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 U.S. ENVIRONMENTAL PROTECTION AGENCY

 + + + + +

 CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE

 OZONE REVIEW PANEL

 + + + + + 

 PUBLIC MEETING

 + + + + +

 WEDNESDAY, SEPTEMBER 12, 2012

 + + + + +

 The meeting convened in State

Ballroom A and B of the Marriott City Center,

500 Fayetteville Street, Raleigh, North

Carolina, at 8:30 a.m., Jonathan Samet, CASAC

Chair, presiding.

CASAC MEMBERS PRESENT:

JONATHAN M. SAMET, Chair

GEORGE A. ALLEN

ANA DIEZ-ROUX

ARMISTEAD (TED) RUSSELL

HELEN SUH

KATHLEEN WEATHERS
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PANEL MEMBERS PRESENT:

ED AVOL

MICHELLE BELL

JOSEPH D. BRAIN 

DAVID CHOCK

WILLIAM MICHAEL FOSTER 

DAVID GRANTZ

JACK HARKEMA

DANIEL JACOB

STEVEN KLEEBERGER 

FREDERICK J. MILLER

HOWARD NEUFELD

JAMES ULTMAN

SVERRE VEDAL

PETER WOODBURY

U.S. ENVIRONMENTAL PROTECTION AGENCY STAFF

PRESENT:

ANGELA NUGENT, Designated Federal Officer

VANESSA VU, Director, SAB Staff Office

SUSAN ANENBERG, OAQPS

CHRISTINE DAVIS, OAQPS

JEFF HERRICK, NCEA

BRYAN HUBBELL, OAQPS

AMY LAMSON, OAQPS

JOHN LANGSTAFF, OAQPS

KRIS NOVAK, NCEA

ZACHARY PEKAR, OAQPS

HEATHER SIMON, OAQPS

TRAVIS SMITH, OAQPS

LYDIA WEGMAN, OAQPS

BEN WELLS, OAQPS

KAREN WESSON, OAQPS

YUQUAN ZHANG, RTI International, Contractor
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 C-O-N-T-E-N-T-S

Chapter 6 - Controlled Human Exposure

 by Dr. Jack Harkema . . . . . . . . . . 22

 by Dr. James Ultman . . . . . . . . . . 26

 by Dr. Steven Kleeberger. . . . . . . . 37

 by Dr. Michael Foster . . . . . . . . . 40

Chapter 7 - Epidemiology

 by Dr. Sverre Vedal . . . . . . . . . . 44

 by Dr. Diez-Roux. . . . . . . . . . . . 58

 by Dr. Fred Miller. . . . . . . . . . . 62

Chapter 8 - National Scale Risk Assessment

 by Dr. Christopher Frey . . . . . . . . 82

 by Dr. Ted Russell. . . . . . . . . . . 91

 by Mr. Ed Avol. . . . . . . . . . . . . 94

Chapter 9 - Synthesis

 by Dr. Michelle Bell. . . . . . . . . .103

 by Dr. Christopher Frey . . . . . . . .107

 by Dr. Ted Russell. . . . . . . . . . .113

Review of Proposed Changes

 by Dr. Hubbell. . . . . . . . . . . . .119

Discussion of Health Risk and

Exposure Assessment

 by Chair Samet, Moderator . . . . . . .122

 by Dr. Travis Smith . . . . . . . . . .206

Ch. 1 - 3, Introduction, Conceptual

Model and Scope

 by Dr. Kathleen Weathers. . . . . . . .227

 by Dr. Peter Woodbury . . . . . . . . .233

Ch. 4 - Air Quality

 by Dr. Daniel Jacob . . . . . . . . . .253

 by Dr. Ted Russell. . . . . . . . . . .268

 by Mr. George Allen . . . . . . . . . .269
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Ch. 5 - Ecological Effects

 by Dr. Kathleen Weathers. . . . . . . .277

 by Dr. Howard Neufeld . . . . . . . . .280

 by Dr. David Grantz . . . . . . . . . .293

 by Dr. Peter Woodbury . . . . . . . . .305

CHAPTER 6 - ECOSYSTEM SERVICES

 by Dr. David Grantz . . . . . . . . . .306

 by Dr. Peter Woodbury . . . . . . . . .308

 by Dr. Kathleen Weathers. . . . . . . .309

 by Dr. Howard Neufeld . . . . . . . . .322

CHAPTER 7 - SYNTHESIS

 by Dr. Howard Neufeld . . . . . . . . .330

 by Dr. Kathleen Weathers. . . . . . . .341

CLARIFYING COMMENTS FROM THE PUBLIC . . . . .349

ADJOURN . . . . . . . . . . . . . . . . . . .393
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1 P-R-O-C-E-E-D-I-N-G-S

2 (8:33 a.m.)

3 DR. NUGENT: Good morning,

4 everyone. It's time to resume our public

5 meeting. The second day of the CASAC ozone

6 review panel meeting.

7 And for the record, again, I'm

8 Angela Nugent, the designated federal officer

9 for the panel today. I want to draw the

10 attention of the public to our additional

11 public comment opportunity this afternoon at

12 5:00 p.m.

13 And this is a public comment

14 period where members of the public need not

15 have contact to the SAB staff office way in

16 advance to provide comment. But I asked them

17 to let me know by 3:00 p.m. today whether they

18 wish to provide some additional comments.

19 And I should stress the importance

20 of additional. We want to hear any new

21 information that they want to bring to the

22 attention of the panel, and especially
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1 clarifying information that may be useful to

2 the panel in its deliberations.

3 So if individuals can send me an

4 email, or come up to me during the meeting or

5 during the break, I'll put their name on the

6 list to provide comments to be no more than

7 three minutes in length. So that's it in

8 terms of formal comments. Dr. Samet?

9 CHAIR SAMET: Welcome back to Day

10 2 of CASAC Survivor, the Raleigh version. I

11 understand that we're voting several people

12 off the show today, including, you too?

13 Wow, and Daniel. Well, you can be

14 replaced. So what we want to do is go back. 

15 Remember we had heard from the commenters on

16 Chapter 5, populations at risk. That was from

17 Helen, Michelle, and Ana.

18 And maybe if we could just recap

19 your comments really quickly as a reminder and

20 then open up for general discussion. So,

21 Helen?

22 DR. SUH: Just quickly, there were
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1 three sets of charge questions. The first

2 charge question asked about the methods. And

3 I think, do you want me to just speak for

4 myself or for --

5 CHAIR SAMET: Yes.

6 DR. SUH: Okay. So I guess the

7 methods were good but complicated. And so it

8 would be helpful to have a figure to

9 illustrate how the APEX model works.

10 Also it would be good to have some

11 more precision clarification about certain

12 aspects of the methods, including about the

13 uncertainty analyses and the methods that will

14 be used, even if it's a qualitative

15 uncertainty analysis.

16 Also information about model

17 performance, more specific, because I

18 understand the model's been improved. But

19 what is the overall effect and, I guess, how

20 the model could be validated, at least in

21 part, so the performance could be quantified

22 in some aspects.
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1 Also in terms of the multiple

2 repeated exposure analysis, how that's going

3 to be done and with what database, given the

4 deficiencies or the limitations of the

5 activity databases that exist currently.

6 So those are some of the main

7 areas. Also in terms of the second charge

8 question, which had to do with the

9 presentation of the results in the

10 interpretation, there was also some

11 clarification that would be very helpful.

12 One is in the overview of the

13 presentation of the figures and tables. It

14 would be helpful. It was very hard for me to

15 figure out which references went to what, so

16 perhaps maybe even a reference to the figure

17 numbers themselves.

18 And also beyond just the overview

19 of what the figures and tables were, it would

20 be good to have a discussion, or a summary, of

21 the overall main findings from those figures.

22 So what are the main points that
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1 you would like the reader to take home from

2 those figures and tables. That would be good

3 to have a text discussion of that.

4 And I guess also, for Section

5 5.6.1, it was unclear how that was going to be

6 incorporated into the subsequent analyses. 

7 Even if it was going to be helpful in the

8 qualitative part of the uncertainty analysis,

9 it would be good to have some discussion to

10 tie that part in.

11 And I guess the other point with

12 the methods is there needs to be more

13 discussion about the databases and their age,

14 and their relevance and appropriateness.

15 I think it's just an inherent

16 limitation. But perhaps there's something

17 that could be done to discuss their impacts,

18 or maybe if there's some other more recent

19 data sets that can be used to even see the

20 trends and how the activity patterns might be

21 changing across the years. That would be

22 helpful.
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1 And there were some more current

2 databases. And maybe some comparisons can be

3 made in those ways.

4 There's also, I guess, very few

5 related to children. And the question is, is

6 that enough, or is that going to be sufficient

7 to characterize for the 16 cities.

8 Then regarding the uncertainties

9 and variability, it was unclear what was going

10 to be done. What was set out sounded good. 

11 The question is, it was unclear what was going

12 to be done.

13 And even if it is a qualitative

14 analysis, I think it would be good to have

15 some more description about what that

16 qualitative analysis is supposed to achieve. 

17 What are the goals of it? And then also what

18 really is going to be done, how is it going to

19 be conducted, would be helpful.

20 Also, I guess, for me I think the

21 averting analysis, the averting behavior

22 analysis, should be incorporated in the
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1 uncertainty analysis somehow.

2 I think it's a nice piece, what's

3 in there now. But it just needs to be tied in

4 better to what's going to happen going

5 forward. 

6 CHAIR SAMET: Okay. Michelle?

7 DR. BELL: So I'll be brief. And

8 so my thoughts were very much in line with

9 Helen's.

10 I thought that a flow chart, the

11 process would be useful. Just as was already

12 mentioned, there needs to be more discussion

13 of the uncertainties.

14 And I had recommended considering

15 a table or some other format where EPA

16 outlines the uncertainties that they're

17 incorporating in a quantitative way, ones

18 they're incorporating in a qualitative way,

19 where they discuss what are the anticipated

20 directions.

21 And then maybe list some

22 uncertainties, where we don't have an idea
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1 what the anticipated direction might be on the

2 results, but remember the benefits and costs

3 of the Clean Air Act had that for health

4 outcomes, ones we can quantify and ones we

5 suspect.

6 So I thought that would be useful

7 here because I think a lot of thought has gone

8 into the uncertainty that could be used to

9 make that table.

10 And it was kind of hard to

11 evaluate some of the methods for the

12 sensitivity analysis because they weren't

13 specified. But, as I mentioned yesterday, I

14 suspect EPA has really good ideas for this. 

15 That it just is hard to evaluate without the

16 details.

17 I suggested consolidating some of

18 the figures and to be a little more careful

19 about labeling them. So the example I've

20 given was using 6-8 for 2006 to 2008. And

21 these are all things that can be fixed. It'll

22 just make them easier to read, especially as
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1 we know more figures are coming.

2 In general, I thought that this

3 chapter could be better referenced. There

4 were a few places where some rationale was

5 given, but it would be helpful for readers to

6 have references. And in my written comments

7 I give a few specific examples.

8 And my last point is that there's

9 a lot of language that I found kind of vague,

10 where again I assume that EPA has really

11 specific ideas about what they mean. And I

12 think it would be useful to put them in.

13 And again, in the written comments

14 I give some specific examples, just to give

15 you a better idea of how I'm thinking about

16 that.

17 CHAIR SAMET: Ana?

18 DR. DIEZ-ROUX: Yes. So I raised

19 a point about the representativeness of the

20 time-activity data and the implications of

21 that for the exposure estimates.

22 Then, in terms of the presentation
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1 of the results, to think about what sub-groups

2 really need to be shown. And I guess that's

3 linked to the sub-groups in which the

4 attributable mortality is going to be

5 estimated, and so forth, but thinking more

6 about that.

7 A second point was what the

8 metrics should be for the exposure estimates

9 that are shown. And I suggested thinking

10 about showing continuous distributions, in

11 addition to these dichotomous measures of yes

12 or no exposed to values above a certain level,

13 looking at the distribution across the whole

14 continuum, and also variability, describing

15 some of the variability in that distribution. 

16 And in terms of the sensitivity

17 analysis, if there was any way to be able to

18 look a little bit at the implications of the

19 assumptions regarding time-activity data and

20 also the backgrounds.

21 CHAIR SAMET: Okay. Thank you. 

22 So let me open this up for further comments
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1 from the panel. Fred?

2 DR. MILLER: I have a comment. 

3 But Chris had his hand up first.

4 CHAIR SAMET: Oh, go ahead, Fred.

5 DR. FREY: No, you go.

6 DR. MILLER: I'd like to bring up,

7 again, the concern relative to the CHAD

8 database and the fact that most of the data in

9 it are more than a couple of decades old.

10 And my grandson, that's 3 1/2, has

11 got a different activity pattern than I know

12 a 3 1/2 year old did 25 years ago. And I have

13 a concern about the representative of this. 

14 And so I'd like to offer up, there

15 is a 2006 paper by, I don't know if you're

16 aware, from the people in Montreal, where they

17 followed 2,200 people, ages 1 month to 96

18 years, more than 20,000 exposure days.

19 And they used doubly label water. 

20 And they have the percentiles of the

21 distribution for total daily and minute

22 ventilation from the 5th to the 99th, in all
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1 these different age groups.

2 And I was wondering -- and I

3 talked briefly with Helen about this and I'm

4 going to send her the paper -- if you could

5 maybe use that as a cross-check from your

6 sampling, where you come up with where you

7 feel that your individuals are compared to

8 where the individual, based upon the daily

9 minute ventilation that you come up with,

10 would be, to see if there's been a shift in

11 that.

12 And it goes to some of the things

13 that have been raised about sensitivity and

14 other kinds of analyses. And they actually

15 made measurements and compared it with

16 different other methods in that.

17 So it seems like it might be

18 something that you could find useful. It's

19 not my area, but for some reason I have the

20 paper.

21 CHAIR SAMET: So, Fred, following

22 yesterday, you should put that into your
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1 individual comments, what the reference is, so

2 that it's there, and into your comments for

3 this charge question.

4 DR. MILLER: Okay. I can't just

5 send it to Helen and let her do it?

6 CHAIR SAMET: Yes, that would be

7 fine. It needs to go into the database.

8 DR. MILLER: Yes. And it clearly

9 needs to be added to the ISA if it's not in

10 there, as they brought up about the McDonnell

11 and other things. So I'll make sure. One way

12 or the other it'll get in there.

13 CHAIR SAMET: And actually, in

14 follow-up, there's certainly a lot of very

15 comparatively elegant time-activity work going

16 on now using more sophisticated monitors.

17 And I wouldn't know that it's

18 going on. I don't know what is in the

19 literature, perhaps other do. But as a

20 general question, have you searched for

21 updates on time-activity patterns?

22 MR. LANGSTAFF: Yes, we've done
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1 that. But we'll look at this paper and

2 compare the ventilation rate distributions to

3 what APEX is estimating.

4 DR. HUBBELL: And one other point. 

5 And we're providing this list of potential

6 changes that you'll have later on today. But

7 one of the things that we've identified is

8 this American Time Use Survey data, which may

9 have some information that helps us look at

10 some more recent activity patterns.

11 But again, we're still in the

12 process of bringing that data in and

13 evaluating it. It's a fairly complex data

14 set, so we're working with that.

15 DR. NUGENT: And MESA Air has

16 collected some time-activity data, right? I'm

17 not sure how detailed it is and how relevant

18 it would be to, well, but not just looking at

19 kids There're no kids. It's only adults. 

20 But that could be something to explore too. 

21 I don't know.

22 CHAIR SAMET: Okay, Chris?
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1 DR. FREY: Yes. Maybe it's more

2 of a policy question, but I think it touches

3 on the CHAD data, the averting behavior. So

4 I looked at Chapter 5 because I had to review

5 the summary, Chapters 8, 9, and that builds on

6 the rest of the document.

7 And I think one thing, I didn't

8 get a clear picture from the current REA as

9 what is EPA's position on averting behavior. 

10 Because it seems like you could certainly look

11 at that in a descriptive way, as far as

12 interpreting epidemiological studies where

13 averting behavior is just an artifact of the

14 activity pattern that was observed.

15 But I think when you're looking

16 prospectively at risk assessments to inform

17 setting of the standard, it raises the

18 question should we be, in a sense, taking

19 credit for people avoiding going outdoors

20 because the air quality is bad?

21 Or should people have,

22 essentially, a right to go outside, even if
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1 they're in a sensitive group. And so it's

2 less clear to me that averting behavior should

3 be done in a prospective risk assessment aimed

4 at setting a standard.

5 And then also we get into some of

6 these issues of the time-activity pattern. 

7 While maybe some kids spend all day in front

8 of video games, others spend all day

9 skateboarding. And so we could look at sub10 groups where there are kids who spend a lot of

11 time outdoors, even in modern-day culture.

12 So I don't know to what extent it

13 makes sense in the REA. But it seems like,

14 well, the REA has to either implicitly or

15 explicitly take this on and make a statement.

16 And is it that averting behavior

17 is assumed to be something from a policy

18 perspective that's going to be a compensating 

19 mechanism for otherwise unacceptable air

20 quality? But that's really, I think, a policy

21 choice, not necessarily a science choice.

22 CHAIR SAMET: Okay.
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1 DR. NUGENT: If I can just add, I

2 had a similar concern. I think one of the

3 places where this can be addressed is in this

4 framework chapter, that we talked about

5 yesterday, that there not being a very clear

6 framework.

7 That would be a place where one

8 could discuss how averting behavior is or is

9 not going to be considered, for example.

10 CHAIR SAMET: Okay, Ed.

11 MR. AVOL: Just to reiterate one

12 aspect of this and sharpen the focus, I think

13 it's useful and important to review the

14 overall CHAD database and look at other time15 activity information across the spectrum.

16 But because there's been so much

17 information that has become available in early

18 life exposures, and the importance of early

19 life exposures in characterizing respiratory

20 and cardiovascular and just a whole range of

21 health outcomes later on.

22 The fact that we really don't have
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1 anything in the zero to five category, in a

2 risk document, I think is an important gap

3 that somehow needs to be addressed.

4 It needs to be described or

5 discussed. It's a critical research need. 

6 But somehow, I think because of all that's

7 been done from asthma to so many other

8 diseases, we need to somehow close that gap.

9 DR. BRAIN: And you could even

10 mention gestational exposures of moms as well.

11 CHAIR SAMET: Okay. Other

12 comments on Chapter 5? Well, Chapter 6? 

13 Okay, so this is controlled human exposure,

14 Jack?

15 DR. HARKEMA: So I have just some

16 quick comments. And I'm not going to go in a

17 lot of detail. But I think Question 1 methods

18 part, I think we've touched on --

19 CHAIR SAMET: Jack, you might pull

20 the mic over just a little bit.

21 DR. HARKEMA: Okay, I'm sorry. 

22 This has been brought up before, but the
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1 methods of McDonnell and Schelegle, the new

2 papers, first of all those have to be

3 documented.

4 You were right yesterday to point

5 that out, that should be documented in the

6 ISA. And it has to be incorporated somewhere

7 in there and then referred back to.

8 But at first I thought that you

9 should compare all three methods. But I think

10 that's labor intensive and it would just be

11 way too labor intensive to do all three of

12 those.

13 But I think that it would be, from

14 what I read, and this isn't my area but it

15 seems like those papers, that would be the

16 crucial threshold model to use in this

17 approach.

18 Now, I have actually almost the

19 same comment that was in Chapter 5. I thought

20 there should be some type of diagram or flow

21 chart on the methodology that will enhance the

22 text as well.
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1 And also where parts of the data

2 will be input into the different aspects, when

3 you're going from controlled human exposures

4 and then all the way through the population

5 base, just give it a flow. Set the stage for

6 it.

7 And then also there's some mention

8 of background in here, but more specifics of

9 where that would be inputted into the model. 

10 And so that may be clear to you, but for

11 someone that's outside of the area if that can

12 be really stated how that would be put in

13 place. 

14 The other thing, I think there

15 needs to be a stronger rationale. I agree

16 that using the FEV1 is the most appropriate in

17 those data. But I think there needs to be a

18 little bit stronger rationale and clearer

19 rationale in this document of why you're using

20 that compared to the other responses. And I

21 think that can be handled very easily.

22 And then there's a lot of
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1 references back to the older documents, but

2 not as much back to the current ISA. So I

3 would put a lot more.

4 Go back to the ISA. And that's

5 where the chapters that deal with controlled

6 exposures there need to be very clear. And

7 then you can reference and build the case

8 while you're using the risk analysis there.

9 The other charge questions, I

10 think the biggest thing is to look at these

11 new papers. But also in terms of the

12 discussion on certainty and variability, I

13 guess I have similar to Chapter 5.

14 If you could build a table or a

15 summary somewhere in the text, where you

16 really clearly state the strength and

17 limitations of each of the models and where

18 the uncertainty and variability is found, so

19 it's very concise rather than a long

20 discussion in the text. So that's about it. 

21 CHAIR SAMET: Okay, thanks. Jim?

22 DR. ULTMAN: Sorry.
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1 CHAIR SAMET: That's all right. 

2 Go ahead.

3 DR. ULTMAN: I didn't realize I

4 was next. I have re-written the first part of

5 my remarks. And I will update them so you'll

6 be able to read them.

7 But I've been giving quite a bit

8 of thought to the use of the FEV1 filtered air

9 correction. It's not a trivial matter. And

10 I know you realize that.

11 So my first comment is, I don't

12 know if you realize it, or maybe I'm not

13 right, but in this REA I don't see any clear

14 definition of how the percent delta FEV1 was

15 calculated.

16 And there're many ways you can

17 calculate that. And it's really not said. 

18 But if I go back to the ISA, there's a very

19 strong leaning toward correcting FEV1

20 decrement by filtered air to come up with a

21 percent delta FEV1.

22 In fact, there's about a page and
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1 half devoted to explaining why that's a good

2 idea to do that. There's a problem though. 

3 And what I'm referring to is the fact that one

4 of the ways of calculating the delta FEV1 is

5 to take the difference between the ozone

6 exposure and the filtered air exposure

7 decrement.

8 So each one is a decrement. You

9 have exposure to ozone. You have the

10 decrement in FEV1. You have an exposure to

11 filtered air. You have a decrement of FEV1. 

12 You take the difference between

13 those and you get a corrected decrement of

14 FEV1 exposed to ozone versus clean air. And

15 that's normalized so you get a percent.

16 But basically you're trying to

17 correct for, by using filtered air. Now what

18 are you correcting for? That's the question. 

19 And I've found in the literature,

20 and also in the REA, that the argument seems

21 to be that if somebody, for example, has

22 hyperreactive airways, and they're exposed to
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1 clean air and they're exercising, they're

2 going to get a decrement in their FEV1.

3 So if they then are exposed to

4 ozone, there's two components that's coming

5 into it. It's the exposure to ozone itself,

6 plus an intrinsic effect of exercise to cause

7 airway closure, or airway narrowing.

8 So the idea is to try to get that

9 effect out. And that's considered important. 

10 So as an aside, I don't know why that effect

11 is any different than age or anything else. 

12 It's an effect modifier and I

13 don't know why it has to be gotten out. Well,

14 it has to be gotten out that way because you

15 don't know if it's there or not. Like age,

16 you can tell what a person's age is, but you

17 can't tell if they're getting hyperreactive or

18 not, if that's the cause.

19 Now the problem comes in that

20 there can also be a day-to-day variation in

21 the measurement system. So when you take that

22 difference, between the clean air and the
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1 filtered air, you're introducing an additional

2 error, due to the measurement system, into it. 

3 So that's the down side of doing

4 it that way. And this all seems like it's

5 very academic until we get down to very low

6 ozone exposures.

7 And what happens at very low ozone

8 exposures is that filtered air contribution,

9 although it's a relatively small contribution

10 at high exposures, it becomes a large

11 contribution.

12 A much larger contribution is when

13 you're at low exposures and your decrements

14 are small. Does that make sense? So you're

15 taking the difference of two small numbers.

16 And you can really amplify. If

17 there's a small error in the measurement from

18 day-to-day, you can really amplify that error. 

19 And that's what concerns me.

20 Because then we're looking at all

21 these small exposures now. And we're

22 introducing an effect which could look, in
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1 fact, some subjects it can completely

2 overestimate the individual subject's

3 response.

4 Because some subjects actually

5 show an improvement after ozone exposure. So

6 maybe their airways are dilating or something. 

7 I don't know. And they show an improvement.

8 So when you take the difference

9 between the decrement and improvement, you get

10 a larger decrement then is apparently due to

11 ozone.

12 So if you look at an individual

13 then, you could really overestimate the

14 decrement that ozone is causing when you're

15 looking at individuals. And that's really

16 what my concern is.

17 And that was, I think, brought up

18 at the last CASAC panel. And there's been

19 some discussion in this literature, I think,

20 also, by Adams, I think was one of the people. 

21 So the question is what to do

22 about it and how to handle it. Is that
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1 something you've been milling around in your

2 minds, also?

3 MR. LANGSTAFF: Yes, it is. And

4 there are several, well, there's more than one

5 way of adjusting for this effect of exercise

6 in clean air.

7 And one thing we've been

8 considering doing, and you can give us your

9 input on that, is just doing the analysis both

10 ways, calculating, for example, the population

11 exposure response function using the FEV1

12 decrements corrected for exercise in clean

13 air, and then without that correction, and

14 just seeing if it makes any difference.

15 Because it doesn't seem that it

16 usually makes much difference in the

17 aggregate. So it may not actually matter. 

18 But it is an important point.

19 DR. ULTMAN: Well, wouldn't it

20 matter? I'm obviously concerned if somebody

21 if focuses in on a group of a few individual

22 subjects. If you do everything in the
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1 aggregate, I think things can wash out pretty

2 well.

3 But the trouble is if people then

4 zoom in to look at individuals, that's what

5 gets me concerned. But at any rate, I think

6 that's a valid way of doing it.

7 There's another idea that

8 McDonnell has in his 2012 paper, which is kind

9 of buried at the end in a sentence or two. 

10 And the way that he actually calculates his

11 percent delta FEV1, is he takes a study, a

12 complete study.

13 Well, it's more complicated than

14 that. But let's suppose that there's a

15 continuous exposure study which he's used. So

16 there's no time varying changes or anything. 

17 So you just measure the pre and

18 post FEV1 changes, and what it does is he'll

19 take in that single study. He'll take all the

20 subjects' clean air exposures, and average

21 those. So he has a single clean air exposure

22 for the group of subjects.
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1 And the advantage of that, as far

2 as the accuracy problems with the

3 instrumentation, which vary from day-to-day as

4 you average those out by grouping subjects

5 together. So that's another method you might

6 consider. Does that make sense?

7 DR. ULTMAN: Yes. I'm aware of

8 how he's done that. We have some concerns

9 about how that washes out the individual

10 variation in a way we might not want. But

11 we're looking at that.

12 MR. LANGSTAFF: Okay, good. Took

13 me awhile to really see through all this. 

14 But, of course, you've been at this for a long

15 time.

16 Okay. So my bottom line is

17 there's clearly not enough explanation in the

18 current version of the REA. Because this

19 could be a very important issue. And

20 hopefully it won't be. But it could be an

21 important issue.

22 CHAIR SAMET: Okay, anything else,
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1 Jim?

2 DR. ULTMAN: Yes. I've got a

3 couple of things here. A lot of the comments

4 you'll just read on my comments, so it won't

5 matter.

6 I guess the McDonnell, Smith and

7 Stewart model is obviously very attractive. 

8 But it overpredicts the current calculation

9 you have, if I understand it, it not over10 predicts, I'm sorry.

11 In the current calculation, it

12 predicts larger values than the other method

13 did. And if that happened, then all the

14 calculations you did, that would become an

15 issue, I think, in terms of really why that's

16 the case.

17 So I assume that you're going to

18 be doing more comparisons to try to see if

19 that holds up or not.

20 MR. LANGSTAFF: That's correct. 

21 Yes. We'll track down the difference for

22 that.
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1 DR. HUBBELL: But there are

2 reasons why it's the case. We expect it to do

3 that.

4 DR. ULTMAN: With respect to the

5 question about age, whether the relationship

6 between age and delta FEV1, the function that

7 you're going to be using, which is just

8 basically you take 35 years old and take it

9 all the way out.

10 In terms of sensitivity analysis,

11 I think that's something that you should just

12 test in your sensitivity analysis. I don't

13 think there's much else you can do.

14 There are a few articles in the

15 literature. And those should be referenced

16 for people who have looked at the effects of

17 ages, two or three maybe.

18 There's not enough for you to do

19 anything in terms of data. But I think that

20 it should be mentioned and explained. And I

21 think the sensitivity analysis would be really

22 important.
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1 Oh, and then the last point was,

2 if I understand correctly, you're using the

3 same exposure response formula, or model, for

4 adults, for children, and for children with

5 asthma. Am I wrong?

6 MR. LANGSTAFF: That's correct, we

7 are.

8 DR. ULTMAN: Okay. So basically

9 then, what that means is the risk analysis,

10 based on the human subject data, the FEV1

11 data, is going to be based on the difference

12 in their activity patterns alone.

13 MR. LANGSTAFF: And there's a

14 difference in physiology.

15 DR. ULTMAN: Oh, physiological --

16 MR. LANGSTAFF: The ventilation

17 rates --

18 DR. ULTMAN: -- physiological

19 parameters. Like ventilation, for example.

20 MR. LANGSTAFF: Right, and body

21 surface area, and so on.

22 DR. ULTMAN: Body surface area,
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1 things of that nature, and activity pattern.

2 MR. LANGSTAFF: Right.

3 DR. ULTMAN: I think that's a huge

4 jump. This is intuitive. But to say that

5 children behave the same as adults with

6 respect to exposure response, well, maybe I

7 can buy that. And there is literature that

8 you quote that kind of backs that up.

9 But then when you extrapolate that

10 to asthmatic children, where I don't think the

11 data really exists to support that, then that

12 to me is a big jump.

13 And I would suggest that's another

14 area where sensitivity analysis is really

15 important, to see if there's any way you can

16 take a look at that. That's all I have.

17 CHAIR SAMET: Okay. Thanks. 

18 Steve?

19 DR. KLEEBERGER: I don't have too

20 many things to discuss. Jack covered Question

21 1 fine. Most of my comments actually come in

22 Question 2.
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1 I had trouble with Table 6-4 on

2 Page 620. It wasn't quite intuitive to me

3 what exactly was being represented there until

4 I looked at it hard enough. And it was kind

5 of a reduction of percentage change.

6 And I can't offer a better way to

7 do it. But it was kind of hard for me to

8 figure out what exactly it was you meant in

9 that table.

10 Jack also mentioned, let's see

11 where are we in here with that, this gets to

12 page, hang on, let me go on to one other thing

13 first.

14 On Page 649, Line 1, the author

15 suggests that the lung function response

16 appears to level off after six hours of

17 exposure. And it's unlikely that longer

18 exposures would change exposure response

19 relationship.

20 But if you look at that figure

21 pretty carefully Schelegle's paper, in fact,

22 I don't think that's the case. I don't think
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1 the response is, in fact, leveling off.

2 I think it's peaking at that

3 point. And I don't think we can actually make

4 that extrapolation out that a couple more

5 hours of exposure wouldn't make a change. In 

6 fact, the change might increase rather than

7 level off. So I would be careful with that

8 interpretation.

9 Question 3, this is where I agree

10 with Jack. I think the rationale needs to be

11 clarified.

12 But I would also add that in

13 animal models, and I believe this is the case

14 in human subjects with controlled exposures --

15 and Mike can address that when it's his turn -

16 - but the responses to ozone are not always

17 correlated. And in mouse genetics what that

18 means is that the phenotypes do not co19 segregate.

20 And so someone who is very

21 responsive in terms of their FEV1 response,

22 may not be responsive to the inflammatory
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1 response, and vice versa. And symptoms could

2 be the same.

3 So if you're basing all your

4 models on FEV1 changes, you may be missing

5 populations that are actually very

6 susceptible. But you don't detect that with

7 your FEV1 phenotype.

8 And so I think that needs to be

9 stated very clearly, that while re-using FEV1

10 there may be other populations that may model

11 very differently. And you may be missing

12 those with these models.

13 I think the other questions are

14 beyond my area of expertise. I've put a few

15 notes in my written comments. But those are

16 my major points.

17 CHAIR SAMET: Thanks. Mike?

18 DR. FOSTER: So I want to

19 reiterate. I think, Jack, Jim and Steve have

20 made some really good comments. I've written

21 up some really good comments for you people

22 and, I think, in a gentle form or way.
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1 But I think I just want to re2 emphasize that in the ISA third draft there're

3 some comments about ethnicity differences that

4 Bill McDonnell's approaches have never taken

5 advantage of, or considered.

6 In addition, some of the databases

7 that Bill has relied on have not really

8 strenuously looked at whether or not obesity

9 in the database was a problem. And we know

10 from retrospective analysis obesity seems to

11 be a risk factor for an FEV1 change.

12 And then we know from the mouse

13 literature, and Stephanie Shore's stuff at the

14 School of Public Health at Harvard, that the

15 model data suggests that it's consistent with

16 the human data.

17 So I would like to emphasize that

18 you at least go back and look at that. And I

19 can easily provide you with the references, if

20 I haven't done that already.

21 I think the other thing that I

22 have concern about is actually in the second
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1 draft. And I've made my comments clear, that

2 if you're relying on Ed Schelegle's paper, and

3 Chong Kim's, and I guess it's Milan Hazucha's

4 papers, they're very different profiles of

5 ozone exposure.

6 And some of those utilize

7 different minute ventilations. So the dose

8 response concentration, times dose, times

9 breathing frequency, will probably be very

10 much different in terms of the exposure times

11 metrics. So they should definitely be taken

12 into consideration.

13 The other point that I'm confused

14 about when I look closely at Ed Schelegle's

15 paper, is for each of the dose exposures that

16 he did, he started out with 30-plus patients.

17 But in every case they ended up

18 with 16 going for the entire 6.6 hours. And

19 I couldn't find any reason why there was such

20 a high drop-out rate. That's an extremely

21 high drop-out rate.

22 So I guess you can easily call
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1 Schelegle and ask Ed what gives. Maybe

2 someone knows. There's got to be a response. 

3 In my laboratory we never see that happen at

4 higher doses.

5 I guess the final thing is, so the

6 practice labs, you might have a subject come

7 in. You screen him, you put him on a

8 treadmill. You find out what their airway

9 responsiveness is based on FEV1.

10 They come back in for a

11 preliminary pre-measurement before and

12 exposure, whether it's filtered air or ozone. 

13 We're actually doing seven visits now. So we

14 have a lot of preliminary data and it's in

15 large numbers. 

16 Our paper last summer was 140

17 sample size, same equipment, same laboratory,

18 same technician, and skin testing. So the

19 careful approaches here is what you need to

20 think about.

21 And I understand the broadness of

22 Bill McDonnell's approach. But you also have
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1 to go with where is the strength and the

2 precision in the controlled laboratory

3 studies.

4 And John, I brought in some

5 references to provide you with about the

6 ethnicity differences that I mentioned

7 yesterday in urban areas. But that's it. 

8 Thanks, John.

9 CHAIR SAMET: Okay, thank you,

10 Mike. Questions? Sverre?

11 DR. VEDAL: Yes, I may have missed

12 it in the discussion. But I think you need to

13 have, I guess, some motivation as to why

14 you're focusing entirely on lung functions. 

15 There's obviously a whole

16 literature on a host of other end points that

17 are controlled clinical study end points.

18 I understand why that's done. 

19 It's quantitative, relatively easy to do,

20 relative to the others. But there's been a

21 change over time, and I think an appropriate

22 one, to relatively de-emphasize the lung
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1 function findings.

2 It wasn't long ago when we were

3 talking about a risk assessment, and all we

4 talked about was spirometry. We didn't have

5 anything else to talk about. Well, now we do. 

6 And I think in this context, I

7 think if you're not going to talk about

8 anything else you need to at least motivate

9 that, as to why you're not looking at the

10 other end points.

11 CHAIR SAMET: Fred?

12 DR. MILLER: I have several

13 comments. But let me just follow-up with what

14 Mike was bringing out about obesity in the

15 McDonnell model body surface area, I would

16 think, helps account for obesity.

17 The other thing about the latest

18 McDonnell paper is, that model is able to

19 simulate the results of every single clinical

20 study that's been done, whether it's square

21 wave, triangle exposure, step function, or you

22 name it.
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1 So it's fairly robust in terms of

2 the differential equations that are in there

3 and the various parameters.

4 But I think Mike's point, in terms

5 of ethnicity, is a very important one. And

6 I'd have to reiterate that. And I think on

7 some of these others though, some of the

8 aspects of it are incorporated into the

9 modeling results.

10 I personally favor the use of the

11 McDonnell model because it allows you to get

12 at population level responses rather than

13 individual responses. And so that, to me, is

14 a significant stream.

15 On Page 610, the staff are talking

16 about the various probability scenarios,

17 90/10, 80/20, and 50/50. And I would say that

18 the 50/50 population scenario is definitely

19 not needed. And if you end up incorporating

20 the McDonnell paper, definitely not.

21 Because that split, in terms of

22 the areas of the two curves that you're trying
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1 to represent the tolerance distribution, as

2 you would go to the ends of the population

3 curves, the tolerance distributions for

4 individual responses has a narrower

5 distribution.

6 If you go back to Finney bioassay

7 101, the tolerance distribution is very tight

8 on the end. And it gets broader as you cut

9 the planes in that.

10 So to assume a 50/50 is just too

11 much, 80/20 and 90/10, I think, cover the

12 scenarios you're concerned about in terms of

13 that.

14 So when we're talking here about

15 trying to cut down on the amount of work on

16 that, that's one that I think you could easily

17 defend not following up on.

18 The panel's in the paper in that I

19 have a suggestion for making it easier to

20 read. And what I would do is, right now you

21 have it split up for city and category of

22 decrements greater than ten, and so on.
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1 And I would think that if you

2 rearranged it for a series of three figures

3 for each city followed by a set of three

4 figures for the next city, where in a given

5 city you have the results for school age

6 children, asthmatic children, and old people

7 for the FEV1 ten decrement, then the next set

8 for the greater than 15 and greater than 20,

9 then go to the next city.

10 Right now, to compare things, I'm

11 scrambling back and forth. And I think you

12 might look at that to see if that gave an

13 easier track to follow.

14 And in some of these, well, not in

15 this chapter as much, but in some of the

16 others, I would actually be advocating that

17 all of these figures be in an appendix and

18 that you only have one in the body of the text

19 that illustrates the points.

20 Because particularly in the later

21 chapters, you're going for pages that are

22 interjected with all kinds of figures before
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1 you get back to the text explaining in that. 

2 So it might help clarify following through. 

3 Then I have just one other

4 comment. And that has to do with on 646 you

5 state, on Line 18, that there're going to be

6 three topic areas that they intend to perform

7 additional analyses.

8 And the first one involves

9 updating the exposure response functions with

10 new clinical data and comparing it to the

11 previous results. And I feel that's a waste

12 of the time.

13 The current is more robust. It

14 includes more studies in that. It's got to be

15 closer to the truth. To do it, to compare it

16 to the oldest, is saying we think we've gone

17 the wrong direction. We've made errors.

18 It's a more robust database. So

19 why waste time comparing it to something from

20 before. I just don't see that.

21 The second area involved

22 determining the relative importance of low
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1 dose extrapolation by looking at the number of

2 percentages of people experiencing adverse

3 response.

4 And if you use the McDonnell

5 threshold model, that's going to assist you in

6 that task, as well as the third one that you

7 identify on that page. Because in his model,

8 you do have age as a factor that can be

9 incorporated.

10 I have some other specific things

11 but they're just with my general comments.

12 CHAIR SAMET: Okay, thank you. 

13 Other comments on this? Okay. Then we'll

14 move on to Chapter 7, epidemiology. Sverre?

15 DR. VEDAL: I'm going to take a --

16 CHAIR SAMET: Wait, wait. I'm

17 reminded that we need a volunteer, Jack, to

18 summarize the stuff here.

19 DR. HARKEMA: Thanks for

20 volunteering me.

21 CHAIR SAMET: Okay, thank you. 

22 Sverre?
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1 DR. VEDAL: I'm going to go

2 through --

3 CHAIR SAMET: Do you want to

4 accept the nomination right now?

5 DR. HARKEMA: What's that?

6 CHAIR SAMET: Do you want to

7 accept the nomination right now? No, go

8 ahead.

9 (Laughter.)

10 (Off the record comments.)

11 DR. VEDAL: -- methodically,

12 unimaginatively, through the charge questions,

13 some of which I'll have very little to say

14 about.

15 The first had to do with the set

16 of epi studies for concentration response

17 functions and such. And I think, over time,

18 I've become convinced that the multi-city

19 study approaches are fine and using the

20 Bayesian shrinkage approach is fine, I think

21 is preferable.

22 It may not come out always, but
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1 when you're using single city studies for some

2 of the functions, qualifying that based on the

3 fact that it is only one city, you do that

4 appropriately, I think, in most instances.

5 On the rationale for selection of

6 studies I think is good. I have no problem

7 with that. The uncertainty and variability

8 discussion, there was a lot of discussion on

9 the uncertainty and variability, and probably

10 because you're missing a lot of chunks from

11 what's going to be in the subsequent version

12 for other parts.

13 It's obviously very qualitative,

14 by necessity. And I've finally become

15 convinced that you just are not able to do a

16 formal probabilistic risk assessment, and this

17 was probably as good as you can do.

18 In ozone, in particular, one of

19 the big issues obviously is exposure

20 measurement error. And it's kind of

21 qualitatively different, I think, from the

22 other sources of uncertainty that you've dealt
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1 with here, that you've dealt with quite

2 thoroughly.

3 I don't know quite how it could be

4 worked in to this type of a framework. But at

5 least some consideration of exposure

6 measurement error needs to be given to it. 

7 Because there's such an element in the room,

8 particularly for ozone.

9 I understand the notion that we do

10 have a metric based on central monitoring

11 sites. And those are the sites that are used

12 in looking at attainment for the NAAQS and

13 such.

14 So it doesn't really matter in

15 some way what's sort of intervening between

16 those, but it does if you're actually looking

17 at it, you want to estimate a risk due to

18 ozone, in particular.

19 And we all struggle with issues of

20 what this actually means. What these

21 associations actually mean is that these low

22 concentrations, where we know that the
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1 exposure to the susceptible population are

2 considerably different, probably a lot lower,

3 people who are inactive, indoors, none of that

4 comes out here. And yet it's a big lurking

5 area of uncertainty.

6 The things that you've dealt with

7 here I think you've dealt with quite well. 

8 Exposure measurement error could also be a

9 source of what you described as variability,

10 as opposed to uncertainty, differences that

11 effect estimates regionally as a result of

12 potentially different degrees of exposure

13 measurement error.

14 The next section is on the results

15 of the risk analysis. And one point that I

16 was struck with, and I'm struck with it every

17 time I see it of course, is the very large

18 effects that are estimated for.

19 And in this example, that's used

20 in the risk assessment, is an exacerbation

21 where the percent of baseline risk

22 attributable to ozone varies somewhere between
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1 12 and 20-some percent, which is big, right,

2 and may not pass the credibility test in some

3 sense. It's that big.

4 So it comes as a shock when you

5 find you're reading that. And obviously it's

6 based on the effect estimate from the Boston

7 study. And that's all you've got to work

8 with. But to the readers, looking at this,

9 we're not talking about changes in ozone

10 concentrations.

11 Your argument is that there was no

12 ozone, regardless of what else, it's

13 interactive with the cause of this. If you

14 remove ozone you're going to have huge

15 reduction in asthma exacerbations. So anyway,

16 it does strike the reader.

17 One thing that I was struck with

18 in this is also, and it's I wouldn't say

19 necessarily a style thing, there's a lot of

20 repetition.

21 And I guess specifically the

22 repetition comes in where it's almost a
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1 formulaic approach, where you repeat things

2 with this is the current situation. And then

3 you repeat things when you go in the just

4 meeting the standards setting.

5 And then you repeat things again

6 when you into a summary. And the summary, in

7 some sense, is that the level of detail is too

8 great. It's almost the same level of detail

9 that was used in the original descriptions of

10 the results. So it's very repetitious and in

11 some way formulaic as you go through it.

12 The presentation of the ozone, the

13 distribution of ozone related mortality, I

14 don't know what everybody else thinks, but I

15 love the eMaps.

16 I know that they're just a

17 representation of the numbers that are in the

18 cells. But nevertheless, I think they're

19 great.

20 And I really like, and again, I'm

21 probably going to be in the minority here, I

22 love going to zero. I love seeing the back of
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1 that, the zero percent concentrations that are

2 playing no role. And then as you march up

3 where it starts to kick in. And I thought it

4 was very informative. And so I'm a big fan of

5 that.

6 One thing that struck me was the,

7 and again it must have something to do with

8 the city estimates and the Zanobetti versus

9 Bell, she's not here, the Bell study, is so

10 dramatically different.

11 You have a very similar estimate

12 effect for New York. And then you look down

13 the tables and you go from like maybe, I don't

14 know, 70 deaths in L.A. And then in then in

15 the other study it's 500 deaths in L.A.,

16 dramatically different. And maybe a little

17 point of discussion about that, because it's

18 so dramatic.

19 The refinements to the second

20 draft, and what you're planning to do and not

21 to do, I'm going to vote not to have you go

22 through a long term respiratory morbidity risk
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1 assessment. That would be my take, based on

2 the findings. I think the rest is fine.

3 And then one final point, there're

4 a lot of studies that get this designation as

5 being the strongest bit of evidence and such

6 and such.

7 And in this setting, it's not the

8 nuance in asthma studies. It's the Lin study

9 from where it's the strongest of the long term

10 ozone exposure respiratory morbidity studies. 

11 And you mess up the references

12 there a little bit. But that's a little issue

13 of tidiness. You either mean that's a really

14 strong study or that what else is out there is

15 really lousy. It's not that strong of a

16 study, and yet it comes off as being this

17 landmark kind of study. That's all I have to

18 say.

19 CHAIR SAMET: Good. Ana?

20 DR. DIEZ-ROUX: Okay. Let's see. 

21 I have, I guess, some similar comments to what

22 Svere said. In terms of the selection of the
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1 studies and the rationale, I agree with Svere

2 and his comments. I have nothing to add to

3 what he said.

4 In terms of the discussion of

5 uncertainty and variability, I thought you did

6 a very thorough, thoughtful job. That's what

7 you can do. I think it's very reasonable.

8 My main comments are related to

9 the presentation of the results. Our first

10 thing has to do -- and maybe because I'm an

11 epidemiologist, we can get a little obsessed

12 with terminology -- but some of the

13 terminology is very confusing, in terms of how

14 you're describing the effects.

15 And this is true in other parts of

16 the document too. Sometimes you talk about

17 total incidents when you should be saying

18 number of deaths attributable to.

19 So I would come up with some

20 standard terminology and then use it

21 throughout. Because it makes if very

22 confusing to understand what you're showing.
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1 And it can be very simple, number

2 of deaths prevented, percent of deaths

3 prevented, reduction in absolute number of

4 deaths, percent reduction in absolute number

5 of deaths.

6 And that's basically what you

7 need, really. So I would go through all the

8 tables and the text, and then use a consistent

9 terminology that's more or less accepted by

10 most people in terms of describing these

11 different estimates.

12 And there might be ways, I think

13 the heat maps are fine. I didn't understand

14 why they were called heat or maps, because

15 they don't seem to have any of those things in

16 them. But I guess it's just a jargon thing.

17 But there might be ways that you

18 could streamline the results, at least at some

19 point, to get a bigger punch, and to be able

20 to more clearly show, summarize the findings.

21 And really what you need is

22 something like, you could have a table where
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1 you have total number of deaths attributable

2 to ozone for your different cutoffs.

3 And I like going down to zero too,

4 myself, actually, total number of deaths

5 attributable to ozone, percent of total deaths

6 attributable to ozone, and then the absolute

7 and the percent reduction using different

8 standards. And that you could summarize

9 pretty succinctly, at some point, for each

10 city and then overall.

11 And that would give people

12 something where everything's together and you

13 can really see what the impact of this would

14 be. Even if you keep some of the more

15 descriptive stuff, I think having some summary

16 like that would be very useful.

17 Let's see, what else. Yes, and I

18 liked showing both the estimates going down to

19 zero and the lowest measured level. I thought

20 that was very informative.

21 And finally the last thing, this

22 question about the long term effects
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1 estimates, I'm not as familiar with that

2 particular literature in terms of the long

3 term respiratory effects as others.

4 But given that it's such an

5 important cause of morbidity and the

6 population impact is so large, I would be

7 inclined to think about whether it's not

8 possible to show something, even with lots of

9 caveats.

10 That's my opinion. I'd be

11 interested in hearing what other people think. 

12 But because the population impact of morbidity

13 from respiratory diseases is so important, I

14 would be inclined to perhaps consider that.

15 CHAIR SAMET: Okay, Fred.

16 DR. MILLER: Do you want only the

17 things relative to the charge questions now or

18 anything on seven in addition to the charge?

19 CHAIR SAMET: Feel free to go

20 ahead.

21 DR. MILLER: Okay. Well, first of

22 all, relative to the heat maps, I like them
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1 also. But I would say that you need some more

2 interpretation, an explanation of them. 

3 Because, for example, when you have the

4 alternative standards you have a shifting

5 down.

6 And it looks like by lowering the

7 standard you're actually causing more

8 mortality at lower exposure levels if you look

9 in the tables, which is counter intuitive, to

10 say the least.

11 The other thing is you might

12 consider the color scale. Right now, 47 to 72

13 deaths are represented by the same color. And

14 I don't know that you don't state the number

15 of death interval that you were matching up. 

16 So get that in there.

17 And it might be possible to use a

18 more narrow scale in terms of, I don't know if

19 20 some deaths is the minimum when you

20 consider the overall.

21 But it just seemed like I might be

22 able to extract more out of the heat maps if
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1 they not only explained their purpose a little

2 better, but also maybe to refine the color

3 scheme. That was just because of the last

4 couple of comments, that I would throw that in

5 now.

6 In terms of selecting the

7 appropriate studies for the concentration

8 response things, I think the document clearly

9 defends what they did.

10 They discussed the procedure that

11 you did for selecting them, including the air

12 quality information, the fact that CR

13 functions had to be available for the in-point

14 of interest and baseline health incident state

15 in that. And so I felt that was a very

16 strong, and it was up front, and it was very

17 good.

18 In terms of the rationale for

19 selecting in the specific studies, on Page 723

20 the four major criteria that you used to

21 select are identified. And it goes through it

22 very clearly.
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1 I would not endorse addressing

2 long term exposure related respiratory

3 morbidity. I'm in agreement with Sverre on

4 that at this time, due to limitations in the

5 data available for such analyses.

6 And I think you're going to have

7 more than enough work to do without that. 

8 Then you also talk about expanding and the

9 factors then that you considered in

10 identifying the effect estimates. And you

11 list six of those.

12 And the shape of the functional

13 risk model is something that might end up

14 needing a little more discussion if you use

15 the latest threshold model from McDonnell. 

16 Because there'll be some text that would need

17 to be added there.

18 In terms of the qualitative

19 discussions of uncertainty, you've done an

20 excellent job, in my opinion, on either

21 identifying aspects that either contribute to

22 uncertainty or variability.
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1 But I point out here, and I think

2 this page or whatever it is that you have got

3 where we can look at today, I strongly

4 recommend a prioritization of the different

5 tasks and what you can do.

6 And in my individual comments, I

7 have said what would be my priority. And I'll

8 be interested in hearing if the rest of the

9 panel agrees with some of those.

10 And right now they're spread out

11 in the document in this section and that. So

12 hopefully what you've pulled together here for

13 today will help us in aiding you to give you

14 our feedback.

15 In terms of the results of the

16 risk analysis, the 11 bulleted paragraphs in

17 Section 762 do a fairly good job of presenting

18 and discussing key results.

19 However, here's where the

20 illustration of the major findings, with some

21 simple graphs that convey the finding of the

22 recent condition scenario versus the scenario
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1 where the current standards are just met,

2 would be useful.

3 The extra verbiage used to convey

4 all of these important findings is just

5 overwhelming. And so the old saying about a

6 picture is worth 1,000 words, if you can

7 develop a couple of figures you could really

8 save a lot there.

9 So I've already told you about the

10 heat maps. And in terms of the characterizing

11 overall confidence, two pages in the document

12 are devoted to characterizing the overall

13 confidence in the risk assessment and risk

14 estimates derived from these studies.

15 The five topics that you discuss

16 appear to be relevant. However, I don't

17 understand why the log linear no-threshold

18 model was used for the full range of exposures

19 down to zero, as this belies the real issues

20 that need to be addressed, that a risk above

21 the PRB.

22 And I would grant Sverre having
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1 one scenario where you go to zero. But this

2 bridges into one of my major concerns about

3 the current analyses that are in this document

4 in the policy assessment.

5 And that is not having estimates

6 of the contributal risk above some definition

7 of policy relevant background. To me, this is

8 absolutely necessary because you have to

9 understand what part of the total risk is

10 controllable by any kind of regulatory action.

11 And without that, you just can't

12 make that. And I feel so strongly as to say

13 you're inviting litigation to the

14 Administrator of the agency without doing

15 this.

16 Because as we get to lower and

17 lower levels, to set a standard where it's

18 below the background levels that exist would

19 be arbitrary and capricious, in terms of the

20 Clean Air Act's wording in that. So somewhere

21 it's got to be included here.

22 And right now, you only go to the
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1 LMLs. And the LML, as I indicated yesterday,

2 and I think it's Table 75, the LMLs in all of

3 these epidemiology studies are a factor of

4 three to five lower than what you present

5 earlier in the document for the regional

6 specific North America background or U.S.

7 background, and so forth. 

8 And so to talk about risks from

9 the LML up, in the epi studies, is to me also

10 incorrect. It's got to come about from some

11 definition of what you think is the non12 empathogenic contribution, and what that level

13 is, and then what are the risks above that.

14 So if I were endorsing two

15 scenarios, it would be down to zero for the

16 total risk. And then define, if you're going

17 to use North America background or USA

18 background, or what, but show the attributable

19 risk reduction that could occur with the

20 alternative standards that incorporate those

21 different background definitions.

22 And I don't understand here the
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1 relative importance of U.S. versus North

2 America background to that, which might be

3 more defensible or more appropriate. That's,

4 I think, in Danielle's bailiwick more than

5 mine.

6 But this is clearly, to me, a

7 major problem, with the current set-up of the

8 analyses, that is just inviting trouble.

9 I like the higher order decoupled

10 method. And we had a little bit of discussion

11 yesterday about the pros and cons. And in the

12 current document, it's not made clear how you

13 would go about, then, estimating risks above

14 some particular cut point.

15 I know it can be done and I had

16 separate discussions. But right now the

17 document just doesn't cover that.

18 So in terms of a couple of the

19 other things, there's a couple of other areas

20 in terms of the number of sensitivity

21 analyses, and that's in Section 7.7.1, that

22 you might look at at the second, they're
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1 discussed there.

2 And here again, I feel a need to

3 prioritize because I just am convinced there

4 isn't enough time and aren't enough resources

5 to do all of them.

6 And my own prioritization would be

7 to prioritize the short term exposure related

8 mortality, then the short term exposure

9 related morbidity, and lastly, by

10 interpolation of missing air quality data.

11 I think the missing air quality

12 data is just going to be masked by the

13 importance of the sensitivity analyses of

14 those first two. So here again is an area

15 that I would suggest putting the lowest

16 priority to trying to get to.

17 I also wondered, in terms of the

18 national study, if right now they're all

19 divided up into the different cities. And

20 then you do the total. And I'm wondering

21 about if you could have some overall

22 quantitation where you weight these by the
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1 population that's at risk in these areas.

2 Because you have different

3 mortality estimates. And, as Sverre brought

4 up, the difference between the Bell and the

5 Schwartz things are quite large in some

6 instances.

7 I think you've explained that it's

8 primarily due to what was the definition of

9 the ozone season in those two different

10 studies. One included many more months, and

11 one was only July, August. Isn't that not

12 true?

13 DR. VEDAL: It's true but it's not

14 going to be an issue.

15 DR. MILLER: Okay. Well, it

16 certainly would need to be adjusted for that. 

17 And then there may still be differences. But

18 I'm not sure they'd be as large as they are

19 now.

20 So that kind of weighting, to make

21 comparisons between studies that did different

22 things, there might be something that helped
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1 to put that into perspective.

2 Let's see, I think that covers

3 most of the major things. I have some

4 specific comments that I'll convey as far as

5 some of the other points that can help the

6 clarification.

7 CHAIR SAMET: Okay, Zach?

8 DR. PEKAR: We just, I think,

9 wanted a little bit of a follow-up on one of

10 the points that you made. I think because

11 this has been mentioned a couple of times at

12 the McDonnell study.

13 If you have a clinical study that

14 shows some of evidence for a threshold, I

15 think, the challenge of translating that into

16 a threshold within an epi based response

17 function, where you have a generalized

18 exposure surrogate based on monitoring data

19 versus a very specific clinical exposure

20 level, I understand that gives us some

21 information we might want to try to use.

22 But the bridge between those
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1 technically can be pretty challenging. And if

2 there's any advice that the committee would

3 have for how we could bridge that, that

4 technical gap, that would be very much

5 appreciated.

6 DR. VEDAL: It's not a gap you

7 need to bridge.

8 DR. PEKAR: Okay.

9 CHAIR SAMET: Sverre, use the mic.

10 DR. VEDAL: It's not something you

11 need to bridge. There are qualitatively

12 different than what you learned.

13 Whether there's a threshold model

14 in the controlled exposure studies for FEV1

15 probably has no relevance to whether there's

16 a threshold for other end points, and in the

17 epi studies as well. I wouldn't generalize

18 that observation to the other risk estimates.

19 CHAIR SAMET: Comments from others

20 on this chapter?

21 DR. DIEZ-ROUX: I have, actually,

22 maybe a clarification question. So my
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1 understanding is when you're estimating the

2 health benefit attributable to just meeting

3 the standard, and then you'll do it further,

4 you are considering the background, right?

5 Because you would generate the

6 exposure distributions, background is

7 considered in that, right? It's not reduced

8 below the background level. It's adjusted. 

9 Is that correct? Is my understanding correct? 

10 So it is incorporated into the estimation?

11 DR. PEKAR: That's correct.

12 DR. HUBBELL: It's incorporated. 

13 So we're not disingenuous about this, I don't'

14 think that's what Fred was getting at, at all.

15 DR. DIEZ-ROUX: So what were you

16 getting at? Can you clarify?

17 DR. MILLER: A portion, while it's

18 incorporated, then they show the total risk. 

19 But they don't show, of that alternative

20 standard risk, how much of the total of that

21 is due to exposures above the policy relevant

22 background levels.
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1 DR. DIEZ-ROUX: In terms of

2 estimating the overall health impact of ozone,

3 I don't think the background levels should be,

4 I disagree with that. I don't think it should

5 be the boundary below which an effect is not

6 estimated.

7 Because, in fact, the background

8 levels differ across the country quite a bit. 

9 And there's no reason to assume some places

10 will achieve a lower level. And that has a

11 health benefit. Why should we ignore that?

12 DR. MILLER: But that's just

13 exactly the point, there's region specific. 

14 And when they're doing their 16 city studies,

15 in that they're in vastly different geographic

16 regions, and we have the background data by

17 season in that, so that you can make that

18 adjustment. And to use one overall value,

19 it's not going to cut it anymore.

20 DR. DIEZ-ROUX: But when they

21 estimate the health benefit, they are

22 accounting for the background variation across
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1 races. Isn't that correct? That's correct.

2 DR. MILLER: It is. But you're

3 still not identifying the fraction of risk

4 that is attributable to exposures above the

5 background. And that is what is going to come

6 into the policy assessment by the

7 Administrator, of within whatever range the

8 science says you could consider, how confident

9 they are in moving that.

10 DR. DIEZ-ROUX: And they're

11 assuming --

12 DR. MILLER: And the agency has

13 talked here about looking at the levels below

14 60 parts per billion. It's an absolute waste

15 of time, in my opinion.

16 And this has to do with the

17 Administrator having the data to make the

18 judgement about how much of this risk is

19 controllable.

20 CHAIR SAMET: So a comment here. 

21 I think we need to return to the general

22 policy related background issue. And probably
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1 the right time to do that is when we get to

2 Chapter 9 in the synthesis. And I think I

3 understand the points of everybody.

4 But I think there's issues here

5 around how the calculations are done, and then

6 how they may be interpreted in light of

7 alternative scenarios that come into the

8 policy analysis, which I think is what Fred is

9 getting at, and where those estimates will

10 come from, in fact, for apportioning the risk

11 for policy consideration.

12 So I think we need to circle back

13 to this, in light of the discussions yesterday

14 and today.

15 DR. DIEZ-ROUX: I think clarifying

16 exactly how background is being utilized in

17 these estimates is very important. Because we

18 need to understand that and make sure we all

19 have the same understanding of it. Because

20 otherwise I think we may be talking about

21 different things.

22 CHAIR SAMET: This is actually one
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1 of those places where a figure would really

2 help, showing zero, what you're doing in

3 background eventually in 16 locations, I

4 guess, and then other considerations as you

5 impose policy scenarios onto this for a

6 portion of the risk. Which I think is what

7 Fred is getting at. Because you are

8 calculating 16 specific --

9 DR. HUBBELL: Yes. To clarify

10 very specifically, we are using background

11 right now as a floor. And even in the HDDM

12 approach, basically it would be incorporated

13 into the model, where you would not reduce air

14 quality below natural background levels in

15 calculating differences between scenarios.

16 What we're not doing, is we're not

17 apportioning risk into risk occurring due to

18 exposures below background and risk occurring

19 due to exposures above background.

20 So we're not apportioning, but

21 we're taking into account background when we

22 simulate just meeting the current standard or
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1 alternative standards. That's the major

2 difference.

3 And I think it's a very clear

4 difference, and it was a direct decision that

5 we made. It was not an accident. It was a

6 decision we made.

7 And that's why we think it is very

8 useful to get all the input we can on this. 

9 But to be clear, we are incorporating it in

10 our simulations. And it's just not called out

11 as a separate attributable factor.

12 (Off microphone comments.)

13 DR. HUBBELL: Yes.

14 DR. VEDAL: Not to belabor this

15 issue, but I think we need to come back to the

16 issue. Because this issue does sort of touch

17 on the issue of feasibility. And just like

18 risk benefit, it's something where it's sort

19 of a no-no.

20 And so the question I have is, is

21 it appropriate to be talking about something

22 like policy relevant background, whatever
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1 background you want to use, given that it is

2 actually a topic relating to feasibility of a

3 standard.

4 MS. WEGMAN: And as I said

5 yesterday, in setting the standards we don't

6 take the feasibility into account. But it is

7 a separate question on which we would like

8 your advice on how we should reflect, if we

9 should reflect, background in looking at the

10 risk estimates, and also in thinking about

11 standard levers.

12 That's a question we have not

13 taken on in the first draft of the risk and

14 exposure assessment or the policy assessment.

15 And so this discussion is

16 extremely helpful to us and the further

17 discussion will be as well. But it's not from

18 the standpoint of feasibility.

19 CHAIR SAMET: Okay, back to

20 Chapter 7, other comments?

21 DR. VEDAL: One little suggestion

22 that I might make, I think Fred also brought
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1 it up, relating to this issue of repetition.

2 When you go from current

3 situations to just meeting the standard, one

4 way of avoiding that is to say, looks exactly

5 the same except for blah, blah, blah. And

6 then you avoid that formulaic presentation.

7 DR. HUBBELL: And we also take

8 into account the suggestion of trying to use

9 some summary tables, rather than doing it in

10 the text, which just leaves a lot of

11 repetitive text. We take into account for

12 sure.

13 CHAIR SAMET: Chris?

14 DR. FREY: I had maybe a

15 clarifying question for Bryan. So I think

16 you did a nice job explaining how the

17 background is accounted for in the rollback

18 approach.

19 And this is jumping ahead a bit. 

20 Maybe we ought to defer it. But my question

21 is at some point we're going to talk about

22 what levels you ought to analyze.
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1 And it could be that because of

2 regional differences in background, one city

3 might be able to go to a lower level than

4 another city. Have you thought about that or

5 anticipated how to handle that?

6 DR. HUBBELL: Obviously, again, in

7 each city we'll have a different estimate of

8 background. And, again, because we're not

9 focusing on the feasibility, in the case of

10 HDM we'd be focusing on what are the NOx or

11 VOC or a combination of NOx and VOC that would

12 lead to just meeting the various different

13 alternative standards.

14 And we're not really going to be

15 discussing the feasibility of getting those

16 particular reductions actions, we're simply

17 identifying the sensitivity of the atmosphere

18 to those reductions in emissions.

19 So we would take it into account

20 but it's not a discussion of feasibility. 

21 It's just a discussion of how sensitive the

22 atmosphere is to those reductions.
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1 CHAIR SAMET: I'm sure we'll come

2 back to this. Fred?

3 DR. MILLER: There's another word,

4 though, that you need to consider. And that's

5 the uncertainty. And as you go to lower

6 levels you're charged with characterizing

7 what's the amount of uncertainty.

8 And I submit, without having this

9 apportionment of the risk into above

10 background and below for whatever the area is,

11 that you are masking the amount of uncertainty

12 that's present. And so there's more than one

13 way to skin a cat in defending what I'm

14 proposing.

15 CHAIR SAMET: Okay. Let's go on

16 to Chapter 8. Chris?

17 DR. NUGENT: Before we do that, do

18 you have anything --

19 CHAIR SAMET: Oh, yes. I thought

20 I heard Sverre volunteer.

21 (Off microphone comments.)

22 DR. FREY: So Sverre's going to do
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1 Chapter 8 also, is that --

2 (Laughter.)

3 DR. FREY: Okay, I wanted to make

4 one observation before I get into Chapter 8,

5 which is that on the general issue of

6 uncertainty, just thinking back, even in my

7 limited time on CASAC, I think EPA's made a

8 lot of progress on this.

9 And I just thought it would be

10 worth pointing that out. We've heard a lot of

11 very positive comments about what EPA's done

12 with uncertainty. And certainly the comments

13 are much more positive than they were some

14 years ago.

15 So there's a very nice positive

16 trend. So I just thought it was worth saying

17 something nice, and congratulating you guys

18 for doing a good job.

19 Okay, so Chapter 8 has to do with

20 the national risk assessment. And I think

21 some of the issues are probably similar with

22 the city scale risk assessments.
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1 And as a reader of Chapter 8, I

2 read Chapter 8 and then I went back and read

3 the rest of the document. So I can tell you

4 my reaction to Chapter 8 as someone who picks

5 and chooses what to read.

6 And I found the conceptual

7 framework lacking in Chapter 8. It would have

8 been nice, and maybe it's just a paragraph, if

9 you just channel Bryan Hubbell for a

10 paragraph, I think. You do a good job

11 explaining these things at the meetings.

12 So if we have a Bryan Hubbell

13 paragraph, or whoever it is, that just says

14 what is the conceptual framework for this in

15 terms of, in particular, thinking of the

16 issues that we just talked about, like

17 background and LML.

18 And as a reader, I can tell you it

19 was extremely confusing to me what the concept

20 was for the national scale risk assessments

21 with regard to those two issues.

22 So I don't want to belabor that
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1 because we've talked a lot about that. But I

2 think a similar thing applies to Chapter 8.

3 After reading Chapter 8, and then

4 reading the whole document, and then hearing

5 some discussion, I think I finally figured out

6 why there's the elemental analysis and the no

7 elemental analysis.

8 But if you're going to have it, it

9 would be nice to say it up front. Because all

10 the detail in the world means nothing to a

11 reader who can't figure out why this is being

12 done. And having said that, I guess I'm not

13 entirely convinced of the need for the LML

14 based analysis.

15 I guess the idea, this is my

16 interpretation, is that the epidemiological

17 models are based on really looking at

18 differences in response versus differences in

19 concentration over some range of observed

20 concentration. So we don't have observations

21 below the LML.

22 And then there's the side issue
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1 of, well, EPA didn't actually use the LML for

2 each epi study. They used a generic one-size3 fits-all LML.

4 And that's maybe a secondary

5 concern is how valid is that. And so maybe

6 the model's not valid for concentration

7 differences down at that range.

8 On the other hand, if we're

9 predicting absolute number of incidences, the

10 epi model does do that in the range of

11 concentrations observed. So to then apply it

12 only above the LML, I guess I would have to

13 rely on the epidemiologist. But it almost

14 seems like that's a bias.

15 So, I don't know. As a reader it

16 would help me to understand the thought

17 process behind why that's being done. And

18 it's sort of described as it gives a bounding

19 analysis, that with and without tells you

20 something about the uncertainty.

21 But I'm not totally convinced of

22 that. But I'm not an epidemiologist. So I
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1 would really like for an epidemiologist to

2 weigh in on that.

3 Let's see. So then on Chapter 8,

4 I wrote a lot of detailed comments. And I'm

5 not going to go through them in detail. A lot

6 of them are technical comments but they

7 pertain to very specific things.

8 So I think my general comments on

9 Chapter 8 are really just two things. One is

10 what I just said. And the other is there's

11 some discussion of different exposure metrics.

12 And I know that two of the epi

13 studies that are used prominently actually use

14 some slightly different exposure metrics in

15 terms of one was based on May through

16 September data. The other was June through

17 August data.

18 And I don't know if the EPA's

19 already done this, but wouldn't it be possible

20 to do some quantitative assessment of the

21 correlation between those, just to get an idea

22 of their similarity or were there differences?
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1 I know there're some other

2 differences in those studies, that one is

3 based on non-accidental mortality and the

4 other's based on total mortality.

5 And those differences, I think

6 the chapter does a pretty good job of

7 conveying that. I think there's one place

8 where it forgot to do it. And I made a note

9 of that.

10 So otherwise I thought, in

11 general, the approach was reasonable. I

12 actually really liked the assessment of the,

13 let me double check so I make sure I remember

14 the right chapter, so this is the chapter

15 where we have the assessment of the

16 representativeness of the city specific

17 studies.

18 And I thought that was really

19 nice. That was a nice part of the chapter. 

20 I thought that was pretty well done.

21 One thing I was thinking is to

22 extend that, it seemed to be written focused
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1 on mortality and how the city-specific

2 modeling is representative with regard to

3 mortality.

4 But I think one could extend that

5 to the representativeness of those activity

6 patterns related to morbidity end points as

7 well. So that's just a suggestion. That's

8 it.

9 CHAIR SAMET: Okay, Ted?

10 DR. RUSSELL: I think one problem

11 with having two engineers doing a review on

12 this one is that very similar comments and

13 similar approaches.

14 I read Chapter 8 first and then

15 went on to read the rest. And like Chris, I

16 started to read Chapter 8 as it sits. And

17 you're wondering why a lot of these decisions

18 were made and what some of the alternatives

19 would be.

20 And I think that just has to be

21 cleaned up in terms of how Chapter 8 really,

22 why they've made their decisions, link it
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1 better to Chapter 7, and how Chapter 7 really

2 motivates some of this.

3 I would still start out with

4 bringing stuff from the ISA about the causal

5 determinations, and how this pushes you

6 forward, just for people like me who might

7 start with the Chapter 8.

8 Going on, actually some of the

9 little things, the way the introduction really

10 doesn't set up well the whole chapter, that

11 was another thing I found.

12 You really don't talk about

13 anything that's going to be happening with the

14 considerations with the Jerrett, et al study.

15 And you don't talk about, again,

16 what I thought like Chris said, the analysis

17 of the representativeness. That's not really

18 captured in the introduction either. And it's

19 a great piece that you've done there. So I

20 think that should be brought in.

21 And then another thing that just,

22 and I find this elsewhere in the document as
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1 well, is that you make a statement like we

2 also have high confidence in extrapolating the

3 national average effect estimates.

4 And I don't know why you have high

5 confidence versus medium or low or whatever,

6 particularly if you've read Chapter 7, the

7 Bell, et al. and Zanobetti and Schwartz, you

8 got different results.

9 We understand why you got

10 different results, but usually when you get

11 really different results that would decrease

12 your confidence in it.

13 Now it turns out, in Chapter 8,

14 that you get remarkably similar results. And

15 I would bring that out in a summary of Chapter

16 8.

17 If two of my students did these

18 two analyses using different time periods, and

19 using different effect estimates, and came out

20 with estimates that close to each other, I'd

21 be amazed. And I think that can be brought

22 out some. So those are my comments on this.
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1 CHAIR SAMET: Okay, Ed?

2 MR. AVOL: So I have some minor

3 comments that are in the submitted text. And

4 actually I've updated them so I'll submit an

5 updated version.

6 But I do have one question for

7 you. In reading this, the issue about

8 applying the national average estimates and

9 the grid cells, downwind of urban for rural

10 areas, et cetera -- and maybe just because

11 it's not my particular field -- but it seemed

12 like that seemed to not quite set with me

13 completely for the following reasons.

14 I'm not a demographer, but as far

15 as I understand the population is not randomly

16 and uniformly distributed across the country. 

17 And there are lots of other reasons, social

18 reasons, for why there are more people living

19 around Los Angeles, maybe, and Houston, than

20 some other places.

21 Those have higher pollution values

22 and so people downwind are going to have
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1 higher exposures applying national averages.

2 I'm not sure whether I'm more

3 concerned about people downwind of places like

4 Los Angeles and Houston, that then get under5 assigned in national averages, or people that

6 are downwind of other places that get over7 assigned in more open.

8 You do say that in the text. And

9 if you go forward and look at assigning the

10 nearest urban area and using that approach,

11 which made me think that was a more relevant

12 approach, so why even do the national

13 estimate.

14 Why not do some sort of assignment

15 or assessment of areas based on the nearest

16 urban area and use the best available

17 information. It seems to me that's better

18 information and a better estimate in some

19 sense.

20 DR. ANENBERG: So our rationale

21 for applying the national average estimates to

22 the rural areas was that we could take
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1 advantage of the broader population, the

2 broader exposure characteristics, given that

3 we do here have lower confidence in

4 extrapolating the epi effect estimates that

5 were based on urban study areas to rural areas

6 with very different populations.

7 We can consider using a different

8 method to do that. In fact, I think one CASAC

9 member suggested that we, as a sensitivity

10 analysis, apply the lowest urban effect

11 estimate and the highest urban effect estimate

12 and see how that effects the results.

13 And I think perhaps that would be

14 a useful method of getting at how the

15 sensitivity falls out here. But I would look

16 for your advice on that.

17 MR. AVOL: Right. I think that

18 sounds like a reasonable thing to do. 

19 Because, again, there was a lot of discussion

20 about uncertainty and variability in all these

21 sorts of issues. So that would be a good

22 application for that consideration.
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1 DR. ANENBERG: Sure.

2 CHAIR SAMET: Anything else to

3 add? Okay, others? Comment on this, Chapter

4 8? Fred?

5 (Off microphone comments.)

6 CHAIR SAMET: Is your name Fred?

7 (Laughter.)

8 DR. MILLER: Please, let him go

9 ahead, please.

10 DR. VEDAL: I wonder if we

11 shouldn't just extend the discussion of using

12 the lowered, the LML, in the utility of that.

13 When I read things I was convinced

14 that there was utility to it. And they were

15 basically for the reasons that Chris said, not

16 extrapolating beyond where there's data, and

17 then giving more of a real world scenario.

18 But I think your comment, in terms

19 of when you look at the heat maps, that is the

20 real world distribution and concentration. So

21 that point really doesn't hold water.

22 It's really an issue of
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1 extrapolating beyond the data and whether

2 there's utility in presenting effects where

3 arguably you might have more confidence in the

4 estimate.

5 I think that's probably the extent

6 of it. And so the issue is whether that's

7 reasonable, given that now we may be

8 recommending, well, who knows what we'll

9 recommend, but whether we'll recommend other

10 estimates that are based on some notion of

11 background as well.

12 And then whether we should just

13 limit it to down to zero versus addressing

14 something related to background, or whether

15 there's some additional utility to having this

16 intermediate concentration, which is the LML.

17 I don't feel strongly about that. 

18 I expect we're going to be swamped with a lot

19 of different estimates if we include all of

20 them.

21 DR. MILLER: Okay. I had several

22 comments in reading this chapter. There's a
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1 lot of duplication of material. There's a

2 couple of paragraphs, for example, in 8-1. 

3 And then it's basically repeated in 8.1.3. 

4 And so put it one or the other, but I don't

5 think you need to have the amount of detail in

6 both.

7 The figure, 1.9, it could benefit

8 from being re-drawn to have the same color for

9 the Bell study, the solid red line in the dash

10 colors using the different cutoffs, that

11 ordinarily you're used to seeing for a given

12 study.

13 And then if you do alternatives

14 with that, it's the same color but a different

15 form. So you're correct in what you present,

16 but it would just be easier for people to

17 follow.

18 I had a suggestion too on 845. 

19 You discussed the ATUS data, and about using

20 that in the second draft. And I would give it

21 a very low priority, given the problems with

22 the data.
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1 And so if you're looking for

2 cutting down work that, to me, it's one of the

3 sensitivities that could drop out.

4 And then I always learn things at

5 these meetings. But the last thing I'd like

6 to ask is education in your tables, where you

7 present population per square mile. Is the

8 square mile determined as the crow flies or

9 from topological maps? Because it makes a

10 hell of a difference.

11 DR. ANENBERG: I don't know the

12 answer to that at this time. We took that

13 from the Census Bureau. And I can get back to

14 you on it.

15 DR. MILLER: Yes. It makes a

16 tremendous difference. And so I'm just

17 curious which it is. If it's just as the crow

18 flies, it's useless.

19 Look at Cincinnati and the ups and

20 downs of the hills versus as the crow flies,

21 and the density per square. So I hope it is

22 topographical, but I wouldn't count on it.
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1 DR. ANENBERG: We can check that.

2 CHAIR SAMET: Okay. Other

3 comments on Chapter 8? So let me get a sense

4 of a couple of things. We're running ahead of

5 schedule, which is okay. Kathy Weathers was

6 coming --

7 DR. NUGENT: About 11:30.

8 CHAIR SAMET: About 11:30. We

9 need a break somewhere. We need to talk about

10 Chapter 9. And then, Bryan and Lydia, this

11 list of things to do, when might we see that?

12 DR. HUBBELL: Well, we have the

13 draft of that ready. We needed to make a few

14 little modifications and then we're going to

15 give it to Angela. We just haven't had a

16 break yet. So we thought we're going to do it

17 at a break.

18 CHAIR SAMET: So let me make a

19 suggestion. I think Chapter 9 might go pretty

20 quickly, well, might not. All right, a break

21 until 10:30.

22 (Whereupon, the above-entitled
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1 matter went off the record at 10:13 a.m. and

2 resumed at 10:33 a.m.)

3 CHAIR SAMET: We are at Chapter 9,

4 Synthesis, and let's see, Michelle, you're the

5 -- I'm sorry, Fred.

6 DR. MILLER: I had one more point

7 about Chapter 8, before we get off of it. To

8 me, there wasn't an adequate explanation in

9 this document why the authors deleted the two

10 highest cities in the Bell study from the

11 analysis that they were doing. I would like

12 a clarification of what was the problem as to

13 why those two highest city studies were

14 eliminated.

15 DR. ANENBERG: If I could just

16 clarify that really quickly, it wasn't deleted

17 from our study. It just wasn't captured in

18 urban study areas that were selected for the

19 urban study area analysis.

20 DR. MILLER: Okay, thank you.

21 CHAIR SAMET: Okay, Chapter 9 --

22 lead reviewers for 8, Sverre, you don't have
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1 to do 8. Chris, would you?

2 DR. FREY: Sure.

3 CHAIR SAMET: Okay.

4 DR. BELL: All right, so I think a

5 lot of the comments that I have for Chapter 9

6 are going to echo some of the other comments

7 people have made about the document in

8 general.

9 So I know that each chapter is not

10 a standalone document. But I do think that

11 it's useful to think about what kind of

12 impression it would give if someone just read

13 that chapter, because that's quite possible.

14 And the issue of the policy

15 relevance of going down to zero really comes

16 up in this chapter and it's not described at

17 all. So my strongest recommendation for this

18 chapter is to discuss the policy relevance of

19 going down to zero.

20 And I agree with Chris's comment

21 about channeling Bryan Hubbell, because when

22 you explained what that was done in the, you
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1 know, the partitioning or distinction not to

2 partition between anthropogenic and biogenic

3 emissions, that was more clear than the text

4 in this chapter.

5 So I think this was just a matter

6 of adding that in. But I think it's really

7 important to think about that, but especially

8 for the policy relevance of going down to

9 zero. Because if you haven't read the other

10 parts of the document, I think it would make

11 no sense at all if you just read Chapter 9.

12 I also thought that, I know this

13 is kind of an early draft, but I thought some

14 of the word choices were maybe not the best. 

15 And I made some specific comments in my

16 written comments. But just to give some

17 examples: the word "significant" is used in

18 the context of something other than

19 statistical significance. That's easy to fix,

20 if you use "substantial" or something else.

21 And talking about two cities, when

22 you're looking at results from four studies
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1 and two studies identified three of those, the

2 same three to have the highest effect and then

3 go on to say that's a significant difference, 

4 that's really confusing.

5 So I think this is another case

6 where I suspect EPA really does have something

7 very reasonable in mind. But you just need to

8 fix the wording.

9 And there are other places in here

10 where I wasn't sure if it was a wording choice

11 or a conceptual difference. So I wasn't sure

12 if everything in this chapter was really what

13 EPA meant to say.

14 And one example is where it says

15 that the urban areas provided good

16 representation of the overall distribution of

17 risk. I mean, they clearly don't, right,

18 because we don't have estimates for rural

19 areas?

20 And so there's sentences like that

21 as an example where I'm not sure what you

22 mean. So I would just check the sentences to
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1 make sure they're what you mean.

2 And another example, just to help

3 readers along, it talks about not specifying

4 U.S. background concentrations because it's

5 incorporated in the modeling. If I didn't

6 know anything about that modeling, I wouldn't

7 understand because you have to specify stuff

8 for the model. So I think you could use

9 language like, "it's estimated by the model",

10 just so that the reader understands what's

11 going on.

12 And then other places were a

13 little bit vague, like there's talking about

14 an improved approach in a few places.

15 Improved, improved how? I think that could be

16 expanded upon.

17 So I thought this was a good start

18 but the main comments are just better

19 explanations and to be really careful about

20 the word choices, and also given the

21 complexity, just to maybe give it one read22 through and pretend you're a reader who hasn't
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1 read the other sections. And just make sure

2 there's nothing there that's going to be taken

3 out of context later. And those are my key

4 points.

5 CHAIR SAMET: Chris?

6 DR. FREY: Okay, yes, so reviewing

7 Chapter 9 it's, as Michelle points out, it's

8 kind of hard to do that without commenting on

9 the document in its entirety. I guess I'll

10 start with my comments that are specific to

11 Chapter 9 and then I wanted to talk a little

12 bit about the issue of the background.

13 So I wrote some detailed comments

14 on Chapter 9. I'm not going to go through all

15 of them but, there's a couple of places in the

16 document, like in here it says, "APEX lacks

17 proficiency to model repeated exposures." And

18 I don't know if "repeated" is meant to mean

19 longitudinal, I guess that's the word I'm more

20 used to seeing, but I assume that's sort of

21 what it means.

22 And there's some other language, I
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1 think it was in Chapter 8 I didn't mention,

2 but it says something like, APEX, you know,

3 there's some statements about APEX that I

4 think were actually unnecessarily negative,

5 you know, that exposure is largely unknown

6 and, you know, so I think on these issues it's

7 not that it, for example, it's not that

8 exposures are largely unknown, I know that's

9 mentioned in Chapter 8, it's just that there's

10 some uncertainty in estimating exposure. I

11 don't think that means we can't make useful

12 estimates.

13 In the case of proficiency to

14 model longitudinal exposures, my thought is

15 that's not really an artifact of APEX, that's

16 an artifact of CHAD, it's really the input

17 data.

18 And to me that's an important

19 distinction. As someone who started my career

20 as a modeler, I'm very sensitive about people

21 criticizing models for what they can't do when

22 in fact, well, the model could do it, we just
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1 don't have the data. It's not that the model

2 is misspecified.

3 And I guess the practical

4 implication is if you wanted to fix it, the

5 way you would fix it would be probably to get

6 more data for CHAD. If you had that data, you

7 could account for it.

8 So I think some discussion, and

9 maybe it goes back to Chapter 7 and 8 where

10 you really are using CHAD, but in my comments

11 in Chapter 8 I did talk about, it would be

12 nice to have a little more discussion of CHAD. 

13 And then that sets up for this kind of

14 statement.

15 In Chapter 9, since I think it's

16 supposed to be a synthesis across the

17 document, one of the things that I think is

18 extremely important is to be consistent. And,

19 you know, you have some endpoints that are, 

20 you know, short-term, city-based. You have

21 others that are long-term national-based, but

22 at least it would be nice to use the same
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1 statistics to summarize them.

2 So for example, you know, if you

3 report a mean estimate and a probability range

4 for each of these results, so that at least

5 it's consistent in that regard, because I find

6 it confusing when it goes between different

7 metrics.

8 And also, some estimates are

9 reported based on three years accumulated,

10 like for the national mortality estimates. 

11 And others I think are annual. And that could

12 generate a lot of confusion, so I would just

13 encourage consistency.

14 I think in terms of being a kind

15 of a synthesis chapter, it would be important

16 to have not a long discussion, but at least a

17 discussion on what wasn't modeled, you know,

18 there are health endpoints that are identified

19 in the ISA that, you know, have some weight of

20 evidence that are not part of the risk

21 assessment. And I think it would be nice to

22 say so.
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1 And likewise, what are the

2 limitations of what was modeled? This doesn't

3 have to be tables and tables of uncertainty

4 estimates but, you know, what are the top

5 three to five limitations? And what could be

6 said about biases in the quantitative

7 estimate, either because of limitations or

8 because of things that weren't modeled, you

9 know, is the true risk to the public

10 underestimated because the model only

11 addressed certain endpoints, or particular

12 endpoints?

13 And then I have some other

14 detailed comments. I think since Jon promised

15 we would talk more about background here, I'll

16 say something about that.

17 I've been listening carefully to

18 Fred's comments and to what Bryan had to say,

19 and I think, it seems to me that we have to be

20 a little careful not to presume what decision

21 the Administrator is going to make.

22 And I think, my understanding of
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1 the statute of the Clean Air Act is that

2 there's nothing in it that specifically

3 addresses background. And it may be that this

4 just wasn't anticipated when the Clean Air Act

5 was written more than 40 years ago that, you

6 know, the criteria pollutants, at least

7 implicitly, are conceptualized as probably

8 having a threshold and probably we're looking

9 at concentrations well above background. This

10 statute works well if those conditions are

11 met.

12 And we're finding, well, with more

13 science, that well, maybe there isn't a

14 threshold or we can't find it yet. And

15 there's health effects at levels that go down

16 to background. I think that's a policy issue

17 for the Administrator to grapple with.

18 So I think from a science

19 perspective, I don't see anything wrong with

20 giving information to the Administrator, such

21 as Fred suggested that, well we ought to

22 estimate the risk associated with exposures up
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1 to background relative to zero or, I think

2 relative to zero to me makes sense, or

3 relative to LML, and I'm not so sure.

4 That doesn't bother me to give

5 that as information but I think we also need

6 to give the Administrator information on the

7 risk from the level of the standard down to

8 either zero or LML, and so that the

9 Administrator has that information.

10 The Administrator can decide what

11 the policy options are for background, if any. 

12 I think we're presuming there's no policy

13 option, maybe the administration comes up with

14 some creative international treaty or, you

15 know, unlikely, but I just don't think we

16 should prejudge that too much, so I just

17 wanted to throw that in.

18 CHAIR SAMET: Okay, thanks. And

19 Ted.

20 DR. RUSSELL: Yes, thank you,

21 Michelle and Chris have captured many of my

22 thoughts, just some other things, you know,
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1 when you see something like 20 to 930 deaths,

2 that doesn't communicate much to me except

3 actually at first level it sounds like there's

4 quite a bit of uncertainty in the results.

5 So I'm not sure that that's

6 telling the story that you really want. And

7 I think it would be better if you came up with

8 a way of capturing the same idea, you know, I

9 was thinking maybe in a table and you specify

10 what the range is really all about.

11 You used the word, you know,

12 another little one and sort of getting back to

13 making sure what you say is what you really

14 mean when you said there is considerable

15 variation, when actually I looked at it and I

16 didn't see that much variation, this had to do

17 with the range in deaths between the two

18 studies.

19 And, you know, in terms of a

20 synthesis, I realize this is still a work in

21 progress. I didn't see that, you know, there

22 was some obvious areas for synthesis that you
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1 could do between the various studies so far,

2 they're just not there yet. And I just assume

3 they're coming. So we'll see that on the next

4 review. Thank you.

5 CHAIR SAMET: Thanks, Ted. I

6 think just to elaborate on Ted's comment, I

7 mean, assuming that this is sort of a first

8 glimpse at what is to come, I mean, if the

9 plot is the synthesis, there is no synthesis,

10 there's not even a heading that says

11 synthesis, there's something that says

12 observations at the end.

13 So I think this is also where the

14 problems with Chapter 2 sort of come back at

15 you, because if you had that true framework

16 that is not there in Chapter 2 then you should

17 be coming back to it here, using that to

18 really frame consideration of what you found

19 with the exposure and risk assessment, and put

20 the whole story together.

21 And I recognize right now that

22 you're not there yet, you don't have all the
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1 pieces together, you're sort of lining them

2 up. But you don't really have, you didn't

3 create the framework initially -- I know you

4 did, because you had to but you certainly have

5 not made it explicit.

6 So I think Chapter 2 should

7 provide the framework and Chapter 9 you come

8 back and say, well, turning to our framework,

9 here's what we found. And then truly have a

10 synthesis section that I think we all agree

11 will be there. So, yes, Howie.

12 DR. NEUFELD: I'd just make a

13 comment too about the clarity of writing,

14 trying to get your point across. I'm just

15 reading this one section here on Page 9-9, you

16 say five to 60 percent of mortality reductions

17 occur due to reductions in ozone on days when

18 ozone is greater than this value.

19 And then you say what would happen

20 after you meet the current standard, and you

21 say it falls to four to 59 percent. But

22 given, you know, error bars and things like
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1 that, it really probably didn't change, right?

2 So, when one says "falls," I mean,

3 do you mean statistically significant, that

4 that one percent is a reduction, or really, is

5 that really just the same? So I think just be

6 careful about when you say something happens,

7 whether that's really statistically

8 significant or not.

9 CHAIR SAMET: Okay, other comments

10 on Chapter 9? David.

11 DR. CHOCK: If you look at Page 9-

12 4, there is an area talking about comparing

13 just meeting the current standard of Los

14 Angeles versus three other cities and claims

15 that children exposed above 0.6 ppm is

16 considerably less, and the range may be two to

17 five percent compared to other cities of 23 to

18 37 percent. And I think this is really an

19 artifact of quadratic rollback, because I

20 think that's a region where the suppression of

21 the concentration is excessive. And I hope

22 you look into that.
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1 CHAIR SAMET: Okay, Fred.

2 DR. MILLER: I have just one

3 comment and that has to do with, on Page 9-11 

4 you're talking about, in the next draft,

5 particularly for the case of short-term

6 exposure and mortality risk, you talk about

7 treating co-pollutants.

8 Personally, I don't want to see

9 any risk estimates that do not adjust for PM

10 because otherwise, to me, you're highly

11 double-counting. And so that, to me, should

12 be the baseline as the minimum, that

13 everything is included. And you didn't do it

14 in the first draft and you say you're going

15 to, you know, consider this. To me, adjusting

16 for PM in your analyses here is an absolute

17 must.

18 CHAIR SAMET: Okay. Yes, Helen.

19 DR. SUH: I guess my understanding

20 of this chapter was really that it wasn't

21 supposed to be a summary but really a

22 synthesis. And building on what Jon had said,
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1 is it your intention for the second round to

2 also include the summary?

3 Because my guess is that lends to

4 some of the repetition that people, you know,

5 we've been mentioning. Because there's the

6 executive summary, there's a summary of

7 results in each of the chapters, and then

8 there's a summary again.

9 And so, I guess I would recommend

10 taking out that summary here and really having

11 it be a synthesis. And I think that would

12 help with some of the perceived repetition.

13 DR. HUBBELL: If I could ask a

14 question about that, because I think that's an

15 interesting point. And we struggled a bit

16 with that, that issue of repetition, you know,

17 and you'll see in the chapters we didn't quite

18 have the consistency that we would like in

19 terms of some of the chapters do a lot of

20 summarization at the end, some of the chapters

21 don't do any.

22 And we're trying to figure out the
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1 correct way to balance this, and the intent of

2 Chapter 9, which was the new chapter just

3 because we looked at this and we said, well we

4 have, you know, exposure, clinical risk,

5 national risk estimates, and the urban case

6 studies, we really ought to have something

7 that synthesizes across.

8 It's actually, as the ISA people

9 are aware, it's a very difficult task to

10 synthesize. And so we're really exploring and

11 working on this so, you know, we're very

12 welcome to ideas as to what would make this

13 most useful and most informative.

14 And, you know, I think if taking

15 those summaries out and putting them in the

16 chapters themselves makes sense, that's fine. 

17 If having a separate summary chapter makes

18 sense, we're willing to consider that as well,

19 or having a section of the synthesis that is

20 summary and synthesis would be fine too.

21 DR. SUH: Yes, I mean, I guess I

22 was thinking that it would be nice to have at
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1 the end of each chapter the summary of the key

2 findings. I don't think every finding needs

3 to be in there, but just the key findings that

4 are going to get brought forward into other

5 chapters or even into the policy assessment.

6 And here, I guess I was thinking

7 that it would be more of a discussion like you

8 might, I mean, I know they're very, very hard

9 to write. A good synthesis is very difficult

10 but, you know, maybe akin to a discussion in

11 a research paper, that it really doesn't talk

12 about all the findings but it talks about the

13 major ones and the ones that have big

14 implications to what might be coming next.

15 And that's the kind of synthesis I

16 think would be so useful and it wouldn't copy

17 the executive summary so much, right, and it

18 would really put all those results from the

19 different chapters together to say, well how

20 do they all fit together to tell, you know, a

21 piece of this or many pieces of this story

22 that need to be told.
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1 I think that would be extremely

2 effective. I know it's very difficult, but I

3 guess that's what I was thinking would be

4 really important for this.

5 CHAIR SAMET: Just to clarify,

6 there will be an executive summary?

7 DR. HUBBELL: Yes, the plan is to

8 have an executive summary for the second

9 draft. And again, the executive summary was

10 considered to be much more of an overview and

11 a more layman's discussion of the whole

12 document.

13 And the synthesis was intended to

14 be, as Helen said, very, much more about

15 integrating. We didn't accomplish that this

16 time but that's what we're hoping to do.

17 CHAIR SAMET: I mean, I certainly

18 want to reinforce what Helen said about having

19 a true integrative synthesis, it actually

20 implies that somebody has looked across the

21 whole document, understood it, understood how

22 it fits together and bought it together. And
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1 I think that's probably sort of a critical

2 piece of your own internal peer review if

3 nothing else.

4 DR. HUBBELL: Yes, and one of the

5 reasons why we didn't, we had attempted to do

6 a little bit more but because we didn't have

7 the clinical risk information available and a

8 reasonable amount of time to redo this, that's

9 why it suffers from that.

10 But, you know, as one of the, as

11 the author of that particular chapter, it's

12 something that we really want to do, and we

13 want to make it something very, very useful

14 for you all to be able to review and

15 understand what we want to bring forward.

16 So it may take a few times to get

17 this right and we're really looking for, you

18 know, I'd love very strong criticisms of what

19 goes in and out of that would be very, very

20 helpful.

21 CHAIR SAMET: Okay, other comments

22 on Chapter 9? Let's see, Michelle, could you
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1 do the --

2 DR. BELL: I volunteered, Jon.

3 CHAIR SAMET: Oh, you did? Okay.

4 DR. FREY: I think Sverre should

5 do it.

6 DR. BELL: I volunteer Sverre.

7 (Laughter.)

8 CHAIR SAMET: So a general comment

9 that I want to make, in doing this assessment,

10 you draw on the old assessment. You draw on

11 the scientific foundations for that

12 assessment, you draw on what is in the ISA

13 that is relevant to refining the last

14 assessment.

15 You have this nice little diagram

16 with an arrow that goes from ISA to REA. But

17 it is certainly not explicit in the text or

18 the structure here. There's some commentary

19 up front, but to me, it would be very helpful

20 if at the start of each of the sort of

21 methodological sections you said, here is what

22 we are using from the last assessment, here is
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1 how we have changed it, here is why we have

2 changed it based on the ISA, and did that

3 explicitly in each chapter because I think

4 that would bring a sharper reality to the

5 relationship between the two documents.

6 There's text like that imbedded in

7 there but it sort of comes in different spots

8 a little bit haphazardly. But I think it

9 would be very helpful and also clarify how you

10 ground this in the ISA. So here really, the

11 ISA serves you for supporting use of

12 particular pieces, concentration response

13 functions, whatever, you know, refinements to

14 exposure methodology, but just be explicit. 

15 I think that would be very helpful in your

16 document. Any other comments on this

17 document?

18 DR. BRAIN: I like that idea and

19 one could imagine that such comments could be

20 bolded so they could easily be identified as

21 you move through the document, what's really

22 happened, what are the significant changes.
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1 CHAIR SAMET: So we are at a

2 point, I think we've finished this, unless

3 anybody has any last comments, I think we've

4 covered it well.

5 I think there's, Kathy's not here

6 yet, expected soon. And I also wonder, if

7 we're going to turn back to the discussion of

8 the PRB, whether we should wait until we go

9 through the welfare risk and exposure

10 assessment and have a more general discussion. 

11 That seems to make sense, I think. So I think

12 there's --

13 DR. HUBBELL: And Jon, we had one

14 clarifying point that Zach wanted to make

15 about one of the chapters if that's okay.

16 CHAIR SAMET: Sure.

17 DR. HUBBELL: Okay, go ahead.

18 DR. PEKAR: Okay. So just 

19 yesterday, the public commenters, there were

20 two comments that were actually made that just

21 for the record, I wanted to clarify a little

22 bit.
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1 It was stated that we didn't use

2 or include confidence intervals on the effect

3 estimates for the Bayesian adjusted, or

4 Bayesian shrunken estimates that we used for

5 short-term mortality, the Bell and the

6 Zanobetti and Schwartz, and that's not

7 factually correct. We did include those

8 conference intervals and did generate 95th

9 percentile confidence estimates. It was the

10 delta risk estimates that don't have those

11 confidence intervals at this point, but we're

12 going to update that.

13 And I think more importantly, and

14 I talked to Dr. Smith about this afterwards,

15 we actually, specifically, in the document

16 referenced his study, looking at the regional

17 Bayesian effect estimates and identify that as

18 an option for the follow-on work. So I just

19 wanted to be clear that we had referenced his

20 study and the option of looking at those

21 Bayesian adjusted priors.

22 CHAIR SAMET: Okay, thanks. All
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1 right, one thing that we can do now and we're

2 going to sit down and have a small conference

3 here about what we're going to do next in

4 light of our schedule, is to take a look --

5 you can put this up now, this is, I think the

6 list of things that you said you were going

7 to, that are still to be done.

8 DR. NUGENT: We're not centered,

9 Greg. I don't know whether that's something

10 you can help with, showing up on my screen as

11 centered but not up there.

12 CHAIR SAMET: Let's see. Actually,

13 Bryan or Lydia, do you want to take us through

14 this.

15 DR. HUBBELL: You want me to walk

16 you through that?

17 CHAIR SAMET: Yes.

18 DR. HUBBELL: Okay, all right. A

19 couple of broad notes on this is that we have

20 laid out by chapter the various different

21 items that we have identified as areas we are

22 considering or have committed ourselves to. 
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1 These are not prioritized across the different

2 chapters. We're obviously looking for your

3 input as to what are the items that you feel

4 are most important to address.

5 There's a couple of items,

6 including this first one which I put in based

7 on conversations yesterday because they made

8 clear sense to us that we'd want to make sure

9 we added them. Obviously, the ones that we've

10 discussed this morning are not added to the

11 list yet, but can be.

12 So the first one was Chapter 2,

13 and I heard a number of times that we need to

14 improve our conceptual framework chapter,

15 possibly by adding a broader conceptual

16 framework diagram and then more carefully

17 laying out the various different elements that

18 we expect to address with our risk modeling. 

19 So that's something we expect to be able to

20 do.

21 Chapter 4, this was the Air

22 Quality Considerations. Most of these have to
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1 do with the CMAQ-HDDM methodology and

2 implementing it. Again, we're continuing to

3 evaluate this methodology. And one thing I

4 wanted to make sure that we conveyed to you is

5 our interest in your comments not only on what

6 is in the main body of the document, but also

7 on the technical memo that describes the HDDM

8 approach that we are developing and how it's

9 specifically being proposed that we implement

10 it in the second draft. And this is the Simon

11 et al. memo which has been posted to the Web

12 site as well.

13 And we, again, especially for

14 those of you who are heavily involved in the

15 air quality modeling side of things, we would

16 really welcome your comments on the specific

17 method of implementing the HDDM approach.

18 An important factor as well within

19 the air quality is that we're planning to

20 update the base platform for the modeling of

21 2007. And that's an important update because

22 it's going to be using average fires rather
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1 than the year-specific fires in that

2 particular modeling.

3 And then there was a number of,

4 I'm not going to go through these

5 specifically, but there's a number of

6 potential updates to the HDDM approach that we

7 think are appropriate for us to be

8 considering.

9 There is also an additional item

10 that we're not considering based on our

11 conversations yesterday and today. And that's

12 evaluating the feasibility of estimating

13 background ozone, both using the CMAQ-HDDM

14 modeling platform as well as looking at other

15 available models for background values. 

16 That includes the GEOS-Chem and

17 CAMx modeling, as well as considering, and,

18 again, this is a really important

19 consideration and I'm hoping that the

20 discussion this afternoon will cover this,

21 which is, if a decision is made that, you

22 know, it's reasonable to look at at least one
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1 definition of background ozone as a way of

2 apportioning risk, the question of what

3 definitions of background are most appropriate

4 for us to be considering. There are a number

5 of different ways to define background and I

6 think it's very important for us to get some

7 input on those definitions.

8 And again, one thing to remember

9 as well is in doing these background

10 estimates, they're heavily dependent on the

11 emissions inputs and the years of evaluation

12 as well. So there's a number of uncertainties

13 that get brought in and the ability to

14 characterize those uncertainties is important.

15 Chapters 5 and 6, again, one of

16 the most important things is that for the

17 exposure and clinical risk will be expanding

18 from four areas to 16 areas. The 16 areas is

19 different than the 12 areas for the Urban Case

20 Study Risk Estimates because those exposure

21 analyses can look at a broader range of cities

22 because they don't require information on
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1 baseline incidence rates or concentration

2 response relationships. And that's the

3 determining factor for the risk, Urban Case

4 Study areas for the epidemiological risk

5 estimates.

6 We're also hoping to add exposure

7 estimates for outdoor workers and for older

8 populations. And we'll be looking at, again,

9 a number of different alternative standards

10 which we have not determined as the number or

11 the specific level yet.

12 Specific to Chapter 5, on the

13 exposure assessment there's a number of

14 different sensitivity analyses we're planning

15 on doing. We heard very loud and clear that

16 there's several that are very important to the

17 panel and we'll certainly take that into

18 account.

19 We are looking at evaluating the

20 representativeness of the CHAD activity

21 patterns as well as developing some potential

22 sensitivity analyses looking at different
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1 years of the CHAD data or different specific

2 populations within that.

3 And another thing we're planning

4 on doing is looking at a different method for

5 spatial interpolation within those urban areas

6 for exposure, you know, the exposure

7 calculations use a much more refined set of

8 information on air quality both temporally and

9 spatially than the epidemiological studies,

10 which use these more aggregate measures of air

11 quality over the urban areas, so there's a

12 need to look at that a little more carefully.

13 And then this idea of looking at

14 repeated exposures, that would give me

15 longitudinal exposures where you have certain

16 individuals who may have sustained activity

17 patterns that can lead to higher exposures, we

18 wanted to look at that.

19 And just a clarification of the

20 types of uncertainty, we heard a good

21 suggestion about trying to classify those into

22 things we can address qualitatively through
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1 sensitivity analyses or things where we don't

2 have enough data to make a determination.

3 On the Lung Function Risk

4 Assessment, again, this is an important issue

5 that we wanted to mention, is that some of the

6 changes we're proposing are conditional on the

7 availability of data from the study authors.

8 We're trying to update the two,

9 both types of lung function estimates with

10 data from additional clinical studies, and

11 we've been working to get the data from the

12 Kim et al. and Schelegle et al., but also

13 looking back at previous estimates from back

14 in the '80s and '90s and not all the data has

15 been able to -- we have not been able to get

16 all the data that we wanted to be able to look

17 at the full range of studies.

18 And then in terms of the McDonnell

19 Study, there has been an issue with being able

20 to obtain the underlying data that McDonnell

21 used to estimate those risk functions. And

22 we're trying to work with that, but some of
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1 the data that McDonnell received, the

2 underlying study authors have not been willing

3 to share that data with EPA and so it becomes

4 a problem for us to be able to replicate the

5 results and be able to do uncertainty

6 assessments. So that's the thing we're trying

7 to deal with, but that has been a limitation

8 in implementing that model.

9 And then for Chapter 7, which is

10 Urban Area Case Studies for the

11 epidemiological risk, again we'll be looking

12 at just attaining alternative standards as

13 well as the current standard. We're going to

14 be adding assessment or evaluating the

15 addition of assessments for respiratory

16 mortality risk for long-term exposures,

17 including potential sensitivity analysis based

18 on using regional effect estimates.

19 We're going to be adding the

20 conference intervals for the risk reductions,

21 again, there's a couple different ways you can

22 do that, one is by comparing the risks at
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1 current levels with the risks at the lowest

2 measured levels. There's also a process in

3 NMMAPS for just specifying the change in air

4 quality and estimating confidence intervals

5 based on that and we're working through those

6 details.

7 And then again, conditional on

8 availability of data from various different

9 study authors, we're considering adding a

10 number of sensitivity analyses for alternative

11 model specifications, some of the issues for

12 the multi-city studies is getting the Bayesian

13 adjusted estimates for different lags or other

14 things that allow us to make a comparative

15 analysis between our primary estimates and

16 these alternative specifications. There's a

17 bit of an apples to oranges if we don't have

18 the city-specific Bayesian shrunken estimates,

19 and that's been an issue. Okay, so that's

20 that one.

21 We're also looking at alternatives

22 to the way we construct the composite monitor,
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1 you know, we have to make certain

2 simplifications to try to match the way the

3 monitors were dealt with in certain studies.

4 NMMAPS specifically uses a very complicated

5 structure for doing trimming of different

6 monitor data, et cetera.

7 And it's not clear to us if we did

8 the same trimming using current air quality,

9 whether that actually gives you the right

10 match to using -- to air quality that was

11 generated during the actual study period. So

12 there's some questions about how best to match

13 that data.

14 We're also planning on further

15 analyzing patterns of interest. Several of

16 the CASAC members have noted that there's some

17 very interesting differences between the

18 Zanobetti and Schwartz and Bell studies in

19 terms of the effect estimates and the

20 magnitude.

21 We like the idea of doing a

22 correlation between the studies both in terms
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1 of the air quality data and in terms of the

2 effect estimates, and we'll be looking at some

3 of those comparisons and trying to better

4 understand why you see such heterogeneity in

5 the cities that's not just explained by

6 population data.

7 The issue of the LML values, it's

8 very tricky. We appreciate you not commenting

9 on the poorness of the surrogate that we used

10 in the first draft. We're planning on

11 improving that LML characterization either by

12 getting the data directly from the authors,

13 which would be preferred, or if that data's

14 not available, we're going to try to construct

15 LML values based on matching the AQS data, the

16 ozone monitoring data to the periods of the

17 study and determining the lowest measured

18 levels in those locations.

19 And then finally, we'll be looking

20 at some sensitivity analysis on interpolation

21 of missing ozone data in deriving our

22 composite monitor values.
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1 For the national scale risk

2 estimates, we're going to be, similar to the

3 urban case studies, we'll be adding the long4 term exposure respiratory mortality estimates. 

5 We'll be correcting the error that was in the

6 maps for the May to September average, versus

7 the full year average.

8 To the extent we're able to get

9 estimates, we're going to try to apply the May

10 to September city-specific concentration

11 response factors from the Bell et al. study.

12 And again, conditional on the

13 availability of the NMMAPS LML values, we

14 wanted to try to apply the city-specific LML

15 values to generate the mortality estimates

16 across the national set of counties that are

17 included, and then potentially also use city18 specific estimates for the Zanobetti and

19 Schwartz estimates as well.

20 And then the final part is in 

21 Chapter 8, Assessment of Representativeness of

22 the Urban Case Study Areas, we are considering
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1 using the American Time Use Survey data to

2 look at national representativeness of the

3 urban areas for time spent outdoors.

4 And the reason why we wanted to do

5 this is that it also helps us to be able to

6 identify issues related to exposure

7 measurement error because that's one of the

8 concerns is that, especially for comparing

9 L.A. and New York and Houston, there are

10 clearly differences going on in terms of the

11 relationship between personal exposures and

12 ambient concentrations. And that's an

13 uncaptured source of variability and

14 heterogeneity between the cities and we're

15 trying to look at that. So that's the list,

16 it's a big list.

17 CHAIR SAMET: Great, thanks. I

18 think this was a useful exercise that probably

19 brings terror to your hearts. But there's a

20 lot potentially to do. I mean, you have a very

21 long list and we haven't looked at the welfare

22 document yet.
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1 So I wonder, you know, what we

2 might do is just go back through, I think

3 you've heard committee opinions along the way

4 about some of these items but perhaps if we

5 went back and at least, you know, were

6 dichotomous about this and said perhaps some

7 things really need to be done and some things

8 are, you know, the kinds of things you think

9 about if you happen to have time, that might

10 be useful.

11 So let me make a suggestion that

12 we go back through the list and as a group,

13 see if we can provide some, perhaps not

14 consensus guidance, but some general guidance. 

15 I think we're in full agreement that we need

16 a better Chapter 2. So I think we can mark

17 that as high. And Fred?

18 DR. MILLER: Can I ask us, when we

19 do this, if EPA's able to provide the division

20 of labor that's involved because we might end

21 up saying, well, all of these, but they're all

22 to the same people or the same subcontractor
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1 that would do the work of that. So it's not

2 clear that we shouldn't also integrate that in

3 our discussion or let them integrate that.

4 CHAIR SAMET: Well, or let them

5 integrate that. We can certainly, yes.

6 DR. HUBBELL: Well, we do have a

7 little bit of contract help on the exposure

8 modeling inputs but we don't have any for the

9 other parts.

10 DR. MILLER: All right, well

11 that's good to know. And I'm sorry to have --

12 I did used to know you guys, if you're still

13 alive at the end of this.

14 CHAIR SAMET: Okay, so let's go, I

15 think we can probably have consensus that we

16 need a good Chapter 2? Yes, okay. So we can

17 mark that H, that means high.

18 All right, and then the air

19 quality considerations, and some of these

20 we've actually discussed along the way. So

21 why don't we run down these and just see if we

22 can give some guidance here, just go down the
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1 list I think is probably the easiest way, so

2 1A?

3 DR. RUSSELL: If I might, excuse

4 me. You're pretty much committed to going to

5 the 2007, right? I mean, that's a done deal,

6 it shouldn't add any real time?

7 MS. WEGMAN: Right, no, we're

8 already using 2007 in this data, but I think

9 the update of the platform is a given. That's

10 something that's already planned and it's

11 already underway and it's already mostly done.

12 DR. RUSSELL: Then 1b, I'll just

13 sort of, unless people stop me, the longer

14 modeling period, I'm not sure, particularly

15 when we start talking about the welfare

16 document, why not move to a whole year? I

17 just don't see why not. And maybe you can

18 give some reasons why not.

19 MS. SIMON: I was going to make

20 the same point.

21 I think it's just a computing

22 resource issue, looking at, you know, what's
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1 necessary. And then obviously, the HDDM model

2 runs take longer than a typical model run, and

3 we're planning to do several iterations at

4 different emissions levels, so I think that's

5 the main concern, is just computer resources

6 and time to complete the modeling and then

7 have adequate time to analyze the data once

8 we've completed the modeling. But we are

9 considering doing an entire year, especially

10 if the committee thinks that it's necessary.

11 MS. WEGMAN: And the only other

12 thing I wanted to mention, you brought up the

13 welfare and HDDM, but at this point in time I

14 don't know if we feel comfortable with using

15 HDDM for rolling back welfare. I think, what

16 we have been analyzing is using HDDM for urban

17 scale rollbacks, but a national scale rollback

18 is a little bit more challenging.

19 And I don't think at this point we

20 know exactly how we would do HDDM on a

21 national scale to make sure that changes in

22 one area are rippling through, through changes
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1 in another area and then, you know, that those

2 are correlating with each other. So I will

3 probably talk about this more when we discuss

4 the welfare REA, but just keep that in mind.

5 DR. RUSSELL: Okay, I'm not sure

6 how it couldn't, but that, so we can talk

7 about that offline.

8 DR. HUBBELL: The other thing I

9 wanted to mention on that is there is a bit of

10 a tradeoff potentially as well in the sense

11 that, you know, we haven't begun starting to

12 evaluate, and again, this is another of the

13 issues we're looking at, but being able to

14 evaluate combinations of NOx and VOC, to the

15 extent we're able to work through a

16 methodology that's acceptable, those are

17 additional runs that would take time. And if

18 we weren't trying to run full year runs you

19 might be able to do more combinations.

20 So it's a very much a tradeoff in

21 terms of, you know, right now we're scheduled

22 for trying to get another complete draft by,
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1 you know, the end of winter. And that just

2 doesn't leave us a huge amount of time to do

3 all the model runs.

4 CHAIR SAMET: Fred.

5 DR. MILLER: What is the value of,

6 in terms of the health endpoints, of going

7 beyond what's recognized in the area as the

8 ozone season? What does it add in terms of

9 the amount of work you have to do versus the

10 potential gain for having, you know, now four

11 months where, you know, the highest ozone

12 exposure level is at 20 or 30 parts per

13 billion and you're now adding up risks on this

14 nature and that.

15 So I'm trying to understand what

16 would be the value going away from your

17 strategy that you did relative to the health

18 basis where these and the EPI studies were

19 done during ozone seasons. So how are you --

20 it doesn't seem like a lot of return for the

21 effort to go to the full year. But maybe

22 Sverre and some others can jump in here about
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1 what's really --

2 MS. SIMON: I think that one of

3 the issues is that in certain areas of the

4 country the ozone season is the full year. So

5 for instance, in L.A., we define the ozone

6 season as being year-round.

7 And so we're planning on doing one

8 national scale modeling run, so if we're

9 doing, you know, the nation for the year, it's

10 not like we would do a shorter period of time

11 for New York than L.A. because it's going to

12 be a national scale modeling.

13 DR. BRAIN: And clearly, the

14 control strategies are all year long, so in

15 fact, the Administrator might like to know

16 that applying these all year might have less

17 benefit in some of these non-ozone months in

18 some areas. But it seems to me it's important

19 information.

20 DR. BELL: Yes, I would say this

21 is, in my view, a high priority, resources and

22 time permitting. Because depending on your
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1 threshold assumptions, there could be health

2 impacts during non-ozone season.

3 There's no such thing as an ozone

4 season that you're going to apply uniformly to

5 every city. And if you start applying

6 different months to different cities, then

7 it's getting difficult to interpret. So I

8 think, resources permitting, going year-round

9 would be fantastic.

10 DR. MILLER: Well, how do the

11 Bayesian estimates that you're going to use

12 for these city-specific things that were

13 derived, not using full year exposures and

14 seasonality, how are those going to be

15 adjusted to incorporate into this? Is this

16 something that's doable?

17 DR. BELL: Yes.

18 MS. SIMON: Just in response to

19 Michelle's comment, although you might want to

20 look at ozone throughout the year for a lot of

21 these areas where EPA has defined an ozone

22 season, they turn off the monitors during
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1 certain months so we're not going to

2 necessarily be able to evaluate the exposure

3 when there's no monitoring data.

4 DR. BELL: That's why I meant to

5 the degree possible.

6 MS. SIMON: Okay.

7 CHAIR SAMET: Okay, let's see, I

8 think Daniel.

9 DR. JACOB: You mentioned about

10 using average fire for this. What did you

11 mean by that?

12 MS. WESSON: So, in the 2007 run

13 that was fused for the national scale, the

14 2007 run actually had real fires in there so

15 you see all the Idaho fires very clearly.

16 So in general, when we run our

17 models, we have a average fire inventory that

18 goes in, which represents an average of fire

19 emissions over several years. And we are

20 recommending that we use that in the 2007 run

21 this time around so that when we fuse that

22 data with three years of ambient data, that
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1 it's a little bit more apples and apples,

2 versus using the gradients from one model run

3 of 2007 with real fire data, paired with three

4 years of ambient data.

5 CHAIR SAMET: Other comments?

6 DR. JACOB: What theory rules do

7 you plan to use in the HDDM for in terms of

8 the sensitivity?

9 MS. SIMON: So what we've done in

10 our initial test cases, we've looked at first

11 and second order sensitivities to U.S.

12 anthropogenic NOx emissions nationwide, all

13 sources, and U.S. anthropogenic VOC emissions. 

14 And I think Bryan can talk a little bit more

15 about why we chose not to differentiate source

16 sectors.

17 DR. JACOB: Yes, why not?

18 DR. HUBBELL: Well, the reason why

19 is that again, when we do these assessments,

20 what we're really trying to do is improve our

21 ability beyond the quadratic rollback method,

22 to reflect the responsiveness of the
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1 atmosphere in these different urban areas to

2 changes in NOx and VOC, without specifically

3 getting into control strategies.

4 Control strategies is more of a

5 function of looking forward into the

6 implementation process. This is simply trying

7 to reflect, and really what we're trying to

8 find is some bounding cases that allowed us to

9 look at the potential for the atmosphere to

10 respond to just meeting the standard, without

11 trying to identify how we would actually get

12 there in terms of specifics.

13 So the reason why we looked at

14 domain wide, for example, rather than looking

15 at specific sources or specific locations, was

16 to be able to recognize that similar to what

17 we've done with the quadratic rollback, you're

18 trying to see what would happen if you just

19 attained in any specific area, without

20 assuming what other areas are going to do.

21 And so, when you look at domain

22 wide, you're simply saying, we're going to
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1 identify how much, you know, when you reduce

2 NOx, NOx per se, all NOx, how much the air

3 quality responds in that particular location,

4 Atlanta or Detroit, and how it responds to

5 VOCs. And what we saw for Atlanta was that

6 VOCs didn't do much but for Detroit you could

7 look at either NOx or VOCs.

8 The limitation of what we've done

9 now is that we haven't been able to look at

10 combinations of NOx and VOC, which I think is

11 very important. And we haven't been able to

12 fully evaluate that yet. But that's, to me,

13 the next level that we really need to be able

14 to get to.

15 Now when we get to, you know,

16 thinking about individual sectors, then you

17 start asking the question: which sectors? And

18 there's any number of combinations you could

19 do. And it's not clear to me what value that

20 adds to the REA, certainly it's something

21 that's addressed when we look at control

22 strategies and things like the RIA. But
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1 that's a different beast all together.

2 So our focus was trying to say,

3 looking at individual locations, you know,

4 ignoring what's going on in other locations,

5 what is the responsiveness of the air quality

6 in that location, at different monitoring

7 within that location, to changes in NOx and

8 VOC, and that's really what we're trying to

9 accomplish.

10 DR. JACOB: So just to follow up,

11 it's the changes in national emissions of the

12 NOx and VOCs, right?

13 DR. HUBBELL: Right.

14 DR. JACOB: So you might then get

15 inconsistent results between different urban

16 areas?

17 DR. HUBBELL: Yes, and you would

18 get that with the quadratic rollback as well,

19 in fact, they're completely inconsistent

20 because they don't take into account how

21 changes in air quality in one location would

22 be reflected in changes in air quality in
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1 another location. It's inherent in every type

2 of urban case study analysis that is looking

3 at just meeting the standard in each

4 individual location.

5 A national assessment would

6 require us to try to find an optimal level of

7 reductions across the country that would just

8 get every location into attainment. And

9 that's the reason why we don't do that for the

10 national scare assessment, is that it's very

11 difficult to simulate just meeting the

12 standard everywhere and not overattaining in

13 some locations and not others.

14 DR. JACOB: What are you going to

15 do outside of those 16 urban areas when you

16 have exceedance of those then, are you going

17 to be applying HDDM also?

18 DR. HUBBELL: We're not doing an

19 assessment of just meeting the standard

20 outside of those 16 areas. It's only in the

21 16 areas that we're looking at just meeting

22 the current standard and alternative
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1 standards.

2 For the national scale assessment

3 it's only looking at current conditions. And

4 it's just purely to give a broader sense of

5 risk characterization, not to try to look at

6 the impacts of attaining alternative

7 standards.

8 DR. RUSSELL: I just need to talk

9 to him offline.

10 CHAIR SAMET: Okay. So let me

11 make a suggestion. Kathy is here, welcome. 

12 So we're actually looking at this list of sort

13 of what to now is left for the Agency to do if

14 they bring the Health Risk Assessment, Health

15 Risk and Exposure Assessment to completion?

16 Let me suggest in terms of

17 schedule, that what we might try and do is go

18 through this a little more quickly than we are

19 doing, hint, and also perhaps before lunch,

20 hear the Agency presentation on the Welfare

21 Risk and Exposure Assessment Document.

22 So what I would suggest that we do
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1 is, do try and progress through these, I think

2 if as just happened, important technical

3 thing, issues are surfaces, we should pause to

4 discuss them. But we've got a lot to get

5 through.

6 MS. WEGMAN: And did we get a

7 ranking, high, medium, low on this?

8 CHAIR SAMET: Well I think on this

9 one I heard high from some, if doable I think,

10 is that --

11 DR. MILLER: On everything for

12 Chapter 4 or just what he talked about?

13 CHAIR SAMET: No, no, no, on no, I

14 think we were only at B, Fred, at the moment.

15 DR. MILLER: Oh, okay.

16 CHAIR SAMET: We may have wandered

17 into others but I think I heard most who

18 spoke, favor in going to the full year. And

19 we heard about the potential feasibility

20 constraints on that. Yes?

21 MS. SIMON: And I'd just like to

22 reiterate what Bryan mentioned previously,
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1 that we've heard a lot of generally positive

2 comments about using HDDM. But we'd also like

3 specific comments on our specific methodology

4 and implementation in the memo, the technical

5 memo that was presented, because I think there

6 was a comment yesterday about, you know, we're

7 not going to be able to necessarily predict

8 all of the committee's reactions to how this

9 is implemented. And so we have done a few

10 case studies where we've laid out a specific

11 methodology and --

12 CHAIR SAMET: Okay, so we'll, we

13 need a note that we are certain to cover that. 

14 So let's go to --

15 DR. MILLER: Jon, was that only if

16 we went and downloaded it or was it sent to

17 us? Because I don't recall seeing this memo.

18 CHAIR SAMET: Download, so it's in

19 the materials on the Web site.

20 DR. MILLER: So you have to go and

21 download it?

22 DR. NUGENT: If you like, I can
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1 send it to you.

2 DR. MILLER: Please do because

3 I'll go, it's another story, never mind.

4 CHAIR SAMET: Okay, all right, 1c,

5 making rapid progress under Chapter 4. So,

6 update to CMAQ v5 if available, et cetera.

7 DR. RUSSELL: Not a high priority.

8 CHAIR SAMET: Yes, I was going to

9 say this sounds like an L?

10 DR. RUSSELL: The coefficients

11 don't change between versions very much.

12 CHAIR SAMET: Okay, D, explore

13 other approaches for grouping model and

14 observe days in the bins?

15 DR. BRAIN: Sounds like a no.

16 CHAIR SAMET: Yes.

17 DR. BRAIN: It's something we can

18 propose, but again offline. And if they want

19 to do it, fine.

20 CHAIR SAMET: Okay, so expand the

21 16 cities, that's a must, that's a definite,

22 that's part of your plans. F, consider
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1 expanding to two DV periods, we only analyzed,

2 at 2008-2010.

3 DR. HUBBELL: Yes, we have to do

4 that.

5 CHAIR SAMET: And then what about

6 G, comments there, combinations --

7 DR. RUSSELL: I'm actually not

8 sure, I think this is something we don't, at

9 least I don't understand what the issue here

10 is.

11 DR. HUBBELL: Let me just explain

12 it real quickly. When we looked at Detroit,

13 for example, to get to attainment it was a 50

14 percent reduction in NOx, or a 70 --

15 MS. SIMON: Eighty-five.

16 DR. HUBBELL: -- 85 percent

17 reduction in VOC. Clearly, the purpose of

18 using VOC emission reductions in a place like

19 Detroit is not to get to attainment all by

20 itself, it's to try to offset some of the NOx

21 disbenefits you might get in the core areas. 

22 So combinations would tell you more about that
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1 than looking at either just NOx or either just

2 VOC because it's not likely that VOC by itself

3 is going to be a dominant strategy.

4 DR. RUSSELL: I'm just, you've got

5 the sensitivity coefficients for each city, so

6 you've got the cross sensitivity and the

7 second order sensitivities, so this isn't even

8 part of recalculation using CMAQ.

9 MS. SIMON: Right. I think there

10 were two holdups about why we haven't used the

11 combination so far. One is that, to my

12 knowledge in the literature there has been a

13 fair amount of evaluation of, you know,

14 against brute force model runs with using NOx

15 or VOC alone, but I have not found any

16 evaluation in the literature where they've

17 evaluated the combination of NOx and VOC cuts

18 against brute force runs. And we would like -

19 -

20 DR. RUSSELL: The Hitomi reference

21 does that.

22 MS. SIMON: Okay. And we would
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1 also like to do that with our own runs before

2 we use the combination term. And the second

3 issue is just that when you get to doing

4 combinations of NOx and VOC cuts, there's,

5 could be potentially infinite combinations of,

6 you know, what percent NOx and what percent

7 VOC, so figuring out if there's some objective

8 way to kind of pick an optimal VOC to NOx,

9 cut, you know, if you're just doing NOx then

10 you don't have to make any of those decisions.

11 DR. MILLER: It's not clear to me,

12 is this for the national or for the 16

13 specific cities that you're talking about now?

14 MS. SIMON: This is for the urban

15 case studies, the 16.

16 DR. MILLER: Okay, is it possible

17 to not do it for all of them but, you know,

18 use your insight and show a couple of examples

19 of what the impact could be, because --

20 DR. HUBBELL: Yes. I think that

21 is, in fact that may be the way we think about

22 it because for example, in Atlanta, we were
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1 able to pretty quickly identify the VOC's not

2 going to be effective at all, and so why

3 bother?

4 DR. RUSSELL: And the Simon memo

5 starts --

6 DR. HUBBELL: It starts down that

7 path and we just need to finish exploring it,

8 I think was the question.

9 CHAIR SAMET: Okay, Item 2 under

10 Chapter 4, evaluation of feasibility of

11 estimating background ozone, both using the

12 CMAQ-HDDM model configuration and other

13 available models, as well, so here we go.

14 DR. HUBBELL: We're opening the

15 door a little bit.

16 DR. JACOB: So here, I think the

17 idea is that you would like to have an

18 estimate of the background that's consistent

19 with your HDDM application. It makes sense

20 that the ISA should include the CMAQ results,

21 which so far doesn't, right?

22 MS. SIMON: Yes, I'm not aware of
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1 any published CMAQ studies that have done,

2 like zero out U.S. emissions.

3 DR. JACOB: Yes, I was talking

4 with Joe Pinto offline yesterday and he was

5 saying that the CMAQ background results are

6 very consistent versus CAMx or GEOS-Chem, but

7 that would need to be documented.

8 DR. RUSSELL: And also, I mean at

9 the same time in the ISA you have to document

10 the use of HDDM, that you really haven't.

11 CHAIR SAMET: So where does this

12 sit, I mean that's, then alternative

13 definitions so how many, what do you have in

14 mind there?

15 DR. HUBBELL: If we're going to go

16 down this road, we're looking for some advice

17 on this, you know, again there's a number of

18 policy considerations that come into selecting

19 different alternative definitions. Doing more

20 than one, it's going to be somewhat

21 prohibitive in terms of just the amount of

22 extra effort to be able to do multiple risk
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1 estimates for multiple backgrounds and so

2 forth.

3 Clearly the one that has the least

4 policy implication from a straight support

5 standpoint is to look at natural background,

6 you know, which would be allowing us to look

7 at the full range of risk that's associated

8 with anthropogenic emissions. And then the

9 question of whether they're controllable or

10 not is not brought up as much.

11 CHAIR SAMET: So let me make a

12 suggestion. I think we need to maybe come

13 back to this after we discuss the Welfare

14 Document. And I think we had said before that

15 we would sort of wait and do that. Let's just

16 hold on this. Then Chapter 5 and 6, we've

17 dealt with urban areas, exposure estimates for

18 outdoor workers and people greater than 65,

19 you are doing, I think? Yes, Howie.

20 DR. NEUFELD: Just ask for a point

21 of clarification, you focus on these urban

22 areas, but is there any consideration in this
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1 talking about people in the rural areas? I

2 mean, what's the emphasis there?

3 DR. HUBBELL: Well, I mean, the

4 emphasis is two things, one is you're trying

5 to get to where you have a large population

6 center to get an idea of the general magnitude

7 of the exposures.

8 The second part is that we tried

9 to look at places that were at or above the

10 current standard. And those tended to be more

11 in the urban locations.

12 DR. NEUFELD: But there is some

13 evidence that people in some rural areas, the

14 ozone's actually higher than the urban areas.

15 DR. HUBBELL: Well, and actually

16 let me just point out one thing on that is

17 that, because I'm not sure if we've made this

18 clear, is that the definitions of the urban

19 areas, the extent of the urban areas that are

20 considered, differ between the exposure and

21 the epidemiological risk analyses because the

22 exposure assessment takes into account the
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1 fact that people commute and travel, so it

2 does bring in areas that are contributing to

3 exposure within the overall urban areas. It's

4 not picking up rural areas necessarily,

5 they're just picking up suburban areas to a

6 certain extent. And I don't know, Jonathan,

7 you want to talk about the rural areas at all?

8 Yes, we had to prioritize and we

9 felt that that was, the priority was to get

10 the areas that would likely experience the

11 highest elevated ozone. Again, if you have

12 specific areas that you can identify for us

13 that you're concerned about, we can certainly

14 take that into consideration.

15 DR. MILLER: Didn't you, in either

16 the national or, I mean, one of these chapters

17 I've read, you indicated the percentage of the

18 U.S. population captured in your 16 cites, and

19 it seemed like it was extremely large

20 percentage of the total U.S. population.

21 DR. HUBBELL: I don't know whether

22 we had that or not, certainly I think we had
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1 that in the national assessment, I don't think

2 it --

3 DR. MILLER: I know I read it

4 somewhere.

5 DR. HUBBELL: Yes, it may have

6 been in the national, I don't remember where

7 we had that for our 16, but we can certainly

8 provide that.

9 DR. MILLER: Well I was just

10 wondering, you know, to kind of put it into

11 perspective of what's the extent of gain, I

12 thought it covered like 75 or 80 percent of

13 the total population.

14 DR. HUBBELL: I'm not sure, but

15 we'll find that out.

16 CHAIR SAMET: Okay, so let's go

17 back, so if I was to understand correctly,

18 let's go to Chapter 5, Exposure Assessment. 

19 I think the things you have, these big items

20 under Chapters 5 and 6 are things you're

21 attending to.

22 DR. HUBBELL: Yes, that's to do.
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1 MR. AVOL: So I have one question,

2 Jon, back on, just scroll back up, that

3 previous comment on the addition of exposure

4 estimates for outdoor workers and people 65-

5 years-old, so I just went back to the Chapter

6 2 framework and looked at at-risk populations,

7 and it lists four at-risk populations. It

8 lists children, outdoor workers, people over

9 65, and people of low socio-economic status. 

10 Where does that fall in here?

11 DR. HUBBELL: John, do you want to

12 comment on this?

13 MR. LANGSTAFF: Well, we are going

14 to be doing an analysis of the lower, the

15 difference in SES and how that impacts, you

16 know, exposure and risk.

17 MS. WEGMAN: But that's going to

18 be -

19 MR. LANGSTAFF: Well, and that's

20 actually an analysis for dealing with high

21 risk groups.

22 DR. HUBBELL: We're not
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1 specifically estimating separate exposures for

2 low SES, but what we are doing is identifying,

3 in the populations that are higher risk, what

4 is the SES status of those individuals in the

5 high risk groups. And it's not quite the same

6 thing but it is a way of addressing it.

7 DR. SUH: I think just, Ed, I

8 don't think it's possible to use a population

9 exposure assessment using APEX for people with

10 low SES, it's just the activity data is not

11 there. And so I think it would be very

12 difficult in this circumstance to do that.

13 MR. AVOL: Right, I'm just saying

14 that there's sort of a logical inconsistency

15 in identifying it but then not --

16 DR. SUH: But there is, I think it

17 was mentioned in the sensitivity analysis that

18 you guys are going to do.

19 DR. HUBBELL: Right, I mean we do

20 have the data there to do it from the census. 

21 We're using trackable data that has SES

22 information.
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1 MR. AVOL: Does the activity

2 barriers, do they contain SES information?

3 DR. HUBBELL: No they don't.

4 DR. MILLER: Yes, so that's the,

5 you wouldn't be able to have specific

6 activities that might get the buy in to this

7 category.

8 DR. SUH: I guess it would just be

9 important to bring that out.

10 CHAIR SAMET: Okay, on to Chapter

11 5 Number 1, which was Your Underexposure

12 Assessment I think this was improve the depth

13 and clarity of documentation presentation,

14 yes.

15 DR. SUH: Wait, wait, why was that

16 low?

17 CHAIR SAMET: What?

18 DR. SUH: I'm sorry, why was the

19 outdoor workers and the --

20 CHAIR SAMET: No, outdoor workers

21 no, they weren't low.

22 DR. SUH: And that wasn't low.
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1 CHAIR SAMET: No, this is all

2 high, no. It was, I think the discussion

3 around SESs where you, perhaps got confused.

4 DR. SUH: I don't even, I don't

5 think that's low I just think it's not, yes --

6 CHAIR SAMET: Yes, it's all -- You

7 can leave all those, the general ones under 5

8 and 6 are all -- And certainly you are going

9 to do, what is it, Number 1 and Number 2,

10 which is, these workers, these are among our

11 key suggestions for the document. Now the

12 sensitivity analysis --

13 DR. SUH: Well, that's high.

14 CHAIR SAMET: Let's see, I guess

15 the question is are all of these high? So for

16 example, considering developing a method for

17 spatial interpolations.

18 DR. RUSSELL: I think that one is

19 low.

20 DR. SUH: Yes, that one I think is

21 low.

22 DR. RUSSELL: Because, I mean,
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1 we've done it and it doesn't make that much

2 difference.

3 CHAIR SAMET: Yes, so that one

4 would be low. The CHAD activity is a yes,

5 that's a high. What about evaluations for

6 above, what do you mean by 3a? I'm confused

7 at this point.

8 DR. HUBBELL: 3a was looking at if

9 we are to generate an alternative set of

10 background concentrations, that's something

11 that we would need to incorporate into the

12 greater risk influence as well.

13 CHAIR SAMET: I see, okay. So

14 potentially high.

15 DR. HUBBELL: Yes, that's

16 conditional.

17 CHAIR SAMET: And D, what do you

18 intend there?

19 MR. LANGSTAFF: Well there's

20 published studies such as Fred referred to

21 that we can compare the APEX similar results.

22 CHAIR SAMET: So this would be
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1 updating, so this would be a potential

2 updating expansion of the database on activity

3 levels or --

4 MR. LANGSTAFF: No, this would be

5 comparing what APEX predicts to measure, you

6 know, studies that have done measurements and

7 just --

8 DR. SUH: They model performance

9 validation.

10 MR. LANGSTAFF: Yes, evaluating

11 APEX essentially.

12 CHAIR SAMET: So this would be a

13 model validation, what's coming out of APEX

14 versus whatever estimates you can find.

15 MR. LANGSTAFF: Right.

16 DR. SUH: I'd say that's very

17 important.

18 DR. MILLER: Yes, I think it's in

19 the same category as I said about this double

20 water labeling and checking relative to the

21 CHAD in the percentile. So it's in that kind

22 of, how well does the model reflect what other
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1 data sources may indicate might be going on --

2 CHAIR SAMET: Okay so it's perhaps

3 poorly described here, that's, okay.

4 DR. HUBBELL: And I was going to,

5 Dr. Miller, if you could provide that

6 reference to that study that you mentioned

7 from Montreal, that would be great.

8 DR. MILLER: I'll wait until

9 tonight and then I can access the internet

10 because, you know, you people that aren't Mac

11 users if you, you have to set up a special

12 account and not go through your original

13 server. So I can't, I can get everything from

14 you but I can't send.

15 CHAIR SAMET: We feel for you,

16 Fred. Repeated exposures, repeated exposures,

17 that's for, analysis of repeated exposure for

18 cohorts of school children.

19 DR. SUH: It's high. I mean, I

20 guess we all mentioned them so I don't know,

21 I mean, I guess I'm just not sure how you're

22 going to do it but --
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1 CHAIR SAMET: Yes, it sounds

2 ambitious in terms of --

3 DR. HUBBELL: Well, one of the

4 things we've discussed and then John can

5 elaborate, one of the things we've discussed

6 is creating scenarios, so we'd not simply be

7 doing a random draws in a way that repeats, it

8 would be simply saying, if there were a school

9 child who had the following activity pattern

10 repeated, what would the exposure be? Again,

11 it's difficult to know how representative that

12 child would be but it's a scenario.

13 DR. SUH: Well you could argue

14 that if they're at camp or in their school,

15 there is a pattern to their activities.

16 DR. HUBBELL: Yes, that's correct.

17 CHAIR SAMET: Okay. And then

18 obviously, additional clarification types of

19 uncertainty characterizations, sure.

20 DR. HUBBELL: Well that's

21 directly, I think, in response to what you

22 guys were asking about so.
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1 CHAIR SAMET: All right, so on to

2 Chapter 6 --

3 DR. MILLER: Wait, whoa, whoa,

4 before you go on, I thought that you couldn't

5 identify individuals with repeated occurrence

6 of attack.

7 DR. HUBBELL: Of --

8 DR. MILLER: Well like in your

9 asthma, when they go back into the pool, you

10 can't tell that it's the same individual that

11 you're getting, that they can have more than

12 one.

13 DR. HUBBELL: Yes, this is purely

14 on the exposure side --

15 DR. MILLER: Okay.

16 DR. HUBBELL: -- simply saying,

17 you know, what are the exposures for people

18 who are engaged in repeated activities or

19 locations that have higher ozone exposures? 

20 Because right now the way that the diaries

21 work, the diary selection process works, you

22 don't get those connected strings of high
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1 activity levels.

2 DR. MILLER: Oh, okay.

3 DR. HUBBELL: And so this is just

4 trying to account for the fact that people

5 have --

6 CHAIR SAMET: Thank you. Okay, so

7 6.1, I think we heard at least one suggestion

8 from Fred that this might be a lower priority? 

9 That there was already a robust body of data,

10 this is on adding additional clinical studies.

11 DR. MILLER: Which one were you

12 talking about?

13 CHAIR SAMET: 6.1, Fred, Chapter

14 6, the first one.

15 DR. MILLER: Yes, it's a high

16 priority --

17 CHAIR SAMET: You want that, I

18 thought you had said before that you were not

19 so sure that was necessary given the --

20 DR. MILLER: No, I said that you

21 don't have to repeat the analysis from the

22 previous, that didn't include this, but if you
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1 use the later models you want to include the

2 data.

3 And that raises the question that

4 you brought up about the availability of

5 McDonnell and some of the data not, I thought

6 if it was federally funded people had to give

7 you the data. So I'm surprised that, and I

8 know it's not, anyway, I'm surprised that

9 you're having trouble getting all of the data

10 that was used in that.

11 MR. LANGSTAFF: For this first

12 one, we already have the data from Kim and

13 Schelegle, so we can do that. And with the

14 other data, Adams is reluctant to give us his

15 data, so that's a huge part of the data set

16 that's used to fit the McDonnell, Stewart and

17 Smith model.

18 DR. MILLER: Why do you call it

19 huge? It pales in comparison to the 59

20 subjects that EPA did.

21 MR. LANGSTAFF: Well there's

22 several studies that he did back in the '80s
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1 and '90s I guess that make up a pretty

2 significant part of that.

3 DR. MILLER: Okay.

4 MR. LANGSTAFF: And we can go

5 ahead and --

6 DR. MILLER: But if McDonnell in

7 his paper shows that he's able to simulate

8 every exposure pattern that's been done, why

9 do you have to have the data from, and not

10 just use the output parameters of the final

11 McDonnell model? Why do you have to go back

12 to the raw database?

13 MR. LANGSTAFF: Well, we need to

14 have the data to look at the uncertainty of

15 the model. But perhaps we can do that with

16 just the data we have --

17 DR. MILLER: I see.

18 MR. LANGSTAFF: -- it's just not

19 going to be quite as good analysis of

20 uncertainty.

21 DR. MILLER: Yes, okay. Mr.

22 Adams, Dr. Adams, hmm.
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1 CHAIR SAMET: Okay, so are both of

2 these then judged high? Is that --

3 DR. KLEEBERGER: Jon, I think

4 there was also some discussion maybe that

5 falls into these two points about the

6 comparison of the McDonnell model with the

7 exposure function model, the differences in

8 the predictivities of those, I think you wrote

9 that up in the, you wrote in the document that

10 you were going to follow up and try to find

11 where --

12 DR. HUBBELL: Right, yes.

13 DR. KLEEBERGER: -- what explains

14 those differences.

15 DR. HUBBELL: We're going to, yes.

16 DR. KLEEBERGER: Okay.

17 CHAIR SAMET: You know, we may

18 make, on this data sharing issue, we may want

19 to make a general comment. I mean, I think

20 these are important databases, these studies

21 are not going to be necessarily replicated in

22 the future.
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1 And even just the issue of

2 somebody curating them so that they're

3 available and archived is, to me, important. 

4 And perhaps, you know, when we write our

5 general comments we should make a comment

6 about the importance in general of access to

7 key data sets.

8 And I think that comment should go

9 in somewhere, I mean, you know, I think

10 certainly the community sense around data

11 sharing has shifted a lot in the last decade. 

12 And I think this is an example where having

13 the data set at the individual level would

14 allow analyses that might be a benefit in many

15 ways. Jim?

16 DR. ULTMAN: And I just thought of

17 a point of clarification in terms of which

18 version -- with the threshold --

19 DR. NUGENT: Excuse me, Jim, could

20 you use the mic please?

21 DR. ULTMAN: Oh I'm sorry, I

22 apologize. Which version of the model of
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1 McDonnell's are you planning to use, the one

2 with the threshold, the one without the

3 threshold, or are you going to compare them? 

4 It seems like there's a lot of work that

5 you've got cut out for you there.

6 MR. LANGSTAFF: Well, we're

7 planning to use the model with the threshold. 

8 We'll do some uncertainty analysis. It would

9 be a better analysis if we had all the data. 

10 But it will be more limited without it but

11 that's the model we're planning on using.

12 DR. ULTMAN: Yes, you know, I read

13 and reread the McDonnell paper and it seems

14 like the difference in the fits to the model

15 is relatively modest between that.

16 MR. LANGSTAFF: Yes, right.

17 DR. ULTMAN: I mean, I wouldn't,

18 in fact I would be very careful not to push

19 the word threshold to the point that it looks

20 like that the paper has uncovered some kind of

21 a physiologic or biologic mechanism, because

22 it's really a model-based kind of a threshold. 
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1 Maybe you can come up with a different word

2 for it.

3 MR. LANGSTAFF: Yes, so it's a

4 dose threshold anyway, not a concentration

5 threshold, and yes, he says there's not much

6 difference between the two models.

7 DR. ULTMAN: Right.

8 CHAIR SAMET: Okay, on to Chapter

9 7 --

10 DR. NUGENT: So both of these are

11 high, is that correct?

12 CHAIR SAMET: They're high, yes. 

13 And then Chapter 7.1, I think that's --

14 DR. HUBBELL: Yes, that's a --

15 CHAIR SAMET: That's a high.

16 DR. HUBBELL: That's a high, yes.

17 CHAIR SAMET: Now I think there

18 was some sense about 2 that, okay what about

19 2?

20 DR. VEDAL: Well, I don't think

21 there's any disagreement on mortality.

22 CHAIR SAMET: It was the
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1 morbidity, that's correct.

2 DR. VEDAL: Right.

3 CHAIR SAMET: Okay. So that gets

4 an H. Confidence intervals you have,

5 apparently, these were going in. 4, this

6 potentially entails a lot of work. I mean I

7 guess I'd say this is something that would be

8 nice but, I don't know, others, Michelle, what

9 do you think?

10 DR. BELL: I agree with you.

11 CHAIR SAMET: Would it make you

12 happy if we put an L by this?

13 DR. MILLER: I kind of say, how

14 can you use data in which they haven't

15 adjusted for other co-pollutants and we're

16 talking in particular the importance of PM?

17 DR. BELL: Because, my view is

18 because there has been a wealth of evidence

19 showing that the ozone estimates are not

20 confounded by particles including specific

21 chemical components of particles. Therefore,

22 even an ozone estimate that doesn't adjust for
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1 particles we could say was quite unlikely to

2 be confounded by PM, given, I mean there's

3 been numerous studies that have looked at that

4 adjustment. But, I agree that if what I just

5 said is EPA's thinking, they could put that in

6 the document.

7 DR. HUBBELL: Yes, we'll do a

8 better job explaining that. And the other

9 issue that comes up, Fred, with this is that,

10 you know, for the large multi-city studies

11 you're dealing with PM data that's not

12 collected on an everyday basis and you have

13 ozone data that's collected on an everyday

14 basis and you lose statistical power when you

15 have to combine those two data sets. And so

16 while this is useful for sensitivity, it very

17 much limits your ability to use large multi18 city studies.

19 DR. MILLER: And I understand that

20 and I understand what Michelle is bringing up. 

21 However, in those studies, when they did

22 include them, they did change the size of the
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1 effect estimate. They didn't change the

2 overall trends.

3 DR. VEDAL: I mean there are

4 complexities here, Fred. One complexity is it

5 tends a little bit on the endpoint as to

6 whether there's going to be confounding by co7 pollutants. But the other issue is multi8 pollutant models and single-pollutant models,

9 the issues are complex. There's the issue of,

10 you know, differential measurement error by

11 pollutants and the impact of that on the

12 respective estimates.

13 So I'm not sure there's any

14 consensus that estimates for multi-pollutant

15 models are actually more valid than estimates

16 from single-pollutant models. Clearly, I mean

17 in both scenarios, there are uncertainties. 

18 You'd like to split the difference but, you

19 know, that's a lot of work.

20 CHAIR SAMET: So of the items

21 listed here, for example lag structure, I mean

22 perhaps if one is to be given higher priority
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1 because in the sense it's intuitive to raise

2 it, it is the question of is there sensitivity

3 to inclusion at least to PM in the models?

4 You do have, I mean putting aside

5 all the complexities that evolve around those

6 comparisons, restrictions, and data sets and

7 so on, there are data sets that would allow

8 you to provide some information on that, and

9 it's probably reasonable to do this.

10 I mean, I would worry about you

11 getting sunk in things like lag structure,

12 different outcomes, and different models. I

13 mean, I think that really could become

14 overwhelming quickly.

15 But I think perhaps of those

16 factors that you listed for the sensitivity

17 analysis, perhaps co-pollutants might receive

18 the greatest emphasis with higher priority.

19 DR. PEKAR: Just to make sure I'm

20 clear on that, so the regional prior

21 adjustment, that was the other thing that was

22 mentioned yesterday by Dr. Smith. And I just
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1 wanted to be explicit about asking in regards

2 to that also, in the Bayesian adjusted effect

3 estimates.

4 DR. MILLER: I endorse it in my

5 written comments that you're going to get.

6 CHAIR SAMET: Michelle, do you

7 want to comment on that?

8 DR. BELL: I wasn't planning to.

9 CHAIR SAMET: Okay, frankly I'd be

10 surprised if it made much difference, might be

11 surprised if it made much difference but

12 that's, you can take a look.

13 DR. HUBBELL: One other question

14 for you is, as you know, there's a literature

15 on each of these elements. One question is,

16 do you think it's appropriate to focus on

17 maybe one or two cities where there may be,

18 you know, a study that has looked at these

19 things?

20 And we may not even need to take

21 it all the way through to the risk estimates

22 as much as just comparing the coefficients
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1 from the, you know, the effect estimates

2 across there and saying look, there's a

3 significant difference in the effect

4 estimates. Once you've applied it through the

5 concentration response functions, it's

6 probably going to have an equivalent impact

7 within a particular city.

8 Some of these, like the regional

9 functions, will obviously make a difference

10 between cities, but for some of the other ones

11 it might be, maybe it would be sufficient

12 simply to point to the literature and talk

13 about the difference.

14 CHAIR SAMET: I think to the

15 extent that you can do that, that would be --

16 DR. HUBBELL: Helpful.

17 CHAIR SAMET: -- valuable.

18 DR. HUBBELL: Okay.

19 CHAIR SAMET: So let's go on. So

20 we're at 7.5, composite monitor of constructs.

21 DR. SUH: I think that there is

22 research showing that it doesn't matter so
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1 much about how you average, more in the

2 process data.

3 DR. PEKAR: I'm not aware of those

4 studies. If you have a reference that would

5 be great.

6 CHAIR SAMET: So here you're

7 referring to things like the NMMAPS trimming

8 and so on?

9 DR. HUBBELL: Yes.

10 DR. BELL: I would personally put

11 this as it would be great if you could do it

12 but a low priority compared to other items

13 would be my assessment.

14 DR. SUH: Yes, and I also think

15 there's some citations you could probably use

16 to bolster what you do, so I think it's

17 probably, I would agree with Michelle.

18 DR. PEKAR: I mean, if you could

19 point us in that direction that would be

20 great. And that does save us a lot of work,

21 just to be clear, it does.

22 CHAIR SAMET: Number 6, addition
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1 of further analysis of patterns of interest in

2 risk assessment?

3 DR. VEDAL: That would be, you

4 know, it's a small bit of work, relatively

5 minor. But it would be kind of neat if you

6 had that.

7 DR. HUBBELL: I think we're going

8 to do that I mean that's --

9 DR. VEDAL: Yes, I think you will.

10 DR. HUBBELL: -- in order to be

11 able to do the synthesis we're going to have

12 to really get a better understanding of that.

13 CHAIR SAMET: Okay, LML, that's 7.

14 DR. VEDAL: Yes, LML, so this is a

15 big discussion that some way, because it's a

16 lot of work, you know, the whole exposition

17 regarding surrogates and all that, you know,

18 is all dependent on whether you're going to do

19 LML in the final analysis.

20 And, you know, I don't know, it's

21 a little bit of a tough call. I think it all

22 hinges on whether you think you're going to be
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1 susceptible to criticism going beyond the

2 limits of the data. And you might be.

3 I personally am not bothered by it

4 and I'd prefer rather just to see going down

5 to background which, or going down to zero

6 which, you know, essentially gives you a

7 flavor of what is actually there and the

8 distribution of concentrations. But I'd like

9 to hear some other views, you know, on that.

10 CHAIR SAMET: And might this be

11 done in some illustrative locations and then

12 stop perhaps? I mean, I -

13 DR. HUBBELL: Well, one thing I'll

14 not on that and Zach can back me up on this,

15 but we were surprised at the amount of

16 variability in the LML values between

17 locations. For example, in New York City we

18 were looking at, it was almost close to zero.

19 And if it turns out that if you

20 think about the way daily values of ozone are,

21 if it's raining on a particular day for

22 example, you can have ozone concentrations
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1 very close to zero.

2 And, you know, for eight hour

3 metrics averaged over a city, you know, you

4 can get a huge amount of variability. And

5 it's not clear, again, one of the problems

6 with LML is it's the bottom of the data. It

7 doesn't tell you very much about data density

8 along the estimation of the concentration

9 response relationships.

10 So that's one of the reasons why

11 in the charge question we asked about are

12 there other places we should be looking? You

13 know, what Dr. Miller said earlier about, you

14 know, the fact that we want to know about the

15 confidence in the concentration response

16 relationship.

17 And sometimes, in previous

18 discussions, I think the discussion of

19 background and the discussion of confidence in

20 the C-R function get conflated. And we're

21 trying to separate those out a bit and get a

22 better understanding of how do we characterize
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1 confidence in the relationship while still

2 providing the full range of information across

3 the board.

4 CHAIR SAMET: So how much work

5 would it be, Bryan, to do what you have in the

6 paren?

7 DR. HUBBELL: And what was the

8 parentheses --

9 (Off microphone discussion)

10 DR. HUBBELL: Ben, how much

11 difficulty is there in constructing the lowest

12 values observed across a particular time

13 period and a location?

14 MR. WELLS: You've got to match to

15 the specific studies.

16 DR. HUBBELL: You've got to come

17 up here. Hold on, you've got to come up to a

18 mic.

19 MR. WELLS: This is Ben Wells.

20 DR. HUBBELL: He's our air quality

21 expert.

22 MR. WELLS: If I was able to get a
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1 list of the epidemiological studies in the

2 study periods and the months used in those

3 periods then it wouldn't be too much

4 difficulty to match the ozone data to those

5 periods and to, you know, determine the lowest

6 measured levels. We have all of the ozone

7 data. It's just a matter of matching it to

8 the health studies.

9 CHAIR SAMET: Fred, aren't those

10 kinds of things in their publications?

11 MR. WELLS: No.

12 DR. MILLER: No, no.

13 DR. PEKAR: No, not that kind of

14 detail.

15 CHAIR SAMET: They don't say,

16 okay, I'm surprised. So they don't say we

17 sampled years 2010 to --

18 DR. PEKAR: Oh, that is there but

19 not the detail of the LML.

20 DR. MILLER: Right, but the

21 specific LMLs are not listed for every

22 location within the statute.
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1 MR. WELLS: And just to be clear,

2 this is not the lowest value at a monitor in

3 urban area, it's the constructed composite

4 values used in the statistical analysis.

5 DR. HUBBELL: Yes, and so again,

6 while we can create this construct that would

7 be based on previous air quality data, the

8 ability to specifically match the actual LML

9 that might have been used in the study itself,

10 the study design, is not something we can do.

11 CHAIR SAMET: Right, because of

12 the construct, right. So I don't know how

13 others feel, Chris?

14 DR. FREY: Well, just some

15 thoughts, you know, the comparison of the

16 results with and without the LML, I mean to me

17 as an engineer, I didn't see that much

18 difference in the risk estimates. So I didn't

19 know that there was that much value added.

20 I could see the point, you know,

21 that Sverre alludes to that you're less likely

22 to, or you're more robust to criticism if you
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1 take it into account. But it just seems like

2 at the margin it doesn't add a lot of value to

3 the results.

4 And I do think it is valuable to

5 do the risk, at least one set of risk

6 estimates should be relative to zero. But

7 ultimately, I think the policy analysis is

8 going to hinge probably more on how the risk

9 estimates compare among different non-zero

10 concentrations.

11 So I think the risk estimate down

12 to zero is kind of like background information

13 for the administrator. And in that sense,

14 maybe it doesn't need the same level of

15 quality as some of the comparative estimates

16 among different alternatives versus

17 background, for example.

18 And I guess, so I would think,

19 maybe as some alluded to, the thing is to pick

20 a few strategic cases and do a sensitivity for

21 those. But I think the work is not just to

22 figure out what is the composite LML, but it's
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1 then to do the risk assessment.

2 So it's a little bit more than I

3 think, the answer that we just heard. And I

4 would rather use that time for something maybe

5 more important.

6 But if there were two or three

7 cities where the LML was, maybe some high

8 LMLs, if there are any that are above ten for

9 example, maybe look at those. And then just

10 say, you know, for LMLs below X, five or ten

11 that it's not going to affect the result more

12 than some percentage and we didn't look at it.

13 CHAIR SAMET: So I think that's an

14 L.

15 DR. PEKAR: I think that's a good

16 point and another thing to make is to a

17 certain extent we'd have to see this with the

18 alternate standards. But the deltas that

19 we're talking about often occur above these

20 LMLs anyway. So it's really just, and that's

21 what becomes important for them.

22 CHAIR SAMET: Okay, what about 8? 
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1 I think I heard L for that earlier.

2 DR. MILLER: Yes, I certainly

3 thought it was an L.

4 CHAIR SAMET: Okay, so 8 is an L. 

5 All right, we're on the Chapter 8.

6 DR. BRAIN: Can I, just one add

7 on? I don't know that it belongs here or

8 somewhere else but, it seems to me at sometime

9 we should comment on next generation of

10 exposure assessment, you know, we should all

11 have iPhone Apps that would measure location

12 activity and ozone remote sensing, you know,

13 personal dosimeters. I mean as I look at this

14 I think, you know, we really need more

15 detailed information. And it doesn't exactly

16 belong here but somewhere I think we need to

17 put forward some of those possibilities.

18 CHAIR SAMET: Okay. Maybe iPhone

19 5 is going to have that. Everybody will want

20 that. Okay, 8, Number 1, this is long-term

21 exposure of respiratory mortality.

22 DR. RUSSELL: Aren't you currently
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1 pretty far down that road, or --

2 DR. HUBBELL: We are I mean again,

3 it's not a matter of can we do that or is it

4 feasible? It is feasible to do that and our

5 plan is to do that. We were just looking for

6 your input as to whether it was supportable to

7 do that.

8 DR. RUSSELL: I think it's high, I

9 mean it is, I think, a critical piece of

10 information to pass on.

11 CHAIR SAMET: Okay, we do want you

12 to correct the error.

13 DR. HUBBELL: Yes.

14 CHAIR SAMET: Let's see, 3.

15 DR. MILLER: Can you explain what

16 that means? You've used very crypted --

17 DR. ANENBERG: Sure, so in the

18 current draft we intended to quantify res from

19 using the effect estimates from Bell et al.

20 (2004) for just May to September, and the

21 Zanobetti and Schwartz, using the Zanobetti

22 and Schwartz effect estimates for the matching
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1 period in that study which is June to August.

2 We actually only had city-specific

3 estimates from Bell et al. (2004) using all

4 year ozone data. So our suggestion was that

5 in the next draft we identify whether it's

6 possible to get just the May to September

7 city-specific estimates and use those.

8 CHAIR SAMET: Did Bell et al. have

9 any comments on this? Or as to availability?

10 DR. BELL: Oh, I'm working on it.

11 CHAIR SAMET: Oh, okay.

12 DR. MILLER: What's the advantage?

13 CHAIR SAMET: Et al. is not.

14 DR. BELL: Are you volunteering,

15 Jon?

16 CHAIR SAMET: What?

17 DR. BELL: Are you volunteering?

18 CHAIR SAMET: No.

19 DR. ANENBERG: So in the National

20 Scale Assessment, and because it's one grid

21 over the entire U.S., it really requires one

22 defined modeling period. So May to September
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1 would be the lowest common denominator for all

2 locations.

3 CHAIR SAMET: So you're really

4 doing additional analysis then.

5 DR. MILLER: I forgot national

6 scale is in the title in this chapter.

7 DR. HUBBELL: Yes, in addition,

8 the other benefit of doing that is it allows

9 for more direct comparability between the

10 studies rather than, you know.

11 DR. MILLER: Yes.

12 CHAIR SAMET: Okay, what about

13 8.4? Sounds to me like this is, we've talked

14 about this, this is really lowish, as is 5. 

15 All right then 8, actually, Fred, you had a

16 comment on these data and then we heard

17 actually from EPA that they did want to look

18 at these data.

19 DR. MILLER: Yes, they did but at

20 least in the write up you said there's an

21 awful lot of problems with the data so I was

22 taking in the context of just that, when they
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1 articulated what might be able to be done with

2 it if they could do it here today, it had a

3 higher, you know, probability or rating if it

4 was up to me. So I guess I would advocate

5 this is as an M, we haven't had an M.

6 DR. HUBBELL: It's one of those

7 things that if we can get the data to work, we

8 think it has some value. But it's kind of a

9 high-risk, high-reward thing if we can get it

10 to work, so yes, and it is a lot of work

11 because it's a big data set. And we are

12 getting some help from our colleagues at ORD

13 but it's a big job.

14 DR. MILLER: I guess I still have

15 to, given everything else that you're doing

16 and knowing you're going to revisit this in

17 another five to eight years, no one has ever

18 made the five year cycle, that this still

19 ought to be an L.

20 DR. HUBBELL: Okay, that's fine

21 too. Thanks, Fred.

22 CHAIR SAMET: I think it's fine to
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1 put L by things. So we will provide this, our

2 guidance to you. If nothing else, hopefully

3 it will allow you to think about the many

4 things you have to do and give some

5 priorities.

6 So what I'm going to suggest is

7 that we not start on the Welfare Risk and

8 Exposure Assessment, we finished the Health

9 Risk and Exposure Assessment, I think

10 adequately. Why don't we reconvene at 1:15

11 and at that point then we'll get back with the

12 schedule that's there for 1:30, okay? So

13 adjourned for an hour and ten minutes or so.

14 (Whereupon, the above-entitled

15 matter went off the record at 12:05 p.m. and

16 resumed at 1:19 p.m.)

17

18

19

20

21

22
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1 A-F-T-E-R-N-O-O-N S-E-S-S-I-O-N

2 (1:19 p.m.)

3 CHAIR SAMET: Okay, welcome back. 

4 If everybody could take their seats, we are

5 ready to move on to the Welfare Risk and

6 Exposure Assessment long awaited by the team

7 to my right so we'll hear I guess from EPA.

8 MS. WEGMAN: This is Dr. Travis

9 Smith who will be doing the presentation.

10 CHAIR SAMET: You don't seem to be

11 on yet. Try now.

12 MS. WEGMAN: Are we ready now? 

13 Okay, so this is Dr. Travis Smith. He's going

14 to do the presentation on the Welfare Risk and

15 Exposure Assessment.

16 DR. SMITH: I think we're on. I

17 think he said we're on so try and get some

18 slides up quick.

19 This will be somewhat shorter than

20 some of the other things. We'll hurry through

21 some of the discussion of air quality because

22 a lot of it's discussion we've already had. 
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1 We have the slides.

2 So just really quick. I won't go

3 through all the people on this. It's just

4 obviously there's a lot of people involved

5 with this project, not just me sitting here

6 talking.

7 We have a lot of help,

8 particularly from the Forest Service with the

9 i-Tree model so just sort of acknowledge that

10 that's a group that we've, they've been very

11 helpful in addition to all the people in the

12 OAQPS with the EPA.

13 The structure of the Welfare

14 Assessment is very similar to the Health

15 Assessment. Couple less chapters I think but

16 we more or less followed the same sequence,

17 introduction, framework, scope.

18 I'm not going to really talk about

19 those in this part of the presentation. We'll

20 talk about those as we move through the

21 afternoon here.

22 And so I'll talk a little bit
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1 about the air quality considerations, the

2 ecological effects and then the ecosystem

3 services and just briefly touch on the

4 synthesis chapter.

5 So we have talked a lot about the

6 air quality already and a lot of the issues

7 with this are going to be the exact same

8 things that have been discussed and so I'll

9 try to touch on what's different here.

10 This is we were using the W126

11 national fused surface for the recent air

12 quality.

13 For the current conditions, we

14 used the 2006 to 2008 monitor data and that

15 was fused with the 2007 CMAQ data and all this

16 was fused together using the enhanced Voronoi

17 Neighbor Averaging, eVNA, technique.

18 And the same for the Health

19 Assessment. The monitor data provided the

20 absolute W126 values and the CMAQ model data

21 determined the gradient between monitors.

22 And so there'll be some discussion
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1 here of in the east there's not a lot of space

2 between monitors. In the western United

3 States there's obviously very large areas with

4 no monitors.

5 To rollback to just meeting the

6 current standard, we rolled back to meeting

7 the eight-hour standard, not the W126

8 standard, because the current secondary

9 standard is equal to the primary.

10 To do this, the monitors were

11 grouped together throughout the U.S., and if

12 at least one monitor was exceeding the 2006 to

13 2008 design value or above 75 ppb, the

14 monitors were rolled back.

15 And then from the rolled back

16 data, we recalculated the W126 and then

17 generated the fused spatial surface a second

18 time.

19 So see what these surfaces look

20 like. These actually highlight some of the

21 technical difficulties that have already been

22 discussed in great length here.
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1 You can see the forest fires in

2 Idaho sticking out in the upper, upper left

3 and in the rollback scenario, that because

4 there's no monitors there to roll back.

5 And there are some other technical

6 issues with this rollback that we know are

7 there and we're exploring different ways to

8 address those for the second draft.

9 And we're certainly hoping to get

10 more input than has already, CASAC's been

11 giving plenty of input on the rollback and the

12 air quality so we'll hopefully get more on

13 this particular surface as we move forward.

14 Then moving onto the ecological

15 effects in Chapter 5, we pulled forward the

16 causal and likely causal effects from the ISA.

17 I think the most important thing

18 here is to point out a lot of these we really

19 don't have anything other than just we know

20 it's a causal effect. We had no way to look

21 at them in a quantitative way for the risk

22 assessment.
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1 And so for the ecological effects,

2 we looked specifically at the reduced

3 vegetation growth and the visible foliar

4 injury and those are the only things that

5 we've really been able to quantitatively look

6 at in this risk assessment.

7 And of those, only the reduced

8 vegetation growth, they're in the form of

9 relative biomass loss, that's the only thing

10 that we have a concentration-response function

11 for to be able to really assess incremental

12 changes in the effects of ozone.

13 In Chapter 6 we do use some of

14 these causal or likely causal effects to

15 qualitatively look at how that would relate to

16 ecosystem surfaces and I'll talk about that in

17 a couple of slides.

18 So looking at the relative biomass

19 loss, basically this analysis was completed in

20 the last review. We've updated it in this

21 review using the newer air quality data.

22 We have one new tree species so
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1 there's 11 tree species that have a

2 concentration-response function for biomass

3 loss.

4 It is important and the biomass

5 loss function is calculated as a comparison

6 between varying ozone scenarios and a clean

7 air quality or zero ozone so that is how it is

8 calculated.

9 And we didn't recalculate those

10 numbers. Those are directly from the last

11 review and the technical documents that were

12 associated with the last review.

13 The species ranges for those

14 analyses were based on published U.S. Forest

15 Service data. Some of that data is very old,

16 from the early '70s.

17 And so for the eastern species, we

18 were able to update some of those ranges with

19 more recent Forest Inventory Analysis data

20 from the Forest Service.

21 For this particular analysis, we

22 were able to look at and assess both the
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1 absolute biomass loss numbers across the range

2 of those individual species.

3 And we also looked at proportional

4 changes between the two air quality scenarios,

5 the recent air quality and then the rolled

6 back air quality surface.

7 A step that we tried to go, as

8 this was addressed some in the last review,

9 we've tried to expand on in this review, is to

10 use some form of abundance-weighted biomass

11 loss for the analysis.

12 And we think this is a very

13 important step of getting away from just

14 looking at effects on individual species to

15 trying to assess larger effects within

16 ecosystems.

17 In this case we used data from the

18 FIA program with the Forest Service to weight

19 the individual biomass loss values for each 

20 individual tree species.

21 And we did it on a cell-by-cell

22 basis within the CMAQ grid but we were using
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1 the analysis of the actual, where we had

2 calculated the original biomass loss values.

3 We used Federal Class 1 and

4 designated Critical Habitat Areas as the

5 geographic endpoints, not necessarily as

6 anything more than an area that has been

7 designated as important to protect to give us

8 something to analyze as an endpoint.

9 Unfortunately, for this analysis

10 we were restricted to only the eastern species

11 so we only had eight species so this is

12 limiting the data.

13 And because there are so few

14 species, it makes it difficult to really

15 interpret what the absolute biomass loss

16 values meant and how that would relate to the

17 overall ecosystem.

18 So we focused in this analysis

19 specifically on the proportional change

20 between the two air quality scenarios.

21 For this draft we included only an

22 assessment of the Great Smoky Mountains. We
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1 plan to include Rocky Mountain and

2 Sequoia/Kings National Parks for the second

3 draft.

4 For this instead of using the FIA

5 data for abundance value, we used U.S.

6 Geologic Survey and National Park Service

7 vegetation mapping of the National Park.

8 And we used abundance data in the

9 form of cover data and cover estimates of

10 individual species to weight the relative

11 biomass loss values.

12 And so I think it's important to

13 clarify because I think there's two different

14 ways we use this data. In this one we were

15 still focused only on the 11 tree species that

16 we had response functions for.

17 And one thing we're hoping to get

18 a little bit of input on is the ozone exposure

19 estimates for this were still based on the

20 national surface.

21 The 12-kilometer CMAQ grid is a

22 much coarser scale than the actual vegetation
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1 mapping is and so we're hoping to get some

2 input on a better way to get a surface for the

3 parks but that's something we'll talk about

4 later.

5 Visible foliar injury is something

6 we're hoping to look at and we did look at

7 some in this draft but is definitely an

8 incomplete analysis in this draft.

9 We're hoping to include ozone

10 monitoring data from the Forest Health

11 Monitoring program where they have an ozone

12 damage index that they've calculated to a

13 number of points.

14 And we're working with the Forest

15 Service. The data need to be fuzzed at some

16 level for privacy issues and so we're working

17 with them to get as accurate of data as we

18 possibly can to reduce uncertainty within the

19 analysis.

20 In that analysis we're hoping to

21 also include moisture data in a regional scale

22 to try to account for moisture in addition to
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1 the ozone.

2 Within the National Park, we used

3 the National Park's ozone-sensitive species

4 list I believe from the Baltimore conference

5 in 2003 and updated again I believe 2007.

6 For all of the eastern tree

7 species that we know are sensitive, we

8 calculated more of a national or at least the

9 eastern U.S. surface of the sensitive tree

10 species. These are not species necessarily

11 sensitive to biomass loss but sensitive to

12 foliar injury.

13 And then we looked at a finer

14 scale using the cover data within the park of

15 all species that are thought to be sensitive

16 and that goes across tress, shrubs and herbs.

17 And so we're hoping to get some

18 input on the best way to continue that

19 assessment because that does not include ozone

20 data at this point. It's purely cover

21 information about sensitive species.

22 And this draft also includes a
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1 screening-level assessment of visible foliar

2 injury risk in National Parks and that's an

3 update of a study that was done by Kohut in

4 2007.

5 And there are some very

6 interesting things with the study. We think

7 it's a very good way to look through the

8 screening level but we also had some questions

9 that we specifically asked for CASAC's input

10 on how to move forward or update the way that

11 assessment is done.

12 So Chapter 6, we move forward into

13 the Ecosystem Services Assessment. We used

14 the Ecosystem Services Framework based on the

15 Millennium Ecosystem Assessment of 2001 or

16 2000.

17 When we could, we did quantitative

18 assessments of ecosystem services that were

19 mostly based on the biomass loss. Some of

20 that included in addition to the actual

21 biomass loss and how that effects the crop

22 species the timber species.
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1 We also looked at a couple of

2 different ways, at carbon sequestration and

3 pollution removal.

4 But as I mentioned before, a lot

5 of those services we just couldn't do a

6 quantitative assessment. There was just no

7 way to look at incremental changes and impacts

8 on ozone.

9 So for those we did include a

10 qualitative assessment, just hoping to provide

11 some context to the scope and magnitude of the

12 potentially effected services because there's

13 a lot of things that can be effected and

14 there's just not very many that we can

15 actually quantitatively assess at this point.

16 So looking at the services we

17 included, we did, the framework includes

18 regulating services, provisioning services,

19 cultural services and supporting services.

20 The services here marked with an

21 asterisk are things that we were able to look

22 at in this draft, includes the climate
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1 regulation in the form of carbon sequestration

2 and food and timber products in the form of

3 the biomass loss curves that we've talked

4 about.

5 There's a number of other things

6 here, water and fire regulation, net primary

7 productivity, community composition and the

8 cultural services of non-use and recreational

9 value, that we just haven't quantitatively

10 assessed in this and we don't think we'll have

11 a way to probably do that.

12 Net primary productivity and

13 community composition are things that we're

14 looking at potential ways to get at those in

15 the second draft including models like PnET or

16 expanding into some other models using the

17 abundance-weighted data to try to assess

18 community composition changes.

19 On a national scale we use the

20 FASOM greenhouse gas model, the Forest and

21 Agricultural Services Optimization Model22 Greenhouse Gas. That's a lot to say.
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1 That quantifies incremental

2 effects of ozone-induced biomass loss on

3 timber and agricultural markets. It also

4 addresses climate regulation in the form of

5 changes in carbon storage.

6 This assessment used the 11 tree

7 species that we used in Chapter 5 in addition

8 to the agricultural species identified in the

9 ISA and all of those are input into this

10 model.

11 And one important note with this

12 model relative to the i-Tree model which we'll

13 talk about next is that this model accounts

14 for trade-offs between these species.

15 And so species that are included

16 in the FASOM model, we had to come up with

17 proxies for those species if we didn't have a

18 specific response function, otherwise they

19 would be given a beneficial preference in the

20 model as a trade-off of assuming they have no

21 effect from ozone.

22 And so we did ask for the panel's
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1 input on the proxies we used and how we went

2 about doing that because that certainly does

3 effect the output of this model as how we deal

4 with the species that we don't have response

5 functions for.

6 For Urban Case Study areas we used

7 the i-Tree model from the Forest Service and

8 the Forest Service that we've been working, I

9 say run in collaboration with.

10 And this collaboration is they did

11 all the work helping us and they've been very

12 helpful, and we did it in specific areas. 

13 This estimates the ozone-induced biomass loss

14 effects on carbon sequestration and pollution

15 removal in urban systems.

16 We did it so far in five areas,

17 Atlanta, Baltimore and Syracuse, the cities,

18 and then the entire larger Chicago region and

19 then all of the urban areas across Tennessee.

20 These areas were selected in part

21 by where the Forest Service had quantified

22 data for the tree inventories in those cities
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1 to be able to run the model and also of the

2 eight species that we can use which cities

3 actually had enough species to work with.

4 And most of these had 2 or 3

5 species present, accounting for 10 to maybe 20

6 percent of the total trees in the city.

7 And this is where i-Tree is

8 different than FASOM, is that i-Tree does not

9 include trade-offs between these species.

10 So the results from the i-Tree

11 model are based specifically just on changes

12 in those two or three tree species that occur

13 in each of those cities.

14 So some quick summary of some of

15 the key points, very incomplete at this point. 

16 Biomass loss, the magnitude and shape of the

17 actual concentration-response functions varied

18 significantly between species.

19 But looking across all of the

20 species, there was about a 30 percent

21 reduction in biomass loss when the ozone

22 concentrations were reduced to just meeting
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1 the current standard.

2 For visible foliar injury,

3 although we weren't able to do a large

4 quantitative assessment, we did find that in

5 some areas, particularly areas near Atlanta

6 and some other areas in the east, up to 80

7 percent of the tree cover are species that are

8 considered sensitive to visible foliar injury.

9 So there are some areas that are

10 certainly at risk but we have not included

11 ozone exposure into that yet. That's just an

12 assessment of the cover of those individual

13 species.

14 For ecosystem services there's a

15 lot of results in the report on the results of

16 FASOM but on average we did find a reduction

17 in commercial timber of about 5.2 percent of

18 biomass loss based on the exposure inputs.

19 In i-Tree, we're not going to go

20 into exact numbers with the i-Tree but we did

21 get significant reductions in carbon

22 sequestration and pollution removal based on
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1 the response functions of the trees that were

2 input into the model.

3 Some potential updates that will

4 be from the second draft, there will be new

5 model runs for both FASOM and i-Tree for

6 alternate standards and we will have alternate

7 standards, same as in the Health Assessment.

8 We do plan to include uncertainty

9 analysis. That's something we're going to

10 hopefully, as we see exactly what analyses are

11 in this, then we'll be able to determine

12 better what kind of uncertainty analysis we

13 can do.

14 There'll be a more quantitative

15 treatment of both fire risk and bark beetle

16 risk in relation to ozone exposure levels.

17 And we're also exploring using the

18 model PnET to assess changes in net primary

19 productivity and water cycling, and we will be

20 completing the National Park studies.

21 So that is it for now, quickly, so

22 we can move on to something else.
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1 CHAIR SAMET: Okay, thank you. 

2 One question, the last slide, it said

3 potential updates. What does potential mean?

4 DR. SMITH: I think that probably

5 is just, probably not worded correctly. That

6 should just say the updates we're planning for

7 --

8 CHAIR SAMET: So these are

9 definitely updates you're planning to make?

10 DR. SMITH: Other than, I think

11 PnET is the one that we're exploring whether

12 or not we can use that. We're hoping to get

13 some input from the panel on the use of PnET.

14 Certainly we will have more runs

15 of FASOM and i-Tree and we will include an

16 uncertainty analysis. We haven't determined

17 exactly what that's going to look like. And

18 National Parks will get done and the bark

19 beetle and fire risk will get done.

20 These are the updates that will be

21 coming and with the exception of PnET we're

22 certain of them, and PnET we're hoping to get
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1 some input and we're still exploring how best

2 to use that.

3 CHAIR SAMET: Okay, thanks. Do

4 panel members have questions?

5 DR. WEATHERS: I have another

6 question along those lines. I thought I

7 understood that the FIA or FHM data would also

8 be used in this next round, either --

9 DR. SMITH: Yes.

10 DR. WEATHERS: -- synthesized by

11 the Forest Service or by you all.

12 DR. SMITH: I am working with John

13 Coulston at the Forest Service. They have a

14 certain range of the amount of distance that

15 they have to fuzz their data points by.

16 And I'm working with him to see if

17 instead of fuzzing the data if he can actually

18 give me the CMAQ grid cell that it falls in so

19 that way I could reduce it from the standpoint

20 of my analyses or the analyses we do with it.

21 That will at least reduce the

22 uncertainty of making sure that I don't have
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1 a data point assigned to the wrong grid cell

2 that we're assessing it in.

3 But, yes, we are planning to do

4 that and that should be on this slide, that we

5 are planning to include that.

6 CHAIR SAMET: Okay, others? Okay,

7 good. Thank you. Then we'll move on to the

8 public comment session. We have one speaker

9 registered to talk, Jacqueline Patterson on

10 behalf of the NAACP. Let's see, Jacqueline

11 Patterson.

12 DR. NUGENT: Ms. Patterson, are

13 you on the line? We understood you were going

14 to provide comments this afternoon.

15 CHAIR SAMET: It's not 2:00 so we

16 will --

17 (Off microphone discussion.)

18 CHAIR SAMET: My timekeeper says

19 we have to wait till 2:00. So we'll move on

20 to then begin our review and then come back

21 for public comment period. So first, Chapters

22 1, 2, 3. This is Kathy and Peter.
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1 DR. WEATHERS: Okay, thanks. We'd

2 like to do something a little bit different

3 for these introductory chapters, which is

4 we'll address the questions but we'd like to

5 raise some overarching concerns and issues

6 that underpin all of this section.

7 And so I'll start and identify a

8 few and then turn it over to Peter and the

9 rest of the welfare folks if they have

10 something to say.

11 So there are some overarching

12 concerns and one of them has to do with

13 looking at individual species versus

14 aggregated species throughout this document

15 and I think that we'll have a healthy

16 discussion about that.

17 But there are some pros and cons

18 and concerns about aggregation, that species

19 aggregation, and we'd like to hear a little

20 bit more from you about the wisdom and

21 motivation of doing that.

22 I think that Peter Woodbury will
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1 address the issue of not using some of the

2 crop yield data to the extent that they might

3 be used and also how sensitive species versus

4 non-sensitive species are used in this

5 analysis.

6 Another overarching issue that I'm

7 sure will come up in later versions has to do

8 with uncertainty, which you touched on, and

9 especially understanding the methods for

10 fusion, for gap filling.

11 The way in which data are

12 spatially extrapolated or combined I think is

13 not illustrated very well in this document and

14 the devil's in the details here.

15 So in future versions and as part

16 of this discussion, we ought to get some

17 clarity on those analyses and fusion analyses.

18 I asked the question about the FIA

19 and FHM data. We may want to have a little

20 bit longer discussion about how those data

21 will be used moving forward. I think those

22 are the major overarching issues that I
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1 identified.

2 There are some specifics as well

3 that appear in the conceptual model and the

4 introduction as well and throughout and those

5 have to do with all of the figure legends and

6 table legends.

7 I guess I would ask that they're

8 clear and that those figures and tables could

9 stand alone with a figure legend.

10 It's kind of a frustration to be

11 looking at data and data presentations and not

12 be clear about what the legends are. Howie,

13 do you have something to add to that?

14 DR. NEUFELD: Yes, just related to

15 that, many of them are missing units so be

16 sure to have units on there.

17 DR. WEATHERS: So with that said,

18 let me briefly go through my answers to the

19 questions. So these are mostly yes and no

20 questions, which is probably good for you.

21 And I think the introductory and

22 background material, especially background on
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1 the previous materials, is appropriately

2 characterized and useful so I found that

3 introduction useful.

4 The conceptual model is also good. 

5 There are some details that I will pass on in

6 my individual comments that we don't need to

7 go through here.

8 This chapter and a number of other

9 ones also need some copy editing clearly, and

10 the scope seems to be well communicated as

11 well.

12 I was struck actually by looking

13 at the PA. It seems that the PA was much more

14 succinct in some ways than this scope document

15 and where there's overlap I would look to the

16 PA for some clear language.

17 And somewhere, and maybe it's in

18 the last chapter and not here in the scope, it

19 seems that it would be really useful to have

20 a list of data and synthesis needs that would

21 result in a more robust analysis five years

22 from now, so keeping those in the forefront I
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1 think would be quite useful.

2 And, again, if history repeats

3 itself, this, you know, kind of stepping aside

4 or, you know, skipping aside the uncertainty

5 and variability section will probably come

6 back to haunt you. Peter, you want to --

7 DR. WOODBURY: Well, I'll also

8 start with some overall topics that pertain to

9 more than one chapter and more generally to

10 the approach used for the risk analysis.

11 I appreciate all the effort that's

12 gone into developing this welfare risk

13 analysis.

14 I think EPA has made a creative

15 effort to try to look at a lot of potential

16 endpoints and different types of effects of

17 ozone, different types of vegetation and

18 different environments.

19 And I think the use of ecosystem

20 services as a conceptual framework is helpful

21 and it's also timely. To some extent it's a

22 change in language but I think it is a helpful
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1 framework.

2 I think as far as sort of a

3 process comment, there's some efficiencies

4 presumably to be gained by getting our

5 comments on a partial draft as you're doing

6 here.

7 You know, if we give comments that

8 indicate you're headed in a productive

9 direction or in some cases otherwise, that

10 might help you be efficient with your efforts

11 but, again, I, you know, maybe open this up to

12 the rest of the panel for comments on this.

13 I think there's also a risk that

14 you're taking that you may get a lot more

15 helpful comments than you're expecting on the

16 next go around when there's a more complete

17 draft.

18 I'm not quite sure how to phrase

19 this, but I think in trying to sort through

20 the parts that were complete and the parts

21 that were not, I guess I'm trying to elaborate

22 on what I just said.
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1 And I think what I just said is

2 with an incomplete draft it's a little harder

3 as a reviewer to pay complete attention to all

4 of the pieces since it's not always clear

5 which are the most important pieces, which

6 pieces are sort of more finished or less

7 finished. I guess I'll stop there.

8 Another sort of, perhaps just a

9 process comment. You know, I appreciate that

10 the charge questions represent things that are

11 on your mind as you're doing the analysis and

12 areas where we might be helpful.

13 I just wanted to reflect that, you

14 know, they were formulated to elicit a yes or

15 no answer in most cases and they were also in

16 most cases quite narrow.

17 So I did not find them

18 particularly helpful in reviewing this

19 document at this stage. I did my best to

20 respond to them and I have sent those

21 comments.

22 I found there were several issues
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1 that I felt strongly need to be addressed in

2 this document that I didn't feel really were

3 reflected in the charge questions.

4 And I'm going to focus the rest of

5 my kind of overall comments on those and I

6 basically went through my more detailed

7 comments and tried to summarize what I felt

8 were these overarching issues.

9 And I will send you these in

10 addition to the more detailed comments that I

11 sent previously, so I've actually sent them to

12 Angela already for distribution.

13 I think my biggest concern is I

14 feel there needs to be a great deal more

15 attention and a different approach for looking

16 at crop yield loss.

17 As summarized in the ISA, there's

18 very strong evidence going back for decades

19 that ambient ozone exposures in many locations

20 and years are causing significant and

21 substantial yield loss for sensitive crop

22 species. There's also similar evidence for
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1 some tree species.

2 In the jargon of ecosystem

3 services, food and feed production are a

4 critical provisioning service and I think

5 there needs to be at least equal effort and

6 analysis dedicated to crop yield loss as there

7 is currently for forest tree analyses.

8 So for sensitive crops such as

9 soybean, I think they should be addressed as

10 individual species.

11 There's obviously a utility in

12 trying to look at multiple species, but there

13 are sensitive species such as soybean that

14 deserve to be looked at as individual species.

15 I also think it's important to use

16 a different approach than FASOM in the way

17 that it's being used to look at the crop yield

18 loss.

19 And part of my concern about that

20 is I think that it's important to look at

21 higher ozone regions and lower ozone regions

22 separately.
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1 And that's because kind of

2 averaging them together or looking only at the

3 total sort of net economic impact I think

4 actually obscures important real impacts in

5 the real world, real detriments to welfare

6 that are not captured in that approach.

7 So what I mean by that is the

8 FASOM model is trying to look at a sectoral

9 impact, so the entire agriculture sector as

10 well as the forest sector nationally.

11 But actually what happens with

12 ozone exposure for a sensitive crop is that

13 there are winners and losers, so in high ozone

14 regions you have significant and substantial

15 yield loss. In low ozone regions you don't.

16 Now, at a national level when you

17 merge those together you're kind of losing

18 that impact. You're getting only a net

19 impact. So the way I'm looking at it, in a

20 high ozone region farmers are losing income

21 due to crop yield loss.

22 And the FASOM analysis essentially
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1 says, well yes, those farmers are losing

2 income but farmers in another region of the

3 country with lower ozone are getting greater

4 income so it kind of comes out as a net wash

5 if you will, a very small net effect.

6 But I think the farmers in a state

7 or a multi-state region with significant yield

8 losses, I don't think they're going to be very

9 happy that they lost income due to ozone.

10 I don't think they're going to

11 feel like, well, that's okay because somebody

12 in another state made more money, that evens

13 it out. I don't think that's an appropriate

14 way to look at yield loss.

15 DR. BRAIN: Why do the other

16 farmers make more money? Because the cost of

17 crops are higher or --

18 DR. WOODBURY: Well, so the

19 approach in an economic model like FASOM is

20 that if you have lower total crop production,

21 then the price per unit will go up.

22 DR. BRAIN: So it's like the
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1 drought this summer where there are winners as

2 well as some losers.

3 DR. WOODBURY: Right, right.

4 DR. BRAIN: If that's the case,

5 then the consumers are also losing and the

6 food prices are going up.

7 DR. WOODBURY: They may but again

8 that, yes, that impact may, the impact to

9 consumers because the crops physically are

10 mixed together and then sold in a commodities

11 market, you may not have as much of a

12 differential impact on consumers unless

13 they're buying that commodity locally.

14 So I kind of envision that this

15 kind of differential impact might be greater

16 for producers than consumers, at least for

17 these commodities, you know, like soybean that

18 are traded pretty widely, traveling pretty

19 far.

20 For other crops that are sold more

21 locally, you could certainly have an effect

22 for consumers.
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1 And another point could be that if

2 ozone is causing substantial yield loss to a

3 sensitive crop species, you know, something

4 like soybeans is a global commodity.

5 So there could also be a loss of

6 competitive advantage between the U.S. and

7 another country such as Brazil if they're not

8 experiencing any ozone-induced yield losses.

9 Another topic that is on your list

10 for getting more attention is competition

11 among species, so that's on your list and I

12 just wanted to elaborate on that point a

13 little bit.

14 So for forest species in the

15 eastern U.S., in many cases they're growing in

16 mixed species stands so you have species that

17 are more sensitive to ozone growing with

18 species that are less sensitive.

19 They are competing for resources

20 such as light and, you know, it's challenging

21 to model that kind of competition and the

22 effect of ozone on that competition.
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1 But I think it's important to look

2 at this fact, that many of these species are

3 growing in competition with each other. It

4 has a couple of important implications.

5 For one thing, since species are

6 competing, this could exacerbate the impacts

7 of ozone on a sensitive species.

8 So if a sensitive species is

9 growing a few percent less per year but it's

10 competing with something that's less

11 sensitive, that impact could be exaggerated.

12 So it's losing competitive ability

13 and the effect could be even worse than just

14 that few percent at the scale of a whole stand

15 of trees.

16 So that's a way that you can have

17 a greater impact than you would predict just

18 by looking at a seedling exposure-response

19 function.

20 On the other hand, because these

21 species are competing, our modeling work and

22 that of other folks that's in the literature
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1 would suggest that if you sum up the ozone

2 impacts on individual species in a stand, that

3 could be greater than the impact on the total

4 growth of the stand because the species that

5 are less sensitive are going to take advantage

6 of the greater light, et cetera.

7 So in that way you can have less

8 effect on total biomass production in a stand

9 than you would expect. That doesn't mean

10 there are no impacts.

11 If you're eliminating ozone

12 sensitive species, some of those are going to

13 be important commercially, important for

14 wildlife, et cetera, and I realize that, you

15 know, this is a challenging thing to model but

16 I think that issue needs to be addressed.

17 Another sort of broad suggestion

18 I'd like to make is do not average effects

19 among sensitive and less sensitive species.

20 So what I mean by that is for the

21 crop impacts if you just look at total effects

22 on all the crops, the ones that are sensitive
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1 and not, it gives an impression of a fairly

2 small, you know, welfare impact.

3 But if you have major species,

4 again, I'm picking on soybean just because

5 it's so widely grown and so well known to be

6 sensitive to ozone, if you have impacts on

7 soybean, that's important even if there's very

8 little impact on another major crop like corn.

9 So when you combine those effects

10 together, I'm not sure you're really

11 addressing the welfare impacts adequately.

12 Similarly, I would suggest not

13 averaging impacts across high ozone and low

14 ozone regions and I sort of touched on that

15 issue already. But, again, I think it can

16 give a false impression that there's very

17 little impact at the national scale.

18 There's nothing wrong with looking

19 at the average impact at the national scale,

20 but if you have large regions of the country

21 with substantial impacts, you also want to pay

22 attention to those regional impacts.
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1 Another general issue is scaling

2 from tree seedlings to mature trees in

3 forests. This is not a new issue. This has

4 gotten a lot of attention in previous

5 documents.

6 And I know you, you know, thought

7 about this but I do think there needs to be a

8 little bit more attention on that issue in

9 this document.

10 Then the issue of background

11 level. I think we've heard something about

12 this previously and it seems I've heard it in

13 previous meetings as well.

14 This is a perennial issue that's

15 not going away and, again, some clarity about

16 how that's going to be handled here will be

17 helpful.

18 And then my last overall comment

19 is I understand the need to keep this document

20 succinct and that's a good thing. I think

21 referring to the ISA is good.

22 And there are some lengthy
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1 appendices to this document, such as the one

2 about the FASOM modeling.

3 And I think it's important that

4 some of the key assumptions in something like,

5 as an example, the FASOM modeling, some of the

6 key assumptions need to come out of that

7 appendix and go into the risk assessment.

8 So I'm not talking about adding

9 pages but just a few sentences that say this

10 is the model, this is why we used it, these

11 were some critical assumptions that we made

12 and this is why we made them. That's it for

13 my sort of overall comments. I can stop

14 there.

15 CHAIR SAMET: Okay. All right,

16 thank you, Peter. It might have been quicker

17 if we asked you what you liked.

18 DR. WOODBURY: What I liked? Yes.

19 (Laughter)

20 CHAIR SAMET: Just a comment. I

21 mean, I think your comments on the process

22 issues were well taken and I think also the
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1 same issues surface with the Health and Human

2 Exposure Assessment.

3 And I've felt that this is

4 something we need to circle back to in general

5 probably tomorrow for discussion. I think you

6 phrased the issues well.

7 Let me ask if others want to

8 comment. I mean, I think there was a

9 tremendous amount of useful guidance on

10 general issues from what was said and

11 presumably we'll come back to some of those as

12 we go through the specific chapters and come

13 to the end, but let me open up for discussion.

14 DR. HUBBELL: If I could make one

15 clarifying comment on the use of FASOM. Would

16 that be okay?

17 Okay. So FASOM, because a lot of

18 people aren't familiar with these types of

19 broad-scale economic models -- just for

20 background, my Ph.D. is in agricultural

21 economics so I've dealt with a lot of these

22 models over time.
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1 But the key thing to remember in

2 all these and what makes it very difficult in

3 modeling them is that you cannot look at any

4 individual species in isolation in these

5 models because they're based entirely on

6 looking at the trade-offs the farmers make in

7 terms of making decisions about the crops they

8 plant, the acreage they plant to them, whether

9 they plant them to agricultural crops or

10 forest crops.

11 And so you simply can't

12 disentangle them in a very easy way. It

13 doesn't mean there aren't other analyses that

14 could accompany those type of assessments.

15 But the models by their very

16 nature are intended to address the fact that

17 farmers do make these trade-offs.

18 It's equivalent to thinking about

19 a stand competition model in a natural forest. 

20 Here it's guided by human actions, not by

21 natural competition.

22 But it's important to be able to
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1 make sure that those are modeled and

2 understanding how welfare impacts occur for

3 people in different parts of the country.

4 Again, the ability to look at

5 disaggregations by region and so forth, that

6 may be something we can do.

7 But the ability to just split out

8 individual species, especially for crops, is

9 very difficult to do while still reflecting

10 how it actually effects welfare from an

11 economic standpoint which is how we've

12 generally judged agricultural impacts, is

13 through the way they impact agricultural

14 markets. So I just wanted to clarify that

15 those are, they're inherently linked.

16 DR. WOODBURY: Thank you and I

17 appreciate that linkage and I understand the

18 motivation and utility of this model and

19 approach and I realize this has been used for

20 a number of years.

21 But I've had the same objection

22 for a number of years as well, that it's
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1 useful but not adequate, and I'm not

2 suggesting trying to create a new model or

3 disaggregate FASOM.

4 I would suggest in addition some

5 very simple spatial extrapolations to say we

6 know what the seasonal ozone exposure is for

7 a number of years. We have the exposure8 response function.

9 You put those two together and you

10 have an estimate of yield loss for a sensitive

11 species throughout the country.

12 I think there are some of these

13 kinds of analyses that can be done with the

14 existing data. That doesn't have to be

15 complicated.

16 It's not really breaking new

17 ground but it's an additional way to look at

18 crop yield loss that I think is important in

19 addition to the national, you know, that kind

20 of integration that happens with FASOM.

21 You know, I don't know if this is

22 a good analogy and I'm not sure I remember the
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1 correct terminology, but maybe it's an analogy

2 in what came up earlier today with human

3 health where people are avoiding ozone

4 exposure.

5 Well, this is an important

6 behavior that we need to think about, but in

7 terms of a risk analysis, it doesn't mean that

8 we just say, well, people are not going

9 outside so there's therefore no risk.

10 I would say if people are choosing

11 to plant something else instead of soybeans

12 because of yield loss, that's something we

13 want to know about.

14 We don't just want that only to be

15 rolled into a model where it's fairly

16 invisible and pretend that nothing happened.

17 CHAIR SAMET: Okay. Any other

18 comments on these introductory chapters? I

19 will say that Chapter 2 in this document was

20 much more of a conceptual model than Chapter

21 2 in the Human Health and Risk Assessment

22 document. You might look back to it. But it
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1 clearly strove to set out a conceptual model.

2 Let's go back to then the public

3 comment period and let me check if Jacqueline

4 Patterson is here or available by telephone. 

5 Okay.

6 DR. NUGENT: Ms. Patterson, are

7 you on the line?

8 CHAIR SAMET: Okay. All right,

9 then we will keep going to air quality. Back

10 to our air quality team.

11 DR. WOODBURY: Excuse me, Jon. If

12 I could just give one comment on Chapter 2.

13 CHAIR SAMET: Sure, Peter. Sure,

14 sure.

15 DR. WOODBURY: So I liked Chapter

16 2. You know, the question was is it useful? 

17 Yes, it's useful and it should be retained. 

18 I do think in this chapter and elsewhere some

19 more effort can be made on language and we

20 heard this earlier today.

21 There's a lot that's not known

22 about ozone effects but there's a fair amount
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1 that is known and I think the policy analysis

2 had some good, clear phrases of what was known

3 and wasn't known.

4 I think there's room for

5 improvement there as well, but I think it

6 would be helpful to have more clarity about

7 what's relatively well known and what's

8 relatively poorly understood.

9 CHAIR SAMET: Okay, thanks. 

10 Daniel.

11 DR. JACOB: Okay. Yes, so Chapter

12 4 on air quality, there is some significant

13 overlap with a similar chapter in the REA for

14 Health and it's overall very good.

15 I have a bunch of comments but I

16 just want to talk here about the most

17 important ones.

18 The first one is how the fused

19 national scale data were obtained. There's a

20 reference from Wells but more information I

21 think would be useful.

22 To tell the truth, I found the
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1 data to look weird but maybe it's just me. 

2 But when I look at Figure 4-3, I see that W126

3 is more of a problem in Utah than in New

4 Jersey.

5 Well, I'm exaggerating. It's

6 about the same in Utah as in New Jersey. I

7 don't know what goes in there.

8 So my understanding from the

9 discussion that we had this morning was that

10 the fused data is really anchored by all of

11 the network data.

12 But then I don't know what happens

13 over much of the Intermountain West. The

14 Intermountain West, when you look at Figure 4-

15 3, is after California where the action's at

16 in terms of trying to meet the W126 standard.

17 And kind of wonder why that is. 

18 Is that because CMAQ is making a lot of ozone

19 from fires? So we have the hotspot over

20 central Idaho which I understand is going to

21 go away after you use the climatological

22 average fires.
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1 But that brings in another

2 question which is that it seems like air

3 quality models are making too much ozone from

4 fires and it's a point of controversy, but

5 when you talk to a lot of people, that's a

6 general opinion.

7 And in particular in 2007 when we

8 had those raging fires in central Idaho, the

9 eight U.S. stations for ozone didn't see any

10 elevated ozone.

11 I looked at the data for Glacier

12 National Park and the CASTNet sites. You

13 don't see any ozone enhancement from the Idaho

14 fires.

15 And I've done aircraft missions

16 with NASA in which we've looked at plumes from

17 boreal forest fires. We don't see any ozone

18 production from fires.

19 Hanwant Singh published a couple

20 papers recently on looking at the fires in

21 2008 in California and those fires he found

22 produced ozone only when mixed with urban
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1 plumes.

2 It is kind of interesting and it's

3 interesting because the exceptional events

4 that are removed from this map are precisely

5 the 2008 California fire events that when you

6 had elevated ozone it probably wasn't just

7 natural.

8 In any case, there is a lot of

9 concerns here associated with fire effects on

10 ozone. This is an important comment I have

11 for the Policy Assessment because it actually,

12 Chapter 1 goes quite a bit into the fires.

13 But I would suggest paying some

14 attention here. You know, there's a lot of

15 uncertainty about the effect on fires. I'm

16 not saying I have a better solution.

17 But clearly some attention should

18 be paid to the potential for some error

19 associated with the ozone production from

20 fires and how it would impact the W126

21 standard. All right, so that was my point.

22 So in terms of getting some more
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1 confidence in those fused data surfaces, you

2 know, I'd like to know for example were all

3 the ozone monitors really used in constructing

4 them?

5 For example, if the CASTNet

6 monitors were not used, then perhaps they

7 could be used as a verification.

8 And perhaps a comparison of the

9 fused data to observations at places where you

10 didn't use those sites for constructive fuse

11 data now would be very, very useful in getting

12 us confident and in particular in the

13 Intermountain West which seems so interesting

14 in terms of the elevated W126.

15 Okay, I mentioned the exceptional

16 events in California and this was a comment I

17 had for the Health REA too. It's not very

18 clear to me why you removed the exceptional

19 events.

20 You know, it's a little bit like

21 removing the natural background but you don't

22 remove the natural background here. Maybe
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1 some explanation would be useful.

2 The last important point I want to

3 make is with regard to the background and

4 Peter said always the background.

5 Well, here again, you know, the

6 background is very important and you look at

7 the Intermountain West and you wonder how much

8 the background is contributing to this W126.

9 And so since I think there's

10 consensus that you're going to go to HDDM for

11 the next draft of the REA and I certainly

12 think that's the best thing to do, it becomes

13 very compelling to properly characterize the

14 background in CMAQ because you're going to be

15 using the CMAQ background in going to the W126

16 and to meeting the W126 standard.

17 And so this might mean going back

18 to the ISA and having a comparison with the

19 CAMx and GEOS-Chem background calculations

20 that are there.

21 And if you don't want to go back

22 to the ISA, I guess that's fine but then there
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1 should be some discussion of this in the REA

2 chapters, either for the Health REA or the

3 Welfare REA.

4 It's more critical for the Welfare

5 REA than for the Health REA because the

6 Welfare W126 standard is much more sensitive

7 to the background, well, more sensitive to the

8 background.

9 The last point I wanted to make

10 was with regard to the strategy for meeting

11 the W126 standard on the national scale and

12 here it's different from the Health REA where

13 you're just focusing on those 16 urban

14 regions.

15 This is actually something that I

16 did not get from the chapter, that you were

17 going to do this, you would try to meet the

18 W126 standard on a national scale using the

19 fused data and now HDDM.

20 And what I learned from Travis's

21 presentation is that you were going to group

22 the data into large areas and apply HDDM into
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1 individual areas. Well, that makes a lot of

2 sense.

3 I didn't see any documentation of

4 this in the chapter. Maybe it's because it's

5 somewhere else in the report or maybe it's

6 because it needs to be there.

7 DR. SMITH: I was just talking

8 specifically about the rollback surface we

9 used and that's how this particular rollback

10 surface in here was calculated. I believe all

11 of that is in the Wells appendix -- appendix.

12 DR. JACOB: Oh, okay.

13 MS. WESSON: Yes, maybe I can

14 address the myriad of comments.

15 Let's see, exceptional events. So

16 the exclusion of the exceptional events, the

17 only thing that was excluded were the fires in

18 2008 from California and that was a mistake on

19 our part so we will in the second draft

20 include those fires in California.

21 Of course when we are rolling back

22 to simulate just meeting the standard, any
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1 concurred exceptional events would not be

2 included in the rollback but it would be

3 included in the surface that would be going

4 into the analysis.

5 You mentioned the HDDM. So we

6 talked about HDDM with respect to --

7 DR. JACOB: Oh, I'm sorry to

8 interrupt, Karen. But, okay, but it was

9 diagnosed an exceptional event in California. 

10 Nobody bothered about the fires in central

11 Idaho, that they also would be exceptional

12 events.

13 MS. WESSON: Actually they weren't

14 because there was no concurred event, so it

15 has to be concurred by the region and then

16 excluded from the data. The only excluded

17 events were the California fires.

18 (Off microphone discussion.)

19 MR. WELLS: So the reason the

20 exceptional events, the concurred exceptional

21 events, are excluded is when we do our rule

22 makings and then when we compare air quality
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1 data to the standards, we remove exceptional

2 event data from the standards.

3 And so to more accurately reflect,

4 you know, just meeting those standards for the

5 rollback scenarios, we want to remove those

6 data also because that's what we would do with

7 those data in the event that we were actually

8 comparing them to a standard.

9 DR. JACOB: Yes, okay, but what

10 about the hotspots in central Idaho?

11 MR. WELLS: Well, that's not based

12 on any monitoring data. There were no

13 concurred exceptional event data in there

14 because none of the monitors were affected by

15 the wildfires. That was purely a modeling

16 artifact.

17 DR. JACOB: I know, but you're

18 still going to, from my understanding, in

19 trying to meet the W126 standard, then you're

20 going to try to beat down the ozone when there

21 were central fires in Idaho and I'm not sure

22 how you're going to do this but it doesn't --
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1 do you understand my problem?

2 MS. WESSON: So I think this is

3 something that we would want some comment on. 

4 So the way that the surface was done for just

5 meeting the current eight-hour standard, the

6 monitors were adjusted to meet the standard

7 but not all grid cells were adjusted to meet

8 the standard.

9 DR. JACOB: Well, that's correct. 

10 My understanding that for the Health REA you

11 only wanted your 16 urban areas to meet the

12 standard, whereas here you're going after the

13 whole country including central Idaho.

14 MS. WESSON: Well, I think that's

15 a question. So what we have done is all the

16 monitors have been rolled back to meet the

17 standard.

18 But because of the way that the

19 fused surface is done, you can have areas

20 between monitors that are higher than the

21 actual monitor reading so we haven't rolled

22 back those areas, so we haven't rolled back
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1 grid cell by grid cell.

2 What we've done is we've rolled

3 back the monitors. I mean, I guess we were

4 thinking more in line with an attainment

5 demonstration. The monitors need to be in

6 attainment. Those would come down.

7 But, you know, there may be

8 unmonitored areas that may have higher

9 concentrations and that's kind of the, you

10 know, the method we followed.

11 But I think, you know, we would

12 like feedback of whether you think that in

13 simulating just meeting the standard if we

14 need every single grid cell to meet that

15 standard.

16 DR. HUBBELL: For this draft we

17 looked at meeting an eight-hour standard. 

18 Monitors were at the eight-hour standard it

19 was being monitored for.

20 What we're really trying to get

21 comment on is as we look towards W126

22 standards and looking at alternative W126
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1 standards which presumably we're going to want

2 to meet in other places than urban areas, what

3 is the best way for us to target or simulate

4 attaining those standards in places that we're

5 most concerned about or where we would be

6 likely to monitor?

7 And that's really the question, is

8 how do we deal with that design issue of how

9 do we simulate attainment when we don't have

10 monitors and we don't have non-attainment

11 areas?

12 MS. WESSON: And then maybe I can

13 address a couple of your other comments. One

14 of the things you mentioned was the HDDM, so

15 we talked about it with respect to the Health

16 analysis.

17 And what we have done there or

18 what we're proposing to do there is to look at

19 a city and then we would look at the monitors

20 within that urban area and roll back those.

21 And as I was alluding to earlier,

22 there's more challenges to looking at
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1 attainment of, you know, 1,000 monitors across

2 the entire United States because when we look

3 at an urban area and we look at, you know, 10

4 monitors there, we simulate just meeting the

5 current standard.

6 But on a national scale, it's how

7 do you make, I mean, which monitor would you

8 choose to just meet the current standard and

9 then are you over-controlling in other areas?

10 And so the way that we have done

11 the rollback is to group certain sections of

12 monitors together and I think that it's

13 documented in the Wells et al. memo, the exact

14 methodology that we followed as far as

15 grouping those monitors.

16 And then we rolled back those

17 monitors to simulating just meeting the

18 standard and we used the quadratic rollback

19 approach to do that.

20 And I think that, you know, we

21 don't have a good idea of how we would apply

22 HDDM in those cases and how we would apply it
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1 nationally to simulate just meeting the

2 standard given the breadth of the monitors

3 that we're talking about.

4 CHAIR SAMET: So let me ask. We

5 still have two of our signed commenters to go

6 through. Do you have more comments here?

7 DR. JACOB: I guess I don't

8 understand why you couldn't use HDDM in this

9 case in the same way as you do for urban

10 areas. You're doing it for individual

11 regions.

12 MS. WESSON: So, I mean, that's a

13 good point. So what we could do is look at

14 individual regions together and think about

15 applying HDDM. But I think applying the

16 rollback with all the different regions

17 interacting together --

18 DR. JACOB: Not interacting.

19 MS. WESSON: Yes.

20 DR. JACOB: Okay.

21 CHAIR SAMET: Fred, to this point

22 or --
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1 DR. MILLER: Just so the health

2 people can follow a little better, could you

3 please tell me the units of W126 because it's

4 missing from the graphs, the figures.

5 So what are the units? I think

6 it's a 90-day moving average or what, could

7 you just define it for us?

8 DR. SMITH: So W126 as it's

9 calculated here, it's a three-year average. 

10 It has the weighted curve and it is in ppm

11 hours, is the units of that.

12 DR. MILLER: I see.

13 DR. SMITH: And it is at the

14 highest three month, summed three month and

15 then averaged over three years.

16 DR. MILLER: Okay, thank you.

17 CHAIR SAMET: Okay, Ted.

18 DR. RUSSELL: Yes, not much to add

19 from what Daniel said. I actually do think

20 that it might be good to show a little bit

21 more about the different dynamics of ozone in

22 a rural area just since it is different than
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1 what a lot of people are thinking about, so I

2 think somewhere here it would be good to show

3 that and comment on it.

4 I do think also here you have to

5 discuss how well the air quality monitors do

6 at more rural sites because typically

7 everybody talks about how they do at urban

8 sites.

9 So I think that issue has to be

10 addressed and I realize we talked about it

11 right before lunch. I still think there are

12 ways to use HDDM.

13 I mean, doing it by the regional

14 approach or something like that do make sense

15 and I think it's just a matter of maybe some

16 more discussion and thinking about that and I

17 do think it would be a preferable way.

18 CHAIR SAMET: Okay, George.

19 MR. ALLEN: I'll join Daniel in my

20 concerns about rural fire ozone production as

21 noted before. The national scale fused

22 surface -- I think is a pretty good approach.
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1 I'm a little concerned out west

2 where the monitors are sparse and the

3 topography is complex. That's a challenge no

4 matter how you slice and dice it and that's

5 not going to go away.

6 The rollback, we keep talking

7 about that, whether or not you could use HDDM. 

8 It is a resource issue and we did talk offline

9 about it before lunch a bit.

10 If we could figure out a way to do

11 it for this, I mean, the way the current draft

12 reads it says rollback, the quadratic

13 rollback, with a U.S. background as a lower

14 bound.

15 But, yes, certainly if we could

16 work out a way where it's not too resource

17 intensive and put HDDM into this as well that

18 would be great, but I acknowledge that the

19 agency's resources are constrained and so is

20 the time given the schedule that we're looking

21 at.

22 But clearly HDDM, if we can do it
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1 in the context of a national scale domain for

2 the secondary, would be an improvement. 

3 That's it.

4 CHAIR SAMET: Okay, let's see. Do

5 my job. Peter, actually would you mind taking

6 on the comments for Chapters 1 and 3 and then

7 Daniel maybe for this chapter?

8 Let me ask for those of us who are

9 neither Daniel, Ted or George and are a little

10 confused at this point between what you're

11 doing for the human side on air quality and

12 the ecological welfare side, I just -- I think

13 people are going to look at this and get

14 confused, ordinary people, and

15 epidemiologists, the broad mass of humanity.

16 (Laughter)

17 MALE PARTICIPANT: Not the

18 engineers.

19 CHAIR SAMET: Not the engineers. 

20 They understand everything. Would it be -- I

21 mean, I'm just throwing this out.

22 Would it be valuable to have a
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1 table that said sort of here's how we're

2 dealing with air quality on the Human Health

3 Risk Assessment, here's how we're doing it on

4 the Ecological Welfare Assessment?

5 There are differences. I

6 understand that there are reasons that there

7 are differences. You're facing somewhat

8 different challenges and I think that's come

9 up in the discussion.

10 But I think it's going to be quite

11 confusing to people who look at these

12 documents and perhaps in the Policy Assessment

13 because there's going to be sort of underlying

14 different modeling and rollback strategies as

15 I understand the story you're telling.

16 And so, you know, I throw this

17 out. I mean, if you had something up there

18 now that had two halves to it that put the

19 story together, it might make sense to me

20 because, right, we keep hearing the same words

21 over and over but the same model is not being

22 used exactly the same way, so I just throw
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1 that out for discussion.

2 DR. GRANTZ: Would you want that

3 to appear in both the Health and Welfare REAs

4 or --

5 CHAIR SAMET: Let's hear from

6 people. Howie, you wanted to comment here?

7 DR. NEUFELD: Yes, I want to

8 comment on something else after you're done

9 with this.

10 CHAIR SAMET: Okay. Ted.

11 DR. RUSSELL: I was thinking about

12 it and since I won't be here tomorrow I'll

13 just put in my two cents worth.

14 I think that belongs in the PA and

15 that there could be a good integrative summary

16 in the PA that goes into that. You know, you

17 might want parts --

18 CHAIR SAMET: Okay, somewhere. So

19 we might just hold the thought but, I mean, I

20 think, and again this goes back to the

21 integration which you do want to do in the end

22 so.
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1 DR. GRANTZ: Jon, I think one

2 reason to have something like that, whether

3 it's a table or just a paragraph explaining

4 those differences and having it here, would be

5 almost as a goad to integrate them when you

6 could and as much as you could.

7 There are going to be some

8 differences but they've tended maybe to drift

9 apart more than they needed to, and if forced

10 to sit down and align them, that might

11 actually be beneficial in terms of the next

12 draft.

13 DR. BRAIN: And I think if you

14 want to talk about the differences, you should

15 also perhaps begin by the similarities. I

16 mean, ozone is ozone. You'd think that some

17 elements of exposure assessment should be the

18 same.

19 So I think, you know, such a piece

20 should include both, you know, what they do

21 have in common as well as perhaps differences

22 in the way they're treated.
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1 CHAIR SAMET: Good point. So

2 think about it and I think maybe the PA,

3 wherever Ted suggests, would be valuable. So

4 let's open up for discussion on --

5 DR. NEUFELD: I just want to

6 clarify some equations there on Page 4-5. You

7 have the N100.

8 For those of us, you know, doing

9 physiology and stuff, we usually treat this

10 slightly differently. We do N100 is any hour

11 equal to 100 or greater and you have only

12 greater than 100.

13 I know that sounds minor but it

14 can add a lot of hours to the index. So just

15 figure out why you picked that one when most

16 people do it the other way around.

17 And then the SUM06. That's not

18 the definition of SUM06 that I know of. 

19 That's an AOT60 or something. I mean, SUM06

20 is just simply the sum of all the hours, 60

21 parts per billion or greater.

22 But if you subtract 60 off it,
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1 that's an AOT60 just like the European AOT40. 

2 So which SUM06 did you actually use? Did you

3 use the traditional SUM06 or, and just get,

4 you know --

5 (Off microphone discussion.)

6 MR. WELLS: So, yes, you're

7 correct. The way it's defined in here is

8 actually more what you were calling the AOT,

9 in this case it would be AOT60, but we could

10 go back to a more traditional SUM06 in the

11 second draft.

12 DR. NEUFELD: Yes, I mean, they're

13 also highly correlated but this is one I've

14 never seen and everybody is very familiar with

15 the SUM60 so.

16 DR. HUBBELL: I think one of the

17 things we'll need to check is which one was

18 actually used in the Kohut analysis since

19 that's what we were replicating.

20 DR. NEUFELD: Okay, okay.

21 (Off microphone discussion.)

22 DR. NEUFELD: I can't recall off
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1 the top of my mind what Bob used but Bob was

2 doing traditional stuff. It was probably a

3 regular SUM60.

4 MALE PARTICIPANT: SUM06.

5 CHAIR SAMET: Okay. Other

6 comments on Chapter 4? Okay, so why don't we

7 do Chapter 5 and then maybe we'll break. 

8 Kathy.

9 DR. WEATHERS: Okay. I'll make

10 some general comments again. The specific

11 comments will be in the individual comments.

12 And maybe we ought to break this

13 down as the comments about the relative

14 biomass loss to begin with and then go to the

15 foliar injury risk.

16 So in general, I like the approach

17 and I'm pleased to see that it's been vetted

18 in the past as well. That's always a good

19 sign.

20 You had some specific questions

21 for how to analyze and compare the data, which

22 I think all of us might respond to a little
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1 bit differently.

2 It seems to me that aggregating

3 the data is necessary for a national

4 assessment and a national standard but I know,

5 again, some of my colleagues will have a

6 little bit different view of that.

7 So I think combining the 11-tree

8 species and combining the quantitative data

9 that exist is important for extrapolating to

10 a national scale assessment.

11 I think the importance values and

12 using the RBLs with the importance values is

13 really important, again for extrapolating to

14 a national scale assessment. I applaud the

15 use of those kinds of metrics and weighting.

16 And I certainly don't have any

17 recommendations for a wider range of tree

18 species given that there aren't data

19 available.

20 Hopefully the FIA and the forest

21 health data may fill some of those holes but

22 it's awfully difficult to extrapolate when
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1 there aren't data there.

2 I also like the idea of using the

3 Class 1 Critical Habitats for case studies. 

4 I would also consider, I know that somewhere

5 you suggested that you might and rejected it,

6 but some of the coastal areas, Acadia or Cape

7 Cod National Seashore.

8 I like those areas in particular

9 because there are finer-scale data as you've

10 mentioned. You're using those data and you

11 had a question about how to fuse those data.

12 I do see that as a major challenge

13 when you've got CMAQ data at 12 kilometer grid

14 cell size and you've got 30 by 30 or 10 by 10

15 meter data that can't be used unless you can

16 figure out some way to generate ozone data at

17 the same scale.

18 So I think there's some potential

19 there and I would be inclined to exploit that

20 potential for getting a finer spatial scale

21 analysis for these national parks where there

22 are much more data than across the nation.
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1 So that kind of takes us to Number

2 10 with the RBLs and the Class 1 area so those

3 are my overarching comments on those

4 questions. Should we try David or Peter or

5 Helen?

6 CHAIR SAMET: Howie will be next.

7 DR. NEUFELD: Okay, I'm going to

8 just highlight a few, well, actually a lot of

9 my comments here but not go through everything

10 that I've written so you can see that. I

11 agree with much of what Kathleen said.

12 I just bring attention to this one

13 paper by Matyssek. I looked to see if it had

14 been cited but it wasn't, but it's

15 distinguishing between the effective dose and

16 the uptake, you know, essentially trying to

17 get a more mechanistic approach here.

18 Now, there may not be the data to

19 do anything with that but at least I think if

20 one mentions that you're aware of it then we

21 know that at least for future purposes that

22 we're going to try to get better at doing
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1 this.

2 I'm a little concerned about that

3 graph in Chapter 5 of the sensitivity, the C4 R, you know, the concentration-response

5 functions.

6 First off, I think you should say

7 where you got that. I couldn't figure out

8 where you got all those from. Is that listed

9 in there? I mean, maybe I missed it but I

10 couldn't tell where each of the tree species,

11 like where you pulled the data from.

12 (Off microphone discussion.)

13 DR. NEUFELD: And I guess my main

14 concern is looking at the Sugar Maple data. 

15 To me that's an inherently insensitive tree

16 and it looks extremely sensitive in this

17 analysis.

18 I went through and I looked up

19 papers on Sugar Maple and ozone, you know,

20 most of them by Mary Topa.

21 And in the 2001 article that she

22 had in Plant, Cell and Environment she states,
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1 "There's no statistical difference in the dry

2 weight of seedlings in the one times and the

3 1.7 ambient treatments."

4 And many of her effects didn't

5 occur until she got to three times ambient, so

6 I just don't understand. Based on that, it

7 sounds like it's very insensitive and then

8 here it sounds like it's very sensitive.

9 DR. SMITH: The preliminary

10 comments, I did look up that paper and in that

11 figure Sugar Maple is actually very

12 insensitive until over a W126 of about 20 to

13 25 ppm.

14 And so without looking, the

15 ambient concentrations in a lot of the places

16 are 10 to 15.

17 And so it is quite possible that

18 that's exactly, but once it does get to the,

19 kind of, that's one of the species that kind

20 of seem to behave almost like a threshold

21 where it was very insensitive and then there

22 was a rapid, a very small area of W126
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1 exposure which it becomes very sensitive.

2 I looked at that paper and it

3 wasn't in the same metrics and I didn't have

4 time to figure out exactly what metrics level,

5 how those related to a W126.

6 But I don't know that it's

7 completely inconsistent with that because the

8 level where it does become very sensitive is

9 actually above 20 to 25 and so that species

10 does behave very interestingly in that figure.

11 DR. NEUFELD: Okay. And it's just

12 amazing I think after 50 years of work that we

13 don't have more, you know, response functions. 

14 It just tells you something about, you know,

15 what's been going on, you know, funding and

16 everything.

17 But did you mine the literature

18 for -- I mean, what were the criteria for

19 determining whether you could include them in

20 this graph?

21 DR. SMITH: From this graph I

22 pulled only from the ISA. They only came from
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1 the ISA and so I did not bring any species

2 that were not listed in the ISA.

3 And so I would have to defer to

4 Jeff and Chris in the back of the room to

5 explain exactly all of their criteria. Most

6 of these species were in the last review,

7 except for cottonwood.

8 DR. NEUFELD: Yes. Maybe I'm

9 thinking if you go back further there are

10 other species possibly that could be mined but

11 maybe there are criteria you have that

12 preclude that.

13 (Off microphone discussion.)

14 MR. HERRICK: Calculate the W126s

15 for it. So these are all the species that we

16 have W126 concentration-response functions

17 for.

18 So there might be others out there

19 but we don't have access to the hourly data so

20 we can't consistently look at them across

21 different species.

22 DR. NEUFELD: So like some of
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1 those earlier studies would just have the

2 total sum at the end of the experiment but

3 didn't tell you the hourly values.

4 MR. HERRICK: Yes, they might have

5 did an average. People in their publications

6 didn't consistently calculate W126 and so we

7 made an effort in the ISA to actually contact

8 authors and we hardly got any.

9 Just a lot of the studies were so

10 old that people didn't have their hourly data

11 anymore so we weren't able to construct more,

12 so this is sort of the best we can do.

13 I mean, we got it from the Gregg

14 study and we got the FACE hourly data but that

15 was about it.

16 CHAIR SAMET: I think an important

17 question here is if somebody went to the ISA,

18 and I can't remember what's there, would they

19 see what you just said set out in a

20 sufficiently transparent way on --

21 MALE PARTICIPANT: I can't hear

22 from over here.
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1 CHAIR SAMET: So my question was

2 and, you know, simply maybe we can look at

3 this again.

4 But if you went to the ISA and

5 tried to understand the search strategy to

6 identify these concentration-response

7 relationships, would you find a transparent

8 exposition of how you came to identify them in

9 a way that could be replicated?

10 MR. HERRICK: Yes. You would find

11 that we cited a report by Henry Lee et al.

12 from EPA who did this for the, I mean, these

13 were put together in 1996 for the 1996 review. 

14 So these are the same exact response functions

15 that we used in 1996 and in 2006 and now.

16 The only thing that's been added

17 is the cottonwood and that's what we added in

18 the last ISA so everything is documented

19 through those reports.

20 DR. NEUFELD: Okay. I just want

21 to emphasize what others have said before,

22 that combining all the analyses of the trees
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1 into one analysis I think puts too much weight

2 on the insensitive species and obscures the

3 sensitive ones.

4 Let's see. If we go to the other

5 alternative geographic areas that could be

6 used, I had mentioned in here a couple of

7 national parks and also the western portion of

8 Pennsylvania there in that -- which is known

9 to be in the high ozone area and also has many

10 of the species that are sensitive to ozone in

11 that area.

12 It's the premier area for growing

13 black cherry, for example, so that would be a

14 good site to concentrate on.

15 The other one I wanted to talk

16 about was if we go to the, let's see which

17 question is it? This would be 11c. This is

18 getting into the Kohut analyses.

19 The REA and the PA, you know, go

20 to great lengths to justify using the

21 cumulative indices such as the W126. And

22 here, based on this analysis, you can move a
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1 park down in its sensitivity category whether

2 or not the N100 is exceeded, solely on that

3 basis.

4 So you could -- like I put here in

5 my comments, you know, you could have fairly

6 high ozone all the way from 60 to 99, or in

7 your case up to 100 actually because it's the

8 way you defined it, and that's going to cause

9 significant biological impacts and that would

10 make the park still at high risk.

11 So I'm wondering, you know, if

12 there's some way of adjusting the way that you

13 did this.

14 And there's also an inverse

15 relationship sometimes between soil, moisture

16 and ozone exposure so you could be at high

17 risk with lower ozone or you could have years

18 that don't have peaks above 100 but still have

19 lots of ozone.

20 So I was suggesting maybe some

21 sort of three-dimensional space if that's

22 possible. I think it's more realistic. It's
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1 what the plants are actually experiencing in

2 terms of their risk.

3 And the last part for this is

4 concerned about, this is question 12b, the

5 appropriateness of the mapping with the

6 understory species, I think is what you're

7 asking in this point.

8 So the understory species, of

9 course, in a dense cove hardwood forest are

10 going to experience much less ozone than the

11 canopy trees so they're not as high risk in

12 that sense.

13 And also there's lower deposition,

14 lower wind speeds, a whole bunch of things

15 which would probably make them less sensitive. 

16 It doesn't mean injury doesn't occur. I mean,

17 we've seen it.

18 But I think if there's a way of

19 taking that into account I think it's just

20 more realistic and I think if you could try to

21 do that.

22 Also I want to also get away from
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1 the idea, I've heard it mentioned several

2 times here, that plants in the understory have

3 better water, moisture conditions, relations

4 and stuff like that.

5 In fact, it could be just the

6 opposite. There's a tremendous amount of

7 competition, root competition, in the

8 understory.

9 And studies have shown, especially

10 at like the Duke Forest here, that when you

11 trench the forest floor, you get an abundance

12 of growth because you've cut off the

13 competition for water from the adult trees.

14 So, in fact, understory herbs may

15 be under more water stress, not less, and that

16 could also effect how they react. So those

17 are my major comments and then the rest of it

18 is --

19 DR. HUBBELL: Can I ask for a

20 clarification on the Kohut analysis that we

21 did, the screen-level assessment?

22 DR. NEUFELD: Yes, yes.
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1 DR. HUBBELL: One of the struggles

2 we're trying to work through is how important

3 it is to stick to the metrics used by the

4 primary author in trying to replicate an

5 existing analysis versus should we develop a

6 new screening analysis methodology that

7 incorporates your comments.

8 And I think we're comfortable

9 going either direction but we wanted to make

10 sure that you understood our motivation was to

11 try to replicate an existing analysis and

12 therefore we kind of were stuck with the

13 assumptions made.

14 DR. NEUFELD: Yes, I guess I was

15 just a little concerned that if you make it

16 all dependent on that one metric, you know,

17 like where you have to have both indices to be

18 classified as high, that you are categorizing

19 a park then as less sensitive when, in fact,

20 it's still sensitive.

21 So I just felt it was too coarse

22 of a cutoff so I was thinking if you could
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1 just refine that.

2 I still think the ideas, you know,

3 essentially soil, moisture and ozone are the

4 same but I think that the cutoffs are too

5 abrupt.

6 DR. HUBBELL: Okay, so the answer

7 then, if I'm interpreting correctly, is it's

8 okay with you as a reviewer if we modify that

9 Kohut model without putting a whole new peer

10 reviewed publication into the ISA? That's

11 what --

12 DR. NEUFELD: Yes, because I think

13 the spirit is still the same and the

14 underlying bases are the same. You're just

15 redefining the thresholds for putting them

16 into the different categories.

17 DR. HUBBELL: Okay, thank you.

18 DR. WOODBURY: I'd like to chime

19 in on that briefly. What you need are

20 essentially the exposure-response function for

21 the foliar injury.

22 I mean, I don't know if those
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1 levels were chosen because there were data,

2 you know, using those levels to identify

3 whether, you know, foliar injury would or

4 would not occur.

5 So I don't remember the exact

6 reason that Bob Kohut chose those levels, but

7 if you have data to support any other

8 quantitative level I think that's fine.

9 DR. GRANTZ: Is your question

10 whether to keep the two standards, so you have

11 to meet the W126 and the N100 standard?

12 DR. HUBBELL: The question was

13 simply that, you know, the Kohut analysis

14 identified certain benchmarks, certain

15 thresholds that were then used to define

16 whether an area was sensitive or not sensitive

17 or at risk or not at risk.

18 And the question was should we

19 incorporate, as you said, take the spirit of

20 the model and incorporate additional

21 information to update it or do we need to

22 stick with what was used there and simply
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1 report the results of applying it to new air

2 quality?

3 And my understanding is that

4 you've provided comments that will allow us to

5 update the model and make it more respective

6 of current science.

7 DR. GRANTZ: Yes, and I would

8 agree with that too.

9 CHAIR SAMET: Okay, Dave.

10 DR. GRANTZ: Yes, I don't think

11 I'm going to go through my step-by-step

12 comments that are written down.

13 But the big overarching issue here

14 for me is how you're going to aggregate

15 species in different areas and whatnot.

16 From a policy perspective, I think

17 it is really important, and I agree with Kathy

18 on this, that there has to be some aggregation

19 so that you're carrying forward a few or

20 single impacts that can be sort of readily

21 interpreted. The risks of that are obvious.

22 So summary one would be that you
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1 really need to lay out as you do it why you're

2 going which direction. You know, why are we

3 aggregating here? Why are we not aggregating

4 here?

5 You know, in my individual

6 comments I make the suggestion that, and it's

7 actually question 8c I think. You suggest

8 that we use the most sensitive species and

9 then carry that sensitivity relationship

10 forward.

11 And I put in my comments that I

12 think that's probably not appropriate because

13 it's not defensible. It makes the entire

14 system seem more sensitive than we might be

15 able to defend.

16 However, we've been having

17 discussions over here the last couple of days

18 and I'm not sure that I still maintain that

19 position, although it has some merit.

20 The problem with that is it

21 doesn't protect the most sensitive species and

22 that can be catastrophic for a number of
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1 reasons that I'm sure you appreciate.

2 So I'm not sure what I'm

3 recommending the right way to go is. I think

4 at this point, being that this is, you know,

5 Version 0.5, you at the very least need to lay

6 out the choices that you've made and why

7 you've made them and what the consequences

8 are, you know, not in 200 pages.

9 But I think that's a really key

10 thing that you just can't finesse. It's

11 really important whether you aggregate or you

12 don't aggregate.

13 And I still maintain that you need

14 some key individual benchmarks that you carry

15 forward or endpoints that you carry forward. 

16 Okay, now, Howie, you want to --

17 DR. NEUFELD: I just wanted to ask

18 you a question, I think maybe Travis or Lydia

19 or Bryan, anyone.

20 So what is the EPA obligated to

21 do, say, if faced with a situation in this

22 ecological context where you have a sensitive
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1 species and if you then aggregate species and

2 you set a higher standard based on that

3 average and it fails to protect that sensitive

4 species which then may be at risk to, say, be

5 extirpated from an area or even go instinct?

6 I give you as an example the

7 recent birch species that was discovered by

8 Virginia Tech researchers where at one point

9 there were maybe 20 trees for the entire

10 species.

11 And birch is known to be sensitive

12 to ozone and if you set it higher and that

13 species was at risk and then those 20 trees

14 died out the whole species would be gone.

15 How does the EPA deal with a

16 situation like that? It's an easy question to

17 answer.

18 MS. WEGMAN: Yes, I was going to

19 say I don't have any simple answer for you on

20 that one. You know, we have to look at the

21 data we have, you know, all the data we have,

22 and see what we are protecting.
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1 You know, when we look at people

2 from the health side or we're looking at

3 obviously just one species but we look at

4 protecting everybody, including sensitive sub5 populations or at-risk populations as we call

6 them now.

7 We don't have to protect every

8 individual but we do have to look at sensitive

9 populations as well as the general population.

10 So if you were to analogize that,

11 we would be trying to protect sensitive

12 species as well as a broad array of species

13 but perhaps not every single individual member

14 of the species.

15 So I don't know if that analogy

16 works so well in the welfare context but

17 that's, you know, an initial way to think

18 about it.

19 DR. HUBBELL: One other thing on

20 that, I'll just elaborate a little bit, is the

21 language used for the primary standards and

22 the secondary standards is different.
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1 And it talks about adverse effects

2 on public welfare and deciding whether

3 protecting an individual species is protecting

4 public welfare. That's something that we're

5 wrestling with.

6 That's one of the reasons why

7 we're moving towards an ecosystem services

8 oriented framework is to try to address

9 impacts on public welfare rather than trying

10 to focus on individual species or individual

11 locations and species.

12 That said, as Travis pointed out,

13 we've now started also trying to identify case

14 study areas that are of specific interest,

15 that have been identified as needing

16 protection, like Federal Class 1 areas or

17 Critical Habitat Areas.

18 So technically if a birch tree was

19 in a Critical Habitat Area for birch trees, we

20 would identify it as something that we would

21 want to protect.

22 MS. WEGMAN: Maybe.
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1 DR. HUBBELL: Maybe, I'm sorry.

2 MS. WEGMAN: Yes, and the other

3 thing of course we're always trying to define

4 is what is adversity? What constitutes

5 adversity and is it adverse if we lose the

6 last 20 birch trees of a specific kind of

7 birch?

8 DR. NEUFELD: Yes, and not to

9 complicate things and I don't want to drag it

10 on but --

11 MS. WEGMAN: I should say adverse

12 to public welfare.

13 DR. NEUFELD: Yes, and if you do

14 set this risk analysis at a point where a

15 species has the potential, and I'm just saying

16 potential, doesn't mean it's going to happen

17 but, to go extinct does that become in

18 violation then of the Endangered Species Act?

19 MS. WEGMAN: That's another

20 question entirely.

21 DR. NEUFELD: Yes.

22 CHAIR SAMET: So we haven't heard
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1 from Peter yet. We have two, are these on

2 this point then for --

3 DR. MILLER: Yes, it is.

4 CHAIR SAMET: Okay, go ahead.

5 DR. MILLER: If you're talking

6 about 5-1 and talking about aggregate or not

7 aggregate, there are a number of statistical

8 issues that you better take account of.

9 These curves clearly show that you

10 do not have parallel line assay and so the

11 combination of things in terms of slope ratio

12 assay and weighted variances and all of this

13 stuff has got to be totally taken into

14 account.

15 And it's no easy job and the

16 answer will depend on each delta down that the

17 answer will change.

18 So those of you that want them to

19 aggregate, realize you're talking about mixing

20 apples and oranges in the hope you get fruit

21 cocktail. It's a tough one.

22 DR. SMITH: I have one
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1 clarification on the aggregation, is in

2 putting that in the idea was not to replace

3 looking at individual species. It was as an

4 additional way to summarize.

5 I mean, I didn't intend that as a

6 way to, we're not going to talk about

7 individual species. It was just a way to,

8 here are all the species and what can we say

9 about this as a whole across the country?

10 So I want to make sure it's clear

11 that that was intended to be a comment not a

12 question.

13 DR. GRANTZ: My comment was

14 intended to encourage you in that regard. I

15 think that is important for regulatory and for

16 public understanding reasons. I think that's

17 really quite important.

18 CHAIR SAMET: Okay, Joe.

19 DR. BRAIN: I want to know what we

20 say about evolution in relation to sensitive

21 species because even if it's sensitive within

22 that species there's variation in
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1 susceptibility.

2 And with slow changes in ozone,

3 isn't it likely that more resistant strains

4 will inevitably take over? Is that an

5 important consideration and is it dealt with

6 adequately?

7 DR. NEUFELD: I'm trying to

8 remember. I think it was discussed somewhat

9 in the ISA. There are some studies that show

10 within just one year changes in genotype

11 frequencies after high ozone years.

12 But there's some controversy with

13 those studies because it's also confounded

14 with drought. But there is some evidence that

15 you can have rapid shifts in population due to

16 selection by ozone.

17 DR. BRAIN: It is different than

18 people. We have huge numbers, shorter

19 generation times. One could expect rapid

20 adaptations given enough diversity.

21 DR. NEUFELD: But we have the

22 paradox that we've been looking at these
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1 things now for 60 years and the sensitive

2 species are still there so the only

3 explanation --

4 DR. BRAIN: I mean, maybe that's

5 worth commenting on, that the sensitivity of

6 a particular species has been relatively

7 stable, you haven't seen drift in response in

8 the higher ozone levels or lower ozone levels.

9 DR. NEUFELD: When there have been

10 controlled studies, they've shown directional

11 selection. But in the field what may be

12 happening is that ozone is fairly stochastic. 

13 Correct me if I'm wrong because you guys are

14 much more up on the year-to-year variation.

15 So you may have directional

16 selection for a few years in high ozone and

17 then you have a period of respite where you

18 have low ozone and the sensitive individuals

19 come back.

20 And so as this oscillates, you

21 know, it keeps them in the population as

22 opposed to completely eliminating them.
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1 DR. SMITH: We are also dealing

2 with, some of the tree species we're talking

3 about short, some of the tree species actually

4 do have very long generation times.

5 And though you may have seedlings

6 one year, the next year it's a different, the

7 seedlings are coming from the same parent tree

8 and so you can get rapid shifts back and forth

9 with that.

10 CHAIR SAMET: Okay, so why don't

11 we go on and hear from Peter? I think he

12 wanted to speak anyway.

13 DR. WOODBURY: Just one last

14 comment on that topic. I think the challenge

15 again is what's an adverse welfare effect? 

16 And I think that's a general issue.

17 You know, this is a challenge you

18 all are well aware of and I think that is a

19 challenge for the ecosystem services framework

20 and you've acknowledged that.

21 There are a lot of linkages, there

22 are a lot of conceptual models that have
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1 substantial support to say here's a chain of

2 events that seems likely. There seems likely

3 to be an influence of ozone.

4 But once you get into those causal

5 chains, it's very challenging to be

6 quantitative with them and, you know, I think

7 this question of, you know, essentially

8 evolutionary selection by ozone is another one

9 of those.

10 CHAIR SAMET: Dr. Grantz.

11 DR. GRANTZ: I wanted to basically

12 finish what I was going to say about Chapter

13 5 if I could.

14 CHAIR SAMET: Oh, okay.

15 DR. GRANTZ: Thanks.

16 CHAIR SAMET: We wandered.

17 DR. GRANTZ: Yes, that was good. 

18 It was a good discussion. But there was a

19 question about this new emphasis on ecosystem

20 services and I wanted to put my two cents in

21 in favor of that.

22 I think that's very important in
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1 terms of bringing back some of these esoteric

2 concepts to humans. I think that's very

3 important.

4 But following on from that, my

5 reading of Chapters 5 and 6 was just a slog,

6 you know? I thought it was incredible

7 repetition and bringing up something and then

8 analyzing it later.

9 And I wondered if, A, those two

10 could be brought together a little bit, even

11 the chapter titles. One is Ecological

12 Effects. The other is Risks to Ecosystem

13 Services.

14 It seems to me that ecosystem

15 services could give more relevance to

16 ecological effects if they were all brought

17 together because each of those services comes

18 from some ecological impact.

19 And if that was just laid out

20 concept by concept, taking it all the way

21 through to what you're going to do with it

22 risk-wise, I think it would be, A, easier to
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1 read and a lot easier to understand.

2 And it would give more relevance

3 to some of the things that you kind of throw

4 out there even though in some cases you're not

5 going to come to a quantitative risk

6 assessment anyway.

7 But you might as well do it right

8 at the time that you bring it up because then

9 it's clear why you're not going to be

10 quantitative about it and it would also

11 shorten this thing a fair bit I think.

12 CHAIR SAMET: Okay, thanks. 

13 Anything else on the chapter?

14 DR. GRANTZ: No, that's all I

15 have.

16 CHAIR SAMET: Okay. So, Peter,

17 comments on Chapter 5.

18 DR. WOODBURY: The reason I'm

19 hesitating is since I spent quite a bit of

20 time on my general comments, I'm quickly

21 trying to see if there's anything that I

22 haven't already covered in here.
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1 DR. WEATHERS: While Peter's doing

2 that, may I ask a question of the EPA, which

3 is focusing on the Class 1 areas makes a lot

4 of sense as I said before.

5 But is it a challenge at all given

6 that many of them have complex and

7 heterogeneous terrain, they have sensitive

8 landscapes, if not species?

9 Does it present a policy challenge

10 if those are the focal areas, or is it a

11 policy advantage because they have to be

12 protected? You know, they're anomalous in

13 some ways.

14 DR. SMITH: Also the availability

15 of ozone monitors.

16 DR. HUBBELL: Yes, there are

17 benefits in terms of some of them have ozone

18 monitors in the parks which is a benefit. It

19 is also a benefit that they have been

20 identified as needing protection.

21 The question of the heterogeneity

22 within the systems and the uniqueness of them,
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1 yes, I'll let Travis discuss whether or not

2 that's a problem or not.

3 But it's certainly something that

4 we wouldn't want to necessarily extrapolate

5 from them to make a statement to that other

6 systems but there are uniquely sensitive

7 systems in some cases that may be parts of the

8 sensitive populations we're interested in

9 trying to protect.

10 But, again, you know, that's

11 something that we can certainly discuss, how

12 to characterize uncertainties there.

13 DR. SMITH: We used the same CMAQ

14 data so it was the same 12 by 12 grids. And

15 so the fact that they are places, like

16 Linville Gorge is one of the areas.

17 And that's certainly an area that

18 I don't know the ozone exactly there but I'm

19 sure that it doesn't match exactly what the

20 CMAQ grid says when you look across the

21 terrain in the gorge.

22 And that's a problem with any of
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1 them. That is what we have. In some of them

2 they're very small and so we're limited. 

3 There's only two or three CMAQ grid cells that

4 actually intersect it.

5 The ozone exposure is a problem in

6 those in that we don't have a way to get into

7 a finer scale than the CMAQ grid, at least for

8 this draft and I don't see a simple way of

9 getting past that for the next draft.

10 And so that is an issue and I

11 think it may be something I need to make sure

12 to be clear that that is a known issue with

13 using those but we're not going to have a way

14 to work with those other than to acknowledge

15 it.

16 CHAIR SAMET: Okay, let's see. 

17 Peter, anything else to add or?

18 DR. WOODBURY: Yes, I'm not going

19 to go through my detailed comments except for

20 just one topic on Table 5-7.

21 And this goes back to the kind of

22 modification of the Kohut risk analysis. I
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1 did want to make a couple of comments on this

2 topic.

3 One, in the table the moderate

4 risk class is not included in this table and

5 I wasn't sure if there was a reason for that. 

6 I think three classes is fairly few and two is

7 fewer.

8 MS. LAMSON: Sure, I can quickly

9 clarify that for you. So this table -- oh,

10 I'm sorry. Hi, I'm Amy Lamson. I did the

11 Kohut analysis.

12 So this table was just showing the

13 higher and lower risk class. The main

14 difference between the moderate and the high

15 rating is the number of years that meet the

16 criteria, so I'm more than happy to add a note

17 to clarify that.

18 So for example, if at least three

19 years met the criteria, then that was

20 classified as high risk. If only one or two

21 years, that was classified as moderate risk.

22 DR. WOODBURY: So I'm still not
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1 hearing a justification for going from three

2 classes to two. I understand what you just

3 said.

4 MALE PARTICIPANT: Why don't you

5 have moderates in there?

6 MS. LAMSON: Because the

7 differential is just something that's not

8 arrayed on this table but I'm happy to add

9 that if that's the recommendation from this

10 panel.

11 DR. HUBBELL: It was actually one

12 of the classes it just wasn't in this one.

13 DR. WOODBURY: Okay, and I guess

14 another comment on this analysis which would

15 be more effort but in general I'm in favor of

16 trying to be quantitative whenever possible.

17 So, you know, I think the Kohut

18 analysis was, you know, trying to look at a

19 lot of parks, some without any exposure data

20 in some kind of tractable approach and I

21 understand wanting to be tractable.

22 But essentially, to the extent
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1 that the underlying data or response

2 relationship supports a quantitative, you

3 know, a number here rather than just a high,

4 medium and low, that would be helpful.

5 And I don't have an opinion right

6 now of, you know, whether the foliar injury

7 data would support that.

8 In other words, do you really

9 have, you know, exposure-response functions

10 that would allow you to say this fraction of,

11 or this is the degree of foliar injury you'd

12 expect to get?

13 But if it's possible to do it

14 quantitatively, that is more informative than

15 these risk classes, so that's an alternative

16 to saying if it's possible to do it that way

17 then you don't need these, you know, somewhat

18 artificial high, medium and low.

19 I'm sorry if that was, I feel like

20 I was kind of rambling there. Does that make

21 sense?

22 DR. HUBBELL: No, I understand. I
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1 just can't remember. We've had conversations

2 about the foliar damage data. We're hoping to

3 get the Forest Health Monitoring program and

4 we've talked about doing some analysis with

5 that using that data.

6 DR. SMITH: No, we're still

7 planning that. We are planning to look at

8 that and that might be something that would

9 help inform what you're talking about for this

10 and I would be very happy if it did.

11 DR. HUBBELL: And one thing that

12 comes up with that as you're probably well

13 aware is any time you do something like that

14 that's a new analysis.

15 It's essentially a research effort

16 and there's questions that come about about

17 what's the proper way to hit that, you know,

18 review in a way we can then use?

19 It's a delicate balance between

20 developing a risk analysis methodology and

21 developing a new concentration-response

22 function which we then use.
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1 So we do have a balancing act

2 about how to introduce new information into

3 our assessments.

4 CHAIR SAMET: Okay, anything else? 

5 I'm going to propose a break. You're past

6 time for a break here. So why don't we break

7 for 15 minutes.

8 (Whereupon, the above-entitled

9 matter went off the record at 3:03 p.m. and

10 resumed at 3:21 p.m.)

11 CHAIR SAMET: So are up to Chapter

12 6. And actually, Kathy, on Chapter 5, would

13 you be the summarizer for the comments? Okay,

14 thanks.

15 Chapter 6, Ecosystem Services. 

16 Dave?

17 Dave, are you ready to -- you're

18 the first commenter on this, on Chapter 6.

19 DR. GRANTZ: I am?

20 CHAIR SAMET: You are, but you can

21 concede to Peter if you want. Would you like

22 to be last?
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1 DR. GRANTZ: No, I just have one

2 real comment that I wanted to make. There's

3 a question, 15, I guess, how appropriate it is

4 to link things that are sort of secondary to

5 ozone. And in California that's bark beetles,

6 but there are other things. And I think

7 that's just very appropriate.

8 And I think to the extent that you

9 can tease out some sort of a fractional or

10 approximate fractional impact of ozone on

11 susceptibility to these other things, I think

12 that is just very appropriate. Now obviously

13 you're not going to have exact quantitative

14 numbers for that, but I do think we can make

15 some estimates given drought intensity and

16 ozone levels in different places that are

17 seeing bark beetle infestations, for example. 

18 You can start to make at least qualitative

19 assessments of the risk that's associated with

20 having ozone as opposed to not having ozone. 

21 So I think in that sense you're on the right

22 track, and so I support that strongly.
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1 CHAIR SAMET: That's it?

2 DR. GRANTZ: I think that's the

3 key point, yes.

4 CHAIR SAMET: Okay, Peter?

5 DR. WOODBURY: So I'm not going to

6 go through all the details, but there's a

7 question, 16B, about extrapolation of

8 concentration response, functions across

9 similar species. I think, yes, this can be

10 done but it needs to based on, you know,

11 physiological factors. And it looked to me,

12 if I understood correctly, in the FASOM

13 appendix that it may have done on kind of

14 economic groupings. So I think sugar beets

15 was in with sorghum and, you know, sugar cane

16 and some other sugar crops, if I read that

17 correctly. So the point would just be, it

18 needs to be done on a sort of physiological

19 basis.

20 DR. HUBBELL: If we could clarify

21 how we did it, because I'd like to be sure

22 that we --
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1 MR. ZHANG: Hi. My name is Yuquan

2 Zhang, who did the FASOM, just your modeling

3 for this ozone impacts work. That's a very

4 good question that why we use the proxy crops

5 and mapping them to the FASOM, just your

6 crops. Basically it's because the limited

7 data. And so in order to incorporate as much

8 information into FASOM model, we have to use

9 the crop mapping. And why we use sorghum to

10 sugar beets, those mapping are based on many

11 previous experiences of FASOM modeling when we

12 were doing the national assessment about

13 climate change. When we don't have

14 information for each crop we have to use some

15 crop mapping.

16 DR. HUBBELL: And were they mapped

17 based on physiological characteristics?

18 MR. ZHANG: Largely they are,

19 although just like you mentioned, I think that

20 mapping sugar beets for sorghum may not make

21 as much sense physiologically, but we can

22 reconsider that issue and then use a better
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1 proxy.

2 DR. HUBBELL: One question for

3 you, Dr. Woodbury, is if there are particular

4 characteristics that you would recommend us to

5 match on would be very helpful.

6 DR. WOODBURY: Yes. I mean

7 there's plenty of literature on this. There

8 is clearly a difference between C3 and C4

9 species. You don't want to mix those together

10 under any circumstances, so that's why that

11 one jumped out to me. Again, let me just

12 emphasize the general point that it's not

13 appropriate to group them based on economics

14 or other criteria. You have to pay attention

15 to some, you know, physiological expectation

16 that there may be a similar ozone response. 

17 So again, beyond the C3 and C4, I don't know

18 if any of my colleagues want to suggest any

19 other guidelines.

20 I mean I guess I would say with,

21 you know, perhaps this is just brainstorming,

22 but if you can come up with some physiological
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1 groupings that do make sense and then look at

2 the data that you have for species within

3 those groupings, come up with sort of an

4 average response function among two or three

5 or four species in a group, you know, that

6 might be a defensible approach that would kind

7 of combine, using the data you have but paying

8 attention to the major differences in

9 physiology.

10 DR. HUBBELL: For tree species we

11 attempted to do this many years ago, like

12 five, six, seven years ago. We attempted to

13 do this based on photosynthesis rates and a

14 few other characteristics for trees, and then

15 we clustered them and tried to do that. They

16 did okay, but it was difficult to find the

17 kind strong patterns that we would have liked

18 to be able to support those groupings. So

19 again, this is helpful to get the information

20 you have, and we'll certainly try to find some

21 other characteristics.

22 MR. ZHANG: Thank you so much.
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1 DR. NEUFELD: I just wanted to

2 reiterate that. I mean you could do

3 functional traits, you know, and so things

4 like early versus late successional species. 

5 Determinate versus indeterminate could be

6 really important, because a tree that's

7 indeterminate can keep replacing leaves that

8 are injured by ozone with new leaves and

9 perhaps not be as sensitive, whereas, a

10 determinate tree, those leaves are going to be

11 out there all the time and that's the only

12 leaves it produces. It's not going to have

13 the capacity say to do that. But you still

14 have to careful.

15 For example, the example I gave in

16 my comments here is black cherry and pin

17 cherry. I mean just from those of us who look

18 in the field, pin cherry appears to be less

19 sensitive to ozone, and that's anecdotally

20 now, but there is one study that's shown, than

21 black cherry and yet they're very closely

22 related and they're both really successional
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1 so you --

2 DR. HUBBELL: Do we understand

3 from the science of why they're different in

4 terms of their --

5 DR. NEUFELD: No.

6 DR. HUBBELL: No, okay.

7 DR. GRANTZ: And just to throw

8 another thing into that. We've shown that

9 there's as much variability among C4 species

10 as there is between C3 and C4 species. Sugar

11 cane is absolutely quite sensitive, some

12 varieties, to ozone. So it's very difficult

13 to do that. I think functional types may be

14 a good point, and also growth habitats may be

15 a good point. And from that sense, sorghum

16 and sugar beet at least some cultivars, might

17 be a fairly good substitution from

18 California's desert areas.

19 CHAIR SAMET: Okay. So let's see. 

20 Peter?

21 DR. WOODBURY: I guess just to

22 briefly say there is a question, 16, in
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1 general about forest yield impacts. And just

2 to clarify my earlier point, I do think it's

3 important to try to look, you know, I

4 appreciate the effort to look at as many tree

5 species as possible and to find a way to

6 aggregate up, so I'm not suggesting,

7 necessarily, that you need to employ a really

8 complex, unwieldy modeling strategy, but you

9 may consider if there are serious issues with

10 integrating multiple species into an analysis

11 I just want to, I guess I'm just asking to

12 account for that in the approach.

13 So for example, if you're looking

14 at net primary production as an endpoint you

15 want to account for this competition among

16 species in some way, and starting in the

17 conceptual model there. And, you know, it may

18 be with some of the limitations about

19 importance values, et cetera, you know, you

20 may want to look at analyses of individual

21 species, you know, for areas where they're

22 relatively important, not necessarily
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1 combining the multiple species together in the

2 same analysis, but looking at some common

3 sensitive species individually and then

4 aggregating those results by region or

5 something.

6 There was a question, 18, about

7 PnET-CN, use of that model. So, you know, and

8 any model represents some processes and not

9 others, and my understanding of PnET is that

10 it's what's called a "big leaf" model so that

11 canopy's kind of model it as one giant leaf. 

12 And I don't know in that context how you

13 account for this issue of competition between

14 ozone sensitive and less sensitive species. 

15 So again, just make sure you're considering

16 that issue in using a model like that.

17 Again for forests that are

18 dominated by a particular species it's not an

19 issue, so that might be a domain where that

20 would appropriate. And again with PnET-CN,

21 just paying attention to this issue of scaling

22 from seedling data to a mature tree or, you
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1 know, the other approach is kind of scaling

2 based on a photosynthetic response and maybe

3 modified with stomatal conductance, you know,

4 to the mature tree. But again just making

5 sure you're looking at those issues as you do

6 the analysis. I think that's it.

7 CHAIR SAMET: Okay, thanks.

8 Kathy?

9 DR. WEATHERS: I'd just add to the

10 comment about PnET is I think it's an

11 interesting idea to try to run that model but

12 being clear about what it is that you're

13 looking for and what the model might tell you,

14 which is probably about what you don't know,

15 is important up front. And I question whether

16 you have all of the data you need to

17 parameterize the model across the country.

18 You might consider DayCent-Chem. 

19 I don't know if you can get your hands on that

20 model or work with the folks who developed it

21 at Colorado State, but that's another bio22 geochemical model that could give you the same
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1 kinds of output that was parameterized with

2 CENTURY, so some flexibility.

3 DR. WOODBURY: Sorry. One other

4 point I forgot to mention related to the PnET

5 model is to be, you know, with this model and

6 any other ones there's going to be a couple of

7 key relationships or factors that kind of

8 drive the results, right, even if the model's

9 relatively complex. So for the tree-grow

10 model it's going to be, how is the

11 photosynthetic response to ozone represented? 

12 So that's essentially parameterizing the

13 model. It's a big complicated model, but if

14 you're looking at multiple species and the

15 differences between them, that's what's going

16 to drive the difference.

17 And with PnEt, if you're looking

18 at hydrologic responses then the critical

19 assumption is going to be, or parameterization

20 will be, what is the effect of ozone on

21 stomatal conductance? And the literature on

22 this topic is unfortunately complex. My
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1 colleagues may have some more clarity than I

2 do, but sometimes stomata close, sometimes

3 they open, sometimes they don't seem to

4 function very well.

5 I guess I would recommend focusing

6 on Sandy McLaughlin's study and other studies

7 of mature trees to the extent, you know, Nancy

8 Grulke's as well, in California, and any other

9 studies of mature trees so that you hopefully

10 can represent that. This is also an example

11 of the kind of key assumption that I would

12 like to see represented in the risk analysis. 

13 Again you can't put everything about the model

14 and you don't need to, but sort of those key

15 relationships and what assumption was made, I

16 think, is important.

17 CHAIR SAMET: Okay.

18 DR. HUBBELL: Can I ask, for Dr.

19 Weathers, do you have a primary reference for

20 that DayCent-Chem model? I don't think that

21 we've evaluated that.

22 DR. WEATHERS: There's several. I
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1 think the most recent one might be in the USGS

2 peer review report, which is Hartman, et. al.,

3 H-A-R-T-M-A-N, and Jill Baron also. I think

4 there's a Baron, et. al.

5 CHAIR SAMET: So actually to bring

6 them to their attention, put them in specific

7 references into your comments.

8 DR. WEATHERS: Yes.

9 CHAIR SAMET: Thank you.

10 DR. WOODBURY: Kathy, do you know

11 if they've been used that they represent ozone

12 response at all?

13 DR. WEATHERS: I don't think they

14 have historically, but it's a fairly detailed

15 biogeochemical model that might be able to set

16 ozone. Other insults to trees, but I don't

17 think ozone.

18 DR. WOODBURY: But that may be

19 getting more into a research model than --

20 DR. WEATHERS: It may be.

21 DR. WOODBURY: -- applying an

22 existing model that has parameters for ozone.
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1 CHAIR SAMET: Okay. Howie, you're

2 the last to comment here.

3 DR. NEUFELD: Okay. Well, I agree

4 with what's been said before. I wanted to go

5 back to the i-Tree model. I was talking to

6 somebody at a meeting a couple months ago,

7 because we were doing some urban forestry

8 stuff, and about the model, and he said it was

9 developed for southern trees.

10 Am I wrong or has it been

11 parameterized, because you guys are using it

12 all over the country. He was a little worried

13 about us even using it up at Appalachian State

14 at Boone. So I was wondering if you could

15 comment on that.

16 MS. DAVIS: Hi. I'm Christine

17 Davis. I'm responsible for Chapter 6. The i18 Tree model, our understanding from working

19 with Dave Nowak at the Forest Service, is that

20 they have used it and parameterized it for

21 many cities across the country and even across

22 the globe, so that it shouldn't be an issue
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1 having it parameterized. They go out and

2 actually have field measurement data for the

3 forests in the particular cities.

4 DR. NEUFELD: Okay.

5 DR. HUBBELL: In fact, one of the

6 sort of unique things about the i-Tree model

7 is that it's using, you know, inventories,

8 urban inventories of trees so it's fairly

9 well, that level of uncertainty about what

10 trees are actually there is much lower than in

11 some of the other types of assessments.

12 DR. NEUFELD: We were just talking

13 to some people from the Southern Forest

14 Experiment Station and they expressed that

15 concern. That's why I brought it up. But if

16 you've got it all taken care of, that's great.

17 DR. HUBBELL: Thank you.

18 DR. SMITH: Part of the selections

19 for that was in working with the Forest

20 Services we gave them species that we had

21 response functions for and they went through

22 the cities that they do have parameterized and
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1 said, here's some cities that we can actually

2 do something with. And so that was a part of

3 the question, is they had the list of what

4 they could do.

5 DR. NEUFELD: I also think it's

6 very good that you are doing the urban

7 forests. I think that's a really important

8 thing to keep track of because it's important

9 both to people in the cities, and they do make

10 up a pretty substantial number of trees across

11 the country.

12 I had the same questions about

13 PnET because it was originally developed, you

14 know, in the Harvard Forest at Hubbard Brook.

15 About, you know, the big leaf

16 model, I'll just give you an example. Art

17 Chappelka's work showed that when he was doing

18 the analysis of full-year injury of trees in

19 the Smokies, it wasn't the leaves that were at

20 the top of the canopy that were the most

21 injured. It was the ones about a meter down

22 in the canopy that had the most full-year
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1 injury from ozone probably because they were

2 less water-stressed. You know, the direction

3 radiation hitting the top of the canopy

4 actually caused the stomata in those leaves to

5 close and they took up less ozone. So there

6 actually was more injury lower down in the

7 canopy, and there's less light there.

8 So I think, you know, assuming

9 it's just a single leaf it's going to miss

10 some stuff and, you know, in terms of how a

11 tree is, and it's very complicated, I mean

12 trying to do a whole canopy, so you have to

13 make some compromise. But just so you know

14 that there's that potential variation going

15 on.

16 And you made the comment, I know

17 we brought it up last time about using this

18 model that Hanqin Tian's using at Auburn, the

19 DLEM, and you sort of said in there, well, we

20 can't use it, but you didn't say why. Could

21 you elaborate on that? I'm just curious.

22 DR. SMITH: So we met with Tian at
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1 ESA in Austin, a couple of us met with him,

2 and it is a proprietary model. And a lot of

3 it is, you know, we just didn't have the

4 resources to be able to utilize his model. So

5 it's not something, it was just we were

6 resource-limited on the ability to access the

7 model.

8 DR. NEUFELD: And the other thing

9 I want to talk about, and this gets into maybe

10 some little controversy stuff here, is it says

11 here in Question 19, regarding ecosystem

12 services related to full-year injury. What do

13 you think about using potential full-year

14 injury as a metric to a sense potential lack

15 of cultural services.

16 Well, having worked in the Smokies

17 on one of the most well-traveled paths, the

18 path up to Clingmans Dome, I would have to say

19 except for one person that I met, Lyle Craker,

20 once, who came up to me. I didn't know that

21 he was an ozone researcher. He was the only

22 person I met in three years that even thought
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1 about ozone and knew what was going on with

2 the plants there.

3 So is there perhaps a better way

4 to get at this? I think if people are pointed

5 out, if it's pointed out to them that these

6 plants that have all these brown leaves, and

7 indeed, it looked like somebody had walked

8 along the trail with a flamethrower when we

9 had the high ozone in the late '90s, but

10 people just thought that was natural. But if

11 you pointed out that that was due to ozone

12 they were very concerned. So perhaps there's

13 some other metric one can get at. But I don't

14 think the average person walking down a path

15 will know or see ozone injury, and on the

16 trees especially because it's way up in the

17 canopy.

18 DR. HUBBELL: So based on what you

19 just said, the question is, are you saying

20 that people didn't notice that there was

21 something wrong, or wouldn't have noticed if

22 it was better, or is it that they, if they
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1 didn't know that it was from ozone? Because

2 if they still saw that it was damaged and

3 thought like, gosh, it would have been nice if

4 that was green instead of brown, then that

5 matters.

6 DR. SMITH: Right. They did not

7 know. Some of them would comment, you know,

8 why are these plants brown? And then we would

9 tell them it was ozone injury, air pollution,

10 okay, and then they were concerned. But

11 others, most people just didn't even notice

12 it. And with trees, you know, where you have

13 to really train yourself to see what foliar

14 injury looks like, most people would not

15 notice the injury on the leaves.

16 So I'm thinking perhaps there's

17 some other way, I mean of assessing the

18 cultural impact to people either through

19 sociological surveys. You know, the EPA

20 queried people in terms of how much a life is

21 worth, you know, doing the cost-benefit

22 analysis by asking how much would they
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1 sacrifice from their salary in order to make

2 the workplace safer to prevent one fatality. 

3 So one could do a similar thing with, you

4 know, people, in saying if they were told it

5 was ozone injury, how much is it worth to

6 them? And then you could try to monetize that

7 and get a risk to the things like that.

8 CHAIR SAMET: Okay, any other

9 comments? Howie, you're not -- okay. 

10 Comments from others on Chapter 6 Ecosystem

11 Services?

12 Steve?

13 DR. KLEEBERGER: I've got more of

14 a point of interest than a question. But this

15 discussion on Chapters 5 and 6 have sort of

16 brought out my old ecology background. And

17 I'm curious whether there are secondary

18 effects of ozone exposure on plants that would

19 impact animal biomass or animal diversity or

20 energy flow or nutrient cycling. I mean have

21 studies been done that could be incorporated

22 into this modeling, or perhaps you've already
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1 done that. I have to admit I haven't read

2 either one of those chapters yet.

3 DR. SMITH: I don't know, right

4 off, of any studies that have carried it that

5 far through. I think in a, paren,

6 theoretical, ecological view, I would say of

7 course there must be some effect, but we would

8 have absolutely no way to quantify that beyond

9 saying this might be an effect. DR. NEUFELD: 

10 Well, let me give you an example. It's a

11 recent paper by people down at Auburn looking

12 at effects of ozone on digestibility of

13 forage. So the Southeast, for example, and

14 just to tell you impact, the Southeast has

15 about 18 million hectares of pasture land,

16 managed pasture land, so they were looking at

17 this as, and used rabbits, which are the main

18 herbivore, and without any foliar symptoms

19 there were decreases, significant decreases in

20 digestibility.

21 So, you know, there's a trophic

22 interaction kind of thing there and people
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1 have done other things with insects and that's

2 been covered in the ISA and so on. And that's

3 why I think, you wrote in the Ecosystem

4 Services of disruption of biogeochemical

5 cycling, so there's some empirical data to

6 support those.

7 CHAIR SAMET: Okay. Other

8 questions?

9 Dave, would you take on the

10 summarizer role of this?

11 DR. GRANTZ: For?

12 CHAIR SAMET: Chapter 6.

13 Chapter 7. Howie?

14 DR. NEUFELD: I thought the

15 synthesis was well done. I didn't really have

16 too many comments here. The only thing I have

17 is a little question, is that there is a

18 bullet explaining biomass losses in the

19 Smokies, which suggests that the response is

20 quite large, and yet when you go back to the 

21 Kohut assessments then the risk to the Park is

22 low. So there's a contradiction there and I
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1 was wondering, how can you reconcile that?

2 DR. SMITH: With The Great

3 Smokies, you can come up with a couple of

4 contradictions of that. One is that the Kohut

5 study is foliar injury compared to biomass

6 loss, which we didn't try to do anything to

7 compare those two, really, in this. And the

8 Smokies actually were slightly different when

9 we looked in the context of the Class I areas

10 compared to when we looked at it at the finer

11 resolution of the Park map. And so just even

12 the resolution of that changed the results

13 slightly in the Great Smoky Mountains when we

14 used the Park mapping data versus just the

15 larger scale FIA data that I had available. 

16 And so I think the contradiction you're

17 talking to is because it's the difference of

18 the foliar injury versus biomass loss. But

19 there was a slight difference between just

20 looking at on the fine scale versus the bigger

21 scale.

22 CHAIR SAMET: Okay, Kathy?
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1 DR. WEATHERS: Yes, I also thought

2 it was useful and it made me wonder whether it

3 ought to go up front. I was a little sorry to

4 see it at the end of the section rather than

5 up front. So I thought it was well done. And

6 the only question I had, whether the reference

7 points of no ozone was reasonable and

8 defensible and we've already been there.

9 CHAIR SAMET: I will say the

10 headings and title match what we saw in the

11 human health and exposure risk assessment,

12 where the title of the chapter was synthesis,

13 but it was actually not synthetic but more of

14 a summary. And I think this same issue we

15 brought up there, I guess, I don't think you

16 were here but, might not pertain here, which

17 is, are you going to write a synthesis here at

18 the end and provide the executive summary at

19 the beginning? I mean recognizing this is a

20 work in progress.

21 DR. HUBBELL: Yes. The plan is to

22 do the executive summary and to make the next
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1 draft have a more comprehensive synthesis. 

2 And if you all are interested, we can also try

3 to make sure that each of the chapters has a

4 summary as we're doing in the health chapter,

5 summary results, and that way you have a

6 consistent process.

7 CHAIR SAMET: Yes. I mean I

8 really think they should be parallel including

9 the comments earlier about showing what has

10 come from the ISA, what's based on prior

11 assessments and so on.

12 Howie?

13 DR. NEUFELD: Can I make one other

14 just structural comment since we're kind of

15 talking about it? It goes across both welfare

16 documents. And that's the large amount of

17 material that is redundant from the ISA, so it

18 adds bulk to both documents.

19 Is it because you have to have

20 these as stand-alone, assuming somebody didn't

21 read the ISA, or could you shorten those

22 sections? You know, where you come up to
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1 where you're justifying the risk analysis,

2 it's almost like completely redoing a

3 literature or science review and then saying

4 what you're going to do. Is there any way of

5 shortening that and sort of just referring

6 back to the ISA so that you get, and what it

7 would do is it would focus your attention more

8 on the risk assessment in these documents as

9 opposed to reading through all the, like a

10 literature review almost.

11 DR. HUBBELL: It's a constant

12 struggle, and we look to you all to decide

13 what's the most appropriate way to move this

14 forward. Because generally when we've tried

15 to cut it down a lot then some people pick it

16 up and go, well, I haven't read the ISA and I

17 don't want to go back and read the ISA. And

18 so it's a struggle to know how much material

19 to carry forward from document to document.

20 You'll see this obviously in the

21 policy assessment as well. There's some

22 duplication of both documents. And it's
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1 really a question for you, the panel, to give

2 us advice on, you know, what do you think is

3 appropriate to carry forward to allow you and

4 the public to give advice and good comment?

5 CHAIR SAMET: Fred?

6 DR. MILLER: Well, I view that

7 they ought to be stand-alone documents, but I

8 also view that it's like a cone, and the ISA

9 is the base of the cone and the PA is the tip

10 of the cone. And the amount that you can cut

11 down between the REA and the PA is probably

12 much greater than you can cut down between the

13 ISA and the HREA, or the welfare part. And I

14 think there are a lot of people who only read

15 one of these three documents.

16 And so we're getting subjected to

17 all of it and we think that it's too much, but

18 for somebody who didn't wade through the 1,177

19 pages of the ISA, which I want to congratulate

20 the agency. That is about half of what it was

21 ten years ago. So I think that you have to

22 consider a little more rather than less, but
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1 paring down as you go. That's my opinion.

2 CHAIR SAMET: You know, I think if

3 you wrote this in a way that it was very clear

4 what you were bringing from document to

5 document up front, and then had some

6 supporting material perhaps, doing what Fred

7 said, so those who have not read the ISA,

8 here's the brief version. So it was very

9 clear what you brought forward so you didn't

10 have to struggle to find it in the midst of a

11 mini-ISA, which I think is what many of us

12 have sort of objected to as we've read the

13 documents. Because in part this sort of

14 cutting and pasting and pulling forward

15 doesn't really work. I think it's hard to

16 write a good synthesis or a good summary.

17 And so I think that what you could

18 do is say, look, here's what I brought forward

19 from the ISA. Here is a brief summary of

20 relevant aspects not only the most critical,

21 and move on. Something like that would be my

22 preference. I mean I think Fred is right
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1 about one thing. The documents certainly

2 should get shorter as you go from the ISA at

3 the base to the subsequent documents. Those

4 who want to comment on this?

5 Pete?

6 DR. WOODBURY: Yes, just briefly. 

7 I agree that this is going to be an ongoing

8 tension. There's never going to be a perfect

9 resolution, but I am in favor of the risk

10 assessments, you know, being stand-alone

11 documents with reference to the ISA.

12 And I guess, you know, we're

13 talking about what's the definition of a

14 stand-alone document. Maybe what will help is

15 clearer sign posting. You certainly don't

16 want to summarize the ISA in the risk

17 assessment. It's really, to my view, the risk

18 assessment is focusing on some key

19 relationships and performing some analyses,

20 and you need just to reference the material

21 that directly supports those. And then to the

22 extent that you're going to have a qualitative
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1 discussion of other end points that maybe

2 can't be quantified, you know, you need a very

3 brief, few sentence support for why those

4 matter.

5 So I guess maybe as a model, when

6 I look at a journal article I like to see a

7 value claim, preferably in the abstract,

8 certainly in the introduction. And a well9 written introduction to a journal article

10 quickly gets you from the general literature

11 to, why am I reading this article and what's

12 the point?

13 CHAIR SAMET: Okay, anything else

14 on this point?

15 Okay. So let's see. Howie, would

16 you summarize the comments on Chapter 7? I

17 don't think there's much to summarize at this

18 point.

19 So let me see if there's anything

20 else on this document and then I'll make a

21 suggestion as to what we might do.

22 Ted, what time are you leaving?
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1 DR. RUSSELL: 4:45.

2 CHAIR SAMET: 4:45. Anybody else

3 leaving before 5:00?

4 Peter?

5 DR. WOODBURY: I just have one

6 more.

7 CHAIR SAMET: And you want to

8 leave?

9 DR. WOODBURY: No, not leaving. 

10 Just on the synthesis, I think that's where it

11 was mentioned that you do plan to look at the

12 forest health monitoring data, and I just want

13 to say I do think as we've heard previously

14 those are really important useful data. I

15 would like to encourage you also to look back

16 at the approach that was used in the previous

17 round of looking at the forestry health

18 monitoring data by, you know, I think it was

19 CMAC grid cells. Jeff can correct me from

20 here.

21 MR. HERRICK: Those were counties.

22 DR. WOODBURY: A County scale,
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1 thanks. So I thought that was a useful

2 analysis and it might be useful to refresh

3 that analysis. It did give a good coverage of

4 much of the country.

5 CHAIR SAMET: So what I'm going to

6 suggest, I think that our discussion on this

7 document seems to be over, and that we now

8 move to the clarifying comments from the

9 public. Here, we have three commenters. And

10 then just return back, I think, to sort of

11 perhaps I'll say our last discussion on the

12 policy related background, et cetera,

13 discussion while we have Greg here.

14 So let me ask, we have three

15 commenters. We've allocated for each of you

16 three minutes to present. And again we, I

17 guess, heard from you before, so what we'd

18 like is your comments that are clarifying or

19 additional based on your attendance here at

20 the meeting or related back to clarifying

21 earlier comments. So let's see, I think are

22 we almost ready. And I think Angela will give
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1 you a warning at --

2 DR. NUGENT: I'll give them

3 warning when two of your three minutes are

4 over.

5 CHAIR SAMET: Okay, so our first

6 commenter is Sonja Sax, on behalf of the

7 American Petroleum Institute.

8 DR. NUGENT: And I believe she has

9 given Greg a slide with this.

10 MS. SAX: Hi again. Thank you

11 again for the opportunity to clarify some of

12 the comments. First of all, I just want to

13 follow up on the general BenMAP approach and

14 this issue of confidence intervals. 

15 Specifically, I was referring to the

16 confidence intervals in the deltas, which were

17 not in the first draft, and the importance of

18 those.

19 Also I just want to show two

20 figures that I think are informative. These

21 are some BenMAP analyses that we conducted. 

22 They're not using exactly the same data that
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1 EPA used, but they do show the estimates down

2 to zero and LML and PRB. And it's a little

3 bit difficult to see the graph, but

4 essentially on the Y axis you have the

5 different concentration response functions

6 that we applied from Smith 2009 summer, the

7 EPA's Zanobetti and Schwartz, the EPA Bell and

8 the all-year Smith, et al., 2009 concentration

9 response functions. The closed circles and

10 triangles and squares are the current

11 conditions, and the rollback conditions are

12 the open circle, triangle and square. And

13 this is basically showing zero, LML and PRB.

14 And what I want to note here is

15 just the confidence bounds. They're really

16 large, so in reality whether you're

17 considering zero, LML or PRB, you know, this

18 really shows that a zero risk can't be ruled

19 out based on the confidence intervals.

20 Another important point in taking

21 the approach of going down to zero or LML,

22 this could really be interpreted as, you know,
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1 these are kind of alternative ozone levels,

2 and I'm not sure we really want to go there. 

3 And based on the uncertainty, you know, it may

4 be this is not the best approach. Also we

5 should note that not all of the uncertainty is

6 in these confidence bounds. Next slide,

7 please.

8 I think it's more appropriate to

9 really try to focus on the deltas, and this is

10 what I have plotted here. Oh, I guess my

11 first, it's hard to tell but there's another

12 column that's very light, I guess, and you

13 can't really see it, but that's the zero

14 level. But essentially, when you're looking

15 at the deltas there's very little difference

16 between the zero, LML, and the PRB. And again

17 there's some differences depending on the

18 concentration response function that you use,

19 but there's no confidence bounds here. So

20 without the confidence bounds we can't really

21 see whether these differences are

22 statistically significant or not. So those
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1 are my two points. Thank you.

2 CHAIR SAMET: Okay, thank you. 

3 Any questions? Okay, thank you.

4 Our next presenter is Chris Emery,

5 on behalf of ENVIRON.

6 MR. EMERY: My comments at this

7 point are more about HDDM. There seems to

8 have been confusion among the community

9 members about how HDDM is going to be used in

10 the health risks and welfare REAs, and the

11 details in each of those documents. And

12 apparently not everyone has seen the Simon

13 memo. So Heather Simons got most of the

14 details in there, but still it's kind of

15 lacking because they're still developing some

16 details.

17 But I just want to reiterate, it's

18 going to be important to run a whole year

19 mainly for several reasons that Heather has

20 already talked about. Mainly, a lot of areas,

21 their ozone season does go over the entire

22 year, and if you're going to use the same run,
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1 which I would expect EPA to do to deal with

2 the scaling for the fused national assessment,

3 then you would want to do a whole year. It

4 adds of course more data points for the entire

5 year so that, you know, more relationships

6 over the entire year can be developed using

7 HDDM. So more data is good.

8 Also you can get background from

9 the HDDM run, if you just push zero omissions

10 through the sensitivity results you do get a

11 background number. In this case, I believe

12 that the EPA is running U.S. background in

13 their HDDM run. Or you can do zero out. But

14 given apparent schedule constraints, I think

15 that you can actually get out of this one run,

16 the U.S. background. And that will be good to

17 push that through APEX as a background, and

18 push through, for example, a 75 ppb standard

19 rollback so that you can quantify the change

20 in risk. It seems like it would be fairly

21 straightforward to do from one HDDM run for

22 the whole country for a whole year.
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1 The other thing is, it seems like

2 there's a lot of concern about really knowing

3 what the HDDM approach is well before the next

4 draft of the REA comes out, and this is kind

5 of important. So would it be possible, just

6 as a suggestion, for EPA to come out with an

7 interim technical memo on kind of the final or

8 at least semi-final details of how the HDDM is

9 being applied for both the health and the

10 welfare risk assessment? Apparently the next

11 draft is going to be coming out in late

12 winter, maybe even early spring, and so it

13 would be good to have --

14 DR. NUGENT: Dr. Emery, one

15 minute.

16 DR. EMERY: Thank you. It would

17 be good to have kind of a CASAC ability, even

18 a public review capacity of some sort of

19 interim technical memo coming from the EPA

20 modeling staff, so that we have a good handle

21 on what's going on with HDDM well before the

22 REA comes out. I know that's another round of
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1 review, but it would answer a lot of questions

2 well before the REA comes out, and maybe some

3 of the modeling experts on the committee could

4 benefit from that.

5 Okay, that's all I have.

6 CHAIR SAMET: Okay, thank you. I

7 don't know if anyone from EPA wants to comment

8 on interim technical memos, but --

9 Why don't you come up to the

10 microphone?

11 DR. HUBBELL: So obviously given

12 the schedule that we showed to you the other

13 day that that's highly infeasible to develop

14 a separate technical document that would then

15 be released as a public document for comment,

16 et cetera. Clearly, we want to be able to

17 provide as much information as possible and,

18 you know, we'll be releasing the document well

19 advance of the CASAC meeting, at which time

20 the public can comment on that as well, just

21 like we did this last time.

22 We tried very hard to provide as
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1 much technical information as we could in this

2 go-around for a very complex, new modeling

3 effort and we'll continue to try to provide

4 that information, but I don't see it being

5 feasible to try to produce an interim document

6 within the schedule we provided earlier.

7 CHAIR SAMET: Okay. All right,

8 fine.

9 Fred?

10 DR. MILLER: Is it required that

11 this HDMA or MEA or -- it's getting late -- be

12 in the ISA? Where does it have to appear that

13 the agency can use this on a go-forward? 

14 We've talked about some other manuscripts that

15 clearly need to be added to the ISA. Is this

16 another category, and maybe there's not a

17 publication, but I'm wanting to make sure that

18 it doesn't drop through the crack as to where

19 it has to be referenced such that it can go

20 forward. And I don't know the answer. I'm

21 asking.

22 CHAIR SAMET: Well, others may
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1 want to comment. I think ideally, presumably,

2 the model would have been presented and

3 reviewed in the ISA and commented on.

4 DR. HUBBELL: Yes, we haven't sat

5 down and talked with our colleagues who are

6 working on the ISA about HDDM specifically. 

7 Clearly we will need to do so.

8 You know, one other point I want

9 to make is that we are preparing a draft for

10 submittal to a journal on this initial HDDM

11 effort, the evaluation and the case studies. 

12 Hopefully it will get into the literature

13 prior to when we have to finalize the REA.

14 But in terms of having this

15 specific methodology and this specific

16 application in the ISA, I don't think that

17 that's going to be possible given the time

18 frame, but the methodology itself, the HDDM

19 methodology, there are articles and literature

20 already on that approach that can be

21 incorporated into the ISA.

22 MS. WEGMAN: And we do plan to
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1 talk to John and his folks about that.

2 CHAIR SAMET: Okay, yes?

3 DR. JACOB: I had a question for

4 Chris. So using HDDM to try to get the

5 background, wouldn't that cause an arch

6 overestimate of the background because of the

7 non-linear depends on ozone on emissions?

8 CHAIR SAMET: Go ahead and please

9 come to the microphone.

10 DR. JACOB: I think it's suggested

11 that we could use the HDDM to infer the

12 background emissions. And I'm wondering if

13 that would lead to a great overestimate of the

14 background because of the non-linear

15 dependence of ozone on its precursor

16 emissions.

17 DR. NOVAK: Well, it depends on

18 how you run HDDM. But the way I understand

19 EPA's approach where they actually have to run

20 it three times, effectively, in order to get

21 the full NOx effect, the non-linear NOx

22 effect, that helps to replicate the U.S.
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1 background really closely relative to a zero

2 outrun. So the non-linearity of the HDDM is

3 taken care of, and so it should be very

4 similar to an actual zero outrun.

5 DR. RUSSELL: Though I would say I

6 mean, and you would know more about this than,

7 I think, anyone in the room is that what it

8 will miss when you do that is essentially the

9 global transport and impact, essentially, upon

10 the U.S. on itself after you, you know, it's

11 gone around the world.

12 DR. JACOB: Both the U.S. and

13 itself.

14 DR. RUSSELL: Yes, so it'll miss

15 that but it should be capturing most

16 everything else. And if they wanted to they

17 could use GEOS-Chem boundary conditions taken

18 from a U.S., removing the U.S. boundaries and

19 then they'd get it.

20 DR. NOVAK: I mean that's a good

21 point, and I don't know how much the U.S.

22 effects itself, but I think EPA, and we have
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1 in our HDDM modeling has compared the HDDM

2 zero omissions case to a zero outrun, an

3 actual physical run. And for cases where

4 you're not super NOx sensitive, the

5 agreement's very good for a single HDDM run,

6 run at one particular case.

7 In EPA's situation where they run

8 three different HDDM runs to get the full

9 range from zero all the way down to zero

10 percent emissions, at least for U.S., I think

11 that that's doing a much better job at

12 replicating the U.S. background. So I think

13 it's probably a good way to get a lot of these

14 answers out with just one, you know, set of

15 HDDM runs.

16 CHAIR SAMET: Fred?

17 DR. MILLER: While you're up here,

18 can you comment on whether or not there would

19 be more reliability or less uncertainty if you

20 used North America background versus U.S.

21 background, or the pros and cons of how this

22 particular model could be influenced by which
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1 of these data sets that you might try to

2 generate?

3 DR. NOVAK: Well, I think that's

4 kind of a policy issue. You know, it's what's

5 going to be controllable.

6 DR. MILLER: That might be a

7 policy issue, but you obviously had a hell of

8 a lot of experience with the model. So I'm

9 asking you, for different data inputs what are

10 some of the things that provide the more

11 severability in the likely outcomes and stuff

12 like that, that might enter into deliberations

13 about which of these to pick?

14 DR. NOVAK: I mean you can run

15 HDDM either way. In our situation we're

16 running U.S. background. I'm not exactly sure

17 how the sensitivity output would respond

18 necessarily to a difference between NAB or

19 U.S. background.

20 DR. RUSSELL: The sensitivity

21 coefficients will be remarkably insensitive to

22 that except toward the boundaries. And just
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1 remember that the starting sensitivities are

2 your base case so that's going to be the same,

3 and so then you just go with some emission

4 changes. So you'll have some impact at the

5 boundary but the internal sensitivities --

6 CHAIR SAMET: So I think we need

7 to hear from our last commenter, and then we

8 will coming back to this issue. So thank you.

9 Our last commenter is Nicole

10 Downey on behalf of Earth Systems Science.

11 DR. CHOCK: Jon, can I make a --

12 CHAIR SAMET: Sure.

13 DR. CHOCK: I think Fred raised

14 the issue about incorporating HDDM into ISA. 

15 I personally see in that because it is really

16 a mathematical theory other than a scientific

17 law or a model. So is similar to a quadratic

18 rollback is the mathematical tool even though

19 it's not science-based, and we don't include

20 that in our ISA. So HDDM is another

21 mathematical tool even though it's a lot more

22 vigorous.
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1 CHAIR SAMET: Okay, thank you.

2 DR. BRAIN: And I have a quick

3 comment to Sonja, since we're looking backward

4 and reflecting.

5 Sonja, the first slide you showed

6 us, you know, you emphasized that here were

7 eight or ten studies and all of them had

8 confidence intervals which overlap zero. But

9 it seems to me, what about the point that

10 every single one, the average was to the

11 right? So even the Wilcoxon signed test would

12 say, you know, the chances of having eight or

13 ten all of the same sign are very, very low. 

14 So I would interpret that slide very

15 differently. Yes, any individual study showed

16 a confidence interval overlapping with zero,

17 but they all showed a positive effect, all of

18 them. So a typical meta-analysis would have

19 drawn some strength from that.

20 CHAIR SAMET: Okay, so please go

21 ahead.

22 DR. DOWNEY: I have one slide. 
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1 I'm just going to talk about data so we'll

2 have to wait for the slide.

3 Okay, so as Chris talked about, ENVIRON

4 has done a one-year HDDM run for the U.S.

5 background case, and so this is very

6 preliminary. Just last week I made these

7 figures, compared the output of an HDDM run to

8 the quadratic rollback, and I just wanted to

9 show you some very preliminary results about

10 what the differences in the distribution looks

11 like because I think it's very interesting.

12 The way that this run was set up

13 is our run has 50 percent NOx and VOCs as a

14 starting point so that we could extrapolate

15 from both sides of 50. And in my case, when

16 I was trying to meet a proposed standard I

17 varied NOx and VOCs together. I just did NOx

18 and VOCs together the same amount.

19 So on the left I've got a couple

20 of figures from Riverside, California, in the

21 LA Basin. This is one of the sort of

22 controlling sites for LA. And the green bars,
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1 which are here, are the 2006 observations, and

2 then the red bars are the quadratic rollback.

3 And then the blue bars show what

4 the HDDM distribution looks like, and I think

5 from this figure you can really see that the

6 HDDM shows a significant shift upward in the

7 lower concentration bends, reflecting this

8 non-linear chemistry of ozone, which is very

9 different from the rollback, which pushes down

10 higher levels and really pins the bottom.

11 And so it reflects much more

12 accurately the atmospheric chemistry that's

13 going on and it's very different. And I think

14 this is going to have some implications for

15 both the health and the welfare assessment

16 because it also changes the seasonal cycle in

17 sort of the same way. You have a different

18 amplitude. And I imagine there will be some

19 diurnal changes as well.

20 DR. NUGENT: One minute.

21 DR. DOWNEY: And I think another

22 important point to note is that yesterday it
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1 was talked about that someone mentioned,

2 ideally, you want to shift the whole ozone

3 distribution down when you change a standard. 

4 And because of the non-linearity in ozone

5 chemistry, when you change emissions that

6 doesn't necessarily happen.

7 And what you end up with is

8 something that's much more peaky. And this is

9 getting Riverside down to 80 ppb is standard,

10 you see HDDM starts piling things up in the

11 middle because that's more reflective of how

12 the chemistry will respond.

13 And I think when you convolve that

14 with the concentration response functions and

15 look at these heat maps, you'll see some very

16 interesting patterns emerge. But this is a

17 preview of what the effect of incorporating

18 HDDM may look like going forward.

19 CHAIR SAMET: Okay, thank you.

20 Any questions?

21 DR. VEDAL: I only see what you're

22 describing on that bottom left plot, really,
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1 where there's that more marked distribution

2 shift. There is some in the, you know, zero

3 whatever values --

4 DR. DOWNEY: Okay, let's look at

5 where I rolled these back to. This is for

6 Riverside, meeting a standard of 95 parts per

7 billion.

8 DR. VEDAL: Right, and I see those

9 are design values.

10 DR. DOWNEY: Design values.

11 DR. VEDAL: Yes.

12 DR. DOWNEY: This is for Riverside

13 meeting 80. And so as you get further and

14 further down, if you're in a place where you

15 have this strong NOx scavenging, you'll see

16 this in more rural areas, you don't have this

17 piling up at zero. So the issue is, when

18 you're in a place with a lot of NOx you get in

19 the observations a piling up of zero and the

20 rollback maintains that. But that's an

21 artifact of the non-linear chemistry of ozone

22 because NOx scavenges ozone. So as you cut
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1 emissions that goes away and that's what this

2 is bringing to light is that there's this

3 shift on both sides. And so certainly this

4 will be more of an urban phenomenon, I think,

5 than a rural phenomenon, if that makes sense.

6 Yes?

7 DR. CHOCK: Do you do this based

8 on the NOx reduction alone?

9 DR. DOWNEY: This was NOx and VOCs

10 together.

11 DR. CHOCK: Together.

12 DR. DOWNEY: Yes.

13 DR. CHOCK: And domain-wide rather

14 than --

15 DR. DOWNEY: Domain-wide, and the

16 domain was the entire U.S.

17 CHAIR SAMET: Okay, thank you. So

18 what I'm going to suggest is that we maybe go

19 back a little bit to this further discussion

20 of, at this point I'm confused about what to

21 call it, but the set of issues sort of

22 associated with policy related background,
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1 various ways to model.

2 And I think what I'd like here is

3 a little help perhaps, Ted, before you go out

4 the door, and then anybody else to sort of lay

5 out at this point what you think the issues

6 are that would benefit from further

7 discussion. We certainly discuss a lot of

8 these issues, and I think the real question is

9 have we come to grips with them enough in a

10 way that we can write a cogent and coherent

11 set of comments about them? So let me push

12 that difficult charge on you.

13 DR. RUSSELL: Oh sure. And Daniel

14 and I can maybe argue about this, or iterate

15 on this.

16 So one of the first questions has

17 to be is if we're going to an HDDM approach

18 there's less need to actually look into the

19 PRB. Although I still think whether they go

20 the HDDM route or not that's an important

21 concept to bring through in the ISA, and I

22 wouldn't be against them actually using some
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1 estimates of the PRB in some of their

2 analyses. And they might even use a couple of

3 different ways of getting there. Because

4 there's essentially the purely modeled base

5 way, which is how they've done it

6 historically, and then there is essentially

7 getting there by starting with the observation

8 and, you know, you've got the fused ozone

9 fields and then you subtract the amount of

10 ozone formed from the U.S. emissions via the

11 HDDM way. However, as I pointed out that

12 actually will still overestimate the

13 background concentrations because it doesn't

14 pick up the impact of the U.S. on itself after

15 it goes around the globe, you know, one, two,

16 three, four times. And I'm not sure how big

17 that is.

18 I mean Daniel, do you have a feel

19 for that?

20 DR. JACOB: It's pretty small, a

21 couple ppb.

22 DR. RUSSELL: A couple ppb. So it
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1 would just have to be recognized that that's

2 going to be a bias in that. There is

3 potential for HDDM to either overestimate or

4 underestimate the variability in the

5 background impact. And I can't convince

6 myself it's going to be one way or another at

7 this point, so that would just be another

8 uncertainty that would have to be addressed.

9 And I think really the exercise,

10 and I'd like to see them do this, to develop

11 their policy-relevant background, you know,

12 the way they've done it so far as well as

13 essentially doing it by the HDDM way,

14 subtracting from the observations is I think

15 it gives you a good idea. It'll help bound it

16 and it'll provide the uncertainties, and I

17 think that would be a useful thing to see. 

18 And then potentially use those uncertainties

19 in some of their analyses, or at least address

20 them. Discuss what, you know, the levels of

21 them.

22 But if they're not going to use,
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1 they're never going to use the policy-relevant

2 background directly, I'm not sure how much

3 further they have to go than they are right

4 now.

5 CHAIR SAMET: Okay. Daniel, do

6 you want to comment for that?

7 DR. JACOB: Yes. And I agree with

8 what Ted is saying. I mean even if you use

9 this so-called observation based approach

10 starting with your data and then applying

11 HDDM, you're still very sensitive to what CMAC

12 thinks should be the background, because this

13 is what's going to define your slopes

14 extrapolated to zero emissions, right.

15 DR. RUSSELL: To a lesser degree.

16 DR. JACOB: Maybe to a lesser

17 degree. But I think it's important. Let's

18 assume that we need to have some good

19 knowledge of the background. Fred has been

20 very insistent in that and then for some good

21 reasons. Then I think we need to figure out

22 what kind of background values scene that
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1 produces, and compares that to the CMACs and

2 GSM values.

3 DR. RUSSELL: Oh, I think that

4 goes without saying.

5 DR. JACOB: Okay, fine. Yes.

6 DR. RUSSELL: And in fact, they

7 should calculate, you know, if they can and I

8 don't see why not. You know, they've already

9 done the calculation very near zero emissions

10 in terms of the sensitivities, just to see if

11 they're very different at a zero. Because if

12 they're not that different at the zero versus

13 ten percent, I don't think you'll see a huge,

14 you know, then what's that saying is that the

15 impact of what, quote, "the background

16 conditions are," is small on the sensitivity

17 coefficients.

18 DR. JACOB: Yes, the other point I

19 wanted to make is that from this approach what

20 you would get is a U.S. background.

21 DR. RUSSELL: Yes.

22 DR. JACOB: All right, so you
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1 wouldn't get the North American background and

2 you certainly would not get the natural

3 background. Yes. I don't think that CMAC

4 could get the natural background even with its

5 hemospheric version since the southern

6 hemisphere is not pristine. So I don't think

7 it --

8 CHAIR SAMET: Okay. Other

9 comments?

10 Fred?

11 DR. MILLER: I have a question for

12 Ted. You mentioned about doing one thing,

13 like I think it means this down to zero, and

14 then the other way they're treating PRB right

15 now. Well, they're not treating PRB right now

16 so I'm not sure what you were, which kind of

17 scenario you were referring to. And I guess

18 I'd also prefer to change my statements in

19 terms of PRB to just talk about U.S.

20 background.

21 You know, I mean it's not a matter

22 of the policy per se, but just what are the
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1 background levels in this country from non2 anthropogenic sources? And maybe some of the

3 sensitivity here, and we can get into the

4 politics of the science policy that we're

5 going to be addressing tomorrow separately.

6 So I wasn't clear, Ted, what other

7 scenario you were referring to when you made

8 that comment.

9 DR. RUSSELL: So the comment in

10 terms of how to calculate this new PRB

11 approach, is what they're doing is they're

12 starting with an observed surface. So they're

13 taking the observations and then they're going

14 to decrease the ozone from there using the

15 sensitivity of ozone to U.S. emissions.

16 And if it was really -- but I

17 won't make Bryan cringe -- if they wanted to

18 they could also look at Canadian and Mexican

19 actually at the same time, but it's just more

20 work. It's more computer time, and I'm not

21 sure that it would add that much information

22 to what they want to do.
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1 Yes?

2 DR. HUBBELL: I was just going to

3 point out that's not off the table. In fact,

4 that's one of the issues we're dealing with

5 when we think about looking at alternative

6 standards is whether we would need to tap into

7 Canadian and Mexican emissions as well as U.S.

8 emissions in order to be able to do the

9 sensitivities. I don't know because we

10 haven't done that yet, but I certainly was not

11 implying that we have made a final decision

12 that we would not look at sensitivity to North

13 American emissions as well as U.S. We haven't

14 really discussed that fully.

15 DR. RUSSELL: Okay, so given that

16 Bryan just agreed, you know, that could be

17 done at the same time and you could answer

18 that question as well and get the separate

19 impact of North American, non-U.S. emissions

20 at the same time and include that in your

21 analyses.

22 Did that answer your question,
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1 Fred?

2 DR. MILLER: Yes, thank you.

3 CHAIR SAMET: I think, Fred, you

4 made a proposal that I just want to make sure

5 I heard it right, which is that the term "PRB"

6 be stricken.

7 DR. MILLER: Yes. I want to be

8 able to get into the analysis background and

9 then convince you tomorrow about why you need

10 the fraction of risk that's attributable to

11 expose above whatever that background level

12 is. And I'm not calling that policy, but

13 that's kind of the analysis it would be in the

14 PA document.

15 DR. RUSSELL: And I will agree

16 with Daniel that you cannot get natural

17 background at this time in that approach.

18 DR. HUBBELL: I'm sorry. Are you

19 saying using the HDDM approach you can't get

20 natural background, correct?

21 DR. RUSSELL: Yes.

22 DR. HUBBELL: Okay.
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1 DR. RUSSELL: Because your

2 boundary conditions, I mean --

3 DR. HUBBELL: Right. But you

4 weren't saying that there's no way to get

5 natural background --

6 DR. RUSSELL: Oh, no, no, no. 

7 Just that approach with the current modeling

8 setup, I mean you would have to go to a global

9 model.

10 CHAIR SAMET: Right. So --

11 DR. BRAIN: I have an adjacent

12 question. Yesterday, Daniel, I think, gave a

13 very clear exposition of the PRB, the U.S.

14 domestic one, the North American one, and

15 said, yes, you know, each one has an identity,

16 a definition. So I'm still a little confused

17 as to whether this committee and the staff has

18 a preference for one of them. Are they going

19 to use all three of them or just two of them? 

20 Where and when will they express, you know,

21 when one is appropriate and when the other is

22 appropriate? It seems to be it will be
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1 confusing if there are too many different

2 approaches to background.

3 DR. RUSSELL: I mean that's truly

4 a policy question.

5 DR. BRAIN: So all three have to

6 be --

7 DR. RUSSELL: I think all three

8 have value.

9 DR. BRAIN: But then shouldn't

10 someone express the relative value of these

11 different --

12 DR. RUSSELL: Yes.

13 DR. BRAIN: -- approaches?

14 DR. RUSSELL: I mean and that

15 would, to me, naturally go in, you could use

16 it in the REA and PA and just say this is why

17 you're using the natural background, U.S.

18 background, North American background.

19 DR. BRAIN: So I guess I'm just

20 asking that there be clear guidance for that.

21 CHAIR SAMET: Okay, other comments

22 on this vexing issue? Is it a little clearer?
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1 DR. DIEZ-ROUX: I mean to me the

2 key issue is how the background is going to be

3 used. And I don't know if we want to talk

4 about that now or tomorrow but that's the key

5 issue.

6 CHAIR SAMET: It's certainly a

7 tomorrow issue.

8 DR. HUBBELL: Well, one thing, I'd

9 like to get clarity on that because I'm

10 assuming that that will then feed back to us,

11 because the question of how it's to be used in

12 the policy assessment will ultimately guide us

13 as to what we need to focus on, on terms of

14 producing estimates. And it's very difficult

15 for us to envision a world where we're going

16 to have to produce four times as many

17 estimates for how ever many alternative

18 standards. So it needs to be a little bit

19 more guidance on that.

20 I think I mentioned this morning,

21 you know, my preference is to focus on natural

22 background if we're going to do a risk-based
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1 run, and then provide information from either

2 the sensitivity runs, et cetera, that gives us

3 information about how you would apportion the

4 air quality changes to the HDDM, based on

5 HDDM. So I think it would be helpful to get

6 a little more guidance than simply, you know,

7 the policy assessment needs to address it in

8 a particular way, because we have to generate

9 the estimates.

10 DR. MILLER: And Daniel, could you

11 redefine for us about natural background

12 versus U.S. background?

13 DR. JACOB: Well, Bryan could --

14 okay, but natural background is no

15 anthropogenic emissions anywhere, and pre16 industrial levels of methane. On the planet,

17 yes.

18 DR. MILLER: Okay.

19 DR. JACOB: U.S. background is no

20 anthropogenic emissions in the United States.

21 Including Canada and Mexico. Now you can get

22 it with either ship emissions, aircraft
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1 emissions, those are --

2 (Off microphone comments)

3 DR. MILLER: But does that mean

4 that one of the three of these has more

5 implications for the flexibility you have and

6 what you might attain by choosing an

7 alternative level of the standard? It looks

8 like --

9 DR. JACOB: Well, I mean it does,

10 but this is as Ted pointed out, the policy

11 question. I'm eager to what Lydia and Bryan

12 have to say about this. Clearly, if you're

13 just using the natural background and on that

14 basis you figure out the risk associated and

15 the costs associated with elevated ozone, and

16 then you say, well, what do I do about it? 

17 Well, you have to talk to China about lowering

18 its emissions. And is this something that's,

19 I mean the point, won't that put you in a

20 pickle? Because then you'll have the standard

21 that cannot really net or is not designed to

22 be solely net proof U.S. emission controls. 
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1 But I'm getting out of my depth here.

2 MS. WEGMAN: So yes, I think we're

3 getting a little bit ahead of where we are

4 right now. Chris, this morning, was talking

5 about the fact, and he's correct, this is a

6 decision for the Administrator to make when

7 she comes to consider, he or she, comes to

8 consider the question of what should the

9 standard level be and how to think about

10 background. So that's not a question we're

11 going to be prepared to answer tomorrow

12 because there are both legal and policy

13 considerations that go into the question of

14 how background is or is not considered in

15 selecting a level of the standard. There are

16 multiple factors that will obviously go into

17 the selection of the level of the standard.

18 So I think what we're looking for

19 today is your best advice from the standpoint

20 of the risk assessment as to how we should

21 think about background. What are the right

22 analyses to do for purpose of the risk
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1 assessment that will then feed into the policy

2 assessment? That's really what we're looking

3 for today. And tomorrow we will talk more

4 about the policy assessment, and I'm sorry Ted

5 and Daniel won't be here for that. But we're

6 looking for your guidance today and that --

7 DR. MILLER: Then doesn't that say

8 that it varies down to zero, and the worst

9 case scenario would be the North America

10 background? Those two define the extrema.

11 DR. HUBBELL: Why North American

12 background rather than natural?

13 DR. MILLER: Because it includes

14 more things that you can't control that are

15 there, that are out there, if I'm

16 understanding it correctly.

17 DR. HUBBELL: But again, I think,

18 one thing I want to be careful about is again,

19 determining what is controllable or not

20 controllable. Again, one of the purposes of

21 trying to broaden our understanding of this

22 was to be able to allow, and again this was a
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1 comment that was also made by CASAC last time,

2 which is understanding what risk is coming

3 from manmade or anthropogenic, the source to

4 ozone. Regardless of whether it's from the

5 U.S. or from North America or from

6 international sources, it's good to know and

7 to be able to think about options for

8 addressing international transport. If you

9 haven't identified the risk it's not possible

10 to do that.

11 Our approach of rolling back the

12 air quality values based on first starting

13 with the U.S. emissions and then considering

14 whether you would need to go to North American

15 and then to outside of North American

16 emissions in looking at alternative,

17 simulating just meeting alternative standards

18 allows you to be able to give that information

19 through the Administrator about, will you be

20 able to attain this alternative standard with

21 U.S. emissions? If not, will you be able to

22 attain it with North American emissions, and
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1 so forth. It allows you to have both sets of

2 information.

3 MS. WEGMAN: But I just want to

4 remind everybody that the feasibility of

5 attaining the standards is not relevant to

6 selecting the standard level.

7 CHAIR SAMET: Let me try and

8 phrase it because I may or not have it, so if

9 I don't, tell me. So if you use zero you

10 obviously have all ozone risk. That's the

11 easy case. If you use U.S. background rolled

12 back to current standard, the difference is

13 that risk attributable to theoretically

14 controllable ozone based on U.S. emissions. 

15 And then you can say of 100 percent so much

16 can be addressed by rolling back to different

17 points.

18 The other way to look at this,

19 which I've think you've heard, is to start

20 with the current standard, roll to all the

21 counties to that point, and then have other

22 scenarios where you have more stringent
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1 standards and look at how much risk is

2 reduced. Is that the other way you are going

3 at this or do I have this wrong?

4 DR. HUBBELL: What we are

5 suggesting is that you would start with the

6 current levels of ozone. You would then

7 attain the current standards.

8 CHAIR SAMET: Right.

9 DR. HUBBELL: And then you would

10 continue to use U.S. emissions based on the

11 HDDM runs to just attain an alternative

12 standard and continue going down. Now at some

13 point we may, again we don't know because we

14 haven't done this, we may run out of U.S.

15 emissions and still not have attained. In

16 which case, if we've done the HDDM run with

17 the tags for North American emissions, we can

18 then say, well, what if we also reduced North

19 American emissions, which would be Canadian

20 and Mexican emissions? And then if you didn't

21 attain at that point you could even go further

22 and say, what if you were to reduce
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1 international emissions?

2 I'm not saying that that's how far

3 we'd have to go, it's just that by having the

4 tags in there you could go down that way. And

5 eventually, obviously, if there was still no

6 attainment then you'd have a problem because

7 you would then be down to naturally occurring

8 ozone. I'm not anticipating that's going to

9 be the case, but again that's the layered

10 approach we would take to be able to look at

11 alternative standards. And again, in each of

12 those cases you don't have to specify

13 background specifically. It's done by taking

14 off the emissions.

15 DR. MILLER: You don't have to

16 specify it, but if I understand, by pushing

17 the right button on the HDMA you can get the

18 fraction of the risk reduction that's due to

19 above whatever you ended up with your estimate

20 of background.

21 DR. HUBBELL: With the exception

22 of natural background, yes.
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1 DR. MILLER: With the exception of

2 natural.

3 DR. HUBBELL: But we could again,

4 we could introduce a background, a natural

5 background would do that.

6 DR. MILLER: So that sounds like a

7 reasonable plan. I'm just arguing that I

8 think the Administrator is going to need that

9 information for making the defining decision

10 on whatever to select. And so I think you're

11 going to be charged with getting it and you

12 might as well do it while you're a part of

13 this process. But it seems to me like your

14 hierarchal step-down is a logical way to go. 

15 The only thing I would add in is you've thrown

16 into here questions about -- well, that's

17 actually for tomorrow. Never mind about below

18 60, forget it.

19 CHAIR SAMET: Okay. Other

20 comments on this issue?

21 Ana?

22 DR. DIEZ-ROUX: I think, to me,
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1 it's helpful to distinguish two separate

2 things. One is estimating the deaths

3 attributable to ozone, period, right now,

4 right. And for that exercise, to me, it makes

5 sense to go all the way down to zero because

6 there's evidence that there's a linear

7 relationship. I mean you could argue whether

8 going below the lowest measure, whatever it

9 calls, the lowest measure introduces more

10 uncertainty, whatever, but in principle, if

11 you want to estimate the total deaths or

12 whatever health outcome are attributable to

13 ozone based on the evidence we have so far, it

14 makes total sense to go down to zero.

15 Now a separate question is how

16 many deaths would be saved if we established

17 a given standard? And when you do that you

18 could take say best case scenario, maximum

19 achievable impact would be if you could

20 achieve a standard and see how that changes. 

21 But we know that in reality that might not be

22 achievable because that might imply going
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1 below the background for some locations. And

2 so then you could show estimates accounting

3 for that but you will be able to go below, you

4 know, this background or that background, and

5 you can show different sensitivity analysis.

6 So you have the potential maximum

7 benefit versus the anticipated realized

8 benefit assuming different backgrounds that

9 you will not be able to go below. And that

10 information, I think, is very useful to the

11 Administrator. But I think the first piece,

12 estimating the overall health impact of ozone

13 should not take into consideration the

14 background, because I think, from my

15 perspective, that just muddles the issue. You

16 still have the information that you need in

17 the other section to allow you, what can you

18 actually do. So to me that would be the most,

19 and I think that's the way you've been doing

20 it.

21 CHAIR SAMET: Okay. Well, I think

22 we will have an interesting discussion
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1 tomorrow. Let's see, I think we'll be absent

2 Ted. We'll have Daniel on by phone.

3 DR. JACOB: Early, yes.

4 CHAIR SAMET: Early. All right,

5 and who else?

6 Okay, and some wish they were on

7 by phone, but you'll be here. So tomorrow

8 we'll be starting at 8:30, and onto the policy

9 assessment. So thanks for all your hard work

10 over this couple days, and more tomorrow.

11 DR. NUGENT: Thank you all very

12 much for all your comments on the REAs.

13 (Whereupon, the above-entitled

14 matter went off the record at 4:39 p.m.)

15

16

17

18

19

20

21

22
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circles 351:9

circumstance

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citations 191:15

cited 280:14 286:11

cites 167:18

cities 10:7 71:19

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Clingmans 334:18

clinical 44:17 45:19

49:10 73:13,19

120:4 123:7

132:17 135:10

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193:18 194:1

328:2 333:5

closed 351:9

closely 42:14

322:21 360:1

closer 49:15

closure 28:7

clustered 321:15

CMAC 348:19

373:11 375:3

CMACs 374:1

CMAQ 159:6

161:8 163:20

164:1,5 208:15,20

213:22 215:21

227:18 254:18

258:14,15 279:13

310:13,20 311:3,7

CMAQ-HDDM

130:1 131:13

163:12

coarse 291:21

coarser 215:22

coastal 279:6

cocktail 301:21

Cod 279:7

coefficients 159:10

161:5 189:22

362:21 374:17

cogent 370:10

coherent 370:10

cohorts 175:18

collaboration

222:9,10

colleagues 204:12

278:5 320:18

328:1 358:5

collected 18:16

186:12,13

color 63:12,13 64:2

99:8,14

Colorado 326:21

colors 99:10

column 352:12

combination 83:11

161:11,17 162:2

301:11

combinations

146:14,19 153:10

153:18 160:6,22

162:4,5

combine 186:15

244:9 321:7

combined 230:12

combining 278:7,8

286:22 325:1

come 6:4 16:6,9

26:20 37:21 43:6

43:10 51:22 59:19

69:10 77:5 78:7

78:10 80:15 84:1

115:8,14 116:7

164:18 165:12

184:1 195:16,17

221:16 228:20

230:7 233:5 246:6

247:11,12 264:6

272:8 304:19

308:5 315:16

320:22 321:3

340:3 342:10,22

355:6 356:9 359:9

370:9

comes 28:19 54:4

55:4,22 58:16

103:15 113:13

125:7 186:9 239:4

307:17 315:12

355:4,22 356:2

384:7,7

comfortable

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coming 13:1 28:4

101:6 115:3,17

121:14 174:13

226:21 305:7

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5:16 15:2 23:19

26:11 49:4 77:20

97:3,18 103:20

115:6 116:13

118:3 124:8

149:19 158:6

169:3,12 181:19

182:5,8 189:7

200:9 203:16

228:8,21 234:3

235:9 245:18

246:20 247:8,15

252:3,12 256:10

257:16 263:3

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356:15,20 358:1

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commenter 316:18

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commenters 6:15

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349:9,15

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42:1 45:13 50:11

50:13 51:10 58:21

59:2,8 64:4 66:6

73:4 74:19 80:12

81:20 84:21 85:11

85:12 89:4,6,8

91:12 93:22 94:3

97:5 98:22 101:3

103:5,6 104:15,16

106:18 107:10,13

109:10 111:14,18

117:9 123:21

125:16,19 126:3

126:20 130:5,16

151:5 158:2,3

160:6 182:5 189:5

202:9 228:14

232:6 234:5,7,12

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complicated 7:7

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composite 137:22

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concentrate 287:14

concentration 42:8

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87:20 88:6 97:20

98:16 117:21

125:12 133:1

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129:14,15 231:3

232:4 233:20

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167:13 265:5

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229:5,12,18 256:9

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concurred 261:1

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conditional 135:6

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156:3 208:13

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conductance 326:3

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cone 344:8,9,10

conference 127:8

128:2 136:20

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confidence 67:11

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96:3 98:3 127:2,9

127:11 137:4

185:4 194:15,19

195:1 257:1

350:14,16 351:15

351:19 352:6,19

352:20 364:8,16

confident 77:8

257:12

configuration

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conflated 194:20

confounded 185:20

186:2 303:13

confounding 187:6

confused 42:13

172:3 173:6

271:10,14 369:20

379:16

confusing 59:13,22

86:19 105:4 110:6

272:11 380:1

confusion 110:12

353:8

congratulate

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congratulating

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connected 177:22

cons 70:11 229:17

361:21

consensus 142:14

143:15 187:14

258:10

consequences

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62:14 63:12,20

77:8 84:4 96:7

118:15 120:18

159:22 279:4

324:9 326:18

344:22 384:7,8

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consolidating

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constitutes 300:4

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continuous 14:10

32:15

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contract 143:7

Contractor 2:21

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contributal 68:6

contribute 65:21

contributing 167:2

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contribution 29:8,9

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control 148:14

152:3,4 153:21

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77:19 165:9 362:5

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39:14 44:2,17

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conversations

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73:4

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conveying 90:7

convince 372:5

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88:21 97:13

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copy 121:16 232:9

core 160:21

corn 244:8

correct 27:17 34:20

36:6 75:9,9,11

77:1,1 99:15

120:1 127:7

176:16 184:11

185:1 201:12

251:1 263:9 276:7

304:13 348:19

378:20 384:5

corrected 27:13

31:12

correcting 26:19

27:18 140:5

correction 26:9

31:13

correctly 36:2

168:17 226:5

292:7 318:12,17

385:16

correlated 39:17

276:13

correlating 146:2

correlation 89:21

138:22

cost 239:16

costs 12:2 383:15

cost-benefit 336:21

cottonwood 284:7

286:17

Coulston 227:13

count 100:22

counter 63:9

counties 140:16

348:21 387:21

country 76:8 94:16

148:4 155:7 239:3

241:7 244:20

249:3 250:11

263:13 302:9

326:17 330:12,21

332:11 349:4

354:22 376:1

County 348:22

couple 15:9 34:3

39:4 64:4 67:7

70:18,19 73:11

99:2 101:4 107:15

128:19 129:5

136:21 162:18

207:15 211:17

219:1 242:4

255:19 265:13

287:6 295:17

312:1 327:6 330:6

334:1 340:3

365:19 371:2,21

371:22 393:10

course 33:14 54:17

260:21 289:9

300:3 338:7 354:4

cove 289:9

cover 47:11 70:17

131:20 158:13

215:9,9 217:14,20

224:7,12

coverage 349:3

covered 37:20

126:4 168:12

308:22 339:2

covers 73:2

co-pollutants 118:7

185:15 188:17

CR 64:12

crack 357:18

Craker 334:19

create 116:3 197:6

250:2

creating 176:6

creative 113:14

233:14

credibility 55:2

credit 19:19

cringe 376:17

criteria 64:20

112:6 283:18

284:5,11 312:16

312:19 320:14

critical 22:5 123:1

201:9 214:4 237:4

246:11 259:4

279:3 299:17,19

327:18 345:20

criticism 193:1

197:22

criticisms 123:18

criticizing 108:21

crop 218:21 230:2

236:16,21 237:6

237:17 238:12,21

239:20 241:3

243:21 244:8

250:18 319:9,14

319:15

crops 237:8 239:17

240:9,20 243:22

248:7,9,10 249:8

318:16 319:4,6

cross 161:6

cross-check 16:5

crow 100:8,17,20

crucial 23:16

crypted 201:16

cultivars 323:16

cultural 219:19

220:8 334:15

336:18

culture 20:11

cumulative 287:21

curating 182:2

curious 100:17

333:21 337:17

current 10:1 19:8

25:2 33:18 34:8

34:11 49:13 56:2

67:1 68:3 70:7,12

79:22 82:2 116:20

117:13 136:13

137:1 138:8

155:22 156:3

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curve 268:10

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cutoff 291:22

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data 9:19 13:20

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18:12,13,16 19:3

24:1,17 35:19

36:10,11 37:11

41:15,16 43:14

49:10 65:5 71:10

71:12 73:18 76:16

77:17 89:16,17

97:16 98:1 99:19

99:22 108:17

109:1,6,6 134:1

135:2,7,10,11,14

135:16,20 136:1,3

137:8 138:6,13

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144:8 145:7 150:3

150:22,22 151:3,4

170:10,20,21

175:1 178:9 179:2

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179:15,15 180:9

180:14,16 181:18

182:7,10,13 183:9

185:14 186:11,13

186:15 188:6,7

191:2 193:2 194:6

194:7 196:4,7

197:7 202:4

203:16,18,21

204:7,11 208:14

208:15,19,20

209:16 211:21

212:15,15,19

213:17 214:12

215:5,8,9,14

216:10,15,17,21

217:14,20 220:17

222:22 227:7,15

227:17 228:1

230:2,11,19,20

231:11,11 232:20

250:14 253:19

254:1,10,11

255:11 257:1,9,11

259:19,22 261:16

262:1,2,6,7,12,13

277:21 278:3,8,18

278:21 279:1,9,10

279:11,13,15,16

279:22 280:18

281:11,14 284:19

285:10,14 293:1,7

297:21,21 310:14

313:19 314:1,7

315:2,5 319:7

321:2,7 325:22

326:16 331:2

339:5 340:14,15

348:12,14,18

350:22 354:4,7

362:1,9 365:1

373:10

database 8:3 15:8

17:7 21:14 41:9

49:18 174:2

180:12

databases 8:5 9:13

10:2 41:6 181:20

data's 139:13

Dave 294:9 316:16

316:17 330:19

339:9

David 2:3,4 4:2,5

117:10 280:4

Davis 2:13 330:16

330:17

day 5:5 6:9 20:7,8

193:21 356:13

DayCent-Chem

326:18 328:20

days 15:18 116:17

159:14 295:17

393:10

day-to-day 28:20

29:18 33:3

deal 25:5 136:7

144:5 222:3

236:14 265:8

297:15 354:1

dealing 169:20

186:11 272:2

305:1 377:4

dealt 52:22 53:1

54:6,7 138:3

165:17 247:21

303:5

death 63:15

deaths 57:14,15

59:18 60:2,2,4,5

61:1,4,5 63:13,19

114:1,17 391:2,11

391:16

decade 182:11

decades 15:9

236:18

decide 113:10

343:12

deciding 299:2

decision 80:4,6

111:20 131:21

377:11 384:6

390:9

decisions 91:17,22

162:10 248:7

decoupled 70:9

decrease 93:11

376:14

decreases 338:19

338:19

decrement 26:20

27:7,8,10,11,13

28:2 30:9,10,14

48:7

decrements 29:13

31:12 47:22

dedicated 237:6

defend 47:17

295:15

defending 84:13

defends 64:9

defensible 70:3

295:13 321:6

341:8

defer 82:20 284:3

deficiencies 8:4

define 69:16 132:5

148:5 268:7

293:15 300:3

373:13 385:10

defined 149:21

202:22 276:7

288:8

defining 390:9

definite 159:21

definitely 42:11

46:18,20 216:7

226:9

definition 26:14

68:6 69:11 72:8

132:1 275:18

346:13 379:16

definitions 69:21

132:3,7 164:13,19

166:18

degree 150:5

314:11 373:15,17

degrees 54:12

deleted 102:9,16

deliberations 6:2

362:12

delicate 315:19

delta 26:14,21 27:4

32:11 35:6 127:10

301:16

deltas 199:18

350:16 352:9,15

demographer

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demonstration

264:5

denominator 203:1

dense 289:9

density 100:21

194:7

depend 301:16

dependence 359:15

dependent 132:10

192:18 291:16

depending 148:22

352:17

depends 359:7,17

deposition 289:13

depth 171:12 384:1
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describing 14:14

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description 10:15

descriptions 56:9

descriptive 19:11

61:15

desert 323:18

deserve 237:14

design 197:10

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designation 58:4

designed 383:21

detail 22:17 56:7,8

87:10 89:5 99:5

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detailed 18:17 89:4

107:13 111:14

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details 12:16 137:6

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detriments 238:5

Detroit 153:4,6

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developed 326:20

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developing 130:8

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233:12 315:20,21

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devil's 230:14

devoted 27:1 67:12

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diagnosed 261:9

diagram 23:20

124:15 129:16

diaries 177:20

diary 177:21

dice 270:4

dichotomous 14:11

142:6

died 297:14

Diez-Roux 1:19 3:7

13:18 58:20 74:21

75:15 76:1,20

77:10 78:15 381:1

390:22

differ 76:8 166:20

difference 27:5,12

28:22 29:15 30:8

31:14,16 34:21

36:11,14 72:4

80:2,4 100:10,16

105:3,11 169:15

173:2 183:14

184:6 187:18

189:10,11 190:3,9

190:13 197:18

282:1 312:14

320:8 327:16

340:17,19 352:15

362:18 387:12

differences 41:3

44:6 54:10 72:17

79:15 83:2 87:18

87:18 88:7 89:22

90:2,5 138:17

141:10 181:7,14

272:5,7 274:4,8

274:14,21 321:8

327:15 352:17,21

365:10

different 15:11

16:1,16 24:2

28:11 42:4,7,10

52:21 54:2,12

57:10,16 60:11

61:2,7 66:4 69:21

71:19 72:2,9,21

74:12 76:15 78:21

83:7,12 89:11,14

93:8,10,11,18,19

96:6,7 98:19

99:10,14 110:6

121:19 125:7

128:20 129:1,17

132:5,19 133:9,14

133:22 134:1,4

136:21 137:8,13

138:5 145:4 149:6

149:6 152:1 154:1

154:6,15 164:19

184:1 188:12,12

198:9,16 208:9

210:7 215:13

219:2 223:8 229:2

233:16,17,18

236:15 237:16

249:3 259:12

267:16 268:21,22

272:8,14 278:6

284:21 292:16

294:15 298:22

303:17 305:6

317:16 323:3

340:8 351:5 361:8

362:9 366:9,13,17

371:3 374:11,12

380:1,11 387:16

392:5,8

differential 46:2

187:10 240:12,15

313:7

differentiate

151:15

differently 40:11

275:10 278:1

364:15

difficult 120:9

121:9 122:2 149:7

155:11 170:12

176:11 214:14

248:2 249:9

278:22 321:16

323:12 351:3

370:12 381:14

difficulties 209:21

difficulty 195:11

196:4

digestibility 338:12

338:20

dilating 30:6

direct 80:4 203:9

direction 12:1

49:17 191:19

234:9 291:9 295:2

333:2

directional 304:10

304:15

directions 11:20

directly 139:12

176:21 212:10

346:21 373:2

Director 2:12

disaggregate 250:3

disaggregations

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disagree 76:4

disagreement

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disbenefits 160:21

discovered 297:7

discuss 9:17 11:19

21:8 37:20 67:15

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64:10 71:1 99:19

129:10 143:20

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209:22 303:8

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discussion 3:16

6:20 8:20 9:3,9,13

11:12 25:12,20

30:19 44:12 52:8

52:8 57:17 59:4

65:14 70:10 81:15

81:17 83:20,21

87:5 89:11 96:19

97:11 109:8,12

110:16,17 121:7

121:10 122:11

126:7,10 131:20

143:3 172:2 181:4

192:15 194:18,19

195:9 206:21,22

208:22 228:17

229:16 230:16,20

247:5,13 254:9

259:1 261:18

269:16 272:9

273:1 275:4 276:5

276:21 281:12

284:13 306:18

337:15 347:1

349:6,11,13

369:19 370:7

392:22

discussions 65:19

70:16 78:13

194:18 295:17

diseases 22:8 62:13

disentangle 248:12

disingenuous 75:13

disruption 339:4

distance 227:14

distinction 104:1

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diurnal 366:19

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division 142:19

DLEM 333:19

doable 149:16

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document 19:6

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documented 23:3,5

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documents 25:1

125:5 212:11

245:5 272:12

342:16,18 343:8

343:22 344:7,15

345:13 346:1,3,11

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doing 29:3 31:8,9

32:6 34:18 43:13

68:14 76:14 79:2

79:16 82:9 85:18

91:11 102:11

124:9 132:9

133:15 134:4

138:5,21 145:9

148:7,9 155:18

156:19 162:3,9

164:19 165:19

169:14 170:2

176:7 203:4,8

204:15 206:9

222:2 229:21

234:5 235:11

267:10 269:13

271:11 272:3

275:8 277:2

280:22 309:1

315:4 319:12

330:7 332:6,17

336:21 342:4

345:6 361:11

372:13 375:12

376:11 392:19

domain 152:14,21

271:1 325:19

369:16

domain-wide

369:13,15

Dome 334:18

domestic 379:14

dominant 161:3

dominated 325:18

door 163:15 370:4

dose 42:7,8,15 50:1

184:4 280:15

doses 43:4

dosimeters 200:13

double 90:13

174:19

double-counting

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doubly 15:19

Downey 363:10

364:22 366:21

368:4,10,12 369:9

369:12,15

download 158:18

158:21

downloaded

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downs 100:20

downwind 94:9,22

95:3,6

Dr 3:3,4,4,5,6,7,8,9

3:10,12,13,13,15

3:18,20,21,22,23

4:1,2,2,3,5,6,7,8

4:13,14 5:3 6:8,22

7:6 11:7 13:18

15:2,5,6 17:4,8

18:4,15 19:1 21:1

22:9,15,21 25:22

26:3 31:19 33:7

34:2 35:1,4 36:8

36:15,18,22 37:3

37:19 40:18 44:11

45:12 50:15,19

51:1,5,11 58:20

62:16,21 72:13,15

73:8 74:6,8,10,21

75:11,12,15,17

76:1,12,20 77:2

77:10,12 78:15

79:9 80:13,14

81:21 82:7,14

83:6 84:3,17,22

85:3 91:10 95:20

97:1,8,10 98:21

100:11,15 101:1,7

101:12 102:6,15

102:20 103:2,4

107:6 113:20

116:12 117:11

118:2,19 119:13

120:21 122:7

123:4 124:2,4,6

125:18 126:13,17

126:18 127:14

128:8,15,18

142:18 143:6,10

144:3,12 146:5,8

147:5 148:13,20

149:10,17 150:4,9

151:6,17,18

154:10,13,14,17

155:14,18 156:8

157:11,15 158:15

158:20,22 159:2,7

159:10,15,17

160:3,7,11,16

161:4,20 162:11

162:16,20 163:4,6

163:14,16 164:3,8

164:15 165:20

166:3,12,15

167:15,21 168:3,5

168:9,14,22

169:11,22 170:7

170:16,19 171:3,4

171:8,15,18,22

172:4,13,18,20,22

173:8,15 174:8,16

174:18 175:4,5,8

175:19 176:3,13

176:16,20 177:3,7

177:8,13,15,16

178:2,3,11,15,20

179:18 180:3,6,17

180:21,22 181:3

181:12,13,15,16

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183:12,17 184:7

184:10,14,16,20

185:2,10,13,17

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196:12,13,18,20

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202:10,12,14,17

202:19 203:5,7,11

203:19 204:6,14

204:20 206:8,13

206:16 226:4,10

227:5,9,10,12

228:12 229:1

231:14,17 233:7

239:15,18,22

240:3,4,7 246:18

247:14 249:16

252:6,11,15

253:11 260:7,12

261:7 262:9,17

263:9 264:16

267:7,18,20 268:1

268:8,12,13,16,18

273:2,7,11 274:1

274:13 275:5

276:12,16,20,22

277:9 280:7

281:13 282:9

283:11,21 284:8

284:22 286:20

290:19,22 291:1

291:14 292:6,12

292:17,18 293:9

293:12 294:7,10

296:17 298:19

300:1,8,13,21

301:3,5,22 302:13

302:19 303:7,17

303:21 304:4,9

305:1,13 306:10

306:11,15,17

308:14,18 309:1

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draws 176:7

drift 274:8 304:7

drive 327:8,16

drop 100:3 357:18

drop-out 42:20,21

drought 240:1

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dry 282:1

due 29:2 30:10

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75:21 79:17,19

116:17 238:21

239:9 303:15

335:11 389:18

Duke 290:10

duplication 99:1

343:22

DV 160:1

dynamics 268:21

E

eager 383:11

earlier 69:5 194:13

200:1 251:2

252:20 265:21

285:1 324:2 342:9

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early 21:17,18

104:13 212:16

322:4 355:12

393:3,4

Earth 363:10

easier 12:22 47:19

48:13 99:16

307:22 308:1

easiest 144:1

easily 24:21 41:19

42:22 47:16

125:20

east 209:1 224:6

eastern 212:17

214:10 217:6,9

241:15

easy 44:19 104:19

248:12 297:16

301:15 387:11

echo 103:6

ecological 4:1

208:2 210:14

211:1 271:12

272:4 296:22

307:11,16,18

338:6

ecology 337:16

economic 238:3

239:19 247:19

249:11 318:14

economics 247:21

320:13

ecosystem 4:4

208:2 211:16

214:17 218:13,14

218:15,18 224:14

233:19 237:2

299:7 305:19

306:19 307:12,14

316:15 334:11

337:10 339:3

ecosystems 213:16

Ed 2:1 3:10 21:10

42:2,14 43:1 94:1

170:7

editing 232:9

education 100:6

effect 7:19 28:6,9

28:10,12 29:22

31:5 54:11 55:6

57:12 65:10 76:5

93:3,19 96:4,10

96:11 105:2 127:2

127:17 136:18

138:19 139:2

187:1 189:2 190:1

190:3 201:19,22

210:20 221:21

222:3 239:5

240:21 241:22

242:13 243:8

256:15 290:16

305:15 327:20

338:7,9 359:21,22

364:17 367:17

effected 219:12,13

effective 122:2

163:2 280:15

effectively 359:20

effects 4:1 35:16

54:18 59:14 61:22

62:3 96:12 98:2

112:15 208:2

210:15,16 211:1

211:12,14 213:14

213:15 218:21

221:2 222:14

233:16 243:18,21

244:9 249:10

252:22 256:9

282:4 299:1

307:12,16 337:18

338:12 360:22

efficiencies 234:3

efficient 234:10

effort 147:21

164:22 233:11,15

237:5 252:19

285:7 313:15

315:15 324:4

357:3 358:11

efforts 234:10

eight 194:2 204:17

214:11 223:2

255:9 364:7,12

Eighty-five 160:15

eight-hour 209:7

263:5 264:17,18

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65:20,21 92:18

111:7 113:8

139:11 153:7

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element 53:7

elemental 87:6,7

elements 129:17

189:15 274:17

elevated 167:11

255:10 256:6

257:14 383:15

elicit 235:14

eliminated 102:14

eliminating 243:11

304:22

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eMaps 56:15

emerge 367:16

Emery 353:4,6

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emission 160:18

363:3 383:22

emissions 83:18

104:3 132:11

145:4 150:19

151:12,13 154:11

164:2 165:8 359:7

359:12,16 361:10

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epi 51:16 69:9

73:16 74:17 88:2

88:10 89:12 96:4

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epidemiological

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epidemiologist

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epidemiologists

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equal 209:9 237:5

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equations 46:2

275:6

equipment 43:17

equivalent 190:6

248:18

error 29:2,17,18

52:20 53:6 54:8

54:13 116:22

140:5 141:7

187:10 201:12

256:18

errors 49:17

ESA 334:1

esoteric 307:1

especially 5:22

12:22 104:7

130:13 141:8

145:9 230:9

231:22 249:8

290:9 335:16

essentially 19:22

174:11 193:6

238:22 280:16

292:3,20 306:7

313:22 315:15

327:12 351:4

352:14 360:8,9

371:4,6 372:13

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estimate 53:17 55:6

57:11 76:21 83:7

95:13,18 96:11,11

98:4 110:3 111:7

112:22 135:21

163:18 185:22

187:1 198:11

250:10 389:19

391:11

estimated 14:5

54:18 76:6 106:9

estimates 13:21

14:8 54:11 57:8

60:11 61:18 62:1

65:10 67:14 68:5

72:3 74:18 78:9

78:17 81:10 93:3

93:19,20 94:8

95:21 96:4 98:10

98:19 105:18

108:12 110:8,10

111:4 118:9 120:5

127:3,4,9,10,17

132:10,20 133:5,7

135:9,13 136:18

137:13,15,18

138:19 139:2

140:2,4,9,15,18

140:19 149:11

165:1,17 169:4

174:14 185:19

187:12,14,15

189:3,21 190:1,4

197:18 198:6,9,15

201:19,22 202:3,7

215:9,19 222:13

317:15 351:1

371:1 381:14,17

382:9 392:2

estimating 18:3

70:13 75:1 76:2

108:10 131:12

137:4 163:11

170:1 391:2

392:12

estimation 75:10

194:8

et 92:14 93:7 94:10

130:11 135:12,12

138:6 140:11

159:6 201:19

202:3,8,13 243:6

243:14 266:13

286:11 324:19

329:2,4 349:12

351:8 356:16

382:2

ethnicity 41:3 44:6

46:5

European 276:1

evaluate 12:11,15

130:3 146:12,14

150:2 153:12

evaluated 161:17

328:21

evaluating 18:13

131:12 133:19

136:14 174:10

evaluation 132:11

161:13,16 163:10

358:11

evaluations 173:5

evens 239:12

event 261:9,14

262:2,7,13

events 256:3,5

257:16,19 260:15

260:16 261:1,12

261:17,20,21

306:2

eventually 79:3

389:5

everybody 56:14

78:3 200:19 206:4

269:7 276:14

298:4 387:4

everyday 186:12

186:13

everything's 61:12

evidence 58:5

73:14 110:20

166:13 185:18

236:18,22 303:14

391:6,13

eVNA 208:17

evolution 302:20

evolutionary 306:8

evolve 188:5

exacerbate 242:6

exacerbation 54:20

exacerbations

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exact 208:7 224:20

266:13 286:14

293:5 317:13

exactly 38:3,8

76:13 78:16 82:4

145:20 200:15

225:10 226:17

272:22 282:18

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exceedance 155:16

exceeded 288:2

exceeding 209:12

excellent 65:20

exception 226:21

389:21 390:1

exceptional 256:3

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excessive 117:21

excluded 260:17

261:16,16,21

exclusion 260:16

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executive 119:6

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31:12 141:18

372:9 391:4

exercising 28:1

exist 8:5 68:18

278:9

existing 250:14

291:5,11 329:22

exists 37:11

expand 159:20

213:9

expanded 106:16

expanding 65:8

132:17 160:1

220:16

expansion 174:2

expect 35:2 98:18

129:18,19 243:9

303:19 314:12

354:1

expectation 320:15

expected 126:6

expecting 234:15

experience 167:10

289:10 362:8

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experiencing 50:2

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experiment 285:2

331:14

expert 195:21

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experts 356:3

explain 160:11

201:15 284:5

explained 35:20

64:1 72:7 103:22

139:5

explaining 27:1

49:1 82:16 86:11

186:8 274:3

339:18

explains 181:13

explanation 33:17

63:2 102:8 258:1

304:3

explanations

106:19

explicit 116:5

124:17 125:14

189:1

explicitly 20:15

125:3

exploit 279:19

explore 18:20

159:12

exploring 120:10

163:7 210:7

225:17 226:11

227:1

expose 378:11

exposed 14:12

27:14,22 28:3

117:15

exposition 192:16

286:8 379:13

exposure 3:2,17 8:2

13:21 14:8 15:18

22:13 27:6,6,9,10

28:5 30:5 31:11

32:15,21 36:3

37:6 38:17,18

39:5 42:5,10

43:12 45:21 49:9

52:19 53:5 54:1,8

54:12 58:10 63:8

65:2 71:7,8 73:18

73:19 74:14 75:6

81:14 89:11,14

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115:19 118:6

120:4 125:14

126:9 132:17,20

133:6,13 134:6,6

140:4 141:6 143:7

147:12 150:2

156:15,21 165:17

166:20,22 167:3

168:18 169:3,16

170:9 175:17

176:10 177:14

180:8 181:7

200:10,21 205:8,9

206:6,15 215:18

224:11,18 225:16

238:12 247:2

250:6,7 251:4

274:17 283:1

288:16 311:5

313:19 337:18

341:11

exposures 21:18,19

22:10 24:3 25:6

29:6,8,10,13,21

32:20 38:18 39:14

42:15 67:18 75:21

77:4 79:18,19

95:1 107:17 108:8

108:14 112:22

134:14,15,17

136:16 141:11

149:13 166:7

170:1 175:16,16

177:17,19 236:19

exposure-response

242:18 292:20

314:9

express 379:20

380:10

expressed 331:14

extend 90:22 91:4

97:11

extent 20:12 98:5

140:8 146:15

166:19 167:6

168:11 190:15

199:17 230:2

233:21 313:22

317:8 328:7

346:22

extinct 300:17

extirpated 297:5

extra 67:3 164:22

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278:22 310:4

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extrapolated

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extrapolating 93:2

96:4 97:16 98:1

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extrapolation 39:4

50:1 318:7

extrapolations

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81:16 86:19

109:18 122:1

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faced 296:21

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fact 15:8 21:22

26:22 27:3 30:1

38:21 39:1,6 52:3

64:12 76:7 78:10

96:8 108:22

148:15 154:19

162:21 167:1

178:4 183:18

194:14 242:2

248:16 290:5,14

291:19 310:15

331:5 374:6 377:3

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factor 41:11 50:8

69:3 80:11 130:18

133:3

factors 65:9 140:11

188:16 318:11

327:7 384:16

factually 127:7

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fair 161:13 252:22

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fairly 18:13 46:1

66:17 244:1

251:15 288:5

304:12 312:6

323:17 329:14

331:8 354:20

fall 169:10

falls 96:15 116:21

117:2 181:5

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false 244:16

familiar 62:1

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fan 57:4

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final 43:5 58:3

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110:5 112:14

152:8 155:6

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311:7 340:10

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finish 163:7 306:12

finished 126:2

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Finney 47:6

fire 150:10,17,18

151:3 220:6

225:15 226:19

256:5,9 269:20

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150:14,15 210:1

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255:14,17,18,20

255:21 256:12,15

256:20 260:17,20

261:10,17 262:21

first 7:1 15:3 23:2,8

26:4,11 38:13

49:8 51:15 59:9

62:21 71:14 81:13

91:14 114:3 115:7

118:14 129:6,12

139:10 151:10

178:14 179:11

228:21 253:18

281:6 316:18

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352:11 364:5

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flies 100:8,18,20

floor 79:11 290:11

flow 11:10 23:20

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focal 309:10

focus 21:12 154:2

165:21 189:16

236:4 299:10

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focused 90:22

214:18 215:15

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83:10 259:13

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217:12 218:1

224:2,8 277:15

292:21 293:3

314:6,11 315:2

336:13 338:18

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213:18 216:10,14

220:20 222:7,8,21

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278:20 289:9

290:10,11 315:3

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277:10,16 298:9

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313:15 320:12

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110:12 127:8

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158:7 159:8 161:3

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181:21 183:3

185:5 187:6 189:5

190:6 192:7,11,18

192:22 193:1,4,5

198:8 199:11

200:19 204:16

205:6 206:13

207:18 208:7

224:19 225:9

226:17 228:13

236:4,18 239:8,10

240:6 243:5,12

245:15,16 251:8

252:9 254:20

258:10,14,15,17

259:17,21 261:3

262:18,20,22

263:12 265:1

270:5 271:13

272:10,13 274:7

280:7,22 283:15

288:8 289:10

291:9 294:11,14

295:2 297:18

300:16 302:6

306:12 307:21

308:5,9 311:13,18

313:1 316:5

317:13 318:5

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333:9,14 335:1

341:17 343:4

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351:21 353:9,18

353:22 355:11,21

358:17 362:5

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366:13,14 367:18

369:18 370:17

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384:11 388:2,12

389:8 390:8,11

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good 5:3 7:7,10

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86:10 90:6 96:21

105:15 106:17

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266:21 267:13

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354:7,16 355:13

355:17,20 360:20

361:5,13 372:15

373:18,20 386:6

gorge 310:16,21

gosh 336:3

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gradients 151:2

grandson 15:10

grant 67:22

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209:22 214:22

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165:18 173:12

239:3 240:15

242:17 243:3,6

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greenhouse 220:20

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grid 94:9 202:20

213:22 215:21

227:18 228:1

263:7 264:1,1,14

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growing 241:15,17
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55:21 58:21 60:16

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119:3,9 120:21

121:6 122:3 171:8

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317:3 320:20

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328:5 341:15

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349:17 352:10,12

375:17 380:19

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haphazardly 125:8

happen 11:4 43:3

116:19 142:9

152:18 300:16

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happening 92:13

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happy 185:12

239:9 312:16

313:8 315:10

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90:14 96:17

101:20 103:4

105:17 115:21

117:1 121:17

123:17 128:1,18

138:9 143:10,18

144:5,7 146:21

154:12,13 159:4

161:9 163:21

170:13,19 174:15

177:1,20 181:12

183:16 184:7

185:2 196:20

197:11,12 200:5

203:15 206:7

240:3,3 246:15

252:8 256:21

269:11 272:20

296:3 308:7 314:5

317:21 327:8

336:6 338:3

345:22 357:7

364:11 368:8

373:3,14 374:22

375:14,15 378:5

379:3,10 384:4,21

388:8 389:17

391:3,4 393:4

rippling 145:22

risk 3:9,16 6:16

19:16 20:3 22:2

25:8 36:9 41:11

45:3 52:16 53:17

54:15,20,21 57:22

65:13 66:16 67:13

67:13,20 68:6,9

69:16,19 72:1

74:18 75:18,20

77:3,18 78:10

79:6,17,17,18

80:18 81:10,13

84:9 85:20,22

86:20 105:17

110:20 111:9

112:22 113:7

115:19 118:6,9

120:4,5 123:7

126:9 127:10

129:18 132:2,17

132:20 133:3,4

135:3,21 136:11

136:16,20 140:1

156:5,14,15,21

164:22 165:7

166:21 169:16,21

170:3,5 173:12

189:21 192:2

197:18 198:5,5,8

198:11 199:1

205:7,9 206:5,14

210:21 211:6

218:2 224:10

225:15,16 226:19

233:10,12 234:13

246:7 251:7,9,21

272:3 277:15

288:10,17 289:2

289:11 293:17,17

297:4,13 300:14

308:5 311:22

312:4,13,20,21

314:15 315:20

317:19 328:12

337:7 339:21

341:11 343:1,8

346:9,16,17

351:18 354:20

355:10 378:10

383:14 384:20,22

386:2,9 387:10,13

388:1 389:18

risks 69:8,13 70:13

136:22 137:1

147:13 294:21

307:12 353:10

risk-based 381:22

risk-wise 307:22

Riverside 365:20

367:9 368:6,12

road 164:16 201:1

robust 46:1 49:13

49:18 178:9

197:22 232:21

Rocky 215:1

role 57:2 339:10

roll 210:4 265:20

387:20

rollback 82:17

117:19 145:17

151:21 152:17

154:18 209:5

210:3,6,11 260:8

260:9 261:2 262:5

266:11,18 267:16

270:6,12,13

272:14 351:11

354:19 363:18

365:8 366:2,9

368:20

rollbacks 145:17

rolled 209:6,14,15

213:5 251:15

263:16,21,22

264:2 266:16

368:5 387:11

rolling 145:15

260:21 386:11

387:16

room 53:7 253:4

284:4 360:7

root 290:7

round 119:1 227:8

348:17 355:22

route 370:20

RTI 2:21

rule 261:21

ruled 351:18

rules 151:6

run 143:21 145:2

146:18 148:8

150:12,14,16,20

151:2 222:9 223:1

326:11 353:18,22

354:9,13,15,21

359:18,19 361:3,5

361:6,7 362:14

365:4,7,12,13

382:1 388:14,16

running 101:4

354:12 362:16

runs 145:2 146:17

146:18 147:3

161:14,18 162:1

225:5 226:14

361:8,15 382:2

388:11

rural 94:9 95:22

96:5 105:18 166:1

166:13 167:4,7

268:22 269:6,20

368:16 369:5

Russell 1:20 3:10

3:13,23 91:10

113:20 144:3,12

146:5 156:8 159:7

159:10 160:7

161:4,20 163:4

164:8 172:18,22

200:22 201:8

268:18 273:11

348:1 360:5,14

362:20 370:13

371:22 373:15

374:3,6,21 376:9

377:15 378:15,21

379:1,6 380:3,7

380:12,14

S

SAB 2:12 5:15

sacrifice 337:1

safer 337:2

salary 337:1

Samet 1:13,16 3:17

6:8,9 7:5 11:6

13:17 14:21 15:4

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40:17 44:9 45:11

50:12,16,21 51:3

51:6 58:19 62:15

62:19 73:7 74:9

74:19 77:20 78:22

81:19 82:13 84:1

84:15,19 91:9

94:1 97:2,6 101:2

101:8,18 102:3,21

103:3 107:5

113:18 115:5

117:9 118:1,18

122:5,17 123:21

124:3,8 126:1,16

127:22 128:12,17

141:17 143:4,14

147:4 150:7 151:5

156:10 157:8,13

157:16 158:12,18

159:4,8,12,16,20

160:5 163:9

164:11 165:11

168:16 171:10,17

171:20 172:1,6,14

173:3,13,17,22

174:12 175:2,15

176:1,17 177:1

178:6,13,17 181:1

181:17 184:8,12

184:15,17,22

185:3,11 187:20

189:6,9 190:14,17

190:19 191:6,22

192:13 193:10

195:4 196:9,15

197:11 199:13,22

200:4,18 201:11

201:14 202:8,11

202:13,16,18

203:3,12 204:22

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scene 373:22

schedule 101:5

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205:12 270:20

354:14 356:12

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Schelegle 23:1 43:1

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Schelegle's 38:21

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scheme 64:3

school 41:14 48:5

175:18 176:8,14

Schwartz 72:5 93:7

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140:19 201:21,22

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science 20:21 77:8

112:13,18 294:6

323:3 343:3

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science-based

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scientific 1:3

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scope 3:19 207:17

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scrambling 48:11

screen 43:7 128:10

screening 218:8

291:6

screening-level

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scroll 169:2

se 153:2 375:22

search 286:5

searched 17:20

Seashore 279:7

season 72:9 76:17

147:8 148:4,6

149:2,4,22 353:21

seasonal 250:6

366:16

seasonality 149:14

seasons 147:19

seats 206:4

second 5:5 8:7 14:7

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section 9:4 54:14

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sections 107:1

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sectoral 238:8

sectors 151:16

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seeing 31:14 56:22

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segregate 39:19

select 64:21 390:10

selected 102:18

222:20

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384:15 387:6

selection 52:5

58:22 177:21

303:16 304:11,16

306:8 384:17

selections 331:18

semi-final 355:8

send 6:3 16:4 17:5

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236:9

sense 19:18 20:13

29:14 33:6 55:3

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sequestration

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serious 324:9

server 175:13

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service 207:8

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220:8,21 224:14

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SES 169:15 170:2,4

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SESs 172:3

session 228:8

set 10:10 18:14

24:5 48:3,7 51:15

68:17 92:10 94:12

134:7 140:16

173:9 175:11

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365:12 369:21

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182:7 186:15

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387:1

setting 19:17 20:4

56:4 58:7 81:5

setup 379:8

set-up 70:7

seven 43:13 62:18

321:12

severability 362:11

shape 65:12 223:16

share 136:3

sharing 181:18

182:11

sharpen 21:12

sharper 125:4

shift 16:10 366:6

367:2 368:2 369:3

shifted 182:11

shifting 63:4

shifts 303:15 305:8

ship 382:22

shock 55:4

Shore's 41:13

short 71:7,8 305:3

shorten 308:11

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shortening 343:5

shorter 148:10

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short-term 109:20

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show 6:12 30:5,7

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75:18,19 162:18

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shown 14:2,9 290:9

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shows 73:14 180:7

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shrinkage 51:20

shrubs 217:16

shrunken 127:4

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sides 365:15 369:3

sign 277:19 346:15

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significant 46:14

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similar 21:2 25:13

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Similarly 244:12

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simulating 264:13

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skipping 233:4

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Smoky 214:22

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soybeans 241:4

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so-called 373:9

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sparse 270:2

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spatially 134:9

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wrestling 299:5

write 121:9 182:4

203:20 341:17

345:16 370:10

writing 116:13

written 13:6,13

40:15,20 90:22

104:16 112:5

189:5 280:10

294:12 347:9

wrong 36:5 49:17

112:19 228:1

244:18 304:13

330:10 335:21

388:3

wrote 89:4 107:13

181:8,9 339:3

345:3

W126 208:10,20

209:7,16 254:2,16

256:20 257:14

258:8,15,16 259:6

259:11,18 262:19

264:21,22 268:3,8

282:12,22 283:5

284:16 285:6

287:21 293:11

W126s 284:14

X

X 199:10

Y

Y 351:4

year 15:12 140:7

144:16 145:9

146:18 147:21

148:4,9,14,16

149:13,20 157:18

202:4 204:18

242:9 303:10

305:6,6 353:18,22

354:3,5,6,22

years 9:21 15:12,18

35:8 85:14 110:9

112:5 132:11

134:1 150:19,22

151:4 196:17

204:17 232:21

236:20 249:20,22

250:7 268:15

283:12 288:17

303:11 304:1,16

312:15,19,21

321:11,12 334:22

344:21

years-old 169:5

year-round 148:6

149:8

year-specific 131:1

year-to-year

304:14

yesterday 12:13

16:22 21:5 23:4

44:7 69:1 70:11

78:13 81:5 126:19

129:7 131:11

158:6 164:4

188:22 366:22

379:12

yield 230:2 236:16

236:21 237:6,17

238:15,21 239:7

239:14 241:2,8

250:10,18 251:12

324:1

York 57:12 141:9

148:11 193:17

Yuquan 2:21 319:1

Z

Zach 73:7 126:14

193:14

ZACHARY 2:17

Zanobetti 57:8

93:7 127:6 138:18

140:18 201:21,21

351:7

zero 22:1 56:22

57:1 61:3,19

67:19 68:1 69:15

79:2 98:13 103:15

103:19 104:9

113:1,2,8 164:2

193:5,18 194:1

198:6,12 212:7

351:2,13,17,18,21

352:13,16 354:9

354:13 360:1,4

361:2,2,9,9 364:8

364:16 368:2,17

368:19 373:14

374:9,11,12

375:13 385:8

387:9 391:5,14

Zhang 2:21 319:1,2

319:18 321:22

zoom 32:4

0

0.5 296:5

0.6 117:15

1

1 3:19 15:17 22:17

37:21 38:14

171:11 172:9

200:20 214:3

228:22 256:12

271:6 279:3 280:2

299:16 309:3

1A 144:2

1b 144:12

1c 159:4

1,000 67:6 266:1

1,177 344:18

1.7 282:3

1.9 99:7

1/2 15:10,12

1:15 205:10

1:19 205:16 206:2

1:30 205:12

10 223:5 266:3

279:14,14 280:2

282:16

10:13 102:1

10:30 101:21

10:33 102:2

100 275:11,12

288:7,18 387:15

101 47:7

103 3:12

107 3:13

11 66:16 212:1

215:15 221:6

11c 287:17

11-tree 278:7

11:30 101:7,8

113 3:13

119 3:15

12 1:9 55:1 132:19

279:13 310:14,14

12b 289:4

12-kilometer

215:21

12:05 205:15

122 3:17

140 43:16

15 48:8 282:16

316:7 317:3

16 10:7 42:18 76:14

79:3,8 132:18,18

155:15,20,21

159:21 162:12,15

167:18 168:7

259:13 263:11

323:22

16B 318:7

18 49:5 325:6

338:15

19 334:11

1996 286:13,13,15

2

2 6:10 37:22 115:14

115:16 116:6

129:12 142:16

143:16 163:9

169:6 172:9

184:18,19 223:4

228:22 251:19,21

252:12,16

2,200 15:17

2:00 228:15,19

20 48:8 63:19 114:1

147:12 223:5

282:12 283:9

297:9,13 300:6

20,000 15:18

20-some 55:1

200 296:8

2000 218:16

2001 218:15 281:21

2003 217:5

2004 201:20 202:3

2006 12:20 15:15

208:14 209:12

286:15 366:1

2007 130:21 144:5

144:8 150:12,14

150:20 151:3

208:15 217:5

218:4 255:7

2008 12:20 208:14

209:13 255:21

256:5 260:18

2008-2010 160:2

2009 351:6,8

2010 196:17

2012 1:9 32:8

206 3:18

22 3:3

227 3:20

23 117:17

233 3:21

25 15:12 282:13

283:9

253 3:22
202-234-4433

Neal R. Gross & Co., Inc.

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8c 295:7

8-1 99:2

8.1.3 99:3

8.4 203:13

8:30 1:13 393:8

8:33 5:2

80 168:12 224:6

367:9 368:13

80s 135:14 179:22

80/20 46:17 47:11

82 3:9

845 99:18

85 160:16

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101:10,19 102:3

102:21 103:5

104:11 107:7,11

107:14 109:15

116:7 117:10,11

120:2 123:22

9-11 118:3

9-9 116:15

90s 135:14 180:1

335:9

90-day 268:6

90/10 46:17 47:11

91 3:10

930 114:1

94 3:10

95 368:6

95th 127:8

96 15:17

99 288:6

99th 15:22
NEAL R. GROSS

COURT REPORTERS AND TRANSCRIBERS 

1323 RHODE ISLAND AVE., N.W. 

(202) 234-4433 WASHINGTON, D.C. 20005-3701 www.nealrgross.com 

C E R T I F I C A T E

This is to certify that the foregoing transcript 

In the matter of: 

Before: 

Date: 

Place: 

was duly recorded and accurately transcribed under 

my direction; further, that said transcript is a 

true and accurate record of the proceedings. 

 

 ----------------------- Court Reporter 

444

Clean Air Scientific Advisory

Committee Ozone Review Panel

US EPA

09-12-12

Raleigh, NC