202-234-4433

Neal R. Gross & Co., Inc.

Page 1

U.S. ENVIRONMENTAL PROTECTION AGENCY (EPA)

+ + + + +

FEDERAL INSECTICIDE, FUNGICIDE AND RODENTICIDE

ACT SCIENTIFIC ADVISORY PANEL (FIFRA SAP)

+ + + + +

REEVALUATION OF THE HUMAN HEALTH EFFECTS OF

ATRAZINE: REVIEW OF EXPERIMENTAL ANIMAL AND IN

VITRO STUDIES AND DRINKING WATER MONITORING

FREQUENCY

+ + + + +

DOCKET NO.: EPA-HQ-OPP-2010-0125

+ + + + +

THURSDAY,

APRIL 29, 2010

+ + + + +

 The Panel convened at 8:30 a.m. in the

Hamilton Ballroom of the Hamilton Crowne Plaza

Hotel, located at 1001 14th Street, N.W.,

Washington, D.C., Steven G. Heeringa, Ph.D.,

Chair, and Kenneth M. Portier, Ph.D., Session

Chair, presiding.

FIFRA SAP MEMBERS PRESENT:

STEVEN G. HEERINGA, Ph.D., Chair

KENNETH M. PORTIER, Ph.D., Session Chair

JOHN R. BUCHER, Ph.D., DABT

JANICE E. CHAMBERS, Ph.D., DABT, ATS

GERALD A. LeBLANC, Ph.D.

DANIEL SCHLENK, Ph.D.
202-234-4433

Neal R. Gross & Co., Inc.

Page 2

FQPA SCIENCE REVIEW BOARD MEMBERS PRESENT:

SUSAN F. AKANA, Ph.D.

RICHARD H. COUPE, Ph.D.

KENNETH BARRY DELCLOS, Ph.D.

PENELOPE A. FENNER-CRISP, Ph.D., DABT

ROBERT J. GILLIOM, Ph.D.

RICHARD GREENWOOD, Ph.D.

WILLIAM L. HAYTON, Ph.D.

STEVEN D. HOLLADY, Ph.D.

TERESA H. HORTON, Ph.D.

KANNAN KRISHNAN, Ph.D.

HERBERT K.H. LEE, Ph.D.

KEVIN T. O'BYRNE, Ph.D.

NU-MAY RUBY REED, Ph.D., DABT

JEAN F.L. REGAL, Ph.D.

DANIEL J. SELVAGE, Ph.D.

CARMEN J. WILLIAMS, M.D., Ph.D.

LINDA J. YOUNG, Ph.D.

ALSO PRESENT:

JOSEPH E. BAILEY, Designated Federal Official
202-234-4433

Neal R. Gross & Co., Inc.

Page 3

 C-O-N-T-E-N-T-S

Opening of Meeting and 

Administrative Procedures

 Joseph Bailey . . . . . . . . . . . . . . . .4

Introduction and Identification

of Panel Members

 Kenneth Portier . . . . . . . . . . . . . . .4

Charge to Panel

 Question 1.9 continued. . . . . . . . . . . .5

Charge to Panel - Question 2

 Approaches to Evaluating Water

 Sampling Strategies and 

 Frequency of Monitoring

Question 2.1. . . . . . . . . . . . . . . . . . . 88

Question 2.2. . . . . . . . . . . . . . . . . . .114

Question 2.3. . . . . . . . . . . . . . . . . . .125

Question 2.4. . . . . . . . . . . . . . . . . . .136

Wrap up and Adjourn . . . . . . . . . . . . . . .169
202-234-4433

Neal R. Gross & Co., Inc.

Page 4

1 P-R-O-C-E-E-D-I-N-G-S

2 8:34 a.m.

3 MR. BAILEY: Okay, this is the last

4 day of the FIFRA Scientific Advisory Panel

5 meeting on atrazine reevaluation. I am Joe

6 Bailey, serving as Designated Federal Official. 

7 Dr. Portier is the chair and I will turn the mike

8 to him.

9 SESSION CHAIR PORTIER: Good morning. 

10 Thank all of you for sticking with us into this

11 fourth day of discussion. Hopefully not a full

12 day of discussion, but it remains to be seen.

13 Just to give the panel and the

14 audience a little kind of idea of what our plans

15 are this morning, I am going to revisit Question

16 1.9, just to get any concluding remarks and kind

17 of make sure that at least the panel is clear

18 that all of our ideas have been captured.

19 We will then go to some presentations

20 from Syngenta and EPA on clarifying comments

21 related to the hydrology and the simulations that

22 they did. The panel members assigned to those
202-234-4433

Neal R. Gross & Co., Inc.

Page 5

1 questions have been working very hard the last

2 three days to really understand what both

3 Syngenta and EPA has done so that they can make

4 sure that their remarks are to the point.

5 And while it has been nice for them to

6 have these discussions on the side, those

7 discussions need to come back into the full room

8 so they are captured into the public record. 

9 That is the gist of a public meeting.

10 So we will have those presentations

11 hopefully no more than a half hour with questions

12 and then we will begin with the four hydrology

13 questions. And at that point, hopefully we will

14 be able to close the public meeting and begin to

15 write our report.

16 So with that, I am going to, I don't

17 know where we are on the slides, but we are going

18 to go back to Question 1.9 which if you remember,

19 these are the questions that relate to the risk

20 assessment and the PK and primarily deal with

21 issues of frequency and duration of water

22 monitoring as it relates to toxicological
202-234-4433

Neal R. Gross & Co., Inc.

Page 6

1 concerns. And I have asked Dr. Reed and Dr.

2 Krishnan, I warned them that I was going to call

3 on them first to start the discussion and then we

4 will open it back up to the panel.

5 Primarily, we don't really want to

6 rehash everything we talked about yesterday but

7 we want to capture any additional thoughts you

8 may have had over the evening. It was an

9 intensive two hours yesterday afternoon and I had

10 to go back and think about a lot of it myself.

11 So with that, Dr. Reed?

12 DR. REED: Yes, the Chair gave me the

13 warning but it was five minutes ago.

14 (Laughter.)

15 SESSION CHAIR PORTIER: You don't get

16 much more for tornadoes, either.

17 DR. REED: Yes, for 1.9, the first

18 part of it, we talked about let me see. I was

19 given 30 seconds.

20 We talked about focusing on downstream

21 events to define the endpoints for risk

22 assessment. So these endpoints have to do with
202-234-4433

Neal R. Gross & Co., Inc.

Page 7

1 something that is adverse in terms of function or

2 anything that you would define.

3 And we talked a little bit about

4 within the mode of action that we have been

5 talking about in the last three days but also any

6 possible other mode of actions. I have asked if

7 anyone who has a sense of sensitivity difference

8 between animals and humans of the particular

9 endpoint that you are interested in having used

10 risk assessment.

11 But most of the comments are really

12 just to go back and look at the entire database

13 and benchmark those analysis to form endpoints to

14 compare the sensitivity of these endpoints and to

15 include all the steps in the key events and

16 mindful about the acute and short and long-terms

17 duration of exposure and mindful about the

18 toxicity of the metabolites and so some form of

19 toxicity equivalence factor could be applied to

20 it to address all the speciation of the

21 chemicals.

22 And our sort of comments or inclusion
202-234-4433

Neal R. Gross & Co., Inc.

Page 8

1 for this issue would be also regarding the lack

2 of data from all the previous discussions.

3 And Dr. Horton had graciously offered

4 to come up with maybe more detail. When we talk

5 about key events and I felt yesterday we were a

6 little bit ambiguous about what are the key

7 events, how many, four, eight, or additional mode

8 of action. And so Dr. Horton graciously offered

9 to come up with a new house and maybe more

10 elaborate.

11 So this will be where we want to hear

12 from her. Dr. Horton.

13 DR. HORTON: Now I know why I didn't

14 go into architecture.

15 Okay, I am still working on this new

16 figure but I can give you an idea of what it will

17 look like for the minutes. And it will be a

18 multi-part figure reflecting the charge to the

19 panel to evaluate the MOA and aid the agency in

20 its preparation for the September 2010 meeting.

21 And the goal of agency by 2010 is to

22 develop a draft weight of evidence document that
202-234-4433

Neal R. Gross & Co., Inc.

Page 9

1 includes points of departures for evaluating risk

2 in infants and children, with the goal of

3 determining the extent to which the current data

4 indicates a need for the Agency to develop a new

5 human health assessment for atrazine and to

6 reconsider, as appropriate, the frequency of

7 drinking water sampling.

8 So to that end, the figure will be a

9 multi-part figure encapsulating the mode of

10 action or the modified mode of action as

11 discussed here, which will be a newly

12 conceptualized mode of action, taking into

13 consideration the data discussed in the last few

14 days. The weight of evidence supporting the

15 various level data that has been discussed using

16 arrows of different weights to indicate the

17 confidence in the various pieces of evidence that

18 have been discussed.

19 And the diagram will recognize that

20 organisms, especially humans, are complex systems

21 and that the systems we have discussing often

22 interact and function as feedback systems with
202-234-4433

Neal R. Gross & Co., Inc.

Page 10

1 some degree with hierarchical organization and

2 that within that context, atrazine may act at

3 several different levels of the hierarchy. And

4 the key events may occur at any point within that

5 hierarchy. And because these are feedback

6 systems, they may ramify at different levels.

7 The diagram will also attempt to

8 identify where new areas of research may be

9 helpful or where areas of research need to be

10 strengthened. Thank you.

11 DR. MENDEZ: Could I just one second

12 please?

13 SESSION CHAIR PORTIER: Yes, sure.

14 DR. MENDEZ: I just want to clarify

15 for the panel that our intent for September, it

16 is to reevaluate the toxicity not just for

17 infants and children but for the entire

18 population.

19 SESSION CHAIR PORTIER: Sorry, this is

20 Ken Portier. Is this on? Yes, I guess so.

21 In talking with Dr. Horton about the

22 diagram, the one thing that caught me is she took
202-234-4433

Neal R. Gross & Co., Inc.

Page 11

1 figure three and she said but the way I see it is

2 this way and with CNS at the top and functional

3 at the bottom. And I think that is one of the

4 major changes.

5 It sounds simple but when she talked

6 about top down or something at the top, you know,

7 I was looking at it as atrazine from this chart

8 and she was looking at it as CNS, looking at it

9 in the transposed way. And to me, that clarified

10 a lot of the conversations she had been making

11 all along because she sees it in this rotated

12 version with the central nervous system at the

13 top and functional capabilities at the bottom and

14 atrazine comes in from the side.

15 Dr. Horton and then Dr. Crisp.

16 DR. HORTON: You have to understand

17 that within the world of reproductive

18 endocrinologists and neuroendocrinologists, we

19 divide the world into those of us who work above

20 the belt and below the belt.

21 (Laughter.)

22 SESSION CHAIR PORTIER: Dr. Fenner-
202-234-4433

Neal R. Gross & Co., Inc.

Page 12

1 Crisp?

2 DR. FENNER-CRISP: I don't know how to

3 top that one.

4 I might suggest that you go back and

5 look in the draft document with respect to the

6 mammary tumor MOA and the figure in there does in

7 fact start with the brain and trickle down.

8 SESSION CHAIR PORTIER: Dr. Cooper?

9 DR. COOPER: I would like to, if I

10 could, also make a comment. I think that figure

11 that you saw there was before it got to

12 Washington. 

13 But I am really pleased to hear you

14 make the suggestions that you made because it is

15 an extraordinarily complex set of issues that we

16 are dealing with and the key point, you have

17 captured it, especially when you think of

18 toxicity pathways that was touched on yesterday,

19 they are not linear. That they are probably

20 masses with multiple, perhaps multiple target

21 sites. And to capture that is an extraordinarily

22 difficult thing, especially in a mixed audience
202-234-4433

Neal R. Gross & Co., Inc.

Page 13

1 like this.

2 The other thing is that the arrows are

3 key to this also. And there is a considerable

4 amount of difference in the weight of evidence. 

5 But the only thing is, is that we are preparing,

6 you are going to make suggestions about the way

7 the arrows, the thickness of them is that in this

8 case, I think there you can bring in some of the

9 in vitro data that is existing.

10 SESSION CHAIR PORTIER: Dr. Bucher?

11 DR. BUCHER: Yes, I just wanted to

12 bring up the fact I have been involved in a

13 number of discussions over the years on MOA and

14 the development of frameworks for the utilization

15 of MOA with respect to evaluating human cancer

16 information. And one of the things that I think

17 is most important is that the MOAs have to be

18 pretty well developed on, I think the words we

19 have used are compelling, scientifically

20 compelling.

21 And what I am a little worried about

22 here is that we have a very, very complicated
202-234-4433

Neal R. Gross & Co., Inc.

Page 14

1 potential figure for an MOA that is being

2 introduced at the very end of the meeting and I

3 wouldn't want it to come out of this meeting as

4 something to the world that has been fully

5 evaluated and agreed upon by this group.

6 So I think that whatever you put

7 together should be cast in the right terms that

8 it is a very, you know, it is a suggestion of a

9 way that the agency might want to look at

10 organizing the information rather than being cast

11 as an MOA.

12 SESSION CHAIR PORTIER: Yes, we have

13 talked about that as well. Dr. Krishnan, do you

14 want to talk about PK issues?

15 DR. KRISHNAN: The second part of this

16 question related to the temporal consideration

17 based on toxicity versus monitoring frequency. 

18 As much as I would like to talk about PK, I am

19 going to have to be a little bit more implicit of

20 both PK and PD. That is what I am going to try

21 to do and invite Dr. O'Bryne to complete or

22 correct me as I make my comments.
202-234-4433

Neal R. Gross & Co., Inc.

Page 15

1 In thinking about this, you know,

2 frequency of monitoring versus the temporal

3 profile or temporal considerations of toxicity,

4 we do it in terms of the time course of the

5 atrazine and metabolite in the body, which is

6 essentially the PK consideration, and then the

7 time course or the precursor, the key events or

8 the precursors measured, which could be altered

9 LH cortical levels.

10 First, in terms of the consideration

11 of the time course of atrazine and metabolites or

12 the internal dose. As we talked about yesterday,

13 the uptake or consumption pattern associated with

14 the drinking water combined with the

15 pharmacokinetic considerations, my colleagues

16 mentioned some of the key aspects, particularly

17 the slow absorption rate for example from the

18 drinking water or from oral administrations, the

19 relative short half-life of the parent chemical

20 combined with an extended half-life of some of

21 the metabolites, these things suggest together

22 that in the integrated measure, the internal
202-234-4433

Neal R. Gross & Co., Inc.

Page 16

1 exposure would be relevant.

2 For example, the chloroforms, the

3 chlorinated form, atrazine plus metabolites that

4 still have the Cl in them. Or there could be an

5 average calculated based on the AUCs over a

6 particular period.

7 So the area under the curves would

8 integrate both the dose and the temporal

9 considerations together, essentially. And I have

10 not seen anything that suggests that the

11 particular Cmax of a parent chemical or

12 metabolite during a specific period is somehow

13 clearly associated with the profile of the

14 precursors measured or the outcome in tox

15 studies.

16 So given those observations, I still

17 tend to think that the overall profile or

18 consideration of pharmacokinetics does not call

19 for very narrow time-based analysis of

20 monitoring. So that is one part of it.

21 The second part is that then you think

22 about the time course of the key events are
202-234-4433

Neal R. Gross & Co., Inc.

Page 17

1 essentially the precursor or the measurements

2 that are made between the internal dose and the

3 toxicological outcome. In this case, the time

4 course of the LH or the cortical levels.

5 The current complication, in my mind

6 at least, arises from the consideration of the

7 more recent data on the short-term changes in the

8 rats summarized in Table 3 on the HPA/HPG axis,

9 essentially in the four-day experiment. I think

10 that is where some of the discussions yesterday

11 focused on.

12 It is kind of unclear to me if the

13 four-day exposures result in an impact on the

14 precursors, for example, LH or cortical levels. 

15 And since they seem to bounce back within the

16 next few days that follow, these four-day

17 experiments which lead to all the discussions of

18 a four-day monitoring frequency, for example, is

19 kind of related to an adapting response, rather

20 than an adverse response. That is a question I

21 ask myself. You know, if it is a four-day dosing

22 that causes an impact on LH and then in four days
202-234-4433

Neal R. Gross & Co., Inc.

Page 18

1 it bounces back, then the cycle, I guess, it gets

2 back afterwards and so forth, it appears to me

3 more of an adapted response, rather than adverse

4 reproductive outcome per se.

5 Further, depending on the critical

6 effect, I mean, that is one of the reasons why we

7 revisited the MOA figure. Depending upon the

8 critical effect that is going to drive the acute

9 versus chronic assessment, I hesitate to see this

10 single MOA funnel in accommodating all of these

11 outcomes. Specifically, I don't think one MOA

12 will fit all effects acute and chronic. That is

13 where some of my concerns come.

14 So the precursor effects appear to

15 make at least convincing sense to me based on

16 chronic exposures and sustained effects on the LH

17 associated with the reproductive outcome and so

18 on which are not questioned at all.

19 But in terms of the acute exposure and

20 I am still a bit reluctant to suggest anything

21 other than an integrated or an average one,

22 rather than focusing on a four-day period based
202-234-4433

Neal R. Gross & Co., Inc.

Page 19

1 on these points.

2 Additionally, as I was thinking about

3 this four-day discussion yesterday and some of it

4 came through during the supper time, is that you

5 know, once you take a benchmark, some level of a

6 NOAEL in a rat study, based on the effects on the

7 precursors and then use that as a basis for

8 deriving oral concentrations appropriate for

9 humans, what is the point of relating then back

10 to the four-day frequency there or the duration

11 of the effect in the rat unless we want to

12 protect the rat? Because that is where some of

13 our confusions came from.

14 The focus should be on the dose

15 because we took the dose based on the rat studies

16 and we said well we will use that as a basis to

17 protect the humans. And then you do the

18 calculations and then I don't see the obvious

19 connection of going back with these numbers, back

20 to the four-day frequency. That obvious link, I

21 mean, that is not an obvious link to me, unless

22 you want to calculate the physiological equal and
202-234-4433

Neal R. Gross & Co., Inc.

Page 20

1 times and so forth. Even there I am not sure you

2 would say it is a worthy effort.

3 As Bob Dedrick, I think, in the '70s

4 derived some of these physiological time

5 equivalents and so forth, those sophisticated

6 calculations could be done but I don't see that

7 being a productive route.

8 So, I don't know if I confused or

9 clarified more but this is what is in my mind,

10 based on discussions we have had.

11 SESSION CHAIR PORTIER: Dr. Greenwood,

12 you wanted to add something to this?

13 DR. GREENWOOD: Yes. I think we were

14 trying to get our heads around this at the end of

15 the session yesterday afternoon. And I think

16 what we saw was that it makes a lot of sense,

17 because you have got to base your protective

18 levels on the hazard. And we saw that there was

19 a lot of sense in basing it on the rodent assay,

20 which is clear, with a clear endpoint and so on,

21 and well supported with evidence.

22 But we couldn't see then why the time
202-234-4433

Neal R. Gross & Co., Inc.

Page 21

1 should be based on the rodent at all. I mean,

2 this is what Dr. Krishnan just said. We just

3 could not see why you would use a time based for

4 a rat effect. Okay, the dose is important. That

5 is what you use it for, to get the hazard. But I

6 don't think it was ever intended to try and set

7 an exposure time. As Dr. Krishnan said, you are

8 not trying to, a lot of people spend money trying

9 to kill rats and not trying to protect them.

10 So I think we felt that if there were

11 to be an exposure time figured in to the water

12 monitoring, it has really got to be something

13 more human-based. But what that would be is

14 difficult because, as Dr. Horton said, it could

15 be just an hour, the critical period for some

16 endpoints and you are never going to capture

17 that.

18 So we felt that if you go for the most

19 vulnerable stage, maybe during development, it

20 could just be an hour but I would prefer to let

21 Dr. Horton speak to that, I think.

22 SESSION CHAIR PORTIER: Dr. Horton?
202-234-4433

Neal R. Gross & Co., Inc.

Page 22

1 DR. HORTON: Yes, this is one of the

2 things we will try to encapsulate in the new

3 diagram is the difference between developmental

4 programming effects, which may have long-term

5 outcomes which may not, while exposure may occur

6 during fetal development and may result from

7 short duration exposure but the actual effects

8 may not appear until later in life versus what

9 Dr. Krishnan referred to as an adaptive response

10 occurring in response to an acute exposure. So

11 we will try to capture those two different things

12 because in a physiological sense, the mechanisms

13 of action may be very different.

14 SESSION CHAIR PORTIER: Dr. Bucher?

15 DR. BUCHER: So I understand and I

16 agree with the comments you made about the time

17 issues. But I think that you said something

18 about the fact that if there was a four-day

19 exposure and there was an effect but it was

20 reversible, it wasn't considered an adverse

21 effect. And I would absolutely disagree with

22 that.
202-234-4433

Neal R. Gross & Co., Inc.

Page 23

1 And if you are talking about an

2 adaptive behavior as simply something that can be

3 reversible, I would disagree with that as well. 

4 I think that is not the correct interpretation of

5 toxicology.

6 SESSION CHAIR PORTIER: Dr. Cooper?

7 DR. COOPER: This is not an atrazine

8 paper but in 1993 we published a paper with the

9 acaricide chlordimeform where we showed that a

10 single dose administered on the critical period

11 on the afternoon of proestrus and the clearance

12 of this stuff is pretty quick, blocked the LH

13 surge and delayed ovulation. And everyone said

14 well that is a reversible effect and if you block

15 ovulation, they ovulate the next day, especially

16 a rat. And rats are rats and humans are humans.

17 Well, it turns out that we went on to

18 examine that further. And when you delay

19 ovulation, the ova ages. And as the ova ages, it

20 becomes, I don't know what the right word is, the

21 viability is such that you end up with things

22 like polyspermia and you end up with polyploidy. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 24

1 I think that was Dr. Stoker's master's thesis.

2 And so when you went on and looked at

3 the mothers of those one-time dosed animals which

4 was reversible, if they became pregnant, then

5 that effect was not reversible. There was

6 delayed development, embryo anomalies, reduced

7 litter size. So sometimes a single dose, even

8 though it may apparently be reversible, may not

9 necessarily be that.

10 SESSION CHAIR PORTIER: Dr. Krishnan?

11 DR. KRISHNAN: I think I was cautious

12 in the way I phrased it, but what I had raised as

13 a question or concern was that those effects

14 during the four days on the precursors if you

15 will, the cortical and the LH, if they are

16 clearly associated with the reproductive outcome

17 or the delayed maturation on some of the other

18 whole animal affects, clearly, then I don't ask

19 question aloud. So it is obvious.

20 My understanding or at least the

21 reason why I raised the question was if it was

22 affecting a cycle on then which bounces back and
202-234-4433

Neal R. Gross & Co., Inc.

Page 25

1 then captures, there is no demonstration of

2 reproductive outcome or other whole animal

3 effects in these studies, which doesn't seem to

4 be the case. If not, please correct me. Then I

5 will take back and I say well three or four-day

6 or even less or more frequent monitoring would be

7 required.

8 So I just want to be clear that I have

9 not misread or misinterpreted.

10 SESSION CHAIR PORTIER: Dr. Reed?

11 DR. REED: Just so that you are having

12 fun here, when I was beginning to do risk

13 assessment and our kids were very young and they

14 asked me what do you do and so I said we just do

15 experiments on rats and mice and bunnies and then

16 we bring that down by hundred-fold. And they got

17 the idea that all the field mice and field rats

18 and bunnies are all very safe.

19 And about the reversibility, I totally

20 agree with Dr. Bucher that reversible effects are

21 still adverse if it is adverse to begin with.

22 Also, a lot of times maternal effects
202-234-4433

Neal R. Gross & Co., Inc.

Page 26

1 may be reversible. Alkaloids is an example that

2 the fetus is not, when it gets to the fetus

3 effect, fetal effects.

4 But I think, my understanding of this

5 issue about reversibility is that you have a

6 cascade of events and going through the networks

7 or the pathway of the mode of action, there may

8 be a step that you would have effect that there

9 is some sort of threshold so that it will not

10 trigger the next step, then that might be

11 something to consider as "reversible" in that

12 context.

13 But my main comments is that we talk

14 a little bit about area under the curve and blood

15 concentration and so forth. But I think it is

16 important, at least when I am doing PAPK model,

17 the hardest thing is to decide on the dose

18 metrics for rats to human equivalents. And I

19 think that when we get to PAPK or use PAPK as a

20 tool, that we should clear about what is the most

21 appropriate target sites. And I would not

22 preclude the possibility of using peak
202-234-4433

Neal R. Gross & Co., Inc.

Page 27

1 concentration and just across the board to say

2 area under the curve is the most appropriate. In

3 terms of target site, it might not be serum but

4 could be more pertinent. Could be brain.

5 And so these are the kind of things

6 that I think we need to consider. I don't think

7 it is necessary for us to set in stone to say

8 area under the curve or what target site, or just

9 serum concentration.

10 SESSION CHAIR PORTIER: Dr. O'Byrne?

11 DR. O'BYRNE: I am just listening to

12 these conversations, I am trying to remind myself

13 why I am here. And it was my thought that we had

14 this new data of this 15-minute activation of the

15 HP axis, which was making us reconsider the

16 sampling frequency.

17 Now, my considered opinion is that

18 this 15-minute HP activation is a red herring and

19 it reminds me of Tony Blair's 45-minute weapons

20 of mass destruction argument. And I think we

21 have got to be very careful here. 

22 The data, I mean, we have heard these
202-234-4433

Neal R. Gross & Co., Inc.

Page 28

1 arguments about development and how sensitive

2 they may be but I haven't actually seen any data. 

3 We are dealing here with a surge as this sort of

4 benchmark which cannot be separated from cycles. 

5 If you don't have surges, you don't have cycles. 

6 And the rat has got a four-day cycle and humans

7 have got a 28-day cycle. And the data that we

8 have got in front of us is that the LOEL for

9 atrazine is 3.6 milligrams over 28 weeks or 6.2

10 over four days. And that is what we have to work

11 with. So, I think these are the critical

12 factors.

13 And the discussions about how to

14 extrapolate from the rat model to humans is also

15 quite important, I think, to appreciate. And to

16 fit the physiology into the toxicology is hugely

17 difficult and we struggle with that. And I don't

18 know whether you can extrapolate from some

19 analogies and experimental data. 

20 There is one that comes to my mind. 

21 If you take a rat and you give it a large enough

22 dose of estrogen, then you will have an LH surge
202-234-4433

Neal R. Gross & Co., Inc.

Page 29

1 every day at 5:00 or whatever until the thing

2 becomes exhausted.

3 If you give a large dose of estrogen

4 to a woman at any stage during the follicular

5 phase, you stop that cycle dead in its tracks and

6 you will wait 14 days for the next spontaneous LH

7 surge. So I don't know whether you can use that

8 temporal concordance or not but it is hellishly

9 difficult.

10 I don't know if that is of any value

11 at all.

12 SESSION CHAIR PORTIER: I am sure the

13 Agency is going to have to deal with that as they

14 kind of move forward and put this all together.

15 I am going to kind of end it at this

16 point. I just wanted to make a last comment that

17 as I was thinking through this and trying to make

18 the transition to the monitoring and sampling,

19 while it is nice to understand the mechanism of

20 action and understand this stuff, when you really

21 think from a population public health level, you

22 know, you start thinking about a hundred million
202-234-4433

Neal R. Gross & Co., Inc.

Page 30

1 women, at any one point, any one day, any one

2 point in time, some of them are at the critical

3 level. So the statistic you are probably looking

4 for for monitoring is going to be some kind of

5 daily average that we are not going to exceed

6 because there is somebody at risk every day.

7 So it is nice to know the duration of

8 the impact of a concentration on an individual

9 but when you integrate it over the population, it

10 is going to be an average value that you are

11 looking at. The question is translating that

12 average to a physiologically critical dose that

13 is safe. Right? And that is the concern.

14 So I hope, Dr. Krishnan and Dr. Reed

15 are going to be able to capture this. The reason

16 for doing this is I know this is a critical

17 question and I really want to make sure as we

18 write this up we kind of capture all of this.

19 Thank you. I think, Dr. Cooper, you

20 are done. We are going to bring up the hydrology

21 point.

22 At this point, I think Syngenta is
202-234-4433

Neal R. Gross & Co., Inc.

Page 31

1 first up. Dr. Hendley, I think has the

2 microphone, followed by Dr. Sielken, followed by

3 Mr. Thurman or Mary Frankenberry. And they

4 promised me to kind of collectively keep this

5 within 30 or 40 minutes. And we will probably,

6 at the end of this presentation, we will probably

7 take our morning break, get our coffee and then

8 go into these questions.

9 A number of pages have been handed out

10 to the panel and they are already being processed

11 to put on the docket, some of which are going to

12 be presented here and some of which have not. A

13 lot of it is clarifying material.

14 We went through the presentation on

15 Monday afternoon kind of quick and that was, I

16 think, the one reason we really want to revisit

17 and make sure we understand what was presented. 

18 I know it is early in the morning but I am sure

19 the toxicology physiologists over here are going

20 to kind of sit back and relax.

21 Dr. Hendley.

22 DR. HENDLEY: Okay, this is Paul
202-234-4433

Neal R. Gross & Co., Inc.

Page 32

1 Hendley from Syngenta again. And we would like

2 to thank you, Mr. Chairman and the panel for

3 giving us an opportunity to clarify some of the

4 handouts that were given.

5 And I think in fairness to the

6 hydrologists, for the rest of you around the

7 panel, you need to realize that between ourselves

8 and EPA, we have talked about five approaches,

9 each of which have examined data in subtly

10 different ways than the hydrologists have

11 properly asked us to clarify exactly how the data

12 were processed so they can better understand the

13 interpretations that have been made.

14 So what I am going to be doing is

15 referring or what we are going to be doing, and

16 incidentally, I have Dr. Sielken on my left and

17 Dr. Chen, the Dr. Chen from Chen et al., which

18 you have seen a number of times, on my right.

19 What we are going to do is talk about

20 the slide number from my presentation and then,

21 if I may, Mr. Chairman, I suggest after these are

22 largely one pages, if we briefly go through the
202-234-4433

Neal R. Gross & Co., Inc.

Page 33

1 one-pager and then ask to make sure we have

2 addressed the questions, it is more efficient

3 than trying to remember what we said. Thank you

4 very much, indeed, then.

5 Okay, the first one refers to --

6 SESSION CHAIR PORTIER: Can you make

7 that full screen?

8 DR. HENDLEY: I can. I can make it

9 bigger but I am afraid it is in Word. Where is

10 the zoom on this version of Word? It is not the

11 one where -- zoom. Thank you. Is that any

12 better? No, not really. Thank you.

13 It is amazing how Windows changes. 

14 You totally forget the version as you move on.

15 Okay, the first one refers to slide

16 ten in the Syngenta presentation, which was about

17 high centiles. There is a very detailed report

18 on the docket, which is Whitmore and Mosquin.

19 The points that I think I would like

20 to clarify that I understand were questions in

21 the minds of the hydrologists were this is

22 finished water. And what we are talking about
202-234-4433

Neal R. Gross & Co., Inc.

Page 34

1 here is an analysis that follows almost exactly

2 the process in the main report, which was done on

3 the full data set of both SDWA 50,000 data points

4 and VMP/AMP, the 48,000 data points. But this

5 time, they looked at the subsets of data based on

6 the water sources, static, flowing, and mixed.

7 And in addition, they looked at the

8 subset of data based across all community water

9 systems but recorded samples between April 1 and

10 July 31.

11 And so briefly, in that process, each

12 subset of the VMP/AMP data set was taken. The

13 data points were weighted, as described in detail

14 in the report. And we probably don't need to go

15 into that today.

16 The key question I think that was in

17 the mind of the panel was were these data points

18 interpolated. And the answer is they were not

19 interpolated. The centile estimates that were

20 given on slide ten were computed directly from

21 the population of measurements. And then the

22 confidence intervals were obtained as described
202-234-4433

Neal R. Gross & Co., Inc.

Page 35

1 in the full report and that led to the table in

2 the handout that accompanied the slides.

3 DR. YOUNG: Linda Young. Okay, Paul,

4 I can't figure out, are you talking about this

5 handout, and this slide ten? I can't find slide

6 ten.

7 DR. HENDLEY: Okay.

8 DR. YOUNG: So, I am quite lost.

9 DR. HENDLEY: Okay. I do not have the

10 slide set up here. It is the slide set that I

11 presented. I think it is near the back of that

12 slide, that package of slides.

13 DR. YOUNG: Okay.

14 DR. HENDLEY: There you go.

15 DR. YOUNG: All right.

16 DR. HENDLEY: No, the slide set that

17 starts with potential atrazine exposure in

18 drinking water. You got it. That is slide ten.

19 So that was on the high centiles and

20 it had the data on the full data set, as well as

21 the subsets by source type, static water, the

22 flowing water. And so that was done with no
202-234-4433

Neal R. Gross & Co., Inc.

Page 36

1 interpolation from the entire set of data points. 

2 Any further questions on that one? Okay.

3 The second one refers to slide 12 in

4 that presentation, which you should find says

5 sampling frequency and then 90-day exposure. 

6 Okay? Right.

7 So, this is a handout that is

8 describing in more detail the process of

9 developing the data. And the key points here, it

10 was raw water and there were 441 community water

11 system years from -- and now we can read it --

12 from 137 community water systems.

13 This was from the atrazine monitoring

14 program. So they were seven-day intervals during

15 the growing season and every two weeks during the

16 rest of the season. And for each one of those

17 441 community water system years, linear

18 interpolation was used in this case to create a

19 365 daily concentration profile. From that

20 interpolated profile, 90-day rolling averages

21 were computed and the maximum one was selected

22 for each community water system year. That was
202-234-4433

Neal R. Gross & Co., Inc.

Page 37

1 the true value against what we are going to

2 compare our samples.

3 There were a number of sampling

4 designs tested and just to be clear, they are as

5 shown on the table on the other side. Overleaf,

6 as the Brits would say. This is the atrazine

7 monitoring protocol, right to extreme right of

8 that table. And in January, there was one every

9 two weeks, which works out to approximately three

10 sets of samples in January, two in February, two

11 in March, and this just happens to be because of

12 calendar weeks and then you go into the weekly

13 period.

14 SESSION CHAIR PORTIER: Time is a15 wasting here.

16 DR. HENDLEY: Yes. Okay. And so we

17 had weekly sampling during April to July and then

18 again went back to the two-weekly schedule for

19 the rest of the year and there were also sampling

20 regimes with 17 samples, nine and six during the

21 year.

22 The sampling design that was used in
202-234-4433

Neal R. Gross & Co., Inc.

Page 38

1 this piece of work by Research Triangle

2 International was a series of windows were

3 created and the approach was to take one sample

4 in each simulation, one sample point within the

5 window with replacement. Each day in the window

6 had an equal probability of selection for all

7 1,000 simulations.

8 And so you took a random day within

9 the week. You moved to the next selection week,

10 took another random day. And the reason for that

11 is because that is actually the instruction that

12 was given to the plan operators, not that they

13 had to go every Tuesday, because that was an

14 unfair burden but they were to take one during

15 the calendar weeks, following that schedule in

16 the table.

17 So for each simulation there was a

18 linear interpolation between the sampled points

19 to come up with a 365-day profile and from that,

20 a 90-day rolling average was created for the days

21 and the maximum simulation 90-day rolling average

22 was obtained.
202-234-4433

Neal R. Gross & Co., Inc.

Page 39

1 And the difference between the

2 simulation 90-day maximum and the true maximum

3 for each of those community water system years

4 was recorded and then you had a set of maximum

5 simulated to maximum and that was worked on as a

6 full data set and as data subsets based on the

7 source-type. So that was the background to how

8 we processed the data for that piece of work. 

9 And I realize you need many hands to hold all the

10 pieces of paper.

11 DR. YOUNG: So the results are in this

12 set of slides?

13 DR. HENDLEY: The results are

14 summarized on slide 12 of the presentation. The

15 90-day exposure slide, which is entitled

16 "Sampling Frequency and Confidence; 90-day

17 Exposure."

18 DR. SIELKEN: This is Dr. Sielken. 

19 There is an analogous set of answers in the other

20 handout that you had that I provided that was

21 done using the procedure that I am going to

22 describe later. But there are also additional
202-234-4433

Neal R. Gross & Co., Inc.

Page 40

1 numerical results for that type of analysis.

2 DR. HENDLEY: And of course, the full

3 background to this, Dr. Young, is in the Chen, et

4 al. 2009, the evaluation of sampling frequency

5 alternatives, which is in the docket.

6 SESSION CHAIR PORTIER: And I think

7 the key conclusion here is that when you use that

8 methodology to look at reduced sampling schemes,

9 so going from roughly 25, 26 samples in a year

10 down to six or nine samples and showed that by

11 that same methodology you got the same

12 distributions with fewer samples.

13 DR. HENDLEY: Precisely. Precisely. 

14 Thank you. Any further questions on that one? 

15 Okay.

16 Okay, the last one of my presentation

17 before Dr. Sielken refers to slide number 14 in

18 the presentation that I made and it is entitled

19 "Sample Frequency, Shorter Exposures." This one

20 will be quicker, Mr. Chairman.

21 The key points of it, unlike the last

22 one where we started off with atrazine monitoring
202-234-4433

Neal R. Gross & Co., Inc.

Page 41

1 program frequency, which was basically seven days

2 apart, this is using daily data sets. And I

3 should say, there is one other data set we

4 haven't been specific about and Dr. Gilliom can

5 tell you more about it, and that is a set from

6 2001 for Lake Perry, which was done by Blomquist,

7 et al., it is submitted, it is in the EPA White

8 Paper and that had daily data for a large static

9 body. And I realize that we never actually

10 mentioned that here. 

11 So as you remember, we had finished

12 data from a community water system in Missouri

13 that was daily or near daily. And we had daily

14 or near daily data from the Eco program and the

15 Heidelberg programs.

16 So for each source of data for each

17 year, we took the measured daily or near daily

18 data. We filled in any gaps with linear

19 interpolation to create a 365-day time series and

20 we identified peak and we computed point

21 estimates of the 95th and 99th centile daily

22 value. And those were the true ones against
202-234-4433

Neal R. Gross & Co., Inc.

Page 42

1 which we compared.

2 DR. YOUNG: So how were those

3 percentiles estimated?

4 DR. HENDLEY: Dr. Chen?

5 DR. CHEN: This is Wenlin Chen,

6 Syngenta.

7 The percentile actually are calculated

8 based on the daily chemograph generated. We

9 looked at the maximum each year at each site. 

10 You have a sequence of events and you keep the

11 maximum as a peak concentration for that year for

12 that site and then calculate those so the 90th

13 centile and the 99th centile.

14 DR. YOUNG: So you used, basically the

15 rate data and took the percentiles of the data?

16 DR. CHEN: Right, yes.

17 DR. YOUNG: Okay.

18 DR. HENDLEY: So again, using that

19 365-day time series for that source of data for

20 that year, we used the same simulation approach

21 using the window approach that we have just

22 discussed. And so for each resulting simulation
202-234-4433

Neal R. Gross & Co., Inc.

Page 43

1 of those one thousand simulations, a linear

2 interpolation between the sample points generated

3 a 365-day profile as before and the peak and 95th

4 and 99th centile of daily values were obtained

5 from that.

6 And then from each distribution of a

7 thousand runs, the example in the handout that

8 was given, this was from Dr. Mosquin, shows the

9 mean, the peak, and the 95th centile daily values

10 and their percentage deviations.

11 So has that clarified the data

12 processing for that?

13 DR. SIELKEN: This is Dr. Sielken. Dr.

14 Mosquin's handout that he is referring to are the

15 two separate pages that were put in the docket

16 from Dr. Mosquin?

17 DR. HENDLEY: Yes, the last two in

18 there. 

19 SESSION CHAIR PORTIER: And the

20 conclusion from this, again getting back to the

21 conclusions of this exercise, is that when you

22 compared a once-a-week sampling to your overall,
202-234-4433

Neal R. Gross & Co., Inc.

Page 44

1 you tend to underestimate the maximum by 20

2 percent, 20 percent to 80 percent for Missouri,

3 and 1.1 to 2.6 multipliers for the others. So as

4 we would expect, you are underestimating the

5 maximum. What you were doing here was estimating

6 the multiplier between the maximum between the

7 sample distribution to the real. Being able to

8 say, if I had sampled once a week during the

9 season and used that to create my profile, I

10 would underestimate the maximum by 20 to 80

11 percent. Right? I mean, is that kind of the

12 conclusion?

13 DR. CHEN: These systems are not --

14 sorry. This is Wenlin Chen. You mentioned the

15 three systems. One is St. Louis which is a true

16 drinking system, the other two are at the Ohio

17 River and the eco that is not drinking water. 

18 That is just ecological water system.

19 SESSION CHAIR PORTIER: And you get

20 the performance with the drinking water profile

21 and the worst performance with the well water

22 profiles, as we would expect.
202-234-4433

Neal R. Gross & Co., Inc.

Page 45

1 Dr. Heeringa?

2 CHAIR HEERINGA: Steve Heeringa. 

3 Paul, there is a chemograph that is illustrated

4 on this handout sheet. Is that typical of one of

5 these water systems? For example, you are using

6 site-specific, year-specific data. So you have

7 seven years for this Missouri finished water

8 system. What do those chemographs look like?

9 DR. HENDLEY: Paul Hendley. The

10 Missouri chemographs have a low maximum value but

11 they are actually quite spiky. In a way, quite

12 surprisingly spiky. So some of the peaks appear

13 to be of the order of maybe three or four days.

14 DR. CHEN: Wenlin Chen. I just wanted

15 to add to that is that we are looking at the

16 ratio, not the absolute of the peak. So when you

17 look at the ratio, it should give you, it cancels

18 out where it is really spiky or not spiky.

19 CHAIR HEERINGA: Steve Heeringa. My

20 concern is that the spiky problem is the tough

21 one. And I assume the Heidelberg data, which are

22 Honey Creek and Rock Creek, that those are spiky
202-234-4433

Neal R. Gross & Co., Inc.

Page 46

1 data as well because those are agricultural

2 drainage, mostly.

3 DR. HENDLEY: Especially for the two

4 that you have selected, which were Honey and

5 Rock, which were about 35 and I think 130 square

6 miles.

7 CHAIR HEERINGA: Thank you.

8 SESSION CHAIR PORTIER: Paul, scroll

9 down so people can see the image Dr. Heeringa was

10 talking about. I think it is further down on

11 your slide. Isn't it?

12 DR. HENDLEY: I think it is on one of

13 the others, yes.

14 SESSION CHAIR PORTIER: There it is.

15 DR. HENDLEY: There you go.

16 SESSION CHAIR PORTIER: And the red

17 lines identify the sampling bins, the periods for

18 the windows.

19 DR. HENDLEY: Absolutely correctly. 

20 So what you are doing is you are getting the

21 computer to pull a sample from each of these

22 windows.
202-234-4433

Neal R. Gross & Co., Inc.

Page 47

1 So with that, if there is no further

2 questions from the hydrology group, we would like

3 to turn to the discussion for underlying -- Where

4 is your presentation? And this underlies slide

5 13

6 DR. SIELKEN: This is Dr. Sielken. 

7 Thank you, Mr. Chairman and the rest of the

8 panel.

9 This is some supplemental explanation

10 to go with the short handout that was part of the

11 docket yesterday. There seemed to be a missing

12 link in the middle of it. And since it was no

13 simulation and no trickery or anything like that,

14 I wanted to make it clear the step-by-step

15 procedure that I was going through and also

16 differentiated a little bit from some of the --

17 slightly different than the Mosquin procedure,

18 although our procedure and our results ended up

19 being within ten percent of each other. So they

20 were very close, even though they were slightly

21 different.

22 The procedure that I applied both to
202-234-4433

Neal R. Gross & Co., Inc.

Page 48

1 all 202 CWS's as well and that was on a multi2 year profile, I also applied to the St. Louis --

3 I'm sorry. I wasn't supposed to say that. The

4 Missouri community water supply system, which was

5 close to daily data. I did that both yearly in

6 groups and multi-year. And I also applied that

7 to, the same system, to the almost daily data in

8 the Heidelberg data sets, the Honey, the Rock,

9 Sandusky, and Maumee. 

10 In all of those cases, the procedure

11 was as follows. And it is almost identical

12 whether you use linear interpolation or step13 wise. So that is not really a big deal.

14 You start out, at least for data sets

15 like this where you followed this nearly weekly

16 or nearly daily sampling. It is a fairly dense

17 data set, as shown on the top of this slide. The

18 slide underneath it is the linear interpolation

19 and that is where we started. And the only

20 modeling that was done was just that linear

21 interpolation. There was nothing else that was

22 simulated.
202-234-4433

Neal R. Gross & Co., Inc.

Page 49

1 SESSION CHAIR PORTIER: And for those

2 who can't read it, that is about 13 years' worth

3 of time series data for one site that you are

4 showing there.

5 DR. SIELKEN: Yes, that is correct. 

6 We had a variable number of years in CWSes. Most

7 of them had around ten years. Well, there were

8 351 profiles that were 13 years long; 351 CWS

9 years came from 13-year profiles. So that was

10 13-year profiles and seven-year profiles were the

11 most common length of profiles.

12 From this linear interpolated profile

13 for multi-years capturing your variability, I

14 went through and overlaid a grid of seven days

15 wide. A window, a seven-day wide window. And

16 then picture of course that seven-day window is

17 not to scale. But you know, put a grid over it

18 of seven days and then started at the beginning

19 of the profile on Monday, went to the next

20 Monday, took another -- and this is in the seven21 day sampling.

22 We actually did the testing because
202-234-4433

Neal R. Gross & Co., Inc.

Page 50

1 you were interested in perhaps changing the

2 frequency of sampling. We also looked at two3 day, three-day, four-day, five-day, six-day,

4 seven-day, weekly, bi-weekly, tri-weekly, and

5 monthly. So we looked at all of those profiles

6 in exactly the same way that I am going to show

7 here and I will use as an example the seven-day

8 spacing. So analogous to what is going on now.

9 Given the linear profile, we overlay

10 a grid of the sample spacing here seven days

11 apart. Start systematically with Mondays and

12 then go to the next Monday and so forth and just

13 march through that entire profile every seven

14 days, starting with a Monday, record those

15 values, take a linear profile between those seven

16 day values -- no. Sorry.

17 Take those seven day values, find the

18 max, and then compare that max to the original

19 profile max, the data profile max. So when we

20 are looking at acute for one day, we just take

21 those seven-day spaced apart samples, compare the

22 max in that set of samples to the max in the
202-234-4433

Neal R. Gross & Co., Inc.

Page 51

1 original profile. So there is no simulation in

2 there.

3 I did that starting on Monday,

4 starting on Tuesday, Wednesday, Thursday, you

5 know, all the seven possible starting points. So

6 there were seven sample maxes to compare to the

7 overall data profile max. And you can see the

8 variability in those numbers on this profile. 

9 You know, 0.97 for Mondays, 0.96 for Tuesdays. 

10 Obviously, one of those was going to hit it and

11 it was Saturday in this example, sort of.

12 I took the average of those seven

13 numbers and that is what I recorded as the single

14 number for that CWS is a ratio of the sample max

15 to the data profile max.

16 And now this next slide is the new

17 one. And that was to say what did I do with

18 those 202 numbers? Well, first of all, that is

19 what I worked with was those 202 numbers; one for

20 each CWS, just a comparison of the sample max to

21 the profile max. I took those 202 numbers and

22 found the percentiles of those 202 numbers. So
202-234-4433

Neal R. Gross & Co., Inc.

Page 52

1 there was no simulation of anything in there.

2 And the little histogram here in the

3 center is just to hopefully eliminate confusion. 

4 If there was a grid, and all of these ratios for

5 seven day spacing and trying to target an acute

6 daily value or a daily value, the ratios between

7 the sample max and the true max ranged from 0.75

8 at the low end up to one. There was 18 from 0.75

9 to 0.80; 31 in the next bin, and so forth.

10 So they were not very widely spread. 

11 The worst case was around 0.75. So you were only

12 off by a quarter, 25 percent, using seven-day

13 sampling to estimate the ratio between a max one

14 day and a profile one day. So for a one-day

15 target, you can use a seven-day sampling and be

16 within 25 percent.

17 Dr. Lee?

18 DR. LEE: For the ones where it is you

19 are sampling say every seven days, did you 

20 average across the possible seven starting days

21 to get the figures in the table?

22 DR. SIELKEN: Yes, I did.
202-234-4433

Neal R. Gross & Co., Inc.

Page 53

1 DR. LEE: Okay, thank you.

2 DR. SIELKEN: So, it was the expected

3 performance of the sampling plan.

4 Okay, that was probably the slide that

5 was most missing from your explanation before.

6 That set of percentiles computed from

7 the 202 values was tabulated in your handout for

8 both one-day sampling, of course that would have

9 been perfect; two-day sampling; three-day

10 spacing; all the way up to 28-day spacing. So

11 this is just a table repeating the analysis for

12 each sample spacing and then just recording the

13 percentile.

14 SESSION CHAIR PORTIER: If I can

15 interpret this and then if you look at the last

16 column, 28 days, it says if instead of sampling

17 every day you sample once a month, kind of on

18 average you are going to whatever it is, 22

19 percent below the kind of in the long run, you

20 are going to be 22 percent below the true daily

21 maximum. Right?

22 DR. SIELKEN: I'm not exactly sure
202-234-4433

Neal R. Gross & Co., Inc.

Page 54

1 where you are seeing 22 --

2 SESSION CHAIR PORTIER: I am looking

3 at the 50th percentile. I'm sorry. The median.

4 DR. SIELKEN: Oh, at the 50th

5 percentile. Okay. Going right across here,

6 0.7548, you are about 25 percent below.

7 SESSION CHAIR PORTIER: It is 0.79. 

8 So it is like 21 percent.

9 DR. SIELKEN: Well okay, yes.

10 SESSION CHAIR PORTIER: You can't read

11 it from there.

12 DR. SIELKEN: Knowing that tables are

13 hard to read, regardless of the magnitude, I put

14 a little picture in and you can't read that

15 either but it gave me the idea that as you varied

16 the sample spacing between one day, which gave

17 you perfect results, to 28 days, how did your

18 ratio drop off at the 95th? Well, the orange, if

19 you can see orange is in the middle, and that is

20 the 95th, there is the 97.5 and 90 below it. So

21 those three profiles of interest, how they change

22 with sample spacing.
202-234-4433

Neal R. Gross & Co., Inc.

Page 55

1 And notice that if you don't like

2 pictures, I put it in numbers again. And they

3 are saying that if you take a seven-day sample

4 spacing, keep your current spacing, you are

5 within a quarter for single-day target within 20

6 percent for a three-day target, and so forth. 

7 Notice that for a 90-day rolling target, you are

8 almost there all the time.

9 So this is showing that regardless of

10 your target, really, you are doing quite well

11 with the existing plan. Now this was to test the

12 sampling performance. 

13 What happened? Oh, well, I reached --

14 well how silly. I want to get back to the

15 folders. Where is the folders? Yes, here we go.

16 That showed that you could stick with

17 the current sampling plan and for finished water

18 or even doing the same example with the

19 Heidelberg Eco, getting approximately comparable

20 results. And those were shown in the first

21 handout that I gave, put on the docket a couple

22 of days ago down here. I apologize here.
202-234-4433

Neal R. Gross & Co., Inc.

Page 56

1 We did that same procedure as Dr.

2 Hendley mentioned, we did that not only for the

3 202 CWS collectively, we also split them up by

4 groups, flowing, mixed and static. We got pretty

5 much the same results. You can see a comparison

6 there between what we got for all the CWS

7 together with what we got for the different

8 partition. That is in the handout that you got

9 on the first day from me, which is going to look

10 like that.

11 And then because this was the 202 CWS,

12 which is a really strong database for capturing

13 year to year variability and the differences

14 between CWS in Indiana, Illinois, Ohio, Texas,

15 you know, covering that entire region and the

16 different year effects. 

17 And of course, if you look at a CWS,

18 there is a lot of year to year variability. Then

19 going from that set as a test case to another set

20 that didn't hardly involve the linear

21 interpolation because you had almost daily

22 sampling, which is the CWS set in Missouri, which
202-234-4433

Neal R. Gross & Co., Inc.

Page 57

1 is shown in this slide. Yes, well if I do that

2 then I lose where I was. 

3 You can notice these numbers down

4 here. The lowest number in that table is, you

5 know, for the hardest target, which is single

6 day, those numbers are about a third. The lowest

7 number there is 65 percent or something like

8 that, 60 percent. That means you are off by 40

9 percent, not even a factor of two.

10 Okay, also you know, this was for

11 almost daily sampling of finished water. This is

12 the Heidelberg data set. Again, the two worst

13 cases and again, this is not drinking water but

14 an ecosystem, your lowest number is around 40

15 percent. So you are off by that much. So again,

16 right around at worst a factor of two.

17 DR. HENDLEY: And just if I can, this

18 is Paul Hendley again, that was why having worked

19 from the strength of the temporal and spatial

20 VMP/AMP database and from the strength of the

21 daily database and coming together and finding

22 the results were coherent was one of the points
202-234-4433

Neal R. Gross & Co., Inc.

Page 58

1 we made earlier.

2 SESSION CHAIR PORTIER: So do we have

3 any follow-up questions of Linda or Dr. Lee? Dr.

4 Heeringa.

5 CHAIR HEERINGA: I just want

6 confirmation that the two sets of analyses, the

7 one done by RTI and then the one done by Sielken

8 and Associates, Bob you just used systematic

9 sampling throughout the year. The other just

10 used the random draw within these time windows

11 that have been set out under the plan. 

12 DR. HENDLEY: That is correct.

13 CHAIR HEERINGA: That is the primary

14 difference between your treatments of these same

15 data?

16 DR. HENDLEY: Yes, that is primarily

17 the difference. The only other difference is

18 they looked at every year individually. And I

19 did, too, but I did a multi-year profile.

20 There was one other comment I wanted

21 to make because you asked it yourself, Dr.

22 Heeringa, was what durations. In the first part
202-234-4433

Neal R. Gross & Co., Inc.

Page 59

1 of the handout, we looked at, you know, gave the

2 full numerical distributions of characteristics

3 for one day, ten day, 30 day and 90 day rolling

4 averages. We gave the concentrations, the

5 distribution of concentrations, the distribution

6 of rolling averages. We also addressed the

7 question that you raised was what about how many

8 days in a row did you exceed certain levels.

9 And there was, in that handout, I will

10 just leave it here, in that handout, there are

11 tables that do for the daily profiles the

12 concentrations and then there is just this one

13 page on page five in that handout where we did

14 look at duration, number of days above specified

15 values. And I only have the one day and the ten

16 day in here. But I can tell you that at the 99th

17 percentile, there was no CWS, none of the 202 CWS

18 for finished water that had durations above

19 twelve and a half for more than a day. And so

20 there was no duration above twelve and a half.

21 Obviously at the extremes there was a

22 little bit, I mean, there was more duration at
202-234-4433

Neal R. Gross & Co., Inc.

Page 60

1 the max but there wasn't, you know, at the 99th

2 percentile, there was none for the day, 10-day,

3 30-day, 90-day, there were no durations above

4 that target value of like twelve and a half.

5 SESSION CHAIR PORTIER: Okay. Yes,

6 Dr. Gilliom?

7 DR. GILLIOM: I just want to make a

8 general point that we will probably come back to

9 in discussing the charge questions but it is

10 important in just interpreting all of this. 

11 There is a tremendous number of numbers. And you

12 can generate tons of numbers from synthetic

13 sampling experiments and everything.

14 I just want to make the general point

15 that the importance of each sampling experiment

16 to a specific objective questions depends

17 entirely on how well the simulated truth

18 represents actual truth.

19 So, if we get to a problem where we

20 are looking at a very short-term occurrence like

21 daily, we have to be sure the starting point is a

22 confident estimate of the true daily
202-234-4433

Neal R. Gross & Co., Inc.

Page 61

1 distribution. And there is a lot and I don't

2 even intend to jump into every individual

3 experiment that has been done by Syngenta or EPA

4 but every one of them has a little different

5 twist on how truth was defined and what time span

6 it is relevant to. 

7 So my general point is, there are so

8 many possibilities here we are going to have to

9 get that objective very clearly defined so that

10 we can then evaluate which one is the right one

11 to use.

12 DR. SIELKEN: I agree with Dr. Gilliom

13 that establishing the truth is an important

14 thing. I would point out that when we did the,

15 we took the 202 CWS and took the linear profile

16 as a starting point, my intention was not that

17 that necessarily captured the max within the

18 water that was actually there but if I took those

19 values which are a representation of reality,

20 took that as a reality, it may not have been

21 quite as much of a reality as some people would

22 have wanted if they were to sample more often.
202-234-4433

Neal R. Gross & Co., Inc.

Page 62

1 But with that as the reality, given

2 that reality, how well did you do with your

3 monitoring program? So that was really the issue

4 was the ability or the performance of the

5 sampling. And that is also why we turned to the

6 more daily profile values and tested it there.

7 Thank you.

8 SESSION CHAIR PORTIER: Paul, you want

9 to wrap up? Final comment? No. 

10 Dr. Akana had a question.

11 DR. AKANA: A small point that you can

12 clarify for me. What I understand correctly

13 though, this data treatment is equally valid on

14 say the raw samples that start much higher as

15 well as lower. For instance, the raw data here

16 mostly the points are like three or under parts

17 per billion but the treatment is equally valid if

18 your dataset runs up to 30?

19 DR. SIELKEN: Yes. Yes, because we

20 were looking at ratios between the sample max and

21 the true max, that ratio would be invariant to

22 whether we were going zero to three, zero to 30. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 63

1 You are absolutely correct. It would be

2 applicable.

3 DR. AKANA: But that does mean if your

4 estimate is say 20 percent under-represented, for

5 three, it is 20 percent and for 30 it is 20

6 percent.

7 DR. SIELKEN: Yes, that is correct.

8 DR. HENDLEY: So Mr. Chairman, I would

9 like to wrap up. I will make one comment on

10 that. Of course, that is a correct statement

11 that as we pointed out before, if you are trying

12 to understand the variability for drinking water

13 values, you are best off using drinking water

14 data where it is all possible. But the raw data

15 was a great way of getting a handle on

16 understanding variability. 

17 However, we do appreciate the time you

18 gave us to try and clarify some of these issues. 

19 So thank you very much. We appreciate it.

20 SESSION CHAIR PORTIER: Thank you. 

21 And now Nelson Thurman and Mary Frankenberry. I

22 always get her last name wrong. I just know her
202-234-4433

Neal R. Gross & Co., Inc.

Page 64

1 as Mary.

2 And also at the table is Don Brady,

3 the vision man. And of course we are way beyond

4 my half hour target. This is the last half hour

5 before our discussion.

6 MS. FRANKENBERRY: Thank you. And

7 again, I can go quickly I hope. Our slides I

8 think are a lot simpler than Syngenta's. We did

9 not get their handouts until one of them this

10 morning and would like to get them from Joe

11 Bailey, I think, certainly before the end of the

12 day.

13 Hopefully with these, it appears that

14 they have done at least in one or two of their

15 exercises something very similar to what we did

16 and I am hoping that these will be easy to

17 understand.

18 Step one, we took a sample chemograph. 

19 That is what we get from the field, 30 to 35

20 samples. We augmented it linearly to 365 days to

21 make a true or reference profile that we are now

22 calling true.
202-234-4433

Neal R. Gross & Co., Inc.

Page 65

1 What we got in step two we would like

2 to consider reality. It could have been instead

3 the Heidelberg dataset that would have started

4 with, we wouldn't have had to do as many

5 interpolations but this is what we got and

6 considered as true.

7 From that we sampled this true profile

8 or what we consider reality out in the field. 

9 Let's just say we will look at the example of

10 sampling every four days. We took this -- this

11 is what we do in the field. We may sample every

12 seven days, actually in the AMP program. For

13 this example, try four days. 

14 What we get from number three then, is

15 what we get our as our sample dataset from

16 Syngenta or from wherever we receive a dataset. 

17 And that will have 90 some values, perhaps or 30

18 to 35 if it is from the AMP.

19 For the purpose of the exercises then

20 we augmented this new sample chemograph up to 365

21 days. In some runs we ran step-wise, others

22 linearly but what we presented in the paper were
202-234-4433

Neal R. Gross & Co., Inc.

Page 66

1 linear interpolation.

2 For that new, that sample that was

3 interpolated from step four, we calculated, as an

4 example, three-day running averages, starting

5 days one to three, two to four, all the way up to

6 363 to 365 days. We did rolling three-day

7 averages there.

8 In step six, we took the maximum

9 three-day running average from this re-sample or

10 the first re-sample, if you will. We set it

11 aside into the bootstrap pile and that is what we

12 will call it without the acronym there.

13 (Laughter.)

14 MS. FRANKENBERRY: I actually made

15 myself laugh last night and then forgot about the

16 public record. 

17 (Laughter.)

18 MS. FRANKENBERRY: In step six, we

19 repeated this 4500 times with replacement until

20 our bootstrap pile contained four to five

21 thousand maximum three-day running averages. So

22 what we have there is a distribution of maximum
202-234-4433

Neal R. Gross & Co., Inc.

Page 67

1 three-day averages, three-day running averages

2 that was derived from sampling every four days. 

3 That is our bootstrap sample of maximum three-day

4 averages. 

5 And in step seven what we did then was

6 we spread it out. We looked at its range from

7 minimum to maximum. We looked at the 50th

8 percentile values, low percentiles, high

9 percentiles, and we compared these to what we

10 would have gotten from the so-called true maximum

11 three-day running average. What we were looking

12 for is how often we captured the true max and

13 when we did not, when underestimated it, how

14 often did that happen and to what magnitude to

15 what extent.

16 Those are those questions. I can come

17 back to that but just to show you, again this

18 graph isn't that easy to see but what you are

19 looking at there, each of these, look at number

20 four. That is the bootstrapped distribution of

21 maximum three-day averages sampled at four-day

22 intervals.
202-234-4433

Neal R. Gross & Co., Inc.

Page 68

1 And we went from underestimating by

2 31.5 percent up to getting it right on with no

3 error. That is the range of our performance

4 there. We want to look down at, I think, number

5 13 was our best run or one of the best

6 chemographs. We went from minus two percent up

7 to no error. So that is a very narrow range and

8 we looked at how often. These are all again

9 maximum running three-day averages.

10 And I think in some of our, let's go

11 back up to this question. Do the highest runs

12 equal or exceed the true max? How much lower

13 than true are the lowest runs? And then if we

14 wanted to create an interval on a given CWS, let

15 me go back to this, from minus 30 to zero, that

16 is one kind of interval. If we wanted to look

17 down at our better ones, number 13, we go from

18 minus two up to no error. That is a kind of

19 interval in itself.

20 One of the questions that we will be

21 asking the panel is what do we do with these as a

22 whole? If it happens and this is totally
202-234-4433

Neal R. Gross & Co., Inc.

Page 69

1 hypothetical, if our health effects people are

2 looking at a three-day average and they say how

3 well would we do if we sampled every four days,

4 we could say, I think on mean runs, the average

5 performance was about five percent under true. 

6 Our lowest runs, I think, were on average about

7 13 percent under true for sampling every four

8 days on a three-day average.

9 Now we have asked them, can you live

10 with that if you would like a four-day interval

11 sampling strategy. The thing is we can look at

12 this and say we average 13 percent in the lowest

13 runs below true but we have some higher values

14 that were 30 percent under true. So do we want

15 to make a competence interval around our

16 performance here or do we want to take, run them

17 all as a whole, look at the lowest one percent of

18 all the CWS and say 99 percent will be at or

19 better than this kind of performance. I think

20 that is what we were getting at in asking for

21 advice on bounds for the population, what to do

22 with that, or can we produce a prediction
202-234-4433

Neal R. Gross & Co., Inc.

Page 70

1 interval because we are looking at what we have. 

2 But what do we expect on new systems?

3 Then when we go from here to

4 individual samples, I think we are relying on 

5 asking your advice on things like the different

6 interpolation levels, which did make some

7 difference, we found, in how well we

8 underestimated or overestimated. It wasn't a lot

9 but I think with stair-step you can overestimate

10 more often than not. And I believe it is

11 possible to underestimate a little more in

12 magnitude but we didn't do a systematic look at

13 that.

14 That and any other methods that you

15 could, kriging, whatever that would help us on

16 the individual level. We are trying to go from

17 talking about bounds on something like this for

18 the population to then what do we do on an

19 individual level.

20 SESSION CHAIR PORTIER: Dr. Heeringa?

21 CHAIR HEERINGA: Steve Heeringa. You

22 bootstrapped this 4500 times but in a systematic
202-234-4433

Neal R. Gross & Co., Inc.

Page 71

1 sample of 365 days, there are only 90 unique

2 samples. So are you not simply just at random

3 sort of getting an expected repetition of five of

4 these?

5 MS. FRANKENBERRY: We repeated, yes,

6 on purpose to get a larger number of samples so

7 that when we looked at the lowest one percent or

8 the highest one percent, we would have more

9 samples to deal with but definitely the finite

10 value for each of the running averages. And we

11 simply re-sampled more to be able to have more to

12 deal with.

13 CHAIR HEERINGA: There were sort of 91

14 or 92 unique values that could occur from your

15 sampling process in CWS?

16 MS. FRANKENBERRY: I think it is more

17 for four days because if you start from day one

18 to day three, then day three, two to four, three

19 to five, all the way up to 365, 363, it is

20 something like 365 minus three or four days,

21 something like that. I think the worst case is

22 with 90-day averaging. I think it is 365 minus
202-234-4433

Neal R. Gross & Co., Inc.

Page 72

1 90. It comes around, something like that.

2 SESSION CHAIR PORTIER: Yes, Dr.

3 Gilliom?

4 DR. GILLIOM: Again, I think this will

5 come up later but just a comment briefly now

6 because of the point just made. And I think this

7 is meant as an illustration of a data analysis

8 process, more than like the final answer. So

9 this won't be meant to be particularly critical.

10 But to evaluate a short-term exposure

11 like this, the way the truth was created is not

12 appropriate, basically.

13 I am going to keep coming back. The

14 starting point in how we define truth is the

15 absolute most critical step in every one of these

16 experiments. Like here, truth on a daily basis

17 was created from samples that were 30 or 35 times

18 a year. It is missing many of the

19 characteristics of short-term fluctuation. So

20 when you simulate sampling from that, you are

21 going to do pretty well recreating what you

22 already got from a limited sampling.
202-234-4433

Neal R. Gross & Co., Inc.

Page 73

1 So basically the underlying data used

2 to define truth has to be denser than the type of

3 time frame you are trying to evaluate. And that

4 is why you see so much work done on these

5 relatively few sights that have daily data. And

6 we will see this as kind of a recurring --

7 SESSION CHAIR PORTIER: This is Ken

8 Portier. Your underlying simulation model has to

9 be complex enough to capture that high resolution

10 variability.

11 DR. GILLIOM: Yes, you have to have

12 some basis to defend that your truth represents

13 truth for the timescale you are going to now

14 experiment with.

15 SESSION CHAIR PORTIER: I think most

16 people understand that.

17 MS. FRANKENBERRY: And we did

18 acknowledge that, I think, in the paper. Some of

19 the Heidelberg datasets were not quite 365 but of

20 course they were much better than 30 samples. So

21 and I think that was our next step in plans. We

22 just only got so far. I think, Nelson, did you
202-234-4433

Neal R. Gross & Co., Inc.

Page 74

1 want to address it later?

2 SESSION CHAIR PORTIER: It doesn't

3 look like we have any additional questions. Mr.

4 Thurman, did you want to have some comments?

5 MR. THURMAN: Yes, actually I want to

6 bring us up to from, I am not going to say down

7 in the BS pile but from down in the weeds, I want

8 to bring us back a little higher elevation in

9 terms of we get into this discussion.

10 And first of all, I do want to

11 distinguish one area where we do not agree with

12 Syngenta on this. I mean, some of the contention

13 is the assumption is we should only be doing this

14 assessment based on finished water because

15 finished water through all the processes are

16 going to be more blended and smoothed out than

17 raw water.

18 However, the best data we see in

19 finished water is weekly sampling. So we don't

20 know what is happening in between. And I

21 actually was pulling up an example last night for

22 something else and it turned out to be a pretty
202-234-4433

Neal R. Gross & Co., Inc.

Page 75

1 good example here. This is for one system in

2 Ohio in one year. The magenta are the raw water

3 samples. The dark blue triangles are the

4 finished water samples. And what you see is the

5 finished water follows the same type of pattern

6 we see in the raw water.

7 There is nothing in this that suggests

8 to me that we would expect the finished water to

9 be less variable on the days that weren't sampled

10 than we see in the raw water in this particular

11 system.

12 So this is why we are looking at as we

13 try to define the truth, what are the best

14 datasets out there to define the truth. And

15 those that have more robust sampling are what we

16 are going to work with.

17 The other thing that is interesting is

18 you look here, you see an early season shorter

19 peak and a longer season, a larger peak later in

20 the season. Very similar to the pattern we were

21 showing in the Missouri site. So this is not a

22 pattern that we would not expect to see in some
202-234-4433

Neal R. Gross & Co., Inc.

Page 76

1 of the community water systems. And so as we are

2 going through the analysis, we are looking at

3 those that have more intensive sampling so that

4 we have a better start with what the truth is.

5 And actually the reason I pulled that

6 sample out in the first place is something Bob

7 Gilliom raised and something some other panelists

8 had asked. We have been talking about atrazine

9 and we are looking at total chlorotriazines. The

10 advantage of the AMP monitoring that Syngenta has

11 done is that they have measured not just atrazine

12 but they have measured the individual components

13 to get at the total chlorotriazines.

14 I was looking at Ohio because I know

15 there is simazine use in Ohio. So I wanted to

16 try to show you an illustration of a site where

17 we have got both atrazine and simazine detected. 

18 And you can see the dark blue triangles here

19 happen to be the simazine pattern. The magenta

20 circles are atrazine and the triangles are the

21 total chlorotriazines.

22 This actually shows that simazine is
202-234-4433

Neal R. Gross & Co., Inc.

Page 77

1 following the same pattern that we see with

2 atrazine but I think that is because it is also

3 being used on corn. In other areas of the

4 country where it has different uses, we may see

5 different timing.

6 We do have this data that we can test

7 that. We can start asking ourselves do we see a

8 different pattern with simazine than we do with

9 atrazine and what effect would that have on

10 there? So we do have that power.

11 Okay, so let's go back to where I was

12 hoping the discussion would kind of help us, and

13 I wanted to explain to the tox people why trying

14 to decide are we looking at one day short-term or

15 longer term and not knowing drives us buggy.

16 This chemograph shows the blue line is

17 your daily measurement at this site. This red

18 line shows that if we were looking at a four-day

19 average, this is what type of -- this is your

20 rolling four-day average concentrations. So you

21 see, there is still a pretty good influence of

22 the shorter day measurements in a four-day
202-234-4433

Neal R. Gross & Co., Inc.

Page 78

1 exposure.

2 I started working on the 90-day

3 exposure and I got this far and I probably didn't

4 realize okay, I needed to go back and get more

5 data and then decided I needed to sleep more than

6 I needed that.

7 But what I wanted to show is the

8 magnitude. This data point here and moving along

9 starts here, starts whenever you get your first

10 90 days averaged in. So you can see these

11 exposures are muted, potentially missing this is

12 not going to have as big an impact on a 90-day

13 exposure period as it is on a four-day exposure

14 period. So that is why knowing what our exposure

15 window is, really helps us in terms of how we

16 decide and how we interpret the data. So I

17 wanted to put that in to keep in mind, as you go

18 along.

19 Now if we were to take a look at that

20 seven-day sampling frequency that I showed it

21 seems like weeks ago but I think it was the

22 beginning of the week, this is the red line is
202-234-4433

Neal R. Gross & Co., Inc.

Page 79

1 the four-day rolling average profile you see

2 estimated with all the daily values. The blue

3 line here is that same four-day rolling average

4 profile estimated with the weekly sampling points

5 that you saw in that previous slide.

6 So this is kind of what we are looking

7 at. We see more of a difference, even on the

8 shorter term averages, depending on the sampling

9 frequency than we would otherwise. So this is

10 the context I want you to think about as we get

11 into these questions. And with that, I am not

12 going to throw any more slide at you.

13 SESSION CHAIR PORTIER: Dr. Akana?

14 DR. AKANA: I have a late thought for

15 you. In the HPA world -- Well, first of all, our

16 small group here decided that a one-day exposure,

17 an acute hit is probably going to be okay with

18 atrazine. But in my personal view in the HPA

19 world, a hit say the third cycle away, so you get

20 a hit on the first cycle and if you get a little

21 hit on the third cycle, we are verging into this

22 where you get an extra effect on that third
202-234-4433

Neal R. Gross & Co., Inc.

Page 80

1 cycle.

2 So now I am interested in little

3 clusters of little spikes. So up one day, down. 

4 And then say six days later there is another one

5 day little up and down spike. That, as I

6 understand it, would not be picked up by most

7 sampling of finished water because of the way the

8 water is processed. But if that actually reached

9 -- Well, in the lab you can do that.

10 It can be just as deleterious and

11 verging into an episodic repeated exposure, which

12 is one spike of chronic. Chronic is not just one

13 up-peak and sustained of say atrazine. Little

14 spikes can be bad, too.

15 Now I am wondering if in your work you

16 can detect clusters of little spikes.

17 MR. THURMAN: Boy, that just made

18 things --

19 If you sample frequent enough, yes. 

20 But that gets back to how frequent do you need to

21 sample. And if I go back, if you are looking at

22 something like this, even a creation -- but you
202-234-4433

Neal R. Gross & Co., Inc.

Page 81

1 can start seeing that. If were say, and I am

2 going to use 20 just as an example and please

3 don't take that as this is what we are looking

4 at, but you can start to estimate how often do we

5 encroach that. And once again, how well we can

6 estimate that. The frequency above a certain

7 threshold value or a certain averaging period

8 threshold value, we can estimate that. It

9 depends on how frequently we have to sample to do

10 that. So, it can be done.

11 SESSION CHAIR PORTIER: I wanted to

12 make the point, too, you know, the spikes can be

13 artificially generated by lifestyle. So suppose

14 the background your tap water is a 20. On day

15 one half of your two-liter is from tap water. On

16 day two you are drinking bottled water. On day

17 three you are drinking tap water again. You have

18 just created that double pulse that you are

19 looking at. And so from EPA's point of view,

20 there is a lifestyle that has got to be

21 integrated into this and how people get their

22 two-liter dose every day, it is not always from
202-234-4433

Neal R. Gross & Co., Inc.

Page 82

1 this.

2 Dr. Heeringa?

3 CHAIR HEERINGA: Steve Heeringa. For

4 Nelson, you look at this double peak pattern

5 which we have seen, particularly in some of the

6 agricultural drainages. Is this typically the

7 result of pre-emergent and post-emergent

8 application of atrazine or is it the result of

9 random rate of all events following application?

10 Because there is information. If it

11 is pre-emergent/post-emergent, you pretty much

12 know planning time and application times, in

13 terms of intensive sampling. So how much do we

14 know about that?

15 MR. THURMAN: I may give you a little

16 more complex answer. I mean, it could very well

17 be the result of when the farmer is getting out

18 of the field in relation to the rainy period. 

19 And it is a conjecture because I don't have the

20 rainfall data at this point. It could be that

21 you had some initial planning going on. And so

22 you had some initial atrazine applications for
202-234-4433

Neal R. Gross & Co., Inc.

Page 83

1 the short -- then you get a rainy period. And so

2 the farmer could not get back out in the field

3 until later. And then you see the second one

4 coming later.

5 It could be there is a difference in

6 intensities of the rainfall events that you see

7 here. This far apart suggests that you had two

8 different application periods. And it could be

9 for any number of reasons. A lot of times what

10 we have seen is that it is weather-related in

11 terms of how much can the farmer get out before

12 the rains and when do the fields dry up enough so

13 that they can get back out again?

14 SESSION CHAIR PORTIER: Dr. Krishnan?

15 DR. KRISHNAN: I just want to add to

16 the discussion that the spikes and their

17 relationship to the hits, I mean something that

18 is in-between is the internal dose measure. And

19 so these spikes may not necessarily translate to

20 the spikes of the appropriate dose measure in the

21 body that drives the sequence of events.

22 Given the rate of absorption and so
202-234-4433

Neal R. Gross & Co., Inc.

Page 84

1 forth, the appropriate dose surrogate tends more

2 like the total chloro products. So I think one

3 of the focus would have to be, well essentially

4 because you can relate those effects more closely

5 to those internal dose measure, rather than to

6 the external spikes and the internal dose measure

7 would be more on the early end of the curve in

8 rating these.

9 And one of the focus would have to be

10 considering the integration of the drinking water

11 input with the PBPK model so that some of the

12 dose metric profiles can be evaluated in the

13 context of the detail evaluation as they go

14 forward. I think that would certainly add to the

15 science basis of qualities evaluations.

16 SESSION CHAIR PORTIER: Excuse me. I

17 have 10:17. We will take a 15 minute break and

18 then we will see if my gamble of increasing

19 understanding reduced the uncertainty in the

20 discussion time.

21 We will return at 10:35.

22 (Whereupon, the foregoing proceeding
202-234-4433

Neal R. Gross & Co., Inc.

Page 85

1 went off the record at 10:18 a.m. and

2 resumed at 10:38 a.m.)

3 SESSION CHAIR PORTIER: Mr. Thurman,

4 I guess you are reading the questions. Hey, we

5 are not on question 1.9 anymore. Okay, Dr.

6 O'Byrne?

7 DR. O'BYRNE: Could I just ask one

8 very brief question? I was very surprised at

9 your graph that you plucked out last night from

10 wherever that the level of atrazine in the

11 finished drinking water showed the same profile

12 as raw, if I understood it properly. And you

13 sort of used that as evidence to criticize

14 Syngenta for focusing on finished water. I may

15 be misinterpreting this.

16 I am absolutely amazed that you see

17 the same profile because it depends on where that

18 finished water came from. I mean, was it a big

19 pool? A small pool? Because I would thought

20 there would have been a massive dilution of

21 anything.

22 MR. THURMAN: And I am not sure I
202-234-4433

Neal R. Gross & Co., Inc.

Page 86

1 would call it a criticism. I think it is a

2 distinction between the way we look at things and

3 the way we interpret things. We have some

4 smaller community water systems that don't have

5 much of a holding period where you do see the

6 atrazine moving through and unless you have

7 sufficient carbon filtration, the atrazine will

8 continue to move through the system.

9 Some of the smaller systems I think

10 you heard Alan Roberson talk about that in the

11 public comments, they do try to treat. And some

12 of the systems we do see where the treatment is

13 knocking down the atrazine levels, sometimes

14 because carbon filtration is expensive, they

15 don't necessarily have it all year round. They

16 try to time it as best as they can. Sometimes

17 they get it, sometimes they miss.

18 Sometimes you see in some of the

19 systems where for the most part it is down but

20 then you will a spike coming through. But there

21 are a few systems that we are looking at that do

22 have a similar profile to what we see.
202-234-4433

Neal R. Gross & Co., Inc.

Page 87

1 And the point we are making is we

2 don't necessarily want to wholesale right off

3 what we think are some valuable robust datasets

4 because they are not finished water sets. It is

5 what Bob Gilliom is talking about, how you

6 defined the truth. And if we have the robust

7 data sets that have sampling patterns and shapes

8 and patterns that are similar to what we are

9 seeing in these community water systems, we think

10 those are still very valuable. In fact, more

11 valuable to develop our statistical analysis and

12 the approach we take to evaluating the monitoring

13 strategies because they do capture that frequency

14 that is smoothed out when you have weekly

15 sampling or less frequent sampling.

16 So that was the point I wanted to try

17 to make in terms of that distinction. It is not

18 meant as a criticism but I think the reason we

19 are not writing off well water samples just

20 because they are not finished drinking water.

21 SESSION CHAIR PORTIER: This is Ken

22 Portier. It probably also reflects their
202-234-4433

Neal R. Gross & Co., Inc.

Page 88

1 conservative nature. Right? So they look for

2 worst-case scenarios and they do their risk

3 assessment from a worst-case scenario, figuring

4 that that is going to be protective for everybody

5 else who are in better case scenarios from a

6 public health point of view.

7 Question 2.1.

8 MR. THURMAN: Okay. In conjunction

9 with the toxicological review presented in the

10 issue paper, the Agency has also discussed

11 methods for re-evaluating the sampling frequency

12 that is necessary for determining, with

13 confidence, concentrations of the pesticide in

14 water that sources drinking water. These have

15 included different methods for estimating

16 pesticide concentrations between known sampling

17 events and examining the performance of different

18 sampling strategies for averaging periods of

19 different durations. The Agency seeks feedback

20 from the Panel with regard to how the uncertainty

21 and variability in both the monitoring data and

22 in the toxicity data (i.e., the point of
202-234-4433

Neal R. Gross & Co., Inc.

Page 89

1 departure) can be integrated to characterize and

2 to interpret the potential significance of

3 atrazine concentrations in drinking water.

4 Given the nature of the temporal

5 patterns of pesticide occurrence in surface

6 waters described in Section 5.2 of the issue

7 paper, including serial correlations from day to

8 day, periodicity in elevated concentrations

9 within seasons and from year to year, detections

10 below quantitation data, and uncertainty in the

11 shape of the pesticide distributions in surface

12 waters, what statistical approaches should the

13 Agency consider in determining confidence bounds

14 on exposure estimates from monitoring data?

15 Please comment on how the approach may vary

16 depending on the duration of concern.

17 SESSION CHAIR PORTIER: Dr. Young?

18 DR. YOUNG: Well, considering

19 confidence bounds on exposure estimates for

20 monitoring data, a key consideration is what is

21 being estimated. Now that sounds pretty simple

22 but here that seems to be quite a challenge.
202-234-4433

Neal R. Gross & Co., Inc.

Page 90

1 So what you do differs on whether we

2 want to estimate a specific quantile given daily

3 data, or rolling average data, or if we want to

4 estimate the peak or whatever we want to do.

5 So, I want to put that out there first

6 because what we suggest will differ depending

7 upon what the final decision is.

8 So, if say I want to -- spikes seem to

9 be important. And if I want to estimate a

10 particular quantile, say the 99.99 quantile, the

11 99th percentile over the course of a period of

12 time, and let's suppose that is my goal for right

13 now. Okay? And if I want to say -- I think

14 another question that has to come up with this

15 particular atrazine, with this particular

16 application, is whether you want that for the

17 whole year or for a concentrated period of time

18 in which the concern is greatest. In other words

19 from prior to the start of planting to harvest or

20 sometime in that time frame. Because you know

21 that in most years, it is not as great of a

22 concern.
202-234-4433

Neal R. Gross & Co., Inc.

Page 91

1 The easiest way to do such a sample

2 set in those confidence bounds is to know the

3 distribution. And then you take advantage of the

4 properties of the distribution to set confidence

5 intervals.

6 The problem is I don't think anyone

7 here is comfortable with knowing what that

8 distribution of values is. And when you do not

9 know that, then you are pretty much moving toward

10 non-parametric approaches. And I see that both

11 in that EPA does and Syngenta does and I think

12 that is the right approach.

13 Now, in order to actually use non14 parametric approaches, one of the big things that

15 comes up is sample size. And basically, you have

16 to have enough data if you want to estimate those

17 extreme quantiles with any precision.

18 So if you want a 0.95 quantile, you

19 need at least 20 observations, at least. The

20 standard there may be unacceptably large for that

21 sample size but that is a minimum and if you want

22 a 0.99, you need a hundred. Okay? That is just
202-234-4433

Neal R. Gross & Co., Inc.

Page 92

1 the reality of what you have to have in order to

2 do a decent job.

3 And I saw a table of the sample sizes

4 in one of the Syngenta documents somewhere here. 

5 And I put a similar one but I think -- and also

6 it is in the ILSI report that you referenced in

7 the Appendix A. I thought the Appendix A in that

8 report did a very nice job of outlining options

9 and certainly those could provide great guidance.

10 In that report, some simulation was

11 done and found that based on the same thing we

12 are seeing is interpolating among values and

13 dense datasets. And what they found was that

14 there was a tendency to overestimate the

15 quantiles, the extreme quantiles. So that is a

16 little conservative. And I believe the reason

17 that is the case is that inherent in these

18 methods is the assumption of a random sample and

19 the presence or correlation results in the

20 effective sample size being smaller than that

21 taken. And so we are actually pushing it out a

22 bit more.
202-234-4433

Neal R. Gross & Co., Inc.

Page 93

1 And so I think work, that is a hard

2 problem, but I think work on how to actually

3 figure out what the effective sample size is and

4 refine those methods may be something you are

5 interested in but it is somewhat comforting from

6 the public standpoint that it is a conservative

7 estimate at the present time.

8 If we move to these rolling averages,

9 then the problem is further complicated because

10 normally we think in an independent sample, the

11 variance goes down. You just divide by the

12 sample size as far as the mean goes but you have

13 this positive correlation, which tends to keep

14 that variance inflated a bit.

15 And so that would need to be

16 considered in these methods. I have also thought

17 a little bit about using an extreme value theory

18 in this setting and especially on the rolling

19 averages because as soon as you begin averaging,

20 then you can begin to appeal to perhaps the

21 central limit theorem and if the data aren't too

22 skewed, then maybe you can begin to use some of
202-234-4433

Neal R. Gross & Co., Inc.

Page 94

1 those ideas to reduce sample size. I haven't

2 fully explored that but I think it is something

3 worth looking into.

4 SESSION CHAIR PORTIER: Thank you. 

5 Dr. Coupe is next.

6 DR. COUPE: Thank you. I think first

7 off I need to clear up something. This corner of

8 the table has been referred to as hydrologists or

9 hydrology. There is actually only two

10 hydrologists and two statisticians.

11 (Laughter.)

12 SESSION CHAIR PORTIER: That is an

13 important point, yes.

14 DR. COUPE: Yes, I spent an hour being

15 told what the difference between Bayesian and a

16 frequentist is. I still have no idea.

17 So my remarks are going to be, I am

18 not going to touch too much on the statistics but

19 mostly on the observations on the hydrology of

20 what we are talking about. 

21 To begin so there is an issue on what

22 data should be used to determine exposure
202-234-4433

Neal R. Gross & Co., Inc.

Page 95

1 assessments. There is data on atrazine

2 concentrations from the intake, the stream or

3 reservoir side, and then there is finished water

4 data. the U.S. EPA has decided to use the intake

5 values for assessment of human exposure.

6 The gentlemen from CropLife and

7 Syngenta suggested that the more appropriate data

8 to use to evaluate human exposure is the finished

9 water values. I can see both sides of this

10 argument as there can be considerable difference

11 between intake concentrations and the

12 concentrations in the finished water. However,

13 there are a number of studies showing that a

14 treatment plant effectiveness in removing

15 atrazine is variable depending upon many factors,

16 which includes the type of water, the organic

17 matter content or the pH, the type of treatment,

18 how well trained the treatment plant operators

19 are, the maintenance of the treatment. In some

20 cases atrazine survives the treatment process

21 relatively unaffected. Given this, I think it is

22 appropriate to use the intake values to estimate
202-234-4433

Neal R. Gross & Co., Inc.

Page 96

1 the exposure to humans with the understanding

2 that this is a conservative estimate and that the

3 actual exposure may be less.

4 Given that, I do think Syngenta made

5 a valid point in bringing up the fact that the

6 community water system, that data that we are

7 shown by the U.S. EPA, would not have been used

8 for drinking water, as that plant selectively

9 pumps from the stream into a holding pond and

10 from there into another larger holding pond.

11 And I think this model is more common

12 in the small community water systems that take

13 directly from a stream. They don't have intakes

14 on the reservoir. And in this case, the

15 appropriate place to sample the water used by the

16 plant to evaluate human exposure, would have been

17 from the intake from the larger pond. The data

18 as shown gives a misleading impression of high

19 exposure from this community water system.

20 There are uses for these data, of

21 course, such as ecological exposure but it seems

22 inappropriate for the question being asked here.
202-234-4433

Neal R. Gross & Co., Inc.

Page 97

1 The duration of concern makes a big

2 difference to the sampling strategy. If the

3 duration of the concern is very short, that

4 intensive sampling during expected high

5 concentration period is probably the only real

6 answer. But as the duration of concern

7 increases, the sampling intervals can be eased

8 and if the duration gets long enough, you can

9 actually estimate it from models such as WARP.

10 And in general, it seems that most

11 sites in the AMP program run by Syngenta, the

12 variability and the amplitude of the data are not

13 that great. If you look at page 94 where it

14 states in the White Paper from the U.S. EPA, it

15 states that 90 to 96 percent of the data are less

16 than three parts per billion. And Syngenta shows

17 pretty much the same thing in their response on

18 pages seven and eight.

19 Now this is good news in the sense,

20 especially when you consider these data are

21 biased with more frequent sampling during

22 expected periods of high concentration. In fact,
202-234-4433

Neal R. Gross & Co., Inc.

Page 98

1 the AMP data suggests that there is only a subset

2 of some 27 or so of highly vulnerable community

3 water systems.

4 And to my mind, these sites probably

5 need to be treated separately and more carefully. 

6 For the other sites, the 120 or so, the

7 concentrations are relatively low and don't

8 exceed the MCL often. But I suspect that is

9 because of the storage dynamics of these

10 reservoirs and these community water systems

11 probably have low --

12 Okay, what I wanted to say was that

13 these community water systems probably have

14 consistently low levels of atrazine in their

15 source water throughout the year because of

16 storage dynamics. And realistically, probably

17 more people are exposed to low concentrations of

18 atrazine for longer periods of time than they are

19 higher concentrations at shorter periods in time.

20 I thought of one other things when we

21 were talking, I think Dr. Akana brought it up and

22 a couple of other people were talking about the
202-234-4433

Neal R. Gross & Co., Inc.

Page 99

1 spikes, and so far mostly what we have seen in

2 our chemographs are, I suspect just very small

3 sites. And so they show a very sharp one or two

4 peaks.

5 If you move to larger sites, you can

6 have multiple peaks. Especially if you take a

7 look at the Missouri data, you can see multiple

8 peaks during drinking water season kind of

9 depending on whether it is raining in Iowa or it

10 is raining in Missouri, or raining in Nebraska. 

11 So you can have, in one community system, you can

12 have a number of small spikes but they can

13 consistently come through your system.

14 Thank you.

15 SESSION CHAIR PORTIER: Thank you. 

16 Dr. Gilliom?

17 DR. GILLIOM: I'll just supplement,

18 rather than repeat some of what has been covered. 

19 The first part of the question is related to what

20 approaches should be used to consider in

21 determining confidence bounds on exposure for

22 monitoring data. And so far we have talked
202-234-4433

Neal R. Gross & Co., Inc.

Page 100

1 mostly about the use of site-specific data to

2 estimate those confidence bounds. And I just

3 want to make the point that there is also a range

4 of more indirect approaches using inference from

5 existing data. And there is basically probably

6 two categories of that. One you have heard a lot

7 about in relation to the sampling experiments of

8 taking these highly sampled data sets and driving

9 relationships that show how confidently we can

10 estimate a particular value with a given sampling

11 frequency. 

12 So those are kind of categorical

13 things. The Crawford 2004 paper and the ILSI

14 appendix are good examples of that, which have

15 then been extended further by Syngenta and EPA

16 and they have plans to do even more.

17 So those give you a good initial

18 estimate in many cases of what kind of confidence

19 bounds to expect on any particular concentration

20 statistic. And you can re-do it for other ones

21 if you need to so it gives you an idea of how

22 much you have to worry about it.
202-234-4433

Neal R. Gross & Co., Inc.

Page 101

1 The second category which we have also

2 just had mentioned here is the WARP model type

3 approach, which is really just a multiple

4 regression approach to relate basing

5 characteristics to concentrations and create

6 predictions. That is another indirect way to

7 make, based on the data we have already

8 accumulated from many sites, make estimates of

9 both the concentration statistic and the

10 confidence bounds for unmonitored sites.

11 And I bring those type of methods up

12 mainly, and particularly the second one on the

13 regression models is that a big part of the

14 approach here in the end is going to be to

15 identify the relatively small proportion of sites

16 that need the most intense energy applied to them

17 for the high sampling problems and so forth.

18 And this gives us a direct and

19 quantitative way of getting at that, both what

20 the expected concentration statistic will be and

21 what the expected confidence bounds on that will

22 be, which can now be done.
202-234-4433

Neal R. Gross & Co., Inc.

Page 102

1 And then the second part of the

2 question is related to the duration of concern

3 and how the approach might vary. And I guess I

4 just want to reiterate the point that I think has

5 already been made a couple of times and it is

6 probably obvious at this point but the shorter

7 the duration of the concentration statistic, the

8 more intense data we need to get truth.

9 And it seems simple but it just

10 reiterates how much the answer that we come up

11 with on monitoring design is driven by how you

12 define very specifically the concentration

13 objective. So the Agency in the end is the one

14 who has to do that from what you all say but we

15 need to know the amount, the time frame, and the

16 confidence bounds required to meet the

17 requirements of the Agency and then you can

18 design around that.

19 SESSION CHAIR PORTIER: This is Ken

20 Portier. Just to clarify, when you say intense

21 data, you mean temporally dense measurement.

22 DR. GILLIOM: I will say in a more
202-234-4433

Neal R. Gross & Co., Inc.

Page 103

1 general way, temporally intense in relation to

2 the sought concentration objective.

3 SESSION CHAIR PORTIER: Dr. Lee?

4 DR. LEE: I don't have too much to add

5 to what has already been said. I just want to

6 make two small points. One is that if we are

7 talking about confidence bands, I think it is

8 important to take into account some of the

9 sources of variability that we are not looking at

10 right now like measurement error.

11 You know, if you go and measure the

12 stream, if two different people do it, you are

13 going to get somewhat different answers probably. 

14 And that sort of variability needs to be taken

15 into account, if you want to make a precise

16 confidence statement. And that doesn't

17 necessarily have to be estimated on a stream-by18 stream basis. I think we can learn a lot about

19 that sort of measurement globally and just apply

20 an estimate there. But that sort of variability

21 should definitely be taken into account.

22 And the second point I want to make is
202-234-4433

Neal R. Gross & Co., Inc.

Page 104

1 that sort of follow on to some of what has

2 already been said, that the way you are going to

3 estimate the confidence band really depends on

4 what sort of model you are using to fill in the

5 gaps. Assuming you are not doing daily sampling,

6 you need some sort of model to say how we are

7 going to fill in the gaps and then the type of

8 confidence band is going to follow from that.

9 So I do want to disagree somewhat with

10 Syngenta's statement that we don't need modeling. 

11 It is a model of some sort, even if we are doing

12 linear interpolation. That is a model. And so

13 either we need to sample daily or we need to use

14 some sort of model to fill in in-between. And

15 most likely, it is not a need to sample daily

16 because we can fill in in-between. But it

17 requires some model and that is going to affect

18 how we are going to compute confidence bounds.

19 SESSION CHAIR PORTIER: Thank you. We

20 will open it up to the rest of the panel. 

21 Comments? Dr. Hayton?

22 DR. HAYTON: Do we have any idea of
202-234-4433

Neal R. Gross & Co., Inc.

Page 105

1 how big a daily dose is the threshold of concern? 

2 Because I am showing these chemographs showing a

3 hundred parts per billion. That is a hundred

4 micrograms and in two liters, 200 microgram daily

5 dose. Would that ring any bells in the BPA/BPH

6 signaling network? And to me, if it doesn't, do

7 we need to catch those peaks?

8 SESSION CHAIR PORTIER: Dr. O'Bryne?

9 DR. O'BYRNE: I don't think we have

10 any evidence at all that the small amounts have

11 any effect. They are orders of magnitude

12 greater.

13 SESSION CHAIR PORTIER: And I think

14 that has been said a couple of times before. 

15 Maybe that is a subheading to our final report.

16 Any additional comments? Yes, Dr.

17 Heeringa.

18 CHAIR HEERINGA: Steve Heeringa. The

19 question may be for EPA. Thinking about the

20 simulations that you have done and that Syngenta

21 did and we get this sort of rough projection from

22 a simulation done on some periodic sampling to at
202-234-4433

Neal R. Gross & Co., Inc.

Page 106

1 least truth as defined in an empirical data set

2 and we get factors like 1.5 or 1.24. How would,

3 in the context of where you set these limits, we

4 have uncertainty factors, is that sort of

5 uncertainty built into what you would think of as

6 your typical uncertainty factors or is this

7 something additional?

8 MR. THURMAN: Okay, we have wrestled

9 with how you do with uncertainty. I think you

10 have heard, for instance, that atrazine you have

11 heard the value is 12.5 and 37.5 used. When we

12 are looking at the weekly monitoring of these

13 community water systems, we have been looking at

14 how is that, because it is more intensive

15 sampling, we have been comparing that to a 37.5,

16 with less intensive sampling because there is

17 uncertainty in that we have been looking at the

18 12.5. So there is kind of a 3X that has been

19 used in that regard.

20 So it is possible that we might hold

21 that monitoring uncertainty, if you will, in as

22 part of the overall uncertainty or safety factors
202-234-4433

Neal R. Gross & Co., Inc.

Page 107

1 that we look at.

2 DR. LOWIT: Anna Lowit. Just to add

3 to that, to sort of talk about the source of

4 where that comes from, as atrazine is regulated

5 under the food quality protection act, the FQPA

6 has a provision that requires the application of

7 the 10X. And that 10X accounts for both hazard

8 and exposure and that 10X value can be reduced,

9 as Nelson was talking about, the three, based on

10 information that the Agency looks like and the

11 science supports doing that. It can also be

12 increased. 

13 So I just wanted to make sure that was

14 explicit of where that would be derived from.

15 SESSION CHAIR PORTIER: Dr. Reed?

16 MR. THURMAN: And by the way, this is

17 why you need hazard and exposure folks working

18 together on this. 

19 DR. REED: I just want to put in a

20 caveat about the comparison, the water level and

21 then two liters per day and compared to the

22 animal study, there is a lot of "uncertainties"
202-234-4433

Neal R. Gross & Co., Inc.

Page 108

1 because you have the interspecies variability

2 considered and also inter-individual

3 consideration. And so a straight comparison

4 probably is not very productive.

5 SESSION CHAIR PORTIER: Any additional

6 comments? Okay, I think we have gotten some good

7 comments, Dr. Young and the four. Dr. Heeringa?

8 CHAIR HEERINGA: Just one additional

9 comment. You know, I support what Dr. Young

10 indicated, too, and I think others that Dr.

11 Gilliom with regard to sort of differential

12 sampling over the years. I mean, if we think

13 about it simply as a sampling problem from a

14 frequentist's perspective, we would essentially

15 allocate sample to these intervals in proportion

16 to the standard deviation, the measures within

17 the intervals. And this can be adaptive, too. 

18 In other words, you could start with a

19 periodicity and depending on the water system and

20 the information that is gleaned over time. So I

21 think rather than trying to hit a home run the

22 first time, try to get into the ballpark and then
202-234-4433

Neal R. Gross & Co., Inc.

Page 109

1 think about refinement as you collect additional

2 data.

3 SESSION CHAIR PORTIER: And this kind

4 of was a question that came up as I was reading

5 this. This is Ken Portier.

6 When these community water systems

7 take these samples and process them, what is the

8 lag time between the sample and the number

9 actually being received back to the process, the

10 CWS manager? Is that hours, days or months?

11 DR. COUPE: All of the above.

12 SESSION CHAIR PORTIER: Really? What

13 is the median on that? Is that weeks?

14 (Laughter.)

15 SESSION CHAIR PORTIER: I didn't want

16 to max and min. I am more of a median. Is that

17 like weeks or days?

18 DR. COUPE: Oh, it is probably on the

19 order of days but there are some very large

20 systems that have large holding times and there

21 are very small systems that --

22 SESSION CHAIR PORTIER: Shift it off. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 110

1 Excuse me. What was that? Weeks.

2 Because you know, -- yes, ma'am? 

3 Please identify yourself.

4 MS. BISCOE: Melanie Biscoe. I am the

5 CRM or Chemical Review Manager for atrazine.

6 I think Syngenta has been talking 

7 amongst themselves a little bit back here, and we

8 actually discussed this earlier this year. So

9 about ten days in the EPA, Syngenta monitoring,

10 CWS monitoring program, that you actually get the

11 numbers back.

12 SESSION CHAIR PORTIER: So this has an

13 impact for some of the things that statisticians

14 and samplers would like to say which is well, you

15 could do adaptive sampling. Once you start to

16 see it go up, you increase the sampling rate. 

17 But if there is a ten-day lag and many of these

18 windows are 20 to 30 days wide, that methodology

19 is probably not feasible here. And then you are

20 stuck with kind of choosing a window of time and

21 increasing your sampling during that window of

22 expected peaks and decreasing it.
202-234-4433

Neal R. Gross & Co., Inc.

Page 111

1 So the current methodology is feasible

2 within the practical limits of the sampling and

3 turn around time.

4 Now of course if you had a dip stick

5 methodology and a color code that said high, you

6 could use something like that to increase your

7 sampling, even to within a day. I mean, you

8 know, if it really was moving fast, you could be

9 picking samples hourly.

10 Dr. Heeringa?

11 CHAIR HEERINGA: Steve Heeringa. Just

12 to be clear, when I was talking about being

13 adaptive, I was thinking the feedback might be

14 annual. But as usual, Dr. Portier has a shorter

15 cycle on these things than I do.

16 SESSION CHAIR PORTIER: So you are

17 talking about using last year's profile to tell -

18 - The unfortunate problem with that, of course,

19 is climate.

20 So you know, a lot of this does seem

21 to have a relationship to rainfall. And so while

22 there is a persistence in climate, it is not
202-234-4433

Neal R. Gross & Co., Inc.

Page 112

1 strong enough that you can really use that.

2 CHAIR HEERINGA: But I would say that

3 there are multiple variables in this work model,

4 including how much crops planted, the absorption

5 of the system, flux through the system. That

6 probably has more temporal permanence than the

7 climate. We are not going to predict the

8 rainfall but we can predict roughly when people

9 are going to plant crops, when the atrazine is

10 going to go on the field and roughly how systems

11 are going to respond in the cross different

12 rainfall events.

13 So I think we are averaging over and

14 we don't want to over adapt. But I am just

15 saying that if you were in a system that clearly

16 showed much more variability than the sort of

17 averages on which you are basing your sampling

18 plans, then there might be an argument over time

19 to intensify sampling for that system, until you

20 were confident that you were getting what you

21 wanted for that system.

22 SESSION CHAIR PORTIER: This is Ken
202-234-4433

Neal R. Gross & Co., Inc.

Page 113

1 Portier. And I think we are only repeating

2 recommendations we made at the SAP a year ago

3 when we looked at the ecological issues related

4 to environmental sampling. So you can go read

5 those.

6 Dr. Gilliom.

7 DR. GILLIOM: Just, one comment here

8 since it is not really brought up in the charge

9 questions later, and it is on this issue of

10 whether there is some short-term adaptive

11 approaches that could be taken. And I don't

12 think it is worth getting heavily into this at

13 this point.

14 But if it was to turn out that there

15 is a really short term concentration objective

16 that is extreme, like a one or two-day type of

17 thing, then it really would be possible to use a

18 quick screening process, like an amino assay test

19 for atrazine triazines and just do it in the

20 water plant and make decisions.

21 So there are tools. They can trigger

22 a laboratory analysis and they are well-known and
202-234-4433

Neal R. Gross & Co., Inc.

Page 114

1 well-characterized. So if that becomes a big

2 issue, I think it is a discussion item that

3 should be thought through.

4 SESSION CHAIR PORTIER: Make sure you

5 add that to the report.

6 Okay, I think we have stretched this

7 one out as long as we can. Why don't we move to

8 question 2.2?

9 MR. THURMAN: I would be glad to. The

10 first two simulation methods presented in Section

11 5.5 are applicable to the specific data sets they

12 describe, although some generalities regarding

13 shape patterns appear to exist. Given this

14 information, please comment on the strengths and

15 weaknesses of the approaches and on the practical

16 merits of pursuing them or some other numerical

17 approach with a larger set of higher

18 concentration systems. Please comment on how the

19 methods for determining confidence bounds might

20 apply given these considerations.

21 SESSION CHAIR PORTIER: Dr. Gilliom.

22 DR. GILLIOM: Okay so some of this has
202-234-4433

Neal R. Gross & Co., Inc.

Page 115

1 been talked about a lot. So I am going to kind

2 of abstract from my comments.

3 Both the methods that are referred to

4 in here follow the same concept we have talked

5 about this morning. And generally it is the

6 concept described in the Crawford paper 2004 in

7 which the actual data for selected sites are

8 interpolated between samples, and then treated as

9 truth, and then sampling experiments are done

10 from them.

11 So I think as I said before, probably

12 my comment would sum up that the approach makes

13 sense if the initial actual data are sufficient

14 to simulate reality for the problem at hand.

15 And so in this specific example, just

16 to pick on the two ones that are in here as

17 examples, is that they are not, they are examples

18 of simulated truth from real data that are not

19 adequate to address a short-term concentration

20 objective. So these were examples generated from

21 30 to 40 samples a year and then they were

22 interpolated to create a simulated truth, and
202-234-4433

Neal R. Gross & Co., Inc.

Page 116

1 then they were subsampled to test how they did

2 for doing shorter term concentrations. It is

3 like a seven-day or four-day average or whatever.

4 So and I think everybody realizes that

5 but they are actually good examples of what to

6 watch out for. The analytical process is fine

7 but the specific application is not appropriate

8 for short-term.

9 And so I think the general point I

10 would leave it with is that yes, the basic

11 approach is good. We have to make sure that once

12 the concentration objectives are defined very

13 precisely and probably more precisely than

14 anybody is going to feel comfortable with on the

15 biology end but the Agency is still going to have

16 to do it is that then we design a sampling

17 analysis process that fits that.

18 And I think I will just leave that

19 there.

20 SESSION CHAIR PORTIER: Dr. Coupe?

21 DR. COUPE: I don't think I have too

22 much to offer to Dr. Gilliom. I just say there
202-234-4433

Neal R. Gross & Co., Inc.

Page 117

1 isn't a great method for extrapolating these

2 data. You have what you have and if you need to

3 find a resolution then you just need to collect

4 more, collect measurements.

5 SESSION CHAIR PORTIER: Dr. Lee?

6 DR. LEE: I don't think I really have

7 anything to add to this one. I will have more to

8 say probably later about modeling and bringing it

9 in but I will hold that to number four.

10 SESSION CHAIR PORTIER: Dr. Young?

11 DR. YOUNG: This is one that probably

12 from your view, unfortunately, I feel strongly

13 about.

14 The real question that is not answered

15 that really has to be answered before we know how

16 to simulate is how important are the peaks and

17 how long do the peaks last? And we don't really

18 have that information right now. But there is

19 one thing that I think needs to be made really

20 clear and I think we have hit around it several

21 times but it so important I want to hit it again.

22 If one wants to draw inference at the
202-234-4433

Neal R. Gross & Co., Inc.

Page 118

1 daily level, based solely on the data collected,

2 sampling must occur at least daily. If one wants

3 four-day rolling averages, you can't have a

4 rolling average without a least two values. So

5 the minimum would be two in those four days and

6 that may not be enough.

7 Simulations, models or any other

8 approach that suggests otherwise is making some

9 strong assumptions about what happens at the

10 finer timescale.

11 Now, if a good understanding of the

12 systems exists, then one may be able to model the

13 results, in which case sampling could be

14 confirmatory. But this requires knowledge of the

15 system and sufficient supporting data to

16 construct such a model for each side, something

17 that is not present here, at least not yet.

18 It does seem to be reasonable to

19 concentrate sampling effort during the time of

20 exposure. However, as Bob has noted several

21 times, the sampling scheme developed for atrazine

22 may not be applicable to other contaminants.
202-234-4433

Neal R. Gross & Co., Inc.

Page 119

1 The thing that bothers me about all

2 the simulations that we have seen here is that

3 they are smooth versions of reality and that is

4 true even for the daily data that we have. There

5 is no accounting for measurement error,

6 variability within the day, or all the other

7 things that happen when you are out in the field. 

8 And anyone who has been there knows how bad that

9 can be.

10 It would seem to me that a more

11 reasonable approach for simulations is to try to

12 bring some modeling information such as the WARP

13 to generate chemographs with typical behavior and

14 then to bring the variability associated with

15 that modeling process to bare. So you would have

16 a realization of the chemograph followed by an

17 analysis in that realizations would be replicated

18 so you would capture all of the variability in

19 the system. You wouldn't just be hinging on one

20 particular realization of the chemograph. 

21 Because even if it is real, it is dead and gone.

22 That is not about what is happening in
202-234-4433

Neal R. Gross & Co., Inc.

Page 120

1 the future and I think we need to capture that

2 variability in the simulations because I think as

3 a consequence of doing it the way we have been

4 doing it, it makes everything look better than it

5 should and that is a little scary.

6 So that is, I think, the primary point

7 I want to make.

8 SESSION CHAIR PORTIER: Additional

9 comments?

10 This is Ken Portier. When I looked at

11 this, you know, to me the strength of the

12 methodology as Linda mentioned in the first

13 question is that is its non-parametric nature. 

14 So a lot of it you are going kind of back to

15 first principles of sampling. You are

16 simulating. So to me, that is the strength of the

17 process. 

18 The weakness is the starting data. It

19 is clear. You know, if the starting data is an

20 inadequate representation of what is really

21 happening that the water system, nothing that we

22 can do statistically is going to improve that
202-234-4433

Neal R. Gross & Co., Inc.

Page 121

1 starting data with any kind of first principle

2 statistical methodology. You are going to have

3 to go to modeling or more sampling. I mean, it

4 is kind of that simple.

5 Now, I personally like the WARP

6 approach because I know that there is other data

7 out there, climate data, soil data, that is

8 sampled even more regularly than these water

9 system data is sampled. And so through modeling

10 we can use the correlation relationship and be

11 able to impute a little more of what is going on.

12 And so I do like that kind of approach

13 because it just uses more data, uses the

14 information. And that includes kind of modeling

15 from the well water to the water that comes out

16 of the community water system. I haven't seen

17 any modeling talking about the effectiveness or

18 the methodology.

19 I mean, you talk about activated 

20 carbon systems but do those fail when the

21 concentrations reach a certain level. I mean,

22 you know, some of these systems work well when
202-234-4433

Neal R. Gross & Co., Inc.

Page 122

1 there is just a little bit coming in. But then

2 if the ability of the carbon to capture these

3 molecules gets overwhelmed by the concentration,

4 you could actually have a discontinuous peak kind

5 of event happening as well. So I think you need

6 that kind of. And that is probably a whole other

7 division in EPA that you have to go and talk to. 

8 Right?

9 Mr. Thurman?

10 MR. THURMAN: I do want to point out

11 on the drinking water treatment, we have actually

12 came to the SAP a number of years ago on drinking

13 water treatment and what we knew, what the state

14 of the published literature was on that. We have

15 continued to keep an eye out on that and update

16 that to the extent we do.

17 Generally when we are doing drinking

18 water exposure assessments that would be used in

19 dietary exposures, we addressed drinking water

20 treatment effects as separate at another end of

21 the process. So we didn't bring this to you

22 because we addressed them separately but it is a
202-234-4433

Neal R. Gross & Co., Inc.

Page 123

1 point well taken.

2 In fact, I looked at it, and you are

3 starting to look at sources of variability. You

4 throw in the drinking water treatments, you start

5 to expand your sources of variability that you

6 have to address. So we were trying to take a

7 simpler approach then we can layer that on. But

8 that is a point we will.

9 SESSION CHAIR PORTIER: I mean, I

10 recognize as a public health risk assessment

11 methodology, you can put limits on raw water

12 because as long as you can argue through that raw

13 water back to a consumption and a risk. 

14 So I mean I recognize that as long as

15 you have kind of got that continuum explained, we

16 can still go back and say well but we don't want

17 to see any input, raw waters above a certain

18 level because we know that gets translated for

19 all these processes, with all these uncertainties

20 into levels that are not safe. So, you know, it

21 is just that, and you guys know.

22 Any additional comments? Dr. Gilliom.
202-234-4433

Neal R. Gross & Co., Inc.

Page 124

1 DR. GILLIOM: Just to add to the

2 thought on the model application. I think we are

3 in a position to very effectively use the

4 available models built from existing data to

5 identify with known probability the sites that

6 need the attention for a specific objective. 

7 So there is a lot of points in there

8 to follow but once the objective is defined we

9 can, in other words, estimate which specific

10 sites. And then I think we have to remember in

11 evaluating all these methods, that and it is the

12 agency's direction, I think appropriately, that

13 once you are to that point, each individual

14 system on its own becomes the focus, not some

15 aggregate statistics for that group or anything. 

16 It is that system and those people that use that

17 system.

18 And at that point, the problem can be

19 further evaluated as needed, including adaptive

20 sampling or in some cases as brought up in

21 examples, differences between the intake screen

22 water, holding reservoirs and finished water. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 125

1 Because at this point, you are down to a very

2 small proportion of the community water supplies.

3 So it is a part of an overall

4 decision-making process that right now we are

5 only talking about he FIFRA add-on, that is what

6 I call it anyway, to regular compliance

7 monitoring. We haven't even, I mean, a

8 compliance monitoring is not involved yet. It is

9 still over there on quarterly sampling per year

10 but it could eventually be affected by this down

11 the road if the Office of Water chooses to do

12 that.

13 SESSION CHAIR PORTIER: Okay, I don't

14 see any additional questions. I think you got a

15 pretty good answer on that one.

16 MR. THURMAN: I think we got an

17 answer.

18 SESSION CHAIR PORTIER: Let's move to

19 2.3.

20 MR. THURMAN: As described in Section

21 5.4.2 of the issue paper, the Agency is

22 considering the use of a confidence interval or
202-234-4433

Neal R. Gross & Co., Inc.

Page 126

1 prediction interval approach to characterize the

2 uncertainty of exposure estimates derived from

3 monitoring data of varying sampling frequencies. 

4 Please comment on the strengths and weaknesses of

5 either placing confidence bounds on the rolling

6 average estimates and comparing to the upper

7 limit from monitoring against the level of

8 concern or, conversely, placing confidence bounds

9 on the LOC. 

10 And I apologize. That was written by

11 committee. I hope you will be glad to clarify

12 that.

13 SESSION CHAIR PORTIER: Dr. Coupe, you

14 are first up. It looks like there were four

15 people assigned to these four questions and then

16 Joe used the random number generator to assign

17 who was first.

18 DR. COUPE: When I saw the list of

19 questions come out without names attached, I

20 looked at 2.3 and said, "Dear God, I hope I don't

21 get that one."

22 (Laughter.)
202-234-4433

Neal R. Gross & Co., Inc.

Page 127

1 DR. COUPE: I think I understand the

2 second part of this. And I will paraphrase and

3 you correct me if I am wrong but you are talking

4 about in the second part of taking the

5 uncertainty in creating an LOC and then creating

6 a bound around that and then testing your data

7 against that.

8 MR. THURMAN: That is correct.

9 DR. COUPE: So, I just leave the

10 particulars of the statistics details to my

11 colleagues doing a 95 percent confidence interval

12 slapped over another 95 percent confidence

13 interval. But I don't really think you want to

14 do that, just given the variability of how an LOC

15 is derived and the different safety factors and

16 moving from animals to humans. I think you

17 probably wanted to stick with the LOC number.

18 But I am going to move into a little

19 more broader thing. I am talking about the

20 variability of using a statistical method to

21 determine future sampling scenarios. I know we

22 had to do that. But it assumes that the
202-234-4433

Neal R. Gross & Co., Inc.

Page 128

1 distribution, the constituent of instrument is

2 going to be the same from year to year and we

3 know it is really not true. 

4 There are many things that change from

5 year to year that might change, that change the

6 distribution of atrazine in the surface water of

7 our basin. So when we talk about year to year

8 variation in rainfall as well as long-term

9 climate change, the changes in crop types, for

10 example, you get a new cold weather variety that

11 can be planted earlier or later.

12 We have a lot money going into the

13 Mississippi River Basin now for BMPs and these

14 have the ability to change how the atrazine is

15 moved into the surface water. And you are going

16 to have changes in the weed population and the

17 weed infestation which have changed your

18 herbicide use.

19 But I just wanted to give you a little

20 brief recap of herbicides in water, just kind of

21 a 101 on the distribution of atrazine in surface

22 water. The distribution of atrazine in surface
202-234-4433

Neal R. Gross & Co., Inc.

Page 129

1 water can really be explained in two terms, a

2 source strength and a hydrology. A source

3 strength being the fact that for atrazine to be

4 detected in the surface water of a basin, it has

5 to be used in that basin. We have mentioned work

6 before but if you look in WARP, the single

7 largest parameter that explains variability and

8 explains more variability than all the rest of

9 the parameters combined was use. So, was

10 atrazine used in the basin? If it was, then some

11 small proportion of the applied amount ended up

12 in surface water.

13 And the other important factor in the

14 transport of atrazine is water. There must be

15 water to move atrazine into the stream of the

16 basin. For the most part, atrazine is also

17 transported atmospherically but the

18 concentrations here are moved off the landscape

19 by water.

20 So the EPA has stated how hard it is

21 to relate concentrations to the streams of flow

22 and I agree that it is because the concentration
202-234-4433

Neal R. Gross & Co., Inc.

Page 130

1 is made extremely high with a very small runoff

2 event soon after application and there may be a

3 very small concentration, a large runoff event a

4 few weeks later. But I submit that hydrology can

5 explain the presence or absence of atrazine in

6 streams.

7 Additionally in larger streams, the

8 variability of concentration may be due to the

9 timing of the arrival of water at the intakes

10 from various parts of the basin. Some may be

11 having more rainfall than others. Or maybe

12 planning was further along in one area of the

13 basin than it was in another one.

14 There is an awful lot of information

15 and expertise on the transport of agricultural

16 chemicals and I am convinced, as opposed to

17 determining if, atrazine poses a health risk. It

18 is not rocket science. A program can be designed

19 that will do what is needed to do if we know what

20 is needed. What you need to know and have to

21 know to design a program is what is being

22 discussed here as what is the endpoint. At what
202-234-4433

Neal R. Gross & Co., Inc.

Page 131

1 level does the atrazine concentration in water

2 matter to humans or the environment? It makes a

3 big difference if it is 25 ppb or 0.25 ppb.

4 That being said, here is a few more

5 random comments. I don't think a one size fits

6 all approach is necessary. Crawford showed in

7 his paper that the larger the basins, the less

8 data you need to reach precision goals. 

9 Conversely, of course, the smaller the basin, the

10 flashier it is and the more samples are needed to

11 ensure precision. Given that there is

12 significant history at these sites and many have

13 shown only small levels of atrazine, I believe

14 the sampling strategy could be tailored for

15 individual community water systems, probably less

16 samples on the larger systems and maybe more on

17 the smaller ones.

18 And earlier, there was a discussion

19 about daily sampling. I just want to iterate

20 that as my colleague Linda put it. If you need

21 fine resolution, and she said it several times

22 already, then you have to sample at a fine
202-234-4433

Neal R. Gross & Co., Inc.

Page 132

1 resolution. If you want a four-day average, I

2 thought it was a very good point, a four-day

3 average, you have to sample twice. Yes, you

4 can't really create, interpolate or model data if

5 that is what you need. I whole heartedly agree

6 with that and that goes for even sub-daily

7 sampling, which we haven't even mentioned about

8 because some of these basins may have variability

9 that relies on the sub-daily.

10 So and also just one last point as we

11 mentioned before. I would like to point out that

12 although there is a lot of very good work on the

13 toxicity of atrazine, from my own experience and

14 as the woman from NRDC stated, you never find

15 atrazine alone in a water sample. There is

16 usually a plethora of other constituents, some

17 herbicides, other kinds of materials that are in

18 there. You just never find atrazine alone.

19 So to study the toxicity of atrazine

20 alone kind of short-sheets safety. I think it is

21 a serious concern. Thank you.

22 SESSION CHAIR PORTIER: Dr. Gilliom?
202-234-4433

Neal R. Gross & Co., Inc.

Page 133

1 DR. GILLIOM: I guess on the response

2 to that specific question, you know, my tendency

3 is to not try to put additional, this is an

4 agreement with Dr. Coupe, try to put confidence

5 bounds on the LOC. It seems by its nature it is

6 a process of putting in safety factors and coming

7 up with a conservative threshold. So at least

8 once that is all done, you do have a fixed value

9 to compare something to. I don't think we should

10 start trying to do even more with that.

11 I do think it is really important that

12 we have agreed upon and predictable ways that

13 confidence bounds are going to be put on the

14 exposure estimates. And part of the reason I say

15 that is that I think in the end we are going to

16 have a whole continuum of approaches being used

17 to estimate exposure. So you could envision

18 potentially a high probability group of sites

19 that you have very dense actual measurements on

20 and very tight estimates of confidence on one

21 hand. And those are going to have a different

22 probability of exceedance issue than if we are
202-234-4433

Neal R. Gross & Co., Inc.

Page 134

1 using an indirect method from a model, which is

2 also going to be an important part of the

3 continuum.

4 So I would kind of view it in terms of

5 we would like to see, I would like to see the

6 objectives stated in terms of acceptable

7 probability of exceeding a given threshold. That

8 way, we can look at any method on a common

9 playing field. So in other words, if the EPA

10 gives us guidance and says okay, the moving

11 average value is seven, seven-day value of 20,

12 and it is okay with us that there is a 50 percent

13 chance that that is exceeded, then we can look at

14 any estimation method on a common basis and

15 evaluate the probability on those terms. And

16 that is not easy to do but in the end, that is

17 what it kind of comes down to, even if you have a

18 very shallow basis for estimating the numbers.

19 SESSION CHAIR PORTIER: Dr. Lee?

20 DR. LEE: Yes, I concur with my

21 colleagues here. And I just want to say that

22 statistically the problem of placing confidence
202-234-4433

Neal R. Gross & Co., Inc.

Page 135

1 bounds on a level of concern is a harder problem

2 than just putting confidence bounds on a rolling

3 average. And there is going to be enough

4 complication perhaps in other things that we want

5 to do with this, that this may not be the place

6 to add additional complication.

7 SESSION CHAIR PORTIER: Dr. Young?

8 DR. YOUNG: I have nothing to add.

9 SESSION CHAIR PORTIER: That just

10 increased my probability of getting through this

11 morning. Anyone else?

12 Yes, I tended to concur also. I mean

13 I thought about this and I thought to myself

14 exactly as you say, it is probably a lot harder

15 to put a confidence interval in a limit of

16 concern and it leads to more public confusion. 

17 The exposure estimate, everybody assumes exposure

18 is going to be variable so a probability

19 statement on exposure is probably much more

20 acceptable than one on a public health limit of

21 concern. So I think you have got a pretty clear

22 answer to this question. It kind of gives you
202-234-4433

Neal R. Gross & Co., Inc.

Page 136

1 the direction.

2 MR. THURMAN: Yes, we appreciate that. 

3 Okay, so question 2.4. We just scared Don off.

4 SESSION CHAIR PORTIER: He had warned

5 us that he had to leave for another meeting.

6 MR. THURMAN: I am just picking on

7 him. I will pay for that later.

8 SESSION CHAIR PORTIER: We won't see

9 him again. Right?

10 MR. THURMAN: Well he has got six

11 months to forget about that.

12 Okay, this is, it looks like a two13 part question. I will read them both and you can

14 -- three part. Oh, gosh. Okay. I apologize in

15 advance for that.

16 Please comment on the relative merits

17 of the various modeling approaches the Agency

18 described in Sections 5.4.1 and 5.6 for

19 interpolating pesticide concentration between

20 sampling points and, in particular, on the

21 strengths and weaknesses of these methods as the

22 frequency of samples decreases. 
202-234-4433

Neal R. Gross & Co., Inc.

Page 137

1 Considering the health endpoints being

2 considered for atrazine, particularly data for

3 the HPA axis, and the exposure time frame needed

4 to induce the health effects, which is shorter

5 than that used in the 2003 risk assessment,

6 please comment on the advantages and

7 disadvantages of each model for evaluating the

8 likely occurrence and exposure via drinking water

9 of short, moderate, and long durations.

10 Please comment on the Agency's

11 proposed approach for evaluating these methods,

12 as described in Section 5.7.1. To what extent

13 should the Agency consider other factors, such as

14 the shape of the chemograph(Section 5.5.3),

15 weather patterns, stream flow, and/or pesticide

16 use patterns in evaluating the modeling

17 approaches?

18 SESSION CHAIR PORTIER: Dr. Lee?

19 DR. LEE: You gave me the long one. 

20 All right. Let's charge in.

21 Section 5.4.1 describes two basic

22 methods for filling in values between the actual 
202-234-4433

Neal R. Gross & Co., Inc.

Page 138

1 measurements one-year interpolation, stair-step

2 imputation. Neither of these methods can ever

3 give you a predicted value that is larger than

4 any of the observed values. So this clearly will

5 lead to underestimation of the maximum value, if

6 the maximum value does not occur on a sampling

7 day. And this carries over then into shorter

8 term averages or any average as well. The

9 shorter the average, the more important this peak

10 is.

11 The stair-step method has, I think,

12 further danger of missing the truth here. In

13 terms of following the curves, you are trying to

14 get an average, the stair-step method will tend

15 to overestimate a decrease in curve when it is

16 concave -- will tend to overestimate a decrease

17 in curve. Linear interpolation also will tend to

18 overestimate if the decrease is concave but by

19 not as much as the stair-step method.

20 From the examples of chemograms that

21 were given in Figure 7, I get the impression that

22 most curves generally will be concave from more
202-234-4433

Neal R. Gross & Co., Inc.

Page 139

1 of the year then convex because there is an

2 initial peak following the application of the

3 pesticide, followed by proportional decrease from

4 the peak. And maybe my hydrologist colleagues

5 can correct me on that but that is the impression

6 I get of these shapes.

7 So among these two methods, the linear

8 interpolation does seem like it is probably going

9 to do a little bit better. But for the most

10 part, it probably doesn't matter. We are missing

11 the maximum significantly. There is the

12 potential for missing the maximum significantly

13 here.

14 For longer term averages, like a 90

15 day average or 26 week average, both the linear

16 interpolation stair-step methods seem to work

17 reasonably well because the underestimation of

18 the peak values can be balanced by overestimation

19 of post-peak values. It is not exactly a ringing

20 endorsement but central limit theorems kicking in

21 for us there.

22 But to accurately estimate a maximum
202-234-4433

Neal R. Gross & Co., Inc.

Page 140

1 value when it is out of sample, it is going to

2 require use of a method that can predict a larger

3 value than those that are observed in the data. 

4 And example of such method is an artificial

5 neural network as described in 5.6 and Appendix

6 C.

7 Let me just briefly mention again what

8 I brought up on, I guess it was Monday. Appendix

9 C does describe the importance of not using too

10 many modes because over-fitting is not good for

11 prediction. Absolutely correct. But if you are

12 using too few nodes, then you will also

13 potentially be not fitting the curve very well. 

14 The shape of the peak may not be correctly

15 categorized and you may be missing the maximum

16 value as was shown in the difference between the

17 three-node fits and the four-node fits in the

18 White Paper.

19 So the importance of finding the right

20 number of nodes is critical for getting good

21 estimates and some sort of basic model selection

22 like a BIC measure would be helpful. That may
202-234-4433

Neal R. Gross & Co., Inc.

Page 141

1 also be able to eliminate the need for fitting

2 autoregressive errors. I think the current

3 approach that involves the neural network with

4 autoregressive errors is just going to be too

5 complicated for a non-expert to implement and

6 thus, it is not necessarily a practical approach

7 but perhaps if we can eliminate the need for

8 autoregressive errors, it may become a more

9 useful approach in this context.

10 Section 5.4.1 also mentions a number

11 of other potential approaches: bootstrapping,

12 kriging, random function models, regression-based

13 models and deterministic models. Bootstrapping

14 methods again are never going to be able to

15 predict a value that is larger than you actually

16 observed. So, that leads to definite worries. 

17 The other four methods do have promise alone or

18 particularly in combination.

19 Kriging, which is the basic case of

20 fitting a Gaussian process model interpolates the

21 data with a smooth curve but does allow the curve

22 to move outside the bounds of the data. And so
202-234-4433

Neal R. Gross & Co., Inc.

Page 142

1 it could be used for estimating a maximum that

2 occurs outside of the sample base.

3 Also like a Gaussian process, like a

4 neural network can be used to smooth noisy data,

5 rather than doing strict interpolation. That is

6 an issue that I don't think has been discussed

7 here. The Agency uses interpolation a lot in the

8 White Paper but there is a difference between

9 strict interpolation which necessarily will go

10 through all the observed points and some sort of

11 curve fitting, which may discount exact values

12 because of say measurement error. And so the

13 curve will get close to the points but it will do

14 some smoothing. The neural network approach is

15 an example of smoothing approach. It is not

16 guaranteed to go through all the data points and

17 we wouldn't necessarily want it to go through all

18 the data points. But the interpolation methods,

19 the linear interpolation stair-step are, by

20 definition, interpolation. They are guaranteed

21 to go through the data points. Kriging, in its

22 basic form is an interpolation method. It will
202-234-4433

Neal R. Gross & Co., Inc.

Page 143

1 go through all the data points but

2 generalizations to Gaussian processes allow more

3 flexibility if that is not necessarily the case.

4 Let's see. I did actually get some

5 examples of the community water system data from

6 Marry Frankenberry and was able to fit some basic

7 kriging models to them and found that well, it

8 doesn't actually do a whole lot better than

9 linear interpolation in most cases. In most

10 cases, I was not able to get a fit that gave me

11 maximum values outside the sampling days that

12 were higher than the observed ones.

13 And my guess just from really basic,

14 you know, I didn't have a whole lot of time to do

15 the analysis in the last two days, my guess is

16 that the shape of the peaks are very sharp. And

17 as such, the correlation structure is somewhat

18 different around the maximum than it is in the

19 rest of the space. And so fitting a standard

20 stationary model as kriging would do, is not

21 adequately characterizing the curve. A more full

22 analysis would involve a non-stationary
202-234-4433

Neal R. Gross & Co., Inc.

Page 144

1 correlation model and these do exist. But again,

2 I think this is going to be far more complicated

3 than you would want to implement on an individual

4 community water system out in the field.

5 One last issue around kriging is that

6 you do need to estimate a correlation structure. 

7 These are difficult to estimate. Empirical

8 chemograms are highly variable and so I would

9 recommend the estimation be done more globally,

10 pooling across years and across water systems. 

11 Okay, that is kriging.

12 Random function models are another

13 approach we can use. Essentially there we are

14 picking a shape for the curve. And we do know a

15 fair amount about what these curves may look

16 like, although they differ from system to system. 

17 Using those shapes can really aid in the

18 determination of a maximum value and can help

19 them say in determining if the maximum value may

20 have occurred on a sampling day or a non-sampling

21 day. And if it is a non-sampling day, be able to

22 estimate how much higher is that peak.
202-234-4433

Neal R. Gross & Co., Inc.

Page 145

1 And there are a number of ways to do

2 this. There is an example in the context of

3 pesticide concentrations given by Vecchia et al.

4 in a 2008 paper, essentially Dr. Gilliom is on

5 that one, essentially using the WARP model to

6 look at predicting -- it is combining the work

7 model with seasonal shaped functions to be able

8 to make predictions about where that peak might

9 be.

10 So that ties into regression-based

11 models. And there has been a number of work by

12 Dr. Gilliom and others at looking at regressions

13 to predict maxima and quantiles and there is a

14 lot of potential there. They do, however, are

15 looking at sort of the yearly total. So just

16 looking at the maxima over the whole year, it is

17 not going to give you a time series on its own

18 there. And the accuracy may not be quite at the

19 level that one would want for an individual water

20 system but I think there is a lot of potential

21 there. In particular, there is a lot of

22 potential for combining these regression-based
202-234-4433

Neal R. Gross & Co., Inc.

Page 146

1 models with other sorts of models to get improved

2 information and help us make out-of-sample

3 predictions.

4 The final method is used, the final

5 method mentioned in the White Paper is

6 deterministic models. These are built from a

7 combination knowledge of the physical and

8 chemical laws of the process, looking at the

9 actual physical process. And those can be really

10 useful for predicting maxima and short-term

11 averages when we don't observe the data directly.

12 But an important issue there is

13 calibration, which involves the setting of inputs

14 and possible tuning parameters so that the

15 predictions do closely match observed values. 

16 And there is the concern that this may need to be

17 done for each watershed individually and then

18 that becomes a complicated problem again.

19 I guess one global theme here is you

20 don't get something for nothing. If you want to

21 be able to make predictions in-between the

22 observed days that are more accurate, it takes
202-234-4433

Neal R. Gross & Co., Inc.

Page 147

1 more work.

2 I do want to mention one other

3 category of models that has not been mentioned in

4 the White Paper and that is using extreme value

5 theory. There is a fair amount of theory in the

6 statistics literature about modeling extreme

7 events and their distributions developed around

8 those. And I think Dr. Young has mentioned some

9 of these earlier. But there are some, and there

10 is some very recent work. There is a paper that

11 just came out in the Journal of the American

12 Statistical Association that looks at methods for

13 modeling extreme values of a correlated process. 

14 Most of the literature involves independent

15 samples but some of the more recent work does

16 look at correlated processes such as chemograph. 

17 And so that could be really useful to be looking

18 at. It is a very new literature and I am not

19 that familiar with it but I think the EPA should

20 at least investigate that literature.

21 MR. THURMAN: If you could put that in

22 your report --
202-234-4433

Neal R. Gross & Co., Inc.

Page 148

1 DR. LEE: I will put all the

2 references --

3 MR. THURMAN: -- that would be great.

4 DR. LEE: -- in the minutes. 

5 Absolutely.

6 Okay, so I think that gets through

7 parts one and two of this question. Part three

8 then is about the procedure for evaluating the

9 effectiveness of the different methods given in

10 Section 5.7.1. And the general approach I think

11 is sound but as mentioned before, it is important

12 to make sure that we are using a truth that is

13 realistic and so has enough level of detail and

14 variability that reflects what we will actually

15 see.

16 And then the important point is the

17 last part of the question asks about other

18 factors. And I think those really, really should

19 be looked at, taking into account possible

20 covariates, like weather patterns, stream flow,

21 pesticide use patterns, would really help in

22 being able to make better estimation or also the
202-234-4433

Neal R. Gross & Co., Inc.

Page 149

1 shape of the chemograph.

2 So combining these sorts of other

3 pieces of information would really help. Trying

4 to estimate the maximum, just looking at the data

5 non-parametrically, we can do that but it is not

6 as powerful as bringing in other information that

7 we do have available.

8 And again, Dr. Gilliom has been

9 involved in some work that relates those pieces

10 of information and I think that would be a

11 critical direction for the agency to further

12 investigate.

13 SESSION CHAIR PORTIER: Thank you. 

14 Dr. Coupe?

15 DR. COUPE: I think anything I wanted

16 to say I have already said. And that was pretty

17 comprehensive. So that is it for now.

18 SESSION CHAIR PORTIER: Dr. Gilliom?

19 DR. GILLIOM: Just I guess one thing

20 to make clear is between Dr. Lee and myself, we

21 will make sure all the references are in there

22 for the various articles. They will be included
202-234-4433

Neal R. Gross & Co., Inc.

Page 150

1 in the write-ups.

2 One, I guess point I want to make is

3 that if the, and it is probably just a repeated

4 implication of the discussion here but if the

5 duration is on the very short end, it is going to

6 be evident pretty quick, I think that we don't

7 have enough range of conditions covered of

8 existing examples of intensively monitored sites. 

9 So we have a few sites from Heidelberg College. 

10 We have got a couple of drinking water sites. It

11 is growing but we don't really have the full

12 geographic range of conditions represented. So

13 there will be some important decisions to make

14 there about what that means about how far we use

15 inference from existing data. But there is no

16 point in addressing that or even trying, I think,

17 until we know the concentration objective from

18 the toxicology side from the Agency.

19 And I think that is enough to add for

20 now. Thank you.

21 SESSION CHAIR PORTIER: Dr. Young?

22 DR. YOUNG: I also thought Dr. Lee did
202-234-4433

Neal R. Gross & Co., Inc.

Page 151

1 a nice job in summarizing. One of the things

2 that kind of pull some ideas that have already

3 been stated that maybe pulling them together is

4 that it seems to me it makes really good sense to

5 use the WARP or some other model to identify the

6 most vulnerable community water systems.

7 And then once you found those, then it

8 is probably worth some time and effort to figure

9 out exactly what should be done for those

10 systems. And the methods that have been

11 proposed, interpolation methods, might work fine

12 as long as a 90-day rolling average is fine. But

13 if we begin shortening them up, they are not

14 going to be sufficient.

15 So some way to get a more realistic

16 chemograph is important and that would seem to

17 call for maybe the simplest I can think of is a

18 regression-type model where you put in some

19 kriging and then once you have that, you can use

20 geostatistical simulation to get an idea of the

21 true variability associated with that and maybe

22 begin putting some bounds. And then you have a
202-234-4433

Neal R. Gross & Co., Inc.

Page 152

1 fairly good idea of what might happen within that

2 system.

3 Now that would take more time but if

4 we narrow the scope of the systems, then perhaps

5 you have more time for individual efforts. And

6 then once you get -- the first one always takes

7 the longest. So that is something to think

8 about. 

9 SESSION CHAIR PORTIER: Any additional

10 comments from the panel?

11 One of the things I thought about, we

12 haven't really talked too much about the rolling

13 average methodology. But you are using a fairly

14 simple approach of just taking a couple of points

15 and doing the regular average. And there is a

16 slight improvement you can do on that, which

17 would be more of a weighted average. I was

18 thinking Linda, isn't it more like a lasso-type

19 approach that still gives you a good average? 

20 But you notice on the graphs that Mr. Thurman

21 showed that with the rolling averages, their

22 average profile is always going to be shifted to
202-234-4433

Neal R. Gross & Co., Inc.

Page 153

1 the right of the real profile and that is a

2 function of just doing a simple average, rather

3 than having a slightly wider window with some

4 weights, some decreasing weights on either side.

5 So there is kind of a weighted

6 smoothing that will give you kind of a similar

7 rolling average but one that kind of matches up

8 in terms of its peaks and its valleys with more

9 of the original profile.

10 The other thing I was thinking of, a

11 comment Dr. Gilliom mentioned about, more

12 intensive sampling from more sites, is that it

13 would be nice to have some "normal sites." The

14 interquartile range sites. You know, to sort of

15 ensure the public that we haven't just looked at

16 worst-case scenarios. We have also looked at

17 some good players, some solid citizens in the

18 middle. CWS's that don't have all these major

19 problems that are overall managed, that our

20 methodology works well in those kind of solid

21 citizen sites as well. You always aren't always

22 in the extreme because you get charged with being
202-234-4433

Neal R. Gross & Co., Inc.

Page 154

1 too extreme at that point. Right? And not

2 really giving us a good picture.

3 And as far as the White Paper, you

4 know, when you looked at the neuro network and

5 showed us how well the smooth to the neuro

6 network worked and then you added in the

7 autoregressive two component, you didn't show

8 really how well that improved that process. And

9 the point that Dr. Lee was making on some other

10 models is that kind of adding is nice

11 statistically and it may improve the R-square

12 three percent but when you looked at the picture

13 of the smooth, it probably isn't something that

14 is really noticeable. And so if you are looking

15 to simplify, statistically that is nice but in

16 terms of complexity, it really adds a lot of

17 complexity to the estimating process to be able

18 to do that. That may not pay off in the long19 run.

20 I think that is the end of my

21 comments. Dr. Heeringa.

22 CHAIR HEERINGA: Just one minor
202-234-4433

Neal R. Gross & Co., Inc.

Page 155

1 statistical observation, too, which I think

2 people probably recognize but we didn't mention

3 and that is in the sampling process itself,

4 regardless of the periodicity with which you draw

5 single samples, the sort of every nth day

6 sampling, systematic sampling would be most

7 efficient if you had sort of long-term monotonic

8 trends, increases or decreases. But if you have

9 arbitrary fluctuations on shorter terms, that

10 systematic sampling may actually give you greater

11 variance then something that is more randomly

12 perturbed within the fixed windows.

13 And I think that between what RTI did

14 and Syngenta and what Dr. Sielken did and what

15 you have done in your simulations, you might

16 actually be able to see that one some

17 chemographs. I don't know if it has been

18 structured that way but you could actually test

19 that.

20 But it is just a small point but I

21 think systematic sampling is great if you are on

22 a monotonic trend but could in any given sample
202-234-4433

Neal R. Gross & Co., Inc.

Page 156

1 lead to greater variance or error than a randomly

2 perturbed sort of fixed window sample. It is

3 just a minor issue but is probably something

4 technically not to lose track of in the process.

5 SESSION CHAIR PORTIER: Yes, Dr.

6 Gilliom?

7 DR. GILLIOM: Just a tangent that

8 reminded me of, that it is maybe important to how

9 we translate ultimately the monitoring

10 requirements from the toxicological requirements

11 is just to remember that all of the data we are

12 looking at so far and what is normally done are

13 instantaneous grab samples for the most part. 

14 There is a few data sets that have auto samplers

15 that are doing composites either flow or time

16 waved and so forth.

17 But if it is an important thing to

18 capture for instance to know that we have a time19 weighted daily value or a time-weighted two-day

20 or three-day value, that is important information

21 to have in the LOC. Because too often, that type

22 of information is left out and then the
202-234-4433

Neal R. Gross & Co., Inc.

Page 157

1 monitoring design goes ahead with variants based

2 on instantaneous samples and all that and it may

3 not capture what you really want it to for the

4 biological effects. So I think we can deal with

5 all that, the Agency can deal with all that but

6 we need to know the specific objective from the

7 biological point of view.

8 SESSION CHAIR PORTIER: I am sitting

9 here thinking we need a synthetic drinker that

10 drinks two liters of water out of it in a day and

11 then composites that and gets an average

12 concentration. That's okay.

13 Any additional comments?

14 So that, I think is the last question.

15 MR. THURMAN: Mercifully so.

16 SESSION CHAIR PORTIER: And before I

17 close, we usually do two things, and one is I am

18 going to open it up to the panel for any final

19 comments from any panel member. If there is some

20 topic you felt that hasn't been brought up that

21 you wish to comment on, we can add this in at

22 this point. I think we pretty intensively
202-234-4433

Neal R. Gross & Co., Inc.

Page 158

1 covered a lot of these topics but we will open it

2 up to anyone.

3 Last chance to say anything before I

4 turn it over to EPA for their closing remarks. I

5 don't see anybody dying to present a new issue. 

6 Dr. Lowit, I know she has a few closing comments.

7 DR. LOWIT: Before I speak on behalf

8 of the team to give you our appreciation and our

9 sort of next, what we will be doing now, I will

10 speak on behalf of myself.

11 A little story. A number of years

12 ago, probably eight maybe nine at this point, I

13 had only been with the Agency a year or so, two

14 years at the most and it was the night before the

15 first big meeting I ever gave a big presentation. 

16 A group of us were meeting with the office

17 director at the time. It was several office

18 directors ago. This person was going around the

19 table giving a little pep talk and it came to my

20 turn for a little pep talk. And the comment that

21 I was given was you will do great. Just don't be

22 flip. And I will forever hold that.
202-234-4433

Neal R. Gross & Co., Inc.

Page 159

1 And yesterday I think I said a couple

2 of things that were flip, unfortunately. And I

3 always think of that day because it absolutely

4 nailed my shortcomings. But I did, I think, make

5 a comment that could have, around the science

6 issues that was probably interpreted by all 20 of

7 you 20 different ways and I just want to take a

8 second and clarify something that I said.

9 At some point yesterday as we were

10 talking about the new review. I don't remember

11 if it was in the point of departure or in sort of

12 the 101 thing that Nelson and I did, I made a

13 statement to the effect that in this analysis we

14 were starting from scratch or with a new slate or

15 something to that effect. Let me just clarify

16 what I intended because there was some context

17 there.

18 Back in the fall our AA, assistant

19 administrator, announced that the Agency, the

20 Pesticides Office, would be doing this special

21 reevaluation of atrazine. As part of that, we

22 would do two major things. The first one is
202-234-4433

Neal R. Gross & Co., Inc.

Page 160

1 focusing heavily on 2003 forward, which is the

2 hundred and plus study that you have all been

3 through and the reconsideration of the drinking

4 water monitoring. The other thing that we

5 committed to do was to ensure that the old risk

6 assessment was safe.

7 There are a lot of ways to interpret

8 that. You can interpret that that you go through

9 every single millions and millions of pages that

10 have been submitted to the Agency and that have

11 been performed by researchers all over the world

12 or other extreme is that you take the overviews

13 and you just pick them up and you go. And I

14 would think that some of you probably had that

15 interpretation or somewhere in-between depending

16 on your personal perspective.

17 I just wanted to make sure that this

18 is what we are doing and it is relevant because

19 of some comments that Penny had commented on

20 yesterday and we wanted to make sure that you

21 understood what we were doing. We have a small

22 army in pesticides terms. They are putting a lot
202-234-4433

Neal R. Gross & Co., Inc.

Page 161

1 of resources to this and I have the honor of

2 keeping the train on the tracks. Our small army

3 has a very large task in a very short time frame. 

4 And so what we are doing is doing what the AA

5 asked of us. We are going to go through the old

6 data in what we consider to be sufficient to

7 ensure that the points of departure and the

8 uncertainty factors in the new risk assessment

9 are safe for human health for every life stage we

10 can find across different durations.

11 That does not mean that we are going

12 to go through millions and millions and millions

13 of pages. What it does mean, however, is that

14 when we select our points of departure, when we

15 come back in September, that we will have been

16 through enough of those pages and pages that we

17 feel confident that we sit here to say that our

18 new proposal is safe. And our new proposal

19 covers all sensitive groups.

20 What that means in practice we have

21 four months to figure out but I can tell you we

22 will start with what we call the data evaluation
202-234-4433

Neal R. Gross & Co., Inc.

Page 162

1 reviews. Yes, data evaluation records, what we

2 call DERs which are essentially summaries of the

3 studies that come from the guidelines. And the

4 guideline studies are submitted, literally come

5 in volumes. A chronic bioassay can have easily

6 six, seven, eight, volumes. So we are talking 

7 mountains of paper.

8 And so what we do is take those

9 mountains of paper and summarize them. And

10 fairly lengthy reviews there can be easily --

11 some of them are 50 pages. We will start with

12 those DERs and go from there. But we will

13 however, focus a great deal of attention on the

14 new studies that are coming in part because we

15 know ahead of time that the doses are lower, the

16 endpoints are precursor events, and so we are

17 pushing the dose responses to the left on the

18 dose response curve. And so that will be a large

19 part of the focus but we will go back and ensure

20 that they are safe, that we are not going to go

21 through every individual animal and submit it to

22 the Agency.
202-234-4433

Neal R. Gross & Co., Inc.

Page 163

1 SESSION CHAIR PORTIER: Dr. Crisp?

2 DR. FENNER-CRISP: I wasn't wishing to

3 suggest you had to go back and re-review a study

4 from scratch.

5 DR. LOWIT: No and I didn't think you

6 were. We just wanted it on the record.

7 DR. FENNER-CRISP: The point I was

8 making was we may know now something a little bit

9 more about a particular endpoint of concern, its

10 mode of action, whatever, some nuance and/or have

11 reached a stage where we now reinterpret certain

12 kinds of data. And it was that mini task that I

13 was suggesting had to be pursued in addition to

14 looking at the new information.

15 Can I ask a question since I had the

16 microphone? It reminded me in this last thing

17 about the exposure time frame needed to induce is

18 shorter than in the 2003, that refers to the

19 chronic number, I presume.

20 DR. LOWIT: No, I refer mostly to the

21 shorter term.

22 DR. FENNER-CRISP: Oh, okay. That was
202-234-4433

Neal R. Gross & Co., Inc.

Page 164

1 what I was going to ask you. Do you have in mind

2 now, looking at the other numbers over all of the

3 course of exposure durations, two of the three

4 categories were driven by data related to our

5 discussion the last few days. The pubertal assay

6 drove the middle number, the LH surge drove the

7 chronic number, but these kinds of data did not

8 drive the acute number.

9 So, could we interpret the possibility

10 that you may be looking at this body of data

11 related to the MOA and its consequences for

12 generating an acute number?

13 DR. LOWIT: I don't know. I am not

14 going to answer that.

15 DR. FENNER-CRISP: Okay.

16 DR. LOWIT: I am sorry, Penny, but I

17 am going to answer a different question. I am

18 being flip again.

19 DR. FENNER-CRISP: I think you said

20 that data set wasn't --

21 DR. LOWIT: Yes, and that is an

22 important point and I think we did hear a fairly
202-234-4433

Neal R. Gross & Co., Inc.

Page 165

1 strong consensus that those single 15-minute, 30-

2 minute cort measures would not make a robust

3 regulatory implant. I think that was one message

4 that we got that was pretty clear.

5 But as I have been sitting here and I

6 went home last night digesting what some of you

7 have been talking, particularly Dr. Krishnan and

8 O'Byrne trying to blend those concepts, as I sit

9 here, I think what we really need is a different

10 kind of assessment. I actually think we are

11 asking the wrong questions.

12 And I think this is, if you follow

13 Kannan's line of thinking around the AUC and you

14 blend that with the chemograph idea. And then if

15 we can do some calculations overlaying AUC and

16 chemograph, there may be a better way of asking

17 these questions. Instead of thinking isolated

18 duration, think about it on an AUC basis. It is

19 a much more sophisticated way of thinking about

20 it and would create havoc for the risk managers.

21 But I think it would bring in some of

22 the ideas that we have heard this morning about
202-234-4433

Neal R. Gross & Co., Inc.

Page 166

1 bringing in the level of concern idea and

2 thinking about different CWSes that seem to fit

3 in different categories of 20 some that have a

4 lot of hits, and a lot of them that don't have

5 any, and some in-between, and if you get these

6 different patterns. I think we have been

7 thinking about it on an AUC basis. We may be

8 able to get more of a better distribution of that

9 way of thinking.

10 And so how that fits into doing an

11 acute risk assessment in a short-term, or in the

12 30 days, or an intermediate term of up to 60 days

13 or six months or whatever it is, I don't know how

14 that fits but I think it is actually answering

15 the question. And I don't know how to do it but

16 we will figure it out.

17 DR. FENNER-CRISP: The other thing you

18 don't have assembled yet and we talked about it

19 today and a little bit at the end of yesterday is

20 the human biology that is relevant to the results

21 that have been gathered in the laboratory animal

22 studies. And when you have that, it will better
202-234-4433

Neal R. Gross & Co., Inc.

Page 167

1 inform the point you have just made.

2 SESSION CHAIR PORTIER: I think Dr.

3 Horton and Dr. Akana will want to comment

4 shortly. Short comments.

5 DR. HORTON: Okay, very short comment. 

6 I want to summarize some things made by other

7 people that I think all come together and one is

8 Dr. Krishnan's comment about the area under the

9 curve plus Dr. Akana's comment that she made

10 earlier today about the fact that one 15-minute

11 exposure to cort may not be a significant event

12 but multiple exposures might be. So that when

13 you start putting all of that together, it might

14 be a significant event so as to put these things

15 together.

16 And Dr. Cooper's comment that perhaps

17 we have been looking under the light post for the

18 keys and I think we were driven by a mode of

19 action based on the mammary gland tumor and the

20 LH surge but in the result of that, a lot of our

21 thinking and a lot of the experiments have led us

22 to some very interesting results and perhaps when
202-234-4433

Neal R. Gross & Co., Inc.

Page 168

1 I finally get this figure done, we can kind of

2 move away from the lamppost and think about how

3 to use this new information in an informed way.

4 SESSION CHAIR PORTIER: Dr. Akana?

5 DR. AKANA: Here is an idea for you. 

6 You take a small community water system. You are

7 measuring atrazine raw and in finished water. 

8 And then in the raw sewage, you measure cortisol.

9 SESSION CHAIR PORTIER: She keeps

10 dropping these gems. I don't know what to do

11 with them.

12 DR. AKANA: It might go with the

13 bootstrap method well.

14 (Laughter.)

15 SESSION CHAIR PORTIER: Dr. Lowit, you

16 know, as I listened to all of this, you are

17 keeping things on track. The train idea. And I

18 keep wondering if these are two trains kind of

19 going along together trying to get in the station

20 at the same time or two trains coming at each

21 other and we don't know if they are even on the

22 same track. So they may come together or they
202-234-4433

Neal R. Gross & Co., Inc.

Page 169

1 may pass in the night. So it will be interesting

2 to see when we get here in September or October.

3 DR. LOWIT: Well the train I am

4 driving is the one that the data is interesting. 

5 So I don't want to sit here and speculate and

6 sort of talk free flow because that is what I was

7 starting to do with Penny.

8 SESSION CHAIR PORTIER: Just thank the

9 panel and let's go.

10 (Laughter.)

11 DR. LOWIT: Yes, I think the point is

12 well taken that we have collectively heard an

13 enormous amount of helpful feedback on a very

14 diverse almost absurdly diverse array of topics. 

15 And to say that we appreciate what you have been

16 doing and that what you do whether it is a

17 permanent panel or first timers, it is a lot of

18 work to come to these meetings. We know we hand

19 you lots of pages and ask you to digest and turn

20 around feedback on something you may have just

21 learned about two weeks ago.

22 And I am always impressed when I come
202-234-4433

Neal R. Gross & Co., Inc.

Page 170

1 to these meetings to the degree to which really

2 smart people come together and do really amazing

3 things and this is just another example of that. 

4 And I want to thank each and every one of you on

5 behalf of the whole team.

6 We will be back in September with the

7 next generation of what you have seen with most

8 likely some more choices that we haven't talked

9 about here, points of departure, that sort of

10 thing. We will do a more explicit evaluation of

11 life stage sensitivity that we really haven't

12 addressed here. Peripherally we have but we will

13 head it straight on a little bit more.

14 We will also be joined by your

15 epidemiology colleagues, both on the team and

16 some people on the panel will join us. So we

17 expect a good conversation around thinking about

18 how animal and human information do and don't

19 match. And I can tell you often they don't match

20 and that is a great challenge.

21 So I will thank each and every one of

22 you. I will thank Laura, and Joe, and Charlene
202-234-4433

Neal R. Gross & Co., Inc.

Page 171

1 and everyone else on the SAP staff. Personal

2 thanks to my team. It is an amazing group of

3 people.

4 SESSION CHAIR PORTIER: Thank you. 

5 For the panel, once the DFO makes his final

6 comments, I want you to just leave all your

7 papers and your computer. We are going to go

8 next door for a short five minute process meeting

9 where we talk about how we produce the final

10 report.

11 And at this point, I want to turn it

12 over to the DFO, Joe Bailey, for final closing

13 remarks.

14 MR. BAILEY: All I want to say is to

15 thank the panel for all their hard work, for

16 agreeing to come to the meeting and I look

17 forward to working with you over the next few

18 weeks getting the report pulled together. And I

19 thank EPA for their presentations. I think they

20 were very well done. And I want to thank the

21 public commenters who came to the meeting as well

22 to present their views.
202-234-4433

Neal R. Gross & Co., Inc.

Page 172

1 And that's it. Thank you.

2 Oh, yes, and we will get the report

3 completed within 90 days from today.

4 SESSION CHAIR PORTIER: Thank you. 

5 That ends the meeting.

6 (Whereupon, at 12:14 p.m., the

7 foregoing meeting was adjourned.)

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22
202-234-4433

Neal R. Gross & Co., Inc.

Page 173

A

AA 159:18 161:4

ability 62:4 122:2

128:14

able 5:14 30:15

44:7 71:11 118:12

121:11 141:1,14

143:6,10 144:21

145:7 146:21

148:22 154:17

155:16 166:8

absence 130:5

absolute 45:16

72:15

absolutely 22:21

46:19 63:1 85:16

140:11 148:5

159:3

absorption 15:17

83:22 112:4

abstract 115:2

absurdly 169:14

acaricide 23:9

acceptable 134:6

135:20

accommodating

18:10

accompanied 35:2

account 103:8,15

103:21 148:19

accounting 119:5

accounts 107:7

accumulated 101:8

accuracy 145:18

accurate 146:22

accurately 139:22

acknowledge 73:18

acronym 66:12

act 1:4 10:2 107:5

action 7:4 8:8 9:10

9:10,12 22:13

26:7 29:20 163:10

167:19

actions 7:6

activated 121:19

activation 27:14,18

actual 22:7 60:18

96:3 115:7,13

133:19 137:22

146:9

acute 7:16 18:8,12

18:19 22:10 50:20

52:5 79:17 164:8

164:12 166:11

adapt 112:14

adapted 18:3

adapting 17:19

adaptive 22:9 23:2

108:17 110:15

111:13 113:10

124:19

add 20:12 45:15

83:15 84:14 103:4

107:2 114:5 117:7

124:1 135:6,8

150:19 157:21

added 154:6

adding 154:10

addition 34:7

163:13

additional 6:7 8:7

39:22 74:3 105:16

106:7 108:5,8

109:1 120:8

123:22 125:14

133:3 135:6 152:9

157:13

Additionally 19:2

130:7

address 7:20 74:1

115:19 123:6

addressed 33:2

59:6 122:19,22

170:12

addressing 150:16

adds 154:16

add-on 125:5

adequate 115:19

adequately 143:21

Adjourn 3:22

adjourned 172:7

administered 23:10

administrations

15:18

Administrative 3:2

administrator

159:19

advance 136:15

advantage 76:10

91:3

advantages 137:6

adverse 7:1 17:20

18:3 22:20 25:21

25:21

advice 69:21 70:5

Advisory 1:4 4:4

affect 104:17

afraid 33:9

afternoon 6:9

20:15 23:11 31:15

agency 1:1 8:19,21

9:4 14:9 29:13

88:10,19 89:13

102:13,17 107:10

116:15 125:21

136:17 137:13

142:7 149:11

150:18 157:5

158:13 159:19

160:10 162:22

agency's 124:12

137:10

ages 23:19,19

aggregate 124:15

ago 6:13 55:22

78:21 113:2

122:12 158:12,18

169:21

agree 22:16 25:20

61:12 74:11

129:22 132:5

agreed 14:5 133:12

agreeing 171:16

agreement 133:4

agricultural 46:1

82:6 130:15

ahead 157:1 162:15

aid 8:19 144:17

Akana 2:2 62:10,11

63:3 79:13,14

98:21 167:3 168:4

168:5,12

Akana's 167:9

al 32:17 40:4 41:7

145:3

Alan 86:10

Alkaloids 26:1

allocate 108:15

allow 141:21 143:2

aloud 24:19

altered 15:8

alternatives 40:5

amazed 85:16

amazing 33:13

170:2 171:2

ambiguous 8:6

American 147:11

amino 113:18

amount 13:4

102:15 129:11

144:15 147:5

169:13

amounts 105:10

AMP 65:12,18

76:10 97:11 98:1

amplitude 97:12

analogies 28:19

analogous 39:19

50:8

analyses 58:6

analysis 7:13 16:19

34:1 40:1 53:11

72:7 76:2 87:11

113:22 116:17

119:17 143:15,22

159:13

analytical 116:6

and/or 137:15

163:10

animal 1:6 24:18

25:2 107:22

162:21 166:21

170:18

animals 7:8 24:3

127:16

Anna 107:2

announced 159:19

annual 111:14

anomalies 24:6

answer 34:18 72:8

82:16 97:6 102:10

125:15,17 135:22

164:14,17

answered 117:14

117:15

answering 166:14

answers 39:19

103:13

anybody 116:14

158:5

anymore 85:5

anyway 125:6

apart 41:2 50:11,21

83:7

apologize 55:22

126:10 136:14

apparently 24:8

appeal 93:20

appear 18:14 22:8

45:12 114:13

appears 18:2 64:13

appendix 92:7,7

100:14 140:5,8

applicable 63:2

114:11 118:22

application 82:8,9

82:12 83:8 90:16

107:6 116:7 124:2

130:2 139:2

applications 82:22

applied 7:19 47:22

48:2,6 101:16

129:11

apply 103:19

114:20

appreciate 28:15

63:17,19 136:2

169:15

appreciation 158:8

approach 38:3

42:20,21 87:12

89:15 91:12 101:3

101:4,14 102:3

114:17 115:12

116:11 118:8
202-234-4433

Neal R. Gross & Co., Inc.

Page 174

119:11 121:6,12

123:7 126:1 131:6

137:11 141:3,6,9

142:14,15 144:13

148:10 152:14,19

approaches 3:9

32:8 89:12 91:10

91:14 99:20 100:4

113:11 114:15

133:16 136:17

137:17 141:11

appropriate 9:6

19:8 26:21 27:2

72:12 83:20 84:1

95:7,22 96:15

116:7

appropriately

124:12

approximately

37:9 55:19

April 1:13 34:9

37:17

arbitrary 155:9

architecture 8:14

area 16:7 26:14

27:2,8 74:11

130:12 167:8

areas 10:8,9 77:3

argue 123:12

argument 27:20

95:10 112:18

arguments 28:1

arises 17:6

army 160:22 161:2

array 169:14

arrival 130:9

arrows 9:16 13:2,7

articles 149:22

artificial 140:4

artificially 81:13

aside 66:11

asked 6:1 7:6 25:14

32:11 58:21 69:9

76:8 96:22 161:5

asking 68:21 69:20

70:5 77:7 165:11

165:16

asks 148:17

aspects 15:16

assay 20:19 113:18

164:5

assembled 166:18

assessment 5:20

6:22 7:10 9:5

18:9 25:13 74:14

88:3 95:5 123:10

137:5 160:6 161:8

165:10 166:11

assessments 95:1

122:18

assign 126:16

assigned 4:22

126:15

assistant 159:18

associated 15:13

16:13 18:17 24:16

119:14 151:21

Associates 58:8

Association 147:12

assume 45:21

assumes 127:22

135:17

Assuming 104:5

assumption 74:13

92:18

assumptions 118:9

atmospherically

129:17

atrazine 1:6 4:5 9:5

10:2 11:7,14 15:5

15:11 16:3 23:7

28:9 35:17 36:13

37:6 40:22 76:8

76:11,17,20 77:2

77:9 79:18 80:13

82:8,22 85:10

86:6,7,13 89:3

90:15 95:1,15,20

98:14,18 106:10

107:4 110:5 112:9

113:19 118:21

128:6,14,21,22

129:3,10,14,15,16

130:5,17 131:1,13

132:13,15,18,19

137:2 159:21

168:7

ATS 1:22

attached 126:19

attempt 10:7

attention 124:6

162:13

AUC 165:13,15,18

166:7

AUCs 16:5

audience 4:14

12:22

augmented 64:20

65:20

auto 156:14

autoregressive

141:2,4,8 154:7

available 124:4

149:7

average 16:5 18:21

30:5,10,12 38:20

38:21 51:12 52:20

53:18 66:9 67:11

69:2,4,6,8,12

77:19,20 79:1,3

90:3 116:3 118:4

126:6 132:1,3

134:11 135:3

138:8,9,14 139:15

139:15 151:12

152:13,15,17,19

152:22 153:2,7

157:11

averaged 78:10

averages 36:20

59:4,6 66:4,7,21

67:1,1,4,21 68:9

71:10 79:8 93:8

93:19 112:17

118:3 138:8

139:14 146:11

152:21

averaging 71:22

81:7 88:18 93:19

112:13

awful 130:14

axis 17:8 27:15

137:3

a.m 1:15 4:2 85:1,2

B

back 5:7,18 6:4,10

7:12 12:4 17:15

18:1,2 19:9,19,19

24:22 25:5 31:20

35:11 37:18 43:20

55:14 60:8 67:17

68:11,15 72:13

74:8 77:11 78:4

80:20,21 83:2,13

109:9 110:7,11

120:14 123:13,16

159:18 161:15

162:19 163:3

170:6

background 39:7

40:3 81:14

bad 80:14 119:8

Bailey 2:22 3:3 4:3

4:6 64:11 171:12

171:14

balanced 139:18

ballpark 108:22

Ballroom 1:15

band 104:3,8

bands 103:7

bare 119:15

BARRY 2:3

base 20:17 142:2

based 14:17 16:5

18:15,22 19:6,15

20:10 21:1,3 34:5

34:8 39:6 42:8

74:14 92:11 101:7

107:9 118:1 157:1

167:19

basic 116:10

137:21 140:21

141:19 142:22

143:6,13

basically 41:1

42:14 72:12 73:1

91:15 100:5

basin 128:7,13

129:4,5,10,16

130:10,13 131:9

basing 20:19 101:4

112:17

basins 131:7 132:8

basis 19:7,16 72:16

73:12 84:15

103:18 134:14,18

165:18 166:7

Bayesian 94:15

beginning 25:12

49:18 78:22

behalf 158:7,10

170:5

behavior 23:2

119:13

believe 70:10 92:16

131:13

bells 105:5

belt 11:20,20

benchmark 7:13

19:5 28:4

best 63:13 68:5,5

74:18 75:13 86:16

better 32:12 33:12

68:17 69:19 73:20

76:4 88:5 120:4

139:9 143:8

148:22 165:16

166:8,22

beyond 64:3

biased 97:21

BIC 140:22

big 48:13 78:12

85:18 91:14 97:1

101:13 105:1

114:1 131:3

158:15,15

bigger 33:9

billion 62:17 97:16

105:3

bin 52:9

bins 46:17

bioassay 162:5

biological 157:4,7

biology 116:15
202-234-4433

Neal R. Gross & Co., Inc.

Page 175

166:20

Biscoe 110:4,4

bit 7:3 8:6 14:19

18:20 26:14 47:16

59:22 92:22 93:14

93:17 110:7 122:1

139:9 163:8

166:19 170:13

bi-weekly 50:4

Blair's 27:19

blend 165:8,14

blended 74:16

block 23:14

blocked 23:12

Blomquist 41:6

blood 26:14

blue 75:3 76:18

77:16 79:2

BMPs 128:13

board 2:1 27:1

Bob 20:3 58:8 76:6

87:5 118:20

body 15:5 41:9

83:21 164:10

bootstrap 66:11,20

67:3 168:13

bootstrapped

67:20 70:22

bootstrapping

141:11,13

bothers 119:1

bottled 81:16

bottom 11:3,13

bounce 17:15

bounces 18:1 24:22

bound 127:6

bounds 69:21

70:17 89:13,19

91:2 99:21 100:2

100:19 101:10,21

102:16 104:18

114:19 126:5,8

133:5,13 135:1,2

141:22 151:22

Boy 80:17

BPA/BPH 105:5

Brady 64:2

brain 12:7 27:4

break 31:7 84:17

brief 85:8 128:20

briefly 32:22 34:11

72:5 140:7

bring 13:8,12 25:16

30:20 74:6,8

101:11 119:12,14

122:21 165:21

bringing 96:5

117:8 149:6 166:1

Brits 37:6

broader 127:19

brought 98:21

113:8 124:20

140:8 157:20

BS 74:7

Bucher 1:21 13:10

13:11 22:14,15

25:20

buggy 77:15

built 106:5 124:4

146:6

bunnies 25:15,18

burden 38:14

C

C 140:6,9

calculate 19:22

42:12

calculated 16:5

42:7 66:3

calculations 19:18

20:6 165:15

calendar 37:12

38:15

calibration 146:13

call 6:2 16:18 66:12

86:1 125:6 151:17

161:22 162:2

calling 64:22

cancels 45:17

cancer 13:15

capabilities 11:13

capture 6:7 12:21

21:16 22:11 30:15

30:18 73:9 87:13

119:18 120:1

122:2 156:18

157:3

captured 4:18 5:8

12:17 61:17 67:12

captures 25:1

capturing 49:13

56:12

carbon 86:7,14

121:20 122:2

careful 27:21

carefully 98:5

CARMEN 2:16

carries 138:7

cascade 26:6

case 13:8 17:3 25:4

36:18 52:11 56:19

71:21 88:5 92:17

96:14 118:13

141:19 143:3

cases 48:10 57:13

95:20 100:18

124:20 143:9,10

cast 14:7,10

catch 105:7

categorical 100:12

categories 100:6

164:4 166:3

categorized 140:15

category 101:1

147:3

caught 10:22

causes 17:22

cautious 24:11

caveat 107:20

center 52:3

centile 34:19 41:21

42:13,13 43:4,9

centiles 33:17

35:19

central 11:12 93:21

139:20

certain 59:8 81:6,7

121:21 123:17

163:11

certainly 64:11

84:14 92:9

chair 1:17,18,20,21

4:7,9 6:12,15

10:13,19 11:22

12:8 13:10 14:12

20:11 21:22 22:14

23:6 24:10 25:10

27:10 29:12 33:6

37:14 40:6 43:19

44:19 45:2,19

46:7,8,14,16 49:1

53:14 54:2,7,10

58:2,5,13 60:5

62:8 63:20 70:20

70:21 71:13 72:2

73:7,15 74:2

79:13 81:11 82:3

83:14 84:16 85:3

87:21 89:17 94:4

94:12 99:15

102:19 103:3

104:19 105:8,13

105:18 107:15

108:5,8 109:3,12

109:15,22 110:12

111:11,16 112:2

112:22 114:4,21

116:20 117:5,10

120:8 123:9

125:13,18 126:13

132:22 134:19

135:7,9 136:4,8

137:18 149:13,18

150:21 152:9

154:22 156:5

157:8,16 163:1

167:2 168:4,9,15

169:8 171:4 172:4

Chairman 32:2,21

40:20 47:7 63:8

challenge 89:22

170:20

CHAMBERS 1:22

chance 134:13

158:3

change 54:21 128:4

128:5,5,9,14

changed 128:17

changes 11:4 17:7

33:13 128:9,16

changing 50:1

characteristics

59:2 72:19 101:5

characterize 89:1

126:1

characterizing

143:21

charge 3:6,8 8:18

60:9 113:8 137:20

charged 153:22

Charlene 170:22

chart 11:7

chemical 15:19

16:11 110:5 146:8

chemicals 7:21

130:16

chemograms

138:20 144:8

chemograph 42:8

45:3 64:18 65:20

77:16 119:16,20

147:16 149:1

151:16 165:14,16

chemographs 45:8

45:10 68:6 99:2

105:2 119:13

155:17

chemograph(Sec...

137:14

Chen 32:17,17,17

40:3 42:4,5,5,16

44:13,14 45:14,14

children 9:2 10:17

chlordimeform

23:9

chlorinated 16:3

chloro 84:2

chloroforms 16:2

chlorotriazines

76:9,13,21

choices 170:8

chooses 125:11

choosing 110:20

chronic 18:9,12,16

80:12,12 162:5
202-234-4433

Neal R. Gross & Co., Inc.

Page 176

163:19 164:7

circles 76:20

citizen 153:21

citizens 153:17

Cl 16:4

clarified 11:9 20:9

43:11

clarify 10:14 32:3

32:11 33:20 62:12

63:18 102:20

126:11 159:8,15

clarifying 4:20

31:13

clear 4:17 20:20,20

25:8 26:20 37:4

47:14 94:7 111:12

117:20 120:19

135:21 149:20

165:4

clearance 23:11

clearly 16:13 24:16

24:18 61:9 112:15

138:4

climate 111:19,22

112:7 121:7 128:9

close 5:14 47:20

48:5 142:13

157:17

closely 84:4 146:15

closing 158:4,6

171:12

clusters 80:3,16

Cmax 16:11

CNS 11:2,8

code 111:5

coffee 31:7

coherent 57:22

cold 128:10

colleague 131:20

colleagues 15:15

127:11 134:21

139:4 170:15

collect 109:1 117:3

117:4

collected 118:1

collectively 31:4

56:3 169:12

College 150:9

color 111:5

column 53:16

combination

141:18 146:7

combined 15:14,20

129:9

combining 145:6

145:22 149:2

come 5:7 8:4,9 14:3

18:13 38:19 60:8

67:16 72:5 90:14

99:13 102:10

126:19 161:15

162:3,4 167:7

168:22 169:18,22

170:2 171:16

comes 11:14 28:20

72:1 91:15 107:4

121:15 134:17

comfortable 91:7

116:14

comforting 93:5

coming 57:21

72:13 83:4 86:20

122:1 133:6

162:14 168:20

comment 12:10

29:16 58:20 62:9

63:9 72:5 89:15

108:9 113:7

114:14,18 115:12

126:4 136:16

137:6,10 153:11

157:21 158:20

159:5 167:3,5,8,9

167:16

commented 160:19

commenters

171:21

comments 4:20

7:11,22 14:22

22:16 26:13 74:4

86:11 104:21

105:16 108:6,7

115:2 120:9

123:22 131:5

152:10 154:21

157:13,19 158:6

160:19 167:4

171:6

committed 160:5

committee 126:11

common 49:11

96:11 134:8,14

community 34:8

36:10,12,17,22

39:3 41:12 48:4

76:1 86:4 87:9

96:6,12,19 98:2

98:10,13 99:11

106:13 109:6

121:16 125:2

131:15 143:5

144:4 151:6 168:6

comparable 55:19

compare 7:14 37:2

50:18,21 51:6

133:9

compared 42:1

43:22 67:9 107:21

comparing 106:15

126:6

comparison 51:20

56:5 107:20 108:3

compelling 13:19

13:20

competence 69:15

complete 14:21

completed 172:3

complex 9:20 12:15

73:9 82:16

complexity 154:16

154:17

compliance 125:6,8

complicated 13:22

93:9 141:5 144:2

146:18

complication 17:5

135:4,6

component 154:7

components 76:12

composites 156:15

157:11

comprehensive

149:17

compute 104:18

computed 34:20

36:21 41:20 53:6

computer 46:21

171:7

concave 138:16,18

138:22

concentrate 118:19

concentrated 90:17

concentration

26:15 27:1,9 30:8

36:19 42:11 97:5

97:22 100:19

101:9,20 102:7,12

103:2 113:15

114:18 115:19

116:12 122:3

129:22 130:3,8

131:1 136:19

150:17 157:12

concentrations

19:8 59:4,5,12

77:20 88:13,16

89:3,8 95:2,11,12

98:7,17,19 101:5

116:2 121:21

129:18,21 145:3

concept 115:4,6

concepts 165:8

conceptualized

9:12

concern 24:13

30:13 45:20 89:16

90:18,22 97:1,3,6

102:2 105:1 126:8

132:21 135:1,16

135:21 146:16

163:9 166:1

concerns 6:1 18:13

concluding 4:16

conclusion 40:7

43:20 44:12

conclusions 43:21

concordance 29:8

concur 134:20

135:12

conditions 150:7

150:12

confidence 9:17

34:22 39:16 88:13

89:13,19 91:2,4

99:21 100:2,18

101:10,21 102:16

103:7,16 104:3,8

104:18 114:19

125:22 126:5,8

127:11,12 133:4

133:13,20 134:22

135:2,15

confident 60:22

112:20 161:17

confidently 100:9

confirmation 58:6

confirmatory

118:14

confused 20:8

confusion 52:3

135:16

confusions 19:13

conjecture 82:19

conjunction 88:8

connection 19:19

consensus 165:1

consequence 120:3

consequences

164:11

conservative 88:1

92:16 93:6 96:2

133:7

consider 26:11

27:6 65:2,8 89:13

97:20 99:20

137:13 161:6

considerable 13:3

95:10

consideration 9:13

14:16 15:6,10

16:18 17:6 89:20

108:3

considerations

15:3,15 16:9

114:20
202-234-4433

Neal R. Gross & Co., Inc.

Page 177

considered 22:20

27:17 65:6 93:16

108:2 137:2

considering 84:10

89:18 125:22

137:1

consistently 98:14

99:13

constituent 128:1

constituents 132:16

construct 118:16

consumption 15:13

123:13

contained 66:20

contaminants

118:22

content 95:17

contention 74:12

context 10:2 26:12

79:10 84:13 106:3

141:9 145:2

159:16

continue 86:8

continued 3:7

122:15

continuum 123:15

133:16 134:3

convened 1:15

conversation

170:17

conversations

11:10 27:12

conversely 126:8

131:9

convex 139:1

convinced 130:16

convincing 18:15

Cooper 12:8,9 23:6

23:7 30:19

Cooper's 167:16

corn 77:3

corner 94:7

correct 14:22 23:4

25:4 49:5 58:12

63:1,7,10 127:3,8

139:5 140:11

correctly 46:19

62:12 140:14

correlated 147:13

147:16

correlation 92:19

93:13 121:10

143:17 144:1,6

correlations 89:7

cort 165:2 167:11

cortical 15:9 17:4

17:14 24:15

cortisol 168:8

country 77:4

Coupe 2:2 94:5,6

94:14 109:11,18

116:20,21 126:13

126:18 127:1,9

133:4 149:14,15

couple 55:21 98:22

102:5 105:14

150:10 152:14

159:1

course 15:4,7,11

16:22 17:4 40:2

49:16 53:8 56:17

63:10 64:3 73:20

90:11 96:21 111:4

111:18 131:9

164:3

covariates 148:20

covered 99:18

150:7 158:1

covering 56:15

covers 161:19

Crawford 100:13

115:6 131:6

create 36:18 41:19

44:9 68:14 101:5

115:22 132:4

165:20

created 38:3,20

72:11,17 81:18

creating 127:5,5

creation 80:22

Creek 45:22,22

Crisp 11:15 12:1

163:1

critical 18:5,8

21:15 23:10 28:11

30:2,12,16 72:9

72:15 140:20

149:11

criticism 86:1

87:18

criticize 85:13

CRM 110:5

crop 128:9

CropLife 95:6

crops 112:4,9

cross 112:11

Crowne 1:15

current 9:3 17:5

55:4,17 111:1

141:2

curve 26:14 27:2,8

84:7 138:15,17

140:13 141:21,21

142:11,13 143:21

144:14 162:18

167:9

curves 16:7 138:13

138:22 144:15

CWS 49:8 51:14,20

56:3,6,11,14,17

56:22 59:17,17

61:15 68:14 69:18

71:15 109:10

110:10

CWSes 49:6 166:2

CWS's 48:1 153:18

cycle 18:1 24:22

28:6,7 29:5 79:19

79:20,21 80:1

111:15

cycles 28:4,5

C-O-N-T-E-N-T-S

3:1

D

D 2:7

DABT 1:21,22 2:4

2:13

daily 30:5 36:19

41:2,8,13,13,13

41:14,17,17,21

42:8 43:4,9 48:5,7

48:16 52:6,6

53:20 56:21 57:11

57:21 59:11 60:21

60:22 62:6 72:16

73:5 77:17 79:2

90:2 104:5,13,15

105:1,4 118:1,2

119:4 131:19

156:19

danger 138:12

DANIEL 1:23 2:15

dark 75:3 76:18

data 8:2 9:3,13,15

13:9 17:7 27:14

27:22 28:2,7,19

32:9,11 34:3,3,4,5

34:8,12,13,17

35:20,20 36:1,9

39:6,6,8 41:2,3,8

41:12,14,16,18

42:15,15,19 43:11

45:6,21 46:1 48:5

48:7,8,14,17 49:3

50:19 51:7,15

57:12 58:15 62:13

62:15 63:14,14

72:7 73:1,5 74:18

77:6 78:5,8,16

82:20 87:7 88:21

88:22 89:10,14,20

90:3,3 91:16

93:21 94:22 95:1

95:4,7 96:6,17,20

97:12,15,20 98:1

99:7,22 100:1,5,8

101:7 102:8,21

106:1 109:2

114:11 115:7,13

115:18 117:2

118:1,15 119:4

120:18,19 121:1,6

121:7,7,9,13

124:4 126:3 127:6

131:8 132:4 137:2

140:3 141:21,22

142:4,16,18,21

143:1,5 146:11

149:4 150:15

156:11,14 161:6

161:22 162:1

163:12 164:4,7,10

164:20 169:4

database 7:12

56:12 57:20,21

dataset 62:18 65:3

65:15,16

datasets 73:19

75:14 87:3 92:13

day 4:4,11,12 23:15

29:1 30:1,6 38:5,8

38:10 49:21 50:3

50:16,17,20 52:5

52:14,14 53:17

54:16 56:9 57:6

59:3,3,3,3,15,16

59:19 60:2 64:12

71:17,18,18 77:14

77:22 80:3,5

81:14,16,16,22

89:7,8 107:21

111:7 119:6 138:7

139:15 144:20,21

144:21 155:5

157:10 159:3

days 5:2 7:5 9:14

17:16,22 24:14

28:10 29:6 38:20

41:1 45:13 49:14

49:18 50:10,14

52:19,20 53:16

54:17 55:22 59:8

59:14 64:20 65:10

65:12,13,21 66:5

66:6 67:2 69:3,8

71:1,17,20 75:9

78:10 80:4 109:10

109:17,19 110:9

110:18 118:5

143:11,15 146:22

164:5 166:12,12

172:3

dead 29:5 119:21

deal 5:20 29:13
202-234-4433

Neal R. Gross & Co., Inc.

Page 178

48:13 71:9,12

157:4,5 162:13

dealing 12:16 28:3

Dear 126:20

decent 92:2

decide 26:17 77:14

78:16

decided 78:5 79:16

95:4

decision 90:7

decisions 113:20

150:13

decision-making

125:4

decrease 138:15,16

138:18 139:3

decreases 136:22

155:8

decreasing 110:22

153:4

Dedrick 20:3

defend 73:12

define 6:21 7:2

72:14 73:2 75:13

75:14 102:12

defined 61:5,9 87:6

106:1 116:12

124:8

definite 141:16

definitely 71:9

103:21

definition 142:20

degree 10:1 170:1

delay 23:18

delayed 23:13 24:6

24:17

DELCLOS 2:3

deleterious 80:10

demonstration

25:1

dense 48:16 92:13

102:21 133:19

denser 73:2

departure 89:1

159:11 161:7,14

170:9

departures 9:1

depending 18:5,7

79:8 89:16 90:6

95:15 99:9 108:19

160:15

depends 60:16 81:9

85:17 104:3

derived 20:4 67:2

107:14 126:2

127:15

deriving 19:8

DERs 162:2,12

describe 39:22

114:12 140:9

described 34:13,22

89:6 115:6 125:20

136:18 137:12

140:5

describes 137:21

describing 36:8

design 37:22

102:11,18 116:16

130:21 157:1

Designated 2:22

4:6

designed 130:18

designs 37:4

destruction 27:20

detail 8:4 34:13

36:8 84:13 148:13

detailed 33:17

details 127:10

detect 80:16

detected 76:17

129:4

detections 89:9

determination

144:18

determine 94:22

127:21

determining 9:3

88:12 89:13 99:21

114:19 130:17

144:19

deterministic

141:13 146:6

develop 8:22 9:4

87:11

developed 13:18

118:21 147:7

developing 36:9

development 13:14

21:19 22:6 24:6

28:1

developmental

22:3

deviation 108:16

deviations 43:10

DFO 171:5,12

diagram 9:19 10:7

10:22 22:3

dietary 122:19

differ 90:6 144:16

difference 7:7 13:4

22:3 39:1 58:14

58:17,17 70:7

79:7 83:5 94:15

95:10 97:2 131:3

140:16 142:8

differences 56:13

124:21

different 9:16 10:3

10:6 22:11,13

32:10 47:17,21

56:7,16 61:4 70:5

77:4,5,8 83:8

88:15,17,19

103:12,13 112:11

127:15 133:21

143:18 148:9

159:7 161:10

164:17 165:9

166:2,3,6

differential 108:11

differentiated

47:16

differs 90:1

difficult 12:22

21:14 28:17 29:9

144:7

digest 169:19

digesting 165:6

dilution 85:20

dip 111:4

direct 101:18

direction 124:12

136:1 149:11

directly 34:20

96:13 146:11

director 158:17

directors 158:18

disadvantages

137:7

disagree 22:21 23:3

104:9

discontinuous

122:4

discount 142:11

discussed 9:11,13

9:15,18 42:22

88:10 110:8

130:22 142:6

discussing 9:21

60:9

discussion 4:11,12

6:3 19:3 47:3

64:5 74:9 77:12

83:16 84:20 114:2

131:18 150:4

164:5

discussions 5:6,7

8:2 13:13 17:10

17:17 20:10 28:13

distinction 86:2

87:17

distinguish 74:11

distribution 43:6

44:7 59:5,5 61:1

66:22 67:20 91:3

91:4,8 128:1,6,21

128:22 166:8

distributions 40:12

59:2 89:11 147:7

diverse 169:14,14

divide 11:19 93:11

division 122:7

docket 1:10 31:11

33:18 40:5 43:15

47:11 55:21

document 8:22

12:5

documents 92:4

doing 26:16 30:16

32:14,15 44:5

46:20 55:10,18

74:13 104:5,11

107:11 116:2

120:3,4 122:17

127:11 142:5

152:15 153:2

156:15 158:9

159:20 160:18,21

161:4,4 166:10

169:16

Don 64:2 136:3

door 171:8

dose 15:12 16:8

17:2 19:14,15

21:4 23:10 24:7

26:17 28:22 29:3

30:12 81:22 83:18

83:20 84:1,5,6,12

105:1,5 162:17,18

dosed 24:3

doses 162:15

dosing 17:21

double 81:18 82:4

downstream 6:20

Dr 4:7 6:1,1,11,12

6:17 8:3,8,12,13

10:11,14,21 11:15

11:15,16,22 12:2

12:8,9 13:10,11

14:13,15,21 20:11

20:13 21:2,7,14

21:21,22 22:1,9

22:14,15 23:6,7

24:1,10,11 25:10

25:11,20 27:10,11

30:14,14,19 31:1

31:2,21,22 32:16

32:17,17 33:8

35:3,7,8,9,13,14

35:15,16 37:16

39:11,13,18,18

40:2,3,13,17 41:4

42:2,4,4,5,14,16

42:17,18 43:8,13

43:13,13,16,17
202-234-4433

Neal R. Gross & Co., Inc.

Page 179

44:13 45:1,9,14

46:3,9,12,15,19

47:6,6 49:5 52:17

52:18,22 53:1,2

53:22 54:4,9,12

56:1 57:17 58:3,3

58:12,16,21 60:6

60:7 61:12,12

62:10,11,19 63:3

63:7,8 70:20 72:2

72:4 73:11 79:13

79:14 82:2 83:14

83:15 85:5,7

89:17,18 94:5,6

94:14 98:21 99:16

99:17 102:22

103:3,4 104:21,22

105:8,9,16 107:2

107:15,19 108:7,7

108:9,10 109:11

109:18 111:10,14

113:6,7 114:21,22

116:20,21,22

117:5,6,10,11

123:22 124:1

126:13,18 127:1,9

132:22 133:1,4

134:19,20 135:7,8

137:18,19 145:4

145:12 147:8

148:1,4 149:8,14

149:15,18,19,20

150:21,22,22

153:11 154:9,21

155:14 156:5,7

158:6,7 163:1,2,5

163:7,20,22

164:13,15,16,19

164:21 165:7

166:17 167:2,3,5

167:8,9,16 168:4

168:5,12,15 169:3

169:11

draft 8:22 12:5

drainage 46:2

drainages 82:6

draw 58:10 117:22

155:4

drinker 157:9

drinking 1:7 9:7

15:14,18 35:18

44:16,17,20 57:13

63:12,13 81:16,17

84:10 85:11 87:20

88:14 89:3 96:8

99:8 122:11,12,17

122:19 123:4

137:8 150:10

160:3

drinks 157:10

drive 18:8 164:8

driven 102:11

164:4 167:18

drives 77:15 83:21

driving 100:8

169:4

drop 54:18

dropping 168:10

drove 164:6,6

dry 83:12

due 130:8

duration 5:21 7:17

19:10 22:7 30:7

59:14,20,22 89:16

97:1,3,6,8 102:2,7

150:5 165:18

durations 58:22

59:18 60:3 88:19

137:9 161:10

164:3

dying 158:5

dynamics 98:9,16

D.C 1:17

E

E 1:22 2:22

earlier 58:1 110:8

128:11 131:18

147:9 167:10

early 31:18 75:18

84:7

eased 97:7

easiest 91:1

easily 162:5,10

easy 64:16 67:18

134:16

eco 41:14 44:17

55:19

ecological 44:18

96:21 113:3

ecosystem 57:14

effect 18:6,8 19:11

21:4 22:19,21

23:14 24:5 26:3,8

77:9 79:22 105:11

159:13,15

effective 92:20 93:3

effectively 124:3

effectiveness 95:14

121:17 148:9

effects 1:6 18:12,14

18:16 19:6 22:4,7

24:13 25:3,20,22

26:3 56:16 69:1

84:4 122:20 137:4

157:4

efficient 33:2 155:7

effort 20:2 118:19

151:8

efforts 152:5

eight 8:7 97:18

158:12 162:6

either 6:16 54:15

104:13 126:5

153:4 156:15

elaborate 8:10

elevated 89:8

elevation 74:8

eliminate 52:3

141:1,7

embryo 24:6

empirical 106:1

144:7

encapsulate 22:2

encapsulating 9:9

encroach 81:5

ended 47:18 129:11

endocrinologists

11:18

endorsement

139:20

endpoint 7:9 20:20

130:22 163:9

endpoints 6:21,22

7:13,14 21:16

137:1 162:16

ends 172:5

energy 101:16

enormous 169:13

ensure 131:11

153:15 160:5

161:7 162:19

entire 7:12 10:17

36:1 50:13 56:15

entirely 60:17

entitled 39:15

40:18

environment 131:2

environmental 1:1

113:4

envision 133:17

EPA 1:1 4:20 5:3

32:8 41:7 61:3

91:11 95:4 96:7

97:14 100:15

105:19 110:9

122:7 129:20

134:9 147:19

158:4 171:19

EPA's 81:19

EPA-HQ-OPP-2...

1:10

epidemiology

170:15

episodic 80:11

equal 19:22 38:6

68:12

equally 62:13,17

equivalence 7:19

equivalents 20:5

26:18

error 68:3,7,18

103:10 119:5

142:12 156:1

errors 141:2,4,8

especially 9:20

12:17,22 23:15

46:3 93:18 97:20

99:6

essentially 15:6

16:9 17:1,9 84:3

108:14 144:13

145:4,5 162:2

establishing 61:13

estimate 52:13

60:22 63:4 81:4,6

81:8 90:2,4,9

91:16 93:7 95:22

96:2 97:9 100:2

100:10,18 103:20

104:3 124:9

133:17 135:17

139:22 144:6,7,22

149:4

estimated 42:3

79:2,4 89:21

103:17

estimates 34:19

41:21 89:14,19

101:8 126:2,6

133:14,20 140:21

estimating 44:5

88:15 134:18

142:1 154:17

estimation 134:14

144:9 148:22

estrogen 28:22

29:3

et 32:17 40:3 41:7

145:3

evaluate 8:19 61:10

72:10 73:3 95:8

96:16 134:15

evaluated 14:5

84:12 124:19

evaluating 3:9 9:1

13:15 87:12

124:11 137:7,11

137:16 148:8

evaluation 40:4

84:13 161:22

162:1 170:10

evaluations 84:15

evening 6:8

event 122:5 130:2,3
202-234-4433

Neal R. Gross & Co., Inc.

Page 180

167:11,14

events 6:21 7:15

8:5,7 10:4 15:7

16:22 26:6 42:10

82:9 83:6,21

88:17 112:12

147:7 162:16

eventually 125:10

everybody 88:4

116:4 135:17

evidence 8:22 9:14

9:17 13:4 20:21

85:13 105:10

evident 150:6

exact 142:11

exactly 32:11 34:1

50:6 53:22 135:14

139:19 151:9

examine 23:18

examined 32:9

examining 88:17

example 15:17 16:2

17:14,18 26:1

43:7 45:5 50:7

51:11 55:18 65:9

65:13 66:4 74:21

75:1 81:2 115:15

128:10 140:4

142:15 145:2

170:3

examples 100:14

115:17,17,20

116:5 124:21

138:20 143:5

150:8

exceed 30:5 59:8

68:12 98:8

exceedance 133:22

exceeded 134:13

exceeding 134:7

Excuse 84:16 110:1

exercise 43:21

exercises 64:15

65:19

exhausted 29:2

exist 114:13 144:1

existing 13:9 55:11

100:5 124:4 150:8

150:15

exists 118:12

expand 123:5

expect 44:4,22 70:2

75:8,22 100:19

170:17

expected 53:2 71:3

97:4,22 101:20,21

110:22

expensive 86:14

experience 132:13

experiment 17:9

60:15 61:3 73:14

experimental 1:6

28:19

experiments 17:17

25:15 60:13 72:16

100:7 115:9

167:21

expertise 130:15

explain 77:13

130:5

explained 123:15

129:1

explains 129:7,8

explanation 47:9

53:5

explicit 107:14

170:10

explored 94:2

exposed 98:17

exposure 7:17 16:1

18:19 21:7,11

22:5,7,10,19

35:17 36:5 39:15

39:17 72:10 78:1

78:3,13,13,14

79:16 80:11 89:14

89:19 94:22 95:5

95:8 96:1,3,16,19

96:21 99:21 107:8

107:17 118:20

122:18 126:2

133:14,17 135:17

135:17,19 137:3,8

163:17 164:3

167:11

exposures 17:13

18:16 40:19 78:11

122:19 167:12

extended 15:20

100:15

extent 9:3 67:15

122:16 137:12

external 84:6

extra 79:22

extraordinarily

12:15,21

extrapolate 28:14

28:18

extrapolating

117:1

extreme 37:7 91:17

92:15 93:17

113:16 147:4,6,13

153:22 154:1

160:12

extremely 130:1

extremes 59:21

eye 122:15

F

F 2:2

fact 12:7 13:12

22:18 87:10 96:5

97:22 123:2 129:3

167:10

factor 7:19 57:9,16

129:13

factors 28:12 95:15

106:2,4,6,22

127:15 133:6

137:13 148:18

161:8

fail 121:20

fair 144:15 147:5

fairly 48:16 152:1

152:13 162:10

164:22

fairness 32:5

fall 159:18

familiar 147:19

far 73:22 78:3 83:7

93:12 99:1,22

144:2 150:14

154:3 156:12

farmer 82:17 83:2

83:11

fast 111:8

feasible 110:19

111:1

February 37:10

Federal 1:3 2:22

4:6

feedback 9:22 10:5

88:19 111:13

169:13,20

feel 116:14 117:12

161:17

felt 8:5 21:10,18

157:20

Fenner 11:22

FENNER-CRISP

2:4 12:2 163:2,7

163:22 164:15,19

166:17

fetal 22:6 26:3

fetus 26:2,2

fewer 40:12

field 25:17,17

64:19 65:8,11

82:18 83:2 112:10

119:7 134:9 144:4

fields 83:12

FIFRA 1:4,19 4:4

125:5

figure 8:16,18 9:8,9

11:1 12:6,10 14:1

18:7 35:4 93:3

138:21 151:8

161:21 166:16

168:1

figured 21:11

figures 52:21

figuring 88:3

fill 104:4,7,14,16

filled 41:18

filling 137:22

filtration 86:7,14

final 62:9 72:8 90:7

105:15 146:4,4

157:18 171:5,9,12

finally 168:1

find 35:5 36:4

50:17 117:3

132:14,18 161:10

finding 57:21

140:19

fine 116:6 131:21

131:22 151:11,12

finer 118:10

finished 33:22

41:11 45:7 55:17

57:11 59:18 74:14

74:15,19 75:4,5,8

80:7 85:11,14,18

87:4,20 95:3,8,12

124:22 168:7

finite 71:9

first 6:3,17 15:10

31:1 33:5,15

51:18 55:20 56:9

58:22 66:10 74:10

76:6 78:9 79:15

79:20 90:5 94:6

99:19 108:22

114:10 120:12,15

121:1 126:14,17

152:6 158:15

159:22 169:17

fit 18:12 28:16

143:6,10 166:2

fits 116:17 131:5

140:17,17 166:10

166:14

fitting 140:13

141:1,20 142:11

143:19

five 6:13 32:8 59:13

66:20 69:5 71:3

71:19 171:8

five-day 50:3

fixed 133:8 155:12

156:2

flashier 131:10

flexibility 143:3

flip 158:22 159:2
202-234-4433

Neal R. Gross & Co., Inc.

Page 181

164:18

flow 129:21 137:15

148:20 156:15

169:6

flowing 34:6 35:22

56:4

fluctuation 72:19

fluctuations 155:9

flux 112:5

focus 19:14 84:3,9

124:14 162:13,19

focused 17:11

focusing 6:20 18:22

85:14 160:1

folders 55:15,15

folks 107:17

follicular 29:4

follow 17:16 104:1

104:8 115:4 124:8

165:12

followed 31:2,2

48:15 119:16

139:3

following 38:15

77:1 82:9 138:13

139:2

follows 34:1 48:11

75:5

follow-up 58:3

food 107:5

foregoing 84:22

172:7

forever 158:22

forget 33:14 136:11

forgot 66:15

form 7:13,18 16:3

142:22

forth 18:2 20:1,5

26:15 50:12 52:9

55:6 84:1 101:17

156:16

forward 29:14

84:14 160:1

171:17

found 51:22 70:7

92:11,13 143:7

151:7

four 5:12 8:7 17:22

24:14 28:10 45:13

65:10,13 66:3,5

66:20 67:2,20

69:3,7 71:17,18

71:20 108:7 117:9

118:5 126:14,15

141:17 161:21

fourth 4:11

four-day 17:9,13

17:16,18,21 18:22

19:3,10,20 22:18

25:5 28:6 50:3

67:21 69:10 77:18

77:20,22 78:13

79:1,3 116:3

118:3 132:1,2

four-node 140:17

FQPA 2:1 107:5

frame 73:3 90:20

102:15 137:3

161:3 163:17

frameworks 13:14

Frankenberry 31:3

63:21 64:6 66:14

66:18 71:5,16

73:17 143:6

free 169:6

frequencies 126:3

frequency 1:8 3:10

5:21 9:6 14:17

15:2 17:18 19:10

19:20 27:16 36:5

39:16 40:4,19

41:1 50:2 78:20

79:9 81:6 87:13

88:11 100:11

136:22

frequent 25:6

80:19,20 87:15

97:21

frequentist 94:16

frequentist's

108:14

frequently 81:9

front 28:8

full 4:11 5:7 33:7

34:3 35:1,20 39:6

40:2 59:2 143:21

150:11

fully 14:4 94:2

fun 25:12

function 7:1 9:22

141:12 144:12

153:2

functional 11:2,13

functions 145:7

FUNGICIDE 1:3

funnel 18:10

further 18:5 23:18

36:2 40:14 46:10

47:1 93:9 100:15

124:19 130:12

138:12 149:11

future 120:1

127:21

F.L 2:14

G

G 1:17,20

gamble 84:18

gaps 41:18 104:5,7

gathered 166:21

Gaussian 141:20

142:3 143:2

gems 168:10

general 60:8,14

61:7 97:10 103:1

116:9 148:10

generalities 114:12

generalizations

143:2

generally 115:5

122:17 138:22

generate 60:12

119:13

generated 42:8

43:2 81:13 115:20

generating 164:12

generation 170:7

generator 126:16

gentlemen 95:6

geographic 150:12

geostatistical

151:20

GERALD 1:22

getting 43:20 46:20

55:19 63:15 68:2

69:20 71:3 82:17

101:19 112:20

113:12 135:10

140:20 171:18

Gilliom 2:4 41:4

60:6,7 61:12 72:3

72:4 73:11 76:7

87:5 99:16,17

102:22 108:11

113:6,7 114:21,22

116:22 123:22

124:1 132:22

133:1 145:4,12

149:8,18,19

153:11 156:6,7

gist 5:9

give 4:13 8:16

28:21 29:3 45:17

82:15 100:17

128:19 138:3

145:17 153:6

155:10 158:8

given 6:19 16:16

32:4 34:20 38:12

43:8 50:9 62:1

68:14 83:22 89:4

90:2 95:21 96:4

100:10 114:13,20

127:14 131:11

134:7 138:21

145:3 148:9

155:22 158:21

gives 96:18 100:21

101:18 134:10

135:22 152:19

giving 32:3 154:2

158:19

glad 114:9 126:11

gland 167:19

gleaned 108:20

global 146:19

globally 103:19

144:9

go 4:19 5:18 6:10

7:12 8:14 12:4

21:18 31:8 32:22

34:14 35:14 37:12

38:13 46:15 47:10

50:12 55:15 64:7

68:10,15,17 70:3

70:16 77:11 78:4

78:17 80:21 84:13

103:11 110:16

112:10 113:4

121:3 122:7

123:16 142:9,16

142:17,21 143:1

160:8,13 161:5,12

162:12,19,20

163:3 168:12

169:9 171:7

goal 8:21 9:2 90:12

goals 131:8

God 126:20

goes 93:11,12 132:6

157:1

going 4:15 5:16,17

6:2 13:6 14:19,20

18:8 19:19 21:16

26:6 29:13,15

30:4,5,10,15,20

31:11,19 32:14,15

32:19 37:1 39:21

40:9 47:15 50:6,8

51:10 53:18,20

54:5 56:9,19 61:8

62:22 72:13,21

73:13 74:6,16

75:16 76:2 78:12

79:12,17 81:2

82:21 88:4 94:17

94:18 101:14

103:13 104:2,7,8

104:17,18 112:7,9

112:10,11 115:1

116:14,15 120:14

120:22 121:2,11

127:18 128:2,12

128:15 133:13,15

133:21 134:2
202-234-4433

Neal R. Gross & Co., Inc.

Page 182

135:3,18 139:8

140:1 141:4,14

144:2 145:17

150:5 151:14

152:22 157:18

158:18 161:5,11

162:20 164:1,14

164:17 168:19

171:7

good 4:9 75:1 77:21

97:19 100:14,17

108:6 116:5,11

118:11 125:15

132:2,12 140:10

140:20 151:4

152:1,19 153:17

154:2 170:17

gosh 136:14

gotten 67:10 108:6

grab 156:13

graciously 8:3,8

graph 67:18 85:9

graphs 152:20

great 63:15 90:21

92:9 97:13 117:1

148:3 155:21

158:21 162:13

170:20

greater 105:12

155:10 156:1

greatest 90:18

Greenwood 2:5

20:11,13

grid 49:14,17 50:10

52:4

group 14:5 47:2

79:16 124:15

133:18 158:16

171:2

groups 48:6 56:4

161:19

growing 36:15

150:11

guaranteed 142:16

142:20

guess 10:20 18:1

85:4 102:3 133:1

140:8 143:13,15

146:19 149:19

150:2

guidance 92:9

134:10

guideline 162:4

guidelines 162:3

guys 123:21

H

H 2:2,8

half 5:11 59:19,20

60:4 64:4,4 81:15

half-life 15:19,20

Hamilton 1:15,15

hand 115:14

133:21 169:18

handed 31:9

handle 63:15

handout 35:2,5

36:7 39:20 43:7

43:14 45:4 47:10

53:7 55:21 56:8

59:1,9,10,13

handouts 32:4 64:9

hands 39:9

happen 67:14

76:19 119:7 152:1

happened 55:13

happening 74:20

119:22 120:21

122:5

happens 37:11

68:22 118:9

hard 5:1 54:13 93:1

129:20 171:15

harder 135:1,14

hardest 26:17 57:5

harvest 90:19

havoc 165:20

Hayton 2:6 104:21

104:22

hazard 20:18 21:5

107:7,17

head 170:13

heads 20:14

health 1:6 9:5

29:21 69:1 88:6

123:10 130:17

135:20 137:1,4

161:9

hear 8:11 12:13

164:22

heard 27:22 86:10

100:6 106:10,11

165:22 169:12

heartedly 132:5

heavily 113:12

160:1

Heeringa 1:17,20

45:1,2,2,19,19

46:7,9 58:4,5,13

58:22 70:20,21,21

71:13 82:2,3,3

105:17,18,18

108:7,8 111:10,11

111:11 112:2

154:21,22

Heidelberg 41:15

45:21 48:8 55:19

57:12 65:3 73:19

150:9

hellishly 29:8

help 70:15 77:12

144:18 146:2

148:21 149:3

helpful 10:9 140:22

169:13

helps 78:15

Hendley 31:1,21,22

32:1 33:8 35:7,9

35:14,16 37:16

39:13 40:2,13

42:4,18 43:17

45:9,9 46:3,12,15

46:19 56:2 57:17

57:18 58:12,16

63:8

HERBERT 2:11

herbicide 128:18

herbicides 128:20

132:17

herring 27:18

hesitate 18:9

Hey 85:4

hierarchical 10:1

hierarchy 10:3,5

high 33:17 35:19

67:8 73:9 96:18

97:4,22 101:17

111:5 130:1

133:18

higher 62:14 69:13

74:8 98:19 114:17

143:12 144:22

highest 68:11 71:8

highly 98:2 100:8

144:8

hinging 119:19

histogram 52:2

history 131:12

hit 51:10 79:17,19

79:20,21 108:21

117:20,21

hits 83:17 166:4

hold 39:9 106:20

117:9 158:22

holding 86:5 96:9

96:10 109:20

124:22

HOLLADY 2:7

home 108:21 165:6

Honey 45:22 46:4

48:8

honor 161:1

hope 30:14 64:7

126:11,20

hopefully 4:11 5:11

5:13 52:3 64:13

hoping 64:16 77:12

Horton 2:8 8:3,8

8:12,13 10:21

11:15,16 21:14,21

21:22 22:1 167:3

167:5

Hotel 1:16

hour 5:11 21:15,20

64:4,4 94:14

hourly 111:9

hours 6:9 109:10

house 8:9

HP 27:15,18

HPA 79:15,18

137:3

HPA/HPG 17:8

hugely 28:16

human 1:6 9:5

13:15 26:18 95:5

95:8 96:16 161:9

166:20 170:18

humans 7:8 9:20

19:9,17 23:16,16

28:6,14 96:1

127:16 131:2

human-based

21:13

hundred 29:22

91:22 105:3,3

160:2

hundred-fold

25:16

hydrologist 139:4

hydrologists 32:6

32:10 33:21 94:8

94:10

hydrology 4:21

5:12 30:20 47:2

94:9,19 129:2

130:4

hypothetical 69:1

I

idea 4:14 8:16

25:17 54:15 94:16

100:21 104:22

151:20 152:1

165:14 166:1

168:5,17

ideas 4:18 94:1

151:2 165:22

identical 48:11

Identification 3:4

identified 41:20

identify 10:8 46:17

101:15 110:3

124:5 151:5

Illinois 56:14

illustrated 45:3
202-234-4433

Neal R. Gross & Co., Inc.

Page 183

illustration 72:7

76:16

ILSI 92:6 100:13

image 46:9

impact 17:13,22

30:8 78:12 110:13

implant 165:3

implement 141:5

144:3

implication 150:4

implicit 14:19

importance 60:15

140:9,19

important 13:17

21:4 26:16 28:15

60:10 61:13 90:9

94:13 103:8

117:16,21 129:13

133:11 134:2

138:9 146:12

148:11,16 150:13

151:16 156:8,17

156:20 164:22

impressed 169:22

impression 96:18

138:21 139:5

improve 120:22

154:11

improved 146:1

154:8

improvement

152:16

imputation 138:2

impute 121:11

inadequate 120:20

inappropriate

96:22

incidentally 32:16

include 7:15

included 88:15

149:22

includes 9:1 95:16

121:14

including 89:7

112:4 124:19

inclusion 7:22

increase 110:16

111:6

increased 107:12

135:10

increases 97:7

155:8

increasing 84:18

110:21

independent 93:10

147:14

Indiana 56:14

indicate 9:16

indicated 108:10

indicates 9:4

indirect 100:4

101:6 134:1

individual 30:8

61:2 70:4,16,19

76:12 124:13

131:15 144:3

145:19 152:5

162:21

individually 58:18

146:17

induce 137:4

163:17

infants 9:2 10:17

inference 100:4

117:22 150:15

infestation 128:17

inflated 93:14

influence 77:21

inform 167:1

information 13:16

14:10 82:10

107:10 108:20

114:14 117:18

119:12 121:14

130:14 146:2

149:3,6,10 156:20

156:22 163:14

168:3 170:18

informed 168:3

inherent 92:17

initial 82:21,22

100:17 115:13

139:2

input 84:11 123:17

inputs 146:13

INSECTICIDE 1:3

instance 62:15

106:10 156:18

instantaneous

156:13 157:2

instruction 38:11

instrument 128:1

intake 95:2,4,11,22

96:17 124:21

intakes 96:13 130:9

integrate 16:8 30:9

integrated 15:22

18:21 81:21 89:1

integration 84:10

intend 61:2

intended 21:6

159:16

intense 101:16

102:8,20 103:1

intensify 112:19

intensities 83:6

intensive 6:9 76:3

82:13 97:4 106:14

106:16 153:12

intensively 150:8

157:22

intent 10:15

intention 61:16

interact 9:22

interest 54:21

interested 7:9 50:1

80:2 93:5

interesting 75:17

167:22 169:1,4

intermediate

166:12

internal 15:12,22

17:2 83:18 84:5,6

International 38:2

interpolate 132:4

interpolated 34:18

34:19 36:20 49:12

66:3 115:8,22

interpolates 141:20

interpolating 92:12

136:19

interpolation 36:1

36:18 38:18 41:19

43:2 48:12,18,21

56:21 66:1 70:6

104:12 138:1,17

139:8,16 142:5,7

142:9,18,19,20,22

143:9 151:11

interpolations 65:5

interpret 53:15

78:16 86:3 89:2

160:7,8 164:9

interpretation 23:4

160:15

interpretations

32:13

interpreted 159:6

interpreting 60:10

interquartile

153:14

interspecies 108:1

interval 68:14,16

68:19 69:10,15

70:1 125:22 126:1

127:11,13 135:15

intervals 34:22

36:14 67:22 91:5

97:7 108:15,17

inter-individual

108:2

introduced 14:2

Introduction 3:4

invariant 62:21

investigate 147:20

149:12

invite 14:21

involve 56:20

143:22

involved 13:12

125:8 149:9

involves 141:3

146:13 147:14

in-between 83:18

104:14,16 146:21

160:15 166:5

Iowa 99:9

isolated 165:17

issue 8:1 26:5 62:3

88:10 89:6 94:21

113:9 114:2

125:21 133:22

142:6 144:5

146:12 156:3

158:5

issues 5:21 12:15

14:14 22:17 63:18

113:3 159:6

item 114:2

iterate 131:19

i.e 88:22

J

J 2:4,15,16,17

JANICE 1:22

January 37:8,10

JEAN 2:14

job 92:2,8 151:1

Joe 4:5 64:10

126:16 170:22

171:12

JOHN 1:21

join 170:16

joined 170:14

Joseph 2:22 3:3

Journal 147:11

July 34:10 37:17

jump 61:2

K

KANNAN 2:9

Kannan's 165:13

keep 31:4 42:10

55:4 72:13 78:17

93:13 122:15

168:18

keeping 161:2

168:17

keeps 168:9

Ken 10:20 73:7

87:21 102:19

109:5 112:22

120:10

Kenneth 1:17,21

2:3 3:5

KEVIN 2:12
202-234-4433

Neal R. Gross & Co., Inc.

Page 184

key 7:15 8:5,6 10:4

12:16 13:3 15:7

15:16 16:22 34:16

36:9 40:7,21

89:20

keys 167:18

kicking 139:20

kids 25:13

kill 21:9

kind 4:14,16 17:12

17:19 27:5 29:14

29:15 30:4,18

31:4,15,20 44:11

53:17,19 68:16,18

69:19 73:6 77:12

79:6 99:8 100:12

100:18 106:18

109:3 110:20

115:1 120:14

121:1,4,12,14

122:4,6 123:15

128:20 132:20

134:4,17 135:22

151:2 153:5,6,7

153:20 154:10

165:10 168:1,18

kinds 132:17

163:12 164:7

knew 122:13

knocking 86:13

know 5:17 8:13

11:6 12:2 14:8

15:1 17:21 19:5

20:8 23:20 28:18

29:7,10,22 30:7

30:16 31:18 49:17

51:5,9 56:15 57:5

57:10 59:1 60:1

63:22 74:20 76:14

81:12 82:12,14

90:20 91:2,9

102:15 103:11

108:9 110:2 111:8

111:20 117:15

120:11,19 121:6

121:22 123:18,20

123:21 127:21

128:3 130:19,20

130:21 133:2

143:14 144:14

150:17 153:14

154:4 155:17

156:18 157:6

158:6 162:15

163:8 164:13

166:13,15 168:10

168:16,21 169:18

knowing 54:12

77:15 78:14 91:7

knowledge 118:14

146:7

known 88:16 124:5

knows 119:8

kriging 70:15

141:12,19 142:21

143:7,20 144:5,11

151:19

Krishnan 2:9 6:2

14:13,15 21:2,7

22:9 24:10,11

30:14 83:14,15

165:7

Krishnan's 167:8

K.H 2:11

L

L 2:6

lab 80:9

laboratory 113:22

166:21

lack 8:1

lag 109:8 110:17

Lake 41:6

lamppost 168:2

landscape 129:18

large 28:21 29:3

41:8 91:20 109:19

109:20 130:3

161:3 162:18

largely 32:22

larger 71:6 75:19

96:10,17 99:5

114:17 130:7

131:7,16 138:3

140:2 141:15

largest 129:7

lasso-type 152:18

late 79:14

laugh 66:15

Laughter 6:14

11:21 66:13,17

94:11 109:14

126:22 168:14

169:10

Laura 170:22

laws 146:8

layer 123:7

lead 17:17 138:5

156:1

leads 135:16

141:16

learn 103:18

learned 169:21

leave 59:10 116:10

116:18 127:9

136:5 171:6

LeBLANC 1:22

led 35:1 167:21

Lee 2:11 52:17,18

53:1 58:3 103:3,4

117:5,6 134:19,20

137:18,19 148:1,4

149:20 150:22

154:9

left 32:16 156:22

162:17

length 49:11

lengthy 162:10

let's 65:9 68:10

77:11 90:12

125:18 137:20

143:4 169:9

level 9:15 19:5

29:21 30:3 70:16

70:19 85:10

107:20 118:1

121:21 123:18

126:7 131:1 135:1

145:19 148:13

166:1

levels 10:3,6 15:9

17:4,14 20:18

59:8 70:6 86:13

98:14 123:20

131:13

LH 15:9 17:4,14,22

18:16 23:12 24:15

28:22 29:6 164:6

167:20

life 22:8 161:9

170:11

lifestyle 81:13,20

light 167:17

limit 93:21 126:7

135:15,20 139:20

limited 72:22

limits 106:3 111:2

123:11

Linda 2:17 35:3

58:3 120:12

131:20 152:18

line 77:16,18 78:22

79:3 165:13

linear 12:19 36:17

38:18 41:18 43:1

48:12,18,20 49:12

50:9,15 56:20

61:15 66:1 104:12

138:17 139:7,15

142:19 143:9

linearly 64:20

65:22

lines 46:17

link 19:20,21 47:12

list 126:18

listened 168:16

listening 27:11

literally 162:4

literature 122:14

147:6,14,18,20

liters 105:4 107:21

157:10

litter 24:7

little 4:14 7:3 8:6

13:21 14:19 26:14

47:16 52:2 54:14

59:22 61:4 70:11

74:8 79:20 80:2,3

80:5,13,16 82:15

92:16 93:17 110:7

120:5 121:11

122:1 127:18

128:19 139:9

158:11,19,20

163:8 166:19

170:13

live 69:9

LOC 126:9 127:5

127:14,17 133:5

156:21

located 1:16

LOEL 28:8

long 49:8 53:19

97:8 114:7 117:17

123:12,14 137:9

137:19 151:12

154:18

longer 75:19 77:15

98:18 139:14

longest 152:7

long-term 22:4

128:8 155:7

long-terms 7:16

look 7:12 8:17 12:5

14:9 40:8 45:8,17

53:15 56:9,17

59:14 65:9 67:19

68:4,16 69:11,17

70:12 74:3 75:18

78:19 82:4 86:2

88:1 97:13 99:7

107:1 120:4 123:3

129:6 134:8,13

144:15 145:6

147:16 171:16

looked 24:2 34:5,7

42:9 50:2,5 58:18

59:1 67:6,7 68:8

71:7 113:3 120:10

123:2 126:20

148:19 153:15,16

154:4,12

looking 11:7,8,8

30:3,11 45:15

50:20 54:2 60:20
202-234-4433

Neal R. Gross & Co., Inc.

Page 185

62:20 67:11,19

69:2 70:1 75:12

76:2,9,14 77:14

77:18 79:6 80:21

81:3,19 86:21

94:3 103:9 106:12

106:13,17 145:12

145:15,16 146:8

147:17 149:4

154:14 156:12

163:14 164:2,10

167:17

looks 107:10

126:14 136:12

147:12

lose 57:2 156:4

lost 35:8

lot 6:10 11:10

20:16,19 21:8

25:22 31:13 56:18

61:1 64:8 70:8

83:9 100:6 103:18

107:22 111:20

115:1 120:14

124:7 128:12

130:14 132:12

135:14 142:7

143:8,14 145:14

145:20,21 154:16

158:1 160:7,22

166:4,4 167:20,21

169:17

lots 169:19

Louis 44:15 48:2

low 45:10 52:8 67:8

98:7,11,14,17

lower 62:15 68:12

162:15

lowest 57:4,6,14

68:13 69:6,12,17

71:7

Lowit 107:2,2

158:6,7 163:5,20

164:13,16,21

168:15 169:3,11

M

M 1:17,21

magenta 75:2

76:19

magnitude 54:13

67:14 70:12 78:8

105:11

main 26:13 34:2

maintenance 95:19

major 11:4 153:18

159:22

making 11:10

27:15 87:1 118:8

154:9 163:8

mammary 12:6

167:19

man 64:3

managed 153:19

manager 109:10

110:5

managers 165:20

march 37:11 50:13

Marry 143:6

Mary 31:3 63:21

64:1

mass 27:20

masses 12:20

massive 85:20

master's 24:1

match 146:15

170:19,19

matches 153:7

material 31:13

materials 132:17

maternal 25:22

matter 95:17 131:2

139:10

maturation 24:17

Maumee 48:9

max 50:18,18,19,19

50:22,22 51:7,14

51:15,20,21 52:7

52:7,13 60:1

61:17 62:20,21

67:12 68:12

109:16

maxes 51:6

maxima 145:13,16

146:10

maximum 36:21

38:21 39:2,2,4,5

42:9,11 44:1,5,6

44:10 45:10 53:21

66:8,21,22 67:3,7

67:10,21 68:9

138:5,6 139:11,12

139:22 140:15

142:1 143:11,18

144:18,19 149:4

ma'am 110:2

MCL 98:8

mean 18:6 19:21

21:1 27:22 43:9

44:11 59:22 63:3

69:4 74:12 82:16

83:17 85:18 93:12

102:21 108:12

111:7 121:3,19,21

123:9,14 125:7

135:12 161:11,13

means 57:8 150:14

161:20

meant 72:7,9 87:18

measure 15:22

83:18,20 84:5,6

103:11 140:22

168:8

measured 15:8

16:14 41:17 76:11

76:12

measurement

77:17 102:21

103:10,19 119:5

142:12

measurements

17:1 34:21 77:22

117:4 133:19

138:1

measures 108:16

165:2

measuring 168:7

mechanism 29:19

mechanisms 22:12

median 54:3

109:13,16

meet 102:16

meeting 3:2 4:5 5:9

5:14 8:20 14:2,3

136:5 158:15,16

171:8,16,21 172:5

172:7

meetings 169:18

170:1

Melanie 110:4

member 157:19

members 1:19 2:1

3:5 4:22

MENDEZ10:11,14

mention 140:7

147:2 155:2

mentioned 15:16

41:10 44:14 56:2

101:2 120:12

129:5 132:7,11

146:5 147:3,8

148:11 153:11

mentions 141:10

Mercifully 157:15

merits 114:16

136:16

message 165:3

metabolite 15:5

16:12

metabolites 7:18

15:11,21 16:3

method 117:1

127:20 134:1,8,14

138:11,14,19

140:2,4 142:22

146:4,5 168:13

methodology 40:8

40:11 110:18

111:1,5 120:12

121:2,18 123:11

152:13 153:20

methods 70:14

88:11,15 92:18

93:4,16 101:11

114:10,19 115:3

124:11 136:21

137:11,22 138:2

139:7,16 141:14

141:17 142:18

147:12 148:9

151:10,11

metric 84:12

metrics 26:18

mice 25:15,17

microgram 105:4

micrograms 105:4

microphone 31:2

163:16

middle 47:12 54:19

153:18 164:6

mike 4:7

miles 46:6

milligrams 28:9

million 29:22

millions 160:9,9

161:12,12,12

min 109:16

mind 17:5 20:9

28:20 34:17 78:17

98:4 164:1

mindful 7:16,17

minds 33:21

mini 163:12

minimum 67:7

91:21 118:5

minor 154:22

156:3

minus 68:6,15,18

71:20,22

minute 84:17 165:2

171:8

minutes 6:13 8:17

31:5 148:4

misinterpreted

25:9

misinterpreting

85:15

misleading 96:18

misread 25:9

missing 47:11 53:5

72:18 78:11

138:12 139:10,12

140:15

Mississippi 128:13

Missouri 41:12
202-234-4433

Neal R. Gross & Co., Inc.

Page 186

44:2 45:7,10 48:4

56:22 75:21 99:7

99:10

mixed 12:22 34:6

56:4

MOA 8:19 12:6

13:13,15 14:1,11

18:7,10,11 164:11

MOAs 13:17

mode 7:4,6 8:7 9:9

9:10,12 26:7

163:10 167:18

model 26:16 28:14

73:8 84:11 96:11

101:2 104:4,6,11

104:12,14,17

112:3 118:12,16

124:2 132:4 134:1

137:7 140:21

141:20 143:20

144:1 145:5,7

151:5,18

modeling 48:20

104:10 117:8

119:12,15 121:3,9

121:14,17 136:17

137:16 147:6,13

models 97:9 101:13

118:7 124:4

141:12,13,13

143:7 144:12

145:11 146:1,1,6

147:3 154:10

moderate 137:9

modes 140:10

modified 9:10

molecules 122:3

Monday 31:15

49:19,20 50:12,14

51:3 140:8

Mondays 50:11

51:9

money 21:8 128:12

monitored 150:8

monitoring 1:7

3:10 5:22 14:17

15:2 16:20 17:18

21:12 25:6 29:18

30:4 36:13 37:7

40:22 62:3 76:10

87:12 88:21 89:14

89:20 99:22

102:11 106:12,21

110:9,10 125:7,8

126:3,7 156:9

157:1 160:4

monotonic 155:7

155:22

month 53:17

monthly 50:5

months 109:10

136:11 161:21

166:13

morning 4:9,15

31:7,18 64:10

115:5 135:11

165:22

Mosquin 33:18

43:8,16 47:17

Mosquin's 43:14

mothers 24:3

mountains 162:7,9

move 29:14 33:14

86:8 93:8 99:5

114:7 125:18

127:18 129:15

141:22 168:2

moved 38:9 128:15

129:18

moving 78:8 86:6

91:9 111:8 127:16

134:10

multi 48:1

multiple 12:20,20

99:6,7 101:3

112:3 167:12

multiplier 44:6

multipliers 44:3

multi-part 8:18 9:9

multi-year 48:6

58:19

multi-years 49:13

muted 78:11

M.D 2:16

N

nailed 159:4

name 63:22

names 126:19

narrow 16:19 68:7

152:4

nature 88:1 89:4

120:13 133:5

near 35:11 41:13

41:14,17

nearly 48:15,16

Nebraska 99:10

necessarily 24:9

61:17 83:19 86:15

87:2 103:17 141:6

142:9,17 143:3

necessary 27:7

88:12 131:6

need 5:7 9:4 10:9

27:6 32:7 34:14

39:9 80:20 91:19

91:22 93:15 94:7

98:5 100:21

101:16 102:8,15

104:6,10,13,13,15

105:7 107:17

117:2,3 120:1

122:5 124:6

130:20 131:8,20

132:5 141:1,7

144:6 146:16

157:6,9 165:9

needed 78:4,5,6

124:19 130:19,20

131:10 137:3

163:17

needs 103:14

117:19

Neither 138:2

Nelson 63:21 73:22

82:4 107:9 159:12

nervous 11:12

network 105:6

140:5 141:3 142:4

142:14 154:4,6

networks 26:6

neural 140:5 141:3

142:4,14

neuro 154:4,5

neuroendocrinol...

11:18

never 21:16 41:9

132:14,18 141:14

new 8:9,15 9:4 10:8

22:2 27:14 51:16

65:20 66:2 70:2

128:10 147:18

158:5 159:10,14

161:8,18,18

162:14 163:14

168:3

newly 9:11

news 97:19

nice 5:5 29:19 30:7

92:8 151:1 153:13

154:10,15

night 66:15 74:21

85:9 158:14 165:6

169:1

nine 37:20 40:10

158:12

NOAEL 19:6

nodes 140:12,20

noisy 142:4

non 91:13

non-expert 141:5

non-parametric

91:10 120:13

non-parametrica...

149:5

non-sampling

144:20,21

non-stationary

143:22

normal 153:13

normally 93:10

156:12

noted 118:20

notice 55:1,7 57:3

152:20

noticeable 154:14

NRDC 132:14

nth 155:5

nuance 163:10

number 13:13 31:9

32:18,20 37:3

40:17 49:6 51:14

57:4,7,14 59:14

60:11 65:14 67:19

68:4,17 71:6 83:9

95:13 99:12 109:8

117:9 122:12

126:16 127:17

140:20 141:10

145:1,11 158:11

163:19 164:6,7,8

164:12

numbers 19:19

51:8,13,18,19,21

51:22 55:2 57:3,6

60:11,12 110:11

134:18 164:2

numerical 40:1

59:2 114:16

NU-MAY 2:13

N.W 1:16

O

objective 60:16

61:9 102:13 103:2

113:15 115:20

124:6,8 150:17

157:6

objectives 116:12

134:6

observation 155:1

observations 16:16

91:19 94:19

observe 146:11

observed 138:4

140:3 141:16

142:10 143:12

146:15,22

obtained 34:22

38:22 43:4

obvious 19:18,20

19:21 24:19 102:6

Obviously 51:10

59:21

occur 10:4 22:5

71:14 118:2 138:6
202-234-4433

Neal R. Gross & Co., Inc.

Page 187

occurred 144:20

occurrence 60:20

89:5 137:8

occurring 22:10

occurs 142:2

October 169:2

offer 116:22

offered 8:3,8

office 125:11

158:16,17 159:20

Official 2:22 4:6

Oh 54:4 55:13

109:18 136:14

163:22 172:2

Ohio 44:16 56:14

75:2 76:14,15

okay 4:3 8:15 21:4

31:22 33:5,15

35:3,7,9,13 36:2,6

37:16 40:15,16

42:17 53:1,4 54:5

54:9 57:10 60:5

77:11 78:4 79:17

85:5 88:8 90:13

91:22 98:12 106:8

108:6 114:6,22

125:13 134:10,12

136:3,12,14

144:11 148:6

157:12 163:22

164:15 167:5

old 160:5 161:5

once 19:5 44:8

53:17 81:5 110:15

116:11 124:8,13

133:8 151:7,19

152:6 171:5

once-a-week 43:22

ones 41:22 52:18

68:17 100:20

115:16 131:17

143:12

one-day 52:14 53:8

79:16

one-pager 33:1

one-time 24:3

one-year 138:1

open 6:4 104:20

157:18 158:1

Opening 3:2

operators 38:12

95:18

opinion 27:17

opportunity 32:3

opposed 130:16

options 92:8

oral 15:18 19:8

orange 54:18,19

order 45:13 91:13

92:1 109:19

orders 105:11

organic 95:16

organisms 9:20

organization 10:1

organizing 14:10

original 50:18 51:1

153:9

outcome 16:14

17:3 18:4,17

24:16 25:2

outcomes 18:11

22:5

outlining 92:8

outside 141:22

142:2 143:11

out-of-sample

146:2

ova 23:19,19

overall 16:17 43:22

51:7 106:22 125:3

153:19

overestimate 70:9

92:14 138:15,16

138:18

overestimated 70:8

overestimation

139:18

overlaid 49:14

overlay 50:9

overlaying 165:15

Overleaf 37:5

overviews 160:12

overwhelmed

122:3

over-fitting 140:10

ovulate 23:15

ovulation 23:13,15

23:19

O'Bryne 14:21

105:8

O'Byrne 2:12

27:10,11 85:6,7

105:9 165:8

P

package 35:12

page 59:13,13

97:13

pages 31:9 32:22

43:15 97:18 160:9

161:13,16,16

162:11 169:19

panel 1:4,15 3:5,6,8

4:4,13,17,22 6:4

8:19 10:15 31:10

32:2,7 34:17 47:8

68:21 88:20

104:20 152:10

157:18,19 169:9

169:17 170:16

171:5,15

panelists 76:7

paper 23:8,8 39:10

41:8 65:22 73:18

88:10 89:7 97:14

100:13 115:6

125:21 131:7

140:18 142:8

145:4 146:5 147:4

147:10 154:3

162:7,9

papers 171:7

PAPK 26:16,19,19

parameter 129:7

parameters 129:9

146:14

parametric 91:14

paraphrase 127:2

parent 15:19 16:11

part 6:18 14:15

16:20,21 47:10

58:22 86:19 99:19

101:13 102:1

106:22 125:3

127:2,4 129:16

133:14 134:2

136:13,14 139:10

148:7,17 156:13

159:21 162:14,19

particular 7:8 16:6

16:11 75:10 90:10

90:15,15 100:10

100:19 119:20

136:20 145:21

163:9

particularly 15:16

72:9 82:5 101:12

137:2 141:18

165:7

particulars 127:10

partition 56:8

parts 62:16 97:16

105:3 130:10

148:7

pass 169:1

pathway 26:7

pathways 12:18

pattern 15:13 75:5

75:20,22 76:19

77:1,8 82:4

patterns 87:7,8

89:5 114:13

137:15,16 148:20

148:21 166:6

Paul 31:22 35:3

45:3,9 46:8 57:18

62:8

pay 136:7 154:18

PBPK 84:11

PD 14:20

peak 26:22 41:20

42:11 43:3,9

45:16 75:19,19

82:4 90:4 122:4

138:9 139:2,4,18

140:14 144:22

145:8

peaks 45:12 99:4,6

99:8 105:7 110:22

117:16,17 143:16

153:8

PENELOPE 2:4

Penny 160:19

164:16 169:7

people 21:8 46:9

61:21 69:1 73:16

77:13 81:21 98:17

98:22 103:12

112:8 124:16

126:15 155:2

167:7 170:2,16

171:3

pep 158:19,20

percent 44:2,2,2,11

47:19 52:12,16

53:19,20 54:6,8

55:6 57:7,8,9,15

63:4,5,6 68:2,6

69:5,7,12,14,17

69:18 71:7,8

97:15 127:11,12

134:12 154:12

percentage 43:10

percentile 42:7

53:13 54:3,5

59:17 60:2 67:8

90:11

percentiles 42:3,15

51:22 53:6 67:8,9

perfect 53:9 54:17

performance 44:20

44:21 53:3 55:12

62:4 68:3 69:5,16

69:19 88:17

performed 160:11

period 16:6,12

18:22 21:15 23:10

37:13 78:13,14

81:7 82:18 83:1

86:5 90:11,17

97:5

periodic 105:22

periodicity 89:8

108:19 155:4

periods 46:17 83:8
202-234-4433

Neal R. Gross & Co., Inc.

Page 188

88:18 97:22 98:18

98:19

Peripherally

170:12

permanence 112:6

permanent 169:17

Perry 41:6

persistence 111:22

person 158:18

personal 79:18

160:16 171:1

personally 121:5

perspective 108:14

160:16

pertinent 27:4

perturbed 155:12

156:2

pesticide 88:13,16

89:5,11 136:19

137:15 139:3

145:3 148:21

pesticides 159:20

160:22

pH 95:17

pharmacokinetic

15:15

pharmacokinetics

16:18

phase 29:5

phrased 24:12

physical 146:7,9

physiological 19:22

20:4 22:12

physiologically

30:12

physiologists 31:19

physiology 28:16

Ph.D 1:17,17,20,21

1:21,22,22,23 2:2

2:2,3,4,4,5,6,7,8,9

2:11,12,13,14,15

2:16,17

pick 115:16 160:13

picked 80:6

picking 111:9

136:6 144:14

picture 49:16 54:14

154:2,12

pictures 55:2

piece 38:1 39:8

pieces 9:17 39:10

149:3,9

pile 66:11,20 74:7

PK 5:20 14:14,18

14:20 15:6

place 76:6 96:15

135:5

placing 126:5,8

134:22

plan 38:12 53:3

55:11,17 58:11

planning 82:12,21

130:12

plans 4:14 73:21

100:16 112:18

plant 95:14,18 96:8

96:16 112:9

113:20

planted 112:4

128:11

planting 90:19

players 153:17

playing 134:9

Plaza 1:15

please 10:12 25:4

81:2 89:15 110:3

114:14,18 126:4

136:16 137:6,10

pleased 12:13

plethora 132:16

plucked 85:9

plus 16:3 160:2

167:9

point 5:4,13 10:4

12:16 19:9 29:16

30:1,2,21,22 38:4

41:20 60:8,14,21

61:7,14,16 62:11

72:6,14 78:8

81:12,19 82:20

87:1,16 88:6,22

94:13 96:5 100:3

102:4,6 103:22

113:13 116:9

120:6 122:10

123:1,8 124:13,18

125:1 132:2,10,11

148:16 150:2,16

154:1,9 155:20

157:7,22 158:12

159:9,11 163:7

164:22 167:1

169:11 171:11

pointed 63:11

points 9:1 19:1

33:19 34:3,4,13

34:17 36:1,9

38:18 40:21 43:2

51:5 57:22 62:16

79:4 103:6 124:7

136:20 142:10,13

142:16,18,21

143:1 152:14

161:7,14 170:9

polyploidy 23:22

polyspermia 23:22

pond 96:9,10,17

pool 85:19,19

pooling 144:10

population 10:18

29:21 30:9 34:21

69:21 70:18

128:16

Portier 1:17,21 3:5

4:7,9 6:15 10:13

10:19,20 11:22

12:8 13:10 14:12

20:11 21:22 22:14

23:6 24:10 25:10

27:10 29:12 33:6

37:14 40:6 43:19

44:19 46:8,14,16

49:1 53:14 54:2,7

54:10 58:2 60:5

62:8 63:20 70:20

72:2 73:7,8,15

74:2 79:13 81:11

83:14 84:16 85:3

87:21,22 89:17

94:4,12 99:15

102:19,20 103:3

104:19 105:8,13

107:15 108:5

109:3,5,12,15,22

110:12 111:14,16

112:22 113:1

114:4,21 116:20

117:5,10 120:8,10

123:9 125:13,18

126:13 132:22

134:19 135:7,9

136:4,8 137:18

149:13,18 150:21

152:9 156:5 157:8

157:16 163:1

167:2 168:4,9,15

169:8 171:4 172:4

poses 130:17

position 124:3

positive 93:13

possibilities 61:8

possibility 26:22

164:9

possible 7:6 51:5

52:20 63:14 70:11

106:20 113:17

146:14 148:19

post 167:17

post-emergent 82:7

post-peak 139:19

potential 14:1

35:17 89:2 139:12

141:11 145:14,20

145:22

potentially 78:11

133:18 140:13

power 77:10

powerful 149:6

ppb 131:3,3

practical 111:2

114:15 141:6

practice 161:20

precise 103:15

precisely 40:13,13

116:13,13

precision 91:17

131:8,11

preclude 26:22

precursor 15:7

17:1 18:14 162:16

precursors 15:8

16:14 17:14 19:7

24:14

predict 112:7,8

140:2 141:15

145:13

predictable 133:12

predicted 138:3

predicting 145:6

146:10

prediction 69:22

126:1 140:11

predictions 101:6

145:8 146:3,15,21

prefer 21:20

pregnant 24:4

preparation 8:20

preparing 13:5

presence 92:19

130:5

present 1:19 2:1,20

93:7 118:17 158:5

171:22

presentation 31:6

31:14 32:20 33:16

36:4 39:14 40:16

40:18 47:4 158:15

presentations 4:19

5:10 171:19

presented 31:12,17

35:11 65:22 88:9

114:10

presiding 1:18

presume 163:19

pretty 13:18 23:12

56:4 72:21 74:22

77:21 82:11 89:21

91:9 97:17 125:15

135:21 149:16

150:6 157:22

165:4

previous 8:2 79:5

pre-emergent 82:7

pre-emergent/po...

82:11
202-234-4433

Neal R. Gross & Co., Inc.

Page 189

primarily 5:20 6:5

58:16

primary 58:13

120:6

principle 121:1

principles 120:15

prior 90:19

probability 38:6

124:5 133:18,22

134:7,15 135:10

135:18

probably 12:19

30:3 31:5,6 34:14

53:4 60:8 78:3

79:17 87:22 97:5

98:4,11,13,16

100:5 102:6

103:13 108:4

109:18 110:19

112:6 115:11

116:13 117:8,11

122:6 127:17

131:15 135:14,19

139:8,10 150:3

151:8 154:13

155:2 156:3

158:12 159:6

160:14

problem 45:20

60:19 91:6 93:2,9

108:13 111:18

115:14 124:18

134:22 135:1

146:18

problems 101:17

153:19

procedure 39:21

47:15,17,18,22

48:10 56:1 148:8

Procedures 3:2

proceeding 84:22

process 34:2,11

36:8 71:15 72:8

95:20 109:7,9

113:18 116:6,17

119:15 120:17

122:21 125:4

133:6 141:20

142:3 146:8,9

147:13 154:8,17

155:3 156:4 171:8

processed 31:10

32:12 39:8 80:8

processes 74:15

123:19 143:2

147:16

processing 43:12

produce 69:22

171:9

productive 20:7

108:4

products 84:2

proestrus 23:11

profile 15:3 16:13

16:17 36:19,20

38:19 43:3 44:9

44:20 48:2 49:12

49:19 50:9,13,15

50:19,19 51:1,7,8

51:15,21 52:14

58:19 61:15 62:6

64:21 65:7 79:1,4

85:11,17 86:22

111:17 152:22

153:1,9

profiles 44:22 49:8

49:9,10,10,11

50:5 54:21 59:11

84:12

program 36:14

41:1,14 62:3

65:12 97:11

110:10 130:18,21

programming 22:4

programs 41:15

projection 105:21

promise 141:17

promised 31:4

properly 32:11

85:12

properties 91:4

proportion 101:15

108:15 125:2

129:11

proportional 139:3

proposal 161:18,18

proposed 137:11

151:11

protect 19:12,17

21:9

protection 1:1

107:5

protective 20:17

88:4

protocol 37:7

provide 92:9

provided 39:20

provision 107:6

pubertal 164:5

public 5:8,9,14

29:21 66:16 86:11

88:6 93:6 123:10

135:16,20 153:15

171:21

published 23:8

122:14

pull 46:21 151:2

pulled 76:5 171:18

pulling 74:21 151:3

pulse 81:18

pumps 96:9

purpose 65:19 71:6

pursued 163:13

pursuing 114:16

pushing 92:21

162:17

put 14:6 29:14

31:11 43:15 49:17

54:13 55:2,21

78:17 90:5 92:5

107:19 123:11

131:20 133:3,4,13

135:15 147:21

148:1 151:18

167:14

putting 133:6

135:2 151:22

160:22 167:13

P-R-O-C-E-E-D-...

4:1

p.m 172:6

Q

qualities 84:15

quality 107:5

quantile 90:2,10,10

91:18

quantiles 91:17

92:15,15 145:13

quantitation 89:10

quantitative

101:19

quarter 52:12 55:5

quarterly 125:9

question 3:7,8,13

3:15,17,19 4:15

5:18 14:16 17:20

24:13,19,21 30:11

30:17 34:16 59:7

62:10 68:11 85:5

85:8 88:7 90:14

96:22 99:19 102:2

105:19 109:4

114:8 117:14

120:13 133:2

135:22 136:3,13

148:7,17 157:14

163:15 164:17

166:15

questioned 18:18

questions 5:1,11,13

5:19 31:8 33:2,20

36:2 40:14 47:2

58:3 60:9,16

67:16 68:20 74:3

79:11 85:4 113:9

125:14 126:15,19

165:11,17

quick 23:12 31:15

113:18 150:6

quicker 40:20

quickly 64:7

quite 28:15 35:8

45:11,11 55:10

61:21 73:19 89:22

145:18

R

R 1:21

rainfall 82:20 83:6

111:21 112:8,12

128:8 130:11

raining 99:9,10,10

rains 83:12

rainy 82:18 83:1

raised 24:12,21

59:7 76:7

ramify 10:6

ran 65:21

random 38:8,10

58:10 71:2 82:9

92:18 126:16

131:5 141:12

144:12

randomly 155:11

156:1

range 67:6 68:3,7

100:3 150:7,12

153:14

ranged 52:7

rat 19:6,11,12,15

21:4 23:16 28:6

28:14,21

rate 15:17 42:15

82:9 83:22 110:16

rating 84:8

ratio 45:16,17

51:14 52:13 54:18

62:21

ratios 52:4,6 62:20

rats 17:8 21:9

23:16,16 25:15,17

26:18

raw 36:10 62:14,15

63:14 74:17 75:2

75:6,10 85:12

123:11,12,17

168:7,8

reach 121:21 131:8

reached 55:13 80:8

163:11

read 36:11 49:2

54:10,13,14 113:4

136:13

reading 85:4 109:4

real 44:7 97:5
202-234-4433

Neal R. Gross & Co., Inc.

Page 190

115:18 117:14

119:21 153:1

realistic 148:13

151:15

realistically 98:16

reality 61:19,20,21

62:1,2 65:2,8 92:1

115:14 119:3

realization 119:16

119:20

realizations 119:17

realize 32:7 39:9

41:9 78:4

realizes 116:4

really 5:2 6:5 7:11

12:13 21:12 29:20

30:17 31:16 33:12

45:18 48:13 55:10

56:12 62:3 78:15

101:3 104:3

109:12 111:8

112:1 113:8,15,17

117:6,15,17,19

120:20 127:13

128:3 129:1 132:4

133:11 143:13

144:17 146:9

147:17 148:18,18

148:21 149:3

150:11 151:4

152:12 154:2,8,14

154:16 157:3

165:9 170:1,2,11

reason 24:21 30:15

31:16 38:10 76:5

87:18 92:16

133:14

reasonable 118:18

119:11

reasonably 139:17

reasons 18:6 83:9

recap 128:20

receive 65:16

received 109:9

recognize 9:19

123:10,14 155:2

recommend 144:9

recommendations

113:2

reconsider 9:6

27:15

reconsideration

160:3

record 5:8 50:14

66:16 85:1 163:6

recorded 34:9 39:4

51:13

recording 53:12

records 162:1

recreating 72:21

recurring 73:6

red 27:18 46:16

77:17 78:22

reduce 94:1

reduced 24:6 40:8

84:19 107:8

Reed 2:13 6:1,11

6:12,17 25:10,11

30:14 107:15,19

reevaluate 10:16

reevaluation 1:6

4:5 159:21

refer 163:20

reference 64:21

referenced 92:6

references 148:2

149:21

referred 22:9 94:8

115:3

referring 32:15

43:14

refers 33:5,15 36:3

40:17 163:18

refine 93:4

refinement 109:1

reflecting 8:18

reflects 87:22

148:14

REGAL 2:14

regard 88:20

106:19 108:11

regarding 8:1

114:12

regardless 54:13

55:9 155:4

regimes 37:20

region 56:15

regression 101:4

101:13

regressions 145:12

regression-based

141:12 145:10,22

regression-type

151:18

regular 125:6

152:15

regularly 121:8

regulated 107:4

regulatory 165:3

rehash 6:6

reinterpret 163:11

reiterate 102:4

reiterates 102:10

relate 5:19 84:4

101:4 129:21

related 4:21 14:16

17:19 99:19 102:2

113:3 164:4,11

relates 5:22 149:9

relating 19:9

relation 82:18

100:7 103:1

relationship 83:17

111:21 121:10

relationships 100:9

relative 15:19

136:16

relatively 73:5

95:21 98:7 101:15

relax 31:20

relevant 16:1 61:6

160:18 166:20

relies 132:9

reluctant 18:20

relying 70:4

remains 4:12

remarks 4:16 5:4

94:17 158:4

171:13

remember 5:18

33:3 41:11 124:10

156:11 159:10

remind 27:12

reminded 156:8

163:16

reminds 27:19

removing 95:14

repeat 99:18

repeated 66:19

71:5 80:11 150:3

repeating 53:11

113:1

repetition 71:3

replacement 38:5

66:19

replicated 119:17

report 5:15 33:17

34:2,14 35:1 92:6

92:8,10 105:15

114:5 147:22

171:10,18 172:2

representation

61:19 120:20

represented 150:12

represents 60:18

73:12

reproductive 11:17

18:4,17 24:16

25:2

require 140:2

required 25:7

102:16

requirements

102:17 156:10,10

requires 104:17

107:6 118:14

research 10:8,9

38:1

researchers 160:11

reservoir 95:3

96:14

reservoirs 98:10

124:22

resolution 73:9

117:3 131:21

132:1

resources 161:1

respect 12:5 13:15

respond 112:11

response 17:19,20

18:3 22:9,10

97:17 133:1

162:18

responses 162:17

rest 32:6 36:16

37:19 47:7 104:20

129:8 143:19

result 17:13 22:6

82:7,8,17 167:20

resulting 42:22

results 39:11,13

40:1 47:18 54:17

55:20 56:5 57:22

92:19 118:13

166:20 167:22

resumed 85:2

return 84:21

reversibility 25:19

26:5

reversible 22:20

23:3,14 24:4,5,8

25:20 26:1,11

review 1:6 2:1 88:9

110:5 159:10

reviews 162:1,10

revisit 4:15 31:16

revisited 18:7

re-do 100:20

re-evaluating

88:11

re-review 163:3

re-sample 66:9,10

re-sampled 71:11

RICHARD 2:2,5

right 14:7 23:20

30:13 32:18 35:15

36:6 37:7,7 42:16

44:11 53:21 54:5

57:16 61:10 68:2

87:2 88:1 90:12

91:12 103:10

117:18 122:8

125:4 136:9

137:20 140:19

153:1 154:1
202-234-4433

Neal R. Gross & Co., Inc.

Page 191

ring 105:5

ringing 139:19

risk 5:19 6:21 7:10

9:1 25:12 30:6

88:2 123:10,13

130:17 137:5

160:5 161:8

165:20 166:11

River 44:17 128:13

road 125:11

Roberson 86:10

ROBERT 2:4

robust 75:15 87:3,6

165:2

Rock 45:22 46:5

48:8

rocket 130:18

rodent 20:19 21:1

RODENTICIDE

1:3

rolling 36:20 38:20

38:21 55:7 59:3,6

66:6 77:20 79:1,3

90:3 93:8,18

118:3,4 126:5

135:2 151:12

152:12,21 153:7

room 5:7

rotated 11:11

rough 105:21

roughly 40:9 112:8

112:10

round 86:15

route 20:7

row 59:8

RTI 58:7 155:13

RUBY 2:13

run 53:19 68:5

69:16 97:11

108:21 154:19

running 66:4,9,21

67:1,11 68:9

71:10

runoff 130:1,3

runs 43:7 62:18

65:21 68:11,13

69:4,6,13

R-square 154:11

S

safe 25:18 30:13

123:20 160:6

161:9,18 162:20

safety 106:22

127:15 132:20

133:6

sample 38:3,4

40:19 43:2 44:7

46:21 50:10 51:6

51:14,20 52:7

53:12,17 54:16,22

55:3 61:22 62:20

64:18 65:11,15,20

66:2 67:3 71:1

76:6 80:19,21

81:9 91:1,15,21

92:3,18,20 93:3

93:10,12 94:1

96:15 104:13,15

108:15 109:8

131:22 132:3,15

140:1 142:2

155:22 156:2

sampled 38:18 44:8

65:7 67:21 69:3

75:9 100:8 121:8

121:9

samplers 110:14

156:14

samples 34:9 37:2

37:10,20 40:9,10

40:12 50:21,22

62:14 64:20 70:4

71:2,6,9 72:17

73:20 75:3,4

87:19 109:7 111:9

115:8,21 131:10

131:16 136:22

147:15 155:5

156:13 157:2

sampling 3:10 9:7

27:16 29:18 36:5

37:3,17,19,22

39:16 40:4,8

43:22 46:17 48:16

49:21 50:2 52:13

52:15,19 53:3,8,9

53:16 55:12,17

56:22 57:11 58:9

60:13,15 62:5

65:10 67:2 69:7

69:11 71:15 72:20

72:22 74:19 75:15

76:3 78:20 79:4,8

80:7 82:13 87:7

87:15,15 88:11,16

88:18 97:2,4,7,21

100:7,10 101:17

104:5 105:22

106:15,16 108:12

108:13 110:15,16

110:21 111:2,7

112:17,19 113:4

115:9 116:16

118:2,13,19,21

120:15 121:3

124:20 125:9

126:3 127:21

131:14,19 132:7

136:20 138:6

143:11 144:20

153:12 155:3,6,6

155:10,21

Sandusky 48:9

SAP 1:4,19 113:2

122:12 171:1

Saturday 51:11

saw 12:11 20:16,18

79:5 92:3 126:18

saying 55:3 112:15

says 36:4 53:16

134:10

scale 49:17

scared 136:3

scary 120:5

scenario 88:3

scenarios 88:2,5

127:21 153:16

schedule 37:18

38:15

scheme 118:21

schemes 40:8

SCHLENK 1:23

science 2:1 84:15

107:11 130:18

159:5

Scientific 1:4 4:4

scientifically 13:19

scope 152:4

scratch 159:14

163:4

screen 33:7 124:21

screening 113:18

scroll 46:8

SDWA 34:3

se 18:4

season 36:15,16

44:9 75:18,19,20

99:8

seasonal 145:7

seasons 89:9

second 10:11 14:15

16:21 36:3 83:3

101:1,12 102:1

103:22 127:2,4

159:8

seconds 6:19

Section 89:6

114:10 125:20

137:12,21 141:10

148:10

Sections 136:18

see 6:18 11:1 18:9

19:18 20:6,22

21:3 46:9 51:7

54:19 56:5 67:18

73:4,6 74:18 75:4

75:6,10,18,22

76:18 77:1,4,7,21

78:10 79:1,7 83:3

83:6 84:18 85:16

86:5,12,18,22

91:10 95:9 99:7

110:16 123:17

125:14 134:5,5

136:8 143:4

148:15 155:16

158:5 169:2

seeing 54:1 81:1

87:9 92:12

seeks 88:19

seen 4:12 16:10

28:2 32:18 82:5

83:10 99:1 119:2

121:16 170:7

sees 11:11

select 161:14

selected 36:21 46:4

115:7

selection 38:6,9

140:21

selectively 96:8

SELVAGE 2:15

sense 7:7 18:15

20:16,19 22:12

97:19 115:13

151:4

sensitive 28:1

161:19

sensitivity 7:7,14

170:11

separate 43:15

122:20

separated 28:4

separately 98:5

122:22

September 8:20

10:15 161:15

169:2 170:6

sequence 42:10

83:21

serial 89:7

series 38:2 41:19

42:19 49:3 145:17

serious 132:21

serum 27:3,9

serving 4:6

session 1:17,21 4:9

6:15 10:13,19

11:22 12:8 13:10

14:12 20:11,15

21:22 22:14 23:6

24:10 25:10 27:10

29:12 33:6 37:14

40:6 43:19 44:19
202-234-4433

Neal R. Gross & Co., Inc.

Page 192

46:8,14,16 49:1

53:14 54:2,7,10

58:2 60:5 62:8

63:20 70:20 72:2

73:7,15 74:2

79:13 81:11 83:14

84:16 85:3 87:21

89:17 94:4,12

99:15 102:19

103:3 104:19

105:8,13 107:15

108:5 109:3,12,15

109:22 110:12

111:16 112:22

114:4,21 116:20

117:5,10 120:8

123:9 125:13,18

126:13 132:22

134:19 135:7,9

136:4,8 137:18

149:13,18 150:21

152:9 156:5 157:8

157:16 163:1

167:2 168:4,9,15

169:8 171:4 172:4

set 12:15 21:6 27:7

34:3,12 35:10,10

35:16,20 36:1

39:4,6,12,19 41:3

41:5 48:17 50:22

53:6 56:19,19,22

57:12 58:11 66:10

91:2,4 106:1,3

114:17 164:20

sets 37:10 41:2 48:8

48:14 58:6 87:4,7

100:8 114:11

156:14

setting 93:18

146:13

seven 41:1 45:7

49:14,18,20 50:10

50:13,15,17 51:5

51:6,12 52:5,19

52:20 65:12 67:5

97:18 134:11

162:6

seven-day 36:14

49:15,16 50:4,7

50:21 52:12,15

55:3 78:20 116:3

134:11

seven-year 49:10

sewage 168:8

shallow 134:18

shape 89:11 114:13

137:14 140:14

143:16 144:14

149:1

shaped 145:7

shapes 87:7 139:6

144:17

sharp 99:3 143:16

sheet 45:4

Shift 109:22

shifted 152:22

short 7:16 15:19

22:7 47:10 83:1

97:3 113:15 137:9

150:5 161:3 167:4

167:5 171:8

shortcomings

159:4

shortening 151:13

shorter 40:19

75:18 77:22 79:8

98:19 102:6

111:14 116:2

137:4 138:7,9

155:9 163:18,21

shortly 167:4

short-sheets 132:20

short-term 17:7

60:20 72:10,19

77:14 113:10

115:19 116:8

146:10 166:11

show 50:6 67:17

76:16 78:7 99:3

100:9 154:7

showed 23:9 40:10

55:16 78:20 85:11

112:16 131:6

152:21 154:5

showing 49:4 55:9

75:21 95:13 105:2

105:2

shown 37:5 48:17

55:20 57:1 96:7

96:18 131:13

140:16

shows 43:8 76:22

77:16,18 97:16

side 5:6 11:14 37:5

95:3 118:16

150:18 153:4

sides 95:9

Sielken 31:2 32:16

39:18,18 40:17

43:13,13 47:6,6

49:5 52:22 53:2

53:22 54:4,9,12

58:7 61:12 62:19

63:7 155:14

sights 73:5

signaling 105:6

significance 89:2

significant 131:12

167:11,14

significantly

139:11,12

silly 55:14

simazine 76:15,17

76:19,22 77:8

similar 64:15 75:20

86:22 87:8 92:5

153:6

simple 11:5 89:21

102:9 121:4

152:14 153:2

simpler 64:8 123:7

simplest 151:17

simplify 154:15

simply 23:2 71:2,11

108:13

simulate 72:20

115:14 117:16

simulated 39:5

48:22 60:17

115:18,22

simulating 120:16

simulation 38:4,17

38:21 39:2 42:20

42:22 47:13 51:1

52:1 73:8 92:10

105:22 114:10

151:20

simulations 4:21

38:7 43:1 105:20

118:7 119:2,11

120:2 155:15

single 18:10 23:10

24:7 51:13 57:5

129:6 155:5 160:9

165:1

single-day 55:5

sit 31:20 161:17

165:8 169:5

site 27:3,8 42:9,12

49:3 75:21 76:16

77:17

sites 12:21 26:21

97:11 98:4,6 99:3

99:5 101:8,10,15

115:7 124:5,10

131:12 133:18

150:8,9,10 153:12

153:13,14,21

site-specific 45:6

100:1

sitting 157:8 165:5

six 37:20 40:10

66:8,18 80:4

136:10 162:6

166:13

six-day 50:3

size 24:7 91:15,21

92:20 93:3,12

94:1 131:5

sizes 92:3

skewed 93:22

slapped 127:12

slate 159:14

sleep 78:5

slide 32:20 33:15

34:20 35:5,5,10

35:10,12,16,18

36:3 39:14,15

40:17 46:11 47:4

48:17,18 51:16

53:4 57:1 79:5,12

slides 5:17 35:2,12

39:12 64:7

slight 152:16

slightly 47:17,20

153:3

slow 15:17

small 62:11 79:16

85:19 96:12 99:2

99:12 101:15

103:6 105:10

109:21 125:2

129:11 130:1,3

131:13 155:20

160:21 161:2

168:6

smaller 86:4,9

92:20 131:9,17

smart 170:2

smooth 119:3

141:21 142:4

154:5,13

smoothed 74:16

87:14

smoothing 142:14

142:15 153:6

soil 121:7

solely 118:1

solid 153:17,20

somebody 30:6

somewhat 93:5

103:13 104:9

143:17

soon 93:19 130:2

sophisticated 20:5

165:19

sorry 10:19 44:14

48:3 50:16 54:3

164:16

sort 7:22 26:9 28:3

51:11 71:3,13

85:13 103:14,19

103:20 104:1,4,6

104:11,14 105:21

106:4 107:3
202-234-4433

Neal R. Gross & Co., Inc.

Page 193

108:11 112:16

140:21 142:10

145:15 153:14

155:5,7 156:2

158:9 159:11

169:6 170:9

sorts 146:1 149:2

sought 103:2

sound 148:11

sounds 11:5 89:21

source 35:21 41:16

42:19 98:15 107:3

129:2,2

sources 34:6 88:14

103:9 123:3,5

source-type 39:7

so-called 67:10

space 143:19

spaced 50:21

spacing 50:8,10

52:5 53:10,10,12

54:16,22 55:4,4

span 61:5

spatial 57:19

speak 21:21 158:7

158:10

special 159:20

speciation 7:20

specific 16:12 41:4

60:16 90:2 114:11

115:15 116:7

124:6,9 133:2

157:6

specifically 18:11

102:12

specified 59:14

speculate 169:5

spend 21:8

spent 94:14

spike 80:5,12 86:20

spikes 80:3,14,16

81:12 83:16,19,20

84:6 90:8 99:1,12

spiky 45:11,12,18

45:18,20,22

split 56:3

spontaneous 29:6

spread 52:10 67:6

square 46:5

St 44:15 48:2

staff 171:1

stage 21:19 29:4

161:9 163:11

170:11

stair-step 70:9

138:1,11,14,19

139:16 142:19

standard 91:20

108:16 143:19

standpoint 93:6

start 6:3 12:7 29:22

48:14 50:11 62:14

71:17 76:4 77:7

81:1,4 90:19

108:18 110:15

123:4 133:10

161:22 162:11

167:13

started 40:22 48:19

49:18 65:3 78:2

starting 50:14 51:3

51:4,5 52:20

60:21 61:16 66:4

72:14 120:18,19

121:1 123:3

159:14 169:7

starts 35:17 78:9,9

state 122:13

stated 129:20

132:14 134:6

151:3

statement 63:10

103:16 104:10

135:19 159:13

states 97:14,15

static 34:6 35:21

41:8 56:4

station 168:19

stationary 143:20

statistic 30:3

100:20 101:9,20

102:7

statistical 87:11

89:12 121:2

127:20 147:12

155:1

statistically 120:22

134:22 154:11,15

statisticians 94:10

110:13

statistics 94:18

124:15 127:10

147:6

step 26:8,10 48:12

64:18 65:1 66:3,8

66:18 67:5 72:15

73:21

steps 7:15

step-by-step 47:14

step-wise 65:21

Steve 45:2,19 70:21

82:3 105:18

111:11

Steven 1:17,20 2:7

stick 55:16 111:4

127:17

sticking 4:10

Stoker's 24:1

stone 27:7

stop 29:5

storage 98:9,16

story 158:11

straight 108:3

170:13

strategies 3:10

87:13 88:18

strategy 69:11 97:2

131:14

stream 95:2 96:9

96:13 103:12,18

129:15 137:15

148:20

streams 129:21

130:6,7

stream-by 103:17

Street 1:16

strength 57:19,20

120:11,16 129:2,3

strengthened 10:10

strengths 114:14

126:4 136:21

stretched 114:6

strict 142:5,9

strong 56:12 112:1

118:9 165:1

strongly 117:12

structure 143:17

144:6

structured 155:18

struggle 28:17

stuck 110:20

studies 1:7 16:15

19:15 25:3 95:13

162:3,4,14 166:22

study 19:6 107:22

132:19 160:2

163:3

stuff 23:12 29:20

subheading 105:15

submit 130:4

162:21

submitted 41:7

160:10 162:4

subsampled 116:1

subset 34:8,12 98:1

subsets 34:5 35:21

39:6

subtly 32:9

sub-daily 132:6,9

sufficient 86:7

115:13 118:15

151:14 161:6

suggest 12:4 15:21

18:20 32:21 90:6

163:3

suggested 95:7

suggesting 163:13

suggestion 14:8

suggestions 12:14

13:6

suggests 16:10 75:7

83:7 98:1 118:8

sum 115:12

summaries 162:2

summarize 162:9

167:6

summarized 17:8

39:14

summarizing 151:1

supper 19:4

supplement 99:17

supplemental 47:9

supplies 125:2

supply 48:4

support 108:9

supported 20:21

supporting 9:14

118:15

supports 107:11

suppose 81:13

90:12

supposed 48:3

sure 4:17 5:4 10:13

20:1 29:12 30:17

31:17,18 33:1

53:22 60:21 85:22

107:13 114:4

116:11 148:12

149:21 160:17,20

surface 89:5,11

128:6,15,21,22

129:4,12

surge 23:13 28:3,22

29:7 164:6 167:20

surges 28:5

surprised 85:8

surprisingly 45:12

surrogate 84:1

survives 95:20

SUSAN 2:2

suspect 98:8 99:2

sustained 18:16

80:13

Syngenta 4:20 5:3

30:22 32:1 33:16

42:6 61:3 65:16

74:12 76:10 85:14

91:11 92:4 95:7

96:4 97:11,16

100:15 105:20

110:6,9 155:14

Syngenta's 64:8

104:10

synthetic 60:12

157:9
202-234-4433

Neal R. Gross & Co., Inc.

Page 194

system 11:12 36:11

36:17,22 39:3

41:12 44:16,18

45:8 48:4,7 75:1

75:11 86:8 96:6

96:19 99:11,13

108:19 112:5,5,15

112:19,21 118:15

119:19 120:21

121:9,16 124:14

124:16,17 143:5

144:4,16,16

145:20 152:2

168:6

systematic 58:8

70:12,22 155:6,10

155:21

systematically

50:11

systems 9:20,21,22

10:6 34:9 36:12

44:13,15 45:5

70:2 76:1 86:4,9

86:12,19,21 87:9

96:12 98:3,10,13

106:13 109:6,20

109:21 112:10

114:18 118:12

121:20,22 131:15

131:16 144:10

151:6,10 152:4

T

T 2:12

table 17:8 35:1

37:5,8 38:16

52:21 53:11 57:4

64:2 92:3 94:8

158:19

tables 54:12 59:11

tabulated 53:7

tailored 131:14

take 19:5 25:5

28:21 31:7 38:3

38:14 50:15,17,20

55:3 69:16 78:19

81:3 84:17 87:12

91:3 96:12 99:6

103:8 109:7 123:6

152:3 159:7

160:12 162:8

168:6

taken 34:12 92:21

103:14,21 113:11

123:1 169:12

takes 146:22 152:6

talk 8:4 14:14,18

26:13 32:19 86:10

107:3 121:19

122:7 128:7

158:19,20 169:6

171:9

talked 6:6,18,20

7:3 11:5 14:13

15:12 32:8 99:22

115:1,4 152:12

166:18 170:8

talking 7:5 10:21

23:1 33:22 35:4

46:10 70:17 76:8

87:5 94:20 98:21

98:22 103:7 107:9

110:6 111:12,17

121:17 125:5

127:3,19 159:10

162:6 165:7

tangent 156:7

tap 81:14,15,17

target 12:20 26:21

27:3,8 52:5,15

55:5,6,7,10 57:5

60:4 64:4

task 161:3 163:12

team 158:8 170:5

170:15 171:2

technically 156:4

tell 41:5 59:16

111:17 161:21

170:19

temporal 14:16

15:2,3 16:8 29:8

57:19 89:4 112:6

temporally 102:21

103:1

ten 33:16 34:20

35:5,6,18 47:19

49:7 59:3,15

110:9

tend 16:17 44:1

138:14,16,17

tended 135:12

tendency 92:14

133:2

tends 84:1 93:13

ten-day 110:17

TERESA 2:8

term 77:15 79:8

113:15 116:2

138:8 139:14

163:21 166:12

terms 7:1 14:7 15:4

15:10 18:19 27:3

74:9 78:15 82:13

83:11 87:17 129:1

134:4,6,15 138:13

153:8 154:16

155:9 160:22

test 55:11 56:19

77:6 113:18 116:1

155:18

tested 37:4 62:6

testing 49:22 127:6

Texas 56:14

thank 4:10 10:10

30:19 32:2 33:3

33:11,12 40:14

46:7 47:7 53:1

62:7 63:19,20

64:6 94:4,6 99:14

99:15 104:19

132:21 149:13

150:20 169:8

170:4,21,22 171:4

171:15,19,20

172:1,4

thanks 171:2

theme 146:19

theorem 93:21

theorems 139:20

theory 93:17 147:5

147:5

thesis 24:1

thickness 13:7

thing 10:22 12:22

13:2,5 26:17 29:1

61:14 69:11 75:17

92:11 97:17

113:17 117:19

119:1 127:19

149:19 153:10

156:17 159:12

160:4 163:16

166:17 170:10

things 13:16 15:21

22:2,11 23:21

27:5 70:5 80:18

86:2,3 91:14

98:20 100:13

110:13 111:15

119:7 128:4 135:4

151:1 152:11

157:17 159:2,22

167:6,14 168:17

170:3

think 6:10 11:3

12:10,17 13:8,16

13:18 14:6 16:17

16:21 17:9 18:11

20:3,13,15 21:6

21:10,21 22:17

23:4 24:1,11 26:4

26:15,19 27:6,6

27:20 28:11,15

29:21 30:19,22

31:1,16 32:5

33:19 34:16 35:11

40:6 46:5,10,12

64:8,11 68:4,10

69:4,6,19 70:4,9

71:16,21,22 72:4

72:6 73:15,18,21

73:22 77:2 78:21

79:10 84:2,14

86:1,9 87:3,9,18

90:13 91:6,11

92:5 93:1,2,10

94:2,6 95:21 96:4

96:11 98:21 102:4

103:7,18 105:9,13

106:5,9 108:6,10

108:12,21 109:1

110:6 112:13

113:1,12 114:2,6

115:11 116:4,9,18

116:21 117:6,19

117:20 120:1,2,6

122:5 124:2,10,12

125:14,16 127:1

127:13,16 131:5

132:20 133:9,11

133:15 135:21

138:11 141:2

142:6 144:2

145:20 147:8,19

148:6,10,18

149:10,15 150:6

150:16,19 151:17

152:7 154:20

155:1,13,21 157:4

157:14,22 159:1,3

159:4 160:14

163:5 164:19,22

165:3,9,10,12,18

165:21 166:6,14

167:2,7,18 168:2

169:11 171:19

thinking 15:1 19:2

29:17,22 105:19

111:13 152:18

153:10 157:9

165:13,17,19

166:2,7,9 167:21

170:17

third 57:6 79:19,21

79:22

thought 27:13

79:14 85:19 92:7

93:16 98:20 114:3

124:2 132:2

135:13,13 150:22

152:11

thoughts 6:7

thousand 43:1,7

66:21

three 5:2 7:5 11:1
202-234-4433

Neal R. Gross & Co., Inc.

Page 195

25:5 37:9 44:15

45:13 54:21 62:16

62:22 63:5 65:14

66:5 71:18,18,18

71:20 81:17 97:16

107:9 136:14

148:7 154:12

164:3

three-day 50:3

53:9 55:6 66:4,6,9

66:21 67:1,1,3,11

67:21 68:9 69:2,8

156:20

three-node 140:17

threshold 26:9 81:7

81:8 105:1 133:7

134:7

throw 79:12 123:4

Thurman 31:3

63:21 74:4,5

80:17 82:15 85:3

85:22 88:8 106:8

107:16 114:9

122:9,10 125:16

125:20 127:8

136:2,6,10 147:21

148:3 152:20

157:15

Thursday 1:12

51:4

ties 145:10

tight 133:20

time 15:4,7,11

16:22 17:3 19:4

20:4,22 21:3,7,11

22:16 30:2 34:5

37:14 41:19 42:19

49:3 55:8 58:10

61:5 63:17 73:3

82:12 84:20 86:16

90:12,17,20 93:7

98:18,19 102:15

108:20,22 109:8

110:20 111:3

112:18 118:19

137:3 143:14

145:17 151:8

152:3,5 156:15,18

158:17 161:3

162:15 163:17

168:20

timers 169:17

times 20:1 25:22

32:18 66:19 70:22

72:17 82:12 83:9

102:5 105:14

109:20 117:21

118:21 131:21

timescale 73:13

118:10

time-based 16:19

time-weighted

156:19

timing 77:5 130:9

today 34:15 166:19

167:10 172:3

told 94:15

tons 60:12

Tony 27:19

tool 26:20

tools 113:21

top 11:2,6,6,13

12:3 48:17

topic 157:20

topics 158:1 169:14

tornadoes 6:16

total 76:9,13,21

84:2 145:15

totally 25:19 33:14

68:22

touch 94:18

touched 12:18

tough 45:20

tox 16:14 77:13

toxicity 7:18,19

10:16 12:18 14:17

15:3 88:22 132:13

132:19

toxicological 5:22

17:3 88:9 156:10

toxicology 23:5

28:16 31:19

150:18

track 156:4 168:17

168:22

tracks 29:5 161:2

train 161:2 168:17

169:3

trained 95:18

trains 168:18,20

transition 29:18

translate 83:19

156:9

translated 123:18

translating 30:11

transport 129:14

130:15

transported 129:17

transposed 11:9

treat 86:11

treated 98:5 115:8

treatment 62:13,17

86:12 95:14,17,18

95:19,20 122:11

122:13,20

treatments 58:14

123:4

tremendous 60:11

trend 155:22

trends 155:8

Triangle 38:1

triangles 75:3

76:18,20

triazines 113:19

trickery 47:13

trickle 12:7

trigger 26:10

113:21

tri-weekly 50:4

true 37:1 39:2

41:22 44:15 52:7

53:20 60:22 62:21

64:21,22 65:6,7

67:10,12 68:12,13

69:5,7,13,14

119:4 128:3

151:21

truth 60:17,18 61:5

61:13 72:11,14,16

73:2,12,13 75:13

75:14 76:4 87:6

102:8 106:1 115:9

115:18,22 138:12

148:12

try 14:20 21:6 22:2

22:11 63:18 65:13

75:13 76:16 86:11

86:16 87:16

108:22 119:11

133:3,4

trying 20:14 21:8,8

21:9 27:12 29:17

33:3 52:5 63:11

70:16 73:3 77:13

108:21 123:6

133:10 138:13

149:3 150:16

165:8 168:19

Tuesday 38:13

51:4

Tuesdays 51:9

tumor 12:6 167:19

tuning 146:14

turn 4:7 47:3 111:3

113:14 158:4,20

169:19 171:11

turned 62:5 74:22

turns 23:17

twelve 59:19,20

60:4

twice 132:3

twist 61:5

two 6:9 22:11 36:15

37:9,10,10 43:15

43:17 44:16 46:3

50:2 57:9,12,16

58:6 64:14 65:1

66:5 68:6,18

71:18 81:16 83:7

94:9,10 99:3

100:6 103:6,12

105:4 107:21

114:10 115:16

118:4,5 129:1

136:12 137:21

139:7 143:15

148:7 154:7

157:10,17 158:13

159:22 164:3

168:18,20 169:21

two-day 53:9

113:16 156:19

two-liter 81:15,22

two-weekly 37:18

type 35:21 40:1

73:2 75:5 77:19

95:16,17 101:2,11

104:7 113:16

156:21

types 128:9

typical 45:4 106:6

119:13

typically 82:6

U

ultimately 156:9

unacceptably

91:20

unaffected 95:21

uncertainties

107:22 123:19

uncertainty 84:19

88:20 89:10 106:4

106:5,6,9,17,21

106:22 126:2

127:5 161:8

unclear 17:12

underestimate 44:1

44:10 70:11

underestimated

67:13 70:8

underestimating

44:4 68:1

underestimation

138:5 139:17

underlies 47:4

underlying 47:3

73:1,8

underneath 48:18

understand 5:2

11:16 22:15 29:19

29:20 31:17 32:12

33:20 62:12 63:12

64:17 73:16 80:6

127:1
202-234-4433

Neal R. Gross & Co., Inc.

Page 196

understanding

24:20 26:4 63:16

84:19 96:1 118:11

understood 85:12

160:21

under-represented

63:4

unfair 38:14

unfortunate 111:18

unfortunately

117:12 159:2

unique 71:1,14

unmonitored

101:10

update 122:15

upper 126:6

uptake 15:13

up-peak 80:13

use 19:7,16 21:3,5

26:19 29:7 40:7

48:12 50:7 52:15

61:11 76:15 81:2

91:13 93:22 95:4

95:8,22 100:1

104:13 111:6

112:1 113:17

121:10 124:3,16

125:22 128:18

129:9 137:16

140:2 144:13

148:21 150:14

151:5,19 168:3

useful 141:9 146:10

147:17

uses 77:4 96:20

121:13,13 142:7

usual 111:14

usually 132:16

157:17

utilization 13:14

U.S 1:1 95:4 96:7

97:14

V

valid 62:13,17 96:5

valleys 153:8

valuable 87:3,10,11

value 29:10 30:10

37:1 41:22 45:10

52:6,6 60:4 71:10

81:7,8 93:17

100:10 106:11

107:8 133:8

134:11,11 138:3,5

138:6 140:1,3,16

141:15 144:18,19

147:4 156:19,20

values 43:4,9 50:15

50:16,17 53:7

59:15 61:19 62:6

63:13 65:17 67:8

69:13 71:14 79:2

91:8 92:12 95:5,9

95:22 118:4

137:22 138:4

139:18,19 142:11

143:11 146:15

147:13

variability 49:13

51:8 56:13,18

63:12,16 73:10

88:21 97:12 103:9

103:14,20 108:1

112:16 119:6,14

119:18 120:2

123:3,5 127:14,20

129:7,8 130:8

132:8 148:14

151:21

variable 49:6 75:9

95:15 135:18

144:8

variables 112:3

variance 93:11,14

155:11 156:1

variants 157:1

variation 128:8

varied 54:15

variety 128:10

various 9:15,17

130:10 136:17

149:22

vary 89:15 102:3

varying 126:3

Vecchia 145:3

verging 79:21

80:11

version 11:12

33:10,14

versions 119:3

versus 14:17 15:2

18:9 22:8

viability 23:21

view 79:18 81:19

88:6 117:12 134:4

157:7

views 171:22

vision 64:3

vitro 1:7 13:9

VMP/AMP 34:4,12

57:20

volumes 162:5,6

vulnerable 21:19

98:2 151:6

W

wait 29:6

want 6:5,7 8:11

10:14 14:3,9,14

19:11,22 25:8

30:17 31:16 55:14

58:5 60:7,14 62:8

68:4 69:14,16

74:1,4,5,7,10

79:10 83:15 87:2

90:2,3,4,5,8,9,13

90:16 91:16,18,21

100:3 102:4 103:5

103:15,22 104:9

107:19 109:15

112:14 117:21

120:7 122:10

123:16 127:13

131:19 132:1

134:21 135:4

142:17 144:3

145:19 146:20

147:2 150:2 157:3

159:7 167:3,6

169:5 170:4 171:6

171:11,14,20

wanted 13:11 20:12

29:16 45:14 47:14

58:20 61:22 68:14

68:16 76:15 77:13

78:7,17 81:11

87:16 98:12

107:13 112:21

127:17 128:19

149:15 160:17,20

163:6

wants 117:22 118:2

warned 6:2 136:4

warning 6:13

WARP 97:9 101:2

119:12 121:5

129:6 145:5 151:5

Washington 1:17

12:12

wasn't 22:20 48:3

60:1 70:8 163:2

164:20

wasting 37:15

watch 116:6

water 1:7 3:9 5:21

9:7 15:14,18

21:11 33:22 34:6

34:8 35:18,21,22

36:10,10,12,17,22

39:3 41:12 44:17

44:18,20,21 45:5

45:7 48:4 55:17

57:11,13 59:18

61:18 63:12,13

74:14,15,17,19

75:2,4,5,6,8,10

76:1 80:7,8 81:14

81:15,16,17 84:10

85:11,14,18 86:4

87:4,9,19,20

88:14,14 89:3

95:3,9,12,16 96:6

96:8,12,15,19

98:3,10,13,15

99:8 106:13

107:20 108:19

109:6 113:20

120:21 121:8,15

121:15,16 122:11

122:13,18,19

123:4,11,13

124:22,22 125:2

125:11 128:6,15

128:20,22 129:1,4

129:12,14,15,19

130:9 131:1,15

132:15 137:8

143:5 144:4,10

145:19 150:10

151:6 157:10

160:4 168:6,7

waters 89:6,12

123:17

watershed 146:17

waved 156:16

way 11:1,2,9 13:6

14:9 24:12 45:11

50:6 53:10 63:15

64:3 66:5 71:19

72:11 80:7 86:2,3

91:1 101:6,19

103:1 104:2

107:16 120:3

134:8 151:15

155:18 165:16,19

166:9 168:3

ways 32:10 133:12

145:1 159:7 160:7

weakness 120:18

weaknesses 114:15

126:4 136:21

weapons 27:19

weather 128:10

137:15 148:20

weather-related

83:10

Wednesday 51:4

weed 128:16,17

weeds 74:7

week 38:9,9 44:8

78:22 139:15

weekly 37:12,17

48:15 50:4 74:19

79:4 87:14 106:12

weeks 28:9 36:15
202-234-4433

Neal R. Gross & Co., Inc.

Page 197

37:9,12 38:15

78:21 109:13,17

110:1 130:4

169:21 171:18

weight 8:22 9:14

13:4

weighted 34:13

152:17 153:5

156:19

weights 9:16 153:4

153:4

well-characterized

114:1

well-known 113:22

Wenlin 42:5 44:14

45:14

went 23:17 24:2

31:14 37:18 49:14

49:19 68:1,6 85:1

165:6

weren't 75:9

White 41:7 97:14

140:18 142:8

146:5 147:4 154:3

Whitmore 33:18

wholesale 87:2

wide 49:15,15

110:18

widely 52:10

wider 153:3

WILLIAM 2:6

WILLIAMS 2:16

window 38:5,5

42:21 49:15,15,16

78:15 110:20,21

153:3 156:2

windows 33:13

38:2 46:18,22

58:10 110:18

155:12

wise 48:13

wish 157:21

wishing 163:2

woman 29:4 132:14

women 30:1

wondering 80:15

168:18

word 23:20 33:9,10

words 13:18 90:18

108:18 124:9

134:9

work 11:19 28:10

38:1 39:8 73:4

75:16 80:15 93:1

93:2 112:3 121:22

129:5 132:12

139:16 145:6,11

147:1,10,15 149:9

151:11 169:18

171:15

worked 39:5 51:19

57:18 154:6

working 5:1 8:15

78:2 107:17

171:17

works 37:9 153:20

world 11:17,19

14:4 79:15,19

160:11

worried 13:21

worries 141:16

worry 100:22

worst 44:21 52:11

57:12,16 71:21

worst-case 88:2,3

153:16

worth 49:2 94:3

113:12 151:8

worthy 20:2

wouldn't 14:3 65:4

119:19 142:17

wrap 3:22 62:9

63:9

wrestled 106:8

write 5:15 30:18

write-ups 150:1

writing 87:19

written 126:10

wrong 63:22 127:3

165:11

Y

year 36:22 37:19

37:21 40:9 41:17

42:9,11,20 48:2

56:13,13,16,18,18

58:9,18 72:18

75:2 86:15 89:9,9

90:17 98:15 110:8

113:2 115:21

125:9 128:2,2,5,5

128:7,7 139:1

145:16 158:13

yearly 48:5 145:15

years 13:13 36:11

36:17 39:3 45:7

49:2,6,7,8,9 90:21

108:12 122:12

144:10 158:11,14

year's 111:17

year-specific 45:6

yesterday 6:6,9 8:5

12:18 15:12 17:10

19:3 20:15 47:11

159:1,9 160:20

166:19

young 2:17 25:13

35:3,3,8,13,15

39:11 40:3 42:2

42:14,17 89:17,18

108:7,9 117:10,11

135:7,8 147:8

150:21,22

Z

zero 62:22,22 68:15

zoom 33:10,11

0

0.25 131:3

0.75 52:7,8,11

0.7548 54:6

0.79 54:7

0.80 52:9

0.95 91:18

0.96 51:9

0.97 51:9

0.99 91:22

1

1 34:9

1,000 38:7

1.1 44:3

1.24 106:2

1.5 106:2

1.9 3:7 4:16 5:18

6:17 85:5

10X 107:7,7,8

10-day 60:2

10:17 84:17

10:18 85:1

10:35 84:21

10:38 85:2

1001 1:16

101 128:21 159:12

114 3:15

12 36:3 39:14

12.5 106:11,18

12:14 172:6

120 98:6

125 3:17

13 47:5 49:2,8 68:5

68:17 69:7,12

13-year 49:9,10

130 46:5

136 3:19

137 36:12

14 29:6 40:17

14th 1:16

15 84:17

15-minute 27:14,18

165:1 167:10

169 3:22

17 37:20

18 52:8

1993 23:8

2

2 3:8

2.1 3:13 88:7

2.2 3:15 114:8

2.3 3:17 125:19

126:20

2.4 3:19 136:3

2.6 44:3

20 44:1,2,10 55:5

63:4,5,5 81:2,14

91:19 110:18

134:11 159:6,7

166:3

200 105:4

2001 41:6

2003 137:5 160:1

163:18

2004 100:13 115:6

2008 145:4

2009 40:4

2010 1:13 8:20,21

202 48:1 51:18,19

51:21,22 53:7

56:3,11 59:17

61:15

21 54:8

22 53:18,20 54:1

25 40:9 52:12,16

54:6 131:3

26 40:9 139:15

27 98:2

28 28:9 53:16 54:17

28-day 28:7 53:10

29 1:13

3

3 17:8

3X 106:18

3.6 28:9

30 6:19 31:5 59:3

62:18,22 63:5

64:19 65:17 68:15

69:14 72:17 73:20

110:18 115:21

165:1 166:12

30-day 60:3

31 34:10 52:9

31.5 68:2

35 46:5 64:19 65:18

72:17

351 49:8,8

363 66:6 71:19

365 36:19 64:20

65:20 66:6 71:1

71:19,20,22 73:19

365-day 38:19

41:19 42:19 43:3

37.5 106:11,15

4
202-234-4433

Neal R. Gross & Co., Inc.

Page 198

4 3:3,5

40 31:5 57:8,14

115:21

441 36:10,17

45-minute 27:19

4500 66:19 70:22

48,000 34:4

5

5 3:7

5.2 89:6

5.4.1 136:18 137:21

141:10

5.4.2 125:21

5.5 114:11

5.5.3 137:14

5.6 136:18 140:5

5.7.1 137:12 148:10

5:00 29:1

50 134:12 162:11

50th 54:3,4 67:7

50,000 34:3

6

6.2 28:9

60 57:8 166:12

65 57:7

7

7 138:21

70s 20:3

8

8:30 1:15

8:34 4:2

80 44:2,10

88 3:13

9

90 54:20 59:3 65:17

71:1 72:1 78:10

97:15 139:14

172:3

90th 42:12

90-day 36:5,20

38:20,21 39:2,15

39:16 55:7 60:3

71:22 78:2,12

151:12

91 71:13

92 71:14

94 97:13

95 127:11,12

95th 41:21 43:3,9

54:18,20

96 97:15

97.5 54:20

99 69:18

99th 41:21 42:13

43:4 59:16 60:1

90:11

99.99 90:10