> US ENVIRONMENTAL PROTECTION AGENCY
>
> SCIENCE ADVISORY BOARD (SAB) STAFF OFFICE
>
> CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE (CASAC)
>
> OXIDES OF NITROGEN PRIMARY NAAQS REVIEW PANEL PUBLIC MEETING
>
> MARRIOTT AT RESEARCH TRIANGLE PARK
>
> 4700 Guardian Drive
>
> Durham, North Carolina 27703
>
> OCTOBER 25, 2007
>
> 8:36 A.M.
>
> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 2
>
> Page 2
>
> 1 U.S. ENVIRONMENTAL PROTECTION AGENCY
>
> 2 CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE
>
> 3 PUBLIC MEETING
>
> 4 OCTOBER 25, 2007
>
> 5 DR. HENDERSON: I want to thank everyone
>
> 6 for being so timely in submitted your paragraphs to
>
> 7 Angela, and I thank Angela for bringing it all
>
> 8 together. Now that what\'s being distributed - -
>
> 9 SPEAKER: Hello?
>
> 10 DR. HENDERSON: Hello.
>
> 11 SPEAKER: Rogene, can you, you need to
>
> 12 speak into the microphone.
>
> 13 DR. HENDERSON: Okay.
>
> 14 SPEAKER: Thank you.
>
> 15 DR. HENDERSON: What\'s being passed out
>
> 16 is a compilation of what was submitted, and these are
>
> 17 all, everything, it\'s truly a compilation, but I\'ve
>
> 18 read it through it, and I compared it with the list,
>
> 19 this small list is, these are the points we listed
>
> 20 yesterday afternoon that we thought should be included.
>
> 21 So, you might, quickly, compare this list with what\'s
>
> 22 in, what you\'d submitted to see if we left anything
>
> 23 out.
>
> 24 But the consideration that we\'re going to be
>
> 25 making is, is this the substance, does this include
>
> Page 4
>
> 1 DR. NUGENT: Good morning.
>
> 2 DR. LARSON: Tim Larson.
>
> 3 DR. NUGENT: Good morning.
>
> 4 DR. ULTMAN: Jim Ultman is.
>
> 5 DR. NUGENT: Jim?
>
> 6 DR. HENDERSON: Jim Ultman.
>
> 7 DR. NUGENT: Ultman. And, Lee Anne, are
>
> 8 you on the phone? Okay, all right, and just a note
>
> 9 about public comments. I\'d mentioned yesterday that we

10 were inviting public comments on yesterday\'s discussion

11 relating to the ISA. No member of the public has asked

12 me to speak this morning about the ISA. I\'ll ask one

13 more time, because we want to be a little structured

14 about how the discussion proceeds. Are there members

15 of the public who\'d like to present some comments?

16 MR. HICE: Angela?

17 DR. NUGENT: Yes?

18 MR. HICE: This is John Hice on the

19 phone.

20 DR. NUGENT: Yes?

21 MR. HICE: I\'d like to make a very, very

22 short comment, if I could.

23 DR. NUGENT: Thank you, okay. I\'ll write

24 that down, and we\'ll turn to you in a moment.

25 MR. HICE: Thank you.

> Page 3
>
> 1 everything you want to say to the Administrator in our
>
> 2 letter. And, it\'s not the exact words, because Angela
>
> 3 and I will have to go through and make it sound like it
>
> 4 was written by one person instead of a committee.
>
> 5 But, that is, it\'s not, it\'s going to be
>
> 6 smoothed out, but does it contain the substance of what
>
> 7 we want to say? Can we agree? Do you feel comfortable
>
> 8 with what is written here as a compilation of
>
> 9 everything that we want to say to the Administrator in
>
> 10 terms of our peer review of the first draft of the ISA?
>
> 11 And, while you\'re reading that, I think with, I\'m going
>
> 12 to, I have neglected to let Angela do a roll call of
>
> 13 who\'s on the phone. So, I will turn it back to her
>
> 14 while you\'re reading it.
>
> 15 DR. NUGENT: Thank you, Rogene. As we
>
> 16 start this second day, and we complete the discussion
>
> 17 of the ISA, and then move ahead to the discussion of
>
> 18 the methods document, I wanted to welcome the people on
>
> 19 the phone, and make sure everyone in the room knows
>
> 20 who\'s on the phone, and then talk a little bit about
>
> 21 the public comment period, here. So, may I ask,
>
> 22 please, what CASAC panel members are on the phone
>
> 23 today? Are the CASAC panel members on the phone right
>
> 24 now?
>
> Page 5
>
> 1 DR. NUGENT: Let me also mention that we
>
> 2 didn\'t explicitly list on the agenda public comments
>
> 3 for the methods document. And, I would like to know
>
> 4 whether, once we complete this ISA discussion, there
>
> 5 are members of the public who\'d like to present some
>
> 6 brief comments on the methods document. Okay, hearing
>
> 7 none, I think we should proceed. Rogene, John Hice has
>
> 8 some remarks, and I propose that we take them now.
>
> 9 DR. HENDERSON: Now would be a great

10 time.

11 DR. NUGENT: Thanks. I think your

12 audio\'s working well for us here, so please, speak into

13 your phone set, and we\'d love, we\'d like to hear your

14 comments now, please.

15 MR. HICE: Thank you very much. I just

16 wanted to reiterate that we\'ll be providing written

17 comments for the record to EPA by that deadline, it was

18 October 31st. And I\'m sure several other groups will

19 also. And I would just ask that the CASAC folks take a

20 look through those comments, at their convenience, and

21 add those thoughts to their own as they think about the

22 review of the next draft. That\'s all.

23 DR. NUGENT: Thank you.

24 DR. HENDERSON: And I thank you, too. I,

25 now, can people hear me if I hold the mike up like

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 3
>
> Page 6
>
> 1 this?
>
> 2 SPEAKER ON PHONE: It\'s fine, Rogene, for
>
> 3 me.
>
> 4 DR. HENDERSON: Okay, that\'s good. This
>
> 5 is probably easier than dragging that hand mike. Okay,
>
> 6 have, Angela sent the list of substantive material that
>
> 7 we want to have in the letter to the Administrator.
>
> 8 Have people had a chance to look at it?
>
> 9 SPEAKER: Rogene, you talking about the
>
> 10 short list or you talking about the big one?
>
> 11 SPEAKER: Big one.
>
> 12 DR. HENDERSON: I kind of meant the big
>
> 13 one. This list, this small list, that doesn\'t have a
>
> 14 time on it, is just the notes I jotted down when we
>
> 15 were talking at the end of the day yesterday, when I
>
> 16 said, you know, what are the substantive issues we want
>
> 17 to convey to the Administrator. And these are simply
>
> 18 my notes. We were in agreement yesterday that this
>
> 19 list included everything we wanted to say.
>
> 20 Now, would, what I\'m asking you, now, do you
>
> 21 think these were captured in the more formal listing
>
> 22 that Angela pulled together from the people who
>
> 23 summarized each charge question? Well, that\'s a good
>
> 24 idea. Though some of these overlap quite a bit. We
>
> 25 have charge question one that, have you had a chance to
>
> Page 8
>
> 1 rates. I don\'t know if that information is available.
>
> 2 It\'s not my area quantitatively, but I was looking for
>
> 3 it, and I didn\'t see it in there.
>
> 4 DR. HENDERSON: Okay.
>
> 5 DR. CRAPO: Also, with respect to
>
> 6 question one, I think that this is an appropriate place
>
> 7 where we need to ask if there could be a better
>
> 8 assessment of issues related to background, and peaks,
>
> 9 and variations in exposure, more data about the

10 variations in exposure across groups, so that we know

11 what the, what percent of, or some idea about what

12 fraction of the country are people, or indoors, or

13 outdoors, is exceeding, or not exceeding the current

14 standard, but substantially higher than the current

15 annual average. So, that the, the focus on an average

16 annual number makes it really hard for me to analyze

17 what the exposures really are. So, I think we need

18 more data on that side of the table.

19 DR. HENDERSON: Okay.

20 SPEAKER: Well, I think that actually

21 falls directly under question two.

22 DR. CRAPO: Two, that be great.

23 SPEAKER: That\'s where ambient mon-,

24 concentrations are. Some of it\'s there, but maybe just

25 what more might need to be there.

> Page 7
>
> 1 look at it? Would you like to have ten minutes just to
>
> 2 look at this one, okay. Gary\'s nodding his head.
>
> 3 Okay, let\'s, we will just, we\'re not breaking. We\'re
>
> 4 just giving you time to read it, because this is
>
> 5 important enough. I\'d like for you to have had a
>
> 6 chance to look at it carefully.
>
> 7 (WHEREUPON, the members read the document.)
>
> 8 DR. HENDERSON: I gather from the
>
> 9 conversation that is starting that people are
>
> 10 approaching the end of their reading. Are you about
>
> 11 ready to move on? Okay, what I\'d like to hear from you
>
> 12 first is, is there anything left out of this that
>
> 13 should be added?
>
> 14 SPEAKER: Are you asking just about
>
> 15 question one, or about all of the questions?
>
> 16 SPEAKER: Let\'s go question by question.
>
> 17 DR. HENDERSON: You want to go question
>
> 18 by question? Okay. We\'ll take charge question one.
>
> 19 Is the response written her, does it include everything
>
> 20 that you think should be included? Ron?
>
> 21 DR. WYZGA: One of the things that I
>
> 22 think could be useful could be, if they could have more
>
> 23 quantitative discussion about the rates of
>
> 24 transformation of, I guess, emissions into different
>
> 25 species of NOx and what are the influences on these
>
> Page 9
>
> 1 DR. HENDERSON: Okay, we\'re looking at it
>
> 2 again. We come to two, does anybody else have things
>
> 3 on one, yes, Terry?
>
> 4 DR. GORDON: Just the general, when I
>
> 5 read over this, I got the feeling that we were, I mean,
>
> 6 it\'s, the ISA is supposed to help us assess things, and
>
> 7 seems like some of the things asked to be added were
>
> 8 just making it more criteria document like, just making
>
> 9 it longer, and not, not helping us decide things.

10 DR. HENDERSON: Well, that\'s the strug- -

11 DR. GORDON: Just a caution.

12 DR. HENDERSON: That\'s the struggle

13 that\'s going on, and some of this might go in the

14 annex. I mean, but, we ask that it be condensed, and

15 that the only policy relevant information given. And

16 what you\'re saying is, now, we\'re asking to expand it

17 in - -

18 DR. GORDON: In some areas.

19 DR. HENDERSON: In some areas. Mary?

20 DR. ROSS: Well, that was a point of

21 clarification I was going to ask for in general. It

22 says in the ISA\'s to include material. Maps, in

23 particular, will make it longer. So, one of the

24 questions is, can we balance between annexes and have,

25 expand the ISA a little bit more, but add more of the

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 4
>
> Page 10
>
> 1 annexes, too, and just a clarification if that\'s what
>
> 2 CASAC means when they say in the ISA, or it really mean
>
> 3 in the ISA.
>
> 4 DR. HENDERSON: Okay. That\'s a very good
>
> 5 point, and if we say ISA, do we really mean the annex,
>
> 6 so.
>
> 7 DR. WYZGA: And Mary, if these could be
>
> 8 cross references to the annex, I think that would be
>
> 9 helpful, too.
>
> 10 DR. HENDERSON: We\'ll go on to question
>
> 11 two, because that is what James asked for, you think,
>
> 12 is that included in this answer to question two?
>
> 13 DR. POSTLETHWAIT: Actually, as a present
>
> 14 follow up to James\' point, I\'m wondering about this
>
> 15 issue about getting a little better handle on, first of
>
> 16 all, exposures. I don\'t know what data is available,
>
> 17 but even if some relative analyses of speciation of NOx
>
> 18 could be included, just to give us a feel. I mean if
>
> 19 NO2 is 95 percent of it, then the rest of it\'s fairly
>
> 20 trivial. If it\'s 25 percent of it, then, you know,
>
> 21 there\'s certainly other issues to consider. And, I
>
> 22 think those two things ought to be in the ISA, and not
>
> 23 in the annex, so the reader has that, sort of that,
>
> 24 visceral feel as he, as they continue on to the health
>
> 25 effects portions, et cetera.
>
> Page 12
>
> 1 view.
>
> 2 So, my recommendation would be much the
>
> 3 opposite as, maybe, minimize this, say that monitoring
>
> 4 height could be important, but if they\'re going to
>
> 5 address it in the ISA, they should do it in a more
>
> 6 conclusive fashion, and look at more monitors where
>
> 7 this, where they could pick up this impact.
>
> 8 DR. HENDERSON: Okay, I know, Dale, you -
>
> 9 -

10 DR. HATTIS: Yeah, I want to slightly

11 disagree with the fact that that\'s over-emphasized. I

12 think that\'s a critical component of the analysis that,

13 if anything, should be extended to an analysis of,

14 actually, what the biases are in, as-, you know, would

15 be, in assuming that the distribution of levels of the

16 existing monitoring sites are representative of outside

17 outdoor levels, because it does mean that you can\'t

18 really directly com-, without an analysis of that

19 problem, you cannot directly compare the levels

20 inferred from monitors with the, it helps you

21 reconcile, to a degree that it\'s possible, the, any

22 concentration response relationships you infer from it,

23 the epidemiological data, with concentration in your

24 response you infer from things like the Australian

25 study and the indoor, other indoor studies, which are

> Page 11
>
> 1 DR. HENDERSON: Present those, the two
>
> 2 points again, so I\'ll be sure and get them right.
>
> 3 DR. POSTLETHWAIT: Well, again, to
>
> 4 speciate NOx, whatever\'s available, and then the issue
>
> 5 of what we know about personal exposure, temporal
>
> 6 paradigms.
>
> 7 DR. HENDERSON: Okay, some of that may
>
> 8 not be available in any of the exploits, but we, and I
>
> 9 agree with you, then, by healing what you want. Yes,
>
> 10 Ted.
>
> 11 DR. RUSSELL: If I might, and this, also,
>
> 12 is captured in response to question three. There\'s the
>
> 13 discussion about the importance of the height of the
>
> 14 monitors that shows up both in the last, sort of,
>
> 15 section on the response here, as well as in, there\'s a
>
> 16 fairly large bullet in the next one.
>
> 17 There\'s, currently, a pretty large section in
>
> 18 the ISA on the impact of monitoring height, and I,
>
> 19 actually, found that was much larger than it should be,
>
> 20 and maybe even a red herring as such, in terms of how
>
> 21 it might be addressed in the ISA. For one, there\'s a
>
> 22 lot of information out there where you could compare
>
> 23 the values between different height monitors, as
>
> 24 opposed to just looking at one special study where they
>
> 25 did it. Which, I think, would, gives you a biased
>
> Page 13
>
> 1 based on - -
>
> 2 DR. RUSSELL: But you now, tremendously
>
> 3 larger impacts horizontally and spatially than
>
> 4 vertically, so I think that that\'s being blown out of
>
> 5 proportion, versus where you\'re placing a monitor close
>
> 6 to a road, or you know, four or five hundred meters
>
> 7 away from a road in a park, because that\'s where you\'re
>
> 8 going to have the bigger differences.
>
> 9 DR. HATTIS: Well, I think that\'s, also,

10 an important area, but this is a systematic error, you

11 know, and the other may well be much more - -

12 DR. RUSSELL: No, it\'s, they\'re both

13 systematic.

14 DR. HATTIS: The health studies are based

15 upon aggregate exposure, agg-, exposures within big

16 cities, okay. And those includes both stuff near

17 roadways and not near roadways. So, essentially, that

18 tends to be biased by the verticality, although, there

19 are, in fact, some sub-populations within cities that

20 are even more exposed, okay, because of their, you

21 know, proximity to roads. So, I think that the

22 influences are different, even though there may be a

23 bigger overall number, ratio in the near roadway, far

24 roadway. This other effect, really, is a substantial,

25 why, I say, now, I don\'t think that it\'s overemphasized

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 5
>
> Page 14
>
> 1 in the existing document. I would like to see a fuller
>
> 2 analysis of both kinds of effects.
>
> 3 DR. HENDERSON: I want to be sure I
>
> 4 understand what you\'re saying here. What is the
>
> 5 difference, say, between, you know, vertically. Does
>
> 6 Albuquerque have a different from San Diego, I mean,
>
> 7 they, is that what you\'re talking about, I mean?
>
> 8 DR. HATTIS: No, no, no. This is a
>
> 9 matter of the fact that, the monitors for all the
>
> 10 cities are high.
>
> 11 DR. HENDERSON: Yeah, and people are
>
> 12 breathing down low.
>
> 13 DR. HATTIS: And people are breathing
>
> 14 down low, so that means that, systematically, the epi
>
> 15 studies are based upon concentrations that are
>
> 16 measured, that are underestimated.
>
> 17 DR. HENDERSON: And what is, what is the
>
> 18 difference, the degree of difference, I mean, that
>
> 19 you\'re taking?
>
> 20 DR. HATTIS: Well, I think that, that
>
> 21 from the, you know, the brief discussion that I
>
> 22 remember from the ISA, that that difference is two-,
>
> 23 three-fold. But that\'s different heights of monitors.
>
> 24 If you go down to the ground level, it looks like, you
>
> 25 know, that could even be a larger factor. The fact
>
> Page 16
>
> 1 level, it\'s fairly sparse.
>
> 2 DR. HATTIS: Well, whatever the best
>
> 3 sources of information are to estimate the effect, you
>
> 4 know, they need to be used. The fact of the matter is
>
> 5 that, the existing epidemiological studies are based
>
> 6 upon, what appear to be, biased measurements of the
>
> 7 concentrations people actually receive. And,
>
> 8 therefore, they are not directly comparable with the
>
> 9 indoor measured concentrations that led to the

10 observations in the Australian study and in the, well,

11 Australian study. So, that\'s a big problem that needs

12 to be addressed.

13 DR. LARSON: I\'m unaware of any, or many

14 NO2 EPA monitors that are actually sited on top of it,

15 is that what we\'re talking about? I don\'t think that\'s

16 true.

17 DR. GORDON: Well, it just seems that,

18 from this discussion, I\'m agreeing with Ted, now,

19 \'cause I thought there was a big verticality problem.

20 And if there is more data out there, this chapter

21 doesn\'t get that across to me. It says there\'s a big

22 vertical problem, but they might be variable by site,

23 and that\'s not brought out. So, maybe Ted\'s right. It

24 should be condensed but expanded in other areas. I

25 mean they both should be discussed.

> Page 15
>
> 1 that it\'s a systematic, you know, biases of several
>
> 2 fold can have a big impact on what you might infer is a
>
> 3 level that was, you know, protective of public health.
>
> 4 DR. LARSON: This is Tim Larson. I think
>
> 5 the EPA monitors are sited, in most cases, in such a
>
> 6 way that the inlet height biases are not capturing what
>
> 7 you\'re thinking about, which is the vertical
>
> 8 distribution in urban areas, primarily, in confined
>
> 9 urban areas. And that, I agree, is a significant
>
> 10 gradient that can be threefold. But, you\'re not going
>
> 11 to see that at most NO2 monitoring sites, because
>
> 12 their, the way their sited, they\'re, they tend to be in
>
> 13 open areas. And the differences in heights of the
>
> 14 inlets in those are-, in those open areas just don\'t
>
> 15 capture the kinds of gradients of exposure that are
>
> 16 important.
>
> 17 So, doing an analysis of all the inlet
>
> 18 heights for all the NO2 monitors that EPA has isn\'t
>
> 19 going to really capture that. And, unfortunately,
>
> 20 there\'s just not a lot of data on the vertical
>
> 21 distribution of the heights in the urban areas that are
>
> 22 systematically done. We\'re doing a big study in New
>
> 23 York City right now, trying to capture some of that,
>
> 24 and there is some European data on this subject, but
>
> 25 compared to the data that\'s measured at or near street
>
> Page 17
>
> 1 DR. HENDERSON: Well, I hear-, what I
>
> 2 hear people saying is that in urban areas, there may be
>
> 3 a difference in, there may be a problem with the siting
>
> 4 of the monitors, as far as the vertical differences in
>
> 5 concentrations with NO2, but we don\'t have much
>
> 6 information. I hear people saying, we don\'t need them,
>
> 7 we don\'t know if that\'s true, so would you like, Dale,
>
> 8 in the letter to say that this is a potential problem?
>
> 9 That should be addressed.

10 DR. LARSON: There is some literature on

11 this. I mean, if you could cite that, I, there, it\'s

12 just not a lot of it.

13 DR. HATTIS: Well, whatever the

14 literature is that\'s relevant to estimating the

15 population exposures, that are true versus the

16 population exposures that are estimated in the

17 epidemiological studies, that\'s relevant to judging the

18 levels at which you expect how many of X.

19 DR. HENDERSON: Sure.

20 DR. LARSON: But I think I\'m on balance

21 on Ted\'s, come down on, with Ted on this. I think

22 relative, the NO2 EPA monitors in urban areas, the

23 biggest gradients are horizontal. And they\'re not

24 proximity to roadway per se. They\'re, actually,

25 proximity to confined roadways, where you can get up

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 6
>
> Page 18
>
> 1 factors of three to five times differences relative to
>
> 2 the same traffic, of the same distance from a road in
>
> 3 an unconfined location. So, you\'re not talking about
>
> 4 twenty percent here. You\'re talking about three to
>
> 5 five hundred percent differences. And similarly,
>
> 6 factors two to three in the verticality at those
>
> 7 confined locations with height of, those are big
>
> 8 effects, none of which are being captured by any of
>
> 9 this.
>
> 10 DR. HENDERSON: For any epi studies,
>
> 11 there\'s always the problem of exposure. I mean, we\'re
>
> 12 never happy with the exposure. Now, and, I think this
>
> 13 is an example of some of the issues that come up. I
>
> 14 think it should be mentioned in the letter. As I
>
> 15 recall in reading through the document, it was
>
> 16 discussed quite a bit, but - -
>
> 17 DR. LARSON: Well, the inlet height
>
> 18 effect of the monitors is discussed, which I\'m not sure
>
> 19 is the important parameter.
>
> 20 DR. HENDERSON: I think in the letter, we
>
> 21 do, we confirmed the fact that we are aware that the
>
> 22 exposures, there\'s always a problem with measuring
>
> 23 personal exposures in an epi study.
>
> 24 DR. HATTIS: Yeah, but this is not just
>
> 25 the usual problem. This is not, the usual problem is a
>
> Page 20
>
> 1 types of measurements that are used in some of the, you
>
> 2 know, the better direct studies establishing the
>
> 3 effects of the NO2.
>
> 4 DR. HENDERSON: To point out the
>
> 5 uncertainties associated with the other, go ahead,
>
> 6 George.
>
> 7 DR. THURSTON: Yeah, right, well, yeah, I
>
> 8 agree with that last part that the problem is when you
>
> 9 go to compare it to, like, indoor measurements and

10 those measurements. But, it\'s not a problem with

11 regard to interpreting and the epidemiology, I think we

12 have to keep that clear, and applying it to for

13 standard setting. Because, ultimately, you know,

14 you\'re applying the standards at the central site

15 monitors.

16 So, that\'s what you want to use in the

17 epidemiology, and the fact that, let\'s say, those

18 levels, let\'s say, they were fifty percent of what

19 people were actually exposed to, it, then you would

20 take all the numbers, double them, and then when you go

21 to set the standards, divide them by two. I mean, it

22 would be a waste of time.

23 So, I think that it\'s a fact, but it\'s not a

24 problem that there are differences in the absolute

25 levels between what\'s at the central site monitor, and

> Page 19
>
> 1 random error. And we know how to deal with that.
>
> 2 DR. HENDERSON: I understand. You\'re
>
> 3 saying that this is - -
>
> 4 DR. HATTIS: This is a systematic error.
>
> 5 DR. HENDERSON: - - systematic because
>
> 6 the, you think the inlets are consistently higher than
>
> 7 the level of - -
>
> 8 DR. HATTIS: Look it, all I know is what
>
> 9 I read in the ISA, and this seems to be, you know, what
>
> 10 the ISA seems to say. And then I, sort of, believe
>
> 11 that they will have located the monitors at elevated
>
> 12 levels. You know, maybe, and if it\'s not true, then
>
> 13 fine, you know, but.
>
> 14 DR. HENDERSON: Well, I, our charge is to
>
> 15 advise them on how to improve the ISA, and are you
>
> 16 saying you\'d like the - -
>
> 17 DR. HATTIS: I\'d like that, and I think
>
> 18 that if it\'s, you know, if the analysis, if the
>
> 19 statements in the ISA are correct, then, you know,
>
> 20 maybe they need to be modified with, including the
>
> 21 information from a larger literature base. But, you
>
> 22 know, if they are, then it\'s worth an a-, worth some
>
> 23 much more quantitative analysis, because it creates a
>
> 24 serious difference between the types of measurements
>
> 25 that are used as the basis of the epi studies, and the
>
> Page 21
>
> 1 what people experience on, at street level. But, only,
>
> 2 the only place where I, you know, I think it is a
>
> 3 problem that I can think of, you know, I agree.
>
> 4 When you go, if we\'re going to put some
>
> 5 importance on these indoor studies, we ought to
>
> 6 remember that those concentrations are not directly
>
> 7 comparable to the central site concentrations. And
>
> 8 that\'s, I think, the key that Dale brought out.
>
> 9 DR. WYZGA: And the clinical studies as

10 well.

11 DR. THURSTON: Yeah, and the clinical

12 studies as well, yeah, that\'s true. Because the actual

13 concentrations associated with the NO2 exposures that

14 we measure at the central site monitors are, actually,

15 higher. And so, that might explain some of the

16 differences that we see between the exposure studies,

17 the indoor studies, and the ambient results. So, it,

18 yeah, so that\'s going to have some importance later on

19 in interpreting the results, so that is an important

20 point to bring up in that respect.

21 DR. HENDERSON: Yeah, I can see that- -

22 DR. HATTIS: A condition of the central,

23 there is always random error, addition, to effect, you

24 know, and that\'s also a problem to be analyzed, but.

25 DR. HENDERSON: Well, okay, I see the

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 7
>
> Page 22
>
> 1 point that, George, that, you know, you just made, and
>
> 2 Dale, too, that there is, the, how something comparing
>
> 3 the clinical and the indoor dose response first to what
>
> 4 you may see in epidemiology. That can be, I think,
>
> 5 clearly stated. Yes, George, do you have - -
>
> 6 DR. THURSTON: I just have one separate
>
> 7 comment. I guess it, I\'m not sure if it goes on two or
>
> 8 three, but I think two, that I brought up something
>
> 9 that I, in my quick review, I don\'t see reflected with,
>
> 10 yesterday, which was that we need to, more clearly,
>
> 11 delineate the difference between personal exposures to
>
> 12 all NOx versus personal exposures to ambient NOx, and
>
> 13 their respective relationships to outdoor central site
>
> 14 monitors. I didn\'t see that written in here anywhere,
>
> 15 and I did bring that up. And I hope that that\'s
>
> 16 included.
>
> 17 DR. LARSON: Well, we had a bullet in
>
> 18 section three on trying to look at the alpha, I guess
>
> 19 I, the ratio of the outdoor to personal ambient.
>
> 20 DR. THURSTON: Is that what that bullet
>
> 21 means?
>
> 22 DR. LARSON: Yeah, alpha.
>
> 23 DR. THURSTON: I didn\'t get it.
>
> 24 DR. LARSON: Okay, we\'ll fix it.
>
> 25 DR. CRAPO: Could I ask a question for
>
> Page 24
>
> 1 want to talk about that in the context of the ISA,
>
> 2 fine, or context of the other is fine, too.
>
> 3 DR. HENDERSON: Were you proposing that,
>
> 4 well, this would be quite a few individual comments,
>
> 5 but can you explain it to us what it\'s saying?
>
> 6 DR. HATTIS: Yeah. What this, I need to
>
> 7 get it in front of me. What this is, is, essentially,
>
> 8 plotting the, it is, basically, a lo-, what these are
>
> 9 called is log normal probability plots. And,

10 essentially, what\'s being plotted is the Z score, which

11 is, essentially, the number of standard deviations that

12 each value represents in the distribution.

13 So, that, for example, the first data point

14 here is, generally, the first per-, is the one

15 percentile level. The next is the, I think the five

16 percentile level, et cetera.

17 But, plotted on a probability scale, so that,

18 if, in fact, the date corresponded to a log normal

19 distribution, which is the usual expectation, then the

20 points would fall on the straight line. The regression

21 equation in each case is an estimate of the, the

22 intercept is the log of the geometr-, it\'s an estimate

23 of the log of the geometric mean, and the slope is the,

24 an estimate of the log of the geometric standard

25 deviation, okay.

> Page 23
>
> 1 clarification as I listened to the conversation. When
>
> 2 we get an average annual level expressed for us, is
>
> 3 that what, is that the average over the whole 24-hour
>
> 4 day, then averaged annually. Or is that the high for
>
> 5 the day averaged annually. Are we talking about - -
>
> 6 SPEAKER: Everything.
>
> 7 DR. CRAPO: - - everything averaged
>
> 8 together, so when NO2\'s have the peaks during the
>
> 9 traffic periods of the day, and it goes down very low
>
> 10 at night, you\'re taking these high levels that occur
>
> 11 during the day and averaging it out with twelve to
>
> 12 twenty hours with, of low levels and getting a fairly
>
> 13 low level out of it. That\'s, so, we need a lot more
>
> 14 information about the peak, \'cause, probably, the
>
> 15 average annual is about the last thing we want to look
>
> 16 at to assess this st-, this substance.
>
> 17 DR. HENDERSON: You know, I think, that\'s
>
> 18 what I have written down for your, what I wrote down
>
> 19 for this. And we need to remember about the pattern.
>
> 20 DR. HATTIS: I\'ve made a series of plots,
>
> 21 actually, of the distributions of, for different
>
> 22 average-, of NO2 levels for different averaging times
>
> 23 from the existing data in one of the annex tables.
>
> 24 And, so, we can talk about that later. Yeah, that\'s
>
> 25 the, yeah, that\'s the graph, so, essentially, so if you
>
> Page 25
>
> 1 So, essentially, these, so these, if these
>
> 2 log transformed values were normally distributed, they
>
> 3 should fall more or less on the line, and they more or
>
> 4 less do. They are not perfect log normal
>
> 5 distributions. In fact, the actual data have, don\'t
>
> 6 have as fat a, in the tails as they should for a
>
> 7 perfect log normal. But, essentially, what this does
>
> 8 is to show the change in the slope is, means,
>
> 9 essentially, the longer averaging time or, you know,

10 more tightly distributive than the shorter averaging

11 time. So, the shorter the averaging time that you

12 take, the data are further spread out, just because of

13 regression of the mean effects. And this says how,

14 how, what?

15 DR. HENDERSON: I mean that\'s what you

16 need said, isn\'t it?

17 DR. HATTIS: Yes, and this, basically,

18 quantifies how much less the dispersed the lo-, the

19 yearly and three averages are relative to the one-hour

20 averages.

21 DR. HENDERSON: And that\'s what James is

22 saying, that they didn\'t give us much information, I

23 mean, yes.

24 DR. CRAPO: So, let me just ask a real

25 practical question from a real simple mind. How many,

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 8
>
> Page 26
>
> 1 if I, and instead of doing the average annual and
>
> 2 saying that was fifteen parts per billion, what would
>
> 3 be, if I took the highest one hour from each day, would
>
> 4 that be two hundred parts per billion?
>
> 5 DR. HATTIS: Those data are in the table,
>
> 6 and I didn\'t plot them.
>
> 7 SPEAKER: Actually, they\'re in the ISA,
>
> 8 too.
>
> 9 SPEAKER: They\'re in the hot spot.
>
> 10 DR. CRAPO: Right, so what\'s the answer?
>
> 11 SPEAKER: 201.
>
> 12 (WHEREUPON, there was a discussion off the record.)
>
> 13 DR. CRAPO: Did I guess, I guessed it
>
> 14 right on the money?
>
> 15 SPEAKER: Yes.
>
> 16 DR. HENDERSON: You get a gold star this
>
> 17 morning.
>
> 18 DR. CRAPO: And the excursion, the high
>
> 19 end excursion is from that? Do we have a significant
>
> 20 if the population is exposed 500 ppd?
>
> 21 SPEAKER: No, that was an excursion.
>
> 22 DR. CRAPO: That is an excursion, I was,
>
> 23 that is the excursion, okay.
>
> 24 DR. HENDERSON: Well, okay, Ed, go ahead.
>
> 25 DR. POSTLETHWAIT: Well, if there are
>
> Page 28
>
> 1 proportionally more while I\'m breathing more.
>
> 2 DR. CRAPO: Ted has just pointed to me
>
> 3 that the 200 ppb that we were talking about is probably
>
> 4 at fifteen feet up and not at ground level, so that the
>
> 5 ground level might twice that level. What?
>
> 6 DR. LARSON: Ron, wait, no way, no way.
>
> 7 DR. RUSSELL: More likely at four meters,
>
> 8 aren\'t more of your monitors at four meters than - -
>
> 9 (WHEREUPON, there was a discussion off the record.)

10 DR. LARSON: Those kinds of gradients

11 don\'t exist.

12 SPEAKER: What?

13 DR. CRAPO: You say gradients of that

14 nature don\'t exist?

15 DR. LARSON: Not that, I mean, not that

16 strong a gradient over three meters.

17 DR. PINTO: Yeah, no, I think you\'re

18 right, I mean. I think what I was trying to say was,

19 no, this particular data point, okay, where it was a

20 change in Lakewood, California, downtown Los Angeles,

21 in other words, and is one of the roadside monitors, so

22 you would expect it a, first of all to be very hot; b,

23 you would also expect the inlet to be at, you know, the

24 standard there, at the standard height, which of the

25 order of three meters or so.

> Page 27
>
> 1 estimates made of personal exposures, won\'t that
>
> 2 capture that issue, as opposed to, when we compare data
>
> 3 back just to annual averages, which gets back to the
>
> 4 central issue of how much stuff are people really
>
> 5 inhaling, versus, you know, what\'s the average floating
>
> 6 around.
>
> 7 DR. HATTIS: Not exactly, because it
>
> 8 depends upon, on the averaging time for the personal
>
> 9 exposures, where you also will have a similar - -
>
> 10 DR. POSTLETHWAIT: Well, that\'s where,
>
> 11 that\'s where temporal plot, if it was possible, would
>
> 12 be really useful, even from a qualitative standpoint.
>
> 13 DR. HATTIS: Yeah. But essentially,
>
> 14 you\'d have to have comparable, you know, different
>
> 15 lengths of time averages to be able to compare. And I,
>
> 16 offhand, I don\'t know whether the internal, the indoor
>
> 17 exposures are more variable with time than the outdoor.
>
> 18 So, you can have a different, you could have different
>
> 19 comparability depending upon the, you know, how, what
>
> 20 that looks like.
>
> 21 I mean, there\'s, also, a likely case that the
>
> 22 indoor exposures will be correlated with differences in
>
> 23 breathing rate. So, for example, it may well be that
>
> 24 while I\'m up and about, one of the things I\'m doing is
>
> 25 cooking on my gas stove, and exposing myself to
>
> Page 29
>
> 1 DR. CRAPO: The reason I\'m bringing this
>
> 2 up is that I think those of us that are really focusing
>
> 3 on the health effects are not even in our heads not
>
> 4 even correlated to the right thing. And we\'re sitting
>
> 5 here looking at the ambient levels and thinking 15 ppb
>
> 6 average annual, and we\'re seeing health effects in
>
> 7 asthmatics and people living near roadsides. When, in
>
> 8 fact, the people near the roadsides are getting 200
>
> 9 ppb. And our correlation, all these correlation

10 coefficients on the things that we\'re looking at are,

11 at least, I\'m not sure that because we\'ve used a, such

12 a bad metric to correlate what\'s going on, I don\'t

13 think that we\'re thinking correctly on the health

14 effects side.

15 DR. HENDERSON: I think one thing we can

16 emphasize in our letter is the importance of the

17 temporal and spatial variability in the NOx exposures,

18 and how that will vary.

19 DR. CRAPO: Because this makes our

20 biological plausibility, the discussion yesterday,

21 change directions completely. It puts us, it, we were

22 arguing that we weren\'t exposing enough to NO2 to get

23 the level. If these things, if these exposure metrics

24 change, then our whole argument yesterday, the

25 biological plausibility is met, becomes much more

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 9
>
> Page 30
>
> 1 likely.
>
> 2 DR. RUSSELL: No, maybe Jill wants to go
>
> 3 into this further. I mean, again, the high monitor is
>
> 4 Lakewood in downtown Los Angeles. It\'s a roadside
>
> 5 monitor, so it\'s going to be high. You know, actually,
>
> 6 some of these monitors are sitting really near
>
> 7 freeways.
>
> 8 DR. CRAPO: Well, so am I.
>
> 9 DR. RUSSELL: Right, but what I\'m saying
>
> 10 is that, keep in mind, when you\'re talking about, these
>
> 11 monitors, many of these monitors are capturing very
>
> 12 much the highest levels that you\'re going to get,
>
> 13 except in a very confined street canyon.
>
> 14 DR. CRAPO: Okay, and that\'s what I\'m
>
> 15 thinking, is that the high levels that are causing the
>
> 16 disease that we\'re seeing, and we\'re not understanding
>
> 17 who\'s got that high level, and where it is, and why.
>
> 18 As well as, at least, the medical side of us are,
>
> 19 because we\'re not dealing with the numbers in the form.
>
> 20 So, I\'m just wondering if the, if this is, a large part
>
> 21 of our discussion yesterday wasn\'t confounded by some
>
> 22 of us not quite understanding the exposure levels that
>
> 23 our sub-populations were being exposed to. And, you
>
> 24 know, the fact that your, having those, no NO2 all
>
> 25 night long is irrelevant to the fact that you get up in
>
> Page 32
>
> 1 you were, had actually got, a see-, cumulative
>
> 2 distribution function, that those are the ones you\'d
>
> 3 see at the upper tail. So, I think we are capturing
>
> 4 those. And, actually, in response to your question
>
> 5 about the biologic plausibility is, I think it goes
>
> 6 both ways, is that in many cases, I think we might be
>
> 7 looking at overestimates of what the potential exposure
>
> 8 to NO2 is in a general population. Because a lot of
>
> 9 people live out in the suburbs, and you know, again,

10 I\'m sort of parochial in knowing at Atlanta, is that,

11 we\'ve got more monitors near busy areas, than we do

12 sort of in the general suburbs.

13 DR. HENDERSON: I, well, again, I think

14 maybe we can cover this by a paragraph discussing the

15 importance of the temporal and spatial variability of

16 the pollutants and this is not special to NOx. It\'s

17 always a problem, and that we, that this should be

18 emphasized to discuss in the ISA. And it is true that

19 we\'ll extend, but I don\'t hear anything that\'s, that

20 couldn\'t be covered under the importance of temporal

21 and spatial variability, and the, what we listed, as

22 far as monitoring and determining exposures that we, we

23 had discussed this yesterday afternoon, there\'s the,

24 you know, you have the indoor outdoor exposures, the

25 spatial and temporal variability, the siting of the

> Page 31
>
> 1 the morning and get a big dose from 8:00 a.m. to 12:00
>
> 2 noon or whatever.
>
> 3 DR. BALMES: This is John Balmes. I have
>
> 4 to, I need a clarification. I thought I heard Tim
>
> 5 Larson say that most of the site, and I don\'t have any
>
> 6 map in front of me here on the phone. I heard Tim
>
> 7 Larson say that most of the regular monitoring sites
>
> 8 are not, they\'re in open areas and are not necessarily
>
> 9 near freeways. But I just heard that a lot of the
>
> 10 monitors are by freeways. That makes a big difference
>
> 11 to me.
>
> 12 DR. LARSON: Not a lot of them. Some of
>
> 13 them are, but not a lot of them.
>
> 14 DR. BALMES: Right, I think that\'s
>
> 15 important to know.
>
> 16 DR. LARSON: Most of them are not.
>
> 17 DR. BALMES: Yes. That was my
>
> 18 understanding, too.
>
> 19 DR. RUSSELL: Yeah, so I misspoke when I
>
> 20 said a lot, but you do have a representative population
>
> 21 of ones that are near freeways.
>
> 22 DR. BALMES: Right.
>
> 23 DR. RUSSELL: And those are the ones, or
>
> 24 very heavily traffic roads, and those are the ones that
>
> 25 you do see on the one extreme of our population. If
>
> Page 33
>
> 1 monitors, all of that is something that we\'ve already
>
> 2 said is very important. So, I would like to just
>
> 3 summarize that in a paragraph in the letter, and
>
> 4 emphasize how the differences between the more precise
>
> 5 measurement indoor in the clinical studies, and, as
>
> 6 opposed to outdoor ambient studies.
>
> 7 Does that, would that co-, I mean, we\'ve,
>
> 8 you\'re absolutely ri-, and if you don\'t have the
>
> 9 correct exposure, the response then is, it can\'t be

10 related to the amount of, precisely towards lead and,

11 but that stands, that\'s always a problem with epi

12 studies. They don\'t have it for very long exposures.

13 DR. AVOL: Just one small point of

14 information, I think what Joe meant was Lynwood,

15 California not Lakewood. The Lynwood station is

16 alongside the Long Beach Freeway, and gets several

17 hundred thousand vehicles a day.

18 SPEAKER: Yeah, thanks, Ed.

19 DR. LARSON: Well, as my comments

20 yesterday, and you know, at each of those, you know,

21 two sites, there is a information in the database on

22 distance from major roads. And you could compile that

23 fairly easily, and probably, compare that with the

24 population, U.S. population at large.

25 DR. HATTIS: I think that\'d be a good

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 10
>
> Page 34
>
> 1 thing to do to try to see what biases one should expect
>
> 2 and what, you know, how do we characterize the
>
> 3 variability and the likely exposures in relation to the
>
> 4 variability that we see in the monitors.
>
> 5 DR. HENDERSON: Is that something, Mary,
>
> 6 that could be done?
>
> 7 DR. ROSS: We can look into it.
>
> 8 DR. PINTO: I mean, perhaps, with help
>
> 9 from the program offices, I mean, I tend to think that
>
> 10 that sort of effort if, you know, if done well, I mean,
>
> 11 could take a bit of time and maybe, even, longer. I\'m
>
> 12 thinking in terms of longer than the time scale for
>
> 13 setting the next draft to come out. But we\'d have to
>
> 14 look into that, Rogene.
>
> 15 DR. HENDERSON: Okay, well, let\'s, I
>
> 16 think we\'ve had a good discussion of this issue, which
>
> 17 is a, certainly, an important one. Can we look now
>
> 18 beyond the first three charge questions to going to the
>
> 19 health, unless there\'s anybody else has something else
>
> 20 on the first three charge questions. The next four
>
> 21 charge questions relate to the health effects. And,
>
> 22 was there, were there things that were left out or
>
> 23 that, yes, Ed.
>
> 24 DR. POSTLETHWAIT: As part of the charge
>
> 25 four things, and this just may be simply an issue of
>
> Page 36
>
> 1 Balmes, again. I think the last sentence in the bullet
>
> 2 here, is, contains key information about that the ISA
>
> 3 would be improved if a plan or process for integration
>
> 4 and study selection is clearly laid out. So, that it
>
> 5 would be clear to some, to a reader, such as Ed, why
>
> 6 studies were included.
>
> 7 DR. HENDERSON: That\'s a good point.
>
> 8 Yeah. Are there other things?
>
> 9 DR. WYZGA: Rogene, I had a couple of

10 things on five, but.

11 DR. HENDERSON: Okay, Ron, and then

12 Joyce, go ahead.

13 DR. WYZGA: Okay. I guess, first of all,

14 I\'m flattered that my name is mentioned, but I would

15 also mention that John, in number five, John Balmes

16 mentioned some toxicological studies that weren\'t

17 included. And I would change the wording to say that,

18 instead of several of the latest NOx human field

19 studies is, basically, several recent epidemiological

20 studies that examine the association between health

21 outcomes and NO2, were, either, not included nor

22 studied correctly, and say, especially, in describing

23 the impacts of other pollutants on the NO2 health

24 associations.

25 DR. HENDERSON: Certainly, and nobody\'s

> Page 35
>
> 1 verbiage. On that third line, where it says only the
>
> 2 key studies that support an NAAQS should be included.
>
> 3 I wasn\'t sure what that meant, in terms of, to support
>
> 4 what we have now; to support a new one, I mean. And
>
> 5 when you read that, it almost sounds like you, as we
>
> 6 discussed yesterday, you could, inadvertently,
>
> 7 introduce selection bias on what studies you were
>
> 8 reporting, positive versus negative.
>
> 9 DR. HENDERSON: Well, this still is from
>
> 10 the, has a little history behind it. And that\'s what
>
> 11 I, how I interpret it. If you looked at the CD, it
>
> 12 includes everything from, you know, a 500 ppm exposure
>
> 13 of a toad frog to, you know, something at ambient
>
> 14 levels. And you\'re right. How do you choose the key
>
> 15 studies. But I think the meaning of this statement is
>
> 16 that, that chapter three could be condensed to even
>
> 17 more to make it less like a CD, and more, just includes
>
> 18 studies that are relevant for setting a standard. I
>
> 19 think that\'s the meaning of it, but you\'re bringing up
>
> 20 a problem which we have discussed, CASAC has discussed,
>
> 21 and who chooses, you know. But we came down that it
>
> 22 was more beneficial for us doing this review to have
>
> 23 the Agency choose what they felt were the key relevant
>
> 24 studies.
>
> 25 DR. BALMES: But that, this is John
>
> Page 37
>
> 1 saying it\'s going to be mentioned in here, I mean.
>
> 2 DR. WYZGA: Okay.
>
> 3 DR. HENDERSON: You know, I mean, as I
>
> 4 said, I mean, this will not be covered - -
>
> 5 DR. WYZGA: But, I guess, part of it is,
>
> 6 it\'s broader than simply the epidemiological is because
>
> 7 there\'s some toxicological studies as well.
>
> 8 DR. HENDERSON: Okay, yeah, okay. And,
>
> 9 is that ment-, it\'s a tox study, yeah, Balmes, oh,

10 yeah. No, we, it\'s mentioned there. Sure, Ron, never

11 in my wildest dreams would I mention names.

12 DR. COTE: This is an opportunity,

13 though, for me to ask people if you, if there are

14 specific papers that you\'re aware of that we don\'t

15 have, please give us the references. Because, you

16 know, we\'ve done this careful lit search, for whatever

17 reason those papers have not popped up. So, you know,

18 if it\'s a flaw in the keywords or whatever, so please

19 help us by giving us the specific references rather

20 than.

21 DR. BALMES: So, I will include, this is

22 John Balmes. I\'ll include the ones that I referred to

23 yesterday in my written comments. But the nitrogen

24 dioxide will get the one paper published in 2005. You

25 don\'t have to get fancy with the keywords.

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 11
>
> Page 38
>
> 1 DR. COTE: Thank you.
>
> 2 DR. HENDERSON: Okay, so, Ron and John
>
> 3 will provide, you will provide those in your written
>
> 4 individual comments. Then that, then in the letter, we
>
> 5 can refer to it. We can, see individual comments of,
>
> 6 with them, okay. Anything about question six that
>
> 7 people have problems with or would like to add.
>
> 8 DR. THURSTON: Well, as to the question
>
> 9 five.
>
> 10 DR. HENDERSON: You back to five, okay.
>
> 11 DR. THURSTON: Yeah, well, I mean,
>
> 12 actually, the reason I put Ron\'s name in there was to,
>
> 13 so that I knew he would respond to that and clarify
>
> 14 that sentence with it. I figured that, otherwise, he
>
> 15 might get ignored and he would make sure it was
>
> 16 correct. So, we could just write members also pointed
>
> 17 out, or something, instead of put, naming names here.
>
> 18 The other thing is, in the iterations, I don\'t know,
>
> 19 either I didn\'t have it in the beginning, or it got
>
> 20 left out or something, but I would add at the, in the
>
> 21 last sentence, just, well, I\'m just going to get this,
>
> 22 finally, examining the epidemiology results, and I
>
> 23 would say, after results, add the words across outcomes
>
> 24 as a function of. Because, the whole idea was to look,
>
> 25 not just individually, but look across outcomes and
>
> Page 40
>
> 1 you say that yesterday?
>
> 2 DR. SHEPPARD: I don\'t think I was the
>
> 3 one that brought that up.
>
> 4 DR. WYZGA: Okay.
>
> 5 DR. SHEPPARD: Probably is an important
>
> 6 point.
>
> 7 DR. HENDERSON: Okay, are there more
>
> 8 comments on the substance of answers to question five?
>
> 9 Go on to six, then.

10 DR. AVOL: I have a question on six.

11 This Ed Avol. My question is this. About seven lines

12 in, there\'s a comment about sensitive populations.

13 There\'s no comment about genetic susceptibility in

14 that, and I just have a question for whoever wrote

15 this, if that was a conscious exclusion because they

16 don\'t believe it\'s sufficient - -

17 DR. CRAPO: It wasn\'t conscious. It was

18 late at night in the middle of a bad Rockies game.

19 SPEAKER: That was a good game.

20 DR. CRAPO: So, let\'s add that, let\'s

21 just add that.

22 DR. HENDERSON: So, what line, at what

23 line, yeah, where is it, what line is it, that - -

24 DR. AVOL: It\'s about seven lines down,

25 the last just in, I\'m sorry, there\'s evidence of

> Page 39
>
> 1 look for coherence. That\'s it.
>
> 2 DR. HENDERSON: Okay, and I will remind
>
> 3 you, as far as wordsmithing, when we finish here this
>
> 4 morning, Angela and I will be drafting the actual
>
> 5 letter, based on the substance of these comments. And
>
> 6 you will be receiving it for concurrence and review.
>
> 7 So, small wordsmithing, you can take care of at that
>
> 8 point if you want. Can we go on to charge question
>
> 9 six, then?
>
> 10 DR. WYZGA: One, on question five.
>
> 11 DR. HENDERSON: Five, I\'m still on five.
>
> 12 DR. WYZGA: And, is Lianne on the phone?
>
> 13 DR. HENDERSON: Lianne wasn\'t coming on
>
> 14 till when, 9:00. What\'s Angela\'s time.
>
> 15 DR. NUGENT: Rogene?
>
> 16 DR. HENDERSON: Yes.
>
> 17 DR. NUGENT: Is Lianne on the phone? She
>
> 18 said she would be on the phone.
>
> 19 DR. SHEPPARD: Yeah, I\'m here.
>
> 20 DR. WYZGA: Okay, Lianne, this is Ron
>
> 21 Wyzga. You said something yesterday, number five,
>
> 22 something about, and I wanted to see if I could capture
>
> 23 it, about looking more systematically or in a better
>
> 24 organized way at the, how one deals with co-pollutants
>
> 25 and interpret studies using co-pollutants. Did I hear
>
> Page 41
>
> 1 adverse health effects in sensitive populations such
>
> 2 as.
>
> 3 DR. CRAPO: And how would you say that,
>
> 4 though. The only ones that have really been studied
>
> 5 are the ones that are mentioned here, really. I mean,
>
> 6 genetic is a theoretic thing, but there\'s no hard study
>
> 7 that says this is a gene that creates susceptibility to
>
> 8 NO2.
>
> 9 DR. HENDERSON: You could add a sentence

10 - -

11 DR. CRAPO: I mean, I believe it\'s true.

12 I just don\'t think, I can\'t think of a study that would

13 prove it.

14 DR. HENDERSON: You could add a sentence

15 saying, genetic polymorphisms may also influence the

16 response.

17 DR. AVOL: I mean, there is some

18 published information from our lab and others on GST,

19 and the sensitive to oxidative stress mechanistic

20 pathways.

21 DR. HENDERSON: We can put into the,

22 genetically perception.

23 DR. POSTLETHWAIT: Will that fall under,

24 stuff under question seven. I mean, does, six and

25 seven are, essentially, addressing the same chapter in

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 12
>
> Page 42
>
> 1 the ISA. And there is this issue of defining
>
> 2 susceptible populations.
>
> 3 DR. AVOL: Yeah, that\'s, I mean, I think
>
> 4 that\'s fine. I\'d be happy to put it into seven. I
>
> 5 just pointed it out there, because it seemed like it
>
> 6 was - -
>
> 7 DR. POSTLETHWAIT: Right, yeah, I mean,
>
> 8 this becomes redundant, just like, you know, I said
>
> 9 it\'s redundant.
>
> 10 DR. HENDERSON: Okay, I noted that that
>
> 11 should be mentioned. Are there other things for six or
>
> 12 seven?
>
> 13 DR. RUSSELL: Rogene, before going on,
>
> 14 I\'m actually curious. On the last sentence, that, I
>
> 15 mean, to me that\'s a rather important sentence.
>
> 16 DR. HENDERSON: Which question are you -
>
> 17 -
>
> 18 DR. RUSSELL: Oh, six. That we concur on
>
> 19 the findings and, et cetera, directly result in adverse
>
> 20 impacts. And this comes, you know, I was sitting here
>
> 21 a little uncomfortably yesterday about the use of the
>
> 22 word likely causal when we, they say the strongest new
>
> 23 evidence comes from epidemiologic studies of ED visits
>
> 24 and hospitalization. And I\'m an air quality person, so
>
> 25 I, the medical end is somewhat beyond me, but,
>
> Page 44
>
> 1 whether, in verbiage that, while it may not be the
>
> 2 chemical species NO2, per se, certainly, there\'s a
>
> 3 linkage between NO2 in the air and all the other
>
> 4 goodies and these adverse health outcomes.
>
> 5 DR. CRAPO: I don\'t remember exactly what
>
> 6 I said. I can tell you that I, last night, I
>
> 7 deliberately wrote this sentence very strong, \'cause I
>
> 8 wanted to make us talk about it. I think we have to
>
> 9 decide, I mean, this is, I think, the heart of the

10 paper right here in that one sentence. It tell them

11 whether we really agree or don\'t agree with the

12 fundamental conclusion of the document. The, when I

13 get, at the end of the day, I\'m impressed that the,

14 it\'s not, we have to cite the few studies that were

15 negative; but in fact, this is a, it\'s a ten to one

16 vote in favor of positive, but it\'s not an equal, half

17 were positive and half were negative.

18 These are, the most of the studies that come

19 out are showing strong effects. And I do think that

20 it\'s likely the effects are tracking primarily the

21 products of combustion, which the paragraph says. The,

22 but the, but there are, overwhelmingly, strong data

23 showing an association that we haven\'t really dealt

24 with. And I\'m concerned that the biggest problem is in

25 our exposure metric. I think that our correlation

> Page 43
>
> 1 recognizing that the epi studies that we\'ve talked
>
> 2 about the problems with the monitoring and the spatial
>
> 3 variability. There are some that find associations.
>
> 4 There\'s others that don\'t. And, it almost struck me
>
> 5 that, I was sitting here going, the strongest new
>
> 6 evidence, and you have some studies that go the other
>
> 7 direction, and I recognize EPA was, sort of, sitting on
>
> 8 the fence on this one, too. Directly result may just
>
> 9 overstate how I think I feel on this. And I\'m
>
> 10 wondering how others feel, too.
>
> 11 DR. HENDERSON: I think that\'s a strong
>
> 12 statement considering our discussion. I agree with
>
> 13 you. I don\'t find, I think we\'re all trying to figure
>
> 14 out is there something here or not, and - -
>
> 15 DR. SHEPPARD: Yeah, I agree as well. I
>
> 16 think that\'s pretty strong.
>
> 17 DR. AVOL: I think, in fact, that
>
> 18 yesterday in the discussion, John Samet challenged the
>
> 19 consistent coherent issue.
>
> 20 DR. POSTLETHWAIT: James, yesterday, you
>
> 21 and I were talking, and you came up with a really great
>
> 22 multi-word descriptor, you know, that might soften this
>
> 23 a bit. And I\'m trying to remember what you said. It
>
> 24 was something about, you know, NO2 appears to be a key
>
> 25 player, I mean, you said it better than that. And
>
> Page 45
>
> 1 needs with, needs to be with the highest one-hour
>
> 2 average.
>
> 3 Not, like ozone was before it changed to an
>
> 4 eight hour averaging time. And if we converted all of
>
> 5 our data to one-hour averaging time, we might have a
>
> 6 lot more confidence in this conclusion. But I don\'t,
>
> 7 but I can\'t walk away from the strength of the data
>
> 8 that\'s summarized in the ISA. It\'s a very strong
>
> 9 document with studies from every dimension from every,

10 from lots of different countries, consistently finding

11 associations with products of combustion that metric

12 was within all.

13 DR. BALMES: So, the, this is John

14 Balmes. I agree with you, Jim, James, that the

15 epidemiologic data taken as a whole from a

16 stratospheric level are pretty impressive. My problem

17 is that I don\'t think the coherence is, necessarily,

18 there with the toxicologic data. We talked about that

19 yesterday. And I don\'t, actually, personally, have a

20 problem with the Agency moving ahead with a new

21 standard if that\'s the ultimate outcome, based on

22 epidemiologic data. I do epidemiology.

23 I appreciate its value. But I think it\'s, I

24 don\'t think the toxicology is really there to support

25 the epidemiologic findings. And it\'s often that

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 13
>
> Page 46
>
> 1 toxicology is behind epi.
>
> 2 So, I think we should recognize that, I mean,
>
> 3 if we have to say that. Because, otherwise, people
>
> 4 will criticize the document for not, sort of, fairly
>
> 5 representing the literature. And I think, with regard
>
> 6 to respiratory infection risk, I think the toxicology
>
> 7 is there.
>
> 8 But I don\'t think that we understand why NO2
>
> 9 causes, or is associated with a kind of lung function
>
> 10 decrements that the children\'s health study found. I
>
> 11 think that\'s a very important finding that should be
>
> 12 very strongly emphasized in the document. But I don\'t,
>
> 13 I, certainly, don\'t understand how that occurs.
>
> 14 DR. CRAPO: Would you say the toxicology
>
> 15 does support it if we\'re speaking about 200 ppb instead
>
> 16 of 15 ppb?
>
> 17 DR. BALMES: Uh - -
>
> 18 DR. CRAPO: \'Cause I\'ve changed my mind
>
> 19 on that one.
>
> 20 DR. BALMES: Well, I think that\'s a
>
> 21 greyer area, but I\'m not sure that 200 ppb, the
>
> 22 toxicology supports 200 ppb.
>
> 23 DR. LARSON: James, Tim Larson, again.
>
> 24 15 ppb is your annual average. The 200 ppb is your
>
> 25 one-hour max. So - -
>
> Page 48
>
> 1 DR. LARSON: I\'m not disagreeing with
>
> 2 your general conclusion, but I\'m just saying, to be
>
> 3 more precise, those epidemiology studies are probably
>
> 4 looking at 24 hour time series. And those 24 hour
>
> 5 averages are certainly at a max greater than 15 ppb.
>
> 6 DR. CRAPO: Yeah, and I\'m, but I\'m also
>
> 7 saying that is a, I think that NO is not driven, NO\'s
>
> 8 health effects are not driven by the daily average.
>
> 9 That\'s probably driven by the peak, and - -

10 DR. LARSON: Right, but - -

11 DR. CRAPO: - - and the, and we never

12 looked at the peak in terms of comparison of.

13 DR. LARSON: Right, because a relevant

14 comparison would be the 24 hour versus daily max hourly

15 average. Because the type, and the epi are based,

16 primarily, I believe, on the 24 hour, but I\'m just

17 saying, those are the two numbers to compare.

18 DR. CRAPO: Well, it is except that we\'re

19 looking at fairly profound health effects, and, I mean,

20 in many of these studies, and - -

21 DR. LARSON: Well, and I\'m agreeing with

22 you. I\'m just saying that the change from one day to

23 the next is greater that 15 ppb. And, you know, in

24 some cases, the one-hour max could be several hundred,

25 and the change from one hour to the next could be

> Page 47
>
> 1 DR. CRAPO: Well, that\'s one-hour max in
>
> 2 monitors on certain places. It\'s not the one-hour
>
> 3 personal max.
>
> 4 DR. LARSON: Right, but what\'s the, what
>
> 5 is the annual average at that monitor. I mean, that,
>
> 6 in terms of, if there isn\'t, I mean, there\'s two things
>
> 7 going on here. One of them is spatial, you know,
>
> 8 proximity to roads, et cetera. The other one is the
>
> 9 annual average versus the one-hour average. Both cause
>
> 10 differences in these numbers. But, when you say 200
>
> 11 versus 15, one\'s a chronic exposure and one of them\'s
>
> 12 acute exposure.
>
> 13 DR. CRAPO: Well, I know that, but the
>
> 14 toxicology\'s almost all acute. And the peaks are
>
> 15 acute. And I, see, yesterday, I was sitting here
>
> 16 thinking about the 15 ppb and saying, I\'ve got an order
>
> 17 of magnitude or two orders of magnitude difference in
>
> 18 my toxicology and my epidemiology. But, in fact, I
>
> 19 don\'t. It\'s, they\'re coming together. The lowest
>
> 20 threshold effects for NO are, you know, are in some, a
>
> 21 few hundred ppb. We saw that yesterday, where they
>
> 22 looked at the lower limits of toxicology having
>
> 23 effects. And now, we\'ve got peak levels at fifteen
>
> 24 feet up in the atmosphere, in certain locations,
>
> 25 pushing the same levels.
>
> Page 49
>
> 1 fairly large, too. So, I mean, the 15 ppb, it seems
>
> 2 like, is, it\'s not even in the range of what we\'re
>
> 3 talking about. I mean, that\'s an annual average, and
>
> 4 it just gets washed out, as you say, by all the
>
> 5 seasonally flow seasons and all the midnights and
>
> 6 everything else, so.
>
> 7 DR. AVOL: This is Ed Avol. Not to go
>
> 8 back to discussion of the health effects, but since the
>
> 9 lung function changes in the children health study we

10 brought up, let me just point out one perspective. And

11 that is that in, of course, in looking at lung function

12 growths or decrements in lung function growth among

13 children, we\'re looking at long-term changes of

14 children that are moving around their communities, and

15 we\'re looking at those annual averages from those

16 central site monitors in those areas. And so, while it

17 may be true that close to roadways or at traffic peaks,

18 there are several hundred parts per billion

19 concentrations, in fact, our relationships with those

20 changes in lung function are with the annual average.

21 DR. CRAPO: Well, I understand that

22 factor. I\'m just hypothesizing, I mean, I\'m saying

23 that the health effects appear to be real. And I,

24 biologically, couldn\'t explain them with the annual

25 average, but then it occurs to me that what\'s likely

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 14
>
> Page 50
>
> 1 happening, maybe I only need to hit my kid with 200 ppb
>
> 2 once a week in order to cause adverse changes in his
>
> 3 growth of his lung. And I take him on my freeway for
>
> 4 three or four hours a week while I\'m driving various
>
> 5 places. So, it could be that that\'s the problem, and
>
> 6 we have-, but my point is, we haven\'t even analyzed it
>
> 7 that way.
>
> 8 DR. LARSON: Right.
>
> 9 DR. HENDERSON: Well, there is no - -
>
> 10 DR. CRAPO: But the health effects are
>
> 11 real. That\'s what this sentence says. The health
>
> 12 effects are real. I don\'t know why yet, but I, there,
>
> 13 and it might not, and it might be a surrogate, but
>
> 14 they\'re, but they are so uniform across so many
>
> 15 studies, that we have to take them serious.
>
> 16 DR. HENDERSON: Now, as far as - -
>
> 17 DR. BALMES: This is John Balmes. I
>
> 18 agree with that. It\'s, but I, so the, the coherence in
>
> 19 the epi, I have, I\'m comfortable with. It\'s, if people
>
> 20 are assuming from that statement that we mean coherence
>
> 21 with the toxicology, I don\'t think we\'re really there
>
> 22 yet. That\'s all I\'m trying to say.
>
> 23 DR. THURSTON: Could I say?
>
> 24 DR. HENDERSON: George?
>
> 25 DR. THURSTON: Yeah, I don\'t really think
>
> Page 52
>
> 1 supports that, too.
>
> 2 DR. CRAPO: Well, I\'ll say it, \'cause,
>
> 3 actually, I wrote it with, we said it several times
>
> 4 yesterday, but when, every time you do the study, you
>
> 5 find that there\'s an adverse health effect, which you
>
> 6 can link it, like, to the roadway, or to the children\'s
>
> 7 study with an open fuel on their furnace in the home,
>
> 8 the powerful correlations that go with this, the fact
>
> 9 the primary source of NOx is combustion. So, it seemed

10 like an obvious to me.

11 DR. RUSSELL: Yeah, that one I have no

12 problems with.

13 DR. HENDERSON: I have no problems with

14 that, either. I think that\'s a solid statement, but

15 George, would you repeat your modification of the last

16 - -

17 DR. THURSTON: Yes, I may have it here.

18 CASAC concurs that the epidemiologic findings indicate,

19 we\'ll see, that current ambient, is directly, no, are

20 associated with. That\'s what epidemiology tells us,

21 associations. Are associated with adverse impacts on

22 the public health, comma, but that the document needs

23 to better - -

24 SPEAKER: Articulate?

25 DR. THURSTON: Well, you could say the

> Page 51
>
> 1 we\'re at with, we may end up where this last sentence
>
> 2 is, but I don\'t think we\'re there yet, in my opinion.
>
> 3 So, I mean, I would just say, concur that the
>
> 4 epidemiologic findings, you know, indicate, let me see.
>
> 5 I had it written down here, too. Yeah, concurs that
>
> 6 the epidemiologic findings indicate that current
>
> 7 ambient NO2 exposures are associated with adverse
>
> 8 effects on the public health. But the document needs
>
> 9 to better document, or better, you know, lay out the
>
> 10 plausibility, a consistency in coherence. I think that
>
> 11 work, that needs to tightened up, and that\'s where we
>
> 12 ought to be focusing this next iteration. I don\'t
>
> 13 think we\'re done yet, and this gives the impression
>
> 14 we\'re done.
>
> 15 DR. HENDERSON: That we\'re done. No, I
>
> 16 would agree with that, and I\'ll let Mary talk on it.
>
> 17 Would you write down your modified sentence so that.
>
> 18 DR. ROSS: While you\'re on that subject,
>
> 19 I just want to draw your attention to a sentence a few
>
> 20 sentences earlier. CASAC recognizes that the primary
>
> 21 associations are between products of combustion and
>
> 22 adverse health effects. That\'s also a strong
>
> 23 conclusion that will have policy implications. And
>
> 24 just wanted to make sure if that\'s something you agreed
>
> 25 with. It\'s helpful if you provide why, you know, what
>
> Page 53
>
> 1 ISA needs to, yeah, the ISA needs to better document
>
> 2 that these findings are plausible, consistent and
>
> 3 coherent with, now do we want to say with toxicology
>
> 4 or something with other evidence?
>
> 5 DR. CRAPO: Well, to me, I would say, in
>
> 6 looking at lots of different medical kinds of issues, I
>
> 7 see more consistency of this data than anything,
>
> 8 virtually, anything that I make medical decisions on.
>
> 9 A lot of consistency across broad settings, where the

10 issue has been plausibility or coherence with the

11 toxicology. And that was a dose issue. It was not,

12 there\'s plenty of toxicology at high dose. There\'s no

13 question about if you\'re talking about 10 ppb, I mean,

14 10 ppm, there\'s no question it correlates. So, the

15 whole issue is, to me, the only issue is dose.

16 SPEAKER: Well, yeah, I mean, that is a

17 big - -

18 DR. THURSTON: Well, I just think it

19 needs to be better. I think you\'re probably right.

20 There is, having read it, you know, there is a lot of

21 that evidence, but it hasn\'t been laid out in a way

22 that makes it obvious that where the coherencies are.

23 DR. BALMES: Well, one thing that might

24 be useful is to look at the relationship between these

25 one-hour peak exposures and the annual averages.

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 15
>
> Page 54
>
> 1 DR. HENDERSON: I thought that was done
>
> 2 in there, but I - -
>
> 3 DR. HATTIS: We do have direct evidence
>
> 4 in a table in the ISA on that point, that essentially,
>
> 5 the 99th percentile of the one-hour maximum is like 72
>
> 6 ppb. It\'s not the highest is like 200, but that\'s the
>
> 7 highest of 288,000 measurements. So, I mean, you\'re a
>
> 8 little bit far out on the scale there with the 200.
>
> 9 But, certainly, the 99th percentile is about, is 72
>
> 10 parts per billion whereas the 99th percentile, the one
>
> 11 hour of the yearly averages is 33 parts per billion.
>
> 12 So, you have a couple fold there, which gets a little
>
> 13 closer to the toxicology, but.
>
> 14 DR. HENDERSON: Mary?
>
> 15 DR. ROSS: You know, when we talk about -
>
> 16 -
>
> 17 DR. HATTIS: But that\'s again, for the
>
> 18 monitors that, some of them, which may be close to
>
> 19 roadways, but they\'re still a little high up, so it
>
> 20 may, may still be some additional distortions.
>
> 21 DR. HENDERSON: Mary?
>
> 22 DR. ROSS: We tried to evaluate the
>
> 23 short-term exposure studies that looked at different
>
> 24 indices, and there\'s a small discussion on page 5-5 of
>
> 25 24 hour studies versus one-hour max studies. And they
>
> Page 56
>
> 1 tended to look at NO2.
>
> 2 DR. CRAPO: Yeah, so I mean, you could
>
> 3 say it either way, but since they interconvert, you\'re
>
> 4 a little bit wondering what it really is you\'re. I
>
> 5 mean, is the NO2 a surrogate for NO-, for all the other
>
> 6 species. And so that\'s why I use them interchangeably,
>
> 7 without being very discretionary. I knew, I need, you
>
> 8 should use the same term.
>
> 9 DR. HENDERSON: So, you\'re saying, I

10 mean, what you\'re suggesting, Ron, is that NOx can be a

11 significant factor?

12 DR. WYZGA: Yes.

13 DR. HENDERSON: Does anybody have an

14 objection, I mean, can we agree on can be?

15 DR. CRAWFORD-BROWN: Now, is that can be

16 in the sentence of can be under some circumstances?

17 DR. WYZGA: Well, we\'ve avoided making

18 definitive conclusion in the last sentence, and we\'re

19 saying, you know, we\'re waiting for the document to,

20 basically, organize and, you know, give us a redraft.

21 And it seems to me to make that conclusion that it is,

22 it can be, and I think that\'s one of the things we\'re

23 waiting on, you know, the next round, to see whether or

24 not the document supports, you know, it is a

25 significant factor.

> Page 55
>
> 1 don\'t find, there\'s not a lot of difference in the
>
> 2 epidemiologic. Now, that is one-hour max on a given
>
> 3 day, but, you know, we did try to evaluate that, and
>
> 4 we\'ll look at if there are any further studies.
>
> 5 DR. HENDERSON: Okay, Ron?
>
> 6 DR. WYZGA: Rogene, I would say, I guess
>
> 7 in the spirit of what we said, if you look at the
>
> 8 previous sentence, we, basically, say NOx is a
>
> 9 significant factor, and I wonder, given what we said
>
> 10 later, if we could change the is to can be.
>
> 11 DR. HENDERSON: Yeah, I know, I see
>
> 12 (WHEREUPON, Dr. Henderson reviewed the document.)
>
> 13 DR. ROSS: And can I ask one more, I\'m
>
> 14 sorry to keep bothering you but, when you say NOx, do
>
> 15 you mean NO2 or NOx, and it\'s one of the things we
>
> 16 battle with all the time is selecting the term.
>
> 17 DR. CRAPO: I use them interchangeably,
>
> 18 because we did it yesterday. I don\'t think we know,
>
> 19 exactly, what the species is, but NO2 seems to be a
>
> 20 good surrogate for it, so you could use NO2, but, in
>
> 21 fact, you\'re measuring the, you\'re measuring the, well,
>
> 22 you\'re using it as a surrogate for NOx, so probably, I
>
> 23 think NOx is your better term, because you don\'t really
>
> 24 know it\'s NO2, do you?
>
> 25 DR. WYZGA: Except the studies have
>
> Page 57
>
> 1 DR. HENDERSON: Yes, that\'s the sense
>
> 2 that I understood. Okay, did we, for the NOx issue, we
>
> 3 decided to keep the same term. What did you decide on
>
> 4 the - -
>
> 5 DR. CRAPO: I like NOx better, because it
>
> 6 - -
>
> 7 DR. KENSKI: I actually would prefer NO2
>
> 8 in the, you know, all of the epi stuff is based on NO2
>
> 9 measurements, and the tox stuff is NO2 measurements,

10 and you know, yes, they are, they do interconvert, but

11 you know, the peak, peak, I mean, you know, what we

12 measure as NOx is, what we measure as NO2 is, you know,

13 the difference between NOx and NO-, so I, I don\'t know.

14 I just think it\'s better to be consistent and keep

15 that, you know, link with NO2.

16 DR. HENDERSON: The toxicity data is

17 based on NO2, I mean.

18 DR. KENSKI: Right.

19 DR. HENDERSON: Yeah, I mean, the

20 clinical and the - -

21 DR. KENSKI: And what\'s repor-, and

22 what\'s reported, granted, it\'s not, you know,

23 absolutely pure, you know, true NO2, but it\'s as close

24 to it - -

25 DR. CRAPO: Well, I wouldn\'t be, I\'d be

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 16
>
> Page 58
>
> 1 happy to accept either one, as long as that, someplace
>
> 2 in it, you defined that three was interconversion and
>
> 3 that NO2 is as, is a critical species in the sequence.
>
> 4 So, you just make, as long as you define what you\'re
>
> 5 using, that\'s fine to use the other term, as far as I\'m
>
> 6 concerned.
>
> 7 DR. KENSKI: It just might be good to add
>
> 8 a sentence, you know, up front saying that, you know,
>
> 9 we acknowledge that, you know, the NO2 that we measure
>
> 10 is not, you know, true a hundred percent - -
>
> 11 DR. WYZGA: But I think the other thing
>
> 12 is that, that we use NO2 with the relationship between
>
> 13 NO2 and the other components of NOx may change
>
> 14 temporally and spatially, and we don\'t really have
>
> 15 enough evidence to say that it\'s consistent. And if
>
> 16 that relationship were consistent, then, I think, we
>
> 17 could jump to NOx, but it\'s not consistent.
>
> 18 DR. KENSKI: Right, and we\'re asking for
>
> 19 a better, you know, definition of some of those.
>
> 20 DR. HENDERSON: Can we go on to seven and
>
> 21 eight. I put in on seven about the genetics. I
>
> 22 thought, I don\'t know who wrote eight, but I thought
>
> 23 that was well written. It was very clearly written.
>
> 24 Somebody wrote that. It\'s Doug. Oh, we, we\'ll get a
>
> 25 my kudos to Doug. I thought that was well written.
>
> Page 60
>
> 1 inherently increased exposure represent susceptibility.
>
> 2 DR. HENDERSON: Oh, okay, or whether it
>
> 3 goes in the exposure.
>
> 4 DR. COTE: Just as a point of
>
> 5 clarification on that. We tend to talk about
>
> 6 susceptible and vulnerable populations, and susceptible
>
> 7 is a more innate quality. And vulnerable being people
>
> 8 at increased risk, or individuals at increased risk for
>
> 9 some not intrinsic attribute. So, exposure would be

10 increased vulnerability.

11 DR. POSTLETHWAIT: Then the first bullet

12 needs to be changed to incorporate, not only defining

13 susceptible, but defining vulnerable. And then, depend

14 upon what the panel feels, you can leave the high

15 exposure in there, or not.

16 DR. HENDERSON: Okay, I see, you, there

17 is the, the people near the roadway are vulnerable

18 because of the high exposure. That makes sense. I,

19 and that\'s what the question asks, susceptible or

20 vulnerable.

21 DR. ROSS: And to expand on that, the

22 vulnerable population includes the two sub-categories,

23 other than the biological, the socio-economic and the

24 geographic were, generally, extrinsically sensitive.

25 So, we could split that vulnerability up into two

> Page 59
>
> 1 The, we\'ve talked about the multi-pollutant aspects,
>
> 2 and I\'m, hope-, I think Ellis, probably, you
>
> 3 contributed that. And, that will be, can be worked
>
> 4 into the letter as a major point that, you know, after
>
> 5 all our discussions, we still have this problem of the
>
> 6 multi-pollutant aspects for, when we try to assess the
>
> 7 risk of air pollutants, particularly the different
>
> 8 oxidant pollutants. But, let\'s see if there\'s any big
>
> 9 changes in seven and eight that we want to make.
>
> 10 Particularly, anything we want to add.
>
> 11 DR. BALMES: I thought I heard yesterday
>
> 12 that some people were uncomfortable with the idea of
>
> 13 defining a susceptible group relative based on their
>
> 14 where they live. That\'s the first bullet in, uh - -
>
> 15 DR. HENDERSON: Page seven.
>
> 16 DR. AVOL: I think Tim\'s right. There
>
> 17 was some discussion about moving the issues of high
>
> 18 exposure locations and near roadway into exposure.
>
> 19 DR. POSTLETHWAIT: Yes, and when I put
>
> 20 this together during the Rockies game - -
>
> 21 DR. CRAWFORD-BROWN: I don\'t think the
>
> 22 Rockies actually had a game.
>
> 23 DR. POSTLETHWAIT: That\'s what I was
>
> 24 thinking. Yeah, I mean, that\'s, it can go wherever.
>
> 25 The question is is whether it does, does increased,
>
> Page 61
>
> 1 components, and that would address, I think, Dr. Avol\'s
>
> 2 questions.
>
> 3 DR. AVOL: Yeah, but I think there is a
>
> 4 interaction here in the sense that vulnerable
>
> 5 populations, those are the high exposure alongside
>
> 6 roadways, are likely, are disproportionately likely to
>
> 7 be lower SES and get into issues of environmental
>
> 8 justice. And then, they may have biological in that
>
> 9 sense, be the former susceptible. They may also fall

10 into the susceptible population as well. So, they get,

11 sort of, a double whammy. But I think that it is true

12 that there are susceptible and vulnerable sub-

13 categories here.

14 DR. HENDERSON: Okay. Any more for

15 charge question seven?

16 DR. COTE: If you have time, I had a

17 quick question. The two on this, on the page five, the

18 partial bullet at the top, the last sentence, the

19 chapter did not address biologic plausibility with

20 regard to specific populations, thus it\'s difficult to

21 attribute health outcomes to direct causal. I think

22 we\'re all in agreement when you have biologic

23 plausibility, you\'re much better off. Or is this

24 intended to mean, though, if you don\'t have mechanism

25 of action information, then you can\'t say there\'s a

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 17
>
> Page 62
>
> 1 causal outcome?
>
> 2 DR. POSTLETHWAIT: Well, my intent was
>
> 3 to, sort of, throw that open for discussion that, would
>
> 4 it make the document more robust to have, why would an
>
> 5 asthmatic be more susceptible to NO2 than a normal.
>
> 6 For example, what is it about the biology of NO2 that
>
> 7 induces susceptibility in a specific subset of the
>
> 8 population. Yes?
>
> 9 DR. COTE: Yeah, I think we can make that
>
> 10 stronger. I\'m not sure we can actually, and in each
>
> 11 case, can be successful, do we not.
>
> 12 DR. CRAPO: The asthmatic has a more
>
> 13 responsive airway, and greater responsive inflammation,
>
> 14 and the NO2 is an irritant, so it could easily be an
>
> 15 oxidant. So, it could easily be - -
>
> 16 DR. POSTLETHWAIT: That one\'s easy. Some
>
> 17 of the others may not be so easy. And we can\'t - -
>
> 18 DR. LARSON: Are we doing, dealing with a
>
> 19 likely causal, and that term. I mean - -
>
> 20 DR. POSTLETHWAIT: Yeah - -
>
> 21 DR. LARSON: - - causal is a diff-, is a
>
> 22 higher standard than likely causal. My understanding
>
> 23 was, you don\'t need the biological plaus-, I mean, you
>
> 24 don\'t need the detailed mechanism to go to likely
>
> 25 causal.
>
> Page 64
>
> 1 you\'ve got injury, repair, growth, and development.
>
> 2 DR. COTE: Okay.
>
> 3 DR. POSTLETHWAIT: And, which is not, two
>
> 4 of those factors are, don\'t occur in the adult.
>
> 5 DR. HATTIS: Yeah, if you want to back
>
> 6 off from unique, you might say distinctive.
>
> 7 DR. POSTLETHWAIT: Sure.
>
> 8 DR. COTE: Thank you.
>
> 9 DR. HENDERSON: Okay. Did you get that,

10 Angela, distinctive. Let\'s look at charge question

11 eight and, uh - -

12 DR. AVOL: Could we just go back.

13 DR. HENDERSON: Oh, sure.

14 DR. AVOL: I\'m actually, I mean, I think

15 that Ed is right. It is unique because of the growth

16 aspect. The tissues are in the period of growth and

17 are more sensitive. And I think that is a unique

18 attribute. But it\'s not a unique population, anything

19 in the population that makes them unique, susceptibles

20 population.

21 DR. HENDERSON: I think we can work that

22 in. Let\'s see.

23 DR. LARSON: The first part of the

24 sentence refers to the children\'s health, California

25 health studies. The second part refers to children in

> Page 63
>
> 1 DR. POSTLETHWAIT: Well, in a perfect
>
> 2 world, the detailed mechanisms would be wonderful, but
>
> 3 I\'m not sure we\'re there yet, or even close.
>
> 4 DR. HENDERSON: And regulations have to
>
> 5 be decided on, you know, even in the absence of
>
> 6 mechanisms for sure.
>
> 7 DR. COTE: The second question I had is
>
> 8 in the next bullet, the word unique. I wasn\'t and sure
>
> 9 what was intended. So, it says a unique and probably
>
> 10 susceptible - -
>
> 11 DR. POSTLETHWAIT: Hey, Jim Ultman,
>
> 12 you\'re up.
>
> 13 DR. HENDERSON: Jim, are you there. You
>
> 14 were there. Not answering.
>
> 15 DR. POSTLETHWAIT: He sent me this. I
>
> 16 cut and pasted it in, so it\'s his fault.
>
> 17 DR. HENDERSON: Oh, okay.
>
> 18 DR. COTE: That\'s o-, it\'s not, it\'s not
>
> 19 a deal breaker either way, so that\'s okay.
>
> 20 DR. HENDERSON: Well, I - -
>
> 21 DR. COTE: Thank you anyway.
>
> 22 DR. HENDERSON: Okay. We can find out.
>
> 23 DR. POSTLETHWAIT: I mean, there is some
>
> 24 uniqueness in children because of the superimposition
>
> 25 of exposure on top of growth and development. So,
>
> Page 65
>
> 1 general.
>
> 2 DR. HENDERSON: Yes.
>
> 3 DR. COTE: I thought the concept of the
>
> 4 injury, repair, growth, development was what was useful
>
> 5 for me.
>
> 6 DR. HENDERSON: Yes, I, and, maybe, we
>
> 7 can put that in, that children, I don\'t know, are
>
> 8 unique in that, you know, injury, growth and repair.
>
> 9 DR. POSTLETHWAIT: Actually, you could

10 throw in a fifth variable, which would be dose, \'cause

11 their running around breathing harder, we hope.

12 DR. HENDERSON: Okay, now can we go on to

13 eight, and I, Doug wrote this. It just seemed like it

14 was very clear and captures many of the concerns that

15 the committee had. The, a multi-pollutant aspect, I

16 think, it will be something we\'ll bring up at the end

17 of the letter as a, you know, general concern we have

18 for all airborne pollutants, and maybe we\'ll suggest

19 the need for, in the future, striving to address, you

20 know, multi-pollutants, rather than one pollutant at a

21 time, a one atmosphere approach, which the Agency is

22 trying to take anyway.

23 DR. LARSON: But, I also thought, based

24 on the discussions yesterday, that there was a general

25 scientific consensus that nitric oxide was not a

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>
> Page 66
>
> 1 confounder either in the palliative, its palliative
>
> 2 effects, or its, you know, irritant effects. There
>
> 3 was no, really no information at these concentrations.
>
> 4 I guess, we don\'t really have the EPA summary of all
>
> 5 the nitric oxide concentrations, but it just seemed
>
> 6 like we had, sort of, generally, concluded that if, in
>
> 7 fact, it is a mixture, it\'s, it doesn\'t seem to be the
>
> 8 nitric oxide that\'s doing much of anything. And then,
>
> 9 when you, when you, if you eliminate that, you\'re, sort
>
> 10 of, the next most abundant thing is NO2, and then you
>
> 11 start going way down in abundant for these other
>
> 12 species that we don\'t know much about in terms - -
>
> 13 DR. HENDERSON: I agree, or what I was
>
> 14 thinking of multi-pollutant was ultra fines and ozone
>
> 15 and - -
>
> 16 DR. LARSON: I agree with that, but I\'m
>
> 17 just saying that, even though we can\'t, necessarily,
>
> 18 say much about that, I think we can say something about
>
> 19 the biological plausibility or the lack of it for
>
> 20 nitric oxide. Because that seemed to be a point of
>
> 21 confusion early on in yesterday\'s discussion.
>
> 22 DR. HENDERSON: Okay, I think it\'s pretty
>
> 23 clear in the document. That\'s where I read it, so in -
>
> 24 -
>
> 25 DR. LARSON: Yeah, I mean, we\'re just
>
> Page 68
>
> 1 to is a major reorganization of the document to move a
>
> 2 lot of information from the earlier parts into chapter
>
> 3 five. That\'s not what we intended, though.
>
> 4 DR. CRAWFORD-BROWN: I need to answer
>
> 5 that, though. I mean, I certainly would agree that the
>
> 6 entire document is the resource to which people need to
>
> 7 turn, but I just don\'t know what findings and
>
> 8 conclusions mean if it isn\'t a summary of the most
>
> 9 important and relevant points from the previous

10 chapters. I, you know, I would think that there will

11 be people who are going to say, look, I don\'t have time

12 to read your whole document.

13 Tell me what I really need to know as a

14 policy maker, as somebody who\'s going to try to do a

15 risk assessment, and so forth. Tell me what I really

16 need to know in order to be able to make those

17 determinations. You\'re the scientist. I\'m not the

18 scientist. So, I just, you know, that chapter needs to

19 be a chapter that does summarize everything from the

20 past as far as relevant conclusions are.

21 DR. COTE: You know, I was going to, I

22 meant to ask this yesterday, if there were specific

23 examples where there were more important conclusions in

24 the body than in the chapter five. If somebody could

25 note those, just when you see them. Things were

> Page 67
>
> 1 re-, I mean, I was just reading it. It seems, it still
>
> 2 seemed to be a point of confusion.
>
> 3 DR. HENDERSON: Okay, I got it. We\'re
>
> 4 coming to the end here, and as again, this is the first
>
> 5 time we\'ve tried this sort of process. And I need to
>
> 6 know if everyone on the phone and sitting around the
>
> 7 table is co-, if we modify as we have discussed here
>
> 8 this morning extensively, if we modify the content of
>
> 9 these points, are you comfortable with these, this
>
> 10 being the substance of the letter that we send to the
>
> 11 Administrator. Now, I\'m not talking about
>
> 12 wordsmithing, et cetera. Because, what will happen is
>
> 13 that this draft letter will go to all of you, so if,
>
> 14 you know, if you have wordsmithing problems, don\'t
>
> 15 worry about it. It\'s the substance of what\'s in the
>
> 16 letter that I want to know if you\'re comfortable with.
>
> 17 And Mary, why are you raising - -
>
> 18 DR. ROSS: May I ask one final, about
>
> 19 question number eight.
>
> 20 DR. HENDERSON: Okay.
>
> 21 DR. ROSS: We had actually intended that
>
> 22 the entire ISA be the document that serves as support
>
> 23 for risk and exposure assessment. If it is intended
>
> 24 that only the conclusions chapter be the resource for
>
> 25 risk and exposure assessment, what that\'s going to lead
>
> Page 69
>
> 1 actually were written by the same people, and I think
>
> 2 chapter five reflected, as we were working, perhaps,
>
> 3 more refinement of thinking, and so, it\'s a little
>
> 4 disturbing if it was better the first, rather than the
>
> 5 second round of thinking, so if you ju- -
>
> 6 DR. CRAWFORD-BROWN: If we combine a list
>
> 7 of the things that are back in the earlier chapters
>
> 8 that - -
>
> 9 DR. COTE: That should be - -

10 DR. CRAWFORD-BROWN: But I think the main

11 issue had to do, also, with the fact that the writing

12 of chapter five does need to be the bridge to the user

13 of this document, and what that user needs, as

14 important information, for the kinds of decisions he

15 or she is going to make. And that\'s where I didn\'t,

16 you know, I, personally, I know Ellis felt the same,

17 didn\'t feel that that connection was quite there, where

18 somebody at chapter five began to ask, what are people

19 actually going to use this for in the end. And that\'s

20 why I raise this issue of integrated. I don\'t think

21 there\'s such a thing as integrated outside of the

22 context of the question that somebody is trying to

23 address.

24 DR. COTE: Was it that there weren\'t

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 19
>
> Page 70
>
> 1 of information were missing, or both?
>
> 2 DR. CRAWFORD-BROWN: I, but I mean, my
>
> 3 personal opinion is that what happens with chapter five
>
> 4 is that, it is, what often happens with concluding
>
> 5 chapters in theses, for example, where it\'s just a
>
> 6 compilation of things from the earlier chapters.
>
> 7 Here\'s a thing from chapter one, and here\'s a thing
>
> 8 from chapter two. Rather than somebody,
>
> 9 systematically, sorting through and saying, what do we
>
> 10 think we really learned from the earlier chapters that
>
> 11 are relevant to the kinds of applications that we
>
> 12 thought we were directing this report towards.
>
> 13 DR. CRAPO: I think a good example is the
>
> 14 issue I was talking about a lot this morning about the
>
> 15 dose metrics being annual average, and not telling you
>
> 16 what the people, the populations were exposed to,
>
> 17 actually, in terms of the more toxic elements of the
>
> 18 high level exposures. And then, a discussion of that,
>
> 19 so that the person who tries to interpret the health
>
> 20 effects data, in relationship to the possible
>
> 21 exposures, both for what they know and don\'t know, is
>
> 22 not there in chapter five. A person that would read
>
> 23 that and just think you had, it would just jump right
>
> 24 from the exposure data, think it had totally supported
>
> 25 all the findings. And so, I think, that disconnect
>
> Page 72
>
> 1 of these 47 statements, or however many it becomes, and
>
> 2 that those that are directly relevant to the issue of
>
> 3 making a judgment about the standard be highlighted or
>
> 4 marked in some way, and that the, so that\'s the first
>
> 5 point. And the second point, in chapter two, you were
>
> 6 asking for examples of then something that was
>
> 7 mentioned in chapter two, but didn\'t show up in the
>
> 8 summary that was in chapter five. And all of the nine
>
> 9 statements in chapter five are relevant to the issue of

10 monitoring alone.

11 DR. COTE: Right, I heard that, yes.

12 DR. COWLING: So, I was just thinking to

13 mention those examples.

14 DR. COTE: Thank you, and what you

15 provided on criteria for judgment, I thought was very

16 good, too.

17 DR. CRAPO: I\'d like to add one more

18 thing. I think the biggest thing that might come out

19 of this review of NO cycle is a recommendation that we

20 go to a one-hour daily average instead of an annual

21 average. And, no matter what the level is set at, it

22 would totally change our science. But, we ought to

23 set, I think that\'s what\'s needed more than anything

24 else, because I think that we\'re measuring the wrong

25 thing. And, I would argue that our document ought to

> Page 71
>
> 1 that we struggled with for two days here needs to be
>
> 2 obvious in chapter five.
>
> 3 DR. HENDERSON: And I lay a few, John
>
> 4 Samet had quite a bit to say about chapter five in his
>
> 5 comments. And he had the, since so many people had the
>
> 6 same conclusion that it was really just a listing of
>
> 7 the, what, of items from the previous chapters,
>
> 8 excluding some, because several people said that, and
>
> 9 not an integration of, you know, all five. So, I think
>
> 10 that it really does need attention.
>
> 11 DR. COTE: Clearly.
>
> 12 DR. HENDERSON: You don\'t have that many
>
> 13 people giving almost the same comments without there
>
> 14 being something that - -
>
> 15 DR. COTE: No, no, I wasn\'t disagreeing.
>
> 16 I was just trying to get more - -
>
> 17 DR. HENDERSON: Just get more examples
>
> 18 and - -
>
> 19 DR. COTE: Yeah.
>
> 20 DR. HENDERSON: Yes, Ellis.
>
> 21 DR. COWLING: I would offer two comments.
>
> 22 One is George\'s suggestion yesterday that, and it\'s
>
> 23 relevant to what Doug was just saying about, what is
>
> 24 relevant to the decisions that were made next. And it
>
> 25 seems to me that he was suggesting that a scan be made
>
> Page 73
>
> 1 set the, ought to, appropriately, set the background
>
> 2 for that type of a recommendation, \'cause that\'s where
>
> 3 I think we\'re headed. And it\'s not in there now.
>
> 4 DR. HENDERSON: Has the shorter averaging
>
> 5 time been considered in the, by the Agency, because in
>
> 6 our discussion, I had the same thought, James, and I
>
> 7 thought, well, gee, maybe we\'re looking at the wrong
>
> 8 averaging time.
>
> 9 DR. CRAPO: Well, both daily and, sure, I

10 mean, one-hour and dailies is, those are two changes to

11 it.

12 DR. HENDERSON: I\'m just curious if the

13 Agency has, that\'s ever come up.

14 DR. ROSS: Well, I mean, you can look in

15 the history of the rule making, and in 1993, actually,

16 you\'d have to ask Karen Martin the specific history,

17 but we made an effort to try to breakdown into short-

18 term and long-term exposure discussions. And within a

19 short term, there are a range of different levels.

20 Many of the epi studies use 24 hour, but we did try to

21 discuss the evidence at, related to averaging time.

22 Tox studies use a whole variety of different exposures.

23 And then, we try to make that available, to the extent

24 we can say something about peak exposures, we will.

25 There aren\'t many epi studies that look at peak

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 20
>
> Page 74
>
> 1 exposures. I\'m not even sure there are many tox
>
> 2 studies, but we\'ll try to bring that up as much as we
>
> 3 can. I mean, we\'re taking that ho-, as a comment that
>
> 4 we need to address.
>
> 5 DR. CRAPO: Remember if the ozone field
>
> 6 goes that direction, for shorter averaging times and
>
> 7 it\'s, because it\'s been there for decades, it has
>
> 8 really influenced the thinking of the evolution in that
>
> 9 field. This data would suggest that NO2 has a toxic
>
> 10 profile similar to ozone. In fact, it interacts with
>
> 11 ozone to make it this toxic product. So, there\'s no
>
> 12 rationale for having a different, an annual averaging
>
> 13 time for NO2, and a short averaging time for ozone. I
>
> 14 would just argue that you can use the science of the
>
> 15 ozone science to justify a lot more evaluation of why
>
> 16 NO2 ought to have the same type of short-term
>
> 17 evaluation on it. And part of our problem is we set it
>
> 18 up wrong thirty years ago, and we\'ve got a bad
>
> 19 collection of data to compare everything to.
>
> 20 DR. BALMES: I guess the other point that
>
> 21 we should emphasize, this is John Balmes, is that if
>
> 22 asthma exacerbation is one of the major endpoints that
>
> 23 we feel the epidemiology supports, and I certainly
>
> 24 think it does, then it makes no, you know, an annual
>
> 25 average does not protect asthmatics from exacerbation.
>
> Page 76
>
> 1 me, the epi drives it. We\'d, logically, might think,
>
> 2 oh, it\'s a short term for that day that really drives
>
> 3 it, but we have to come up with a value, and if all the
>
> 4 epi is driven by lo-, the annual averages, we got a
>
> 5 tough task.
>
> 6 DR. HENDERSON: That\'s a good point.
>
> 7 DR. CRAPO: For particulates, we have
>
> 8 two, so we could keep both, then. We could put a
>
> 9 short-term and a long-term standard in. Well, we

10 couldn\'t, but the Administrator could.

11 DR. COTE: And if you were, if you\'re

12 thinking about two different kind of health effects,

13 like lung growth and asthma, there\'s no reason to think

14 it would be the same. It might be, but I\'m not sure, I

15 don\'t know.

16 DR. BALMES: And, there\'s also no reason

17 to have the same type of siting criteria for your

18 monitors if you\'re going to go to a short-term

19 standard. Because, I can walk down a street canyon for

20 an hour and get a completely different exposure than I

21 will at a EPA monitoring site for an hour.

22 DR. ROSS: Just to remind people, we\'re a

23 little ahead of the process here, talking about the

24 standards already and the sited criteria.

25 DR. HENDERSON: No, we tend to jump over

> Page 75
>
> 1 DR. HENDERSON: Okay, thank you. I was
>
> 2 thinking of the ozone and, you know, the eight hour
>
> 3 standard make sense because it\'s much higher during the
>
> 4 daylight hours.
>
> 5 DR. CRAPO: And so NO2 is the same- -
>
> 6 DR. HENDERSON: Is, is NO2 in the same -
>
> 7 -
>
> 8 (WHEREUPON, there was a discussion off the record.)
>
> 9 DR. CRAPO: NO\'s shorter than the ozone
>
> 10 peak, isn\'t it?
>
> 11 DR. RUSSELL: It\'s actually a very
>
> 12 different shape.
>
> 13 DR. HENDERSON: Yeah, I think we\'d have
>
> 14 to be a little careful, but you know, I - -
>
> 15 (WHEREUPON, there was a discussion off the record.)
>
> 16 DR. HENDERSON: Terry, Terry has his hand
>
> 17 up, or you want to go to Dale?
>
> 18 DR. GORDON: I had a feeling that this
>
> 19 conversation was going to go this way, and I was
>
> 20 wondering if it did, we went to shorter term. Are we
>
> 21 going to lose something. It sounds like people are
>
> 22 leaning toward a short term, not a long term, and how
>
> 23 would that effect the true long-term studies, such as
>
> 24 the children\'s lung growth studies, which might be more
>
> 25 correlated epi wise with annual averages. I mean, to
>
> Page 77
>
> 1 to the endpoints, so let\'s, but, I really would like to
>
> 2 draw this together so that we complete our peer review
>
> 3 of the ISA document, and Ellis, would, I\'ll give you
>
> 4 the last call before I ask.
>
> 5 DR. COWLING: Well, I would just like to
>
> 6 support what Jim Crapo has suggested here, with the
>
> 7 additional suggestion, and this is where they did do
>
> 8 what Karen Martin told us yesterday. What was in the
>
> 9 mind of the Administrator, and what are the policy

10 implications of having an annual standard, and what are

11 the policy implications of having a daily standard, or

12 any other standard. And it seems to me that we ought

13 to know what was the rationale in 1971, when an annual

14 standard was selected. And now, and then, we have the

15 other iterations in \'93 and so on, what was in the mind

16 of those who made the decisions at that time. And I

17 think if that is clarified, it would provide a more

18 rational basis for a decision about what is the proper

19 averaging time.

20 DR. BALMES: Ellis, I can tell you one

21 thing. I was, way back in my youth, I was in the

22 public health service at EPA. And the very first

23 criteria document, as you know, were very thin, and

24 committees were about five or six people, and the

25 process took a day, so.

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 21
>
> Page 78
>
> 1 DR. HENDERSON: Oh, you\'re making us all
>
> 2 jealous. Anyway, I do want, we are skipping way ahead.
>
> 3 We\'re going to the next step. And we, before we go
>
> 4 there, we need to complete our review of the ISA, which
>
> 5 is looking at the science. So, I want to know if
>
> 6 everyone in the room and on the phone is comfortable
>
> 7 with the substance of what we\'re going to put in the
>
> 8 letter. And you will see this, the draft come-, the
>
> 9 letter come out, and you will get to, we will seek
>
> 10 concurrences.
>
> 11 We always do, so, is there anyone who is not
>
> 12 comfortable with it? John Samet, are you on the phone?
>
> 13 Oh, he\'s coming this afternoon, okay. Well, we have a
>
> 14 quorum of the chartered members of CASAC here who are
>
> 15 all comfortable with this, so I consider that the
>
> 16 charter members have approved this, the substance of
>
> 17 this letter that\'s going to go out.
>
> 18 What comes out next is going to be the draft
>
> 19 letter, and with Angela\'s able help, I hope we can get
>
> 20 it out fairly soon. And then, you must look at it very
>
> 21 carefully, and we will seek concurrence before it
>
> 22 actually goes in. And any questions about that
>
> 23 process? Well, I thank everybody for cooperating so
>
> 24 well with this new way of doing things. We\'ll see if
>
> 25 it works out. I don\'t know if it, it hasn\'t quite
>
> Page 80
>
> 1 and I would ask, the first speaker is going to be
>
> 2 Lydia?
>
> 3 MS. WEGMAN: Yeah.
>
> 4 DR. HENDERSON: Okay, and Lydia, maybe,
>
> 5 you know, as you go through, you can introduce the
>
> 6 others from the Air office who are going to be
>
> 7 participating. So, it\'s a real privilege to have Lydia
>
> 8 with us. She always clarifies things.
>
> 9 MS. WEGMAN: Well, I don\'t know about

10 that, Rogene, but thank you very much. My name\'s Lydia

11 Wegman, and I am the Director of the Health and

12 Environmental Impacts Division in the Office of Air

13 Quality Planning and Standards. And we are the folks

14 who will be working on the exposure and risk

15 assessment, and ultimately, the advanced notice of

16 proposed rule making and the proposed rule and final

17 rule. And I do want to introduce the folks who are

18 with me, or the ones who have done the real work on the

19 scope and methods plan for the exposure and risk

20 assessment. And Dr. Karen Martin, who will speak in a

21 moment after I\'m done, Dr. Scott Jenkins, Dr. Stephen

22 Graham, and Dr. Harvey Richmond.

23 MR. RICHMOND: I\'m no doctor.

24 MS. WEGMAN: Oh, no doctor, you should be

25 a doctor, though. You do the work of a doctor. So,

> Page 79
>
> 1 reached it conclusion, but all of you, by participating
>
> 2 so readily, I think have helped it, and we may continue
>
> 3 to do this.
>
> 4 The next thing on our agenda was to move on
>
> 5 to the next document. I think maybe it\'s time for a
>
> 6 break, and we\'ll take a fifteen minute break, then
>
> 7 we\'ll come back and we\'ll hear from the Air office.
>
> 8 And they are going to move us in the direction we keep
>
> 9 trying to go.
>
> 10 (WHEREUPON, a break was taken.)
>
> 11 DR. HENDERSON: If everybody could take
>
> 12 their seats. Thank you, Doug. Okay. We\'re going to
>
> 13 be moving on, here comes Ron, if others could take
>
> 14 their seats. We\'re going to be moving on to a
>
> 15 consultation now for our next document, which is the
>
> 16 exposure risk assessment methods document. And, as
>
> 17 we\'ve been saying, we keep jumping in this direction
>
> 18 from going from the science assessment to wanting to
>
> 19 participate in this part of the process. And this is
>
> 20 our opportunity. As a consultation, this is where we
>
> 21 can, early on in the development of this process,
>
> 22 provide advice to the Agency on the methods for
>
> 23 exposure and risk assessment. So, we had quite a bit
>
> 24 of discussion on exposure assessment this morning. It
>
> 25 is, we\'re going to hear first, then, from the Agency,
>
> Page 81
>
> 1 this is our team on the exposure and risk planning for
>
> 2 the NOx review, the primary NOx review, and we\'ll be
>
> 3 coming to talk with some of you next week about our NOx
>
> 4 and SOx secondary review.
>
> 5 I first want to say thank you all for taking
>
> 6 the time to review the work we\'ve done, and to spend
>
> 7 the couple of days you\'re spending here in RTP, either
>
> 8 in person or by phone, to offer us your comments. Your
>
> 9 comments are invaluable to us, and without the work of

10 CASAC, we would not be able to perform our work.

> And I

11 just want to say how very important your work is to us,

12 to the Agency as a whole, and to public health. And

13 thank you very much for all the work that you do.

14 I, also, just want to make one point, as you

15 offer us comments on the scope and methods plan. As

16 you have seen, and I know several of you have commented

17 on, we\'ve got a tiered assessment, both for the

18 exposure and risk assessments. And, one of the reasons

19 we have the tiering is that, we don\'t know whether

20 we\'ll have the scientific evidence to go through all

21 tiers of these assessments, and we are very much

22 looking to you for advice on how to prioritize these

23 assessments, and what we do need to do, based on what

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 22
>
> Page 82
>
> 1 are the resource constraints that we face in our
>
> 2 office. We have constraints, not only in terms of
>
> 3 people and money, which are constraints that we always
>
> 4 face, and it does seem that we face them more every
>
> 5 year with declining budgets. But, we also have a time
>
> 6 constraint in this case. As you know, we have a court
>
> 7 order. It\'s currently under review by the public, but
>
> 8 we very much anticipate having a final court order that
>
> 9 gives us firm dates by which we do have to complete the
>
> 10 proposed and final rules, as well as the ISA. And,
>
> 11 that does limit what we can do. And I want to alert
>
> 12 you to that, because I know that there is a desire on
>
> 13 all of our parts to do the maximum amount of assessment
>
> 14 that we possibly can do with the science that we have.
>
> 15 But we will, in fact, be facing some constraints, and I
>
> 16 want to seek your help in knowing what is the most
>
> 17 important thing to do within the time and resource
>
> 18 constraints we have. So, as you think about these
>
> 19 issues and give us your advice today, I\'d appreciate it
>
> 20 if you kept that in mind.
>
> 21 And now, I\'m going to turn it over to Karen,
>
> 22 who is going to offer a few thoughts on multi-pollutant
>
> 23 assessments.
>
> 24 DR. MARTIN: Just, while this is, you all
>
> 25 have been talking some about the issue of multi-
>
> Page 84
>
> 1 step, in deciding how to approach our exposure and risk
>
> 2 assessments; what is it we\'re assessing the risk of.
>
> 3 We can, obviously, make some up-front assumptions, and
>
> 4 my comments yesterday were intended to help get your,
>
> 5 at least, preliminary thinking to help us do that. But
>
> 6 our assumptions can, then, be further refined, as we go
>
> 7 through the process of doing a first phase and a second
>
> 8 phase of a risk assessment, so that, in the end, we
>
> 9 can, are in the best position to characterize what, in

10 fact, we think our quantitative assessments reflect the

11 risk of, in this case, only SO2, SO2 in combination

12 with other pollutants, SO2 as a surrogate for other

13 pollutants. All those things are things we are, in the

14 end, going to have to speak very clearly to. So the

15 more you help us, at this early stage in the game, with

16 some of your thinking at this stage, and recognizing we

17 can further refine that as we go about characterizing,

18 in the end, the results that we do produce.

19 Beyond that, the issue of multi-pollutant

20 standards and multi-pollutant strategies, obviously,

21 has much broader implications for all of our NAAQS

22 reviews, and for what the Office does in implementing

23 programs to address the NAAQS. And I would just make

24 the observation that, some of the comments I heard, I,

25 perhaps, unintentionally, have the, sort of sounded

> Page 83
>
> 1 pollutant approaches, and multi-pollutant standards,
>
> 2 and multi-pollutant interpretations of scientific
>
> 3 evidence, and the question is, the tangentially
>
> 4 related to the subject we\'re here to talk about, our
>
> 5 plan for doing exposure and risk assessment, but it\'s,
>
> 6 obviously, more broadly related to our ultimate review
>
> 7 of these primary and of two standards, and our review
>
> 8 of standards in general. And I just wanted to take a
>
> 9 moment and make a few observations about the
>
> 10 discussion, and sort of, our view of it.
>
> 11 In the context of science assessment
>
> 12 documents, it\'s clearly extremely important that those
>
> 13 assessment documents do everything they can to tease
>
> 14 out, what do we know about any individual pollutants
>
> 15 effects, and what do we know about the interactions of
>
> 16 that pollutant with other pollutants, and to what
>
> 17 extent can we define specific effects related
>
> 18 individual pollutants versus to what extent does the
>
> 19 evidence limit us to only making more general
>
> 20 observations about associations of air pollution more
>
> 21 broadly.
>
> 22 All those issues are extremely important, and
>
> 23 your discussions are helping, I think, to sharpen the
>
> 24 science assessment document in that regard. It becomes
>
> 25 important for us, of course, at least as an initial
>
> Page 85
>
> 1 like, there is an inherent mismatch between setting
>
> 2 standards for individual pollutants, and crafting
>
> 3 control strategies that most efficiently and
>
> 4 effectively get at the mix of pollutants that are, in
>
> 5 fact, of concern.
>
> 6 And I would offer the observation that I
>
> 7 don\'t, really, perceive that to be a mismatch. It\'s
>
> 8 clearly a distinction, but one can, clearly, have
>
> 9 standards for individual pollutants in conjunction with

10 air quality management programs that are very multi-

11 pollutant oriented, and seek to find the most efficient

12 strategies for addressing all the pollutants for which

13 we have standards. And, you all, I mean, different

14 people have different views on that, but I just wanted

15 to make the observation that there isn\'t, necessarily,

16 an inherent mismatch, or contradiction in doing those

17 things.

18 The one pollutant that we truly have a multi-

19 pollutant standard for is, of course, particulate

20 matter, which is a collection of thousands of

21 pollutants. And if you think about it, we have

22 established that as a multi-pollutant standard under

23 the guise of PM mass, and a great deal of the research

24 right now is focused on trying to tease out what are

25 the differing relative toxicities of the individual

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 23
>
> Page 86
>
> 1 components within that mix. So, when we
>
> 2 do find ourselves faced with what truly is a multiple
>
> 3 pollutant standard, what we set about to do is trying
>
> 4 to figure out how to separate it out. And I think
>
> 5 that\'s sort of informative as to what utility there
>
> 6 might be in trying to aggregate all the other
>
> 7 pollutants into one standard, with interaction terms,
>
> 8 wouldn\'t our next step, logically, be trying to sort
>
> 9 out the relative toxicity.
>
> 10 So, I just wanted to offer those
>
> 11 observations, in terms of, the issue is really an
>
> 12 important one, but perhaps, it\'s not as much of a
>
> 13 mismatch or contradiction as one might originally
>
> 14 think.
>
> 15 Those were the points, observation points I
>
> 16 wanted to offer before we get into it, so if there\'s
>
> 17 nothing else we need to deal with, why don\'t we just
>
> 18 jump in to the overview presentation we wanted to make.
>
> 19 DR. HENDERSON: I think that\'s, that
>
> 20 would be good. Donna has a question.
>
> 21 DR. KENSKI: Well, just a response, I
>
> 22 guess, to the idea that single pollutant standards
>
> 23 don\'t, necessarily, preclude multi-pollutant controls.
>
> 24 Well, I, you know, it\'s clear that, you know, a control
>
> 25 on one pollutant will almost always, you know, have an
>
> Page 88
>
> 1 states, it\'s the Saint Louis area. And so, we, in
>
> 2 fact, are very mindful of that issue. As far as the
>
> 3 planning goes, I think Karen is addressing, you know,
>
> 4 the way in which we set standards right now, which does
>
> 5 not, in fact, preclude multi-pollutant planning. And
>
> 6 we can set-, you know, at this point, we do need to
>
> 7 look at the pollutants individually, but that doesn\'t
>
> 8 prevent us from moving forward to multi-pollutant
>
> 9 planning. And that\'s, definitely, what we are trying

10 to do.

11 DR. HENDERSON: Thank you. And so,

12 Karen, are you going to, who is our first speaker for

13 the - -

14 DR. MARTIN: Scott\'s going to take the

15 lead in covering the opening, and Stephen and Harvey

16 will round out the opening presentation.

17 DR. HENDERSON: Okay.

18 DR. JENKINS: Okay, thanks. My name is

19 Scott Jenkins, and I\'m the health lead for the NO2

20 review and OAQPS. And I\'m going to be talking through,

21 probably, three or four slides on giving a little bit

22 of background on the current approach that we have

23 proposed in the scope and methods plan. And then,

24 Stephen is going to talk through the exposure part of

25 it, and Harvey is going to talk through the risk part

> Page 87
>
> 1 effect on other pollutants, it still, you know, the
>
> 2 burden on the states to comply with the single
>
> 3 pollutant standard requires that they produce a, you
>
> 4 know, state implementation plan. And in that plan,
>
> 5 they have to provide for how they\'re going to control
>
> 6 that single pollutant, not multi-pollutants. So, I
>
> 7 think the, you know, it would, while we get these, sort
>
> 8 of, indirect, you know, controls on other pollutants,
>
> 9 it would be more straightforward, I think, to, you
>
> 10 know, have a multi-pollutant approach that really, you
>
> 11 know, dictated this, you know, need to control all
>
> 12 pollutants, not just single pollutants. So, while, you
>
> 13 know, while, yes, we do get controls, still the burden
>
> 14 on states is to demonstrate their control of a single
>
> 15 pollutant for a single standard.
>
> 16 MS. WEGMAN: Yeah, and I\'ll just respond
>
> 17 briefly. We are very mindful of the air quality
>
> 18 manager port that the NRC issued, and we did, there is,
>
> 19 in fact, a subcommittee of the Clean Air Act Advisory
>
> 20 Committee that has looked at all the recommendations
>
> 21 coming out of the air quality management report,
>
> 22 including the one to develop multi-pollutant plans, and
>
> 23 we, in fact, have a project going on in our office to
>
> 24 pilot multi-pollutant planning with three states, North
>
> 25 Carolina, New York, and Illinois, Missouri, four
>
> Page 89
>
> 1 of it.
>
> 2 Okay, so we\'re all aware, by now, that our
>
> 3 purpose here to solicit feedback on our proposed
>
> 4 approach to assessing risks and exposures. I just went
>
> 5 through this, so I\'m going to talk through, a little
>
> 6 ahead on the schedule a little bit, talk about the
>
> 7 previous review, and give a little bit of what, I hope,
>
> 8 is historical perspective. And then, talk about the
>
> 9 scope of the plan.

10 Okay, first schedule, and Mary presented the

11 same slide yesterday, so I\'m not going to go through

12 anything in detail, other than to point out that the

13 next time we will be soliciting, or will be meeting

14 with CASAC will be Spring of \'08, where we\'ll be asking

15 for feedback on the second draft of the ISA, and the

16 first draft of the risk and exposure assessment, and

17 then, again, September of \'08, for the second draft of

18 the risk and exposure assessment. And then, the final

19 date here, this is our, the date that we anticipate

20 will become our court ordered date. This is about five

21 months earlier, just to point out, five months earlier

22 than the dates we had originally proposed.

23 Okay, so a little bit of background, and

24 this, and I\'m going to expand on this side a little

25 bit, just based on the conversation that we just had

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 24
>
> Page 90
>
> 1 prior to the break, and that is, providing a little bit
>
> 2 of historical perspective for how the original standard
>
> 3 was set.
>
> 4 So, the original standard was based on
>
> 5 epidemiology studies that were conducted in
>
> 6 Chattanooga, Tennessee, basically, where the long-term
>
> 7 annual average levels of NO2 were correlated with
>
> 8 health effects. The issue that arose later with those
>
> 9 studies was that the issue of confounding with other
>
> 10 pollutants, an issue of the measurement approach to,
>
> 11 for, from measuring NO2 in the studies.
>
> 12 So, what happened was that, the original
>
> 13 standard was set based on those long-term epi studies.
>
> 14 And then, every review since then has focused,
>
> 15 essentially, on the short-term issues. And the crux of
>
> 16 the decisions that the Administrator has made are how
>
> 17 well does that existing long-term annual standard
>
> 18 protect against short peak exposures.
>
> 19 So, I think that\'ll become clear when I go
>
> 20 through the slide a little bit, but that just gives you
>
> 21 a little bit of a historical context.
>
> 22 So, and their talk-, specifically, about the
>
> 23 last review of the NO2 NAAQS, and the Administrator had
>
> 24 a, made a couple of conclusions regarding the
>
> 25 sufficiency and the necessity of the existing annual
>
> Page 92
>
> 1 arrived at was that we conduct-, we and OAQPS
>
> conducted
>
> 2 an air quality assessment, essentially, evaluating the
>
> 3 relationship between the annual average NO2 levels and
>
> 4 short-term one-hour average NO2 levels. And, as part
>
> 5 of that evaluation, we looked at the number of
>
> 6 exceedance as a very short-term benchmark values, with
>
> 7 the assumption that were just meeting the current
>
> 8 standard.
>
> 9 So, those benchmark values were derived,

10 again, from these clinical studies that I just

11 mentioned, and, basically, the result was that, if you

12 assume that the existing annual standard is being

13 attained, the short-term levels of NO2 of potential

14 concern would be very unlikely in most parts of the

15 country. I think Los Angeles had a few exceedances at

16 the .2 ppm level, but that was the only spot where

17 those exceedances were found.

18 So, that was, this was the structure of the

19 la-, the con-, of the decision framework for the last

20 review. And now, I\'m going to move to the current

21 review, and talk just for just a minute about the scope

22 of the planned risk and exposure assessment, and then

23 I\'m going to turn it over to Stephen.

24 We hit on this a little bit yesterday, this,

25 using NO2 versus other oxides of nitrogen. Obviously,

> Page 91
>
> 1 standard. The first conclusion was that the existing
>
> 2 annual standard will maintain annual NO2 concentrations
>
> 3 well below levels, long-term levels that are of
>
> 4 potential concern. And those long-term levels of
>
> 5 potential concern were derived from the animal tox
>
> 6 literature. And this is, basically, a fi-, based on
>
> 7 findings that if you expose animals for relatively
>
> 8 long periods of time to relatively high levels of NO2,
>
> 9 you get emphysema-like lesions in the lung. And then,
>
> 10 we\'re talking about, at least months of exposure to,
>
> 11 say, at least 5 ppm NO2 here.
>
> 12 So, it\'s pretty much, it\'s pretty easy to see
>
> 13 that, yes, the existing annual standard of .053 ppm
>
> 14 will protect against those sorts of long-term effects.
>
> 15 The other conclusion, and this is, really,
>
> 16 more of the focus of the last review, the other
>
> 17 conclusion was that the existing annual standard will
>
> 18 provide protection against the short-term peak NO2
>
> 19 levels that are of concern. And those short-term
>
> 20 levels of concern were derived from the human clinical
>
> 21 literature. This came from a set of studies showing
>
> 22 that, in asthmatics, if you expose asthmatics to levels
>
> 23 as low as, say, .2 to .3 ppm NO2, you can get increased
>
> 24 airway response in this.
>
> 25 So, the way that the second conclusion was
>
> Page 93
>
> 1 NO2 is but one of the oxides of nitrogen that include
>
> 2 both gaseous and particulate species.
>
> 3 There are really two issues, I think,
>
> 4 embedded in this. And that is, first, that is using
>
> 5 NO2 as a surrogate for the gaseous nitrogen oxide, and
>
> 6 the other issue is using, is focusing this review on
>
> 7 the gaseous nitrogen oxide.
>
> 8 So, regarding the first, the first statement,
>
> 9 you know, and I think this was borne out yesterday,

10 we\'re thinking-, we\'re planning to use NO2 as a

11 surrogate for the gaseous species, basically, because

12 the lack, relative lack of health effects data, and

13 actually, it came out yesterday, also, the relative

14 lack of monitoring data for gaseous species other than

15 NO2.

16 In the case of the particulate, the second

17 point that I made, the particulate nitrogen oxide, and

18 we made this point in our integrated review plan, and

19 we, this point, also, came up at our last consultation

20 with you last spring, that the particulate species are

21 addressed by the current NAAQS, and the rationale

22 provided right here, basically, the last review for

23 the, of the PM standard concluded that size

24 fractionated particle mass, rather than chemical

25 composition was the most appropriate way to address

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 25
>
> Page 94
>
> 1 ambient PM.
>
> 2 This conclusion, obviously, is going to be
>
> 3 reassessed in the next review, or I should say, the
>
> 4 current review, since it\'s already kicked off. We had
>
> 5 the opening workshop. But at present, it would be
>
> 6 redundant to, also, use the NO2 NAAQS to protect
>
> 7 against the health effects of particulate nitrogen
>
> 8 oxide.
>
> 9 Other than that, I just want to point out
>
> 10 that the assessment is going to evaluate, this will be
>
> 11 a recurring theme throughout Stephen and Harvey\'s part
>
> 12 of the talk, that the current assessment, we\'re
>
> 13 planning to assess both recent ambient levels of NO2,
>
> 14 ambient levels that are associated with just meeting
>
> 15 the current standard, and ambient levels that are
>
> 16 associated with just meeting the potential alternative
>
> 17 standards, which will be identified as we move forward.
>
> 18 And the assessment\'s going to focus on both short- and
>
> 19 long-term exposures.
>
> 20 So, that\'s all that I had to say in the way
>
> 21 of background and introduction. I\'m going to turn it
>
> 22 over now to Stephen, who is going to talk us through
>
> 23 the proposal, proposed plan for the exposure
>
> 24 assessment.
>
> 25 DR. GRAHAM: Thank you, Scott. Could you
>
> Page 96
>
> 1 fashion, that is going from, in a sense, a qualitative
>
> 2 evaluation and progressing to a quantitative evaluation
>
> 3 if, of course, data exists to support that type of an
>
> 4 evaluation.
>
> 5 So, the tier one, as I mentioned, is a air
>
> 6 quality characterization, and the purpose there is to
>
> 7 estimate the potential exposures, using the current, as
>
> 8 well as historical air quality data that we have
>
> 9 available to us, and use that as a surrogate for

10 exposure. In addition, we are proposing to take a

11 glance at some of these near roadway exposures, using

12 the ambient data, using enhancement factors. And then,

13 of course, any available concentration data and

14 emissions data that may be available to look at the

15 influence from sources, particular sources that may be

16 outdoors or indoors.

17 The locations that we considered are

18 outlined, based on the, those criteria that is, air

19 quality trans data availability, you know, number of

20 monitors, whether the data are quality assured and

21 comprehensive, and in addition, to some other criteria.

22 And we\'ve selected Los Angeles, Houston, Atlanta,

23 Philadelphia, and Chicago, and possibly, aggregation,

24 based on some of the analyses that are going to be

25 performed here.

> Page 95
>
> 1 work the clicker? Sometimes I have a habit of talking
>
> 2 with my hands. Okay, all right, thank you. So, of
>
> 3 course, the general, broad goals of this exposure
>
> 4 assessment are to estimate both short-term and long-
>
> 5 term exposures, short-term being hourly, and that is
>
> 6 associated with these current levels of ambient NO2,
>
> 7 and assuming alternative levels of NO2.
>
> 8 Also, to develop these quantitative
>
> 9 relationships, based on the form of the current
>
> 10 standard, which is long-term, annual average and the
>
> 11 relationship between that average and the short-term
>
> 12 peak concentrations, which was done in the prior review
>
> 13 as well. But in addition, I want to, also, consider
>
> 14 local source influences, which we saw was important in
>
> 15 the review of the ISA, and the impact on the exposure
>
> 16 estimates.
>
> 17 As far as the approach, it\'s already been
>
> 18 mentioned that we have three tiers. The tier one is
>
> 19 air quality characterization. And I\'ll go through each
>
> 20 of these in greater detail, I guess, or of course, it\'s
>
> 21 been in greater detail in the scope of methods
>
> 22 document. Populations considered include the general
>
> 23 population, as well as the individuals identified as
>
> 24 susceptible or vulnerable. And the assessment of
>
> 25 uncertainty is also going to be approached in a tiered
>
> Page 97
>
> 1 The expected output is, of course,
>
> 2 descriptive statistics for NO2 in some of these
>
> 3 selected locations; relationships between the short-
>
> 4 term peak levels and the long-term average levels; and,
>
> 5 of course, identification of additional areas to be
>
> 6 modeled in the tier two and tier three, dependent on
>
> 7 the analysis outcome.
>
> 8 Uncertainty will, primarily, be qualitative
>
> 9 at this stage, and of course, these tier one exposure

10 assessment, the outcome is going to be used for

11 comparison with some of the health benchmarks, once

12 they are identified.

13 So, in tier two, we\'ve got, the purpose is to

14 improve that relationship. So, now, we\'re trying to

15 link the actual concentrations, themselves, to persons,

16 to humans. And we are going to, of course, consider

17 both the on roadway and, as well as, near roadway,

18 using dispersion modeling and or enhancement factors as

19 well.

20 The model concentrations for other outdoor

21 sources, if there are any identified, as well as the

22 indoor sources, if they are identified as being

23 important in influencing these exposure estimates or,

24 shall I say, the relationship between, I\'m sorry, the

25 contribution, the relative contribution between the

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>
> Page 98
>
> 1 two, that will be done as well. And, of course,
>
> 2 consider influential factors, that is, time that people
>
> 3 do spend in particular microenvironments, as well as
>
> 4 the limited decay of NO2 indoors, and populations
>
> 5 residing near roadways.
>
> 6 The locations, of course, are going to be,
>
> 7 it\'s going to be a more focused analysis, and it\'s
>
> 8 going to be focused on the locations that have been
>
> 9 identified in the tier one analysis.
>
> 10 And the output is going to be broken up into
>
> 11 two different exposure metrics. We\'ve got short-term
>
> 12 exposure outcome, where we have, in addition to the
>
> 13 temporal and spatially resolved ambient air quality
>
> 14 concentration fields, that account for local sources,
>
> 15 like emissions from roadways and other sources that are
>
> 16 identified as important. We\'ve got estimates of the
>
> 17 number of individuals who may experience exposures of
>
> 18 concern. Not to suggest that it\'s an individual
>
> 19 analysis. It\'s more of a cohort-based analysis.
>
> 20 And then, of course, long-term exposure
>
> 21 estimates will include annual average exposure levels
>
> 22 within a given census tract, and could be considered at
>
> 23 a more finer resolution, say a block group or block.
>
> 24 And it\'s not just the annual, but also, I believe,
>
> 25 we\'ll be able to estimate the daily average.
>
> Page 100
>
> 1 So, as I said, the locations are the same as
>
> 2 those identified previously. The expected output is
>
> 3 going to be the counts of people exposed one or more
>
> 4 times to several NO2 levels, based on, of course,
>
> 5 health information obtained from the ISA, and because
>
> 6 APEX is a time series model, and the averaging time,
>
> 7 that is of interest, can be taken out of the, or shall
>
> 8 I say developed as an exposure metric. And we also
>
> 9 have counts of personal occurrences of a particular

10 exposure.

11 And the uncertainty can be a little bit more

12 quantitative in a sense. We can look at, again, model

13 inputs, where data exists for describing both a

14 variability and the uncertainty in them, and of

15 course, model formulation. If we have estimates of

16 personal exposure that are available to compare that,

17 as well as microenvironmental concentrations. That\'s

18 it. Thank you.

19 MR. RICHMOND: Thank you, Stephen. I\'m

20 going to walk you through a few slides on the risk

21 assessment. First of all, overview goals of the risk

22 assessment are to estimate the number of occurrences of

23 short-term air quality events and number of people

24 exposed at, or above, various potential health effect

25 benchmarks associated with alternative NO2 scenarios.

> Page 99
>
> 1 And, in addition, we\'ve got the ratios of
>
> 2 exposure to ambient, which could be useful for
>
> 3 extrapolating to other areas that we had not modeled.
>
> 4 And uncertainty would be addressed, of course, through
>
> 5 various sensitivity analyses, limited sensitivity
>
> 6 analyses, based on input distributions and other model
>
> 7 inputs, as well as measured comparisons, if there are
>
> 8 data that exist for particular microenvironments. That
>
> 9 would be compared to model estimates.
>
> 10 In tier three, of course, it\'s a more refined
>
> 11 approach, and here we are focusing on addressing more
>
> 12 particulars about human physiology, including time,
>
> 13 well, that\'s not physiology, but time, location,
>
> 14 activity patterns, and their physiology. Using the air
>
> 15 concentration fields developed from a tier-two
>
> 16 approach, that is where we have the on and near roadway
>
> 17 concentrations, and using the EPA\'s APEX model for
>
> 18 estimating exposures.
>
> 19 Locations, of course, are built upon what had
>
> 20 been identified in the tier one and used in the tier
>
> 21 two analysis. And, oh, I forgot to mention, of course,
>
> 22 the APEX model is capable of estimating individual
>
> 23 exposure estimates. It\'s a time series exposure model.
>
> 24 And we also have capabilities to estimate indoor
>
> 25 sources using that model.
>
> Page 101
>
> 1 If a tier two assessment is conducted, we\'d also
>
> 2 provide health risk estimates for NO2 health endpoints
>
> 3 associated with alternative scenarios. And for any
>
> 4 tier, we\'ll, of course, identify and characterize key
>
> 5 assumptions and the variability and uncertainty
>
> 6 associated with the assessments.
>
> 7 As Scott and Stephen have said, the scenarios
>
> 8 evaluated are for both recent air quality, simulating
>
> 9 the current standard, which is a difficult challenge,

10 given the levels are much lower than the current

11 standard; and air quality levels just meeting potential

12 alternative standards, which could be short- or long-

13 term standards.

14 There\'s a two tiered approach here. Proposed

15 one is the, in tier one, potential health effect

16 benchmark levels, which will be based on a review of

17 the revised ISA, would be compared to, first, air

18 quality and then, exposure estimates generated by the

19 tiers that Stephen\'s gone through.

20 Tier two, if it\'s judged feasible, and

21 they\'re, and of sufficient utility for decision making,

22 we involve combining concentration response, if it\'s

23 based on epi; or exposure response based on controlled

24 human exposure data, with exposure estimates to

25 generate population risk estimates. It\'s what we\'d

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>
> Page 102
>
> 1 like to have, ideally. We may or may not be able to
>
> 2 get there.
>
> 3 And next slide, okay. The tier one, as I\'ve
>
> 4 said, the air quality levels from a tier one exposure
>
> 5 assessment, or estimated exposure levels from tier two
>
> 6 or three, would be compared to potential health effect
>
> 7 benchmark levels for several example urban areas.
>
> 8 Those would be the same areas that Stephen\'s talked for
>
> 9 the air quality and exposure tiers.
>
> 10 We have identified, very tentatively, in a
>
> 11 benchmark of, in the .2 to .3 ppm one-hour averaging
>
> 12 time range, based on the controlled human exposure
>
> 13 studies, of effects that have been observed in
>
> 14 asthmatic, both children and adult asthmatic. There\'s
>
> 15 uncertainty about those health effect benchmarks that
>
> 16 we see, we\'d be using alternative benchmark levels to
>
> 17 illustrate the impact of alternative choices about the
>
> 18 lowest exposure levels of concern.
>
> 19 In terms of variability, we address that by
>
> 20 doing the analysis in different geographic areas.
>
> 21 Population variability in response but it would have to
>
> 22 be addressed qualitatively. We don\'t have, I think,
>
> 23 data to distinguish that very well. And the projected
>
> 24 outcomes would be the number of occurrences of air
>
> 25 quality levels at or above several benchmarks, or
>
> Page 104
>
> 1 tier two assessment would be conducted, including the
>
> 2 outcome and insights gained from the tier one
>
> 3 assessments, both with the exposure and risk
>
> 4 assessment, and availability of information and data
>
> 5 required to conduct a tier two assessment on the
>
> 6 adequacy of concentration response functions, baseline,
>
> 7 and getting baseline incidents data for things like
>
> 8 hospital admissions and emergency department visits for
>
> 9 the example urban areas.

10 Then, the utility or value added to the

11 decision process beyond the insights provided by a tier

12 one assessment, and the feasibility of conducting the

13 assessment within the time constraints that we have.

14 Next slide. Based on our preliminary

15 analysis of the first draft ISA, the most likely

16 candidate endpoints are listed here. I think that was,

17 generally, in agreement with what I heard yesterday in

18 the discussion on ISA. But the strongest evidence from

19 the epi would be for respiratory related morbidity

20 endpoints, including hospital admissions, especially

21 for asthmatics; respiratory related emergency

22 department visits; and respiratory symptoms, such as

23 cough and wheeze, particular in children and

24 asthmatics.

25 Risk estimates, if we do proceed to this

> Page 103
>
> 1 number of times in a given year that a population or
>
> 2 individual experiences various exposure levels of
>
> 3 concern.
>
> 4 To our next slide. And a tier two, if
>
> 5 conducted, would estimate the number of individuals in
>
> 6 selected populations for several example urban areas
>
> 7 expected to experience specified health effects more
>
> 8 similar to the ozone and PM risk assessments that we\'ve
>
> 9 completed in the last couple of years.
>
> 10 We judged that it would be more likely that
>
> 11 would be based on the epidemiological literature.
>
> 12 Preliminary judgment is that controlled human exposure
>
> 13 studies don\'t provide enough information to identify
>
> 14 credible exposure response relationships. There\'s
>
> 15 enough information to judge benchmarks for the health
>
> 16 endpoints, but it\'s difficult to see how to get
>
> 17 exposure response relationships across the range of
>
> 18 interest.
>
> 19 We\'re still evaluating. We\'re, obviously,
>
> 20 listened carefully to what you\'ve said over the last
>
> 21 day and a half. And look forward to seeing how the
>
> 22 revised ISA responds to those, in terms of whether
>
> 23 there\'s sufficient epidemiological evidence adequate to
>
> 24 conduct a credible quantitative risk assessment.
>
> 25 The criteria listed here for determining if a
>
> Page 105
>
> 1 stage, we would propose to conduct both sing-, use both
>
> 2 single and multi-pollutant models. And uncertainty
>
> 3 would be addressed similar to how we\'ve handled in PM,
>
> 4 the statistical or sample size uncertainty, that we
>
> 5 would provide confidence intervals around point
>
> 6 estimates of risk, and representing a range of results,
>
> 7 based on different epidemiological studies.
>
> 8 Expected outputs are listed here, in terms of
>
> 9 we would look at estimated incidents that can express

10 the results in a number of different ways. Incidents

11 per hundred thousand and or percent of incidents. And

12 this would address hypothetical change in incidents

13 associated with moving from just meeting the current

14 standard to just meeting potential alternative

15 standard.

16 The final part of the risk characterization

17 is several things that we\'ve tried to either put the

18 more limited example, like, you know, urban areas

19 analysis, is one to summarize U.S. air quality

20 information, and discuss the various health effects

21 that we were not able to quantify from the ISA. So,

22 that would certainly be part of the exposure risk

23 report to provide context for those things that we do

24 deal with quantitatively, and would include those air

25 quality statistics for all air as the U.S. based on the

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>
> Page 106
>
> 1 NO2 monitoring data. And that we\'d also provide
>
> 2 information, national scale information on the size of
>
> 3 potentially susceptible or vulnerable populations as
>
> 4 part of that.
>
> 5 DR. JENKINS: Okay and this slide just
>
> 6 has the charge questions to CASAC. This is actually a
>
> 7 condensed version of those charge questions, because we
>
> 8 actually had too many to fit on a single slide, but I
>
> 9 think this captures them.
>
> 10 DR. HENDERSON: Thank you. Karen, do you
>
> 11 have more to present?
>
> 12 DR. MARTIN: I don\'t believe so, unless
>
> 13 there are some specific questions.
>
> 14 DR. HENDERSON: Okay, now, Lianne, you\'re
>
> 15 on the phone?
>
> 16 DR. SHEPPARD: I am.
>
> 17 DR. HENDERSON: When are you leaving?
>
> 18 I\'m just trying to - -
>
> 19 DR. SHEPPARD: I have two more hours.
>
> 20 DR. HENDERSON: Oh, okay. I was going-,
>
> 21 that\'s good, Lianne. I just wanted to allow you to ask
>
> 22 questions if you were leaving.
>
> 23 DR. BALMES: Rogene?
>
> 24 DR. HENDERSON: Yes.
>
> 25 DR. BALMES: This is John Balmes. I\'m
>
> Page 108
>
> 1 lot of concern about this morning. But let\'s start in
>
> 2 on the discussion of the air quality section, which is
>
> 3 very brief in this report, and has lots of discussants.
>
> 4 So, we may have more discussion and, than there is
>
> 5 text, here.
>
> 6 SPEAKER: Than material.
>
> 7 DR. HENDERSON: But, what I\'d like to do
>
> 8 is run through, you know, there\'s three, the air
>
> 9 quality section, the exposure section, and the risk

10 assessment section. We\'re going to have, open the

11 discussion up to anyone who has any questions. We

12 tried to group people as to their interest, rightly or

13 wrongly, so let\'s start out with, on the air quality,

14 Ellis, did you have some comments you wanted to make,

15 or you know.

16 DR. COWLING: I must say that the general

17 impression I have is that the approach is being, that

18 is being proposed is reasonable, and that I have

19 confidence that it will be pursued within the limits of

20 time available that were mentioned. The five months

21 shorter time frame, I\'m sure has caused some anxiety

22 within the staff about how to get all the things done

23 that they had hoped that they can accomplish. But, I\'m

24 satisfied that the approach being proposed is

25 reasonable.

> Page 107
>
> 1 going to have to leave to teach in a few minutes, and I
>
> 2 will be returning in time for the health effects
>
> 3 discussion, which is currently scheduled for, what is
>
> 4 it, 2:15 your time?
>
> 5 DR. HENDERSON: We may be getting to that
>
> 6 a little earlier, because I plan to have a working
>
> 7 lunch.
>
> 8 DR. BALMES: Well, I just, I have to
>
> 9 teach, so I won\'t be able to join the call till a
>
> 10 little bit after 2:00 your time.
>
> 11 DR. HENDERSON: Okay.
>
> 12 DR. BALMES: Just so you know.
>
> 13 DR. HENDERSON: Okay.
>
> 14 DR. BALMES: I don\'t have a specific
>
> 15 question right now, though, just letting you know.
>
> 16 DR. HENDERSON: Okay, and if you, let us
>
> 17 know when you join in. Okay, I found it fascinating
>
> 18 our discussion just before we started this about, you
>
> 19 know, the annual average standard and how that relates
>
> 20 to peak exposures. And this group has, obviously,
>
> 21 addressed that, and I, in a statement, if the existing
>
> 22 annual standard is obtained, short-term NO2 levels of
>
> 23 potential concern would be unlikely in most parts of
>
> 24 the country.
>
> 25 That, would, addresses some things we had a
>
> Page 109
>
> 1 DR. HENDERSON: Thank you, Ellis. Donna,
>
> 2 do you have any comments on this, the proposed methods
>
> 3 for air quality evaluation?
>
> 4 DR. KENSKI: Well, I guess I have a
>
> 5 question.
>
> 6 DR. HENDERSON: You need to get close to
>
> 7 your mike.
>
> 8 DR. KENSKI: Okay, sorry. In answer to
>
> 9 the, I guess, the charge question about whether it was

10 appropriate to use historic data, I thought that was

11 the logical approach. I, the question I had was in

12 the, how you\'re modeling expected exceedances, and it

13 gives an exponential model here, and I just wondered if

14 there were any discussion about that choice of model,

15 and whether you considered other models.

16 DR. GRAHAM: That\'s a good question,

17 thank you. We\'ve got, that, actually, had been used in

18 the previous review. So, as I was looking to, I guess,

19 duplicate that effort, but in addition to that, we are

20 also going to look at an alternative model that looks

21 at, like a logistic regression, so it\'d be more

22 probability based.

23 DR. KENSKI: Okay, yeah, I think that\'s

24 appropriate.

25 DR. GRAHAM: Thanks.

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>
> Page 110
>
> 1 DR. HENDERSON: Is that all, though, or
>
> 2 did you - -
>
> 3 DR. KENSKI: Oh, well, another question
>
> 4 about your choice of, and I\'m not sure if this really
>
> 5 fits in, maybe it does. Your choice of cities, New
>
> 6 York wasn\'t on this list, and that seemed odd to me
>
> 7 that, \'cause it was one of the higher, definitely one
>
> 8 of the higher concentration cities. So, if you\'re
>
> 9 looking for peak exposures, it seems like you\'d choose
>
> 10 those urban locations, and it has lots of monitors.
>
> 11 DR. GRAHAM: Right, I think Philadelphia
>
> 12 was selected over New York, per se, because of, it was
>
> 13 representative of a northeastern region, but the key
>
> 14 feature there was the availability of additional data,
>
> 15 including very refined roadway counts, and other data
>
> 16 that had been developed previously through other
>
> 17 research. So, I thought it would be a slam dunk, per
>
> 18 se.
>
> 19 DR. KENSKI: Okay.
>
> 20 DR. HENDERSON: Is that all? Tim Larson,
>
> 21 are you on the phone?
>
> 22 DR. LARSON: Yes, I am.
>
> 23 DR. HENDERSON: Okay, did you have
>
> 24 comments on the methods for the air quality section?
>
> 25 DR. LARSON: Yes, a couple. It wasn\'t, I
>
> Page 112
>
> 1 I guess, the question is, how do you see that going
>
> 2 down?
>
> 3 MR. RICHMOND: Okay, this Harvey
>
> 4 Richmond. Let me try to address that. In, if we can,
>
> 5 the risk assessment has said, at first one tier,
>
> 6 there\'s, if we\'re looking at results of either the air
>
> 7 quality in tier one or exposure, either from a tier two
>
> 8 or tier three, either APEX or otherwise exposure
>
> 9 estimates, we\'re comparing, we\'re using all of the air

10 quality information.

11 We\'re using the monitoring, but also,

12 enhancing that with additional information to try to

13 estimate, either, a surrogate for exposure or getting

14 the distribution of exposures. That\'s then going to be

15 compared with a health benchmark levels that are based

16 on the controlled human exposure studies.

17 So, we\'re trying to match exposure with an

18 exposure response or an exposure, you know, an effect

19 observed in a clinical setting. That\'s one use of it.

20 And separately, if the epidemiology is deemed that it\'s

21 sufficiently one that is likely causal, or you know,

22 whatever we decide to go down on that continuum of

23 causality, that we\'re going to quantify, if we quantify

24 an effect from the epidemiological literature, those

25 studies, I agree with you, are based on the ambient

> Page 111
>
> 1 guess, I have to calibrate my thinking here. The
>
> 2 short-term standard, the health effects for that are
>
> 3 based on the analysis of existing monitoring sites for
>
> 4 short-term NO2 levels, I assume.
>
> 5 Is that the, I mean, is, it seems to me that
>
> 6 the relationship between the short-term values and the
>
> 7 long-term values, and how you do that, depends on what
>
> 8 sort of the health basis you\'re deriving, using to
>
> 9 derive that short-term value. If it\'s the epi, then
>
> 10 that\'s one thing, and then, would you just, sort of,
>
> 11 use the statistics from existing sites.
>
> 12 If it\'s independent toxicology clinical human
>
> 13 exposures, et cetera, then it seems like, potentially,
>
> 14 there are different relationships between the long-term
>
> 15 and short-term values for parts of urban areas that may
>
> 16 be more relevant than ones at the monitoring sites.
>
> 17 So, I\'m not clear, it\'s not clear to me which is the
>
> 18 basis for your health risk assessments.
>
> 19 To the extent that it\'s the epi, then I
>
> 20 suspect that the existing monitoring statistics are
>
> 21 relevant. To the extent it\'s other, then I would
>
> 22 suggest that the relationship, I mean, in the extreme
>
> 23 case, for instance, the one-hour peak exposure is while
>
> 24 you\'re commuting, which has nothing to do with any of
>
> 25 the statistics or distributions at the monitoring. So,
>
> Page 113
>
> 1 monitoring fixed site monitors, usually the average
>
> 2 across several monitors.
>
> 3 And if we were to do a risk assessment, based
>
> 4 on epi, it would not be based on the exposure analysis.
>
> 5 It would be based on the ambient fixed site monitors.
>
> 6 I hope that clarifies it.
>
> 7 DR. LARSON: Yeah, that helps, thank you.
>
> 8 Well, at least to the extent that it\'s based on the
>
> 9 human clinical studies, I would suggest caution here

10 using your near roadway or traffic related impact as

11 models, because I think you get very different

12 relationships spatially and temporally in a flat road,

13 than you would in a built up urban area. So, I mean,

14 the residential areas of Philadelphia or parts of

15 Chicago or Houston maybe that\'s, your approach is fine.

16 But we\'re doing studies in Chicago, and I

17 know for a fact that, is part of our cohort lives in

18 downtown Chicago, and we can\'t and don\'t have any

19 success using those kinds of approaches in that area.

20 So, if there was some way to qualitatively screen those

21 parts of the urban areas that are subject, is more

22 complicated confinement effects, so that you would

23 limit your exposure assessment in some way, based on

24 that.

25 I would think that\'s more defensible. You

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>
> Page 114
>
> 1 get a qualitatively different in a distribution and
>
> 2 it\'s not only, then becomes determined by the geometry
>
> 3 of your urban area, but also you end up with this
>
> 4 question about the vertical distributions within those
>
> 5 confined areas.
>
> 6 And then it, then you really up, you really,
>
> 7 you know, it\'s an open question as to where the,
>
> 8 whether you\'ve got buildings with open windows, or
>
> 9 whether you\'ve got inlets of a building in a
>
> 10 residential apartment on the roof or on the ground or
>
> 11 whatever, and it gets real complicated in a hurry. And
>
> 12 the, I guess, the basic point is that a simple, sort
>
> 13 of, Gaussian dispersion model do not have much skill in
>
> 14 those parts of the urban area.
>
> 15 So, I mean, maybe it\'s a qualitative way to
>
> 16 assess, you know, there are methods for doing that.
>
> 17 You know, you can look at building footprints, and
>
> 18 overlay building heights. We\'ve done that in New York,
>
> 19 and we\'re doing it in Chicago. And you can, sort of,
>
> 20 identify the areas that are, you can just look at
>
> 21 Google, actually, and probably do the same thing, but
>
> 22 more quantitatively, you could do it that way. But I
>
> 23 would say your proposed approach air mod, et cetera, is
>
> 24 fine as long as it\'s, sort of, single family
>
> 25 residential, but otherwise, it\'s questionable.
>
> Page 116
>
> 1 the exposure field as to what they think about that.
>
> 2 DR. HENDERSON: And that brings me to a
>
> 3 point that I had meant to bring up earlier. For all of
>
> 4 you who have not yet submitted your individual comments
>
> 5 on this methods document, that\'s just essential in this
>
> 6 case, because the letter will not, it will be more pro
>
> 7 forma when it\'s not going to list the consensus. So,
>
> 8 the advice that you want to give to the Agency in this
>
> 9 consultation will come in the form of your individual

10 written comments, so that increases their importance.

11 Tim, is that, are you, have you completed your comments

12 on the air quality section?

13 DR. LARSON: Well, I, yeah, I think

14 that\'s, wait a minute. It certainly, EPA, I think, is

15 sponsoring some of the work I mentioned, so, you know,

16 we certainly can do our best to work with the, provide

17 whatever information we have. We have quite a bit of,

18 Lianne is involved with this, too. We have quite a bit

19 of NO2 passive monitoring data, saturation data in

20 several cities that we\'re talking about.

21 DR. HENDERSON: I\'m sure they\'d

22 appreciate having all the information you can give

23 them. Ted, would you - -

24 MR. RICHMOND: Can I just ask one

25 clarifying question?

> Page 115
>
> 1 DR. HENDERSON: Do you have a response to
>
> 2 that, I mean, to that, Harvey?
>
> 3 MR. RICHMOND: One thing is I\'ll note
>
> 4 historically, it\'s one of the reasons why there hasn\'t
>
> 5 been an NO2 exposure analysis in the reviews that I was
>
> 6 involved in \'85 and \'93, is doing this is much more
>
> 7 challenging than doing an ozone exposure assessment.
>
> 8 And we acknowledge that.
>
> 9 I think we\'re, you know, we\'re saying, we\'re
>
> 10 trying to push the envelope as far as we can, and we\'re
>
> 11 still in the learning phases as to how far that is, and
>
> 12 we\'d certainly be interested in the kind of information
>
> 13 that, Tim, that you\'ve cited that, you know, if we, for
>
> 14 some of the example cities, if you have relevant
>
> 15 information, or ideas on how best to do it, or if we,
>
> 16 simply, you think if the advice of this committee is
>
> 17 we, simply, aren\'t able to credibly do certain parts of
>
> 18 the analysis.
>
> 19 Obviously, from a public health standpoint,
>
> 20 you\'d be interested in those levels in those places
>
> 21 where you\'re saying it\'s most difficult to conduct the
>
> 22 assessment.
>
> 23 So, that\'s the challenge we face, and this is
>
> 24 a general road map. We\'ll see as we get into it, but
>
> 25 we look to the advice of this committee and experts in
>
> Page 117
>
> 1 DR. HENDERSON: Sure.
>
> 2 MR. RICHMOND: Tim, that saturation data,
>
> 3 is it for short-term averaging?
>
> 4 DR. LARSON: It\'s, no, it\'s two-week
>
> 5 average - -
>
> 6 MR. RICHMOND: Two-week average.
>
> 7 DR. LARSON: Yeah, so that\'s a problem,
>
> 8 but, yeah, it\'s a problem. But it - -
>
> 9 MR. RICHMOND: Okay, I just wanted to

10 clarify it.

11 DR. LARSON: It can identi-, I mean,

12 we\'ve done models, though. We, in New York,

13 specifically, we\'ve done, we\'ve implemented the OSPM

14 model for New York City, which is kind of an

15 interesting exercise. And, to do that, you have to do,

16 or you have to have information on building footprints.

17 It\'s a massive undertaking, but so, we have, we have

18 hourly predictions compared to our measurements. And

19 they compare pretty well.

20 I mean, it is reasonably, if you expect to

21 model that. So, they do have skill, and they, and we

22 do have predictions on an hourly basis, both as an

23 urban background model, and superimposed on that is a,

24 is an OSPM model, and using Mobile Six and the traffic

25 - - model for New York City.

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 31
>
> Page 118
>
> 1 DR. HENDERSON: Okay, let\'s move on to
>
> 2 Ted.
>
> 3 DR. RUSSELL: Okay, very much like the
>
> 4 prior individuals, I was, generally, pleased with the
>
> 5 scope document. One of the things, I think, that comes
>
> 6 out of just recognizing that you\'re going to be doing a
>
> 7 number of these, if you look at it more than one a
>
> 8 year, to get it down to a well-oiled, sort of,
>
> 9 approach, is one of my first recommendations.
>
> 10 And I realize you all are, may not have tons
>
> 11 of resources or whatever, but just something that could
>
> 12 be done such that these things become very automatic.
>
> 13 You know, I look at where we\'ve come in other areas of
>
> 14 the modeling world, and I think that this could be done
>
> 15 when you\'re looking at exposure and risk analysis as
>
> 16 well, just to make it so it\'s not as, such a huge
>
> 17 effort every time.
>
> 18 A few things, one of them was, in regards to
>
> 19 what you, when you were presenting this morning, you
>
> 20 said something about locations considered, and you have
>
> 21 the five cities, and then, an aggregation of others.
>
> 22 And then you said, possibly, or maybe you said possibly
>
> 23 before you said that. And I, it brings back what we
>
> 24 discussed in the lead panel was that, we\'re, I think,
>
> 25 as a whole, more interested in the U.S. than in
>
> Page 120
>
> 1 read this, it wasn\'t just peak exposures. It was all
>
> 2 exposures. So, I\'m just hoping that you\'re looking at
>
> 3 doing the exposure analysis at a national level, not
>
> 4 just for five cities, both peak as well as long term.
>
> 5 DR. GRAHAM: The air quality data is
>
> 6 going to be evaluated nationally. When we look at the
>
> 7 focused exposure analyses, that\'s going to be on
>
> 8 individual cities that had been identified.
>
> 9 DR. RUSSELL: I think we would have some

10 interest in, maybe I\'m not, maybe I\'m singular here, of

11 really the interest at a national level somehow

12 extrapolating or doing something to get, to give us an

13 idea of what\'s happening nationally, not just at those

14 cities. Because it came up in the lead. It came up in

15 the ozone, as well, that that would be some important

16 information.

17 And, let\'s see, one, just a minor comment, in

18 your model, in terms of how to go away from roads use-

19 -

20 DR. MARTIN: Just, if I may?

21 DR. RUSSELL: Yes.

22 DR. MARTIN: Just to come back to the

23 point you just made. I can\'t not make the observation

24 that, as we move to exposure modeling, what we and you

25 both are looking for is to develop enhancements to

> Page 119
>
> 1 individual cities.
>
> 2 I\'m just wondering how you can, how you would
>
> 3 plan not to do an aggregation of others, because I
>
> 4 think that would be somewhat more instructive to us, to
>
> 5 show us, on a national basis, the exposures of concern.
>
> 6 So, do you want to respond to that, how you\'re going to
>
> 7 make that decision not to do an aggregation of others,
>
> 8 or?
>
> 9 DR. GRAHAM: Delete possible.
>
> 10 DR. RUSSELL: Okay.
>
> 11 DR. GRAHAM: No, the intent was, in the
>
> 12 original analysis, they had done Los Angeles as a
>
> 13 separate area, and everything else was just lumped
>
> 14 together. And here I was proposing, okay, we can do
>
> 15 multiple locations, and if it\'s of value, we can look
>
> 16 at these other locations as well. And the criteria, in
>
> 17 a sense, for selecting the individual areas is based on
>
> 18 the fact that, we do have some information on the fact
>
> 19 that there are more peak occurrences.
>
> 20 In these other locations, there may not be.
>
> 21 So, in a sense, the model for predicting peak exposures
>
> 22 over a particular level, it just may fall apart,
>
> 23 because there are no peak exposures. So, that\'s why I
>
> 24 say possible.
>
> 25 DR. RUSSELL: Okay, though, the way I
>
> Page 121
>
> 1 those models to be, to address the location-specific
>
> 2 issues that are so central to understanding exposures
>
> 3 to NO2.
>
> 4 Once you zoom out and now say, oh, let\'s do
>
> 5 that on a national scale, you lose all your local
>
> 6 specificity, which is why we would approach a national
>
> 7 look on the basis of air quality, recognizing it\'s a
>
> 8 pretty gross approximation, and looking on the local
>
> 9 level, to try to tease out the nuances of exposures

10 around roadways and building canyons and those sorts of

11 things that we couldn\'t possibly do on a national

12 scale.

13 So, I mean, we\'re not trying to be resistant

14 to say, yes, it would be nice to know exposures on a

15 national scale, but I think the best we can reasonably

16 do is to do the more generalized air quality, look on

17 the national scale and to tease out the details

18 locally.

19 DR. RUSSELL: Dale has a comment, I

20 think, in response.

21 DR. HATTIS: Yeah, but, yes, essentially,

22 you\'ve got three legs of a parallelogram approach here,

23 at least, in your plan. You\'ve got the local

24 assessment of air quality. You\'ve got the local

25 assessment of exposure. You\'ve got the national

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 32
>
> Page 122
>
> 1 assessment of air quality.
>
> 2 I think it\'s not too much to hope that you
>
> 3 apply the lessons from the comparison of the local
>
> 4 assessment of air quality and the local assessment of
>
> 5 exposure to make, at least, a preliminary national
>
> 6 projection of the exposures, if the regularities you\'ve
>
> 7 observed in your fully analyzed cases apply to some
>
> 8 portion of the national air quality data.
>
> 9 I mean, you, probably, wouldn\'t apply the
>
> 10 Philadelphia comparison directly to South Dakota, but
>
> 11 you, probably, want to apply it to some portion of
>
> 12 South Dakota, maybe, in a fraction of Fargo or
>
> 13 whatever, and, to some extent, get.
>
> 14 But anyway, that\'s the basic idea, is that
>
> 15 it\'s, it might not be a tremendous expense and of
>
> 16 effort to do that distributional projection for an
>
> 17 appropriate fraction of the country, or the country as
>
> 18 a whole, you know, even though you don\'t want to do,
>
> 19 in detail, the country as a whole. You can get an
>
> 20 approximation from that, from the comparison.
>
> 21 DR. MARTIN: And then, of course, you\'re
>
> 22 left with making the judgment, does the approximation
>
> 23 so assume away all the details that are important that
>
> 24 you\'re left with, clearly we could create numbers, but
>
> 25 would they be meaningful. And if they\'re not going to
>
> Page 124
>
> 1 as opposed to standing by the roadside. So, I guess
>
> 2 that I\'m thinking that a lot of your, most of your key
>
> 3 data that\'s going to explain health effects are going
>
> 4 to come from understanding local variability.
>
> 5 And I, and it\'s where I\'m going with this is,
>
> 6 is the, you need to associate locally, but you\'d
>
> 7 probably have to have wide, a careful thought and
>
> 8 discussion about the variances even locally, \'cause
>
> 9 you\'re only very, barely touching that, that part of

10 the parameter.

11 Sounds to me like the, if you\'re doing a near

12 roadway, for example, comparison, you might have

13 various people that are experiencing that for which,

14 that have levels that are many, many times others that

15 are in the same environment because of the way they,

16 the way they were exposed to it. And so, I\'m curious

17 how you\'d handle that variability as you let that

18 average out.

19 And that\'s just, actually, taking exactly

20 your comment and taking it the other direction, even

21 more extreme at the local level that we, actually, need

22 to understand the health effects.

23 DR. GRAHAM: Well, that is part of the

24 plan.

25 DR. CRAPO: Yeah, I like that part, that

> Page 123
>
> 1 be meaningful, then we would argue, they\'re not worth
>
> 2 creating in the first place.
>
> 3 So, that\'s what we have to deal with, and we
>
> 4 can clearly look at it, but that\'s always the tension,
>
> 5 as you point to with lead as well. And, I didn\'t want
>
> 6 to just slip by this and so.
>
> 7 DR. CRAPO: I wanted amplify what you\'re
>
> 8 saying, and maybe, even take it further the other
>
> 9 direction, which is that, your data on local data is
>
> 10 really still coming from your primary monitoring
>
> 11 stations, I assume. That\'s correct? I mean, your,
>
> 12 like your, near roadway monitoring stations, and things
>
> 13 like that, gives you local data that you\'ll use for
>
> 14 looking at some of the variation at the more local
>
> 15 level.
>
> 16 But, in fact, what we\'re learning, as we, or
>
> 17 the mornings is, or what we\'re learning is that there,
>
> 18 the local level has even tremendous variability within
>
> 19 that. There\'s each gra-, I mean, gradients across the
>
> 20 roadway are falling away as a, you know, being 10 feet
>
> 21 versus 100 feet versus 1,000 feet for roadway has a
>
> 22 huge impact on the levels. And even being in a car
>
> 23 dramatically changes the level.
>
> 24 I was told during the break that the school
>
> 25 buses are 4.6 times higher than ambient inside the bus,
>
> Page 125
>
> 1 you had one-hour average, I just really compliment you
>
> 2 on the fact that your, you did have the one-hour
>
> 3 averaging goals in there.
>
> 4 DR. GRAHAM: Well, and to take into
>
> 5 account, specifically, roadway, on roadway and within,
>
> 6 given buffer distances of the roadway.
>
> 7 DR. CRAPO: And what about inside cars?
>
> 8 DR. GRAHAM: Sure, on road equals in
>
> 9 vehicle.

10 DR. CRAPO: \'Cause that could\'ve, that

11 could, actually, do you have, are you going to have

12 personal monitoring measurements to give you that data?

13 DR. GRAHAM: No, based on modeling.

14 DR. CRAPO: You\'re just going to model

15 it, okay. That would be key to this whole thing.

16 DR. HENDERSON: Okay, Ted, did you have

17 more to - -

18 DR. RUSSELL: I think that was,

19 primarily, it, and I\'m not sure where we ended up on

20 it.

21 DR. HENDERSON: Well, I heard that we

22 need the local data to be able to relate it to health

23 effects. I mean, that\'s, to me, that\'s, but - -

24 DR. RUSSELL: Right, I think we want the

25 local data, and, just, when we sit here and talk about

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>
> Page 126
>
> 1 things later, I think we\'re going to want to know what
>
> 2 are the national implications. And maybe I\'m one of
>
> 3 those who says, if you give me an approximate number
>
> 4 and say it\'s really approximate, that\'s a fine thing.
>
> 5 You know, I don\'t, that doesn\'t bother me, because I
>
> 6 know what I\'m basing my discussions on.
>
> 7 That is, it\'s a number and there\'s
>
> 8 significant uncertainty, but it, at least, it\'s
>
> 9 something that gives me an idea of what\'s happening
>
> 10 nationally. So that was the, that\'s my major concern
>
> 11 there. And, let\'s see. Also, one of the other issues
>
> 12 that I, or thoughts I had on this was, how do you plan
>
> 13 to provide ambient versus total exposure risks, total
>
> 14 exposures and related risks in your assessment, \'cause
>
> 15 I think having that comparison would be insightful.
>
> 16 MR. RICHMOND: Well, in the past, and
>
> 17 we\'ve addressed this in the CO exposure model, where we
>
> 18 included passive smoking in gas stoves, we\'re able to,
>
> 19 since it\'s driven by a model, the exposure part, at
>
> 20 that tier of the assessment, to both report total, as
>
> 21 well as, just with the ambient.
>
> 22 In other words, basically, turn the indoor
>
> 23 sources off in the model, and how much is the ambient,
>
> 24 both including ambient outdoors and the ambient that
>
> 25 penetrated indoors, but in the absence of those indoor
>
> Page 128
>
> 1 DR. GRAHAM: In a sense, it\'s no longer
>
> 2 supported, so it may have been used traditionally, and
>
> 3 it may look like a reasonable approach now. I guess,
>
> 4 I\'m trying to think of the future. And AERMOD is
>
> 5 actually a little bit more advanced. It\'s based on
>
> 6 boundary layer theory versus stability classes, and has
>
> 7 additional capabilities addressing turbulence and
>
> 8 meandering.
>
> 9 And, I know that the AERMOD doesn\'t have a

10 line source option right now, but it is something that

11 is being considered in the near future, not in time for

12 this particular review, but it will have that

13 capability. Right now, what we are proposing is to do

14 link-based emissions, so, and I think it is being

15 applied right now in New Haven, Connecticut, and there

16 has been a paper published recently using CALPUFF,

17 which is a similar type of dispersion model, to do this

18 near roadway estimation.

19 So, it\'s, I think, not an unreasonable

20 approach, and there will be, I guess, portions of it

21 that, of CALINE that may be investigated. I think you

22 had also mentioned in your comments earlier about the

23 conversion from NOx to, or shall I say NO to NO2. So,

24 that may be an important feature there.

25 But, again, it was to look for, look towards

> Page 127
>
> 1 sources. So, we do try to provide that perspective of
>
> 2 how much is due to the ambient problem, as opposed to
>
> 3 indoor concentrations as well.
>
> 4 DR. RUSSELL: And that will be included
>
> 5 in this?
>
> 6 MR. RICHMOND: Yes.
>
> 7 DR. HENDERSON: Okay, Christian, do you
>
> 8 have?
>
> 9 DR. SEIGNEUR: Yes, I only have one point
>
> 10 I want to address. It\'s, when it does a tier two
>
> 11 exposure assessment and the use of the model used to
>
> 12 calculate the air quality concentrations, in your
>
> 13 document, you mentioned you plan to use AERMOD.
>
> You,
>
> 14 also, mentioned the model CALINE4.
>
> 15 My understanding is that AERMOD was the route
>
> 16 for stacks, dispersion of protons from stacks. CALINE4
>
> 17 is most specific to roadways. So, could you clarify
>
> 18 why you\'re planning to use AERMOD other than
>
> CALINE4?
>
> 19 DR. GRAHAM: Absolutely. While it had
>
> 20 been recommended to me that I use CALINE for, I\'m
>
> 21 sorry, AERMOD for few reasons, CALINE, from what I
>
> 22 understand is, the developer of that model had recently
>
> 23 retired, and Air B has no initiative to continue on
>
> 24 developing that model. So, we can consider that an
>
> Page 129
>
> 1 the future, and, in addition, the fact that we are,
>
> 2 also, going to look at additional sources, some of
>
> 3 which are stationary type sources. So, why not use one
>
> 4 model to head all the emission sources.
>
> 5 DR. SEIGNEUR: Okay, yeah, that will be
>
> 6 fine. My recommendations, though, would be that if
>
> 7 you\'re going to use AERMOD for roadways, that it would
>
> 8 be evaluated prior to, with data graded near roadways
>
> 9 prior to its application. \'Cause EPA, typically,

10 requires people to evaluate the models before they are

11 applied. So, in this case, since AERMOD has not been

12 formerly evaluated for roadway application, that, you

13 know, EPA would do that.

14 DR. GRAHAM: Right, yeah, and I did

15 forget to mention that, that AERMOD is the recommended

16 model, at least for dispersion.

17 DR. HENDERSON: Now, does anyone else

18 have something they want to comment on the air quality

19 section, \'cause after this, we\'ll move on to the

20 exposure. Yeah, Ron.

21 DR. WYZGA: I have some questions. Let

22 me say that, I\'m very impressed by the approach, and, I

23 think, a really good understanding of the exposures

24 here, and I think you\'re, really, getting it.

25 I think you, it\'s going to be a challenging

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>
> Page 130
>
> 1 job, but my question was, when you talk about just
>
> 2 meeting the current standard scenario, I just want to
>
> 3 see if I understand it.
>
> 4 The current standard is 0.053 ppm, and let\'s
>
> 5 say, that you come up with a health benchmark, a new
>
> 6 one, of .05. And that, if, let\'s say, one of your
>
> 7 cities, your concentration is .04. Does this mean
>
> 8 that, in your risk assessment, you\'re going to assume
>
> 9 that the people are exposed to .053, as opposed to .04?
>
> 10 DR. GRAHAM: That\'s a tough one.
>
> 11 MR. RICHMOND: Our dilemma is, for our
>
> 12 purpose, not an impact assessment. We\'re looking at,
>
> 13 in the country, we have levels, typically, down at .03
>
> 14 and below. So, we\'re well below the current standard,
>
> 15 annual averages for the current standard.
>
> 16 How do we assess what the risk is for meeting
>
> 17 the current standard. It\'s not the risk from recent
>
> 18 air quality, which is lower. Is there a scenario, you
>
> 19 know, that we look at as a hypothetical scenario, that
>
> 20 matches exactly the current standard at the monitoring
>
> 21 network, in the design monitor just meets the 53, you
>
> 22 know, ppb.
>
> 23 There are two, you know, sort of, basic
>
> 24 choices, and I\'d be interested in comments from the
>
> 25 committee. The one we put forward but were, like to
>
> Page 132
>
> 1 standard.
>
> 2 DR. WYZGA: My only concern is that, I
>
> 3 think, the risk estimate that you come up with is going
>
> 4 to be misread as to, this is the current risk, and - -
>
> 5 MR. RICHMOND: Right, and it\'s not, and I
>
> 6 appreciate that.
>
> 7 DR. WYZGA: - - and I think that\'s
>
> 8 something that, really, if it\'s done this way, you need
>
> 9 a very strong statement telling people what it is not.

10 MR. RICHMOND: Right, and we agree. It\'s

11 a very hypothetical, and I agree, sort of, with your

12 comments that if we go down this path, whichever way we

13 do to simulate the current standard, we need to make it

14 clear how unlikely that is, given current NOx

15 stationary controls, given NOx vehicle controls, you

16 know, that\'s a very unlikely scenario. But, that is,

17 sort of, the baseline if you\'re looking at, what are

18 the risks that would be, if you were just meeting the

19 current standard.

20 DR. HENDERSON: Important point. Yes,

21 Kent.

22 DR. PINKERTON: Although this may be

23 somewhat of a trivial question, I noticed in figure

24 two, when it shows the NOx emissions that, where

25 they\'re coming from, and I understand that the focus

> Page 131
>
> 1 hear views on, is to use recent, meaning recent the
>
> 2 last time places did not attain or were just in
>
> 3 attainment of standard. In L.A., it wasn\'t that long
>
> 4 ago, had levels that were approximately the current
>
> 5 standard.
>
> 6 That\'s one approach to use historical air
>
> 7 quality in the \'90\'s, generally, when some of these
>
> 8 locations were just meeting the current standard. The
>
> 9 other choice is to use some kind of roll up approach.
>
> 10 And then, the question is, do you do it proportionately
>
> 11 and roll all the monitors up from current levels to
>
> 12 just meeting standard. And, I believe, UARG had some
>
> 13 comments about that very issue, so I\'d encourage you to
>
> 14 look at that.
>
> 15 They were actually arguing not to use the
>
> 16 historical approach, but that it would be better
>
> 17 rolling up the monitors that were nearest the road, and
>
> 18 then, rolling up the other monitors not as much based
>
> 19 on relationships between near roadway monitors and the
>
> 20 other monitors. So, that\'s an alternative approach.
>
> 21 And I don\'t think we\'re fixed, yet, on exactly which
>
> 22 approach, but we put forward as, to get reaction, at
>
> 23 least. You know, how else are you going to do it.
>
> 24 Otherwise, we don\'t have any results for risk or
>
> 25 exposure or air quality that approximate the current
>
> Page 133
>
> 1 has been, primarily, on urban areas and near roadways,
>
> 2 but I noticed that close to 20 percent of NOx emissions
>
> 3 come from off highways. And so, I\'m just curious if
>
> 4 rural areas or areas of high agricultural activity, do
>
> 5 they contribute to NOx emissions, and are we missing
>
> 6 something by only focusing on urban areas or near
>
> 7 highways?
>
> 8 MR. RICHMOND: I don\'t know if we\'re
>
> 9 prepared to say much. The one thing I will note, I

10 think we were going to look at an air quality tier one,

11 was major power plants sometimes are sited. I know in,

12 I think it was Charlotte Mecklenburg, I know the case

13 where it was sited, just outside the ozone non-

14 attainment area, coal-fired power plant. And we have

15 the ability of modeling to see what kind of NOx levels,

16 NO2 levels would we expect around some of those point

17 sources.

18 They may or may not be peaks of concern.

19 Maybe they\'re still, with controls that we have on,

20 don\'t reach those levels. I don\'t know if on, have any

21 information on that, but that\'s the kind of thing we

22 will look at in the screen analysis, do we have any

23 potential problems outside due to some of these may be

24 that we know which sources from the emission inventory

25 are major contributors to that.

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>
> 1 DR. KENSKI: Kent, maybe I could clarify
>
> 2 a little bit. That off-highway category includes
>
> 3 sources like construction equipment and marine, you
>
> 4 know, boats, lawnmowers, all those things. And so, you
>
> 5 could make the assumption that they, generally, follow,
>
> 6 sort of, a population distribution. I mean, the
>
> 7 distribution of emissions in that category would be
>
> 8 highly correlated with population. So, to that extent,
>
> 9 you could assume that it was more urban and less rural.
>
> 10 Although, certainly, you know, farm equipment is a part
>
> 11 of that category.
>
> 12 DR. PINKERTON: Okay, thanks.
>
> 13 DR. HENDERSON: Okay, I think we\'ve,
>
> 14 actually, already moved into the second section, the,
>
> 15 our exposure section, and - -
>
> 16 DR. SHEPPARD: Rogene, before we
>
> 17 continue, I wanted to comment a little bit more on the
>
> 18 air quality modeling.
>
> 19 DR. HENDERSON: Go right ahead.
>
> 20 DR. SHEPPARD: You know, well, before I
>
> 21 start, if everybody who\'s on the phone could mute their
>
> 22 line. That would be really helpful. You can press
>
> 23 star six if you don\'t have a mute button. So, the
>
> 24 complexity of the modeling, I think, is ex-, of the air
>
> 25 quality model is extremely challenging. And Tim
>
> Page 136
>
> 1 important again, depending upon the purpose of what,
>
> 2 what the analysis is trying to do.
>
> 3 The other comment I have, beyond that, is, if
>
> 4 it could simplify the work, or at least make it clearer
>
> 5 to those of us who are reviewing it, how much each tier
>
> 6 is completely conditional on the previous tier, and to,
>
> 7 perhaps, take out anything that\'s overlapping. For
>
> 8 instance, between tier one and two, that looks like
>
> 9 there\'s some different overlapping efforts that are

10 going to be done, and can those, can some of that be

11 removed and done in only one tier.

12 DR. HENDERSON: Do you have a response,

13 anybody want to respond to a question about the

14 overlapping of the tiers. Again, we\'ve gotten into the

15 exposure area, but that\'s fine. That\'s where we\'re

16 supposed to go. No comments.

17 DR. GRAHAM: We\'ll take a look at that.

18 DR. HENDERSON: You\'ll take a look at,

19 okay. That\'s all we need. Thank you, Lianne. Did you

20 have more to, comments to make?

21 DR. SHEPPARD: I do, but maybe I\'ll wait

22 until other people talk about exposure, and then chime

23 in later.

24 DR. HENDERSON: Okay, well, I know you

25 still have, you\'ll still be there another hour or so?

> Page 135
>
> 1 touched on that a bit with the street canyon issue.
>
> 2 And there\'s so many assumptions that are in here. And
>
> 3 it, it\'s, also, I was, it really hit home, the comment
>
> 4 about the resource limitations. And this is a,
>
> 5 potentially, a huge effort to get it right.
>
> 6 I think simplifications are possible,
>
> 7 depending on the purpose of the analysis, and I\'m, it
>
> 8 strikes me as this air quality modeling is being done
>
> 9 for many different purposes, which means that
>
> 10 simplifications, if you had only one purpose, may not
>
> 11 be as easy.
>
> 12 You know, if you just want exceedances, you
>
> 13 might be able to simplify in different ways, than if
>
> 14 you wanted predictions. Because you\'re going to be
>
> 15 using the predictions, for instance, in the APEX model.
>
> 16 If you\'re focusing just on long-term exposure, there
>
> 17 are simplifications; but if you want the short-term,
>
> 18 one-hour, that means a lot more complex model.
>
> 19 It\'s not so clear that temporal and spatial
>
> 20 variation in NOx are separable in the sense that, when
>
> 21 you\'re really near roads, the temporal patterns are,
>
> 22 probably, really different than locations far away from
>
> 23 roads.
>
> 24 So, thinking about which monitors are
>
> 25 representative for the analysis becomes really
>
> Page 137
>
> 1 DR. SHEPPARD: Yeah, right.
>
> 2 DR. HENDERSON: Doug, you have comments
>
> 3 on the exposure method, the tiered approach, et cetera.
>
> 4 DR. CRAWFORD-BROWN: There\'s no lunch
>
> 5 first, then?
>
> 6 DR. HENDERSON: No.
>
> 7 DR. CRAWFORD-BROWN: Just wanted to know
>
> 8 where we stood with lunch, that\'s all.
>
> 9 DR. HENDERSON: Oh, well, lunch is coming

10 in thirty minutes. Oh, lunch is ready. I suggest we

11 have a working lunch. I thought, maybe, according to

12 our schedule, we would go to, we would have lunch at

13 12:30, that we might make a little more progress, but

14 if hunger pangs are striking, I don\'t mind.

15 DR. CRAWFORD-BROWN: I\'m not taking the

16 rap for this here. I just wanted - -

17 DR. GORDON: Let me take the rap.

18 DR. HENDERSON: When you said to know,

19 it\'s good to know what the plan is. Let\'s work a

20 little longer, I feel like.

21 DR. CRAWFORD-BROWN: Okay, I don\'t care.

22 DR. HENDERSON: I\'m not hungry yet.

23 DR. CRAWFORD-BROWN: Is this on? Is this

24 one on? I\'m not, is there a reason you\'re holding the?

25 DR. HENDERSON: It\'s just because I can\'t

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>
> Page 138
>
> 1 reach and yeah, it\'s on.
>
> 2 DR. CRAWFORD-BROWN: Okay, well, first, I
>
> 3 like the exposure section, as I mentioned in my
>
> 4 comments. It, really, is very much in the line of a
>
> 5 wide range of other kinds of assessments that the EPA
>
> 6 has done over the years. And it, really, is, I would
>
> 7 say, you know, partially in answer to Ted\'s issue, the
>
> 8 exposure and the risk side is starting to get pretty
>
> 9 automated these days.
>
> 10 The models are not quite plug and chug,
>
> 11 because situations change quite dramatically. I was
>
> 12 very comforted when you said that you would use the
>
> 13 epidemiological results with air quality information
>
> 14 and the clinical studies for the, I hope I\'m getting
>
> 15 this right, for the actual inter-subject variability
>
> 16 kinds of calculation, \'cause I always worry about using
>
> 17 the epidemiological results to get your slope factor,
>
> 18 or whatever, and then, also, doing inter-subject
>
> 19 variability.
>
> 20 Because the epi results, in fact, already
>
> 21 have that convolved inside of it. And so, I hope I\'m
>
> 22 understanding that correctly.
>
> 23 MR. RICHMOND: That is correct. And if
>
> 24 you\'ll look at the ozone staff paper, and risk
>
> 25 assessment, you\'ll see that\'s exactly what we say - -
>
> Page 140
>
> 1 you could do is, you could imagine that the different
>
> 2 tiers are different levels of uncertainty, or you can
>
> 3 imagine that the different tiers address different
>
> 4 kinds of questions of one, the lower tiers having to do
>
> 5 with questions about the upper percentiles of exposure.
>
> 6 And the other ones covering the whole exposure realm.
>
> 7 And then, the only other comment I would make now, the
>
> 8 rest are all in my written comments, is, I do think on
>
> 9 the uncertainty side, you\'ve got a significant amount

10 of work to do there.

11 You always will have that. You\'ve got this

12 challenge of combining the, what are going to be

13 necessarily qualitative aspects of uncertainty with

14 more quantitative aspects of uncertainty, aspects of

15 uncertainty that have to do with scenario

16 specification, and so forth, and other aspects having

17 to do with uncertainty in parameter values.

18 And I\'ll be interested to see how you fold

19 those things together into some, sort of, overall

20 judgment of uncertainty here. I agree with the

21 direction you were, sort of, heading, which is to make

22 it, you know, to leave this sort of expert judgment as,

23 and sort of semi-quantitative uncertainty bounds in the

24 assessment.

25 I think that will be important, rather than

> Page 139
>
> 1 DR. CRAWFORD-BROWN: Yeah, exactly the
>
> 2 same thing, yeah.
>
> 3 MR. RICHMOND: - - where we had clinical
>
> 4 data and epi data. We made that point.
>
> 5 DR. CRAWFORD-BROWN: Yeah, good, okay. I
>
> 6 wasn\'t quite clear on one thing, which has to do with,
>
> 7 as you move from tier to tier, are you moving from tier
>
> 8 to tier because of things that you see in the previous
>
> 9 tiers assessment, like a screening method, for example,
>
> 10 that say, oh, if I look at the upper 95 percentile,
>
> 11 boy, that\'s really large.
>
> 12 So, I, that risk is large, so I better do a
>
> 13 more detailed one. Or I look at it and it\'s very
>
> 14 small, so I don\'t need to do the more detailed one. Or
>
> 15 are you moving from tier to tier based on whether the
>
> 16 data are available to move to the next tier. I\'m
>
> 17 assuming, maybe, a little bit of a combination of
>
> 18 those.
>
> 19 DR. GRAHAM: Right, I\'d say both. And
>
> 20 the hope would be that the prior tier is, in a sense,
>
> 21 more conservative or, well, I don\'t want to say,
>
> 22 hopefully, it\'s more uncertain, but we want to reduce
>
> 23 the uncertainty in progression from going from, say, a
>
> 24 tier one to a tier two or tier three.
>
> 25 DR. CRAWFORD-BROWN: Okay, \'cause is
>
> Page 141
>
> 1 thinking that everything can be reduced entirely to the
>
> 2 kind of more quantitative probability density functions
>
> 3 on uncertainty. But, in the end, you\'ll just have to
>
> 4 figure out how you\'re going to present that as a story,
>
> 5 the overall uncertainty. But I thought the
>
> 6 methodologies were quite good. That\'s all.
>
> 7 DR. HENDERSON: Thank you. Terry Gordon,
>
> 8 do you have some assessment, or advice to give on the
>
> 9 exposure section?

10 DR. GORDON: Well, I\'d say quantitative

11 risk assessment is a weak point of mine, so I don\'t

12 have really much to say, except exposure.

13 DR. HENDERSON: Well, this is exposure,

14 yes.

15 DR. GORDON: Well, see my confusion of

16 the terms shows - -

17 DR. HENDERSON: Well, we\'re talking about

18 the exposure section, not the risk assessment. I mean,

19 the tiered exposure approach.

20 DR. GORDON: So, not the-, we\'re on the

21 health effects or not?

22 DR. HENDERSON: No.

23 DR. GORDON: Oh, no.

24 DR. HENDERSON: So, are you still, are

25 you just still, okay, that\'s fine.

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 37
>
> Page 142
>
> 1 DR. GORDON: Well, I was going to read
>
> 2 Ellis\' comment.
>
> 3 DR. GORDON: I have no experience in
>
> 4 which to basically form judgment.
>
> 5 DR. CRAWFORD-BROWN: That didn\'t stop me
>
> 6 from talking.
>
> 7 DR. GORDON: Well.
>
> 8 DR. HENDERSON: I got the message. Okay,
>
> 9 Jim Ultman, are you on the phone? Have to wait for
>
> 10 people to un-mute. Lianne, you said you wanted to save
>
> 11 your comments. Do you have any further comments, and
>
> 12 then I\'ll open it up to the whole group.
>
> 13 DR. SHEPPARD: Well, you know, I\'m
>
> 14 looking over what I wrote. And I prepared an extensive
>
> 15 set of comments, but a lot of them are fairly detailed,
>
> 16 and probably aren\'t worth discussing now. But the, my
>
> 17 comment about the purpose of the tiers, and I also, it
>
> 18 resonated with me, the previous comment about, exactly,
>
> 19 what is the goal of each tier, and are they
>
> 20 representing different kinds of questions, or are they,
>
> 21 really, just progressions of better information.
>
> 22 Because, in the exposure tier, it states pretty clearly
>
> 23 that they\'ll be using interpolated hourly NO2
>
> 24 concentrations.
>
> 25 It says measurements, but presumably,
>
> Page 144
>
> 1 I missed it on Terry, but anyone should have, should
>
> 2 feel free to comment on the approach they\'re using for
>
> 3 exposure assessment, particularly the tiered approach.
>
> 4 Do we have any more comments?
>
> 5 DR. HATTIS: Yeah, I just want to just
>
> 6 reinforce that, you know, I do think that if you stop
>
> 7 at the some of the lower tiers, you\'ll probably not
>
> 8 produce the kind of information that will later be
>
> 9 needed in, at least, impact assessments, if not the

10 primary decisions.

11 DR. HENDERSON: Good, and I believe Frank

12 Speizer had a similar comment that he would be

13 disappointed if you stopped at tier one.

14 DR. GORDON: Rogene, I - -

15 DR. HENDERSON: Okay, Terry.

16 DR. GORDON: I might be making myself

17 more confused, but on page 22, it has a long-term

18 exposure approach, as if it\'s going to be using annual

19 averages. And then, when I get to page 31, I was going

20 to talk about some, one health comment. It says

21 they\'re not going to use that in the health risk

22 assessment. So, if that\'s true, why are you going to

23 do this work? I don\'t agree with not doing it,

24 actually.

25 DR. GRAHAM: It was probably found, I

> Page 143
>
> 1 there\'ll also be predictions, because they\'ll be over
>
> 2 space as well. And, you know, that is just a, I mean,
>
> 3 we\'re, as Tim alluded to, we\'re struggling with that
>
> 4 here in the project we\'re doing.
>
> 5 That\'s a huge, huge undertaking to do that
>
> 6 well. So, there has to be a number of simplifying
>
> 7 assumptions to even do it at all. And, of course, you
>
> 8 know, then you start to question how good it is. And,
>
> 9 again, it depends on the purpose. I guess the only
>
> 10 other comment, with respect to the exposure modeling
>
> 11 is, it seems to me that the, well, when it moves to the
>
> 12 level of the Monte Carlo simulation with the APEX
>
> 13 model, that could be expanded to incorporate some key
>
> 14 assumptions.
>
> 15 So, not just doing sensitivity analyses,
>
> 16 looking at one or two different assumptions; but
>
> 17 incorporating explicit structure for what those
>
> 18 different assumptions or models could be. And, then,
>
> 19 getting a more explicit estimate of the uncertainty
>
> 20 that has to do with more than just the distribution, or
>
> 21 just the underlying assumptions, but also variability
>
> 22 in what those assumptions are.
>
> 23 DR. HENDERSON: Thank you, Lianne. Now,
>
> 24 do others, as I say, these names listed here are just,
>
> 25 trying to divide up according to interest. Obviously,
>
> Page 145
>
> 1 don\'t want to say probably. It was founded in the
>
> 2 current form of the standard. That is, it\'s a annual
>
> 3 average.
>
> 4 DR. HENDERSON: Is that the answer
>
> 5 you\'re, I mean, does that answer your question?
>
> 6 DR. GORDON: Tradition, yeah.
>
> 7 DR. HENDERSON: Yeah, okay.
>
> 8 MR. RICHMOND: Well, one of the, I mean,
>
> 9 this is sort of the linkage to get back and forth

10 between the exposure and risk. One could envision

11 doing a long-term, addressing long-term air quality in

12 the tier one, or long-term exposure, doesn\'t mean that

13 quantitatively that we have enough information to

14 address, or we don\'t, you know, depending on both

15 causality and the level of information on concentration

16 response relationships from the epidemiology, do we

17 have enough to make a credible quantitative risk

18 assessment, not qualitative con-, you know, concerns

19 about the health endpoints that may be shown, but

20 enough to, basically, move to that next step

21 quantitatively. And, so I mean, there could be very

22 much a distinction between whether we do long-term air

23 quality or exposure, as opposed to a tier two,

24 quantitative, long-term risk assessment.

25 The question on benchmark is it\'s difficult,

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>
> Page 146
>
> 1 given the issue that Doug and others have brought up,
>
> 2 the epi studies long-term don\'t tell you, like the
>
> 3 clinical studies, what\'s a benchmark exposure level.
>
> 4 You don\'t know what the exposure was. You know what
>
> 5 the ambient monitors were. That\'s all.
>
> 6 DR. HATTIS: Well, because you don\'t,
>
> 7 they don\'t directly tell you about it, they certainly
>
> 8 give you a clue, from which you can reason, given your
>
> 9 other information about what they\'re likely to have
>
> 10 been.
>
> 11 MR. RICHMOND: It, again, depends,
>
> 12 there\'s all sorts of questions about is it really the
>
> 13 cumulative long-term average, or is it, as Dr. Crapo
>
> 14 mentioned earlier, is it that someone sees a peak so
>
> 15 many times per week. There\'s all sorts of
>
> 16 possibilities, from a health standpoint, in terms of
>
> 17 what the real, underlying cause of those long-term
>
> 18 effects are.
>
> 19 DR. HATTIS: Yeah, and I think you need
>
> 20 to fairly characterize those uncertainties by doing it
>
> 21 a couple of different ways, and say, okay, what are the
>
> 22 differences in expectations that we get from these
>
> 23 different possible states of the world.
>
> 24 DR. HENDERSON: Thank you, Ed?
>
> 25 DR. AVOL: I submitted some written
>
> Page 148
>
> 1 the current standard?
>
> 2 DR. HENDERSON: Yeah, go ahead, Harvey.
>
> 3 We talked about that earlier.
>
> 4 MR. RICHMOND: Yeah, I thought we had
>
> 5 discussed that about twenty minutes ago, but - -
>
> 6 DR. HENDERSON: Yeah.
>
> 7 DR. SHEPPARD: Yeah. Well, maybe, okay,
>
> 8 maybe I just missed it.
>
> 9 MR. RICHMOND: Oh, okay, but, well, what

10 I laid out is, it is a problematic challenge to, you

11 know, to deal with how do you assess the exposures or

12 risk just meeting the current standard. There are

13 different approaches. One is to use historical air

14 quality when the levels were just meeting the current

15 standard back typically in the 90\'s for some of these

16 example urban areas.

17 The other approach would be to rec-, you

18 know, do some statistical adjustment just like we\'ve

19 done in ozone and PM, where it\'s been to ra-, you know,

20 adjust things, air quality adjustment procedures to

21 adjust the distributions downward to meet a standard,

22 but effectively, rolling up distributions to just meet

23 the standard. And there are different ways you could

24 do that, as to whether it\'s proportionately all the

25 monitors, or whether we make distinctions between the

> Page 147
>
> 1 comments. I won\'t go through all those, but one area I
>
> 2 did want to ask about was the issue of the Tiger
>
> 3 mapping, in terms of the road designations, and in
>
> 4 terms of modeling and assessing the portion of
>
> 5 population that may be within or near roadways.
>
> 6 There are ways, I point out one way that we,
>
> 7 sort of, found out there was a problem and what we did
>
> 8 about it, but there are other ways to do it. But I
>
> 9 just wanted to get some confirmation that, in fact,
>
> 10 you\'re either going to ground troop it or do some
>
> 11 sensitivity analysis or something, or move to something
>
> 12 other than that.
>
> 13 DR. GRAHAM: I\'m sorry, I was looking at
>
> 14 my notes. Yeah, we are aware of that data as well.
>
> 15 And, it\'s not to say that we were just going to look at
>
> 16 the one data source that I mentioned, but the Tiger
>
> 17 road, it wouldn\'t be exclusive. But the Tele Atlas
>
> 18 would be used.
>
> 19 DR. HENDERSON: Are there any other
>
> 20 comments or questions people have about the exposure
>
> 21 assessment section?
>
> 22 DR. SHEPPARD: Yeah, this is Lianne
>
> 23 Sheppard. I have another question, and what does it
>
> 24 mean to look at exposure for just meeting current
>
> 25 standard, when so many of the measurements are below
>
> Page 149
>
> 1 near roadway and non-near roadway monitors.
>
> 2 So, we were looking for feedback from the,
>
> 3 particularly, the air quality experts on this
>
> 4 committee.
>
> 5 DR. SHEPPARD: So, I guess, my question,
>
> 6 then, is, should we even do that, since the mo-, since
>
> 7 the current data are below the standard.
>
> 8 MR. RICHMOND: Right, the other
>
> 9 alternative is, then, we, otherwise, have no exposure

10 or risk associated with the current standard. It would

11 really be the recent air quality and what standard that

12 would be associated. So, if a place that only maximum

13 has .03 today annual average, that they were,

14 basically, looking at standards at that level and

15 below. That\'s the choices we face, and you know.

16 DR. CRAWFORD-BROWN: Rogene, may I,

17 several points have touched on that.

18 DR. HENDERSON: Yes, go ahead, Doug.

19 DR. CRAWFORD-BROWN: This worries me just

20 a little bit, because the question comes up as to what

21 do you mean by meeting the standards. If what you mean

22 by meeting the standards is that everybody goes to

23 0.053, then that\'s one thing.

24 But my argument is going to be, that when

25 people meet the standards, kind of like in water, it\'s

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>
> Page 150
>
> 1 the same issue, they put in place mitigation strategies
>
> 2 that do drop significant areas well below the
>
> 3 regulatory limit, and that is part and parcel with
>
> 4 meeting the standard.
>
> 5 So, I don\'t think that a scenario in which
>
> 6 everybody has gone up to .053 is, in fact, a scenario
>
> 7 that is meeting the standard in the way that meeting
>
> 8 the standard actually plays itself out.
>
> 9 MR. RICHMOND: Yeah, let me clarify that.
>
> 10 When we say meeting the standard, standards, typically,
>
> 11 have been implemented by, for example, large regional
>
> 12 areas. The CMSA basis, it\'s not just L.A. County, but
>
> 13 it\'s the L.A. at CMSA is, typically, a definition for a
>
> 14 non-attainment area. When we say adjusting the air
>
> 15 quality, we mean at the highest monitor in that area.
>
> 16 We\'re not talking about using or adjusting air quality
>
> 17 so that every single monitor in an area is just at the
>
> 18 current standard. It is the design monitor within that
>
> 19 urban area. So, it\'s like, for the whole New York
>
> 20 area, if we did New York, or Philadelphia.
>
> 21 DR. CRAWFORD-BROWN: Okay, so in your - -
>
> 22 MR. RICHMOND: So that, so none of it,
>
> 23 nowhere have we ever adjusted air quality in any of our
>
> 24 analyses in the past so that all monitors with an area,
>
> 25 when we do just meeting standard scenarios, it means
>
> Page 152
>
> 1 one of the things, having looked at your comments, we
>
> 2 may want to, at least, take maybe the worst case
>
> 3 situation, maybe it\'s Los Angeles, and look, you know,
>
> 4 look at the mobile models. What if we were to double
>
> 5 vehicle things, would we still have a problem?
>
> 6 We still might not come up to that level
>
> 7 given control technologies and given stationary
>
> 8 technologies, that was the industry argument, that even
>
> 9 given what\'s in place and can\'t be rolled back, that

10 you can\'t envision the scenario that gets there, but

11 that\'s something we could look through modeling in a

12 more limited number of areas.

13 DR. HATTIS: Yeah, and I think that\'s a

14 reasonable alternative, to say, okay, what is the worst

15 possible deterioration that we can reasonably imagine

16 under the current scenario. I mean, that can include,

17 you know, non-attainment of ideal compliance with

18 everything, but.

19 DR. LARSON: Harvey, this is Tim again.

20 When your scenario of just meeting the standard is

21 going to based on the actual location of the, the

22 worst, the highest EPA monitor in that area?

23 I mean, what if you did your modeling

24 exercise and found that there were a whole bunch of

25 places that currently don\'t meet that standard within

> Page 151
>
> 1 only at the design monitor, and what are the
>
> 2 relationships at the other monitors as they flow up and
>
> 3 down.
>
> 4 DR. CRAWFORD-BROWN: Oh, okay, okay.
>
> 5 Okay, there\'s something you said earlier that made me
>
> 6 think it was different from that. Thank you.
>
> 7 MR. RICHMOND: All right, okay.
>
> 8 DR. HENDERSON: Well, and, I think Ron
>
> 9 brought up the important point. You wouldn\'t want to
>
> 10 say that the risk associated with meeting the current
>
> 11 standard, or the current risk when it\'s, actually,
>
> 12 lower. So, I, that\'s, you could misinterpret that.
>
> 13 Dale, did you have something?
>
> 14 DR. HATTIS: Yeah, I think the concern is
>
> 15 to have a realistic scenario. And in the, the
>
> 16 realistic scenario that could get you to back up to the
>
> 17 roll up, type methodology that you want to think about,
>
> 18 is imagine a future of possible growth in traffic or
>
> 19 other things that you could reasonably imagine, where
>
> 20 you could be deteriorating the air quality enough to
>
> 21 get you to near the, near compliance with the current
>
> 22 thing. So, I think it may well be that your roll up
>
> 23 scenarios are the easiest thing to do along those
>
> 24 lines.
>
> 25 MR. RICHMOND: Right, and in that vein,
>
> Page 153
>
> 1 the urban area.
>
> 2 MR. RICHMOND: Well, what you, are you
>
> 3 talking about the exposure modeling, air qu-, I\'m a
>
> 4 little lost as to which point - -
>
> 5 DR. LARSON: Yeah, I mean, your, yeah. I
>
> 6 mean, is it based on the current, the - -
>
> 7 MR. RICHMOND: Whether you meet the
>
> 8 standard, by definition, is at the monitors. It, you
>
> 9 know, the mo-, we\'re supposed to be, have taken that

10 into account.

11 We realize in setting the CO standard and the

12 ozone standard that, no, the highest level may not, you

13 know, depending on the pollutant, may not be at the

14 monitor, but designing scenarios for alternative

15 standards is based on the monitoring network, not,

16 we\'ll then look at the implications through exposure

17 analysis in modeling to see what\'s the distribution of

18 exposures in the population, no matter where they are,

19 but it is based on simulating standards that are met at

20 the monitoring network and by definition of the

21 standard at the design monitor.

22 DR. LARSON: Okay.

23 DR. HENDERSON: Okay, does anyone who is

24 on the phone have any comments? I don\'t want to ignore

25 you. I\'ve already heard from Lianne and Tim. Okay, I

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> Page 154
>
> 1 suggest that there seems to be a strong move towards
>
> 2 lunch. We have lunch next door. I would like for us
>
> 3 to use this as sort of a working lunch. Maybe be
>
> 4 prepared to start our discussions again at 1:00 in
>
> 5 here. You can bring your lunch in here and eat, or you
>
> 6 can eat there, or do whatever you want to do. But I\'d
>
> 7 like to start the discussions at 1:00 and Vanessa has
>
> 8 something.
>
> 9 DR. VU: Since we are short of time, I\'d
>
> 10 also like to invite OAR representatives to join, grab a
>
> 11 lunch here so you can quickly go back here.
>
> 12 DR. CRAWFORD-BROWN: With us?
>
> 13 DR. VU: No, no, I mean, get the lunch
>
> 14 and come back here at 1:00, since it\'s a, you know,
>
> 15 sandwich buffet, whatever.
>
> 16 DR. CRAWFORD-BROWN: They get in line
>
> 17 first, then.
>
> 18 DR. HENDERSON: Well, I think that\'s
>
> 19 great, so we will begin our discussions here at 1:00,
>
> 20 and you can use your time now to eat in there or eat
>
> 21 in, to bring it in here, whatever you want.
>
> 22 (WHEREUPON, the morning session was concluded.)
>
> 23 DR. HENDERSON: We want to, very good.
>
> 24 Doug is my bell ringer. We want to get started
>
> 25 discussing this last section of the methods document.
>
> Page 156
>
> 1 is data from several studies looking at children\'s lung
>
> 2 function, for example, which is a long-term, sort of,
>
> 3 exposure. And so, it seems like there needs to be some
>
> 4 reconciliation.
>
> 5 Yesterday, we had some discussion about,
>
> 6 well, perhaps maybe there is a need for a short-term
>
> 7 NO2 standard. But, so, I just want to offer that up as
>
> 8 a comment for discussion, or at least for
>
> 9 consideration. In any case, here, it just, sort of,

10 says, well, so, we\'re just not going to do it. But I

11 think it needs to be, at the very least, it needs to

12 be, sort of, just supported or substantiated or

13 something.

14 The other comment, which is a small comment.

15 I, also, have some small written comments. But,

16 there\'s a, in section 4.3, there\'s a discussion about

17 health responses. And again, it, sort of, focuses more

18 on short-term effects. And, sort of, disregards long-

19 term losses in lung function. And yet, based on what

20 we talked about yesterday and today, one of the

21 conclusions in the ISA is going to be that long-term

22 lung function is an important issue. So, it seems like

23 the two documents are not going to be quite consistent.

24 MR. RICHMOND: In response, we said

25 preliminary, based on what we saw in the first draft

> Page 155
>
> 1 That is the one on the health risk assessment. And I
>
> 2 think there\'ll be a lot of comments on that. A lot of
>
> 3 people interested in it. John Samet is calling in a
>
> 4 1:45, so we\'ll have his comments later.
>
> 5 But, we have all of our Air Office crew here.
>
> 6 Well, that was a great lunch, and I thank Vanessa and,
>
> 7 who has already left for arranging it for us. That was
>
> 8 a nice way of handling things.
>
> 9 So, now, we\'re going to open our discussion
>
> 10 on the health risk assessment approach, and what, and
>
> 11 offer it, our advice to the Agency as to whether we
>
> 12 think they\'re using the right approach, or if it could
>
> 13 be improved. And Ed, you are one of the first
>
> 14 discussants.
>
> 15 DR. AVOL: Okay, thank you. I have, I
>
> 16 guess, two comments. One has to do with the risk
>
> 17 assessment scope overview itself. The, what\'s laid out
>
> 18 on page 31 talks about how the draft ISA leads to a
>
> 19 suggestion that the strongest health findings are for
>
> 20 one-hour and twenty-four-hour averaging times, so
>
> 21 there\'s not going to be any risk assessment for longer
>
> 22 term exposures. And that\'s disquieting, I guess.
>
> 23 The current standard is an annual, long-term
>
> 24 standard. The document says, or this document says,
>
> 25 we\'re only going to look at short-term exposure. There
>
> Page 157
>
> 1 ISA, which we\'re waiting, and we said we will make the
>
> 2 assessments based on the second draft ISA that will be
>
> 3 coming out before the risk assessment. So, we will
>
> 4 look at and work closely with and see it to see how
>
> 5 those issues are addressed in the second draft ISA.
>
> 6 DR. HENDERSON: Okay, now, is, John
>
> 7 Balmes, are you on the phone?
>
> 8 DR. SHEPPARD: Rogene, I think he said he
>
> 9 was coming back at 2:15.

10 DR. HENDERSON: Oh, that\'s right. I knew

11 that. I\'m sorry.

12 DR. SHEPPARD: Can I take his place? I\'m

13 leaving kind of earl-, soon.

14 DR. HENDERSON: So, would you like to

15 make your comments now?

16 DR. SHEPPARD: Yeah, and I\'d like to

17 follow up on Ed\'s comment, because one of my main

18 concerns was for each of the tiers of the risk

19 assessment is that, I think we need to have very

20 clearly stated criteria for what particular outcomes

21 and populations and so on will be used. And those

22 should be specified in advance. And presumably, they

23 will come from the results of the ISA, as has been

24 stated. But, the criteria for choosing them, for

25 instance, would it be only the out-, the outcomes that

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> Page 158
>
> 1 are measure-, assessed as likely causal, as an example.
>
> 2 Those, then, get brought forwarded to the risk
>
> 3 assessment.
>
> 4 My other, fairly major comment, with respect
>
> 5 to the risk assessment is, I think it needs to be
>
> 6 expanded to have three tiers. And the first tier
>
> 7 should be the qualitative risk assessment. And, so,
>
> 8 that it doesn\'t, it becomes as important as the other
>
> 9 tiers, and it also becomes the foundation for the
>
> 10 quantitative risk assessment.
>
> 11 So, all the different outcomes are reviewed,
>
> 12 but some of them, presumably, can\'t be easily
>
> 13 quantified in a quantitative risk assessment, but
>
> 14 they\'re still important, and they get discussed in the,
>
> 15 in what I would suggest would be the first tier, which
>
> 16 is the qualitative assessment. And then, some of them
>
> 17 meet the criteria for being brought forward for
>
> 18 quantitative assessment, and they, therefore, go up to
>
> 19 the next levels. So, I think that\'s a fairly major
>
> 20 change in the organization that I recommend.
>
> 21 I think the criteria for even discussing the
>
> 22 quantitative or even conducting the quantitative risk
>
> 23 assessment need to be specified in advance as well in
>
> 24 this document. And I, actually, recommend that all
>
> 25 tiers of all, both the exposure and the risk
>
> Page 160
>
> 1 I guess, looking back at the ozone criteria
>
> 2 document, when we had a fair amount of discussion about
>
> 3 the linear versus logistic function, you ended up
>
> 4 using, doing a new analysis, which had different
>
> 5 weights for the linear and the logistic function.
>
> 6 That is an example of moving in that
>
> 7 direction. It was presented more as a sensitivity
>
> 8 analysis, but the, you know, that\'s the, that\'s what
>
> 9 I\'m thinking, that a lot of, a number of different

10 assumptions and uncertainties of those would be

11 incorporated into the estimates that are produced.

12 MR. RICHMOND: But again, that was a

13 sensitivity analysis. There wasn\'t an assignment of

14 how much weight to put on the different choices. We

15 put forth a base case assumption, and we looked at the

16 impact of alternative assumptions. I\'m hearing you say

17 that you want us to do more than that. I\'m still left

18 puzzled as to what you would be recommending us to do

19 differently.

20 DR. SHEPPARD: Okay, I\'ll try to

21 articulate that more clearly in writing.

22 DR. HENDERSON: Okay, is that all you had

23 to say right now, Lianne?

24 DR. SHEPPARD: Yes.

25 DR. HENDERSON: Okay, thank you. And

> Page 159
>
> 1 assessment, be discussed, even if the discussion is
>
> 2 that these are the reasons why we can\'t do this, if
>
> 3 that is the ultimate decision.
>
> 4 From what I\'ve seen, I think that all tiers
>
> 5 should be done, at least so far, but that remains to be
>
> 6 seen.
>
> 7 And then my last, fairly major comment is the
>
> 8 quantitative risk assessment needs to, also,
>
> 9 incorporate some integrated uncertainty assessment that
>
> 10 goes beyond the sensitivity analysis.
>
> 11 DR. HENDERSON: Okay.
>
> 12 MR. RICHMOND: Just on the last point,
>
> 13 this is Harvey Richmond. Could you clarify what
>
> 14 approaches, either in written comments or today, when
>
> 15 you use the word integrated uncertainty assessments,
>
> 16 what you would envision that, what approaches being
>
> 17 used to carry out such integrated assessments?
>
> 18 DR. SHEPPARD: Yeah, and the devil of
>
> 19 those, that kind of thing is in the details, of course.
>
> 20 I\'m sure that\'s why you asked the question. And I
>
> 21 guess, the idea here is that the, and I\'ll try to
>
> 22 expand a little bit more than I have already. The idea
>
> 23 is that the, we go beyond sensitivity where we assume a
>
> 24 different set of fixed assumptions to allowing for
>
> 25 multiple different assumptions.
>
> Page 161
>
> 1 then, James Crapo, do you have something?
>
> 2 DR. CRAPO: Yeah, I think I\'ve made most
>
> 3 of the points, so to make the, I like the risk
>
> 4 assessment model and the health endpoints. I thought
>
> 5 you were choosing the appropriate ones, and I like,
>
> 6 particularly, that you were, included a focus on short-
>
> 7 term exposures and short averaging times.
>
> 8 And, I assume, if I read it correctly, you\'re
>
> 9 going to continue that as you do the tier two

10 epidemiology. You\'re also going to look at the

11 possibility of using correlations with something other

12 than the national average, annual average, but rather

13 the short-term exposure peaks, which I\'d really

14 encourage that.

15 Because I think that one of the most

16 important outcomes that can come out of this analysis

17 to help us develop data that would convince us it\'s

18 important to change the form of the standard or not.

19 Because I think that, I think form of the standard is,

20 probably, one of the more important questions to

21 address at this point in time. And I\'d like to see the

22 risk assessment provide us better information to make

23 an informed decision on that. But I think that\'s

24 already part of your goal as I read it, so I\'m very

25 pleased with what I see.

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> Page 162
>
> 1 DR. HENDERSON: Thank you, James. Is
>
> 2 Steve Kleeberger still here? I don\'t see him. He\'s
>
> 3 not, he wasn\'t going to be on the phone, was he?
>
> 4 DR. NUGENT: That\'s correct, and he did
>
> 5 send an email this morning saying he may be unlikely to
>
> 6 be here.
>
> 7 DR. HENDERSON: Oh, okay. Then we\'ll go
>
> 8 on to Kent Pinkerton.
>
> 9 DR. PINKERTON: Okay, again, I think this
>
> 10 is a really well-executed document. I think the
>
> 11 concern that I have is one that was also expressed by
>
> 12 Frank Speizer. And that\'s just the concern that with
>
> 13 the tiered approach, that one may be tempted to stop at
>
> 14 tier one and not go beyond. And I think that becomes
>
> 15 problematic, because there\'s such rare occurrences of
>
> 16 excursions of above the set standard, that one could
>
> 17 argue, well, there is no need to go to tier two and
>
> 18 look at these potential health effects.
>
> 19 But since we see so many instances of
>
> 20 significant health effects associated with ambient
>
> 21 concentrations of NO2 that are well below the
>
> 22 established standard, that I think it\'s just important
>
> 23 that that be really emphasized, that many of these
>
> 24 studies really need to go beyond just tier one and go
>
> 25 on to tier two and, occasionally, to tier three.
>
> Page 164
>
> 1 national priority - -
>
> 2 DR. HATTIS: - - but I do think that
>
> 3 getting an idea of the quantitative significance of the
>
> 4 health effects that you think are likely is important
>
> 5 from a national priority setting standpoint. You know,
>
> 6 one, of course, your main job is to inform on decisions
>
> 7 about the revision of the criteria, this particular
>
> 8 criteria standard.
>
> 9 But, also, it seems to me, that you are also

10 feeding into a national discussion about how we should

11 devote our resources to changing the mix of air

12 pollutants that we are exposed to, and, as well as

13 other problems. And so, trying to be as thorough as

14 you can about allowing people to project national

15 impacts is an important function, okay.

16 Because this isn\'t going to stop at the, with

17 your meeting of the deadlines that you have in front of

18 you. And, you know, people are going to continue to

19 try to understand how, you know, how they should be

20 devoting resources to this problem. Because of that,

21 in part, I think you ought to really, seriously,

22 consider stretching a bit to include the kind of

23 effects that are based upon the chronic observations,

24 particularly the children\'s lung function growth.

25 Let me say that that\'s important because it

> Page 163
>
> 1 But, again, I think the way it is written,
>
> 2 the health endpoints seem to be very appropriate.
>
> 3 Again, keep in mind, as you look at susceptible
>
> 4 populations, again, to keep in mind children, those who
>
> 5 have asthmatic conditions. Also, I don\'t know if we\'ve
>
> 6 really reached a point at this point in time, but are
>
> 7 there potential differences based on gender, with
>
> 8 regard to the health effects associated with nitrogen
>
> 9 oxide exposures.
>
> 10 Again, I think, some of my questions about
>
> 11 why only cities and not areas that are not city or
>
> 12 population based are not being included, but I think,
>
> 13 Donna, you helped me understand that a little bit
>
> 14 better. So, and I think those are, pretty much, the
>
> 15 extent of my comments.
>
> 16 DR. HENDERSON: Thank you, Kent. Dale
>
> 17 Hattis observed that I\'m not too sharp here this
>
> 18 morning. I skipped him. That was not intentional,
>
> 19 Dale. So, we\'ll hear from Dale right now.
>
> 20 DR. HATTIS: That\'s all right. Anyway, I
>
> 21 want to second the thing that some of the, many of the
>
> 22 comments that, in fact, Kent Pinkerton has just made.
>
> 23 But just to say, a little more strongly. I\'m going to
>
> 24 be really disappointed if you stop at tier one. Of
>
> 25 course, avoiding disappointing me is not a huge
>
> Page 165
>
> 1 sets, if, in fact, it\'s true that the NOx or NO2
>
> 2 changes that, that sets the baseline for lung function
>
> 3 over a lifetime, which deteriorates, which grows
>
> 4 through childhood and early adulthood, and then starts
>
> 5 to deteriorate over time until you get to less and less
>
> 6 function as you get to our age. And so, that\'s, and
>
> 7 that is, in fact, directly related to mortality as
>
> 8 well.
>
> 9 So, that has, sort of, long term implications

10 for lifetime function and survival that might not be

11 apparent from just saying, okay, well, we\'re going to

12 lose X percent of FEV1 for kids who have more than they

13 need to begin with, you know.

14 So, I think that\'s, so I think that\'s a

15 reason to take that possibility seriously, and to, it\'s

16 worth a little bit of a stretch, if you have to admit

17 that you have three or four or even tenfold uncertainty

18 in that, well, okay, it might still be important.

19 DR. HENDERSON: Thank you, Dale. I\'m

20 going to ask Ronald Wyzga to make his comments, and

21 then we\'ll open it up, and everybody can make comments.

22 DR. WYZGA: Thank you. I think the plan

23 as written is a very good plan. I think it\'s very

24 thorough, very thoughtful. I think the difficulties

25 are going to be in the implementation. It\'s a

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>
> Page 166
>
> 1 formidable task. You have a tremendous challenge, and
>
> 2 I think that to come up with something that\'s going to
>
> 3 be, with your resources available and with the data
>
> 4 available, is going to be, accepted by a wide community
>
> 5 is going to be a challenge.
>
> 6 But, I applaud the approach you\'ve taken. I
>
> 7 think, when I think, you know, particularly, tier
>
> 8 three, and you look at the current epidemiology
>
> 9 studies, they tend to look, they tend to use linear
>
> 10 models, which suggest that there\'s no threshold. And,
>
> 11 that\'s one area, what worries me particularly, where
>
> 12 you\'re looking at the just meeting current standard, in
>
> 13 the sense that if an area is still well below any
>
> 14 standard or proposed standard, because you\'re using a
>
> 15 linear model, you\'re going to overestimate the risks
>
> 16 for that area.
>
> 17 And I really worry that that could be
>
> 18 misinterpreted, and I urge you to, sort of, think about
>
> 19 both how you present that, and is there some way to get
>
> 20 around that problem. I don\'t have an obvious solution
>
> 21 to it. I think you\'ve done some thinking about it, but
>
> 22 I urge you to think further about it, and to the extent
>
> 23 that you can\'t resolve it, it\'s going to be very
>
> 24 important how you present the results so that they
>
> 25 don\'t mislead the public. But, otherwise, thank you, I
>
> Page 168
>
> 1 point in time.
>
> 2 DR. HENDERSON: I think that\'s the
>
> 3 problem of double counting, are we double counting. I
>
> 4 mean, how many deaths, how many times can a man die. I
>
> 5 mean, I\'m just joking, of course, but what I mean is -
>
> 6 -
>
> 7 DR. HENDERSON: - - is there any double
>
> 8 counting. I think that\'s a logical question.
>
> 9 MR. RICHMOND: No, Rogene, no, we didn\'t,

10 really, indicate mor-, we didn\'t include mortality in

11 that preliminary list. It\'s morbidity endpoints, and

12 the hospital admission studies, that have NO2 may, or

13 may not, be some of the same studies that pointed to

14 ozone or PM where they were using - -

15 DR. CRAWFORD-BROWN: Yeah, I don\'t know.

16 I just get a sense that I keep seeing the same kinds of

17 studies appear in documents, and, you know.

18 DR. HENDERSON: Yeah, you\'re right, it\'s

19 the morbidity we\'re concerned about, but, okay. Is

20 there anybody on the phone who wants to make a comment

21 and has not? We are - -

22 DR. POSTLETHWAIT: I think Ted wants to

23 comment.

> Page 167
>
> 1 think you\'ve done a great job.
>
> 2 DR. HENDERSON: Are there other people
>
> 3 who want to make comments on the health effects. Doug,
>
> 4 I think does.
>
> 5 DR. CRAWFORD-BROWN: Just one small one,
>
> 6 and it\'s more of a medit issue. As we\'ve been having
>
> 7 this discussion about the epidemiological studies, and
>
> 8 the contribution of NOx and ozone and PM and so forth,
>
> 9 this, probably, is a time for you to start thinking
>
> 10 about looking at, what I would sort of call, the mass
>
> 11 balance of the various risk studies that you\'re doing
>
> 12 to see if they add up to something more than the total
>
> 13 decrement that\'s seen in the epidemiological studies.
>
> 14 I just wonder, if you add it up, what you calculate for
>
> 15 NOx, and what you calculate for ozone, and what you
>
> 16 calculate for PM.
>
> 17 They\'re all based on, sort of, the same kind
>
> 18 of epidemiological results from which, we hope, we\'re
>
> 19 tearing apart the various relevant contributions, but I
>
> 20 just don\'t know. I don\'t know. If you added them up,
>
> 21 would this be something like TRIM, for example, where
>
> 22 TRIM had problems with more stuff coming out of a
>
> 23 compartment than ever went into the compartment, you
>
> 24 know. It\'s sort of a mass balance, kind of, thing
>
> 25 there, that I thought I would find interesting at some
>
> Page 169
>
> 1 quick. We\'ve got all the time in the world.
>
> 2 DR. RUSSELL: No, I prefer making it
>
> 3 quick. Just, carrying on something from what James
>
> 4 said is that, if we\'re going to be looking at a,
>
> 5 possibly, a new standard, when you look at your table
>
> 6 two, or any of the other analyses, just keep in mind
>
> 7 that, maybe look at various alternative standards and
>
> 8 forms of standards for our assessment.
>
> 9 DR. HENDERSON: Yes, I think that would

10 be very good for the analysis to see. Well, it\'s been

11 a very productive day and a half. I\'m, I really want

12 to thank everybody for working so hard, and for

13 staying, most of you staying to the end of the meeting.

14 And, I hate that we\'re going to miss John Samet,

15 apparently, but he has written, has he sent in his

16 comments, his written comments?

17 DR. NUGENT: I don\'t think we have

18 comments on the methods document, but his assistant

19 said he\'d be on the line at 1:45. This was scheduled

20 for 2:15 on the agenda.

21 DR. HENDERSON: Oh, I know, I know the

22 problem, but I\'m just sitting here. I don\'t think

23 people want to sit fifteen minutes to wait, I mean.

24 DR. HATTIS: He can sign on and say his

25 piece and that\'s fine, but we won\'t hear it.

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> Page 170
>
> 1 DR. HENDERSON: But you all will be a the
>
> 2 airport.
>
> 3 DR. CRAWFORD-BROWN: I think it\'s \$14.38
>
> 4 we\'ll earn during that time, so keep at it.
>
> 5 DR. HENDERSON: Okay, well, it, I do
>
> 6 value John and, the two Johns comments, but I think
>
> 7 that they have, since we\'re not trying to reach a
>
> 8 consensus, I mean, we\'re trying to get all of
>
> 9 everybody\'s comments, that we can just get their
>
> 10 written comments, which is what is needed. And again,
>
> 11 if you haven\'t turned in your written comments, well,
>
> 12 be sure you do that. Ed is wanting to say something.
>
> 13 DR. AVOL: Yeah, I would just ask that if
>
> 14 the Agency staff have any questions, based on what
>
> 15 they\'ve heard, that they would like to get
>
> 16 clarification on.
>
> 17 MR. RICHMOND: I have one.
>
> 18 DR. HENDERSON: Okay.
>
> 19 MR. RICHMOND: We put forth, I know it\'s
>
> 20 preliminary for putting aside the long-term children\'s
>
> 21 health study, but for short-term, from the clinical, we
>
> 22 identified a preliminary range of .2 to .3. Are we in
>
> 23 the right ballpark, or do the people who are familiar
>
> 24 with the clinical evidence think it\'s something other
>
> 25 than that range for one hour, based on the controlled
>
> Page 172
>
> 1 we\'re - -
>
> 2 DR. CRAPO: - - and homes and things like
>
> 3 that. So, you\'re going to extrapolate that that might
>
> 4 be there and use that. I like that idea.
>
> 5 MR. RICHMOND: But I\'m saying, on the
>
> 6 health side, are we in the right range - -
>
> 7 DR. CRAPO: On the health side, but the,
>
> 8 so you\'re going to, actually, look to see if there\'s
>
> 9 health effects, and you\'re going to model with that,

10 perhaps, might be that will be - -

11 MR. RICHMOND: Well, we\'re going to see

12 if there are exposures of, what we call, our term is

13 exposures of concern, which doesn\'t mean that everyone

14 who sees that exposure will, necessarily, be affected,

15 and we\'ve explained - -

16 DR. CRAPO: I know, and I understand

17 that. But this going to be all extrapolation data,

18 based on modeling from - -

19 MR. RICHMOND: Model data that\'s a

20 combination of both ambient and modeled inputs through

21 the exposure model.

22 DR. CRAPO: I think that\'s a very good

23 idea. I\'d love to see the data, \'cause it addresses

24 exactly what I\'ve been talking about.

25 DR. HENDERSON: And I think he was,

> Page 171
>
> 1 studies that we have, based on the evaluation in the
>
> 2 ISA.
>
> 3 DR. CRAPO: I have a question, what do
>
> 4 you mean by .2 or .3. Are you looking for, are you
>
> 5 going to model the - -
>
> 6 MR. RICHMOND: A level of concern, if
>
> 7 you\'re going to compare either air quality or
>
> 8 exposures. We did this in ozone, as you remember.
>
> 9 DR. CRAPO: Right.
>
> 10 MR. RICHMOND: But we had .06, .07, .08,
>
> 11 so we don\'t have to settle on a single level, but is
>
> 12 that lev-, a range at which we, at least, are
>
> 13 interpreting our evaluations, because - -
>
> 14 DR. CRAPO: All right, well, let me be
>
> 15 sure I unders- -
>
> 16 MR. RICHMOND: - - I think that that\'s
>
> 17 where the clinical studies start to, kind of, you know,
>
> 18 the lowest level at which effects in asthmatics are
>
> 19 being observed.
>
> 20 DR. CRAPO: I like this, except I\'m not
>
> 21 sure how you\'re going to model it. Because, in fact,
>
> 22 we don\'t have any documented exposures at that level
>
> 23 from the air monitoring stations. You have to
>
> 24 extrapolate into cars and buses and - -
>
> 25 MR. RICHMOND: Well, that\'s what
>
> Page 173
>
> 1 Harvey was probably asking Ed clinically that was what
>
> 2 you would consider appropriate levels. Haven\'t you
>
> 3 done studies with children?
>
> 4 DR. AVOL: Not children in chambers at .2
>
> 5 or .3, no. I mean, the work we did in chambers was a
>
> 6 higher, was with adults. So, I don\'t know that it\'s, I
>
> 7 can directly relate to this.
>
> 8 MR. RICHMOND: You talking about, there
>
> 9 were a number, there\'s a table summarizing a number of

10 asthmatic studies that go down as low as .2.

11 DR. AVOL: And so, I mean, in that sense,

12 again, as James said, I mean, I think, what you lay out

13 is fine. My, not withstanding my previous comment

14 about long-term studies, long-term is more - -

15 DR. HATTIS: Let me make a somewhat

16 modest further comment on that, and that is that, you

17 should, when you measure a statistically significant

18 decrement at lung function, or a change in the

19 responsiveness, you\'re talking about, not necessarily,

20 a uniform response or a uniform susceptibility within

21 that group that\'s measured.

22 And we have prior information about how

23 variable people are in their susceptibility in a large

24 number of other context. We have a database of that

25 that\'s on our website.

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 45
>
> Page 174
>
> 1 But, anyhow, so, in pharmacodynamic
>
> 2 variability, in that kind of local responsiveness,
>
> 3 happens to be, tend to be more variable than lots of
>
> 4 other stuff, basically, of the order of geometric
>
> 5 standard deviation of three or a bit more for that kind
>
> 6 of variability, if I remember correctly.
>
> 7 But, you know, so, you can, in fact, by
>
> 8 imposing a, kind of a log-probit function, with that
>
> 9 amount of spread, you can make a, just as you can make
>
> 10 distributional characterizations of the exposures, you
>
> 11 can make distributional characterizations of the likely
>
> 12 variability in susceptibility, and get something more
>
> 13 that may, in fact, you know, say, you know, for your
>
> 14 first percentile population, you might have sensitivity
>
> 15 that\'s outside of the range that you\'ve measured for
>
> 16 the average concentration that\'s capable of changing
>
> 17 this group, right.
>
> 18 And so, any how, so, it is possible to do a
>
> 19 slightly more involved analysis that, maybe, take you
>
> 20 half hour rather than fifteen minutes, that you might
>
> 21 do for the una, with single variable analysis.
>
> 22 You also asked the question about how do we
>
> 23 do an integrated, you know, characterization of
>
> 24 uncertainty. And there\'s a, this is, perhaps, a longer
>
> 25 answer, but basically, you characterize the
>
> Page 176
>
> 1 there are ways of dealing with this, although, they do
>
> 2 require a bit of creativity and maybe creativity is a
>
> 3 bad word.
>
> 4 MR. RICHMOND: A lot of creativity on
>
> 5 work.
>
> 6 DR. HATTIS: Yeah, you know, but,
>
> 7 nevertheless, you know, it\'s possible. And I think
>
> 8 it\'s possible without inordinate resources. I mean, I
>
> 9 think of the, you know, the poor guy who\'s over in the

10 other part of ORD dealing with trichloroethylene and

11 he\'s having to deal with Markov chain Monte Carlo

12 simulations of to do uncertainties and variability for

13 trichloroethylene and project from animal data to

14 people. And, you know, and he\'s large-, mostly one

15 guy, you know. So, lots of people have, you know,

16 resource constraints, but you know, it\'s a hard problem

17 to do quantitative assessments. But it can, you know,

18 it\'s not impossible.

19 DR. HENDERSON: Thank you, Dale. You

20 almost took up the fifteen minutes, but not quite.

21 DR. HATTIS: I\'m sorry.

22 DR. HENDERSON: No, it\'s okay.

23 Yeah, I can just change it. Kent, go ahead.

24 DR. PINKERTON: This is a, just a

25 question about, under the risk assessment overview, you

> Page 175
>
> 1 uncertainties in the exposure, and it\'s faster to say
>
> 2 that than to do it, and the uncertainties in the
>
> 3 susceptibility and concentration response slopes and
>
> 4 other things of that sort, and you, basically, convo-,
>
> 5 you know, basically, convolute those two with a Monte
>
> 6 Carlo simulation.
>
> 7 But you do have to, you know, sometimes these
>
> 8 are better done than other times, and it is a matter of
>
> 9 an evolving art as to how to choose the distributions
>
> 10 that you use to characterize each of the uncertainties.
>
> 11 MR. RICHMOND: And in this area, it is
>
> 12 not straightforward. The clinical data, even when I\'m
>
> 13 suggesting .2 to .3, some asthmatic studies, controlled
>
> 14 studies, have found effects. Some have them have been
>
> 15 repeated, and haven\'t found the same level under the
>
> 16 same kind of conditions. So, this is no easy matter to
>
> 17 assign probability or simply pick distributions out.
>
> 18 DR. HATTIS: Right, and so, you might
>
> 19 want to, you know, do some combined analysis that says,
>
> 20 well, there\'s some chance that the population
>
> 21 distribution of susceptibilities is in this range, and
>
> 22 some with this kind of mean in standard deviation, and
>
> 23 some chance that it\'s in some other range that would be
>
> 24 compatible with the observation that, you know, was not
>
> 25 found in a particular population. So, anyhow, so,
>
> Page 177
>
> 1 had mentioned there that the EPA would not develop risk
>
> 2 estimates for NO2 related effects associated with long-
>
> 3 term NO2 exposures. And, I think you stated that you
>
> 4 wouldn\'t do that, based on the fact that the findings
>
> 5 are inconclusive, or at best, suggestive. And I\'m just
>
> 6 wondering, does that mean that you think that in doing
>
> 7 short-term exposure assessments, you might be able to
>
> 8 address issues that may have, with regard to NO2
>
> 9 exposures, that may have long-term effects?

10 MR. RICHMOND: No, I don\'t think that\'s

11 what we\'re saying. One, as Lianne mentioned, there, we

12 have applied in the past and was envisioning here,

13 it\'s, selecting which health endpoint is first looking

14 at causality, and in the past, we have and proposed

15 here to do things that were likely causal, not to

16 quantitate risk, or develop risk, quantitative risk

17 estimates for things that were only suggestive or

18 limited whatever final terms the ISA ends up.

19 So, we are, that is, part of the screening

20 criteria in terms of determining how far we go, and

21 then, looking at what kind of information we have as

22 well, in terms of even once you get past. There were

23 endpoints for ozone like inflammation, which were

24 clearly likely causal, or causal.

25 But we didn\'t have sufficient information to

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 46
>
> Page 178
>
> 1 generate, we felt, a credible exposure response
>
> 2 relationship, and we still considered that in the
>
> 3 review, that endpoint, in the discussion and
>
> 4 evaluation, but we didn\'t do a quantitative, let\'s
>
> 5 produce how many people have different degrees of
>
> 6 inflammation.
>
> 7 So, just because we don\'t quantitate
>
> 8 something in terms of producing some number of people
>
> 9 have this many health effects, doesn\'t mean we\'re
>
> 10 ignoring the other health endpoints.
>
> 11 DR. HENDERSON: I have a question that
>
> 12 has occurred to me as I sit here. I seem to remember
>
> 13 that some of the toxicology studies suggested
>
> 14 development of a tolerance to NO2, am I right, does
>
> 15 that happen, or am I getting it confused with ozone
>
> 16 or - -
>
> 17 MR. RICHMOND: I\'ll defer to Ed.
>
> 18 DR. HENDERSON: Are there development,
>
> 19 are there animal tox studies showing development of
>
> 20 tolerance?
>
> 21 DR. PINKERTON: I think there are, yes.
>
> 22 Oftentimes, NO2, as people are likely to be aware,
>
> 23 behaves in a similar manner to ozone. It\'s just that
>
> 24 you have to have much higher concentrations to get the
>
> 25 equivalent response. But I do believe that there is a
>
> Page 180
>
> 1 first draft of the actual document and the second
>
> 2 draft. So, we should be seeing this document two more
>
> 3 times, and - -
>
> 4 MR. RICHMOND: Just to clarify, no, you
>
> 5 won\'t be seeing this document. You\'ll be seeing a
>
> 6 draft exposure risk assessment report.
>
> 7 DR. HENDERSON: Oh, okay.
>
> 8 MR. RICHMOND: I mean, there\'s a huge
>
> 9 difference. This was the road map, this plan. We

10 don\'t, we plan to take into account your comments and

11 the comments of the public in figuring out what we

12 ultimately do. But the revised methods and what we

13 actually do will be in, along with the results, in the

14 first draft risk assessment, that\'s targeted for March.

15 DR. HENDERSON: Your report. Okay,

16 that\'s good, and I\'m glad you - -

17 MR. RICHMOND: I just want to make sure

18 they do, and on the scheduling and we don\'t, under the

19 new process, we don\'t produce a final of this plan.

20 DR. HENDERSON: Oh, no.

21 MR. RICHMOND: The plan is a living - -

22 DR. HENDERSON: No, no, no, no. I\'m

23 just, but this is leading to a document that will be

24 reviewed two more times.

25 MR. RICHMOND: Right.

> Page 179
>
> 1 tolerance that is developed with persistent exposure to
>
> 2 NO2.
>
> 3 DR. HENDERSON: And that\'s very hard to
>
> 4 take into account at setting any standard, I\'m sure,
>
> 5 but. Well, okay, I think we, now. I will do as Ed did
>
> 6 earlier. Anything, any advice or that you were
>
> 7 expecting to get that you haven\'t gotten and would like
>
> 8 for us to comment while you have this great group of
>
> 9 investigators here?
>
> 10 DR. GRAHAM: After your other comment, I
>
> 11 did have a question specifics about, we had selected, I
>
> 12 think, five locations, and I briefly said there were
>
> 13 criteria in their selection. And, I think it was Ed
>
> 14 had commented, why not Phoenix and Denver, and I was
>
> 15 just wondering, why those might want to be included?
>
> 16 DR. AVOL: I picked those two in looking
>
> 17 at previous annual standards in violations of the
>
> 18 standards, and just thinking about distribution and
>
> 19 representation of the national picture that Phoenix and
>
> 20 Denver offered other sorts of geography and exposure
>
> 21 issues than just the urbanized cities like New York and
>
> 22 Philadelphia, sort of thing.
>
> 23 DR. HENDERSON: Any more advice for the
>
> 24 Agency. We will be seeing this again, of course, but
>
> 25 the consultation comes first, and then there\'s the
>
> Page 181
>
> 1 DR. HENDERSON: But thank you for
>
> 2 correcting. This has gotten so complicated with the
>
> 3 new process, but it\'s good to be precise, so I\'m glad
>
> 4 you corrected that. And, perhaps, we can - -
>
> 5 DR. MARTIN: If I might, perhaps, it
>
> 6 might be worth saying, just a little bit of
>
> 7 clarification about what you can expect to see in the
>
> 8 first draft - -
>
> 9 DR. HENDERSON: Good.

10 DR. MARTIN: - - of the risk assessment

11 report versus what you can expect to see in the second

12 draft of the risk assessment report. And I\'ll just

13 layout an initial major distinction, and you folks can

14 add to it as you will. We talked about estimating

15 exposures and risks associated with various alternative

16 standards.

17 And first of all, looking at just air

18 quality, current levels of air quality, and then, just

19 attaining the current standard. Those are the

20 scenarios that we anticipate putting into the first

21 draft of the risk report, and that you will be seeing

22 those results and estimates associated with those two

23 scenarios in the Spring, at the same time you see the

24 second draft integrated science assessment.

25 Subsequent to that, in the second draft, we

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 47
>
> Page 182
>
> 1 would then, at that point, decide what alternative
>
> 2 standards we would then, additionally, do exposure and
>
> 3 risk estimates for. Because at that point, we would
>
> 4 have the benefit of the second draft science
>
> 5 assessment, and the benefit of your review of that
>
> 6 document, to help inform an appropriate range of
>
> 7 alternative standards that would reasonably be applied
>
> 8 to finish out the exposure and risk assessment.
>
> 9 So, those results you\'ll see in the second
>
> 10 draft assessment.
>
> 11 DR. HENDERSON: Okay, that\'s helpful.
>
> 12 DR. MARTIN: And I wanted to make that
>
> 13 point, because in the past, when we\'ve come out with
>
> 14 the first draft assessment, what everyone\'s looking for
>
> 15 is, what is the risk associated with the range of
>
> 16 alternative standards, and that, that\'s what we\'ll do
>
> 17 in the second draft.
>
> 18 DR. HENDERSON: Second draft, in the
>
> 19 first draft, you\'ll have the risks associated with the
>
> 20 current exposures and the, if you reach the higher
>
> 21 levels of the - -
>
> 22 DR. MARTIN: And it relates to the
>
> 23 comment I made yesterday. We really don\'t want to get
>
> 24 ahead of ourselves. We don\'t want to start projecting
>
> 25 to what alternative standards may be appropriate to
>
> Page 184
>
> 1 until I guess receive sort of the next step. So, as a
>
> 2 starting point, I think it was fine. And then, I think
>
> 3 we\'ll have to see what comes.
>
> 4 DR. HENDERSON: Okay, John. Yeah, and
>
> 5 we\'ve had a very good clarification of what the next
>
> 6 steps will be. Did you get to hear that?
>
> 7 DR. BALMES: Yeah, I did hear that. I
>
> 8 heard that, yeah.
>
> 9 DR. HENDERSON: Okay, so that was very

10 informative. And, okay, John. Well, we hope you

11 didn\'t rush over to, we were just waiting for you to,

12 in order to adjourn, to tell you the truth.

13 DR. BALMES: Oh, okay, well, then. I\'m

14 sorry to hold anybody up from adjourning.

15 DR. HENDERSON: Well, we\'ve had

16 interesting discussions. Really, this last discussion

17 was most helpful, and we wouldn\'t have had it if we

18 hadn\'t have been kind of waiting for you.

19 DR. BALMES: Oh, okay, okay.

20 DR. HENDERSON: So you contributed.

21 DR. BALMES: I\'ll be in person next time.

22 DR. HENDERSON: Okay. Thanks a lot,

23 John, for calling in.

24 DR. BALMES: Okay, bye.

25 DR. HENDERSON: Okay, I think we are

> Page 183
>
> 1 consider until we\'ve had the benefit of your review of
>
> 2 the second draft of the science assessment, where the
>
> 3 inferences and conclusions are more sharply defined.
>
> 4 DR. HENDERSON: Good advice, Karen.
>
> 5 DR. MARTIN: Did you offer anything.
>
> 6 SPEAKER: No, that\'s okay, well stated.
>
> 7 DR. HENDERSON: Good.
>
> 8 SPEAKER: Rogene, somebody may have
>
> 9 just - -
>
> 10 DR. HENDERSON: That\'s what I thought.
>
> 11 Is there someone that had come on the phone?
>
> 12 DR. BALMES: ;Yeah, this is John, hi.
>
> 13 DR. HENDERSON: Oh, you have no idea how
>
> 14 happy we are to hear from you, John.
>
> 15 DR. BALMES: Yeah, no, I just listened.
>
> 16 DR. HENDERSON: Okay, well, we would like
>
> 17 your comments on the risk assessment part of this
>
> 18 methods document. And, I\'ll tell you that the response
>
> 19 before you has been generally positive. You probably
>
> 20 didn\'t get to hear all that, but - -
>
> 21 DR. BALMES: No, I, you know, I\'ll tell
>
> 22 you, Rogene. I don\'t, I didn\'t provide written
>
> 23 comments on it at this point. I didn\'t have, I guess,
>
> 24 very much to say, because, in a sense, it was such a
>
> 25 general template that I didn\'t see too much to say
>
> Page 185
>
> 1 finished in our, it is the job of our DFO to adjourn
>
> 2 us.
>
> 3 DR. NUGENT: Well, thank you all for
>
> 4 being here, and then the next steps will be for me to
>
> 5 send around a draft of the document we spoke about this
>
> 6 morning, and send a draft of the minutes around for
>
> 7 your comments. And I guess, even before I do that,
>
> 8 I\'ll be contacting you about scheduling the May meeting
>
> 9 to get your availability, so, I look forward to seeing

10 you again and thank you. Meeting\'s adjourned.

11 (WHEREUPON, the PUBLIC MEETING was adjourned at 1:45

12 p.m.)

13

14

15

16

17

18

19

20

21

22

23

24

25

> US EPA CASAC PUBLIC MEETING 10/25/07 CCR# 15676-2 Page 48
>
> Page 186

Page 188

> 1 CAPTION 1
>
> 2 The foregoing matter was taken on the date, 2
>
> 3 and at the time and place set out on the Title page 3
>
> 4 hereof. 4
>
> 5 It was requested that the matter be taken by 5
>
> 6 the reporter and that the same be reduced to 6
>
> 7 typewritten form. 7
>
> 8
>
> 8 Further, as relates to depositions, it was 9
>
> 9 agreed by and between counsel and the parties that 10
>
> 10 the reading and signing of the transcript, be and 11
>
> 11 the same is hereby waived. 12
>
> 12 13
>
> 13 14
>
> 14 15
>
> 15 16
>
> 16 17
>
> 17 18
>
> 18 19
>
> 19 20
>
> 20 21
>
> 21 22
>
> 22 23
>
> 23 24
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> 24 25
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> 25
>
> Page 187
>
> 1 CERTIFICATE OF REPORTER
>
> 2 COMMONWEALTH OF VIRGINIA
>
> 3 AT LARGE:
>
> 4 I do hereby certify that the witness in the
>
> 5 foregoing transcript was taken on the date, and at
>
> 6 the time and place set out on the Title page hereof
>
> 7 by me after first being duly sworn to testify the
>
> 8 truth, the whole truth, and nothing but the truth;
>
> 9 and that the said matter was recorded
>
> 10 stenographically and mechanically by me and then
>
> 11 reduced to typewritten form under my direction, and
>
> 12 constitutes a true record of the transcript as
>
> 13 taken, all to the best of my skill and ability.
>
> 14 I further certify that the inspection, reading
>
> 15 and signing of said deposition were waived by
>
> 16 counsel for the respective parties and by the
>
> 17 witness.
>
> 18 I certify that I am not a relative or employee
>
> 19 of either counsel, and that I am in no way
>
> 20 interested financially, directly or indirectly, in
>
> 21 this action.
>
> 22
>
> 23
>
> 24 MARK REIF, COURT REPORTER / NOTARY
>
> 25 SUBMITTED ON OCTOBER 25, 2007
>
> 0 **41** 41:1 141:1
>
> **0.053** 130:1 149:23
>
> **00** 100:1
>
> **0002** 2:1
>
> **0003** 3:1
>
> **0004** 4:1
>
> **0005** 5:1
>
> **0006** 6:1
>
> **0007** 7:1
>
> **0008** 8:1
>
> **0009** 9:1
>
> **01** 101:1
>
> **02** 102:1
>
> **03** 103:1 130:13
>
> 149:13
>
> **04** 104:1 130:1, 1
>
> **05** 105:1 130:1
>
> **053** 91:13 130:1
>
> 150:1
>
> **06** 106:1 171:10
>
> **07** 107:1 171:10
>
> **08** 89:14, 17 108:1
>
> 171:10
>
> **09** 109:1
>
> 1
>
> **1,000** 123:21
>
> **10** 10:1 53:13, 14
>
> 110:1 123:20
>
> **100** 123:21
>
> **11** 11:1 111:1
>
> **12** 12:1 112:1
>
> **12:00** 31:1
>
> **12:30** 137:13
>
> **13** 13:1 113:1
>
> **14** 14:1 114:1
>
> **15** 15:1 29:1
>
> 46:16, 24 47:11,
>
> 16 48:1, 23 49:1
>
> 115:1
>
> **16** 16:1 116:1
>
> **17** 17:1 117:1
>
> **18** 18:1 118:1
>
> **19** 19:1 119:1
>
> **1971** 77:13
>
> **1993** 73:15
>
> **1:00** 154:1, 1, 14,
>
> 19
>
> **1:45** 155:1 169:19
>
> 185:11

2

**2** 91:23 92:16 102:11

> 170:22 171:1
>
> 173:1, 10 175:13

**20** 20:1 120:1 133:1

**200** 28:1 29:1 46:15,

> 21, 22, 24 47:10
>
> 50:1 54:1, 1

**2005** 37:24

**2007** 2:1

**201** 26:11

**21** 21:1 121:1

**22** 22:1 122:1 144:17

**23** 23:1 123:1

**24** 24:1 48:1, 1, 14,

> 16 54:25 73:20
>
> 124:1

**24-hour** 23:1

**25** 2:1 10:20 25:1

> 125:1

**26** 26:1 126:1

**27** 27:1 127:1

**28** 28:1 128:1

**288,000** 54:1

**29** 29:1 129:1

**2:00** 107:10

**2:15** 107:1 157:1

> 169:20
>
> 3

**3** 91:23 102:11

> 170:22 171:1 173:1
>
> 175:13

**30** 30:1 130:1

**31** 31:1 131:1 144:19

> 155:18

**31st** 5:18

**32** 32:1 132:1

**33** 33:1 54:11 133:1

**34** 34:1 134:1

**35** 35:1 135:1

**36** 36:1 136:1

**37** 37:1 137:1

**38** 38:1 138:1

**39** 39:1 139:1

> 4

**4.3** 156:16

**4.6** 123:25

**40** 40:1 140:1

**42** 42:1 142:1

**43** 43:1 143:1

**44** 44:1 144:1

**45** 45:1 145:1

**46** 46:1 146:1

**47** 47:1 72:1 147:1

**48** 48:1 148:1

**49** 49:1 149:1

> 5

**5** 91:11

**5-5** 54:24

**50** 50:1 150:1

**500** 26:20 35:12

**51** 51:1 151:1

**52** 52:1 152:1

**53** 53:1 130:21 153:1

**54** 54:1 154:1

**55** 55:1 155:1

**56** 56:1 156:1

**57** 57:1 157:1

**58** 58:1 158:1

**59** 59:1 159:1

> 6

**60** 60:1 160:1

**61** 61:1 161:1

**62** 62:1 162:1

**63** 63:1 163:1

**64** 64:1 164:1

**65** 65:1 165:1

**66** 66:1 166:1

**67** 67:1 167:1

**68** 68:1 168:1

**69** 69:1 169:1

> 7

**70** 70:1 170:1

**71** 71:1 171:1

**72** 54:1, 1 72:1

> 172:1

**73** 73:1 173:1

**74** 74:1 174:1

**75** 75:1 175:1

**76** 76:1 176:1

**77** 77:1 177:1

**78** 78:1 178:1

**79** 79:1 179:1

> **abundant** 66:10, 11

30:1 32:1, 1 38:12

> 8
>
> **80** 80:1 180:1
>
> **81** 81:1 181:1
>
> **82** 82:1 182:1
>
> **83** 83:1 183:1
>
> **84** 84:1 184:1
>
> **85** 85:1 115:1 185:1
>
> **86** 86:1
>
> **87** 87:1
>
> **88** 88:1
>
> **89** 89:1
>
> **8:00** 31:1
>
> 9
>
> **90** 90:1
>
> **90\'s** 131:1 148:15
>
> **91** 91:1
>
> **92** 92:1
>
> **93** 77:15 93:1 115:1
>
> **94** 94:1
>
> **95** 10:19 95:1 139:10
>
> **96** 96:1
>
> **97** 97:1
>
> **98** 98:1
>
> **99** 99:1
>
> **99th** 54:1, 1, 10
>
> **9:00** 39:14

**acknowledge** 58:1

> 115:1

**add** 5:21 9:25

> 38:1, 20, 23
>
> 40:20, 21 41:1, 14
>
> 58:1 59:10 72:17
>
> 167:12, 14 181:14

**added** 7:13 9:1

> 104:10 167:20

**addition** 21:23 95:13

> 96:10, 21 98:12
>
> 99:1 109:19 129:1

**additional** 54:20

> 77:1 97:1 110:14
>
> 112:12 128:1 129:1

**additionally** 182:1

**address** 12:1 61:1,

> 19 65:19 69:23
>
> 74:1 84:23 93:25
>
> 102:19 105:12
>
> 112:1 121:1 127:10
>
> 140:1 145:14
>
> 161:21 177:1

**addressed** 11:21

> 16:12 17:1 93:21
>
> 99:1 102:22
>
> 105:1 107:21
>
> 126:17 157:1
>
> 42:14 45:19 52:1
>
> 57:1 59:22 62:10
>
> 64:14 65:1 67:21
>
> 69:1, 19 70:17
>
> 73:15 75:11
>
> 78:22 93:13 106:1,
>
> 1 109:17 114:21
>
> 124:19, 21
>
> 125:11 128:1
>
> 131:15 134:14
>
> 144:24 150:1
>
> 151:11 158:24
>
> 172:1 180:13

**acute** 47:12, 14, 15

**adjourn** 184:12 185:1 **adjourned** 185:10, 11 **adjourning** 184:14
**adjust** 148:20, 21 **adjusted** 150:23 **adjusting** 150:14, 16
**adjustment** 148:18,

> 20

**administrator** 3:1, 1

> 6:1, 17 67:11
>
> 76:10 77:1
>
> 90:16, 23

**admission** 168:12

**admissions** 104:1, 20

> **addresses** 107:25

**admit** 165:16

> A
>
> **a.m** 31:1
>
> **ability** 133:15
>
> **able** 27:15 68:16
>
> 78:19 81:10
>
> 98:25 102:1 105:21
>
> 107:1 115:17
>
> 125:22 126:18
>
> 135:13 177:1
>
> **accept** 58:1
>
> **accepted** 166:1 **accomplish** 108:23 **according** 137:11
>
> 143:25
>
> **account** 98:14
>
> 125:1 153:10 179:1
>
> 180:10
>
> **absence** 63:1 126:25
>
> **absolute** 20:24
>
> **absolutely** 33:1
>
> 57:23 127:19
>
> 172:23

**addressing** 41:25

> 85:12 88:1 99:11
>
> 128:1 145:11

**adequacy** 104:1

**adequate** 103:23

**across** 8:10 16:21

> 38:23, 25 50:14
>
> 53:1 103:17
>
> 113:1 123:19

**act** 87:19

**action** 61:25

**activity** 99:14 133:1

**actual** 21:12 25:1

> 39:1 97:15
>
> 138:15 152:21
>
> 180:1

**actually** 8:20

> 10:13 11:19
>
> 12:14 16:1, 14
>
> 17:24 20:19
>
> 21:14 23:21 26:1

**adult** 64:1 102:14 **adulthood** 165:1 **adults** 173:1 **advance**
157:22

> 158:23

**advanced** 80:15 128:1

**adverse** 41:1 42:19

> 44:1 50:1 51:1, 22
>
> 52:1, 21

**advice** 79:22 81:22

> 82:19 115:16, 25
>
> 116:1 141:1 155:11
>
> 179:1, 23 183:1

**advise** 19:15

**advisory** 2:1 87:19 **affected** 172:14 **aermod** 127:13, 15,

> 18, 21 128:1, 1
>
> 129:1, 11, 15

**against** 90:18 91:14,

> 18 94:1
>
> **age** 165:1
>
> **agency** 2:1 35:23
>
> 45:20 65:21
>
> 73:1, 13 79:22, 25
>
> 81:12 116:1 155:11
>
> 170:14 179:24
>
> **agenda** 5:1 79:1
>
> 169:20
>
> **agg** 13:15
>
> **aggregate** 13:15 86:1
>
> **aggregation** 96:23
>
> 118:21 119:1, 1
>
> **afternoon** 2:20 32:23
>
> 78:13
>
> **ago** 74:18 131:1
>
> 148:1
>
> **ahead** 3:17 20:1
>
> 26:24 36:12
>
> 45:20 76:23 78:1
>
> 89:1 134:19
>
> 148:1 149:18
>
> 176:23 182:24
>
> **agreed** 51:24
>
> **agreeing** 16:18 48:21
>
> **agreement** 6:18 61:22
>
> 104:17
>
> **agricultural** 133:1
>
> **air** 2:1 42:24 44:1
>
> 59:1 79:1 80:1, 12
>
> 83:20 85:10 87:17,
>
> 19, 21 92:1
>
> 95:19 96:1, 1,
>
> 18 98:13 99:14
>
> 100:23 101:1,
>
> 11, 17 102:1, 1,
>
> 24 105:19, 24,
>
> 25 108:1, 1, 13
>
> 109:1 110:24
>
> 112:1, 1 114:23
>
> 116:12 120:1
>
> 121:1, 16, 24
>
> 122:1, 1, 1
>
> 127:12, 23
>
> 129:18 130:18
>
> 131:1, 25 133:10
>
> 134:18, 24 135:1
>
> 138:13 145:11,
>
> 22 148:13, 20
>
> 149:1, 11
>
> 150:14, 16, 23
>
> 151:20 153:1 155:1
>
> 164:11 171:1, 23
>
> 181:17, 18

**airborne** 65:18

**airport** 170:1 **airway** 62:13 91:24 **albuquerque** 14:1 **alert**
81:25 82:11 **allow** 106:21 **allowing** 159:24

> 164:14

**alluded** 143:1

**am** 30:1 80:11 106:16

> 110:22 178:14, 15

**ambient** 8:23 21:17

> 22:12, 19 29:1
>
> 33:1 35:13 51:1
>
> 52:19 94:1, 13,
>
> 14, 15 95:1
>
> 96:12 98:13 99:1
>
> 112:25 113:1
>
> 123:25 126:13, 21,
>
> 23, 24, 24 127:1
>
> 146:1 162:20
>
> 172:20

**alone** 72:10

**alongside** 33:16 61:1 **alpha** 22:18, 22 **already** 33:1 76:24

> 94:1 95:17
>
> 134:14 138:20
>
> 153:25 155:1
>
> 159:22 161:24

**alternative** 94:16

> 95:1 100:25 101:1,
>
> 12 102:16, 17
>
> 105:14 109:20
>
> 131:20 149:1
>
> 152:14 153:14
>
> 160:16 169:1
>
> 181:15 182:1, 1,
>
> 16, 25

**analyses** 10:17 96:24

> 99:1, 1 120:1
>
> 143:15 150:24
>
> 169:1

**analysis** 12:12,

> 13, 18 14:1
>
> 15:17 19:18, 23
>
> 97:1 98:1, 1,
>
> 19, 19 99:21
>
> 102:20 104:15
>
> 105:19 111:1 113:1
>
> 115:1, 18 118:15
>
> 119:12 120:1
>
> 133:22 135:1, 25
>
> 136:1 147:11
>
> 153:17 159:10
>
> 160:1, 1, 13
>
> 161:16 169:10
>
> 174:19, 21 175:19

**analyze** 8:16

**analyzed** 21:24

> 50:1 122:1

**among** 49:12

**amount** 33:10 82:13

> 140:1 160:1 174:1

**amplify** 123:1

**angela** 2:1, 1 3:1,

> 12 4:16 6:1, 22
>
> 39:1 64:10

**angela\'s** 39:14 78:19

**angeles** 28:20 30:1

> 92:15 96:22 119:12
>
> 152:1

**animal** 91:1 176:13

> 178:19

**animals** 91:1

**anne** 4:1

**annex** 9:14 10:1,

> 1, 23 23:23

**annexes** 9:24 10:1

**annual** 8:15, 16

> 23:1, 15 26:1 27:1
>
> 29:1 46:24 47:1, 1
>
> 49:1, 15, 20, 24
>
> 53:25 70:15
>
> 72:20 74:12, 24
>
> 75:25 76:1
>
> 77:10, 13 90:1,
>
> 17, 25 91:1, 1,
>
> 13, 17 92:1, 12
>
> 95:10 98:21, 24
>
> 107:19, 22
>
> 130:15 144:18
>
> 145:1 149:13
>
> 155:23 161:12
>
> 179:17

**annually** 23:1, 1

**answer** 10:12 26:10

> 68:1 109:1 138:1
>
> 145:1, 1 174:25

**answering** 63:14

**answers** 40:1

**anticipate** 82:1

> 89:19 181:20
>
> **apart** 119:22 167:19
>
> **apartment** 114:10
>
> **anxiety** 108:21
>
> **apex** 99:17, 22 100:1
>
> 112:1 135:15
>
> 143:12
>
> **anybody** 9:1 34:19
>
> 56:13 136:13
>
> 168:20 184:14
>
> **anyhow** 174:1 175:25
>
> **anyone** 78:11
>
> 108:11 129:17
>
> 144:1 153:23
>
> **anything** 2:22 7:12
>
> 12:13 32:19 38:1
>
> 53:1, 1 59:10
>
> 64:18 66:1 72:23
>
> 89:12 136:1
>
> 179:1 183:1
>
> **anyway** 63:21 65:22
>
> 78:1 122:14 163:20
>
> **anywhere** 22:14
>
> **apparent** 165:11 **apparently** 169:15 **appear** 16:1 49:23
>
> 168:17
>
> **appears** 43:24
>
> **applaud** 166:1
>
> **application** 129:1,
>
> 12
>
> **applications** 70:11
>
> **applied** 128:15
>
> 129:11 177:12
>
> 182:1
>
> **apply** 122:1, 1, 1,
>
> 11
>
> **applying** 20:12, 14
>
> **appreciate** 45:23
>
> 82:19 116:22 132:1
>
> **approach** 65:21
>
> 84:1 87:10 88:22
>
> 89:1 90:10 95:17
>
> 99:11, 16 101:14
>
> 108:17, 24
>
> 109:11 113:15
>
> 114:23 118:1
>
> 121:1, 22 128:1,
>
> 20 129:22 131:1,
>
> 1, 16, 20, 22
>
> 137:1 141:19
>
> 144:1, 1, 18
>
> 148:17 155:10,
>
> 12 162:13 166:1

**approached** 95:25

**approaches** 83:1

> 113:19 148:13
>
> 159:14, 16

**approaching** 7:10

**appropriate** 8:1

> 93:25 109:10, 24
>
> 122:17 161:1 163:1
>
> 173:1 182:1, 25

**appropriately** 73:1

**approved** 78:16

**approximate** 126:1, 1

> 131:25

**approximately** 131:1

**approximation**

> 121:1 122:20, 22

**area** 8:1 13:10 46:21

> 88:1 113:13, 19
>
> 114:1, 14 119:13
>
> 133:14 136:15
>
> 147:1 150:14,
>
> 15, 17, 19, 20, 24
>
> 152:22 153:1
>
> 166:11, 13, 16
>
> 175:11

**areas** 9:18, 19 15:1,

> 1, 13, 14, 21
>
> 16:24 17:1, 22
>
> 31:1 32:11 49:16
>
> 97:1 99:1 102:1,
>
> 1, 20 103:1
>
> 104:1 105:18
>
> 111:15 113:14,
>
> 21 114:1, 20
>
> 118:13 119:17
>
> 133:1, 1, 1, 1
>
> 148:16 150:1, 12
>
> 152:12 163:11

**aren\'t** 28:1 73:25

> 115:17 142:16

**argue** 72:25 74:14

> 123:1 162:17

**arguing** 29:22 131:15

**argument** 29:24

> 149:24 152:1

**arose** 90:1

**arranging** 155:1 **arrived** 92:1 **aside** 170:20

**art** 175:1

**articulate** 52:24

> 160:21

**aspect** 64:16 65:15

**aspects** 59:1, 1

> 140:13, 14, 14, 16

**assess** 9:1 23:16

> 59:1 94:13
>
> 114:16 130:16
>
> 148:11

**assessed** 158:1

**assessing** 84:1

> 89:1 147:1

**assessment** 8:1

> 67:23, 25 68:15
>
> 79:16, 18, 23,
>
> 24 80:15, 20 81:17
>
> 82:13 83:1, 11,
>
> 13, 24 84:1 89:16,
>
> 18 92:1, 22 94:10,
>
> 12, 24 95:1, 24
>
> 97:10 100:21, 22
>
> 101:1 102:1 103:24
>
> 104:1, 1, 1, 12,
>
> 13 108:10 112:1
>
> 113:1, 23 115:1,
>
> 22 121:24, 25
>
> 122:1, 1, 1
>
> 126:14, 20
>
> 127:11 130:1, 12
>
> 138:25 139:1
>
> 140:24 141:1,
>
> 11, 18 144:1, 22
>
> 145:18, 24
>
> 147:21 155:1,
>
> 10, 17, 21
>
> 157:1, 19 158:1,
>
> 1, 1, 10, 13,
>
> 16, 18, 23
>
> 159:1, 1, 1 161:1,
>
> 22 169:1 176:25
>
> 180:1, 14
>
> 181:10, 12, 24
>
> 182:1, 1, 10, 14
>
> 183:1, 17

**assessment\'s** 94:18

**assessments** 81:18,

> 21, 23 82:23 84:1,
>
> 10 101:1 103:1
>
> 104:1 111:18 138:1
>
> 144:1 157:1
>
> 159:15, 17
>
> 176:17 177:1
>
> **assign** 175:17
>
> **assignment** 160:13 **assistant** 169:18 **associate** 124:1
>
> 71:10

**attribute** 60:1 61:21

> 64:18

**availability** 96:19

> 64:12, 14 146:25
>
> 155:15 170:13
>
> 173:1, 11 179:16

**avol\'s** 61:1

> **associated** 20:1

104:1 110:14 185:1

> 21:13 46:1 51:1
>
> 52:20, 21 94:14,
>
> 16 95:1 100:25
>
> 101:1, 1 105:13
>
> 149:10, 12
>
> 151:10 162:20
>
> 163:1 177:1
>
> 181:15, 22 182:15,
>
> 19
>
> **association** 36:20
>
> 44:23
>
> **associations** 36:24
>
> 43:1 45:11 51:21
>
> 52:21 83:20
>
> **assume** 92:12 111:1
>
> 122:23 123:11
>
> 130:1 134:1 159:23
>
> 161:1
>
> **assuming** 12:15 50:20
>
> 95:1 139:17
>
> **assumption** 92:1
>
> 134:1 160:15
>
> **assumptions** 84:1,
>
> 1 101:1 135:1
>
> 143:1, 14, 16, 18,
>
> 21, 22 159:24,
>
> 25 160:10, 16
>
> **assured** 96:20
>
> **asthma** 74:22 76:13
>
> **asthmatic** 62:1, 12
>
> 102:14, 14 163:1
>
> 173:10 175:13
>
> **asthmatics** 29:1
>
> 74:25 91:22, 22
>
> 104:21, 24 171:18
>
> **atlanta** 32:10 96:22
>
> **atlas** 147:17
>
> **atmosphere** 47:24
>
> 65:21
>
> **audio\'s** 5:12
>
> **attain** 131:1 **attained** 92:13 **attaining** 181:19
> **attainment** 131:1
>
> 133:14
>
> **attention** 51:19

**available** 8:1

> 10:16 11:1, 1
>
> 73:23 96:1, 13, 14
>
> 100:16 108:20
>
> 139:16 166:1, 1

**average** 8:15, 15

> 23:1, 1, 15, 22
>
> 26:1 27:1 29:1
>
> 45:1 46:24 47:1,
>
> 1, 1 48:1, 15
>
> 49:1, 20, 25 70:15
>
> 72:20, 21 74:25
>
> 90:1 92:1, 1
>
> 95:10, 11 97:1
>
> 98:21, 25 107:19
>
> 113:1 117:1, 1
>
> 124:18 125:1 145:1
>
> 146:13 149:13
>
> 161:12, 12 174:16

**averaged** 23:1, 1, 1

**averages** 25:19, 20

> 27:1, 15 48:1
>
> 49:15 53:25
>
> 54:11 75:25 76:1
>
> 130:15 144:19

**averaging** 23:11,

> 22 25:1, 10, 11
>
> 27:1 45:1, 1 73:1,
>
> 1, 21 74:1, 12, 13
>
> 77:19 100:1 102:11
>
> 117:1 125:1 155:20
>
> 161:1

**australian** 12:24

> 16:10, 11

**automated** 138:1

**automatic** 118:12

**aware** 18:21 37:14

> 89:1 147:14 178:22

**away** 13:1 45:1

> 120:18 122:23
>
> 123:20 135:22

**avoided** 56:17

**avoiding** 163:25

**avol** 33:13 40:10,

> 11, 24 41:17
>
> 42:1 43:17 49:1, 1
>
> 59:16 61:1
>
> B

**background** 8:1

> 73:1 88:22 89:23
>
> 94:21 117:23

**bad** 29:12 40:18

> 74:18 176:1

**balance** 9:24 17:20

> 167:11, 24

**ballpark** 170:23

**balmes** 3:25, 25

> 31:1, 1, 14, 17,
>
> 22 35:25 36:1,
>
> 15 37:1, 21, 22
>
> 45:13, 14 46:17,
>
> 20 50:17, 17 53:23
>
> 59:11 74:20, 21
>
> 76:16 77:20
>
> 106:23, 25, 25
>
> 107:1, 12, 14
>
> 157:1 183:12,
>
> 15, 21 184:1,
>
> 13, 19, 21, 24

**barely** 124:1

**base** 19:21 160:15

**based** 13:1, 14 14:15

> 16:1 39:1 45:21
>
> 48:15 57:1, 17
>
> 59:13 65:23
>
> 81:23 89:25
>
> 90:1, 13 91:1 95:1
>
> 96:18, 24 99:1
>
> 100:1 101:16,
>
> 23, 23 102:12
>
> 103:11 104:14
>
> 105:1, 25 109:22
>
> 111:1 112:15, 25
>
> 113:1, 1, 1, 1, 23
>
> 119:17 125:13
>
> 128:1 131:18
>
> 139:15 152:21
>
> 153:1, 15, 19
>
> 156:19, 25 157:1
>
> 163:1, 12 164:23
>
> 167:17 170:14,
>
> 25 171:1 172:18
>
> 177:1

**baseline** 104:1, 1

> 132:17 165:1
>
> **basic** 114:12
>
> 122:14 130:23
>
> **basically** 24:1 25:17
>
> 36:19 55:1 56:20
>
> 90:1 91:1 92:11
>
> 93:11, 22 126:22
>
> 142:1 145:20
>
> 149:14 174:1, 25
>
> 175:1, 1
>
> **basing** 126:1
>
> **basis** 19:25 77:18
>
> 111:1, 18 117:22
>
> 119:1 121:1 150:12
>
> **battle** 55:16
>
> **beach** 33:16
>
> **become** 89:20 90:19
>
> 118:12
>
> **becomes** 29:25 42:1
>
> 72:1 83:24 114:1
>
> 135:25 158:1, 1
>
> 162:14
>
> **begin** 154:19 165:13
>
> **beginning** 38:19 **behaves** 178:23 **behind** 35:10 46:1
> **believe** 19:10
>
> 40:16 41:11
>
> 48:16 98:24 106:12
>
> 131:12 144:11
>
> 178:25
>
> **bell** 154:24
>
> **benchmark** 92:1, 1
>
> 101:16 102:1,
>
> 11, 16 112:15
>
> 130:1 145:25 146:1
>
> **benchmarks** 97:11
>
> 100:25 102:15,
>
> 25 103:15
>
> **beneficial** 35:22
>
> **benefit** 182:1, 1
>
> 183:1
>
> **best** 16:1 84:1
>
> 115:15 116:16
>
> 121:15 177:1
>
> **better** 8:1 10:15
>
> 20:1 39:23 43:25
>
> 51:1, 1 52:23
>
> 53:1, 19 55:23
>
> 57:1, 14 58:19
>
> 61:23 69:1
>
> 131:16 139:12
>
> 142:21 161:22
>
> 163:14 175:1

**beyond** 34:18 42:25

> 84:19 104:11 136:1
>
> 159:10, 23 162:14,
>
> 24

**bias** 35:1

**biased** 11:25 13:18

> 16:1

**biases** 12:14 15:1, 1

> 34:1

**bigger** 13:1, 23

**biggest** 17:23

> 44:24 72:18

**billion** 26:1, 1

> 49:18 54:10, 11

**biologic** 32:1 61:19,

> 22

**biological** 29:20, 25

> 60:23 61:1 62:23
>
> 66:19

**biologically** 49:24

**biology** 62:1

**bit** 3:20 6:24 9:25

> 18:16 34:11
>
> 43:23 54:1 56:1
>
> 71:1 79:23 88:21
>
> 89:1, 1, 23, 25
>
> 90:1, 20, 21 92:24
>
> 100:11 107:10
>
> 116:17, 18 128:1
>
> 134:1, 17 135:1
>
> 139:17 149:20
>
> 159:22 163:13
>
> 164:22 165:16
>
> 174:1 176:1 181:1

**block** 98:23, 23

**blown** 13:1 **boats** 134:1 **body** 68:24 **borne** 93:1 **bother**
126:1

**bothering** 55:14 **boundary** 128:1 **bounds** 140:23 **boy** 139:11

**break** 79:1, 1, 10

> 90:1 123:24

**breakdown** 73:17

**breaker** 63:19

**breaking** 7:1

**breathing** 14:12,

> 13 27:23 28:1
>
> 65:11

**bridge** 69:12

**brief** 5:1 14:21

> 108:1

**briefly** 87:17 179:12

**bring** 21:20 22:15

> 65:16 74:1 116:1
>
> 154:1, 21

**bringing** 2:1 29:1

> 35:19

**brings** 116:1 118:23

**broad** 53:1 95:1 **broader** 37:1 84:21 **broadly** 83:1, 21
**broken** 98:10 **brought** 16:23 21:1

> 22:1 40:1 49:10
>
> 146:1 151:1 158:1,
>
> 17

**budgets** 82:1

**buffer** 125:1 **buffet** 154:15 **building** 114:1,

> 17, 18 117:16
>
> 121:10

**buildings** 114:1

**built** 99:19 113:13

**bullet** 11:16

> 22:17, 20 36:1
>
> 59:14 60:11
>
> 61:18 63:1

**bunch** 152:24

**burden** 87:1, 13

**bus** 123:25

**buses** 123:25 171:24

**busy** 32:11 **button** 134:23 **bye** 184:24

> C

**calculate** 127:12

> 167:14, 15, 16

**calculation** 138:16

**calibrate** 111:1

**california** 28:20

> 33:15 64:24

**caline** 127:20, 21

> 128:21

**caline4** 127:14,

> 16, 18
>
> **calpuff** 128:16
>
> **candidate** 104:16
>
> **canyon** 30:13 76:19
>
> 135:1
>
> **canyons** 121:10
>
> **capabilities** 99:24
>
> 128:1
>
> **capability** 128:13
>
> **capable** 99:22 174:16
>
> **capture** 15:15, 19,
>
> 23 27:1 39:22
>
> **captured** 6:21
>
> 11:12 18:1
>
> **captures** 65:14 106:1
>
> **capturing** 15:1 30:11
>
> 32:1
>
> **car** 123:22
>
> **care** 39:1 137:21
>
> **careful** 37:16
>
> 75:14 124:1
>
> **carefully** 7:1
>
> 78:21 103:20
>
> **carlo** 143:12 175:1
>
> 176:11
>
> **carolina** 87:25
>
> **carry** 159:17 **carrying** 169:1 **cars** 125:1 171:24 **casac**
> 3:22, 23
>
> 5:19 10:1 35:20
>
> 51:20 52:18
>
> 78:14 81:10
>
> 89:14 106:1
>
> **case** 24:21 27:21
>
> 62:11 82:1 84:11
>
> 93:16 111:23 116:1
>
> 129:11 133:12
>
> 152:1 156:1 160:15
>
> **cases** 15:1 32:1
>
> 48:24 122:1
>
> **categories** 61:13
>
> **category** 134:1, 1,
>
> 11
>
> **causal** 42:22 61:21
>
> 62:1, 19, 21,
>
> 22, 25 112:21
>
> 158:1 177:15,
>
> 24, 24
>
> **causality** 112:23
>
> 145:15 177:14

**cause** 16:19 23:14

> 44:1 46:18 47:1
>
> 50:1 52:1 65:10
>
> 73:1 110:1 124:1
>
> 125:10 126:14
>
> 129:1, 19 138:16
>
> 139:25 146:17
>
> 172:23

**caused** 108:21

**causes** 46:1

**causing** 30:15 **caution** 9:11 113:1 **cd** 35:11, 17

**census** 98:22

**central** 20:14, 25

> 21:1, 14, 22 22:13
>
> 27:1 49:16 121:1

**certain** 47:1, 24

> 115:17

**certainly** 10:21

> 34:17 36:25 44:1
>
> 46:13 48:1 54:1
>
> 68:1 74:23
>
> 105:22 115:12
>
> 116:14, 16
>
> 134:10 146:1

**cetera** 10:25 24:16

> 42:19 47:1 67:12
>
> 111:13 114:23
>
> 137:1

**chain** 176:11

**challenge** 101:1

> 115:23 140:12
>
> 148:10 166:1, 1

**challenged** 43:18

**challenging** 115:1

> 129:25 134:25

**chambers** 173:1, 1

**chance** 6:1, 25 7:1

> 175:20, 23

**change** 25:1 28:20

> 29:21, 24 36:17
>
> 48:22, 25 55:10
>
> 58:13 72:22 105:12
>
> 138:11 158:20
>
> 161:18 173:18
>
> 176:23

**changed** 45:1 46:18

> 60:12

**changes** 49:1, 13, 20

> 50:1 59:1 73:10
>
> 123:23 165:1

**changing** 164:11

> 174:16

**chapter** 16:20

> 35:16 41:25
>
> 61:19 67:24
>
> 68:1, 18, 19, 24
>
> 69:1, 12, 18, 25
>
> 70:1, 1, 1, 22
>
> 71:1, 1 72:1, 1,
>
> 1, 1

**chapters** 68:10

> 69:1 70:1, 1, 10
>
> 71:1

**characterization**

> 95:19 96:1
>
> 105:16 174:23

**characterizations**

> 174:10, 11

**characterize** 34:1

> 84:1 101:1
>
> 146:20 174:25
>
> 175:10

**characterizing** 84:17

**charge** 6:23, 25 7:18

> 19:14 34:18, 20,
>
> 21, 24 39:1
>
> 61:15 64:10 106:1,
>
> 1 109:1

**charlotte** 133:12

**charter** 78:16 **chartered** 78:14 **chattanooga** 90:1 **chemical**
44:1 93:24 **chicago** 96:23

> 113:15, 16, 18
>
> 114:19

**childhood** 165:1

**children** 49:1, 13,

> 14 63:24 64:25
>
> 65:1 102:14 104:23
>
> 163:1 173:1, 1

**children\'s** 46:10

> 52:1 64:24 75:24
>
> 156:1 164:24
>
> 170:20

**chime** 136:22

**choice** 109:14 110:1,

> 1 131:1

**choices** 102:17

> 130:24 149:15
>
> 160:14

**choose** 35:14, 23

> 110:1 175:1
>
> **chooses** 35:21
>
> **choosing** 157:24
>
> 161:1
>
> **christian** 127:1
>
> **chronic** 47:11 164:23 **chug** 138:10 **circumstances** 56:16
> **cite** 17:11 44:14 **cited** 115:13
>
> **cities** 13:16, 19
>
> 14:10 110:1, 1
>
> 115:14 116:20
>
> 118:21 119:1
>
> 120:1, 1, 14 130:1
>
> 163:11 179:21
>
> **city** 15:23 117:14,
>
> 25 163:11
>
> **clarification** 9:21
>
> 10:1 23:1 31:1
>
> 60:1 170:16
>
> 181:1 184:1
>
> **clarified** 77:17
>
> **clarifies** 80:1 113:1
>
> **clarify** 38:13 117:10
>
> 127:17 134:1 150:1
>
> 159:13 180:1
>
> **clarifying** 116:25
>
> **classes** 128:1 **clean** 2:1 87:19 **clear** 20:12 36:1
>
> 65:14 66:23
>
> 86:24 90:19
>
> 111:17, 17
>
> 132:14 135:19
>
> 139:1
>
> **clearer** 136:1
>
> **clearly** 22:1, 10
>
> 36:1 58:23 71:11
>
> 83:12 84:14
>
> 85:1, 1 122:24
>
> 123:1 142:22
>
> 157:20 160:21
>
> 177:24
>
> **clicker** 95:1
>
> **clinical** 21:1, 11
>
> 22:1 33:1 57:20
>
> 91:20 92:10 111:12
>
> 112:19 113:1
>
> 138:14 139:1 146:1
>
> 170:21, 24
>
> 171:17 175:12

**clinically** 173:1

**close** 13:1 49:17

> 54:18 57:23 63:1
>
> 109:1 133:1

**closely** 157:1

**closer** 54:13

**clue** 146:1

**cmsa** 150:12, 13

**co** 33:1 67:1

> 126:17 153:11

**co-pollutants** 39:24,

> 25

**coal-fired** 133:14

**coefficients** 29:10

**coherence** 39:1 45:17

> 50:18, 20 51:10
>
> 53:10

**coherencies** 53:22

**coherent** 43:19 53:1

**cohort** 113:17 **cohort-based** 98:19 **collection** 74:19

> 85:20

**com** 12:18

**combination** 84:11

> 139:17 172:20

**combine** 69:1

**combined** 175:19

**combining** 101:22

> 140:12

**combustion** 44:21

> 45:11 51:21 52:1

**comes** 42:20, 23

> 78:18 79:13
>
> 118:1 149:20
>
> 179:25 184:1

**comfortable** 3:1

> 50:19 67:1, 16
>
> 78:1, 12, 15

**comforted** 138:12

**coming** 39:13 47:19

> 67:1 78:13 81:1
>
> 87:21 123:10
>
> 132:25 137:1
>
> 157:1, 1 167:22

**comma** 52:22

**comment** 3:21 4:22

> 22:1 40:12, 13
>
> 74:1 120:17 121:19
>
> 124:20 129:18
>
> 134:17 135:1 136:1
>
> 140:1 142:1, 17,
>
> 18 143:10 144:1,
>
> 12, 20 156:1,
>
> 14, 14 157:17
>
> 158:1 159:1
>
> 168:20, 23 173:13,
>
> 16 179:1, 10
>
> 182:23

**commented** 81:16

> 179:14

**comments** 4:1, 10, 15

> 5:1, 1, 14, 17, 20
>
> 24:1 33:19 37:23
>
> 38:1, 1 39:1
>
> 40:1 71:1, 13,
>
> 21 81:1, 1, 15
>
> 84:1, 24 108:14
>
> 109:1 110:24
>
> 116:1, 10, 11
>
> 128:22 130:24
>
> 131:13 132:12
>
> 136:16, 20 137:1
>
> 138:1 140:1
>
> 142:11, 11, 15
>
> 144:1 147:1, 20
>
> 152:1 153:24
>
> 155:1, 1, 16
>
> 156:15 157:15
>
> 159:14 163:15,
>
> 22 165:20, 21
>
> 167:1 169:16,
>
> 16, 18 170:1, 1,
>
> 10, 11 180:10,
>
> 11 183:17, 23
>
> 185:1

**committee** 2:1 3:1

> 65:15 87:20
>
> 115:16, 25
>
> 130:25 149:1

**committees** 77:24

**communities** 49:14 **community** 166:1 **commuting** 111:24
**comparability** 27:19 **comparable** 16:1 21:1

> 27:14

**compare** 2:21 11:22

> 12:19 20:1 27:1,
>
> 15 33:23 48:17
>
> 74:19 100:16
>
> 117:19 171:1
>
> **compared** 2:18
>
> 15:25 99:1
>
> 101:17 102:1
>
> 112:15 117:18
>
> **comparing** 22:1 112:1
>
> **comparison** 48:12, 14
>
> 97:11 122:1, 10,
>
> 20 124:12 126:15
>
> **comparisons** 99:1
>
> **compartment**
>
> 167:23, 23
>
> **compatible** 175:24
>
> **compilation** 2:16, 17
>
> 3:1 70:1
>
> **compile** 33:22
>
> **complete** 3:16 5:1
>
> 77:1 78:1 82:1
>
> **completed** 103:1
>
> 116:11
>
> **completely** 29:21
>
> 76:20 136:1
>
> **complex** 135:18
>
> **complexity** 134:24
>
> **compliance** 151:21
>
> 152:17
>
> **complicated** 113:22
>
> 114:11 181:1
>
> **compliment** 125:1
>
> **comply** 87:1 **component** 12:12 **components** 58:13
>
> 61:1 86:1
>
> **composition** 93:25
>
> **comprehensive** 96:21 **con** 92:19 145:18 **concentration** 12:22,
>
> 23 96:13 98:14
>
> 99:15 101:22 104:1
>
> 110:1 130:1 145:15
>
> 174:16 175:1
>
> **concentrations**
>
> 8:24 14:15 16:1, 1
>
> 17:1 21:1, 1, 13
>
> 49:19 66:1, 1 91:1
>
> 95:12 97:15, 20
>
> 99:17 100:17
>
> 127:1, 12 142:24
>
> 162:21 178:24
>
> **concept** 65:1
>
> **concern** 65:17 85:1
>
> 91:1, 1, 19, 20
>
> 92:14 98:18 102:18
>
> 103:1 107:23 108:1
>
> 119:1 126:10 132:1
>
> 133:18 151:14
>
> 162:11, 12 171:1
>
> 172:13

**concerned** 44:24 58:1

> 168:19

**concerns** 65:14

> 145:18 157:18

**concluded** 66:1 93:23

> 154:22

**concluding** 70:1

**conclusion** 44:12

> 45:1 48:1 51:23
>
> 56:18, 21 71:1
>
> 79:1 91:1, 15, 17,
>
> 25 94:1

**conclusions** 67:24

> 68:1, 20, 23 90:24
>
> 156:21 183:1

**conclusive** 12:1

**concur** 42:18 51:1

**concurrence** 39:1

> 78:21

**concurrences** 78:10

**concurs** 51:1 52:18

**condensed** 9:14 16:24

> 35:16 106:1

**condition** 21:22

**conditional** 136:1

**conditions** 163:1

> 175:16

**conduct** 92:1

> 103:24 104:1 105:1
>
> 115:21

**conducted** 90:1

> 92:1 101:1 103:1
>
> 104:1

**conducting** 104:12

> 158:22

**confidence** 45:1

> 105:1 108:19

**confined** 15:1

> 17:25 18:1 30:13
>
> 114:1

**confinement** 113:22

**confirmation** 147:1 **confirmed** 18:21 **confounded** 30:21
**confounder** 66:1

**confounding** 90:1

**confused** 144:17

> 178:15

**confusion** 66:21 67:1

> 141:15

**conjunction** 85:1

**connecticut** 128:15 **connection** 69:17 **conscious** 40:15, 17
**consensus** 65:25

> 116:1 170:1

**conservative** 139:21

**consider** 10:21 78:15

> 95:13 97:16 98:1
>
> 127:24 164:22
>
> 173:1 183:1

**consideration** 2:24

> 156:1

**considered** 73:1

> 95:22 96:17
>
> 98:22 109:15
>
> 118:20 128:11
>
> 178:1

**considering** 43:12

**consistency** 51:10

> 53:1, 1

**consistent** 43:19

> 53:1 57:14
>
> 58:15, 16, 17
>
> 156:23

**consistently** 19:1

> 45:10

**constraint** 82:1

**constraints** 82:1, 1,

> 1, 15, 18 104:13
>
> 176:16

**construction** 134:1

**consultation**

> 79:15, 20 93:19
>
> 116:1 179:25

**contacting** 185:1

**contain** 3:1 **contains** 36:1 **content** 67:1 **context** 24:1, 1

> 69:22 83:11
>
> 90:21 105:23
>
> 173:24

**continue** 10:24

> 79:1 127:23 134:17
>
> 161:1 164:18
>
> **continuum** 112:22
>
> **contradiction**
>
> 85:16 86:13
>
> **contribute** 133:1
>
> **contributed** 59:1
>
> 184:20
>
> **contribution**
>
> 97:25, 25 167:1
>
> **contributions** 167:19
>
> **contributors** 133:25
>
> **control** 85:1 86:24
>
> 87:1, 11, 14 152:1
>
> **controlled** 101:23
>
> 102:12 103:12
>
> 112:16 170:25
>
> 175:13
>
> **controls** 86:23 87:1,
>
> 13 132:15, 15
>
> 133:19
>
> **convenience** 5:20
>
> **conversation** 7:1
>
> 23:1 75:19 89:25
>
> **conversion** 128:23
>
> **converted** 45:1 **convey** 6:17 **convince** 161:17 **convo** 175:1
> **convolute** 175:1 **convolved** 138:21 **cooking** 27:25
> **cooperating** 78:23 **correct** 19:19 33:1
>
> 38:16 123:11
>
> 138:23 162:1
>
> **corrected** 181:1
>
> **correcting** 181:1
>
> **correctly** 29:13
>
> 36:22 138:22 161:1
>
> 174:1
>
> **correlate** 29:12
>
> **correlated** 27:22
>
> 29:1 75:25 90:1
>
> 134:1
>
> **correlates** 53:14
>
> **correlation** 29:1,
>
> 1 44:25
>
> **correlations** 52:1
>
> 161:11
>
> **corresponded** 24:18
>
> **cote** 37:12 38:1 60:1
>
> 61:16 62:1 63:1,
>
> 18, 21 64:1, 1
>
> 65:1 68:21 69:1,
>
> 24 71:11, 15, 19
>
> 72:11, 14 76:11

**cough** 104:23

**could\'ve** 125:10 **counting** 168:1, 1, 1 **countries** 45:10
**country** 8:12 92:15

> 107:24 122:17, 17,
>
> 19 130:13

**counts** 100:1, 1

> 110:15

**county** 150:12

**couple** 36:1 54:12

> 81:1 90:24 103:1
>
> 110:25 146:21

**course** 49:11 83:25

> 85:19 95:1, 20
>
> 96:1, 13 97:1,
>
> 1, 1, 16 98:1,
>
> 1, 20 99:1, 10,
>
> 19, 21 100:1, 15
>
> 101:1 122:21 143:1
>
> 159:19 163:25
>
> 164:1 168:1 179:24

**court** 82:1, 1 89:20

**cover** 32:14

**covered** 32:20 37:1 **covering** 88:15 140:1 **cowling** 71:21

> 72:12 77:1 108:16

**crafting** 85:1

**crapo** 8:1, 22

> 22:25 23:1 25:24
>
> 26:10, 13, 18,
>
> 22 28:1, 13
>
> 29:1, 19 30:1,
>
> 14 40:17, 20 41:1,
>
> 11 44:1 46:14,
>
> 18 47:1, 13
>
> 48:1, 11, 18 49:21
>
> 50:10 52:1 53:1
>
> 55:17 56:1 57:1,
>
> 25 62:12 70:13
>
> 72:17 73:1 74:1
>
> 75:1, 1 76:1
>
> 77:1 123:1
>
> 124:25 125:1,
>
> 10, 14 146:13
>
> 161:1, 1 171:1, 1,
>
> 14, 20 172:1, 1,
>
> 16, 22

**crawford-brown** 56:15

> 59:21 68:1 69:1,
>
> 10 70:1 137:1,
>
> 1, 15, 21, 23
>
> 138:1 139:1, 1, 25
>
> 142:1 149:16, 19
>
> 150:21 151:1
>
> 154:12, 16 167:1
>
> 168:15 170:1

**create** 122:24

**creates** 19:23 41:1 **creating** 123:1 **creativity** 176:1, 1,

> 1

**credible** 103:14,

> 24 145:17 178:1

**credibly** 115:17

**crew** 155:1

**criteria** 9:1 72:15

> 76:17, 24 77:23
>
> 96:18, 21 103:25
>
> 119:16 157:20,
>
> 24 158:17, 21
>
> 160:1 164:1, 1
>
> 177:20 179:13

**critical** 12:12 58:1

**criticize** 46:1

**cross** 10:1

**crux** 90:15

**cumulative** 32:1

> 146:13

**curious** 42:14

> 73:12 124:16 133:1

**current** 8:13, 14

> 51:1 52:19 88:22
>
> 92:1, 20 93:21
>
> 94:1, 12, 15 95:1,
>
> 1 96:1 101:1, 10
>
> 105:13 130:1, 1,
>
> 14, 15, 17, 20
>
> 131:1, 1, 11, 25
>
> 132:1, 13, 14,
>
> 19 145:1 147:24
>
> 148:1, 12, 14
>
> 149:1, 10 150:18
>
> 151:10, 11, 21
>
> 152:16 153:1
>
> 155:23 166:1, 12
>
> 181:18, 19 182:20

**currently** 11:17 82:1

> 107:1 152:25
>
> **cut** 63:16
>
> **cycle** 72:19
>
> D
>
> **dailies** 73:10
>
> **daily** 48:1, 14 72:20
>
> 73:1 77:11 98:25
>
> **dakota** 122:10, 12
>
> **dale** 12:1 17:1
>
> 21:1 22:1 75:17
>
> 121:19 151:13
>
> 163:16, 19, 19
>
> 165:19 176:19
>
> **data** 8:1, 18 10:16
>
> 12:23 15:20, 24,
>
> 25 16:20 23:23
>
> 24:13 25:1, 12
>
> 26:1 27:1 28:19
>
> 44:22 45:1, 1, 15,
>
> 18, 22 53:1
>
> 57:16 70:20, 24
>
> 74:1, 19 93:12, 14
>
> 96:1, 1, 12, 13,
>
> 14, 19, 20 99:1
>
> 100:13 101:24
>
> 102:23 104:1, 1
>
> 106:1 109:10
>
> 110:14, 15 116:19,
>
> 19 117:1 120:1
>
> 122:1 123:1, 1, 13
>
> 124:1 125:12,
>
> 22, 25 129:1
>
> 139:1, 1, 16
>
> 147:14, 16 149:1
>
> 156:1 161:17 166:1
>
> 172:17, 19, 23
>
> 175:12 176:13
>
> **database** 33:21
>
> 173:24
>
> **date** 24:18 89:19,
>
> 19, 20
>
> **dates** 82:1 89:22
>
> **day** 3:16 6:15
>
> 23:1, 1, 1, 11
>
> 26:1 33:17 44:13
>
> 48:22 55:1 76:1
>
> 77:25 103:21
>
> 169:11
>
> **daylight** 75:1
>
> **days** 71:1 81:1 138:1 **deadline** 5:17 **deadlines** 164:17

**deal** 19:1 63:19

> 85:23 86:17 105:24
>
> 123:1 148:11
>
> 176:11

**dealing** 30:19

> 62:18 176:1, 10

**deals** 39:24

**dealt** 44:23 **deaths** 168:1 **decades** 74:1 **decay** 98:1

**decide** 9:1 44:1 57:1

> 112:22 182:1

**decided** 57:1 63:1

**deciding** 84:1

**decision** 77:18 92:19

> 101:21 104:11
>
> 119:1 159:1 161:23

**decisions** 53:1 69:14

> 71:24 77:16
>
> 90:16 144:10 164:1

**declining** 82:1

**decrement** 167:13

> 173:18

**decrements** 46:10

> 49:12

**deemed** 112:20

**defensible** 113:25 **defer** 178:17 **define** 58:1 83:17

**defined** 58:1 183:1

**defining** 42:1

> 59:13 60:12, 13

**definitely** 88:1

> 110:1

**definition** 58:19

> 150:13 153:1, 20

**definitive** 56:18

**degree** 12:21 14:18 **degrees** 178:1 **delete** 119:1
**deliberately** 44:1 **delineate** 22:11 **demonstrate** 87:14
**density** 141:1 **denver** 179:14, 20

**department** 104:1, 22 **depend** 60:13 **dependent** 97:1
**depending** 27:19

> 135:1 136:1 145:14
>
> 153:13

**depends** 27:1 111:1

> 143:1 146:11

**derive** 111:1

**derived** 91:1, 20

> 92:1

**deriving** 111:1

**describing** 36:22

> 100:13

**descriptive** 97:1

**descriptor** 43:22

**design** 130:21 150:18

> 151:1 153:21

**designations** 147:1

**designing** 153:14 **desire** 82:12 **detail** 89:12

> 95:20, 21 122:19

**detailed** 62:24

> 63:1 139:13, 14
>
> 142:15

**details** 69:25 121:17

> 122:23 159:19

**deteriorate** 165:1

**deteriorates** 165:1 **deteriorating** 151:20 **deterioration** 152:15
**determinations** 68:17 **determined** 114:1 **determining** 32:22

> 103:25 177:20

**develop** 87:22 95:1

> 120:25 161:17
>
> 177:1, 16

**developed** 99:15

> 100:1 110:16 179:1

**developer** 127:22

**developing** 127:24

**development** 63:25

> 64:1 65:1 79:21
>
> 178:14, 18, 19

**deviation** 24:25

> 174:1 175:22

**deviations** 24:11

**devil** 159:18 **devote** 164:11 **devoting** 164:20 **dfo** 185:1
**dictated** 87:11 **die** 168:1

**diego** 14:1

> **diff** 62:21
>
> **difference** 14:1, 18,
>
> 18, 22 17:1
>
> 19:24 22:11
>
> 31:10 47:17 55:1
>
> 57:13 180:1
>
> **differences** 13:1
>
> 15:13 17:1 18:1, 1
>
> 20:24 21:16
>
> 27:22 33:1 47:10
>
> 146:22 163:1
>
> **different** 7:24 11:23
>
> 13:22 14:1, 23
>
> 23:21, 22 27:14,
>
> 18, 18 45:10
>
> 53:1 54:23 59:1
>
> 73:19, 22 74:12
>
> 75:12 76:12, 20
>
> 85:13, 14 98:11
>
> 102:20 105:1, 10
>
> 111:14 113:11
>
> 114:1 135:1, 13,
>
> 22 136:1 140:1, 1,
>
> 1, 1 142:20
>
> 143:16, 18 146:21,
>
> 23 148:13, 23
>
> 151:1 158:11
>
> 159:24, 25
>
> 160:1, 1, 14 178:1
>
> **differently** 160:19
>
> **differing** 85:25
>
> **difficult** 61:20
>
> 101:1 103:16
>
> 115:21 145:25
>
> **difficulties** 165:24
>
> **dilemma** 130:11 **dimension** 45:1 **dioxide** 37:24 **direct**
> 20:1 54:1
>
> 61:21
>
> **directing** 70:12
>
> **direction** 43:1
>
> 74:1 79:1, 17
>
> 123:1 124:20
>
> 140:21 160:1
>
> **directions** 29:21
>
> **directly** 8:21 12:18,
>
> 19 16:1 21:1 42:19
>
> 43:1 52:19 72:1
>
> 122:10 146:1 165:1
>
> 173:1
>
> **director** 80:11

**disagree** 12:11

**disagreeing** 48:1

> 71:15

**disappointed**

> 144:13 163:24

**disappointing** 163:25

**disconnect** 70:25 **discretionary** 56:1 **discuss** 32:18

> 73:21 105:20

**discussants** 108:1

> 155:14

**discussed** 16:25

> 18:16, 18 32:23
>
> 35:1, 20, 20
>
> 67:1 118:24
>
> 148:1 158:14 159:1

**discussing** 32:14

> 142:16 154:25
>
> 158:21

**discussion** 3:16,

> 17 4:10, 14 5:1
>
> 7:23 11:13 14:21
>
> 16:18 26:12 28:1
>
> 29:20 30:21
>
> 34:16 43:12, 18
>
> 49:1 54:24 59:17
>
> 62:1 66:21 70:18
>
> 73:1 75:1, 15
>
> 79:24 83:10 104:18
>
> 107:1, 18 108:1,
>
> 1, 11 109:14 124:1
>
> 155:1 156:1, 1, 16
>
> 159:1 160:1 164:10
>
> 167:1 178:1 184:16

**discussions** 59:1

> 65:24 73:18
>
> 83:23 126:1 154:1,
>
> 1, 19 184:16

**disease** 30:16

**dispersed** 25:18

**dispersion** 97:18

> 114:13 127:16
>
> 128:17 129:16

**disproportionately**

> 61:1

**disquieting** 155:22

**disregards** 156:18 **distance** 18:1 33:22 **distances** 125:1
**distinction** 85:1

> 145:22 181:13

**distinctions** 148:25

**distinctive** 64:1, 10 **distinguish** 102:23 **distortions** 54:20
**distributed** 2:1 25:1 **distribution** 12:15

> 15:1, 21 24:12, 19
>
> 32:1 112:14
>
> 114:1 134:1, 1
>
> 143:20 153:17
>
> 175:21 179:18

**distributional**

> 122:16 174:10, 11

**distributions**

> 23:21 25:1 99:1
>
> 111:25 114:1
>
> 148:21, 22
>
> 175:1, 17

**distributive** 25:10

**disturbing** 69:1 **divide** 20:21 143:25 **division** 80:12
**doctor** 80:23, 24,

> 25, 25

**document** 3:18 5:1, 1

> 7:1 9:1 14:1 18:15
>
> 44:12 45:1 46:1,
>
> 12 51:1, 1 52:22
>
> 53:1 55:12
>
> 56:19, 24 62:1
>
> 66:23 67:22
>
> 68:1, 1, 12
>
> 69:13 72:25
>
> 77:1, 23 79:1, 15,
>
> 16 83:24 95:22
>
> 116:1 118:1 127:13
>
> 154:25 155:24,
>
> 24 158:24 160:1
>
> 162:10 169:18
>
> 180:1, 1, 1, 23
>
> 182:1 183:18 185:1

**documented** 171:22

**documents** 83:12,

> 13 156:23 168:17

**done** 15:22 34:1,

> 10 37:16 51:13,
>
> 14, 15 54:1 80:18,
>
> 21 81:1 95:12 98:1
>
> 108:22 114:18
>
> 117:12, 13 118:12,
>
> 14 119:12 132:1
>
> 135:1 136:10, 11
>
> 138:1 148:19 159:1
>
> 166:21 167:1 173:1
>
> 175:1
>
> **donna** 86:20 109:1
>
> 163:13
>
> **door** 154:1
>
> **dose** 22:1 31:1
>
> 53:11, 12, 15
>
> 65:10 70:15
>
> **double** 20:20 61:11
>
> 152:1 168:1, 1, 1
>
> **doug** 58:24, 25 65:13
>
> 71:23 79:12
>
> 137:1 146:1 149:18
>
> 154:24 167:1
>
> **downtown** 28:20
>
> 30:1 113:18
>
> **downward** 148:21
>
> **dr** 2:1, 10, 13, 15
>
> 3:15, 25 4:1, 1,
>
> 1, 1, 1, 1, 1, 17,
>
> 20, 23 5:1, 1, 11,
>
> 23, 24 6:1, 12
>
> 7:1, 17, 21 8:1,
>
> 1, 19, 22 9:1,
>
> 1, 10, 11, 12, 18,
>
> 19, 20 10:1, 1,
>
> 10, 13 11:1, 1, 1,
>
> 11 12:1, 10
>
> 13:1, 1, 12, 14
>
> 14:1, 1, 11, 13,
>
> 17, 20 15:1
>
> 16:1, 13, 17 17:1,
>
> 10, 13, 19, 20
>
> 18:10, 17, 20,
>
> 24 19:1, 1, 1,
>
> 1, 14, 17 20:1,
>
> 1 21:1, 11, 21,
>
> 22, 25 22:1, 17,
>
> 20, 22, 23, 24, 25
>
> 23:1, 17, 20 24:1,
>
> 1 25:15, 17, 21,
>
> 24 26:1, 10, 13,
>
> 16, 18, 22, 24, 25
>
> 27:1, 10, 13 28:1,
>
> 1, 1, 10, 13,
>
> 15, 17 29:1, 15,
>
> 19 30:1, 1, 1,
>
> 14 31:1, 12, 14,
>
> 16, 17, 19, 22, 23
>
> 32:13 33:13, 19,
>
> 25 34:1, 1, 1, 15,

24 35:1, 25

36:1, 1, 11, 13,

25 37:1, 1, 1,

1, 12, 21 38:1, 1,

1, 10, 11 39:1,

10, 11, 12, 13,

15, 16, 17, 19, 20

40:1, 1, 1, 1, 10,

17, 20, 22, 24

41:1, 1, 11, 14,

17, 21, 23 42:1,

1, 10, 13, 16,

18 43:11, 15,

17, 20 44:1

45:13 46:14, 17,

18, 20, 23 47:1,

1, 13 48:1, 1, 10,

11, 13, 18, 21

49:1, 21 50:1,

1, 10, 16, 17, 23,

24, 25 51:15, 18

52:1, 11, 13,

17, 25 53:1, 18,

23 54:1, 1, 14,

15, 17, 21, 22

55:1, 1, 11, 12,

13, 17, 25 56:1,

1, 12, 13, 15,

17 57:1, 1, 1, 16,

18, 19, 21, 25

58:1, 11, 18, 20

59:11, 15, 16, 19,

21, 23 60:1, 1,

11, 16, 21 61:1,

1, 14, 16 62:1, 1,

12, 16, 18, 20, 21

63:1, 1, 1, 11,

13, 15, 17, 18,

20, 21, 22, 23

64:1, 1, 1, 1,

1, 1, 12, 13,

14, 21, 23 65:1,

1, 1, 1, 12, 23

66:13, 16, 22,

25 67:1, 18, 20,

21 68:1, 21

69:1, 1, 10, 24

70:1, 13 71:1, 11,

12, 15, 17, 19,

20, 21 72:11,

12, 14, 17 73:1,

1, 12, 14 74:1, 20

75:1, 1, 1, 1, 11,

13, 16, 18 76:1,

1, 11, 16, 22,

25 77:1, 20 78:1

79:11 80:1, 20,

21, 21, 22 82:24

86:19, 21 88:11,

14, 17, 18 94:25

106:1, 10, 12, 14,

16, 17, 19, 20,

23, 24, 25

107:1, 1, 11,

12, 13, 14, 16

108:1, 16 109:1,

1, 1, 1, 16, 23,

25 110:1, 1, 11,

19, 20, 22, 23, 25

113:1 115:1 116:1,

13, 21 117:1, 1,

1, 11 118:1, 1

119:1, 10, 11,

25 120:1, 1, 20,

21, 22 121:19,

21 122:21 123:1

124:23, 25

125:1, 1, 1, 10,

13, 14, 16, 18,

21, 24 127:1, 1,

1, 19 128:1 129:1,

14, 17, 21

130:10 132:1, 1,

20, 22 134:1,

12, 13, 16, 19, 20

136:12, 17, 18,

21, 24 137:1, 1,

1, 1, 1, 1, 15,

17, 18, 21, 22,

23, 25 138:1

139:1, 1, 19, 25

141:1, 10, 13, 15,

17, 20, 22, 23, 24

142:1, 1, 1, 1, 1,

13 143:23 144:1,

11, 14, 15, 16, 25

145:1, 1, 1 146:1,

13, 19, 24, 25

147:13, 19, 22

148:1, 1, 1 149:1,

16, 18, 19

150:21 151:1, 1,

14 152:13, 19

153:1, 22, 23

154:1, 12, 13, 16,

18, 23 155:15

> 157:1, 1, 10,
>
> 12, 14, 16 159:11,
>
> 18 160:20, 22, 24,
>
> 25 161:1 162:1, 1,
>
> 1, 1 163:16, 20
>
> 164:1 165:19, 22
>
> 167:1, 1 168:1, 1,
>
> 15, 18, 22, 24, 25
>
> 169:1, 1, 17,
>
> 21, 24 170:1, 1,
>
> 1, 13, 18 171:1,
>
> 1, 14, 20 172:1,
>
> 1, 16, 22, 25
>
> 173:1, 11, 15
>
> 175:18 176:1,
>
> 19, 21, 22, 24
>
> 178:11, 18, 21
>
> 179:1, 10, 16,
>
> 23 180:1, 15,
>
> 20, 22 181:1, 1,
>
> 1, 10 182:11,
>
> 12, 18, 22
>
> 183:1, 1, 1, 10,
>
> 12, 13, 15, 16, 21
>
> 184:1, 1, 1, 13,
>
> 15, 19, 20, 21,
>
> 22, 24, 25 185:1
>
> **draft** 3:10 5:22
>
> 34:13 67:13
>
> 78:1, 18 89:15,
>
> 16, 17 104:15
>
> 155:18 156:25
>
> 157:1, 1 180:1, 1,
>
> 1, 14 181:1, 12,
>
> 21, 24, 25
>
> 182:1, 10, 14, 17,
>
> 18, 19 183:1
>
> 185:1, 1
>
> **drafting** 39:1
>
> **dragging** 6:1
>
> **dramatically**
>
> 123:23 138:11
>
> **draw** 51:19 77:1
>
> **dreams** 37:11
>
> **driven** 48:1, 1, 1
>
> 76:1 126:19
>
> **drives** 76:1, 1
>
> **driving** 50:1
>
> **drop** 150:1
>
> **due** 127:1 133:23 **dunk** 110:17 **duplicate** 109:19

**during** 23:1, 11

> 59:20 75:1
>
> 123:24 170:1
>
> E

**earl** 157:13

**earlier** 51:20 68:1

> 69:1 70:1, 10
>
> 89:21, 21 107:1
>
> 116:1 128:22
>
> 146:14 148:1 151:1
>
> 179:1

**early** 66:21 79:21

> 84:15 165:1

**earn** 170:1

**easier** 6:1 **easiest** 151:23 **easily** 33:23

> 62:14, 15 158:12

**easy** 62:16, 17 91:12

> 135:11 175:16

**eat** 154:1, 1, 20, 20

**ed** 26:24 33:18 34:23

> 36:1 40:11 42:23
>
> 49:1 64:15
>
> 146:24 155:13
>
> 170:12 173:1
>
> 178:17 179:1, 13

**ed\'s** 157:17

**effect** 13:24 16:1

> 18:18 21:23 52:1
>
> 75:23 87:1
>
> 100:24 101:15
>
> 102:1, 15
>
> 112:18, 24

**effectively** 85:1

> 148:22

**effects** 10:25 14:1

> 18:1 20:1 25:13
>
> 29:1, 1, 14
>
> 34:21 41:1
>
> 44:19, 20 47:20,
>
> 23 48:1, 19
>
> 49:1, 23 50:10, 12
>
> 51:1, 22 66:1, 1
>
> 70:20 76:12 83:15,
>
> 17 90:1 91:14
>
> 93:12 94:1
>
> 102:13 103:1
>
> 105:20 107:1 111:1
>
> 113:22 124:1, 22
>
> 125:23 141:21
>
> 146:18 156:18
>
> 162:18, 20 163:1
>
> 164:1, 23 167:1
>
> 171:18 172:1
>
> 175:14 177:1, 1
>
> 178:1

**efficient** 85:11

**efficiently** 85:1

**effort** 34:10 73:17

> 109:19 118:17
>
> 122:16 135:1

**efforts** 136:1

**eight** 45:1 58:21, 22

> 59:1 64:11 65:13
>
> 67:19 75:1

**either** 36:21 38:19

> 52:14 56:1 58:1
>
> 63:19 66:1 81:1
>
> 105:17 112:1, 1,
>
> 1, 13 147:10
>
> 159:14 171:1

**elements** 70:17

**elevated** 19:11 **eliminate** 66:1 **ellis** 59:1 69:16

> 71:20 77:1, 20
>
> 108:14 109:1 142:1

**email** 162:1

**embedded** 93:1 **emergency** 104:1, 21 **emission** 129:1

> 133:24

**emissions** 7:24 96:14

> 98:15 128:14
>
> 132:24 133:1, 1
>
> 134:1

**else** 9:1 34:19, 19

> 49:1 72:24 86:17
>
> 119:13 129:17
>
> 131:23

**encourage** 131:13

> 161:14

**endpoint** 177:13

> 178:1

**endpoints** 74:22 77:1

> 101:1 103:16
>
> 104:16, 20
>
> 145:19 161:1 163:1
>
> 168:11 177:23
>
> 178:10

**emphasize** 29:16 33:1

> 74:21
>
> **emphasized** 32:18
>
> 46:12 162:23
>
> **emphysema-like** 91:1
>
> **enhancement** 96:12
>
> 97:18
>
> **enhancements** 120:25
>
> **enhancing** 112:12 **entire** 67:22 68:1 **entirely** 141:1
>
> **epa** 5:17 15:1, 18
>
> 16:14 17:22 43:1
>
> 66:1 76:21 77:22
>
> 116:14 129:1, 13
>
> 138:1 152:22 177:1
>
> **epa\'s** 99:17
>
> **envelope** 115:10 **environment** 124:15 **environmental** 2:1
>
> 61:1 80:12
>
> **envision** 145:10
>
> 152:10 159:16
>
> **envisioning** 177:12
>
> **epi** 14:14 18:10,
>
> 23 19:25 33:11
>
> 43:1 46:1 48:15
>
> 50:19 57:1
>
> 73:20, 25 75:25
>
> 76:1, 1 90:13
>
> 101:23 104:19
>
> 111:1, 19 113:1
>
> 138:20 139:1 146:1
>
> **epidemiologic**
>
> 42:23 45:15, 22,
>
> 25 51:1, 1 52:18
>
> 55:1
>
> **epidemiological**
>
> 12:23 16:1 17:17
>
> 36:19 37:1 103:11,
>
> 23 105:1 112:24
>
> 138:13, 17
>
> 167:1, 13, 18
>
> **epidemiology**
>
> 20:11, 17 22:1
>
> 38:22 45:22
>
> 47:18 48:1 52:20
>
> 74:23 90:1
>
> 112:20 145:16
>
> 161:10 166:1
>
> **equal** 44:16
>
> **equals** 125:1
>
> **equation** 24:21

**equipment** 134:1, 10

**equivalent** 178:25

**error** 13:10 19:1,

> 1 21:23

**et** 10:25 24:16 42:19

> 47:1 67:12
>
> 111:13 114:23
>
> 137:1

**especially** 36:22

> 104:20

**essential** 116:1

**essentially** 13:17

> 23:25 24:1, 10, 11
>
> 25:1, 1, 1 27:13
>
> 41:25 54:1 90:15
>
> 92:1 121:21

**established** 85:22

> 162:22

**establishing** 20:1

**estimate** 16:1 24:21,

> 22, 24 95:1 96:1
>
> 98:25 99:24 100:22
>
> 103:1 112:13 132:1
>
> 143:19

**estimated** 17:16

> 102:1 105:1

**estimates** 27:1 95:16

> 97:23 98:16, 21
>
> 99:1, 23 100:15
>
> 101:1, 18, 24,
>
> 25 104:25 105:1
>
> 112:1 160:11
>
> 177:1, 17 181:22
>
> 182:1

**estimating** 17:14

> 99:18, 22 181:14

**estimation** 128:18

**evaluate** 54:22

> 55:1 94:10 129:10

**evaluated** 101:1

> 120:1 129:1, 12

**evaluating** 92:1

> 103:19

**evaluation** 74:15, 17

> 92:1 96:1, 1, 1
>
> 109:1 171:1 178:1

**evaluations** 171:13

**events** 100:23

**everybody** 78:23

> 79:11 134:21
>
> 149:22 150:1
>
> 165:21 169:12

**everybody\'s** 170:1

**everyone** 2:1 3:19

> 67:1 78:1 172:13

**everyone\'s** 182:14

**everything** 2:17 3:1,

> 1 6:19 7:19
>
> 23:1, 1 35:12 49:1
>
> 68:19 74:19
>
> 83:13 119:13 141:1
>
> 152:18

**european** 15:24

**evidence** 40:25 42:23

> 43:1 53:1, 21 54:1
>
> 58:15 73:21
>
> 81:20 83:1, 19
>
> 103:23 104:18
>
> 170:24

**evolution** 74:1

**evolving** 175:1 **ex** 134:24 **exacerbation**

> 74:22, 25

**exact** 3:1

**exactly** 27:1 44:1

> 55:19 124:19
>
> 130:20 131:21
>
> 138:25 139:1
>
> 142:18 172:24

**examine** 36:20

**examining** 38:22

**example** 18:13

> 24:13 27:23 62:1
>
> 70:1, 13 102:1
>
> 103:1 104:1 105:18
>
> 115:14 124:12
>
> 139:1 148:16
>
> 150:11 156:1 158:1
>
> 160:1 167:21

**examples** 68:23 71:17

> 72:1, 13

**exceedance** 92:1

**exceedances** 92:15,

> 17 109:12 135:12

**exceeding** 8:13, 13

**except** 30:13 48:18

> 55:25 141:12
>
> 171:20

**excluding** 71:1

**exclusion** 40:15 **exclusive** 147:17 **excursion** 26:18, 19,

> 21, 22, 23
>
> **excursions** 162:16
>
> **exercise** 117:15
>
> 152:24
>
> **exist** 28:11, 14 99:1
>
> **existing** 12:16
>
> 14:1 16:1 23:23
>
> 90:17, 25 91:1,
>
> 13, 17 92:12
>
> 107:21 111:1,
>
> 11, 20
>
> **exists** 96:1 100:13
>
> **expand** 9:16, 25
>
> 60:21 89:24 159:22
>
> **expanded** 16:24
>
> 143:13 158:1
>
> **expect** 17:18
>
> 28:22, 23 34:1
>
> 117:20 133:16
>
> 181:1, 11
>
> **expectation** 24:19
>
> **expectations** 146:22
>
> **expected** 97:1
>
> 100:1 103:1
>
> 105:1 109:12
>
> **expecting** 179:1
>
> **expense** 122:15
>
> **experience** 21:1
>
> 98:17 103:1 142:1
>
> **experiences** 103:1
>
> **experiencing** 124:13
>
> **expert** 140:22
>
> **experts** 115:25 149:1
>
> **explain** 21:15 24:1

33:1 35:12 44:25

47:11, 12 54:23

59:18, 18 60:1, 1,

1, 15, 18 61:1

63:25 67:23, 25

70:24 73:18

76:20 79:16, 23,

24 80:14, 19 81:1,

18 83:1 84:1 88:24

89:16, 18 91:10

92:22 94:23

95:1, 15 96:10

97:1, 23 98:11,

12, 20, 21 99:1,

23, 23 100:1,

10, 16 101:18, 23,

24, 24 102:1, 1,

1, 12, 18 103:1,

12, 14, 17 104:1

105:22 108:1

111:23 112:1, 1,

13, 16, 17, 18, 18

113:1, 23 115:1, 1

116:1 118:15

120:1, 1, 24

121:25 122:1

126:13, 17, 19

127:11 129:20

131:25 134:15

135:16 136:15,

22 137:1 138:1,

1 140:1, 1

141:1, 12, 13, 18,

19 142:22 143:10

144:1, 18

> 11 98:17 99:18
>
> 107:20 110:1
>
> 111:13 112:14
>
> 119:1, 21, 23
>
> 120:1, 1 121:1, 1,
>
> 14 122:1 126:14
>
> 129:23 148:11
>
> 153:18 155:22
>
> 161:1 163:1 171:1,
>
> 22 172:12, 13
>
> 174:10 177:1, 1
>
> 181:15 182:20

**express** 105:1

**expressed** 23:1

> 162:11

**extend** 32:19

**extended** 12:13 **extensive** 142:14 **extensively** 67:1 **extent**
73:23

> 83:17, 18
>
> 111:19, 21 113:1
>
> 122:13 134:1
>
> 163:15 166:22

**extrapolate** 171:24

> 172:1

**extrapolating** 99:1

> 120:12

**extrapolation** 172:17

**extreme** 31:25 111:22

> 124:21

**extremely** 83:12,

> 22 134:25

**extrinsically** 60:24

> 49:24 124:1

145:10, 12, 23

> **explained** 172:15 **explicit** 143:17, 19 **explicitly** 5:1
> **exploits** 11:1 **exponential** 109:13 **expose** 91:1, 22
> **exposed** 13:20
>
> 20:19 26:20
>
> 30:23 70:16 100:1,
>
> 24 124:16 130:1
>
> 164:12
>
> **exposing** 27:25 29:22
>
> **exposure** 8:1, 10
>
> 11:1 13:15 15:15
>
> 18:11, 12 21:16
>
> 29:23 30:22 32:1
>
> 146:1, 1 147:20,
>
> 24 149:1 153:1, 16
>
> 155:25 156:1
>
> 158:25 161:13
>
> 172:14, 21 175:1
>
> 177:1 178:1 179:1,
>
> 20 180:1 182:1, 1

**exposures** 8:17 10:16

> 13:15 17:15, 16
>
> 18:22, 23 21:13
>
> 22:11, 12 27:1, 1,
>
> 17, 22 29:17
>
> 32:22, 24 33:12
>
> 34:1 51:1 53:25
>
> 70:18, 21 73:22,
>
> 24 74:1 89:1 90:18
>
> 94:19 95:1 96:1,
>
> F

**face** 82:1, 1, 1

> 115:23 149:15

**faced** 86:1

**facing** 82:15

**fact** 12:11 13:19

> 14:1, 25 16:1
>
> 18:21 20:17, 23
>
> 24:18 25:1 29:1
>
> 30:24, 25 43:17
>
> 44:15 47:18
>
> 49:19 52:1 55:21
>
> 66:1 69:11 74:10
>
> 82:15 84:10 85:1
>
> 87:19, 23 88:1,
>
> 1 113:17 119:18,
>
> 18 123:16 125:1
>
> 129:1 138:20 147:1
>
> 150:1 163:22
>
> 165:1, 1 171:21
>
> 174:1, 13 177:1
>
> **factor** 14:25 49:22
>
> 55:1 56:11, 25
>
> 138:17
>
> **factors** 18:1, 1 64:1
>
> 96:12 97:18 98:1
>
> **factual** 69:25
>
> **fair** 160:1
>
> **fairly** 10:19 11:16
>
> 16:1 23:12 33:23
>
> 46:1 48:19 49:1
>
> 78:20 142:15
>
> 146:20 158:1, 19
>
> 159:1
>
> **fall** 24:20 25:1
>
> 41:23 61:1 119:22
>
> **falling** 123:20
>
> **falls** 8:21 **familiar** 170:23 **family** 114:24 **fancy** 37:25
> **fargo** 122:12 **farm** 134:10
>
> **fascinating** 107:17 **fashion** 12:1 96:1 **faster** 175:1
>
> **fat** 25:1 **fault** 63:16 **favor** 44:16
>
> **feasibility** 104:12 **feasible** 101:20 **feature** 110:14
>
> 128:24
>
> **feedback** 89:1, 15
>
> 149:1
>
> **feeding** 164:10
>
> **feel** 3:1 10:18, 24
>
> 43:1, 10 69:17
>
> 74:23 137:20 144:1
>
> **feeling** 9:1 75:18
>
> **feels** 60:14
>
> **feet** 28:1 47:24
>
> 123:20, 21, 21
>
> **felt** 35:23 69:16
>
> 178:1
>
> **fence** 43:1
>
> **fev1** 165:12
>
> **fi** 91:1

**field** 36:18 74:1,

> 1 116:1

**fields** 98:14 99:15

**fifteen** 26:1 28:1

> 47:23 79:1
>
> 169:23 174:20
>
> 176:20

**fifth** 65:10

**fifty** 20:18

**figure** 43:13 86:1

> 132:23 141:1

**figured** 38:14

**figuring** 180:11

**final** 67:18 80:16

> 82:1, 10 89:18
>
> 105:16 177:18
>
> 180:19

**finally** 38:22

**finding** 45:10 46:11

**findings** 42:19 45:25

> 51:1, 1 52:18 53:1
>
> 68:1 70:25 91:1
>
> 155:19 177:1

**fine** 6:1 19:13 24:1,

> 1 42:1 58:1 113:15
>
> 114:24 126:1 129:1
>
> 136:15 141:25
>
> 169:25 173:13
>
> 184:1

**finer** 98:23

**fines** 66:14

**finish** 39:1 182:1 **finished** 185:1 **firm** 82:1

**first** 3:10 7:12

> 10:15 22:1
>
> 24:13, 14 28:22
>
> 34:18, 20 36:13
>
> 59:14 60:11
>
> 64:23 67:1 69:1
>
> 72:1 77:22 79:25
>
> 80:1 81:1 84:1
>
> 88:12 89:10, 16
>
> 91:1 93:1, 1, 1
>
> 100:21 101:17
>
> 104:15 112:1 118:1
>
> 123:1 137:1
>
> 138:1 154:17
>
> 155:13 156:25
>
> 158:1, 15 174:14
>
> 177:13 179:25
>
> 180:1, 14 181:1,
>
> 17, 20 182:14, 19

**fit** 106:1

**fits** 110:1

**five** 13:1 18:1, 1

> 24:15 36:10, 15
>
> 38:1, 10 39:10,
>
> 11, 11, 21 40:1
>
> 61:17 68:1, 24
>
> 69:1, 12, 18, 25
>
> 70:1, 22 71:1,
>
> 1, 1 72:1, 1 77:24
>
> 89:20, 21 108:20
>
> 118:21 120:1
>
> 179:12

**fix** 22:24

**fixed** 113:1, 1

> 131:21 159:24

**flat** 113:12

**flattered** 36:14 **flaw** 37:18 **floating** 27:1 **flow** 49:1 151:1
**focus** 8:15 91:16

> 94:18 132:25 161:1

**focused** 85:24

> 90:14 98:1, 1
>
> 120:1

**focuses** 156:17

**focusing** 29:1

> 51:12 93:1 99:11
>
> 133:1 135:16

**fold** 15:1 54:12

> 140:18

**folks** 5:19 80:13, 17

> 181:13

**footprints** 114:17

> 117:16

**forget** 129:15

**forgot** 99:21

**form** 30:19 95:1

> 116:1 142:1
>
> 145:1 161:18, 19

**forma** 116:1

**formal** 6:21 **former** 61:1 **formerly** 129:12 **formidable** 166:1
**forms** 169:1

**formulation** 100:15

**forth** 68:15 140:16

> 145:1 160:15 167:1
>
> 170:19
>
> **forward** 88:1 94:17
>
> 103:21 130:25
>
> 131:22 158:17
>
> 185:1
>
> **forwarded** 158:1
>
> **foundation** 158:1 **founded** 145:1 **fraction** 8:12
>
> 122:12, 17
>
> **fractionated** 93:24
>
> **frame** 108:21 **framework** 92:19 **frank** 144:11 162:12 **free**
> 144:1
>
> **freeway** 33:16 50:1
>
> **freeways** 30:1
>
> 31:1, 10, 21
>
> **frog** 35:13
>
> **front** 24:1 31:1 58:1
>
> 164:17
>
> **fuel** 52:1
>
> **fuller** 14:1 **fully** 122:1 **function** 32:1
>
> 38:24 46:1 49:1,
>
> 11, 12, 20
>
> 156:1, 19, 22
>
> 160:1, 1 164:15,
>
> 24 165:1, 1, 10
>
> 173:18 174:1
>
> **functions** 104:1
>
> 141:1
>
> **fundamental** 44:12
>
> **furnace** 52:1
>
> **future** 65:19
>
> 128:1, 11 129:1
>
> 151:18
>
> G
>
> **gained** 104:1
>
> **game** 40:18, 19
>
> 59:20, 22 84:15
>
> **gary\'s** 7:1
>
> **gas** 27:25 126:18
>
> **gaseous** 93:1, 1,
>
> 1, 11, 14
>
> **gather** 7:1
>
> **gaussian** 114:13
>
> **gee** 73:1
>
> **gender** 163:1

**gene** 41:1

**general** 9:1, 21

> 32:1, 12 48:1
>
> 65:1, 17, 24 83:1,
>
> 19 95:1, 22 108:16
>
> 115:24 183:25

**generalized** 121:16

**generally** 24:14

> 60:24 66:1
>
> 104:17 118:1 131:1
>
> 134:1 183:19

**generate** 101:25

> 178:1

**generated** 101:18

**genetic** 40:13

> 41:1, 15

**genetically** 41:22

**genetics** 58:21

**geographic** 60:24

> 102:20

**geography** 179:20

**geometr** 24:22

**geometric** 24:23,

> 24 174:1

**geometry** 114:1

**george** 20:1 22:1,

> 1 50:24 52:15

**george\'s** 71:22

**gets** 27:1 33:16 49:1

> 54:12 114:11
>
> 152:10

**getting** 10:15

> 23:12 29:1 104:1
>
> 107:1 112:13
>
> 129:24 138:14
>
> 143:19 164:1
>
> 178:15

**given** 9:15 55:1, 1

> 98:22 101:10 103:1
>
> 125:1 132:14, 15
>
> 146:1, 1 152:1, 1,
>
> 1

**gives** 11:25 51:13

> 82:1 90:20
>
> 109:13 123:13
>
> 126:1

**giving** 7:1 37:19

> 71:13 88:21

**glad** 180:16 181:1

**glance** 96:11

**goal** 142:19 161:24

**goals** 95:1 100:21

> 125:1

**gold** 26:16

**gone** 101:19 150:1 **goodies** 44:1 **google** 114:21

**gordon** 9:1, 11, 18

> 16:17 75:18 137:17
>
> 141:1, 10, 15, 20,
>
> 23 142:1, 1, 1
>
> 144:14, 16 145:1

**gotten** 136:14

> 179:1 181:1

**gra** 123:19

**grab** 154:10

**graded** 129:1

**gradient** 15:10 28:16

**gradients** 15:15

> 17:23 28:10, 13
>
> 123:19

**graham** 80:22 94:25

> 109:16, 25
>
> 110:11 119:1, 11
>
> 120:1 124:23
>
> 125:1, 1, 13
>
> 127:19 128:1
>
> 129:14 130:10
>
> 136:17 139:19
>
> 144:25 147:13
>
> 179:10

**granted** 57:22

**graph** 23:25

**great** 5:1 8:22 43:21

> 85:23 154:19 155:1
>
> 167:1 179:1

**greater** 48:1, 23

> 62:13 95:20, 21

**greyer** 46:21

**gross** 121:1

**ground** 14:24 28:1, 1

> 114:10 147:10

**group** 59:13 98:23

> 107:20 108:12
>
> 142:12 173:21
>
> 174:17 179:1

**groups** 5:18 8:10

**grows** 165:1

**growth** 49:12 50:1

> 63:25 64:1, 15, 16
>
> 65:1, 1 75:24
>
> 76:13 151:18
>
> 164:24
>
> **growths** 49:12
>
> **gst** 41:18
>
> **guess** 7:24 22:1,
>
> 18 26:13 36:13
>
> 37:1 55:1 66:1
>
> 74:20 86:22
>
> 95:20 109:1, 1, 18
>
> 111:1 112:1 114:12
>
> 124:1 128:1, 20
>
> 143:1 149:1
>
> 155:16, 22
>
> 159:21 160:1
>
> 183:23 184:1 185:1
>
> **guessed** 26:13
>
> **guise** 85:23
>
> **guy** 176:1, 15
>
> H
>
> **habit** 95:1
>
> **half** 44:16, 17
>
> 103:21 169:11
>
> 174:20
>
> **hand** 6:1 75:16
>
> **handle** 10:15 124:17 **handled** 105:1 **handling** 155:1
>
> **hands** 95:1
>
> **happen** 67:12 178:15 **happened** 90:12 **happens** 70:1, 1
>
> 174:1
>
> **happy** 18:12 42:1
>
> 58:1 183:14
>
> **hard** 8:16 41:1
>
> 169:12 176:16
>
> 179:1
>
> **harder** 65:11
>
> **harvey** 80:22
>
> 88:15, 25 112:1
>
> 115:1 148:1 152:19
>
> 159:13 173:1
>
> **harvey\'s** 94:11
>
> **hate** 169:14
>
> **hattis** 12:10 13:1,
>
> 14 14:1, 13, 20
>
> 16:1 17:13 18:24
>
> 19:1, 1, 17
>
> 21:22 23:20 24:1
>
> 25:17 26:1 27:1,
>
> 13 33:25 54:1,
>
> 17 64:1 121:21
>
> 144:1 146:1, 19
>
> 151:14 152:13
>
> 163:17, 20 164:1
>
> 169:24 173:15
>
> 175:18 176:1, 21

**haven** 128:15

**haven\'t** 44:23 50:1

> 170:11 173:1
>
> 175:15 179:1

**having** 30:24 47:22

> 53:20 74:12 77:10,
>
> 11 82:1 116:22
>
> 126:15 140:1, 16
>
> 152:1 167:1 176:11

**he\'d** 169:19

**he\'s** 78:13 162:1

> 176:11, 14

**head** 7:1 129:1

**headed** 73:1 **heading** 140:21 **heads** 29:1 **healing** 11:1

**health** 10:24 13:14

> 15:1 29:1, 1, 13
>
> 34:19, 21 36:20,
>
> 23 41:1 44:1 46:10
>
> 48:1, 19 49:1,
>
> 1, 23 50:10, 11
>
> 51:1, 22 52:1,
>
> 22 61:21 64:24, 25
>
> 70:19 76:12
>
> 77:22 80:11
>
> 81:12 88:19 90:1
>
> 93:12 94:1 97:11
>
> 100:1, 24 101:1,
>
> 1, 15 102:1, 15
>
> 103:1, 15 105:20
>
> 107:1 111:1, 1, 18
>
> 112:15 115:19
>
> 124:1, 22 125:22
>
> 130:1 141:21
>
> 144:20, 21
>
> 145:19 146:16
>
> 155:1, 10, 19
>
> 156:17 161:1
>
> 162:18, 20
>
> 163:1, 1 164:1
>
> 167:1 170:21
>
> 172:1, 1, 1 177:13
>
> 178:1, 10

**hear** 5:13, 25 7:11

> 17:1, 1, 1 32:19
>
> 39:25 79:1, 25
>
> 131:1 163:19
>
> 169:25 183:14,
>
> 20 184:1, 1

**heard** 31:1, 1, 1

> 59:11 72:11
>
> 84:24 104:17
>
> 125:21 153:25
>
> 170:15 184:1

**hearing** 5:1 160:16

**heart** 44:1

**heavily** 31:24

**height** 11:13, 18, 23

> 12:1 15:1 18:1, 17
>
> 28:24

**heights** 14:23 15:13,

> 18, 21 114:18

**hello** 2:1, 10

**help** 9:1 34:1

> 37:19 78:19
>
> 82:16 84:1, 1,
>
> 15 161:17 182:1

**helped** 79:1 163:13

**helpful** 10:1 51:25

> 134:22 182:11
>
> 184:17

**helping** 9:1 83:23

**helps** 12:20 113:1

**henderson** 2:1, 10,

> 13, 15 4:1 5:1, 24
>
> 6:1, 12 7:1, 17
>
> 8:1, 19 9:1, 10,
>
> 12, 19 10:1, 10
>
> 11:1, 1 12:1 14:1,
>
> 11, 17 17:1, 19
>
> 18:10, 20 19:1, 1,
>
> 14 20:1 21:21,
>
> 25 23:17 24:1
>
> 25:15, 21 26:16,
>
> 24 29:15 32:13
>
> 34:1, 15 35:1
>
> 36:1, 11, 25 37:1,
>
> 1 38:1, 10 39:1,
>
> 11, 13, 16 40:1,
>
> 22 41:1, 14, 21
>
> 42:10, 16 43:11
>
> 50:1, 16, 24 51:15
>
> 52:13 54:1, 14, 21
>
> 55:1, 11, 12 56:1,
>
> 13 57:1, 16, 19
>
> 58:20 59:15
>
> 60:1, 16 61:14
>
> 63:1, 13, 17,
>
> 20, 22 64:1, 13,
>
> 21 65:1, 1, 12
>
> 66:13, 22 67:1, 20
>
> 71:1, 12, 17, 20
>
> 73:1, 12 75:1,
>
> 1, 13, 16 76:1, 25
>
> 78:1 79:11 80:1
>
> 86:19 88:11, 17
>
> 106:10, 14, 17,
>
> 20, 24 107:1,
>
> 11, 13, 16 108:1
>
> 109:1, 1 110:1,
>
> 20, 23 115:1
>
> 116:1, 21 117:1
>
> 118:1 125:16, 21
>
> 127:1 129:17
>
> 132:20 134:13,
>
> 19 136:12, 18,
>
> 24 137:1, 1, 1,
>
> 18, 22, 25
>
> 141:1, 13, 17, 22,
>
> 24 142:1 143:23
>
> 144:11, 15
>
> 145:1, 1 146:24
>
> 1, 15, 17 53:12
>
> 54:19 59:17 60:14,
>
> 18 61:1 70:18 91:1
>
> 133:1

**higher** 8:14 19:1

> 21:15 62:22 75:1
>
> 110:1, 1 123:25
>
> 173:1 178:24
>
> 182:20

**highest** 26:1 30:12

> 45:1 54:1, 1
>
> 150:15 152:22
>
> 153:12

**highlighted** 72:1

**highly** 134:1 **highways** 133:1, 1 **historic** 109:10
**historical** 89:1

> 90:1, 21 96:1
>
> 131:1, 16 148:13

**historically** 115:1

**history** 35:10 73:15,

> 16

**hit** 50:1 92:24 135:1

**ho** 74:1

**hourly** 48:14 95:1

> 117:18, 22 142:23

**hours** 23:12 50:1

> 75:1 106:19

**houston** 96:22 113:15

**huge** 118:16 123:22

> 135:1 143:1, 1
>
> 163:25 180:1

**human** 36:18 91:20

> 99:12 101:24
>
> 102:12 103:12
>
> 111:12 112:16
>
> 113:1

**humans** 97:16

**hundred** 13:1 18:1

> 26:1 33:17 47:21
>
> 48:24 49:18
>
> 58:10 105:11

**hunger** 137:14

**hungry** 137:22

**hurry** 114:11 **hypothesizing** 49:22 **hypothetical**

> 105:12 130:19
>
> 132:11
>
> 147:19 148:1, 1

**hold** 5:25 184:14

> 149:18 151:1
>
> 153:23 154:18,
>
> 23 157:1, 10, 14
>
> 159:11 160:22,
>
> 25 162:1, 1 163:16
>
> 165:19 167:1
>
> 168:1, 1, 18, 25
>
> 169:1, 21 170:1,
>
> 1, 18 172:25
>
> 176:19, 22 178:11,
>
> 18 179:1, 23
>
> 180:1, 15, 20,
>
> 22 181:1, 1
>
> 182:11, 18
>
> 183:1, 1, 10,
>
> 13, 16 184:1, 1,
>
> 15, 20, 22, 25
>
> **here\'s** 70:1, 1
>
> **herring** 11:20
>
> **hey** 63:11
>
> **hi** 183:12
>
> **hice** 4:16, 18, 18,
>
> 21, 25 5:1, 15
>
> **high** 14:10 23:1,
>
> 10 26:18 30:1,

**holding** 137:24 **home** 52:1 135:1 **homes** 172:1

**hope** 22:15 59:1

> 65:11 78:19 89:1
>
> 113:1 122:1
>
> 138:14, 21
>
> 139:20 167:18
>
> 184:10

**hoped** 108:23

**hopefully** 139:22 **hoping** 120:1 **horizontal** 17:23
**horizontally** 13:1 **hospital** 104:1, 20

> 168:12

**hospitalization**

> 42:24

**hot** 26:1 28:22

**hour** 26:1 45:1 48:1,

> 1, 14, 16, 25
>
> 54:11, 25 73:20
>
> 75:1 76:20, 21
>
> 136:25 170:25
>
> 174:20
>
> I

**i\'d** 4:1, 21 7:1,

> 11 19:17 42:1
>
> 57:25 72:17
>
> 82:19 108:1 130:24
>
> 131:13 139:19
>
> 141:10 154:1, 1
>
> 157:16 161:13,
>
> 21 172:23

**i\'ll** 4:12, 23 11:1

> 37:22 51:16 52:1
>
> 77:1 87:16 95:19
>
> 115:1 136:21
>
> 140:18 142:12
>
> 159:21 160:20
>
> 168:24 178:17
>
> 181:12 183:18,
>
> 21 184:21 185:1

**i\'m** 3:11 5:18 6:20

> 10:14 16:13, 18
>
> 17:20 18:18 22:1
>
> 27:24, 24 28:1
>
> 29:1, 11 30:1, 14,
>
> 20 32:10 34:11
>
> 36:14 38:21 39:11,
>
> 19 40:25 42:14, 24
>
> 43:1, 23 44:13, 24
>
> 46:21 48:1, 1,
>
> 1, 1, 16, 21, 22
>
> 49:22, 22 50:1,
>
> 19, 22 55:13
>
> 58:1 59:1 62:10
>
> 63:1 64:14 66:16
>
> 67:11 68:17
>
> 73:12 74:1 76:14
>
> 80:21, 23 82:21
>
> 88:19, 20 89:1,
>
> 11, 24 92:20, 23
>
> 94:21 97:24 100:19
>
> 106:18, 25 108:21,
>
> 23 110:1 111:17
>
> 116:21 119:1
>
> 120:1, 10, 10
>
> 124:1, 1, 16
>
> 125:19 126:1, 1
>
> 127:20 128:1
>
> 129:22 133:1 135:1
>
> 137:15, 22, 24
>
> 138:14, 21
>
> 139:16 142:13
>
> 147:13 153:1
>
> 157:11, 12
>
> 159:20 160:1,
>
> 16, 17 161:24
>
> 163:17, 23
>
> 165:19 168:1
>
> 169:11, 22
>
> 171:20 172:1
>
> 175:12 176:21
>
> 177:1 179:1
>
> 180:16, 22 181:1
>
> 184:13
>
> **i\'ve** 2:17 23:20
>
> 46:18 47:16
>
> 102:1 153:25 159:1
>
> 161:1 172:24
>
> **idea** 6:24 8:11 38:24
>
> 59:12 86:22 120:13
>
> 122:14 126:1
>
> 159:21, 22 164:1
>
> 172:1, 23 183:13
>
> **ideal** 152:17
>
> **ideally** 102:1 **ideas** 115:15 **identi** 117:11
>
> **identification** 97:1
>
> **identified** 94:17
>
> 95:23 97:12, 21,
>
> 22 98:1, 16
>
> 99:20 100:1 102:10
>
> 120:1 170:22

**identify** 101:1

> 103:13 114:20

**ignore** 153:24

**ignored** 38:15 **ignoring** 178:10 **illinois** 87:25 **illustrate**
102:17 **imagine** 140:1, 1

> 151:18, 19 152:15

**inadvertently** 35:1

**incidents** 104:1

> 105:1, 10, 11, 12

**include** 2:25 7:19

> 9:22 37:21, 22
>
> 93:1 95:22 98:21
>
> 105:24 152:16
>
> 164:22 168:10

**included** 2:20 6:19

> 7:20 10:12, 18
>
> 22:16 35:1 36:1,
>
> 17, 21 126:18
>
> 127:1 161:1 163:12
>
> 179:15

**includes** 13:16

> 35:12, 17 60:22
>
> 134:1

**including** 19:20

> 87:22 99:12 104:1,
>
> 20 110:15 126:24

**inconclusive** 177:1

**incorporate** 60:12

> 143:13 159:1

**incorporated** 160:11

**incorporating** 143:17

**increased** 59:25

> 60:1, 1, 1, 10
>
> 91:23

**increases** 116:10

**independent** 111:12

**indicate** 51:1, 1

> 52:18 168:10

**indices** 54:24

**indirect** 87:1

**individual** 24:1

> 38:1, 1 83:14,
>
> 18 85:1, 1, 25
>
> 98:18 99:22
>
> 103:1 116:1, 1
>
> 119:1, 17 120:1

**individually** 38:25

> 88:1

**individuals** 60:1

> 95:23 98:17
>
> 103:1 118:1

**indoor** 12:25, 25

> 16:1 20:1 21:1, 17
>
> 22:1 27:16, 22
>
> 32:24 33:1 97:22
>
> 99:24 126:22, 25
>
> 127:1

**indoors** 8:12 96:16

> 98:1 126:25

**induces** 62:1

**industry** 152:1

**infection** 46:1

**infer** 12:22, 24 15:1 **inferences** 183:1 **inferred** 12:20
**inflammation** 62:13

> 177:23 178:1

**influence** 41:15

> 96:15

**influenced** 74:1

**influences** 7:25

> 13:22 95:14

**influencing** 97:23

**influential** 98:1 **inform** 164:1 182:1 **information** 8:1 9:15

> 11:22 16:1 17:1
>
> 19:21 23:14
>
> 25:22 33:14, 21
>
> 36:1 41:18 61:25
>
> 66:1 68:1 69:14
>
> 70:1 100:1 103:13,
>
> 15 104:1 105:20
>
> 106:1, 1 112:10,
>
> 12 115:12, 15
>
> 116:17, 22
>
> 117:16 119:18
>
> 120:16 133:21
>
> 138:13 142:21
>
> 144:1 145:13, 15
>
> 146:1 161:22
>
> 173:22 177:21, 25

**informative** 86:1

> 184:10

**informed** 161:23

**impact** 11:18 12:1

> 15:1 95:15
>
> 102:17 113:10
>
> 123:22 130:12
>
> 144:1 160:16
>
> **impacts** 13:1 36:23
>
> 42:20 52:21
>
> 80:12 164:15
>
> **implementation**
>
> 87:1 165:25
>
> **implemented** 117:13
>
> 150:11
>
> **implementing** 84:22
>
> **implications** 51:23
>
> 77:10, 11 84:21
>
> 126:1 153:16 165:1
>
> **importance** 11:13
>
> 21:1, 18 29:16
>
> 32:15, 20 116:10
>
> **important** 7:1 12:1
>
> 13:10 15:16
>
> 18:19 21:19
>
> 31:15 33:1 34:17
>
> 40:1 42:15 46:11
>
> 68:1, 23 69:14
>
> 81:11 82:17 83:12,
>
> 22, 25 86:12 95:14
>
> 97:23 98:16 120:15
>
> 122:23 128:24
>
> 132:20 136:1
>
> 140:25 151:1
>
> 156:22 158:1, 14
>
> 161:16, 18, 20
>
> 162:22 164:1,
>
> 15, 25 165:18
>
> 166:24
>
> **imposing** 174:1
>
> **impossible** 176:18
>
> **impressed** 44:13
>
> 129:22
>
> **impression** 51:13
>
> 108:17
>
> **impressive** 45:16
>
> **improve** 19:15 97:14 **improved** 36:1 155:13 **inhaling** 27:1
> **inherent** 85:1, 16 **inherently** 60:1 **initial** 83:25 181:13
> **initiative** 127:23 **injury** 64:1 65:1, 1 **inlet** 15:1, 17 18:17
>
> 28:23

**inlets** 15:14 19:1

> 114:1

**innate** 60:1

**inordinate** 176:1

**input** 99:1

**inputs** 99:1 100:13

> 172:20

**inside** 123:25

> 125:1 138:21

**insightful** 126:15

**insights** 104:1, 11

**instance** 111:23

> 135:15 136:1
>
> 157:25

**instances** 162:19

**instead** 3:1 26:1

> 36:18 38:17
>
> 46:15 72:20

**instructive** 119:1

**integrated** 69:20, 21

> 93:18 159:1, 15,
>
> 17 174:23 181:24

**integration** 36:1

> 71:1

**intended** 61:24

> 63:1 67:21, 23
>
> 68:1 84:1

**intent** 62:1 119:11

**intentional** 163:18

**inter-subject**

> 138:15, 18

**interaction** 61:1

> 86:1

**interactions** 83:15

**interacts** 74:10 **intercept** 24:22 **interchangeably**

> 55:17 56:1

**interconversion** 58:1

**interconvert** 56:1

> 57:10

**interest** 100:1

> 103:18 108:12
>
> 120:10, 11 143:25

**interested** 115:12,

> 20 118:25 130:24
>
> 140:18 155:1

**interesting** 117:15

> 167:25 184:16

**internal** 27:16

**interpolated** 142:23

**interpret** 35:11

> 39:25 70:19

**interpretations** 83:1

**interpreting** 20:11

> 21:19 171:13

**intervals** 105:1

**intrinsic** 60:1

**introduce** 35:1 80:1,

> 17

**introduction** 94:21

**invaluable** 81:1 **inventory** 133:24 **investigated** 128:21
**investigators** 179:1 **invite** 154:10 **inviting** 4:10 **involve**
101:22 **involved** 115:1

> 116:18 174:19

**isa** 3:10, 17 4:11,

> 12 5:1 9:1, 25
>
> 10:1, 1, 1, 22
>
> 11:18, 21 12:1
>
> 14:22 19:1, 10,
>
> 15, 19 24:1 26:1
>
> 32:18 36:1 42:1
>
> 45:1 53:1, 1
>
> 54:1 67:22 77:1
>
> 78:1 82:10 89:15
>
> 95:15 100:1 101:17
>
> 103:22 104:15,
>
> 18 105:21 155:18
>
> 156:21 157:1, 1,
>
> 1, 23 171:1 177:18

**isa\'s** 9:22

**irrelevant** 30:25 **irritant** 62:14 66:1 **it\'d** 109:21

**it\'s** 2:17 3:1, 1,

> 1 6:1 8:1, 24
>
> 9:1 10:19, 20
>
> 12:21 13:12, 25
>
> 15:1 16:1 17:11
>
> 19:12, 18, 22
>
> 20:10, 23, 23
>
> 24:1, 22 30:1, 1
>
> 32:16 37:1, 1,
>
> 1, 10, 18 40:16,
>
> 24 41:11 42:1
>
> 44:14, 15, 16,
>
> 20 45:1, 23, 25
>
> 47:1, 19 49:1
>
> 50:18, 19 51:25
>
> 54:1 55:15, 24
>
> 57:14, 22, 23
>
> 58:15, 17, 24
>
> 61:20 63:16, 18,
>
> 18 64:18 66:1,
>
> 22 67:15 69:1 70:1
>
> 71:22 73:1 74:1, 1
>
> 75:1, 11 76:1 79:1
>
> 80:1 82:1 83:1, 12
>
> 85:1 86:12, 24
>
> 88:1 91:12, 12
>
> 94:1 95:17, 20
>
> 98:1, 1, 18, 19,
>
> 24 99:10, 23
>
> 101:20, 22, 25
>
> 103:16 111:1,
>
> 12, 17, 19, 21
>
> 112:20 113:1
>
> 114:1, 1, 15,
>
> 24, 25 115:1, 21
>
> 116:1 117:1, 1, 1,
>
> 17 118:16 119:15
>
> 121:1 122:1, 15
>
> 124:1 126:1, 1, 1,
>
> 19 127:10 128:1,
>
> 1, 19 129:25
>
> 130:17 132:1, 1,
>
> 10 135:1, 19
>
> 137:19, 25 138:1
>
> 139:13, 22
>
> 144:18 145:1, 25
>
> 147:15 148:19,
>
> 24 149:25
>
> 150:12, 13, 19
>
> 151:11 152:1
>
> 154:14 161:17
>
> 162:22 165:1,
>
> 15, 23, 25
>
> 166:23 167:1, 24
>
> 168:11, 18
>
> 169:10 170:1,
>
> 19, 24 173:1
>
> 175:1, 23 176:1,
>
> 1, 16, 18, 22
>
> 177:13 178:23
>
> 181:1
>
> **isn\'t** 15:18 25:16
>
> 47:1 68:1 75:10
>
> 85:15 164:16
>
> **items** 71:1
>
> **iteration** 51:12
>
> **iterations** 38:18
>
> 77:15

**issue** 10:15 11:1

> 27:1, 1 34:16,
>
> 25 42:1 43:19
>
> 53:10, 11, 15,
>
> 15 57:1 69:11,
>
> 20 70:14 72:1, 1
>
> 82:25 84:19
>
> 86:11 88:1 90:1,
>
> 1, 10 93:1
>
> 131:13 135:1 138:1
>
> 146:1 147:1
>
> 150:1 156:22 167:1

**issued** 87:18

**issues** 6:16 8:1

> 10:21 18:13 53:1
>
> 59:17 61:1 82:19
>
> 83:22 90:15 93:1
>
> 121:1 126:11 157:1
>
> 177:1 179:21
>
> J

**james** 10:11, 14

> 25:21 43:20
>
> 45:14 46:23 73:1
>
> 161:1 162:1
>
> 169:1 173:12

**jealous** 78:1

**jenkins** 80:21 88:18,

> 19 106:1

**jill** 30:1

**jim** 4:1, 1, 1

> 45:14 63:11, 13
>
> 77:1 142:1

**job** 130:1 164:1

> 167:1 185:1

**joe** 33:14

**john** 3:25 4:18 5:1

> 31:1 35:25
>
> 36:15, 15 37:22
>
> 38:1 43:18 45:13
>
> 50:17 71:1 74:21
>
> 78:12 106:25 155:1
>
> 157:1 169:14 170:1
>
> 183:12, 14
>
> 184:1, 10, 23

**johns** 170:1

**join** 107:1, 17

> 154:10

**joking** 168:1

**jotted** 6:14

**joyce** 36:12

**ju** 69:1

**judge** 103:15

**judged** 101:20 103:10 **judging** 17:17 **judgment** 72:1, 15

> 103:12 122:22
>
> 140:20, 22 142:1

**jump** 58:17 70:23

> 76:25 86:18

**jumping** 79:17

**justice** 61:1

**justify** 74:15

> K

**karen** 73:16 77:1

> 80:20 82:21
>
> 88:1, 12 106:10
>
> 183:1

**kenski** 57:1, 18,

> 21 58:1, 18
>
> 86:21 109:1, 1, 23
>
> 110:1, 19 134:1

**kent** 132:21 134:1

> 162:1 163:16, 22
>
> 176:23

**key** 21:1 35:1, 14,

> 23 36:1 43:24
>
> 101:1 110:13 124:1
>
> 125:15 143:13

**keywords** 37:18, 25

**kicked** 94:1 **kid** 50:1 **kids** 165:12

**kinds** 14:1 15:15

> 28:10 53:1 69:14
>
> 70:11 113:19
>
> 138:1, 16 140:1
>
> 142:20 168:16

**kleeberger** 162:1

**knew** 38:13 56:1

> 157:10

**kudos** 58:25

> L

**l.a** 131:1 150:12, 13

**la** 92:19

**lab** 41:18

**lack** 66:19 93:12,

> 12, 14

**laid** 36:1 53:21

> 148:10 155:17
>
> **lakewood** 28:20
>
> 30:1 33:15
>
> **large** 11:16, 17
>
> 30:20 33:24 49:1
>
> 139:11, 12
>
> 150:11 173:23
>
> 176:14
>
> **larger** 11:19 13:1
>
> 14:25 19:21
>
> **larson** 4:1, 1
>
> 15:1, 1 16:13
>
> 17:10, 20 18:17
>
> 22:17, 22, 24
>
> 28:1, 10, 15 31:1,
>
> 1, 12, 16 33:19
>
> 46:23, 23 47:1
>
> 48:1, 10, 13, 21
>
> 50:1 62:18, 21
>
> 64:23 65:23 66:16,
>
> 25 110:20, 22,
>
> 25 113:1 116:13
>
> 117:1, 1, 11
>
> 152:19 153:1, 22
>
> **last** 11:14 20:1
>
> 23:15 36:1 38:21
>
> 40:25 42:14 44:1
>
> 51:1 52:15 56:18
>
> 61:18 77:1 90:23
>
> 91:16 92:19 93:19,
>
> 20, 22 103:1, 20
>
> 131:1 154:25
>
> 159:1, 12 184:16
>
> **late** 40:18
>
> **later** 21:18 23:24
>
> 55:10 90:1 126:1
>
> 136:23 144:1 155:1
>
> **latest** 36:18
>
> **lawnmowers** 134:1
>
> **lay** 51:1 71:1 173:12
>
> **layer** 128:1 **layout** 181:13 **lead** 33:10 67:25
>
> 88:15, 19 118:24
>
> 120:14 123:1
>
> **leading** 180:23
>
> **leads** 155:18 **leaning** 75:22 **learned** 70:10 **learning**
> 115:11
>
> 123:16, 17
>
> **least** 29:11 30:18
>
> 83:25 84:1
>
> 91:10, 11 113:1
>
> 121:23 122:1 126:1
>
> 129:16 131:23
>
> 136:1 144:1
>
> 152:1 156:1, 11
>
> 159:1 171:12

**leave** 60:14 107:1

> 140:22

**leaving** 106:17, 22

> 157:13

**led** 16:1

**lee** 4:1

**legs** 121:22 **lengths** 27:15 **lesions** 91:1

**less** 25:1, 1, 18

> 35:17 134:1 165:1,
>
> 1

**lessons** 122:1

**let\'s** 7:1, 16 20:17,

> 18 34:15 40:20, 20
>
> 59:1 64:10, 22
>
> 77:1 108:1, 13
>
> 118:1 120:17 121:1
>
> 126:11 130:1, 1
>
> 137:19 178:1

**letter** 3:1 6:1

> 17:1 18:14, 20
>
> 29:16 33:1 38:1
>
> 39:1 59:1 65:17
>
> 67:10, 13, 16
>
> 78:1, 1, 17, 19
>
> 116:1

**letting** 107:15

**lev** 171:12

**level** 14:24 15:1

> 16:1 19:1 21:1
>
> 23:1, 13 24:15, 16
>
> 28:1, 1, 1 29:23
>
> 30:17 45:16
>
> 70:18 72:21
>
> 92:16 119:22
>
> 120:1, 11 121:1
>
> 123:15, 18, 23
>
> 124:21 143:12
>
> 145:15 146:1
>
> 149:14 152:1
>
> 153:12 171:1,
>
> 11, 18, 22 175:15

**levels** 12:15, 17, 19

> 17:18 19:12 20:18,
>
> 25 23:10, 12, 22
>
> 29:1 30:12, 15, 22
>
> 35:14 47:23, 25
>
> 73:19 90:1 91:1,
>
> 1, 1, 1, 19, 20,
>
> 22 92:1, 1, 13
>
> 94:13, 14, 15
>
> 95:1, 1 97:1, 1
>
> 98:21 100:1
>
> 101:10, 11, 16
>
> 102:1, 1, 1, 16,
>
> 18, 25 103:1
>
> 107:22 111:1
>
> 112:15 115:20
>
> 123:22 124:14
>
> 130:13 131:1, 11
>
> 133:15, 16, 20
>
> 140:1 148:14
>
> 158:19 173:1
>
> 181:18 182:21

**lianne** 39:12, 13,

> 17, 20 106:14,
>
> 21 116:18 136:19
>
> 142:10 143:23
>
> 147:22 153:25
>
> 160:23 177:11

**lifetime** 165:1, 10

**likely** 27:21 28:1

> 30:1 34:1 42:22
>
> 44:20 49:25
>
> 61:1, 1 62:19, 22,
>
> 24 103:10 104:15
>
> 112:21 146:1 158:1
>
> 164:1 174:11
>
> 177:15, 24 178:22

**limit** 82:11 83:19

> 113:23 150:1

**limitations** 135:1

**limited** 98:1 99:1

> 105:18 152:12
>
> 177:18

**limits** 47:22 108:19

**line** 24:20 25:1 35:1

> 40:22, 23, 23
>
> 128:10 134:22
>
> 138:1 154:16
>
> 169:19

**linear** 160:1, 1

> 166:1, 15

**lines** 40:11, 24

> 151:24

**link** 52:1 57:15

> 97:15
>
> **link-based** 128:14
>
> **linkage** 44:1 145:1
>
> **list** 2:18, 19, 21
>
> 5:1 6:1, 10, 13,
>
> 13, 19 69:1
>
> 110:1 116:1 168:11
>
> **listed** 2:19 32:21
>
> 103:25 104:16
>
> 105:1 143:24
>
> **listened** 23:1 103:20
>
> 183:15
>
> **listing** 6:21 71:1
>
> **lit** 37:16
>
> **literature** 17:10, 14
>
> 19:21 46:1 91:1,
>
> 21 103:11 112:24
>
> **little** 3:20 4:13
>
> 9:25 10:15 35:10
>
> 42:21 54:1, 12, 19
>
> 20 131:1 135:22
>
> 179:12

**log** 24:1, 18, 22,

> 23, 24 25:1, 1, 1

**log-probit** 174:1

**logical** 109:11 168:1 **logically** 76:1 86:1 **logistic** 109:21

> 160:1, 1

**long** 30:25 33:12, 16

> 58:1, 1 75:22 91:1
>
> 95:1 101:12 114:24
>
> 120:1 131:1 156:18
>
> 165:1 177:1

**long-term** 49:13

> 73:18 75:23 76:1
>
> 90:1, 13, 17 91:1,
>
> 1, 14 94:19
>
> 95:10 97:1 98:20
>
> 173:10

**lower** 47:22 61:1

> 101:10 130:18
>
> 140:1 144:1 151:12

**lowest** 47:19

> 102:18 171:18

**lumped** 119:13

**lunch** 107:1 137:1,

> 1, 1, 10, 11, 12
>
> 154:1, 1, 1, 1,
>
> 11, 13 155:1

**lung** 46:1 49:1,

> 11, 12, 20 50:1
>
> 75:24 76:13 91:1
>
> 156:1, 19, 22
>
> 164:24 165:1
>
> 173:18

**lydia** 80:1, 1, 1, 10

**lynwood** 33:14, 15

> 56:1 69:1 75:14

111:1, 14 135:16

> 76:23 88:21
>
> 89:1, 1, 1, 23, 24
>
> 90:1, 20, 21 92:24
>
> 100:11 107:1, 10
>
> 128:1 134:1, 17
>
> 137:13, 20
>
> 139:17 149:20
>
> 153:1 159:22
>
> 163:13, 23
>
> 165:16 181:1
>
> **live** 32:1 59:14
>
> **lives** 113:17
>
> **living** 29:1 180:21 **lo** 24:1 25:18 76:1 **local** 95:14 98:14
>
> 121:1, 1, 23, 24
>
> 122:1, 1 123:1,
>
> 13, 14, 18
>
> 124:1, 21
>
> 125:22, 25 174:1
>
> **locally** 121:18
>
> 124:1, 1
>
> **located** 19:11
>
> **location** 18:1
>
> 99:13 152:21
>
> **location-specific**
>
> 121:1
>
> **locations** 18:1 47:24
>
> 59:18 96:17 97:1
>
> 98:1, 1 99:19
>
> 100:1 110:10
>
> 118:20 119:15, 16,
>
> 144:17 145:11, 11,
>
> 12, 22, 24
>
> 146:1, 13, 17
>
> 155:23 156:1, 21
>
> 170:20 173:14,
>
> 14 177:1

**longer** 9:1, 23

> 25:1 34:11, 12
>
> 128:1 137:20
>
> 155:21 174:24

**los** 28:20 30:1 92:15

> 96:22 119:12 152:1

**lose** 75:21 121:1

> 165:12

**losses** 156:19

**lost** 153:1

**lot** 11:22 15:20

> 17:12 23:13
>
> 31:1, 12, 13, 20
>
> 32:1 45:1 53:1, 20
>
> 55:1 68:1 70:14
>
> 74:15 108:1
>
> 124:1 135:18
>
> 142:15 155:1, 1
>
> 160:1 176:1 184:22

**lots** 45:10 53:1

> 108:1 110:10 174:1
>
> 176:15

**louis** 88:1

**love** 5:13 172:23

**low** 14:12, 14

> 23:1, 12, 13 91:23
>
> M

**magnitude** 47:17, 17

**main** 69:10 157:17

> 164:1

**maintain** 91:1

**major** 33:22 59:1

> 68:1 74:22
>
> 126:10 133:11,
>
> 25 158:1, 19 159:1
>
> 181:13

**maker** 68:14

**man** 168:1

**management** 85:10

> 87:21

**manager** 87:18

**manner** 178:23

**map** 31:1 115:24

> 180:1

**mapping** 147:1

**maps** 9:22 **march** 180:14 **marine** 134:1 **marked** 72:1
**markov** 176:11

**martin** 73:16 77:1

> 80:20 82:24
>
> 88:14 106:12
>
> 120:20, 22
>
> 122:21 181:1, 10
>
> 182:12, 22 183:1

**mary** 9:19 10:1

> 34:1 51:16
>
> 54:14, 21 67:17
>
> 89:10
>
> **mass** 85:23 93:24
>
> 167:10, 24
>
> **massive** 117:17
>
> **match** 112:17 **matches** 130:20 **material** 6:1 9:22
>
> 108:1
>
> **matter** 14:1 16:1
>
> 72:21 85:20 153:18
>
> 175:1, 16
>
> **max** 46:25 47:1, 1
>
> 48:1, 14, 24 54:25
>
> 55:1
>
> **maximum** 54:1 82:13
>
> 149:12
>
> **may** 3:21 11:1 13:11,
>
> 22 17:1, 1 22:1
>
> 27:23 34:25
>
> 41:15 43:1 44:1
>
> 49:17 51:1 52:17
>
> 54:18, 20, 20
>
> 58:13 61:1, 1
>
> 62:17 67:18 79:1
>
> 96:14, 15 98:17
>
> 102:1, 1 107:1
>
> 108:1 111:15
>
> 118:10 119:20,
>
> 22 120:20 128:1,
>
> 1, 21, 24 132:22
>
> 133:18, 18, 23
>
> 135:10 145:19
>
> 147:1 149:16
>
> 151:22 152:1
>
> 153:12, 13
>
> 162:1, 13
>
> 168:12, 13
>
> 174:13 177:1, 1
>
> 182:25 183:1 185:1
>
> **maybe** 8:24 11:20
>
> 12:1 16:23
>
> 19:12, 20 30:1
>
> 32:14 34:11 50:1
>
> 65:1, 18 73:1 79:1
>
> 80:1 110:1
>
> 113:15 114:15
>
> 118:22 120:10,
>
> 10 122:12 123:1
>
> 126:1 133:19 134:1
>
> 136:21 137:11
>
> 139:17 148:1, 1
>
> 152:1, 1 154:1
>
> 156:1 169:1 174:19
>
> 176:1

**mean** 9:1, 14 10:1,

> 1, 18 12:17
>
> 14:1, 1, 18
>
> 16:25 17:11
>
> 18:11 20:21
>
> 24:23 25:13, 15,
>
> 23 27:21 28:15, 18
>
> 30:1 33:1 34:1, 1,
>
> 10 35:1 37:1, 1, 1
>
> 38:11 41:1, 11,
>
> 17, 24 42:1, 1, 15
>
> 43:25 44:1 46:1
>
> 47:1, 1 48:19
>
> 49:1, 1, 22
>
> 50:20 51:1
>
> 53:13, 16 54:1
>
> 55:15 56:1, 1, 10,
>
> 14 57:11, 17, 19
>
> 59:24 61:24 62:19,
>
> 23 63:23 64:14
>
> 66:25 67:1 68:1, 1
>
> 70:1 73:10, 14
>
> 74:1 75:25 85:13
>
> 111:1, 22 113:13
>
> 114:15 115:1
>
> 117:11, 20
>
> 121:13 122:1
>
> 123:11, 19
>
> 125:23 127:25, 25,
>
> 25 130:1 134:1
>
> 141:18 143:1
>
> 145:1, 1, 12, 21
>
> 147:24 149:21,
>
> 21 150:15
>
> 152:16, 23
>
> 153:1, 1 154:13
>
> 168:1, 1, 1 169:23
>
> 170:1 171:1 172:13
>
> 173:1, 11, 12
>
> 175:22 176:1 177:1
>
> 178:1 180:1

**meandering** 128:1

**meaning** 35:15, 19

> 131:1

**meaningful** 122:25

> 123:1

**means** 10:1 14:14

> 22:21 25:1
>
> 135:1, 18 150:25

**meant** 6:12 33:14

> 35:1 68:22 116:1

**measure** 21:14 57:12,

> 12 58:1 158:1
>
> 173:17

**measured** 14:16 15:25

> 16:1 99:1 173:21
>
> 174:15

**measurement** 33:1

> 90:10

**measurements** 16:1

> 19:24 20:1, 1,
>
> 10 54:1 57:1, 1
>
> 117:18 125:12
>
> 142:25 147:25

**measuring** 18:22

> 55:21, 21 72:24
>
> 90:11

**mechanism** 61:24

> 62:24

**mechanisms** 63:1, 1

**mechanistic** 41:19 **mecklenburg** 133:12 **medical** 30:18

> 42:25 53:1, 1

**medit** 167:1

**meet** 148:21, 22

> 149:25 152:25
>
> 153:1 158:17

**meeting** 2:1 89:13

> 92:1 94:14, 16
>
> 101:11 105:13,
>
> 14 130:1, 16
>
> 131:1, 12 132:18
>
> 147:24 148:12,
>
> 14 149:21, 22
>
> 150:1, 1, 1, 10,
>
> 25 151:10 152:20
>
> 164:17 166:12
>
> 169:13 185:1, 11

**meeting\'s** 185:10

**meets** 130:21 **member** 4:11 **members** 3:22, 23

> 4:14 5:1 7:1 38:16
>
> 78:14, 16

**ment** 37:1

**mention** 5:1 36:15

> 37:11 72:13
>
> 99:21 129:15

**mentioned** 4:1

> 18:14 36:14, 16
>
> 37:1, 10 41:1
>
> 42:11 72:1 92:11
>
> 95:18 96:1
>
> 108:20 116:15
>
> 127:13, 14
>
> 128:22 138:1
>
> 146:14 147:16
>
> 177:1, 11
>
> **message** 142:1
>
> **met** 29:25 153:19
>
> **meters** 13:1 28:1, 1,
>
> 16, 25
>
> **method** 137:1 139:1
>
> **methodologies** 141:1 **methodology** 151:17 **methods** 3:18 5:1,
>
> 1 79:16, 22
>
> 80:19 81:15
>
> 88:23 95:21
>
> 109:1 110:24
>
> 114:16 116:1
>
> 154:25 169:18
>
> 180:12 183:18
>
> **metric** 29:12 44:25
>
> 45:11 100:1
>
> **metrics** 29:23
>
> 70:15 98:11
>
> **microenvironmental**
>
> 100:17
>
> **microenvironments**
>
> 98:1 99:1
>
> **microphone** 2:12
>
> **middle** 40:18
>
> **midnights** 49:1
>
> **mike** 5:25 6:1 109:1
>
> **mind** 25:25 30:10
>
> 46:18 77:1, 15
>
> 82:20 137:14
>
> 163:1, 1 169:1
>
> **mindful** 87:17 88:1
>
> **mine** 141:11 **minimize** 12:1 **minor** 120:17 **minute** 79:1
> 92:21
>
> 116:14
>
> **minutes** 7:1 107:1
>
> 137:10 148:1
>
> 169:23 174:20
>
> 176:20 185:1
>
> **misinterpret** 151:12
>
> **misinterpreted**
>
> 166:18

**mislead** 166:25

**mismatch** 85:1, 1, 16

> 86:13

**misread** 132:1

**miss** 169:14

**missed** 144:1 148:1 **missing** 70:1 133:1 **missouri** 87:25
**misspoke** 31:19 **mitigation** 150:1

**mix** 85:1 86:1 164:11

**mixture** 66:1

**mo** 149:1 153:1 **mobile** 117:24 152:1 **mod** 114:23

**model** 97:20 99:1, 1,

> 17, 22, 23, 25
>
> 100:1, 12, 15
>
> 109:13, 14, 20
>
> 114:13 117:14, 21,
>
> 23, 24, 25
>
> 119:21 120:18
>
> 125:14 126:17, 19,
>
> 23 127:11, 14, 22,
>
> 24 128:17 129:1,
>
> 16 134:25
>
> 135:15, 18
>
> 143:13 161:1
>
> 166:15 171:1, 21
>
> 172:1, 19, 21

**modeled** 97:1 99:1

> 172:20

**modeling** 97:18

> 109:12 118:14
>
> 120:24 125:13
>
> 133:15 134:18,
>
> 24 135:1 143:10
>
> 147:1 152:11, 23
>
> 153:1, 17 172:18

**models** 105:1

> 109:15 113:11
>
> 117:12 121:1
>
> 129:10 138:10
>
> 143:18 152:1
>
> 166:10

**modest** 173:16

**modification** 52:15 **modified** 19:20 51:17 **modify** 67:1, 1

**moment** 4:24 80:21

> 83:1

**mon** 8:23

**money** 26:14 82:1

**monitor** 13:1 20:25

> 30:1, 1 47:1
>
> 130:21 150:15, 17,
>
> 18 151:1 152:22
>
> 153:14, 21

**monitoring** 11:18

> 12:1, 16 15:11
>
> 31:1 32:22 43:1
>
> 72:10 76:21
>
> 93:14 106:1 111:1,
>
> 16, 20, 25
>
> 112:11 113:1
>
> 116:19 123:10,
>
> 12 125:12 130:20
>
> 153:15, 20 171:23

**monitors** 11:14, 23

> 12:1, 20 14:1,
>
> 23 15:1, 18
>
> 16:14 17:1, 22
>
> 18:18 19:11
>
> 20:15 21:14
>
> 22:14 28:1, 21
>
> 30:1, 11, 11 31:10
>
> 32:11 33:1 34:1
>
> 47:1 49:16 54:18
>
> 76:18 96:20 110:10
>
> 113:1, 1, 1
>
> 131:11, 17, 18,
>
> 19, 20 135:24
>
> 146:1 148:25 149:1
>
> 150:24 151:1 153:1

**monte** 143:12 175:1

> 176:11

**months** 89:21, 21

> 91:10 108:20

**mor** 168:10

**morbidity** 104:19

> 168:11, 19

**morning** 4:1, 1, 12

> 26:17 31:1 39:1
>
> 67:1 70:14 79:24
>
> 108:1 118:19
>
> 154:22 162:1
>
> 163:18 185:1

**mornings** 123:17

**mortality** 165:1

> 168:10

**mostly** 176:14

**move** 3:17 7:11

> 68:1 79:1, 1 92:20
>
> 94:17 118:1 120:24
>
> 129:19 139:1, 16
>
> 145:20 147:11
>
> 154:1
>
> **moved** 134:14
>
> **moves** 143:11
>
> **moving** 45:20 49:14
>
> 59:17 79:13, 14
>
> 88:1 105:13 139:1,
>
> 15 160:1
>
> **multi** 82:25 85:10,
>
> 18
>
> **multi-pollutant**
>
> 59:1, 1 65:15
>
> 66:14 82:22
>
> 83:1, 1 84:19,
>
> 20 85:22 86:23
>
> 87:10, 22, 24
>
> 88:1, 1 105:1
>
> **multi-pollutants**
>
> 65:20 87:1
>
> **multi-word** 43:22
>
> **multiple** 86:1 119:15
>
> 159:25
>
> **mute** 134:21, 23
>
> **myself** 27:25 144:16
>
> N
>
> **naaqs** 35:1 84:21, 23
>
> 90:23 93:21 94:1
>
> **name\'s** 80:10
>
> **national** 106:1 119:1
>
> 120:1, 11 121:1,
>
> 1, 11, 15, 17,
>
> 25 122:1, 1
>
> 126:1 161:12
>
> 164:1, 1, 10, 14
>
> 179:19
>
> **nationally** 120:1, 13
>
> 126:10
>
> **nature** 28:14
>
> **nearest** 131:17
>
> **necessarily** 31:1
>
> 45:17 66:17
>
> 85:15 86:23 140:13
>
> 172:14 173:19
>
> **necessity** 90:25
>
> **negative** 35:1 44:15,
>
> 17
>
> **neglected** 3:12

**network** 130:21

> 153:15, 20

**nevertheless** 176:1

**nice** 121:14 155:1

**night** 23:10 30:25

> 40:18 44:1

**nine** 72:1

**nitric** 65:25 66:1,

> 1, 20

**nitrogen** 37:23 92:25

> 93:1, 1, 1, 17
>
> 94:1 163:1

**no\'s** 48:1 75:1

**no2** 10:19 15:11,

> 18 16:14 17:1,
>
> 22 20:1 21:13
>
> 23:22 29:22
>
> 30:24 32:1
>
> 36:21, 23 41:1
>
> 43:24 44:1, 1 46:1
>
> 51:1 55:15, 19,
>
> 20, 24 56:1, 1
>
> 57:1, 1, 1, 12,
>
> 15, 17, 23 58:1,
>
> 1, 12, 13 62:1, 1,
>
> 14 66:10 74:1, 13,
>
> 16 75:1, 1 88:19
>
> 90:1, 11, 23 91:1,
>
> 1, 11, 18, 23
>
> 92:1, 1, 13, 25
>
> 93:1, 1, 10, 15
>
> 94:1, 13 95:1, 1
>
> 97:1 98:1 100:1,
>
> 25 101:1 106:1
>
> 107:22 111:1 115:1
>
> 116:19 121:1
>
> 128:23 133:16
>
> 142:23 156:1
>
> 162:21 165:1
>
> 168:12 177:1, 1, 1
>
> 178:14, 22 179:1

**no2\'s** 23:1

**nobody\'s** 36:25

**nodding** 7:1

**non** 133:13

**non-attainment**

> 150:14 152:17

**non-near** 149:1

**none** 5:1 18:1 150:22

**noon** 31:1

**nor** 36:21

**normal** 24:1, 18

> 25:1, 1 62:1

**normally** 25:1

**north** 87:24 **northeastern** 110:13 **note** 4:1 68:25 115:1

> 133:1

**noted** 42:10

**notes** 6:14, 18

> 147:14

**nothing** 86:17 111:24

**notice** 80:15

**noticed** 132:23 133:1

**nowhere** 150:23

**nox** 7:25 10:17

> 11:1 22:12, 12
>
> 29:17 32:16
>
> 36:18 52:1 55:1,
>
> 14, 15, 22, 23
>
> 56:10 57:1, 1, 12,
>
> 13 58:13, 17 81:1,
>
> 1, 1 128:23
>
> 132:14, 15, 24
>
> 133:1, 1, 15
>
> 135:20 165:1
>
> 167:1, 15

**nrc** 87:18

**nuances** 121:1

**nugent** 3:15 4:1,

> 1, 1, 1, 17, 20,
>
> 23 5:1, 11, 23
>
> 39:15, 17 162:1
>
> 169:17 185:1
>
> O

**oaqps** 88:20 92:1 **oar** 154:10 **objection** 56:14

**occasionally** 162:25 **occur** 23:10 64:1 **occurred** 178:12
**occurrences** 100:1,

> 22 102:24 119:19
>
> 162:15

**occurs** 46:13 49:25

**observation** 84:24

> 85:1, 15 86:15
>
> 120:23 175:24

**observations** 16:10

> 83:1, 20 86:11
>
> 164:23

**obtained** 100:1

> 107:22
>
> **obvious** 52:10
>
> 53:22 71:1 166:20
>
> **obviously** 83:1 84:1,
>
> 20 92:25 94:1
>
> 103:19 107:20
>
> 115:19 143:25
>
> **odd** 110:1
>
> **october** 2:1 5:18
>
> **off-highway** 134:1
>
> **offer** 71:21 81:1, 15
>
> 82:22 85:1
>
> 86:10, 16 155:11
>
> 156:1 183:1
>
> **offered** 179:20
>
> **offhand** 27:16
>
> **office** 79:1 80:1, 12
>
> 82:1 84:22 87:23
>
> 155:1
>
> **offices** 34:1
>
> **oftentimes** 178:22
>
> **oh** 37:1 42:18
>
> 58:24 60:1 63:17
>
> 64:13 76:1 78:1,
>
> 13 80:24 99:21
>
> 106:20 110:1 121:1
>
> 137:1, 10 139:10
>
> 141:23 148:1 151:1
>
> 157:10 162:1
>
> 169:21 180:1, 20
>
> 183:13 184:13, 19
>
> **okay** 2:13 4:1, 23
>
> 5:1 6:1, 1 7:1, 1,
>
> 11, 18 8:1, 19 9:1
>
> 10:1 11:1 12:1
>
> 13:16, 20 21:25
>
> 22:24 24:25 26:23,
>
> 24 28:19 30:14
>
> 34:15 36:11, 13
>
> 37:1, 1, 1 38:1,
>
> 1, 10 39:1, 20
>
> 40:1, 1 42:10 55:1
>
> 57:1 60:1, 16
>
> 61:14 63:17, 19,
>
> 22 64:1, 1 65:12
>
> 66:22 67:1, 20
>
> 75:1 78:13 79:12
>
> 80:1 88:17, 18
>
> 89:1, 10, 23
>
> 95:1 102:1
>
> 106:1, 14, 20
>
> 107:11, 13, 16, 17
>
> 109:1, 23
>
> 110:19, 23 112:1
>
> 117:1 118:1, 1
>
> 119:10, 14, 25
>
> 125:15, 16 127:1
>
> 129:1 134:12, 13
>
> 136:19, 24
>
> 137:21 138:1
>
> 139:1, 25 141:25
>
> 142:1 144:15 145:1
>
> 146:21 148:1, 1
>
> 150:21 151:1, 1,
>
> 1, 1 152:14
>
> 153:22, 23, 25
>
> 155:15 157:1
>
> 159:11 160:20, 22,
>
> 25 162:1, 1 164:15
>
> 165:11, 18
>
> 168:19 170:1, 18
>
> 176:22 179:1
>
> 180:1, 15 182:11
>
> 183:1, 16 184:1,
>
> 1, 10, 13, 19, 19,
>
> 22, 24, 25

**one\'s** 47:11 62:16

**one-hour** 25:19 45:1,

> 1 46:25 47:1, 1, 1
>
> 48:24 53:25
>
> 54:1, 25 55:1
>
> 72:20 73:10 92:1
>
> 102:11 111:23
>
> 125:1, 1 135:18
>
> 155:20

**ones** 31:21, 23, 24

> 32:1 37:22 41:1, 1
>
> 80:18 111:16 140:1
>
> 161:1

**open** 15:13, 14

> 31:1 52:1 62:1
>
> 108:10 114:1, 1
>
> 142:12 155:1
>
> 165:21

**opening** 88:15, 16

> 94:1

**opinion** 51:1 70:1

**opportunity** 37:12

> 79:20

**opposed** 11:24 27:1

> 33:1 124:1 127:1
>
> 130:1 145:23

**opposite** 12:1

**option** 128:10

**ord** 176:10

**order** 28:25 47:16

> 50:1 68:16 82:1, 1
>
> 174:1 184:12

**ordered** 89:20

**orders** 47:17 **organization** 158:20 **organize** 56:20
**organized** 39:24 **oriented** 85:11 **original** 90:1, 1, 12

> 119:12

**originally** 86:13

> 89:22

**orphan** 127:25

**ospm** 117:13, 24

**others** 41:18 43:1,

> 10 62:17 79:13
>
> 80:1 118:21 119:1,
>
> 1 124:14 143:24
>
> 146:1

**otherwise** 38:14 46:1

> 112:1 114:25
>
> 131:24 149:1
>
> 166:25

**ought** 10:22 21:1

> 51:12 72:22, 25
>
> 73:1 74:16 77:12
>
> 164:21

**over-emphasized**

> 12:11

**overall** 13:23 140:19

> 141:1

**overemphasized** 13:25

**overestimate** 166:15 **overestimates** 32:1 **overlap** 6:24
**overlapping** 136:1,

> 1, 14

**overlay** 114:18

**overstate** 43:1

**overview** 86:18

> 100:21 155:17
>
> 176:25

**overwhelmingly** 44:22

**ourselves** 86:1

> 182:24

**outcome** 45:21 62:1

> 97:1, 10 98:12
>
> 104:1

**outcomes** 36:21

> 38:23, 25 44:1
>
> 61:21 102:24
>
> 157:20, 25
>
> 158:11 161:16
>
> **outdoor** 12:17 22:13,
>
> 19 27:17 32:24
>
> 33:1 97:20
>
> **outdoors** 8:13
>
> 96:16 126:24
>
> **outlined** 96:18
>
> **output** 97:1 98:10
>
> 100:1
>
> **outputs** 105:1
>
> **outside** 12:16
>
> 69:21 133:13, 23
>
> 174:15
>
> **oxidant** 59:1 62:15
>
> **oxidative** 41:19
>
> **oxide** 65:25 66:1, 1,
>
> 20 93:1, 1, 17
>
> 94:1 163:1
>
> **oxides** 92:25 93:1
>
> **ozone** 45:1 66:14
>
> 74:1, 10, 11,
>
> 13, 15 75:1, 1
>
> 103:1 115:1 120:15
>
> 133:13 138:24
>
> 148:19 153:12
>
> 160:1 167:1, 15
>
> 168:14 171:1
>
> 177:23 178:15, 23
>
> P
>
> **page** 54:24 59:15
>
> 61:17 144:17, 19
>
> 155:18
>
> **palliative** 66:1, 1
>
> **p.m** 185:12
>
> **panel** 3:22, 23 60:14
>
> 118:24
>
> **pangs** 137:14
>
> **paper** 37:24 44:10
>
> 128:16 138:24
>
> **papers** 37:14, 17
>
> **paradigms** 11:1
>
> **paragraph** 32:14 33:1
>
> 44:21
>
> **paragraphs** 2:1
>
> **parallelogram** 121:22
>
> **parameter** 18:19
>
> 124:10 140:17

**parcel** 150:1

**park** 13:1 **parochial** 32:10 **partial** 61:18 **partially** 138:1
**participate** 79:19

**participating** 79:1

> 80:1

**particle** 93:24

**particular** 9:23

> 28:19 96:15 98:1
>
> 99:1 100:1
>
> 104:23 119:22
>
> 128:12 157:20
>
> 164:1 175:25

**particularly** 59:1,

> 10 144:1 149:1
>
> 161:1 164:24
>
> 166:1, 11

**particulars** 99:12

**particulate** 85:19

> 93:1, 16, 17, 20
>
> 94:1

**particulates** 76:1

**passed** 2:15

**passive** 116:19

> 126:18

**past** 68:20 126:16

> 150:24 177:12, 14,
>
> 22 182:13

**pasted** 63:16

**path** 132:12 **pathways** 41:20 **pattern** 23:19 **patterns** 99:14

> 135:21

**peak** 23:14 47:23

> 48:1, 12 53:25
>
> 57:11, 11 73:24,
>
> 25 75:10 90:18
>
> 91:18 95:12 97:1
>
> 107:20 110:1
>
> 111:23 119:19, 21,
>
> 23 120:1, 1 146:14

**peaks** 8:1 23:1 47:14

> 49:17 133:18
>
> 161:13

**peer** 3:10 77:1

**penetrated** 126:25

**people** 3:18 5:25

> 6:1, 22 7:1 8:12
>
> 14:11, 13 16:1
>
> 17:1, 1 20:19 21:1
>
> 27:1 29:1, 1
>
> 32:1 37:13 38:1
>
> 46:1 50:19 59:12
>
> 60:1, 17 68:1,
>
> 11 69:1, 18
>
> 70:16 71:1, 1,
>
> 13 75:21 76:22
>
> 77:24 82:1 85:14
>
> 98:1 100:1, 23
>
> 108:12 124:13
>
> 129:10 130:1 132:1
>
> 136:22 142:10
>
> 147:20 149:25
>
> 155:1 164:14, 18
>
> 167:1 169:23
>
> 170:23 173:23
>
> 176:14, 15
>
> 178:1, 1, 22

**per** 17:24 24:14

> 26:1, 1 44:1 49:18
>
> 54:10, 11 105:11
>
> 110:12, 17 146:15

**perceive** 85:1

**percent** 8:11

> 10:19, 20 18:1,
>
> 1 20:18 58:10
>
> 105:11 133:1
>
> 165:12

**percentile** 24:15, 16

> 54:1, 1, 10 139:10
>
> 174:14

**percentiles** 140:1

**perception** 41:22 **perfect** 25:1, 1 63:1 **perform** 81:10
**performed** 96:25 **perhaps** 34:1 69:1

> 84:25 86:12
>
> 136:1 156:1 172:10
>
> 174:24 181:1, 1

**period** 3:21 64:16

**periods** 23:1 91:1 **persistent** 179:1 **person** 3:1 42:24

> 70:19, 22 81:1
>
> 184:21

**personal** 11:1

> 18:23 22:11, 12,
>
> 19 27:1, 1 47:1
>
> 70:1 100:1, 16
>
> 125:12
>
> **personally** 45:19
>
> 69:16
>
> **persons** 97:15
>
> **perspective** 49:10
>
> 89:1 90:1 127:1
>
> **pharmacodynamic**
>
> 174:1
>
> **phase** 84:1, 1
>
> **phases** 115:11
>
> **philadelphia** 96:23
>
> 110:11 113:14
>
> 122:10 150:20
>
> 179:22
>
> **pick** 12:1 175:17
>
> **picked** 179:16 **picture** 179:19 **phoenix** 179:14, 19 **phone**
> 3:13, 19,
>
> 20, 22, 23 4:1, 19
>
> 5:13 6:1 31:1
>
> 39:12, 17, 18 67:1
>
> 78:1, 12 81:1
>
> 106:15 110:21
>
> 134:21 142:1
>
> 153:24 157:1 162:1
>
> 168:20 183:11
>
> **piece** 169:25
>
> **pieces** 69:25 **pilot** 87:24 **pinkerton** 132:22
>
> 134:12 162:1, 1
>
> 163:22 176:24
>
> 178:21
>
> **pinto** 28:17 34:1
>
> **physiology** 99:12,
>
> 13, 14
>
> **places** 47:1 50:1
>
> 115:20 131:1
>
> 152:25
>
> **placing** 13:1
>
> **plan** 36:1 80:19
>
> 81:15 83:1 87:1, 1
>
> 88:23 89:1 93:18
>
> 94:23 107:1
>
> 119:1 121:23
>
> 124:24 126:12
>
> 127:13 137:19
>
> 165:22, 23
>
> 180:1, 10, 19, 21
>
> **planned** 92:22

**planning** 80:13

> 81:1 87:24 88:1,
>
> 1, 1 93:10 94:13
>
> 127:18

**plans** 87:22

**plant** 133:14 **plants** 133:11 **plaus** 62:23 **plausibility**

> 29:20, 25 32:1
>
> 51:10 53:10 61:19,
>
> 23 66:19

**plausible** 53:1

**player** 43:25

**plays** 150:1

**please** 3:22 5:12, 14

> 37:15, 18

**pleased** 118:1 161:25

**plenty** 53:12

**pm** 85:23 93:23

> 94:1 103:1 105:1
>
> 148:19 167:1, 16
>
> 168:14

**plot** 26:1 27:11

**plots** 23:20 24:1 **plotted** 24:10, 17 **plotting** 24:1

**plug** 138:10

**point** 9:20 10:1,

> 14 20:1 21:20 22:1
>
> 24:13 28:19
>
> 33:13 36:1 39:1
>
> 40:1 49:10 50:1
>
> 54:1 59:1 60:1
>
> 66:20 67:1 72:1, 1
>
> 74:20 76:1 81:14
>
> 88:1 89:12, 21
>
> 93:17, 18, 19 94:1
>
> 105:1 114:12 116:1
>
> 120:23 123:1 127:1
>
> 132:20 133:16
>
> 139:1 141:11 147:1
>
> 151:1 153:1 159:12
>
> 161:21 163:1, 1
>
> 168:1 182:1, 1, 13
>
> 183:23 184:1

**pointed** 28:1 38:16

> 42:1 168:13

**points** 2:19 11:1

> 24:20 67:1 68:1
>
> 86:15, 15 149:17
>
> 161:1

**policy** 9:15 51:23

> 68:14 77:1, 11

**pollutant** 65:20

> 83:1, 16 85:11,
>
> 18, 19 86:1, 22,
>
> 25 87:1, 1, 15
>
> 153:13

**pollutants** 32:16

> 36:23 59:1, 1
>
> 65:18 83:14, 16,
>
> 18 84:12, 13 85:1,
>
> 1, 1, 12, 21
>
> 86:1 87:1, 1,
>
> 12, 12 88:1
>
> 90:10 164:12

**pollution** 83:20

**polymorphisms** 41:15

**ppb** 28:1 29:1, 1

> 46:15, 16, 21, 22,
>
> 24, 24 47:16, 21
>
> 48:1, 23 49:1 50:1
>
> 53:13 54:1 130:22

**poor** 176:1

**ppd** 26:20

**popped** 37:17

**population** 17:15, 16

> 26:20 31:20, 25
>
> 32:1 33:24, 24
>
> 60:22 61:10 62:1
>
> 64:18, 19, 20
>
> 95:23 101:25
>
> 102:21 103:1
>
> 134:1, 1 147:1
>
> 153:18 163:12
>
> 174:14 175:20, 25

**populations** 40:12

> 41:1 42:1 60:1
>
> 61:1, 20 70:16
>
> 95:22 98:1 103:1
>
> 106:1 157:21 163:1

**port** 87:18

**portion** 122:1, 11

> 147:1

**portions** 10:25

> 128:20

**position** 84:1

**positive** 35:1 44:16,

> 17 183:19

**possibilities** 146:16

**possibility** 161:11

> 165:15

**possible** 12:21 27:11

> 70:20 119:1, 24
>
> 135:1 146:23
>
> 151:18 152:15
>
> 174:18 176:1, 1
>
> **possibly** 82:14 96:23
>
> 118:22, 22
>
> 121:11 169:1
>
> **postlethwait** 10:13
>
> 11:1 26:25 27:10
>
> 34:24 41:23 42:1
>
> 43:20 59:19, 23
>
> 60:11 62:1, 16, 20
>
> 63:1, 11, 15, 23
>
> 64:1, 1 65:1
>
> 168:22
>
> **potential** 17:1
>
> 32:1 91:1, 1 92:13
>
> 94:16 96:1
>
> 100:24 101:11,
>
> 15 102:1 105:14
>
> 107:23 133:23
>
> 162:18 163:1
>
> **potentially** 106:1
>
> 111:13 135:1
>
> **power** 133:11, 14
>
> **powerful** 52:1
>
> **ppm** 35:12 53:14
>
> 91:11, 13, 23
>
> 92:16 102:11 130:1
>
> **practical** 25:25
>
> **precise** 33:1 48:1
>
> 181:1
>
> **precisely** 33:10
>
> **preclude** 86:23 88:1 **predicting** 119:21 **predictions**
>
> 117:18, 22 135:14,
>
> 15 143:1
>
> **prefer** 57:1 169:1
>
> **preliminary** 84:1
>
> 103:12 104:14
>
> 122:1 156:25
>
> 168:11 170:20, 22
>
> **prepared** 133:1
>
> 142:14 154:1
>
> **present** 4:15 5:1
>
> 10:13 11:1 94:1
>
> 106:11 141:1
>
> 166:19, 24
>
> **presentation** 86:18
>
> 88:16
>
> **presented** 89:10
>
> 160:1

**presenting** 118:19

**press** 134:22

**presumably** 142:25

> 157:22 158:12

**pretty** 11:17 43:16

> 45:16 66:22 91:12,
>
> 12 117:19 121:1
>
> 138:1 142:22
>
> 163:14

**prevent** 88:1

**previous** 55:1 68:1

> 71:1 89:1 109:18
>
> 136:1 139:1 142:18
>
> 173:13 179:17

**previously** 100:1

> 110:16

**primarily** 15:1 44:20

> 48:16 97:1
>
> 125:19 133:1

**primary** 51:20 52:1

> 81:1 83:1 123:10
>
> 144:10

**prior** 90:1 95:12

> 118:1 129:1, 1
>
> 139:20 173:22

**prioritize** 81:22

**priority** 164:1, 1

**privilege** 80:1

**pro** 116:1

**probability** 24:1, 17

> 109:22 141:1
>
> 175:17

**probably** 6:1 23:14

> 28:1 33:23 40:1
>
> 48:1, 1 53:19
>
> 55:22 59:1 63:1
>
> 88:21 114:21
>
> 122:1, 11 124:1
>
> 135:22 142:16
>
> 144:1, 25 145:1
>
> 161:20 167:1 173:1
>
> 183:19

**problem** 12:19 16:11,

> 19, 22 17:1, 1
>
> 18:11, 22, 25,
>
> 25 20:1, 10, 24
>
> 21:1, 24 32:17
>
> 33:11 35:20
>
> 44:24 45:16, 20
>
> 50:1 59:1 74:17
>
> 117:1, 1 127:1
>
> 147:1 152:1 164:20
>
> 166:20 168:1
>
> 169:22 176:16

**problematic** 148:10

> 162:15

**problems** 38:1 43:1

> 52:12, 13 67:14
>
> 133:23 164:13
>
> 167:22

**procedures** 148:20

**proceed** 5:1 104:25 **proceeds** 4:14 **process** 36:1 67:1

> 76:23 77:25
>
> 78:23 79:19, 21
>
> 84:1 104:11 180:19
>
> 181:1

**produce** 84:18 87:1

> 144:1 178:1 180:19

**produced** 160:11

**producing** 178:1 **product** 74:11 **productive** 169:11 **products**
44:21 45:11

> 51:21

**profile** 74:10

**profound** 48:19

**program** 34:1

**programs** 84:23 85:10 **progress** 137:13 **progressing** 96:1
**progression** 139:23 **progressions** 142:21 **project** 87:23

> 143:1 164:14
>
> 176:13

**projected** 102:23

**projecting** 182:24 **projection** 122:1, 16 **proper** 77:18
**proportion** 13:1 **proportionally** 28:1 **proportionately**

> 131:10 148:24

**proposal** 94:23

**propose** 5:1 105:1

**proposed** 80:16, 16

> 82:10 88:23
>
> 89:1, 22 94:23
>
> 101:14 108:18,
>
> 24 109:1 114:23
>
> 166:14 177:14
>
> **proposing** 24:1 96:10
>
> 119:14 128:13
>
> **protect** 74:25
>
> 90:18 91:14 94:1
>
> **protection** 2:1 91:18
>
> **protective** 15:1 **protons** 127:16 **prove** 41:13 **provide**
> 38:1, 1
>
> 51:25 77:17
>
> 79:22 87:1 91:18
>
> 101:1 103:13
>
> 105:1, 23 106:1
>
> 116:16 126:13
>
> 127:1 161:22
>
> 183:22
>
> **provided** 72:15 93:22
>
> 104:11
>
> **providing** 5:16 90:1
>
> **proximity** 13:21
>
> 17:24, 25 47:1
>
> **public** 2:1 3:21 4:1,
>
> 10, 11, 15 5:1,
>
> 1 15:1 51:1
>
> 52:22 77:22
>
> 81:12 82:1
>
> 115:19 166:25
>
> 180:11 185:11
>
> **published** 37:24
>
> 41:18 128:16
>
> **pulled** 6:22
>
> 158:1, 16

**qualitatively** 102:22

> 113:20 114:1

**quality** 42:24 60:1

> 80:13 85:10 87:17,
>
> 21 92:1 95:19
>
> 96:1, 1, 19, 20
>
> 98:13 100:23
>
> 101:1, 11, 18
>
> 102:1, 1, 25
>
> 105:19, 25
>
> 108:1, 1, 13 109:1
>
> 110:24 112:1, 10
>
> 116:12 120:1
>
> 121:1, 16, 24
>
> 122:1, 1, 1 127:12
>
> 129:18 130:18
>
> 131:1, 25 133:10
>
> 134:18, 25 135:1
>
> 138:13 145:11,
>
> 23 148:14, 20
>
> 149:1, 11
>
> 150:15, 16, 23
>
> 151:20 171:1
>
> 181:18, 18

**quantified** 158:13

**quantifies** 25:18

**quantify** 105:21

> 112:23, 23

**quantitate** 177:16

> 178:1

**quantitative** 7:23

> 64:10 67:19
>
> 69:22 83:1 86:20
>
> 107:15 109:1, 1,
>
> 11, 16 110:1 112:1
>
> 114:1, 1 116:25
>
> 130:1 131:10
>
> 132:23 136:13
>
> 143:1 145:1, 25
>
> 147:23 149:1, 20
>
> 159:20 168:1 171:1
>
> 174:22 176:25
>
> 178:11 179:11

**questionable** 114:25

**questions** 7:15

> 9:24 34:18, 20, 21
>
> 61:1 78:22
>
> 106:1, 1, 13, 22
>
> 108:11 129:21
>
> 140:1, 1 142:20
>
> 146:12 147:20
>
> 161:20 163:10
>
> 170:14

**quick** 22:1 61:17

> 168:24 169:1, 1

**quickly** 2:21 154:11

**quite** 6:24 18:16

> 24:1 30:22 69:17
>
> 71:1 78:25 79:23
>
> 116:17, 18 138:10,
>
> 11 139:1 141:1
>
> 156:23 176:20

**quorum** 78:14

> **pure** 57:23

19:23 84:10 95:1

> **purpose** 89:1 96:1
>
> 97:13 130:12
>
> 135:1, 10 136:1
>
> 142:17 143:1
>
> **purposes** 135:1
>
> **pursued** 108:19 **push** 115:10 **pushing** 47:25 **puts** 29:21
> **putting** 170:20
>
> 181:20
>
> **puzzled** 160:18
>
> Q
>
> **qu** 153:1
>
> **qualitative** 27:12
>
> 96:1 97:1 114:15
>
> 140:13 145:18
>
> 96:1 100:12 103:24
>
> 140:14 141:1, 10
>
> 145:17, 24 158:10,
>
> 13, 18, 22, 22
>
> 159:1 164:1 176:17
>
> 177:16 178:1

**quantitatively** 8:1

> 105:24 114:22
>
> 145:13, 21

**question** 6:23, 25

> 7:15, 16, 16,
>
> 17, 18, 18 8:1, 21
>
> 10:10, 12 11:12
>
> 22:25 25:25 32:1
>
> 38:1, 1 39:1, 10
>
> 40:1, 10, 11, 14
>
> 41:24 42:16 53:13,
>
> 14 59:25 60:19
>
> 61:15, 17 63:1

R

**ra** 148:19 **raise** 69:20 **raising** 67:17

**random** 19:1 21:23

**range** 49:1 73:19

> 102:12 103:17
>
> 105:1 138:1
>
> 170:22, 25
>
> 171:12 172:1
>
> 174:15 175:21,
>
> 23 182:1, 15

**rap** 137:16, 17

**rare** 162:15 **rate** 27:23 **rates** 7:23 8:1

**rather** 37:19 42:15

> 65:20 69:1 70:1
>
> 93:24 140:25
>
> 161:12 174:20
>
> **ratio** 13:23 22:19
>
> **rational** 77:18
>
> **rationale** 74:12
>
> 77:13 93:21
>
> **ratios** 99:1
>
> **re** 67:1
>
> **reach** 133:20 138:1
>
> 170:1 182:20
>
> **reached** 79:1 163:1
>
> **reaction** 131:22 **reader** 10:23 36:1 **readily** 79:1 **reading**
> 3:11, 14
>
> 7:10 18:15 67:1
>
> **ready** 7:11 137:10
>
> **real** 25:24, 25 49:23
>
> 50:11, 12 80:1, 18
>
> 114:11 146:17
>
> **realistic** 151:15, 16
>
> **realize** 118:10
>
> 153:11
>
> **really** 8:16, 17
>
> 10:1, 1 12:18
>
> 13:24 15:19
>
> 27:1, 12 29:1 30:1
>
> 41:1, 1 43:21
>
> 44:11, 23 45:24
>
> 50:21, 25 55:23
>
> 56:1 58:14 66:1, 1
>
> 68:13, 15 70:10
>
> 71:1, 10 74:1 76:1
>
> 77:1 85:1 86:11
>
> 87:10 91:15 93:1
>
> 110:1 114:1, 1
>
> 120:11 123:10
>
> 125:1 126:1
>
> 129:23, 24 132:1
>
> 134:22 135:1,
>
> 21, 22, 25
>
> 138:1, 1 139:11
>
> 141:12 142:21
>
> 146:12 149:11
>
> 161:13 162:10, 23,
>
> 24 163:1, 24
>
> 164:21 166:17
>
> 168:10 169:11
>
> 182:23 184:16
>
> **realm** 140:1
>
> **reason** 29:1 37:17
>
> 38:12 76:13, 16
>
> 137:24 146:1
>
> 165:15

**reasonable** 108:18,

> 25 128:1 152:14

**reasonably** 117:20

> 121:15 151:19
>
> 152:15 182:1

**reasons** 81:18

> 115:1 127:21 159:1

**reassessed** 94:1

**rec** 148:17

**recall** 18:15 **receive** 16:1 184:1 **receiving** 39:1 **recent**
36:19 94:13

> 101:1 130:17
>
> 131:1, 1 149:11

**recently** 127:22

> 128:16

**recognize** 43:1 46:1

**recognizes** 51:20

**recognizing** 43:1

> 84:16 118:1 121:1

**recommend** 158:20, 24

**recommendation**

> 12:1 72:19 73:1

**recommendations**

> 87:20 118:1 129:1

**recommended** 127:20

> 129:15

**recommending** 160:18

**reconcile** 12:21 **reconciliation** 156:1 **record** 5:17 26:12

> 28:1 75:1, 15

**recurring** 94:11

**red** 11:20 **redraft** 56:20 **reduce** 139:22 **reduced** 141:1

**redundant** 42:1, 1

> 94:1

**refer** 38:1

**references** 10:1

> 37:15, 19

**referred** 37:22

**refers** 64:24, 25 **refine** 84:17 **refined** 84:1 99:10

> 110:15

**refinement** 69:1

**reflect** 84:10

**reflected** 22:1 69:1

**regard** 20:11 46:1

> 61:20 83:24
>
> 163:1 177:1

**regarding** 90:24 93:1

**regards** 118:18 **region** 110:13 **regional** 150:11 **regression**
24:20

> 25:13 109:21

**regular** 31:1

**regularities** 122:1 **regulations** 63:1 **regulatory** 150:1
**reinforce** 144:1 **reiterate** 5:16 **relate** 34:21

> 125:22 173:1

**related** 8:1 33:10

> 73:21 83:1, 1,
>
> 17 104:19, 21
>
> 113:10 126:14
>
> 165:1 177:1

**relates** 107:19

> 182:22

**relating** 4:11

**relation** 34:1

**relationship** 53:24

> 58:12, 16 70:20
>
> 92:1 95:11
>
> 97:14, 24 111:1,
>
> 22 178:1

**relationships**

> 12:22 22:13
>
> 49:19 95:1 97:1
>
> 103:14, 17
>
> 111:14 113:12
>
> 131:19 145:16
>
> 151:1

**relative** 10:17 17:22

> 18:1 25:19 59:13
>
> 85:25 86:1
>
> 93:12, 13 97:25

**relatively** 91:1, 1

**relevant** 9:15 17:14,

> 17 35:18, 23 48:13
>
> 68:1, 20 70:11
>
> 71:23, 24 72:1,
>
> 1 111:16, 21
>
> 115:14 167:19

**remains** 159:1

> **remarks** 5:1
>
> **remember** 14:22
>
> 21:1 23:19 43:23
>
> 44:1 74:1 171:1
>
> 174:1 178:12
>
> **remind** 39:1 76:22
>
> **removed** 136:11 **reorganization** 68:1 **repair** 64:1 65:1, 1
> **repeat** 52:15 **repeated** 175:15 **repor** 57:21
>
> **report** 70:12 87:21
>
> 105:23 108:1
>
> 126:20 180:1, 15
>
> 181:11, 12, 21
>
> **reported** 57:22
>
> **reporting** 35:1 **represent** 60:1 **representation**
>
> 179:19
>
> **representative** 12:16
>
> 31:20 110:13
>
> 135:25
>
> **representatives**
>
> 154:10
>
> **representing** 46:1
>
> 105:1 142:20
>
> **represents** 24:12
>
> **require** 176:1 **required** 104:1 **requires** 87:1 129:10
> **research** 85:23
>
> 110:17
>
> **residential** 113:14
>
> 114:10, 25
>
> **residing** 98:1
>
> **resistant** 121:13 **resolution** 98:23 **resolve** 166:23
> **resolved** 98:13 **resonated** 142:18 **resource** 67:24
>
> 68:1 82:1, 17
>
> 135:1 176:16
>
> **resources** 118:11
>
> 164:11, 20 166:1
>
> 176:1
>
> **respect** 8:1 21:20
>
> 143:10 158:1
>
> **respective** 22:13

**respiratory** 46:1

> 104:19, 21, 22

**respond** 38:13

> 87:16 119:1 136:13

**responds** 103:22

**response** 7:19 11:12,

> 15 12:22, 24
>
> 22:1 32:1 33:1
>
> 41:16 86:21
>
> 91:24 101:22, 23
>
> 102:21 103:14,
>
> 17 104:1 112:18
>
> 115:1 121:20
>
> 136:12 145:16
>
> 156:24 173:20
>
> 175:1 178:1, 25
>
> 183:18

**responses** 156:17

**responsive** 62:13, 13

**responsiveness**

> 173:19 174:1

**rest** 10:19 140:1

**result** 42:19 43:1

> 92:11

**results** 21:17, 19

> 38:22, 23 84:18
>
> 105:1, 10 112:1
>
> 131:24 138:13, 17,
>
> 20 157:23 166:24
>
> 167:18 180:13
>
> 181:22 182:1

**retired** 127:23

**returning** 107:1

**review** 3:10 5:22

> 22:1 35:22 39:1
>
> 72:19 77:1 78:1
>
> 81:1, 1, 1, 1 82:1
>
> 83:1, 1 88:20 89:1
>
> 90:14, 23 91:16
>
> 92:20, 21 93:1,
>
> 18, 22 94:1, 1
>
> 95:12, 15 101:16
>
> 109:18 128:12
>
> 178:1 182:1 183:1

**reviewed** 55:12

> 158:11 180:24

**reviewing** 136:1

**reviews** 84:22 115:1

**revised** 101:17

> 103:22 180:12

**revision** 164:1

**ri** 33:1

**richmond** 80:22, 23

> 100:19 112:1, 1
>
> 115:1 116:24
>
> 117:1, 1, 1 126:16
>
> 127:1 130:11
>
> 132:1, 10 133:1
>
> 138:23 139:1 145:1
>
> 146:11 148:1, 1
>
> 149:1 150:1, 22
>
> 151:1, 25 153:1, 1
>
> 156:24 159:12,
>
> 13 160:12 168:1
>
> 170:17, 19
>
> 171:1, 10, 16,
>
> 25 172:1, 11, 19
>
> 173:1 175:11 176:1
>
> 177:10 178:17
>
> 180:1, 1, 17,
>
> 21, 25

**rightly** 108:12

**ringer** 154:24

**risk** 46:1 59:1 60:1,

> 1 67:23, 25
>
> 68:15 79:16, 23
>
> 80:14, 19 81:1, 18
>
> 83:1 84:1, 1, 1,
>
> 11 88:25 89:16, 18
>
> 92:22 100:20, 21
>
> 101:1, 25 103:1,
>
> 24 104:1, 25
>
> 105:1, 16, 22
>
> 108:1 111:18 112:1
>
> 113:1 118:15
>
> 130:1, 16, 17
>
> 131:24 132:1, 1
>
> 138:1, 24 139:12
>
> 141:11, 18
>
> 144:21 145:10, 17,
>
> 24 148:12 149:10
>
> 151:10, 11
>
> 155:1, 10, 16,
>
> 21 157:1, 18
>
> 158:1, 1, 1, 10,
>
> 13, 22, 25 159:1
>
> 161:1, 22 167:11
>
> 176:25 177:1,
>
> 16, 16, 16
>
> 180:1, 14
>
> 181:10, 12, 21
>
> 182:1, 1, 15
>
> 183:17

**risks** 89:1 126:13,

> 14 132:18 166:15
>
> 181:15 182:19
>
> **road** 13:1, 1 18:1
>
> 113:12 115:24
>
> 125:1 131:17
>
> 147:1, 17 180:1
>
> **roads** 13:21 31:24
>
> 33:22 47:1
>
> 120:18 135:21, 23
>
> **roadside** 28:21
>
> 30:1 124:1
>
> **roadsides** 29:1, 1
>
> **roadway** 13:23, 24
>
> 17:24 52:1 59:18
>
> 60:17 96:11 97:17,
>
> 17 99:16 110:15
>
> 113:10 123:12, 20,
>
> 21 124:12 125:1,
>
> 1, 1 128:18 129:12
>
> 131:19 149:1, 1
>
> **roadways** 13:17, 17
>
> 17:25 49:17
>
> 54:19 61:1 98:1,
>
> 15 121:10 127:17
>
> 129:1, 1 133:1
>
> 147:1
>
> **robust** 62:1
>
> **rockies** 40:18 59:20,
>
> 22
>
> **rogene** 2:11 3:15 5:1
>
> 6:1, 1 34:14
>
> 36:1 39:15 42:13
>
> 55:1 80:10
>
> 106:23 134:16
>
> 144:14 149:16
>
> 157:1 168:1 183:1,
>
> 22
>
> **roll** 3:12 131:1,
>
> 11 151:17, 22
>
> **rolled** 152:1
>
> **rolling** 131:17, 18
>
> 148:22
>
> **ron** 7:20 28:1
>
> 36:11 37:10 38:1
>
> 39:20 55:1 56:10
>
> 79:13 129:20 151:1
>
> **ron\'s** 38:12
>
> **ronald** 165:20 **roof** 114:10 **room** 3:19 78:1
>
> **ross** 9:20 34:1 51:18
>
> 54:15, 22 55:13
>
> 60:21 67:18, 21
>
> 73:14 76:22

**round** 56:23 69:1

> 88:16

**route** 127:15

**rtp** 81:1

**rule** 73:15 80:16,

> 16, 17

**rules** 82:10

**run** 108:1

**running** 65:11 **rural** 133:1 134:1 **rush** 184:11 **russell**
11:11

> 13:1, 12 28:1
>
> 30:1, 1 31:19,
>
> 23 42:13, 18 52:11
>
> 75:11 118:1
>
> 119:10, 25
>
> 120:1, 21 121:19
>
> 125:18, 24 127:1
>
> 168:24 169:1

S

**saint** 88:1

**samet** 43:18 71:1

> 78:12 155:1 169:14

**sample** 105:1

**san** 14:1

**sandwich** 154:15 **satisfied** 108:24 **saturation** 116:19

> 117:1

**scale** 24:17 34:12

> 54:1 106:1
>
> 121:1, 12, 15, 17

**scan** 71:25

**save** 142:10

**saw** 47:21 95:14

> 156:25

**scenario** 130:1,

> 18, 19 132:16
>
> 140:15 150:1, 1
>
> 151:15, 16 152:10,
>
> 16, 20

**scenarios** 100:25

> 101:1, 1 150:25
>
> 151:23 153:14
>
> 181:20, 23

**schedule** 89:1, 10

> 137:12

**scheduled** 107:1

> 169:19

**scheduling** 180:18

> 185:1

**school** 123:24

**science** 72:22 74:14,

> 15 78:1 79:18
>
> 81:24 82:14 83:11,
>
> 24 181:24 182:1
>
> 183:1

**scientific** 2:1 65:25

> 81:20 83:1

**scientist** 68:17, 18

**scope** 80:19 81:15

> 88:23 89:1 92:21
>
> 95:21 118:1 155:17

**score** 24:10

**scott** 80:21 88:19

> 94:25 101:1

**scott\'s** 88:14

**screen** 113:20 133:22

**screening** 139:1

> 177:19

**se** 17:24 44:1

> 110:12, 18

**search** 37:16

**seasonally** 49:1 **seasons** 49:1 **seats** 79:12, 14 **second** 3:16
63:1

> 64:25 69:1 72:1
>
> 84:1 89:15, 17
>
> 91:25 93:16 134:14
>
> 157:1, 1 163:21
>
> 180:1 181:11,
>
> 24, 25 182:1, 1,
>
> 17, 18 183:1

**secondary** 81:1

**section** 11:15, 17

> 22:18 108:1, 1, 1,
>
> 10 110:24 116:12
>
> 129:19 134:14,
>
> 15 138:1 141:1, 18
>
> 147:21 154:25
>
> 156:16

**seeing** 29:1 30:16

> 103:21 168:16
>
> 179:24 180:1, 1, 1
>
> 181:21 185:1

**seek** 78:1, 21

> 82:16 85:11

**seem** 66:1 82:1 163:1

> 178:12
>
> **seemed** 42:1 52:1
>
> 65:13 66:1, 20
>
> 67:1 110:1
>
> **seems** 9:1 16:17
>
> 19:1, 10 49:1
>
> 55:19 56:21 67:1
>
> 71:25 77:12
>
> 110:1 111:1, 13
>
> 143:11 154:1
>
> 156:1, 22 164:1
>
> **seen** 81:16, 24
>
> 159:1, 1 167:13
>
> **sees** 146:14 172:14
>
> **seigneur** 127:1 129:1
>
> **selected** 77:14 96:22
>
> 97:1 103:1
>
> 110:12 179:11
>
> **selecting** 55:16
>
> 119:17 177:13
>
> **selection** 35:1
>
> 36:1 179:13
>
> **semi-quantitative**
>
> 140:23
>
> **send** 67:10 162:1
>
> 185:1, 1
>
> **sense** 57:1 60:18
>
> 61:1, 1 75:1
>
> 96:1 100:12
>
> 119:17, 21
>
> 127:25 128:1
>
> 135:20 139:20
>
> 166:13 168:16
>
> 173:11 183:24
>
> **sensitive** 40:12
>
> 41:1, 19 60:24
>
> 64:17
>
> **sensitivity** 99:1,
>
> 1 143:15 147:11
>
> 159:10, 23
>
> 160:1, 13 174:14
>
> **sent** 6:1 63:15
>
> 169:15
>
> **sentence** 36:1 38:14,
>
> 21 41:1, 14 42:14,
>
> 15 44:1, 10
>
> 50:11 51:1, 17, 19
>
> 55:1 56:16, 18
>
> 58:1 61:18 64:24
>
> **sentences** 51:20
>
> **separable** 135:20
>
> **separate** 22:1 86:1
>
> 119:13

**separately** 112:20

**september** 89:17 **sequence** 58:1 **series** 23:20 48:1

> 99:23 100:1

**serious** 19:24 50:15

**seriously** 164:21

> 165:15

**serves** 67:22

**service** 77:22 **ses** 61:1 **session** 154:22 **sets** 165:1, 1
**setting** 20:13

> 34:13 35:18 85:1
>
> 112:19 153:11
>
> 164:1 179:1

**settings** 53:1

**settle** 171:11

**seven** 40:11, 24

> 41:24, 25 42:1, 12
>
> 58:20, 21 59:1, 15
>
> 61:15

**several** 5:18 15:1

> 33:16 36:18, 19
>
> 48:24 49:18 52:1
>
> 71:1 81:16 100:1
>
> 102:1, 25 103:1
>
> 105:17 113:1
>
> 116:20 149:17
>
> 156:1

**shape** 75:12

**sharp** 163:17 **sharpen** 83:23 **sharply** 183:1

**sheppard** 39:19 40:1,

> 1 43:15 106:16, 19
>
> 134:16, 20
>
> 136:21 137:1
>
> 142:13 147:22,
>
> 23 148:1 149:1
>
> 157:1, 12, 16
>
> 159:18 160:20, 24

**short** 4:22 6:10

> 73:17, 19 74:13
>
> 75:22 76:1 90:18
>
> 94:18 97:1
>
> 101:12 154:1
>
> 161:1, 1

**short-term** 54:23

> 74:16 76:1, 18
>
> 90:15 91:18, 19
>
> 92:1, 1, 13
>
> 95:1, 1, 11
>
> 98:11 100:23
>
> 107:22 111:1, 1,
>
> 1, 1, 15 117:1
>
> 135:17 155:25
>
> 156:1, 18 161:13
>
> 170:21 177:1

**shorter** 25:10, 11

> 73:1 74:1 75:1, 20
>
> 108:21

**showing** 44:19, 23

> 91:21 178:19

**shown** 145:19

**shows** 11:14 132:24

> 141:16

**sign** 169:24

**significance** 164:1

**significant** 15:1

> 26:19 55:1
>
> 56:11, 25 126:1
>
> 140:1 150:1 162:20
>
> 173:17

**similar** 27:1 74:10

> 103:1 105:1 128:17
>
> 144:12 178:23

**similarly** 18:1

**simple** 25:25 114:12

**simplifications**

> 135:1, 10, 17

**simplify** 135:13

> 136:1

**simplifying** 143:1

**simply** 6:17 34:25

> 37:1 115:16, 17
>
> 175:17

**simulate** 132:13

**simulating** 101:1

> 153:19

**simulation** 143:12

> 175:1

**simulations** 176:12

**sing** 105:1

**single** 86:22 87:1,

> 1, 12, 14, 15
>
> 105:1 106:1 114:24
>
> 150:17 171:11
>
> 174:21

**singular** 120:10

> **sit** 125:25 169:23
>
> 178:12
>
> **site** 16:22 20:14, 25
>
> 21:1, 14 22:13
>
> 31:1 49:16 76:21
>
> 113:1, 1
>
> **sited** 15:1, 12 16:14
>
> 76:24 133:11, 13
>
> **sites** 12:16 15:11
>
> 31:1 33:21
>
> 111:1, 11, 16
>
> **siting** 17:1 32:25
>
> 76:17
>
> **sitting** 29:1 30:1
>
> 42:20 43:1, 1
>
> 47:15 67:1 169:22
>
> **situation** 152:1
>
> **situations** 138:11
>
> **six** 38:1 39:1
>
> 40:1, 10 41:24
>
> 42:11, 18 77:24
>
> 117:24 134:23
>
> **size** 93:23 105:1
>
> 106:1
>
> **skill** 114:13 117:21
>
> **skipped** 163:18 **skipping** 78:1 **slam** 110:17
>
> **slide** 89:11 90:20
>
> 102:1 103:1 104:14
>
> 106:1, 1
>
> **slides** 88:21 100:20
>
> **slightly** 12:10
>
> 174:19
>
> **slip** 123:1
>
> **small** 2:19 6:13
>
> 33:13 39:1 54:24
>
> 139:14 156:14,
>
> 15 167:1
>
> **slope** 24:23 25:1
>
> 138:17
>
> **slopes** 175:1
>
> **smoking** 126:18
>
> **smoothed** 3:1
>
> **so2** 84:11, 11, 12 **socio-economic** 60:23 **soften** 43:22
>
> **solicit** 89:1 **soliciting** 89:13 **solid** 52:14 **solution**
> 166:20

**space** 143:1

**sparse** 16:1

**spatial** 29:17 32:15,

> 21, 25 43:1 47:1
>
> 135:19

**spatially** 13:1 58:14

> 98:13 113:12

**somebody** 58:24

> 68:14, 24 69:18,
>
> 22 70:1 183:1

**somehow** 120:11

**someone** 146:14

> 183:11

**someplace** 58:1

**somewhat** 42:25 119:1

> 132:23 173:15

**speak** 2:12 4:12 5:12

> 80:20 84:14

**speaker** 2:1, 11,

> 14 6:1, 1, 11
>
> 7:14, 16 8:20,
>
> 23 23:1 26:1, 1,
>
> 11, 15, 21 28:12
>
> 33:18 40:19
>
> 52:24 53:16 80:1
>
> 88:12 108:1 183:1,
>
> 1

**speaking** 46:15

**special** 11:24 32:16 **speciate** 11:1 **speciation** 10:17
**species** 7:25 44:1

> 55:19 56:1 58:1
>
> 66:12 93:1, 11,
>
> 14, 20

**specific** 37:14, 19

> 61:20 62:1 68:22
>
> 73:16 83:17 106:13
>
> 107:14 127:17

**specifically** 90:22

> 117:13 125:1

**specification** 140:16

**specificity** 121:1 **specifics** 179:11 **specified** 103:1

> 157:22 158:23

**speizer** 144:12

> 162:12

**spend** 81:1 98:1

**spending** 81:1

**sorry** 40:25 55:14

> 97:24 109:1 127:21
>
> 147:13 157:11
>
> 176:21 184:14

**sort** 10:23 11:14

> 19:10 32:10, 12
>
> 34:10 43:1 46:1
>
> 61:11 62:1 66:1, 1
>
> 67:1 83:10 84:25
>
> 86:1, 1 87:1
>
> 111:1, 10
>
> 114:12, 19, 24
>
> 118:1 130:23
>
> 132:11, 17 134:1
>
> 140:19, 21, 22, 23
>
> 145:1 147:1
>
> 154:1 156:1, 1,
>
> 12, 17, 18 165:1
>
> 166:18 167:10, 17,
>
> 24 175:1 179:22
>
> 184:1

**sorting** 70:1

**sorts** 91:14 121:10

> 146:12, 15 179:20

**spirit** 55:1

**sound** 3:1

**sounded** 84:25

**sounds** 35:1 75:21

> 124:11

**source** 52:1 95:14

> 128:10 147:16

**sources** 16:1

> 96:15, 15 97:21,
>
> 22 98:14, 15 99:25
>
> 126:23 127:1
>
> 129:1, 1, 1
>
> 133:17, 24 134:1

**south** 122:10, 12

**split** 60:25 **sox** 81:1 **spoke** 185:1

**sponsoring** 116:15 **spot** 26:1 92:16 **spread** 25:12 174:1
**spring** 89:14 93:20

> 181:23

**st** 23:16

**stability** 128:1 **stacks** 127:16, 16 **staff** 108:22

> 138:24 170:14

**stage** 84:15, 16 97:1

> 105:1
>
> **standard** 8:14
>
> 20:13 24:11, 24
>
> 28:24, 24 35:18
>
> 45:21 62:22 72:1
>
> 75:1 76:1, 19
>
> 77:10, 11, 12,
>
> 14 85:19, 22 86:1,
>
> 1 87:1, 15 90:1,
>
> 1, 13, 17 91:1, 1,
>
> 13, 17 92:1, 12
>
> 93:23 94:15
>
> 95:10 101:1, 11
>
> 105:14, 15 107:19,
>
> 22 111:1 130:1, 1,
>
> 14, 15, 17, 20
>
> 131:1, 1, 1, 12
>
> 132:1, 13, 19
>
> 145:1 147:25
>
> 148:1, 12, 15, 21,
>
> 23 149:1, 10, 11
>
> 150:1, 1, 1, 10,
>
> 18, 25 151:11
>
> 152:20, 25
>
> 153:1, 11, 12,
>
> 21 155:23, 24
>
> 156:1 161:18, 19
>
> 162:16, 22 164:1
>
> 166:12, 14, 14
>
> 169:1 174:1 175:22
>
> 179:1 181:19
>
> **standards** 20:14,
>
> 21 76:24 80:13
>
> 83:1, 1, 1 84:20
>
> 85:1, 1, 13
>
> 86:22 88:1 94:17
>
> 101:12, 13 149:14,
>
> 21, 22, 25
>
> 150:10 153:15,
>
> 19 169:1, 1
>
> 179:17, 18
>
> 181:16 182:1, 1,
>
> 16, 25
>
> **standing** 124:1
>
> **standpoint** 27:12
>
> 115:19 146:16
>
> 164:1
>
> **stands** 33:11
>
> **star** 26:16 134:23
>
> **start** 3:16 66:11
>
> 108:1, 13 134:21
>
> 143:1 154:1, 1
>
> 167:1 171:17
>
> 182:24

**started** 107:18

> 154:24

**starting** 7:1 138:1

> 184:1

**starts** 165:1

**state** 87:1

**stated** 22:1

> 157:20, 24 177:1
>
> 183:1

**statement** 35:15

> 43:12 50:20
>
> 52:14 93:1
>
> 107:21 132:1

**statements** 19:19

> 72:1, 1

**states** 87:1, 14,

> 24 88:1 142:22
>
> 146:23

**station** 33:15

**stationary** 129:1

> 132:15 152:1

**stations** 123:11,

> 12 171:23

**statistical** 105:1

> 148:18

**statistically** 173:17

**statistics** 97:1

> 105:25 111:11, 20,
>
> 25

**staying** 169:13, 13

**step** 78:1 84:1

> 86:1 145:20 184:1

**stephen** 80:21 88:15,

> 24 92:23 94:11, 22
>
> 100:19 101:1

**stephen\'s** 101:19

> 102:1

**steps** 184:1 185:1

**steve** 162:1

**sub** 61:12

**sub-categories** 60:22

**sub-populations**

> 13:19 30:23

**subcommittee** 87:19

**subject** 15:24

> 51:18 83:1 113:21

**submitted** 2:1, 16,

> 22 116:1 146:25

**subsequent** 181:25

**subset** 62:1

**substance** 2:25 3:1

> 23:16 39:1 40:1
>
> 67:10, 15 78:1, 16

**substantial** 13:24

**substantially** 8:14 **substantiated** 156:12 **substantive** 6:1, 16
**suburbs** 32:1, 12 **success** 113:19 **successful** 62:11 **stood**
137:1

**stop** 142:1 144:1

> 162:13 163:24
>
> 164:16

**stopped** 144:13

**story** 141:1 **stove** 27:25 **stoves** 126:18

**sufficiency** 90:25

**sufficient** 40:16

> 101:21 103:23
>
> 177:25

**sufficiently** 112:21

**straight** 24:20

**straightforward** 87:1

> 175:12

**strategies** 84:20

> 85:1, 12 150:1

**stratospheric** 45:16

**street** 15:25 21:1

> 30:13 76:19 135:1

**strength** 45:1

**stress** 41:19 **stretch** 165:16 **stretching** 164:22 **strikes**
135:1 **striking** 137:14 **striving** 65:19 **strong** 28:16

> 43:11, 16 44:1,
>
> 19, 22 45:1
>
> 51:22 132:1 154:1

**stronger** 62:10

**strongest** 42:22 43:1

> 104:18 155:19

**strongly** 46:12

> 163:23

**struck** 43:1

**structure** 92:18

> 143:17
>
> **structured** 4:13
>
> **strug** 9:10 **struggle** 9:12 **struggled** 71:1 **struggling**
> 143:1
>
> **suggest** 65:18 74:1
>
> 98:18 111:22 113:1
>
> 137:10 154:1
>
> 158:15 166:10
>
> **suggested** 77:1
>
> 178:13
>
> **suggesting** 56:10
>
> 71:25 175:13
>
> **suggestion** 71:22
>
> 77:1 155:19
>
> **suggestive** 177:1, 17
>
> **studied** 36:22 41:1
>
> **studies** 12:25
>
> 13:14 14:15 16:1
>
> 17:17 18:10
>
> 19:25 20:1 21:1,
>
> 1, 12, 16, 17
>
> 33:1, 1, 12
>
> 35:1, 1, 15, 18,
>
> 24 36:1, 16, 19,
>
> 20 37:1 39:25
>
> 42:23 43:1, 1
>
> 44:14, 18 45:1
>
> 48:1, 20 50:15
>
> 54:23, 25, 25
>
> 55:1, 25 64:25
>
> 73:20, 22, 25 74:1
>
> 75:23, 24 90:1, 1,
>
> 11, 13 91:21 92:10
>
> 102:13 103:13
>
> 105:1 112:16, 25
>
> 113:1, 16 138:14
>
> 146:1, 1 156:1
>
> 162:24 166:1
>
> 167:1, 11, 13
>
> 168:12, 13, 17
>
> 171:1, 17 173:1,
>
> 10, 14 175:13,
>
> 14 178:13, 19
>
> **stuff** 13:16 27:1
>
> 41:24 57:1, 1
>
> 167:22 174:1
>
> **summarize** 33:1 68:19
>
> 105:19
>
> **summarized** 6:23 45:1
>
> **summarizing** 173:1
>
> **summary** 66:1 68:1
>
> 72:1

**superimposed** 117:23

**superimposition**

> 63:24

**support** 35:1, 1, 1

> 45:24 46:15
>
> 67:22 77:1 96:1

**supported** 70:24

> 128:1 156:12

**supports** 46:22

> 52:1 56:24 74:23

**supposed** 9:1

> 136:16 153:1

**sure** 3:19 5:18

> 11:1 14:1 17:19
>
> 18:18 22:1 29:11
>
> 35:1 37:10 38:15
>
> 46:21 51:24
>
> 62:10 63:1, 1, 1
>
> 64:1, 13 73:1 74:1
>
> 76:14 108:21 110:1
>
> 116:21 117:1
>
> 125:1, 19 159:20
>
> 170:12 171:15,
>
> 21 179:1 180:17

**surrogate** 50:13

> 55:20, 22 56:1
>
> 84:12 93:1, 11
>
> 96:1 112:13

**survival** 165:10

**susceptibilities**

> 175:21

**susceptibility** 40:13

> 41:1 60:1 62:1
>
> 173:20, 23
>
> 174:12 175:1

**susceptible** 42:1

> 59:13 60:1, 1, 13,
>
> 19 61:1, 10, 12
>
> 62:1 63:10 95:24
>
> 106:1 163:1

**susceptibles** 64:19

**suspect** 111:20 **symptoms** 104:22 **systematic** 13:10, 13

> 15:1 19:1, 1

**systematically** 14:14

> 15:22 39:23 70:1
>
> T

**table** 8:18 26:1 54:1

> 67:1 169:1 173:1

**tables** 23:23

**tail** 32:1

**tails** 25:1

**taking** 14:19 23:10

> 74:1 81:1
>
> 124:19, 20 137:15

**talk** 3:20 23:24 24:1

> 44:1 51:16 54:15
>
> 60:1 81:1 83:1
>
> 88:24, 25 89:1, 1,
>
> 1 90:22 92:21
>
> 94:12, 22 125:25
>
> 130:1 136:22
>
> 144:20

**talked** 43:1 45:18

> 59:1 102:1 148:1
>
> 156:20 181:14

**talking** 6:1, 10,

> 15 14:1 16:15
>
> 18:1, 1 23:1
>
> 28:1 30:10 43:21
>
> 49:1 53:13 67:11
>
> 70:14 76:23
>
> 82:25 88:20
>
> 91:10 95:1
>
> 116:20 141:17
>
> 142:1 150:16 153:1
>
> 172:24 173:1, 19

**talks** 155:18

**tangentially** 83:1 **targeted** 180:14 **task** 76:1 166:1 **teach**
107:1, 1 **team** 81:1

**tearing** 167:19

**tease** 83:13 85:24

> 121:1, 17

**technologies**

> 152:1, 1

**ted** 11:10 16:18

> 17:21 28:1
>
> 116:23 118:1
>
> 125:16 168:22

**ted\'s** 16:23 17:21

> 138:1

**tele** 147:17

**template** 183:25

**temporal** 11:1

> 27:11 29:17 32:15,
>
> 20, 25 98:13
>
> 135:19, 21
>
> **temporally** 58:14
>
> 113:12
>
> **tempted** 162:13
>
> **ten** 7:1 44:15
>
> **tend** 15:12 34:1 60:1
>
> 76:25 166:1, 1
>
> 174:1
>
> **tended** 56:1
>
> **tends** 13:18 **tenfold** 165:17 **tennessee** 90:1 **tension**
> 123:1
>
> **tentatively** 102:10
>
> **term** 55:16, 23
>
> 56:1 57:1 58:1
>
> 62:19 73:18, 19
>
> 75:20, 22, 22 76:1
>
> 95:1 97:1 101:13
>
> 120:1 155:22
>
> 156:19 161:1 165:1
>
> 172:12 177:1
>
> **terms** 3:10 11:20
>
> 34:12 35:1 47:1
>
> 48:12 66:12
>
> 70:17 82:1 86:1,
>
> 11 102:19 103:22
>
> 105:1 120:18
>
> 141:16 146:16
>
> 147:1, 1 177:18,
>
> 20, 22 178:1
>
> **terry** 9:1 75:16,
>
> 16 141:1 144:1, 15
>
> **text** 108:1
>
> **thank** 2:1, 1, 14
>
> 3:15 4:23, 25
>
> 5:15, 23, 24
>
> 38:1 63:21 64:1
>
> 72:14 75:1 78:23
>
> 79:12 80:10
>
> 81:1, 13 88:11
>
> 94:25 95:1 100:18,
>
> 19 106:10 109:1,
>
> 17 113:1 136:19
>
> 141:1 143:23
>
> 146:24 151:1
>
> 155:1, 15 160:25
>
> 162:1 163:16
>
> 165:19, 22
>
> 166:25 169:12
>
> 176:19 181:1
>
> 185:1, 10
>
> **thanks** 5:11 33:18
>
> 88:18 109:25
>
> 134:12 184:22

**that\'d** 33:25

**that\'ll** 90:19

**that\'s** 5:22 6:1,

> 23 8:23 9:10,
>
> 12, 13 10:1, 1
>
> 12:11, 12 13:1, 1,
>
> 1 14:23 15:25
>
> 16:11, 15, 23
>
> 17:1, 14, 17 20:16
>
> 21:1, 12, 18, 24
>
> 22:15 23:13, 17,
>
> 24, 25 25:15, 21
>
> 27:10, 11 30:14
>
> 31:14 32:19
>
> 33:11 35:10, 19
>
> 36:1 39:1 42:1, 1,
>
> 15 43:11, 16 45:1,
>
> 21 46:11, 20
>
> 47:1 48:1 49:1
>
> 50:1, 11, 22
>
> 51:11, 22, 24
>
> 52:14, 20 54:1, 17
>
> 56:1, 22 57:1 58:1
>
> 59:14, 23, 24
>
> 60:19 63:18, 19
>
> 66:1, 23 67:25
>
> 68:1 69:15, 19
>
> 72:1, 23 73:1,
>
> 13 76:1 78:17
>
> 86:1, 19 88:1
>
> 94:20 99:13 100:17
>
> 106:21 109:16,
>
> 23 111:10
>
> 112:14, 19 113:15,
>
> 25 115:23 116:1,
>
> 14 117:1 119:23
>
> 120:1 122:14
>
> 123:1, 1, 11
>
> 124:1, 19
>
> 125:23, 23
>
> 126:1, 10 130:10
>
> 131:1, 20 132:1,
>
> 16 133:21 136:1,
>
> 15, 15, 19 137:1
>
> 138:25 139:11
>
> 141:1, 25 143:1
>
> 144:22 146:1
>
> 149:15, 23
>
> 151:12 152:11,
>
> 13 154:18 155:22
>
> 157:10 158:19
>
> 159:20 160:1, 1
>
> 161:23 162:1, 12
>
> 163:20 164:25
>
> 165:1, 14, 14
>
> 166:1, 11 167:13
>
> 168:1, 1 169:25
>
> 171:16, 25 172:19,
>
> 22 173:21, 25
>
> 174:15, 16
>
> 177:10 179:1
>
> 180:14, 16 182:11,
>
> 16 183:1, 10

**them\'s** 47:11

**theme** 94:11 **themselves** 97:15 **theoretic** 41:1 **theory** 128:1

**there\'ll** 143:1 155:1

**there\'s** 10:21 11:12,

> 15, 17, 21 15:20
>
> 16:21 18:11, 22
>
> 27:21 32:23
>
> 34:19 37:1
>
> 40:12, 13, 25 41:1
>
> 43:1 44:1 47:1
>
> 52:1 53:12, 12, 14
>
> 54:24 55:1 59:1
>
> 61:25 69:21
>
> 74:11 76:13, 16
>
> 86:16 101:14
>
> 102:14 103:14,
>
> 23 108:1 112:1
>
> 123:19 126:1 135:1
>
> 136:1 137:1
>
> 146:12, 15 151:1
>
> 155:21 156:16,
>
> 16 162:15 166:10
>
> 172:1 173:1 174:24
>
> 175:20 179:25
>
> 180:1

**therefore** 16:1

> 158:18

**theses** 70:1

**they\'d** 116:21

**they\'ll** 142:23 143:1

**they\'re** 12:1 13:12

> 15:12 17:23, 24
>
> 26:1, 1 31:1 47:19
>
> 50:14 54:19 87:1
>
> 101:21 122:25
>
> 123:1 132:25
>
> 133:19 144:1, 21
>
> 146:1 155:12
>
> 158:14 167:17
>
> **they\'ve** 170:15
>
> **thin** 77:23
>
> **third** 35:1
>
> **thirty** 74:18 137:10
>
> **thorough** 164:13
>
> 165:24
>
> **thoughtful** 165:24
>
> **thoughts** 5:21
>
> 82:22 126:12
>
> **thousand** 33:17
>
> 105:11
>
> **thousands** 85:20
>
> **tier** 95:18 96:1
>
> 97:1, 1, 1, 13
>
> 98:1 99:10, 20, 20
>
> 101:1, 1, 15, 20
>
> 102:1, 1, 1
>
> 103:1 104:1, 1, 1,
>
> 11 112:1, 1, 1,
>
> 1 126:20 127:10
>
> 133:10 136:1, 1,
>
> 1, 11 139:1, 1, 1,
>
> 1, 15, 15, 16, 20,
>
> 24, 24, 24 142:19,
>
> 22 144:13
>
> 145:12, 23
>
> 158:1, 15 161:1
>
> 162:14, 17, 24,
>
> 25, 25 163:24
>
> 166:1
>
> **tier-two** 99:15
>
> **tiered** 81:17 95:25
>
> 101:14 137:1
>
> 141:19 144:1
>
> 162:13
>
> **tiering** 81:19
>
> **tiers** 81:21 95:18
>
> 101:19 102:1
>
> 136:14 139:1
>
> 140:1, 1, 1 142:17
>
> 144:1 157:18
>
> 158:1, 1, 25 159:1
>
> **three-fold** 14:23
>
> **threefold** 15:10
>
> **threshold** 47:20
>
> 166:10
>
> **throughout** 94:11
>
> **throw** 62:1 65:10 **tiger** 147:1, 16 **tightened** 51:11

**tightly** 25:10

**thurston** 20:1

> 21:11 22:1, 20, 23
>
> 38:1, 11 50:23, 25
>
> 52:17, 25 53:18

**thus** 61:20

**till** 39:14 107:1

**tim** 4:1 15:1 31:1, 1

> 46:23 110:20
>
> 115:13 116:11
>
> 117:1 134:25 143:1
>
> 152:19 153:25

**tim\'s** 59:16

**timely** 2:1 **tissues** 64:16 **toad** 35:13

**today** 3:23 82:19

> 149:13 156:20
>
> 159:14

**tolerance** 178:14, 20

> 179:1

**tons** 118:10

**top** 16:14 61:18

> 63:25

**total** 126:13, 13, 20

> 167:12

**totally** 70:24 72:22

**touched** 135:1 149:17

**touching** 124:1 **tough** 76:1 130:10 **toward** 75:22 **towards**
33:10

> 70:12 128:25 154:1

**tox** 37:1 57:1

> 73:22 74:1 91:1
>
> 178:19

**toxic** 70:17 74:1, 11

**toxicities** 85:25 **toxicity** 57:16 86:1 **toxicologic** 45:18
**toxicological**

> 36:16 37:1

**toxicology** 45:24

> 46:1, 1, 14, 22
>
> 47:18, 22 50:21
>
> 53:1, 11, 12 54:13
>
> 111:12 178:13

**toxicology\'s** 47:14

**tracking** 44:20 **tract** 98:22 **tradition** 145:1

**traditionally** 128:1

**traffic** 18:1 23:1

> 31:24 49:17 113:10
>
> 117:24 151:18

**trans** 96:19

**transformation** 7:24 **transformed** 25:1 **tremendous** 122:15

> 123:18 166:1

**tremendously** 13:1

**trichloroethylene**

> 176:10, 13

**tried** 54:22 67:1

> 105:17 108:12

**tries** 70:19

**trim** 167:21, 22 **trivial** 10:20 132:23 **troop** 147:10

**true** 16:16 17:1,

> 15 19:12 21:12
>
> 32:18 41:11
>
> 49:17 57:23
>
> 58:10 61:11
>
> 75:23 144:22 165:1

**truly** 2:17 85:18

> 86:1

**truth** 184:12

**try** 34:1 55:1 59:1

> 68:14 73:17, 20,
>
> 23 74:1 112:1,
>
> 12 121:1 127:1
>
> 159:21 160:20
>
> 164:19

**trying** 15:23 22:18

> 28:18 43:13, 23
>
> 50:22 65:22
>
> 69:22 71:16 79:1
>
> 85:24 86:1, 1, 1
>
> 88:1 97:14
>
> 106:18 112:17
>
> 115:10 121:13
>
> 128:1 136:1 143:25
>
> 164:13 170:1, 1

**turbulence** 128:1

**turn** 3:13 4:24

> 68:1 82:21 92:23
>
> 94:21 126:22

**turned** 170:11

**twelve** 23:11

**twenty** 18:1 23:12

> 148:1
>
> **twenty-four-hour**

46:1, 13 49:21

**usually** 113:1

> 155:20

124:22 127:22

> **twice** 28:1
>
> **two-week** 117:1, 1
>
> **type** 48:15 73:1
>
> 74:16 76:17 96:1
>
> 128:17 129:1
>
> 151:17
>
> **types** 19:24 20:1
>
> **typically** 129:1
>
> 130:13 148:15
>
> 150:10, 13
>
> U
>
> **uarg** 131:12
>
> **u.s** 2:1 33:24
>
> 105:19, 25 118:25
>
> **uh** 46:17 59:14 64:11
>
> **ultimate** 45:21
>
> 83:1 159:1
>
> **ultimately** 20:13
>
> 80:15 180:12
>
> **ultman** 4:1, 1, 1,
>
> 1 63:11 142:1
>
> **ultra** 66:14
>
> **un-mute** 142:10 **una** 174:21 **unaware** 16:13 **uncertain**
> 139:22
>
> **uncertainties** 20:1
>
> 146:20 160:10
>
> 175:1, 1, 10
>
> 176:12
>
> **uncertainty** 95:25
>
> 97:1 99:1
>
> 100:11, 14 101:1
>
> 102:15 105:1, 1
>
> 126:1 139:23
>
> 140:1, 1, 13,
>
> 14, 15, 17, 20, 23
>
> 141:1, 1 143:19
>
> 159:1, 15 165:17
>
> 174:24
>
> **uncomfortable** 59:12
>
> **uncomfortably** 42:21 **unconfined** 18:1 **underestimated** 14:16
> **underlying** 143:21
>
> 146:17
>
> **unders** 171:15
>
> **understand** 14:1 19:1
>
> 130:1 132:25
>
> 163:13 164:19
>
> 172:16

**understanding** 30:16,

> 22 31:18 62:22
>
> 121:1 124:1 127:15
>
> 129:23 138:22

**understood** 57:1

**undertaking** 117:17

> 143:1

**unfortunately** 15:19

**uniform** 50:14

> 173:20, 20

**unintentionally**

> 84:25

**unique** 63:1, 1 64:1,

> 15, 17, 18, 19
>
> 65:1

**uniqueness** 63:24

**unless** 34:19 106:12

**unlikely** 92:14

> 107:23 132:14,
>
> 16 162:1

**unreasonable** 128:19

**up-front** 84:1

**upon** 13:15 14:15

> 16:1 27:1, 19
>
> 60:14 99:19
>
> 136:1 164:23

**upper** 32:1 139:10

> 140:1

**urban** 15:1, 1, 21

> 17:1, 22 102:1
>
> 103:1 104:1 105:18
>
> 110:10 111:15
>
> 113:13, 21
>
> 114:1, 14 117:23
>
> 133:1, 1 134:1
>
> 148:16 150:19
>
> 153:1

**urbanized** 179:21

**urge** 166:18, 22

**useful** 7:22 27:12

> 53:24 65:1 99:1

**user** 69:12, 13

**utility** 86:1

> 101:21 104:10

**usual** 18:25, 25

> 24:19
>
> V

**value** 24:12 45:23

> 76:1 104:10
>
> 111:1 119:15 170:1

**values** 11:23 25:1

> 92:1, 1 111:1,
>
> 1, 15 140:17

**vanessa** 154:1 155:1

**variability** 29:17

> 32:15, 21, 25
>
> 34:1, 1 43:1
>
> 100:14 101:1
>
> 102:19, 21
>
> 123:18 124:1, 17
>
> 138:15, 19
>
> 143:21 174:1, 1,
>
> 12 176:12

**variable** 16:22 27:17

> 65:10 173:23
>
> 174:1, 21

**variances** 124:1

**variation** 123:14

> 135:20

**variations** 8:1, 10

**variety** 73:22

**various** 50:1 99:1

> 100:24 103:1
>
> 105:20 124:13
>
> 167:11, 19 169:1
>
> 181:15

**vary** 29:18

**vehicle** 125:1 132:15

> 152:1

**vehicles** 33:17

**vein** 151:25 **verbiage** 35:1 44:1 **version** 106:1 **versus** 13:1
17:15

> 22:12 27:1 35:1
>
> 47:1, 11 48:14
>
> 54:25 83:18
>
> 92:25 123:21, 21
>
> 126:13 128:1 160:1
>
> 181:11

**vertical** 15:1, 20

> 16:22 17:1 114:1

**verticality** 13:18

> 16:19 18:1

**vertically** 13:1 14:1

> **view** 12:1 83:10
>
> **views** 85:14 131:1 **violations** 179:17 **virtually** 53:1
> **visceral** 10:24 **visits** 42:23
>
> 104:1, 22
>
> **vote** 44:16
>
> **vu** 154:1, 13
>
> **vulnerability** 60:10,
>
> 25
>
> **vulnerable** 60:1,
>
> 1, 13, 17, 20,
>
> 22 61:1, 12
>
> 95:24 106:1
>
> W
>
> **wait** 28:1 116:14
>
> 136:21 142:1
>
> 169:23
>
> **waiting** 56:19, 23
>
> 157:1 184:11, 18
>
> **walk** 45:1 76:19
>
> 100:20
>
> **washed** 49:1
>
> **wasn\'t** 30:21 35:1
>
> 39:13 40:17 63:1
>
> 71:15 110:1, 25
>
> 120:1 131:1
>
> 139:1 160:13 162:1
>
> **waste** 20:22
>
> **water** 149:25
>
> **ways** 32:1 105:10
>
> 135:13 146:21
>
> 147:1, 1 148:23
>
> 176:1
>
> **we\'d** 5:13, 13
>
> 34:13 75:13 76:1
>
> 101:1, 25 102:16
>
> 106:1 115:12
>
> **we\'ll** 4:24 5:16 7:18
>
> 10:10 22:24
>
> 32:19 52:19 55:1
>
> 58:24 65:16, 18
>
> 74:1 78:24 79:1,
>
> 1, 1 81:1, 20
>
> 89:14 98:25
>
> 101:1 115:24
>
> 129:19 136:17
>
> 153:16 155:1 162:1
>
> 163:19 165:21
>
> 170:1 182:16 184:1

**we\'re** 2:24 7:1, 1

> 9:1, 16 15:22
>
> 16:15 18:11 21:1
>
> 29:1, 1, 10, 13
>
> 30:16, 16, 19
>
> 43:13 46:15
>
> 48:18 49:1, 13, 15
>
> 50:21 51:1, 1, 13,
>
> 14, 15 56:18,
>
> 19, 22 58:18 61:22
>
> 63:1 66:25 67:1
>
> 72:24 73:1, 1 74:1
>
> 76:22 78:1, 1
>
> 79:12, 14, 25 83:1
>
> 84:1 89:1 91:10
>
> 93:10, 10 94:12
>
> 97:14 103:19, 19
>
> 108:10 112:1, 1,
>
> 1, 11, 17, 23
>
> 113:16 114:19
>
> 115:1, 1, 1, 10
>
> 116:20 118:24
>
> 121:13 123:16,
>
> 17 126:1, 18
>
> 130:12, 14
>
> 131:21 133:1
>
> 136:15 141:17,
>
> 20 143:1, 1, 1
>
> 150:16 153:1
>
> 155:1, 25 156:10
>
> 157:1 165:11
>
> 167:18 168:19
>
> 169:1, 14 170:1, 1
>
> 172:1, 11 177:11
>
> 178:1

**we\'ve** 29:11 32:11

> 33:1, 1 34:16
>
> 37:16 43:1 47:23
>
> 56:17 59:1 67:1
>
> 74:18 79:17
>
> 81:1, 17 96:22
>
> 97:13 98:11, 16
>
> 99:1 103:1
>
> 105:1, 17 109:17
>
> 114:18 117:12, 13,
>
> 13 118:13 126:17
>
> 134:13 136:14
>
> 148:18 163:1 167:1
>
> 169:1 172:15
>
> 182:13 183:1
>
> 184:1, 15

**weak** 141:11

**website** 173:25

**week** 50:1, 1 81:1

> 146:15

**wegman** 80:1, 1,

> 11, 24 87:16

**weight** 160:14

**weights** 160:1

**welcome** 3:18

**well-executed** 162:10 **well-oiled** 118:1 **whammy** 61:11

**whatever** 16:1

> 17:13 31:1
>
> 37:16, 18 112:22
>
> 114:11 116:17
>
> 118:11 122:13
>
> 138:18 154:1,
>
> 15, 21 177:18

**whatever\'s** 11:1

**wheeze** 104:23 **whereas** 54:10 **whereupon** 7:1

> 26:12 28:1 55:12
>
> 75:1, 15 79:10
>
> 154:22 185:11

**wherever** 59:24

**whether** 5:1 27:16

> 44:1, 11 56:23
>
> 59:25 60:1 81:19
>
> 96:20 103:22
>
> 109:1, 15 114:1, 1
>
> 139:15 145:22
>
> 148:24, 25 153:1
>
> 155:11

**whichever** 132:12

**who\'s** 3:13, 20 30:17

> 68:14 134:21 176:1

**whoever** 40:14

**whole** 23:1 29:24

> 38:24 45:15
>
> 53:15 68:12
>
> 73:22 81:12 118:25
>
> 122:18, 19
>
> 125:15 140:1
>
> 142:12 150:19
>
> 152:24

**wide** 124:1 138:1

> 166:1

**wildest** 37:11

**windows** 114:1

**wise** 75:25

**withstanding** 173:13

> **wonder** 55:1 167:14
>
> **wondered** 109:13 **wonderful** 63:1 **wondering** 10:14
>
> 30:20 43:10 56:1
>
> 75:20 119:1
>
> 177:1 179:15
>
> **wording** 36:17
>
> **wordsmithing** 39:1, 1
>
> 67:12, 14
>
> **work** 51:11 64:21
>
> 80:18, 25 81:1, 1,
>
> 10, 11, 13 95:1
>
> 116:15, 16 136:1
>
> 137:19 140:10
>
> 144:23 157:1 173:1
>
> 176:1
>
> **worked** 59:1
>
> **working** 5:12 69:1
>
> 80:14 107:1 137:11
>
> 154:1 169:12
>
> **works** 78:25
>
> **workshop** 94:1
>
> **world** 63:1 118:14
>
> 146:23 169:1
>
> **worries** 149:19
>
> 166:11
>
> **worry** 67:15 138:16
>
> 166:17
>
> **worst** 152:1, 14, 22
>
> **worth** 19:22, 22
>
> 123:1 142:16
>
> 165:16 181:1
>
> **write** 4:23 38:16
>
> 51:17
>
> **writing** 69:11 160:21
>
> **written** 3:1, 1
>
> 5:16 7:19 22:14
>
> 23:18 37:23 38:1
>
> 51:1 58:23, 23, 25
>
> 69:1 116:10
>
> 140:1 146:25
>
> 156:15 159:14
>
> 163:1 165:23
>
> 169:15, 16 170:10,
>
> 11 183:22
>
> **wrong** 72:24 73:1
>
> 74:18
>
> **wrongly** 108:13
>
> **wrote** 23:18 40:14
>
> 44:1 52:1 58:22,
>
> 24 65:13 142:14

**wyzga** 7:21 10:1 21:1

> 36:1, 13 37:1, 1
>
> 39:10, 12, 20,
>
> 21 40:1 55:1, 25
>
> 56:12, 17 58:11
>
> 129:21 132:1, 1
>
> 165:20, 22
>
> Y

**yearly** 25:19 54:11

**yesterday** 2:20 4:1

> 6:15, 18 22:10
>
> 29:20, 24 30:21
>
> 32:23 33:20 35:1
>
> 37:23 39:21 40:1
>
> 42:21 43:18, 20
>
> 45:19 47:15, 21
>
> 52:1 55:18 59:11
>
> 65:24 68:22
>
> 71:22 77:1 84:1
>
> 89:11 92:24
>
> 93:1, 13 104:17
>
> 156:1, 20 182:23

**yesterday\'s** 4:10

> 66:21

**yet** 50:12, 22

> 51:1, 13 63:1
>
> 116:1 131:21
>
> 137:22 156:19

**york** 15:23 87:25

> 110:1, 12 114:18
>
> 117:12, 14, 25
>
> 150:19, 20 179:21

**you\'ll** 123:13

> 136:18, 25 138:24,
>
> 25 141:1 144:1
>
> 180:1 182:1, 19

**you\'ve** 64:1 81:24

> 103:20 114:1, 1
>
> 115:13 121:22, 23,
>
> 24, 25 122:1
>
> 140:1, 11 166:1,
>
> 21 167:1 174:15

**youth** 77:21

> Z

**zoom** 121:1