0.00012 0.00014 0.00016 0.00018

0.0002Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15

EventAtrazine Runoff/Break-through (kg/ha)

### Page 90 of 96 Figure D.3

- 74 . Comparison of observed and predicted atrazine concentrations for the

MO

- 02 watershed in 2008.

Figure D.3

- 75 . Com parison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 02 watershed in 2008. 

### MO-02 2008 Grab

0 10 20 30 40 50 60 70

3/13 4/2 4/22 5/12 6/1 6/21 7/11 7/31 8/20 9/9 9/29

DateAtrazine(μg/L)

0 5000 10000 15000 20000 25000 30000 35000 40000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.00005

0.0001 0.00015

0.0002 0.00025

0.0003 0.00035

0.0004 0.00045

0.0005Rain1Rain3Rain5Rain7Rain9Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain29Rain31Rain33Rain35

EventAtrazine Runoff/Break-through (kg/ha)

### Page 91 of 96 Figure D.3

- 76 . Comparison of observed and predicted atrazine concentrations for th e

MO

- 02 watershed in 2008.

Figure D.3

- 77 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 02 watershed in 2008. 

### MO-02 2008 Auto-samples

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

3/13 4/2 4/22 5/12 6/1 6/21 7/11 7/31 8/20 9/9 9/29

DateAtrazine(μg/L)

0 5000 10000 15000 20000 25000 30000 35000 40000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.00005

0.0001 0.00015

0.0002 0.00025Rain1Rain3Rain5Rain7Rain9Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain29Rain31Rain33Rain35

EventAtrazine Runoff/Break-through (kg/ha)

### Page 92 of 96 Figure D.3

- 78 . Comparison of observed and predicted atrazine concentrations for the

MO

- 02 watershed in 2008.

Figure D.3

- 79 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 02 watershed in 2008. 

### MO-02 2008 Combined

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

2/22 4/12 6/1 7/21 9/9 10/29

DateAtrazine(μg/L)

0 5000 10000 15000 20000 25000 30000 35000 40000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Grab Sample Conc, ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.00005

0.0001 0.00015

0.0002 0.00025

0.0003Rain1Rain3Rain5Rain7Rain9Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain29Rain31Rain33Rain35

EventAtrazine Runoff/Break-through (kg/ha)

### Page 93 of 96 Figure D.3

- 80 . Comparison of observed and predicted atrazine concentrations for the

MO

- 04b watershed in 2008.

Figure D.3

- 81 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 04b watershed in 2008. 

### MO-04b 2008 Grab

0.00 10.00 20.00 30.00 40.00 50.00 60.00

2/22 4/12 6/1 7/21 9/9 10/29

DateAtrazine(μg/L)

0 20000 40000 60000 80000 100000 120000 140000 160000 180000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.0005

0.001 0.0015

0.002 0.0025

0.003 0.0035

0.004 0.0045Rain1Rain3Rain5Rain7Rain9Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain31Rain33

EventAtrazine Runoff/Break-through (kg/ha)

### Page 94 of 96 Figure D.3

- 82 . Comparison of observed and predicted atrazine concentrations for the

MO

- 04b watershed in 2008

.

Figure D.3

- 83 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 04b watershed in 2008. 

### MO-04b 2008 Auto-samples

0 20 40 60 80 100 120 140

2/22 4/12 6/1 7/21 9/9 10/29

DateAtrazine(μg/L)

0 20000 40000 60000 80000 100000 120000 140000 160000 180000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.0005

0.001 0.0015

0.002 0.0025

0.003 0.0035

0.004 0.0045Rain1Rain3Rain5Rain7Rain9Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain31Rain33

EventAtrazine Runoff/Break-through (kg/ha)

### Page 95 of 96 Figure D.3

- 84 . Comparison of obs erved and predicted atrazine concentrations for the

MO

- 04b watershed in 2008.

Figure D.3

- 85 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the MO

- 04b wat ershed in 2008. 

### MO-04b 2008 Combined

0 10 20 30 40 50 60 70

2/22 4/12 6/1 7/21 9/9 10/29

DateAtrazine(μg/L)

0 20000 40000 60000 80000 100000 120000 140000 160000 180000Daily Average Flow Yield (L/day/ha) Auto Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.0005

0.001 0.0015

0.002 0.0025

0.003 0.0035

0.004 0.0045Rain1Rain3Rain5Rain7Rain9Rain11Rain13Rain15Rain17Rain19Rain21Rain23Rain25Rain27Rain31Rain33

EventAtrazine Runoff/Break-through (kg/ha)

### Page 96 of 96 Figure D.3

- 86 . Comparison of observed and predicted atrazine concentrations for the

NE

- 02 watershed in 2005.

Figure D.3

- 87 . Comparison of atrazine runoff or break

- through during rain events in
excess of 0.4 cm over the period of monitoring for the NE

- 02 watershed in 2005. 

### NE-02 2005 Grab

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00

3/9 3/29 4/18 5/8 5/28 6/17 7/7 7/27 8/16 9/5

DateAtrazine(μg/L)

0 50000 100000 150000 200000 250000 300000 350000Daily Average Flow Yield (L/day/ha) Grab Sample Conc, ug/L

Storm Events > 0.4 cm

Predicted Conc ug/L

Avg Daily Flow Yeild (L/day ha)

0 0.001 0.002 0.003 0.004 0.005 0.006Rain1Rain2Rain3Rain4Rain5Rain6Rain7Rain8Rain9Rain10Rain11Rain12Rain13Rain14Rain15Rain16Rain17Rain18Rain19Rain20

EventAtrazine Runoff/Break-through (kg/ha)