202-234-4433 Neal R. Gross & Co., Inc. 

### Page 1 

## U.S. ENVIRONMENTAL PROTECTION AGENCY

+ + + + + CLEAN AIR SCIENTIFIC ADVISORY COMMITTEE

PARTICULATE MATTER REVIEW PANEL

+ + + + +

PUBLIC MEETING

+ + + + + WEDNESDAY MARCH 10, 2010

+ + + + +

The meeting convened in Salon ABC of the Marriott Research Triangle Park, 4700 Guardian Drive, Durham, North Carolina, at 8:30 a.m., Jonathan Samet, Chairman, presiding.

CASAC MEMBERS PRESENT:

JONATHAN SAMET, Chairman JOSEPH BRAIN H. CHRISTOPHER FREY ARMISTEAD RUSSELL HELEN SUH202-234-4433 Neal R. Gross & Co., Inc. Page 2

PANEL MEMBERS PRESENT: LOWELL ASHBAUGH ED AVOL WAYNE CASCIO

DAVID GRANTZ JOSEPH HELBLE ROGENE HENDERSON PHILIP HOPKE MORTON LIPPMANN

ROBERT PHALEN

RICH POIROT FRANK SPEIZER SVERRE VEDAL

SCIENCE ADVISORY BOARD STAFF:

HOLLY STALLWORTH, Designated Federal Officer Page 3

TABLE OF CONTENTS AGENDA ITEM PAGE

Convene Meeting and Welcome 4

Introductions, Review of Agenda 5 Background/Schedule of NAAQS Review 9 Overview of Policy Assessment (Chapters 1-3) 11

Highlights of Health Risk Assessment 25 Public Comments on Health Risk Assessment 75 Panel Discussion and Charge Questions for 85 Health Risk Assessment

Overview of Policy Assessment 266 Chapters 4-5

Highlights of Urban-Focused Visibility 271 Assessment Charge Questions 275

Highlights and Discussion of Charge 324 Questions for Review of Policy Assessment

Adjourn 387 Page 4

1 P R O C E E D I N G S 2 (8:30 a.m.) 3 DR. STALLWORTH: I am going to go 4 ahead and start with my opening statement. It 5 is kind of like the one you have to listen to

6 from the airline stewardess. 7 I am Holly Stallworth. I am the 8 Designated Federal Officer for the Clean air 9 Scientific Advisory Panel, Particulate Matter

10 Review Panel. 11 I just want to remind everybody, 12 this is a Federal Advisory Committee. It 13 operates under the requirements of the Federal 14 Advisory Committee Act, and the Clean Air 15 Scientific Advisory Committee is empowered by 16 law to provide advice to the Administrator. 17 Consistent with the requirements 18 of FACA and with EPA policy, the deliberations 19 of this Panel are conducted in public at 20 meetings for which advance public notice is 21 given. Discussions and deliberations, the 22 interactions of the Panel with the public, and

Page 5 1 the agencies are conducted also in public in 2 sessions where I, as DFO, am present to ensure 3 that the requirements of FACA are met. 4 These include the requirements for 5 open meetings, for maintaining records, making 6 available public summaries of meetings, and 7 providing opportunities for public comment. 8 Today I have just one request for 9 public comment from the American Petroleum 10 Institute. Is that correct? Anyone else? 11 Last, I want to say that our 12 office has reviewed financial disclosures from 13 each of you, and determined that there are no 14 conflict of interest or appearance of a lack 15 of impartiality issues. 16 I will turn the meeting over to

17 Dr. Jon Samet. 18 CHAIRMAN SAMET: Good morning, 19 everyone, and welcome to yet another CASAC 20 meeting. 21 I think, just so we can remind 22 each other who others are, a quick round of202-234-4433 Neal R. Gross & Co., Inc. Page 6 1 introductions of the committee for the public. 2 So, Jon Samet from the University of Southern 3 California. 4 DR. FREY: I am Chris Frey at 5 North Carolina State University. 6 DR. LIPPMANN: Mort Lippmann, New 7 York University. 8 MR. AVOL: Ed Avol, University of 9 Southern California. 10 DR. ASHBAUGH: Lowell Ashbaugh, 11 University of California at Davis. 12 MR. POIROT: Rich Poirot, State of

13 Vermont. 14 DR. HENDERSON: Rogene Henderson, 15 Lovelace Respiratory Research Institute. 16 DR. RUSSELL: Ted Russell, Georgia

17 Tech. 18 DR. GRANTZ: David Grantz, 19 University of California at Riverside. 20 DR. SUH: Helen Suh, Harvard 21 School of Public Health 22 DR. BRAIN: Joe Brian, Harvard. Page 7 1 DR. SPEIZER: Frank Speizer, 2 Harvard University. 3 DR. CASCIO: Wayne Cascio, East 4 Carolina University. 5 DR. PHALEN: Bob Phalen, 6 University of California, Irvine. 7 DR. VEDAL: Sverre Vedal, 8 University of Washington. 9 DR. HOPKE: Phil Hopke, Clarkson 10 University. 11 DR. HELBLE: Joe Helble, Dartmouth 12 College. 13 CHAIRMAN SAMET: Good. I notice 14 that the Harvard trio are all sitting in a 15 row. One is at the Harvard School of Public 16 Health, one is at Harvard, and one is at 17 Harvard University. I guess you all know 18 where you are coming from. 19 Just to remind everybody what we 20 are doing on our agenda, we are reviewing the 21 second draft, Health Risk Assessment today, 22 and then tomorrow we are turning to the urban Page 8 1 focused visibility assessment, and we are also 2 discussing, I guess, what is now dubbed the 3 first draft policy assessment, reminding 4 everyone that we saw the 0.2 draft of the 5 policy assessment at our last meeting. 6 We may want to look at the 7 schedule tomorrow as the day comes. In part, 8 I think the visibility may go quicker, and 9 there probably, I suspect, will be perhaps 10 more than an hour's worth of committee 11 interest in discussing on the policy 12 assessment. So maybe we can just sort of see 13 what the day looks like and how we want to 14 discuss that. 15 Also, I think, for CASAC we may be 16 able to move relatively quickly through our 17 review of the second draft Quantitative Health 18 Risk Assessment and, if we are able to do so, 19 I am going to suggest that we then try and 20 take time to work on the letter and get our 21 comments as together as possible today, I 22 think, just for efficiency. Page 9

1 So, again, let's just see what 2 happens. Perhaps the discussions will fill 3 the time, but I suspect they will not. 4 So I think, with that, let me turn 5 things to EPA. 6 DR. MARTIN: Good morning, 7 everyone. First of all, let me welcome you 8 all here and tell you who much we appreciate 9 your making the time in your schedules to 10 participate in this. 11 I also want to extend greetings 12 from Lydia Wegman who, because of prior travel 13 commitments, wasn't able to be here. She was 14 very sorry to be missing this meeting. 15 What we wanted to do, as outlined 16 on the agenda here, was first of all, just to 17 touch on the schedule for where we are in the 18 review schedule and what is coming next, and 19 then we were asked to provide some overview of 20 the policy assessment document, the first 21 draft of that document, which you have just 22 very recently received.202-234-4433 Neal R. Gross & Co., Inc. Page 10 1 So, obviously, you haven't had a 2 chance to read it and consider it, but the 3 sense was that providing some overview of it, 4 how it is organized, the issues it deals with, 5 would help provide some context for your 6 discussions of the Health Risk Assessment 7 today and the Visibility Assessment tomorrow. 8 So that is what we intended to do. 9 First of all, turning to the 10 schedule, what we have here is, in black type, 11 the items on the schedule associated with the 12 completed Innovative Science Assessment and 13 with the second drafts of the documents that 14 we are reviewing today that were released in 15 January and early February. 16 It also indicates that following 17 this meeting today and tomorrow on those 18 documents, we will be planning to finalize 19 those two documents in April. 20 The blue type here highlights the 21 items related to the Policy Assessment, the 22 first draft of that assessment which were just Page 11 1 released Friday evening and, hopefully, has 2 gotten to you all by now. 3 We have the meeting scheduled with 4 you for April 8th and 9th to formally review 5 that document after you have had sufficient 6 time to read it and consider it, and we then 7 intend quite quickly to turn around a second 8 draft of that document by late May, with the 9 idea that we would ask for your review of that 10 second draft in June. 11 The target goal is to finalize it 12 in July, and the rulemaking schedule that the 13 agency has laid out is to have a proposed rule 14 by the end of November and a final rule July 15 next year. That is an ambitious schedule, and 16 hopefully, we will all be able to keep on 17 track with that. 18 Before we get into focusing on 19 some specific elements of the Policy 20 Assessment, I just wanted to make some general 21 comments about it, about its purpose, about 22 how we think about the information it Page 12 1 includes, how it draws from the other 2 documents that you all are looking at, how it 3 is organized. 4 Then what I want to do is turn to 5 focus now today on the chapters that 6 specifically relate to considering the primary 7 standards of Chapter 2 on fine particle 8 standards, Chapter 3 on standards for thoracic 9 coarse particles.10 With me here are Beth Hassett11 Sipple, who is the overall project lead for 12 the PM review and also the lead author on 13 Chapter 2, and Scott Jenkins who is the lead 14 author on Chapter 3. Then tomorrow we will 15 talk about the other two chapters that relate 16 to visibility and other welfare effects. 17 First of all, just to touch on the 18 purpose of the document, the purpose of the 19 Policy Assessment document -- and this is sort 20 of a reincarnation of former staff papers that 21 some of you have seen over the years -- is to 22 serve the purpose of looking at the scientific Page 13 1 evidence and thinking about how one can 2 translate the information derivable from those 3 scientific studies into a basis for 4 considering the adequacy of the current 5 standards and appropriate alternative 6 standards that may be appropriate for 7 consideration. 8 It is also to look at the 9 quantitative assessments that we do, the 10 health risk assessment, the visibility 11 assessment, and ask the question how do we 12 interpret that information and the 13 uncertainties surrounding that information, 14 again as a basis for evaluating the adequacy 15 of the current standards and to identify 16 potential alternative standards that are 17 appropriate for consideration. 18 In doing this, we draw directly 19 from the integrated science assessment and 20 from the documents that you are reviewing here 21 at this meeting, and in doing that, we think 22 about what information comes from that and202-234-4433 Neal R. Gross & Co., Inc. Page 14

1 what factors are relevant to our consideration

2 of the standards. 3 I want to just touch on this point 4 briefly because it is very important. One of 5 the things we do in all the documents we 6 produce is we include a section that outlines 7 what are the legislative requirements in terms 8 of what information is relevant to our 9 standard setting process and what information 10 is not relevant to our standard setting 11 process. 12 We try to make very sure that, in 13 laying out the reasoning and the rationale 14 that underlines our views with regard to 15 adequacy of standards and alternative 16 standards to consider, that we put in place a 17 line of reasoning based on the information 18 available to us, and make sure that we are not 19 considering factors that aren't relevant to 20 the decision.

21 One of those factors that I 22 specifically want to take note of is issues Page 15 1 related to the cost of attaining standards or 2 even the attainability of the standards

3 themselves. 4 I think those of you who have 5 heard overview presentations I have made

6 before have heard the statement that those 7 factors simply are not permissible as a basis 8 for the reasoning that underlines the agency's 9 decisions about setting standards. 10 So it is what you do not see in 11 the documents we present is any discussion 12 about specific measures that people might take 13 to actually attain the range of alternative 14 standards that we are identifying because that 15 isn't relevant to our consideration. 16 The Act was intended to be 17 technology forcing. The Act was intended to 18 create standards that truly represented what 19 it took to protect public health with an 20 adequate margin of safety, what it took to 21 protect public welfare from known or 22 anticipated adverse effects, and those are the Page 16 1 only issues that we are permitted to consider. 2 Therefore, I encourage you as 3 well, as part of your mandate in providing 4 advice to the agency, to ground your thinking, 5 to ground the reasoning that underlies the 6 advice you provide to us on factors that are 7 part of the factors that the agency can take 8 into account. 9 One observation I will make about 10 the first draft document that we have 11 presented is I am sure it is longer than you 12 were hoping it would be, and I thought, rather 13 than listening to you tell me that, I would 14 offer that observation myself. 15 I will make two points about the 16 length of the document.

17 CHAIRMAN SAMET: I am not sure 18 that is a fair strategy, actually. 19 DR. MARTIN: The best defense is a 20 good offense, or something like that, yes. 21 One observation is it is a first 22 draft, and that means we have time to take out Page 17 1 things that got in there that are more 2 detailed than are necessary, that go off on 3 tangents that don't turn out to be relevant or 4 germane or pertinent to the judgments we need 5 to make, the conclusions we need to draw. 6 So it certainly is our goal to 7 continue to try to streamline the document as 8 we move forward, in part based on 9 consideration of your input as to what you 10 found most relevant, most useful information, 11 that you did not find particularly useful or 12 helpful in terms of reaching the conclusions

13 that we need to reach. 14 The other observation I will make 15 about the length of the document is it will 16 necessarily, I think, in the end be longer 17 than you would ideally like it to be, and that 18 is because we feel very strongly that we need 19 to fully articulate the underlying reasoning 20 behind the conclusions we are reaching, that 21 when we put forward to the Administrator 22 conclusions and offer information for her202-234-4433 Neal R. Gross & Co., Inc. Page 18 1 consideration, including the advice you offer 2 her, part of the most important pieces of that 3 is the underlying reasoning, the rationale. 4 What information are we placing weight on? 5 What information are we deciding is not 6 deserving of placing weight on, and how do we 7 reason ourselves from the underlying 8 information to the conclusions and judgments

9 that need to be made? 10 Without that underlying reasoning, 11 our conclusions, and even your advice and 12 recommendations, have far less ability to 13 impact the decisions made. 14 I can guarantee you the first 15 question we get when we present information to 16 the Administrator in terms of our conclusions 17 or your advice is, and what was it based on, 18 why did they say that, why did you conclude 19 that, what information were you looking at. 20 That is always the first question 21 because when the agency puts forward final 22 decisions on these standards, we need to be Page 19

1 able to defend them; and when we defend them, 2 what we are defending isn't so much the bottom 3 line answer we reached, but it is did we have 4 a reasoned basis for getting there? Did we 5 clearly articulate how we looked at all the 6 evidence, how we looked at the risk 7 information, and how we weighted that 8 information to get to where we end up? 9 So I take this time, hopefully 10 not trying your patience too much, to urge you 11 to think about the importance to us in 12 receiving your advice packaged together with 13 underlying rationale, underlying reasoning, 14 and part of the length that you see in the 15 policy document we create is there expressly 16 for the purpose of laying out potential ways 17 that we might develop this reasoning for the 18 purpose of giving you the opportunity to see 19 that and provide input to us on the lines of 20 reasoning that we are pursuing. 21 DR. STALLWORTH: Karen, for the 22 benefit of those of us who need to improve Page 20 1 these letters, can you give us some examples 2 of where a CASAC letter did not provide 3 adequate reasoning for your purposes? 4 DR. MARTIN: Well, I think that, 5 rather than pointing out to one example where 6 a final letter didn't, I would just note that 7 some of the letters go into more detail with 8 regard to the underlying basis for the 9 recommendations you offer than others. 10 I can note that some individual 11 comments sometimes will point to issues that 12 aren't germane to the decisions. We sometimes 13 get input reflecting views about what range of 14 levels of standards we ought to analyze in the 15 assessment, and that was the case here with 16 some preliminary comments. 17 To the extent that those views are 18 grounded in intuition about whether or not 19 different ranges of standards might be 20 attainable, that is not a particularly useful 21 kind of input to us because we simply can't 22 use that in -- Page 21

1 DR. STALLWORTH: Right. So I am 2 trying to get a sense of what, for you, would 3 be adequate reasoning to support CASAC's 4 advice, and short of writing a dissertation 5 the length of the policy assessment, is it 6 adequate for CASAC to say, as stated in the 7 policy assessment or for the reasons stated in 8 this document or that document? 9 DR. MARTIN: Surely, it would be, 10 so long as that reflects the Committee's 11 consensus view. 12 DR. STALLWORTH: Well, sure. Of

13 course.

14 DR. MARTIN: One of the reasons 15 why we pursue different lines of reasoning in 16 the drafts of the policy assessment document 17 is to give you all the opportunity to think 18 about different ways we might go from the 19 underlying information to standards and to 20 provide us your thoughts as to those you found 21 more meriting further consideration or where 22 you found levels of discomfort in terms of how202-234-4433 Neal R. Gross & Co., Inc. Page 22 1 the reasoning considered evidence or risk 2 information. 3 One of the big purposes of this 4 document is to help facilitate your advice to 5 us by presenting our thinking and our 6 reasoning and giving you the opportunity to 7 weigh in on it, so weighing in on not just the 8 bottom line conclusions we reached, but the 9 process by which we got there is equally 10 important. 11 DR. STALLWORTH: Thank you. 12 DR. MARTIN: So just to wrap up, I 13 will say that the document is organized, as I 14 have said, in that series of chapters that 15 touch upon the primary standards and the 16 secondary standards and welfare effects. 17 We lay out at the beginning of the 18 chapters approaches that have been used in 19 previous reviews in order to reason from the 20 available evidence to the decisions that were 21 made in the past reviews. 22 We also spend some time laying out Page 23 1 the decisions that were reached by the court 2 in the review of the 2006 Final Decisions in 3 which, as you know, the court remanded 4 decisions on the primary annual standard, 5 remanded decisions on the visibility

6 standards. 7 We thought it would be useful to 8 you and the general audience for this document 9 to take some time to lay out the reasoning 10 that was used in the past, how the reviewing 11 court thought about that reasoning and 12 incorporated into their judgments, to help 13 inform the approach we are articulating, that 14 we are taking in this review or considering 15 taking, again, to give you more background 16 basis to think about the line of reasoning 17 that you might want to work through and 18 suggest for the agency.

19 The document focuses a whole lot 20 on the underlying evidence. It also brings in 21 a lot of information about the quantitative 22 assessments and, hopefully, what you see is Page 24 1 that in the past and now we consider that 2 looking at the evidence directly to provide a 3 very substantial amount of the information 4 that the agency will, in the end, think about 5 in reaching decisions on the standard, that 6 the quantitative assessments can be extremely 7 useful in terms of helping to make judgments 8 about the adequacy of the current standard, 9 our risk remaining big or small. 10 It can help a lot in terms of 11 helping to sort out the different roles that 12 different standards within a suite of 13 standards play, the annual versus 24-hour 14 standards; but so long as we are working off 15 concentration response functions that exhibit 16 no discernible thresholds, we will always have 17 the case that we will continue to estimate 18 risk down to the very lowest level. 19 Therefore, it raises the question, how do we 20 appropriately characterize how uncertainty 21 increases as you continue to look at lower and

22 lower levels. Page 25

1 With that, I think those were the 2 points I wanted to cover just as sort of a 3 general overview of our thinking about the 4 document. What I want to do now is just ask 5 Beth and Scott to walk through the underlying 6 series of questions that we use to frame the 7 discussion in the document. Our thinking was 8 that if we posed a series of questions that 9 derive from the questions that were originally 10 presented in the integrated review plan that 11 framed this review from the beginning, that it 12 would help better focus us on presenting 13 information that truly helped answer 14 questions, rather than be more expository in 15 nature and be more summarizing in nature. 16 So with that, why don't we turn to

17 Beth. 18 MS. HASSETT-SIPPLE: Karen is 19 right. The structure of this document is very 20 different than the structure of the 21 preliminary draft that you saw in September, 22 and it is, in point, to respond to your202-234-4433 Neal R. Gross & Co., Inc. Page 26 1 comments to make it a much more policy focused 2 document and not to reiterate the information 3 in the ISA and in the assessment document. 4 So, hopefully, we have been able to do that. 5 What we have done is tee up two 6 overarching questions and then ask a series of 7 questions within those overarching questions 8 to get at the key policy relevant points. 9 Specifically, as we look at the 10 adequacy of the current fine particle 11 standards, we ask the question, does the 12 currently available scientific evidence and 13 risk-based information as reflected in the ISA 14 and the second draft risk assessment support 15 or call into question that adequacy of the 16 protection afforded by the current suite of

17 standards? 18 Here we look at the evidence and 19 ask a series of questions, namely, to what 20 extent does the newly available scientific 21 evidence strengthen or call into question the 22 evidence of associations between fine Page 27

1 particles and a variety of health effects. 2 Second, what do we know 3 differently this time about susceptible 4 populations, susceptible populations that had 5 been identified in previous reviews or 6 additional susceptible populations that may 7 have been identified in this review? 8 Thirdly, we ask the question, not 9 only looking at the evidence of association, 10 but also at what levels, what air quality 11 concentrations, those associations have been 12 associated -- reported with. So here we are 13 looking at understanding the air quality 14 concentrations to see if they are lower than 15 have been previously reported or that we see 16 health effects in areas that would currently

17 meet the current standards.

18 And here we have asked for -- we 19 look at the air quality information as 20 reported in the particular studies, but we 21 have also gone out in -- OAQPS to ask for 22 additional air quality information from study Page 28 1 authors to have a better understanding of the 2 air quality distributions evaluated in those 3 studies. 4 On the risk-based side, we also 5 look at -- we ask three specific questions to 6 look at the nature and magnitude of the long 7 term exposure and short term exposure related 8 risks remaining upon simulating just meeting 9 the current standards, and look at the 10 confidence we have in those risk estimates. 11 We also, as Karen mentioned, look

12 at the role of the annual standard and the 24- 13 hour standard and how they work together to 14 provide public health protection and 15 specifically -- and you will see Zach and his 16 team talk about this today as you review the 17 second draft risk assessment -- look at how 18 representative the risk estimates that were 19 generated for the 15 urban case study areas 20 are from a national perspective. 21 Then we actually turn to 22 alternative standards, and we ask a second Page 29 1 overarching question, what alternative suites 2 of standards are supported by the currently3 available scientific information and risk4 based information, again, as reflected in the 5 ISA and in the risk assessment document. 6 Here, we look at the four elements 7 of the standards. So as we focus in, 8 questions associated with looking at 9 indicator, we ask a series of questions, 10 specifically, does the currently available 11 evidence provide support for the continued use 12 of PM2.5 as the indicator as a mass-based 13 indicator for fine particles. Secondly, does 14 the currently available information provide 15 support for considering a separate indicator 16 for additional size fractions. In particular, 17 we are focusing on ultrafine particles. 18 Lastly, we ask a series of 19 questions about PM2.5 components and sources 20 of fine particles and whether or not the 21 currently available information provides 22 support for considering a separate indicator202-234-4433 Neal R. Gross & Co., Inc. Page 30 1 targeting for a particular component or source 2 category of fine particles. 3 The flip side of that question is, 4 is there enough information to provide support 5 for eliminating any component or sources of 6 fine particles from the mix of fine particles 7 that would be considered within the PM2.5 8 mass-based indicator.

9 The second element of the standard 10 is averaging time. Here we ask a series of 11 questions, does the currently available 12 information continue to provide support for 13 the averaging times that are currently used,

14 the annual standard and the 24-hour standard. 15 Then a series of questions, is 16 there enough information to support other -- 17 alternative averaging times, and we looked 18 specifically at an averaging time of less than 19 24 hours to specifically look at exposures -- 20 sub-daily exposures. We also looked at 21 information on seasonal exposures to see if 22 there was enough information to support an Page 31

1 alternative standard that would focus on

2 seasons. 3 The third element of the standard, 4 which is form. In Chapter 2 for fine 5 particles, we discussed the form of the 6 standard in conjunction with the level of the 7 standard. For the annual standard, we have a 8 three-year annual average that allows spatial 9 averaging, allows -- there was a tightening in 10 the 2006 decision that narrowed the 11 constraints for spatial averaging, but they 12 are still there. 13 We discussed that, and we had 14 planned to conduct some additional air quality 15 analyses to inform whether or not it is 16 appropriate to continue to retain the spatial 17 averaging provisions for the annual standard. 18 The 24-hour standard. In this 19 first draft, we have not considered 20 alternative forms of the 24-hour standard, and 21 we have discussed level, just in conjunction 22 with the current form, which is a 98 Page 32 1 percentile form. 2 We expect to conduct additional 3 air quality analysis to inform the second 4 draft policy assessment, to better understand 5 the peak to mean ambient concentrations of

6 PM2.5. 7 For level, we look at both the 8 evidence and risk-based considerations to 9 inform a range of levels that we feel is 10 supported by the evidence and also provided 11 evidence from the risk-based considerations to 12 provide protection for both long and short 13 term exposures. 14 I will note again that in the 15 evidence based considerations, here we are 16 looking again at the air quality data, not 17 only that reported in the epi studies, but 18 also the additional information that we have 19 gathered from study authors, as well as 20 additional analyses that we have done to 21 better understand the air quality 22 distributions in the areas where the epiPage 33

1 studies were conducted. 2 Again, we look at information we 3 have on susceptible populations to better 4 understand the impacts in those particular 5 populations. From a risk-based consideration, 6 we look at the nature, the magnitude, and 7 uncertainties of the long term and short term 8 exposure related risks related to simulations 9 of alternative suites of standards. That is 10 what is contained in the alternative suites 11 that are currently contained in a second draft 12 risk assessment. 13 More importantly, we look again at

14 the roles of the annual and 24-hour standards 15 and how they work together to provide public 16 health protection. 17 Lastly, we integrate these two 18 approaches into informing our conclusions for 19 alternative standards that we believe right 20 now are supported by the current evidence and 21 the risk-based information. 22 DR. JENKINS: Thanks, Beth. I am202-234-4433 Neal R. Gross & Co., Inc. Page 34 1 going to talk through really quickly an 2 overview of Chapter 3, which -- 3 DR. STALLWORTH: Can you introduce 4 yourself? 5 DR. JENKINS: Oh, sorry. I am 6 Scott Jenkins, and I am the primary author on 7 Chapter 3, which is the primary standard for 8 thoracic coarse particles. 9 So similar to the organization of 10 Chapter 2 that Beth just got through walking 11 through, Chapter 3 is organized around a 12 series of policy relevant questions. 13 We deal first with the adequacy of 14 the current standard, and I will just remind 15 you that, in the case of thoracic coarse 16 particles, we have a 24-hour PM10 standard, 17 which is meant to protect against exposures to 18 thoracic coarse particles. 19 So in considering this standard 20 and potential alternative standards for 21 protecting against thoracic coarse particles, 22 we walk through first the adequacy, and then Page 35 1 on the next slide I will talk about potential 2 alternative standards in terms of the elements 3 of the standard, the indicator, averaging 4 time, form, and level, just as Beth did. 5 So first in considering adequacy, 6 there are two main issues that we dealt with 7 with regard to the adequacy of the current 8 PM10 standard. That is, first, the 9 appropriateness of maintaining a standard to 10 protect against effects associated with 11 exposures to thoracic coarse particles, and 12 second is the adequacy of the protection -- 13 public health protection that is actually 14 provided by the current 24-hour PM10 standard. 15 So in addressing these two 16 questions, we walk through the available 17 scientific evidence, and so I will point out 18 here, as you are all aware, we didn't do a 19 quantitative assessment of risks for thoracic 20 coarse particles. So here we are relying on 21 the evidence base considerations in 22 considering the standard. Page 36

1 So we looked at the evidence that 2 was available in the last review, at evidence 3 that has become available since the last 4 review, and that includes, as you are well 5 aware, a number of new epidemiologic studies, 6 multi-city studies in the U.S. and Canada and 7 Europe, studies that have been conducted in a 8 lot of different locations, some of the9 studies that have looked at confounding by co10 pollutants. So there are a number of new 11 studies that are available that can inform 12 decisions on the adequacy of the PM10

13 standard. 14 Chapter 3 has a really limited 15 discussion of susceptible populations. In 16 large part, we refer back to Chapter 2, the 17 discussion in Chapter 2, and back to the ISA. 18 So we don't repeat everything that is in 19 Chapter 2 on susceptible populations. 20 In considering the adequacy of the 21 level of protection provided by the PM10 22 standard, we do bring in PM10 air quality data Page 37 1 from epi study locations. 2 So here what we are talking about 3 is we look at the epi study -- or the study 4 locations where epidemiologic studies have 5 reported positive and statistically 6 significant associations between PM10-2.5 and 7 mortality or morbidity, and we look at whether 8 the PM10 concentrations in those locations 9 were above or below the concentrations that 10 are permitted by the current PM10 standard, so

11 an assessment of whether the current PM10 12 standard is providing adequate public health 13 protection against effects associated with 14 thoracic coarse particles. 15 Then important for the thoracic 16 coarse particle standard is the consideration 17 of the uncertainties, and these include 18 uncertainties due to limitations in the 19 monitoring network, uncertainties due to the 20 limited number of co-pollutant studies that 21 have been conducted, the limited nature of the 22 experimental supporting evidence.202-234-4433 Neal R. Gross & Co., Inc. Page 38 1 So that when you look at all of 2 this evidence together with the air quality 3 information, what this part of Chapter 3 4 presents is basically different ways of 5 considering the evidence and its associated 6 uncertainties and how those different 7 approaches to considering the evidence and 8 uncertainties could lead you to different 9 decisions on the adequacy of the current

10 standard. 11 As Karen mentioned, we are 12 obviously looking for feedback on the 13 approaches that we -- the considerations that 14 we have laid out, as well as the potential 15 conclusions that you reached. 16 So in terms of laying out 17 potential alternative standards for thoracic 18 coarse particles, as I mentioned, it is broken 19 down -- Chapter 3 is broken down into 20 discussion of indicator, averaging time, level

21 and form. 22 Indicator deserves some special Page 39 1 mention here because currently, as I 2 mentioned, we have a PM10 indicator, and of 3 course, PM10 includes both thoracic coarse 4 particles and fine particles, and so a 5 consequence of that is that, for a PM10 6 standard, it is going to allow lower 7 concentrations of thoracic coarse particles in 8 areas with high fine particle concentrations 9 than in areas with low fine particle 10 concentrations. 11 This tends to correlate with lower 12 thoracic coarse particle concentrations being 13 allowed in urban areas than rural areas, and 14 in the last review that was judged to be 15 appropriate. So what we lay out in the 16 indicator section is really a consideration of 17 the available evidence that reconsiders this 18 issue. 19 Does it remain appropriate to have 20 a PM10 standard that essentially allows lower 21 concentrations of thoracic coarse particles in 22 urban areas than in non-urban or rural areas, Page 40 1 while at the same time providing some measure 2 of protection against all thoracic coarse 3 particles? So that is the indicator 4 discussion. 5 With regard to averaging time, we 6 focused really in on the 24-hour averaging 7 time here. So if you recall back to the ISA, 8 the ISA reached conclusions that there wasn't 9 enough evidence to draw conclusions regarding 10 the relationship between long term exposures 11 to thoracic coarse particles and adverse 12 health effects, and with regard to short term 13 the evidence was suggestive, concluded to be 14 suggestive of a causal relationship. 15 So we focused in on short term 16 exposures, effects associated with short term 17 exposures, and specifically the vast majority 18 of the evidence, specifically the epi 19 evidence, is using 24-hour average thoracic 20 coarse particle concentrations. 21 There are a couple of controlled 22 human exposure studies that look at around Page 41 1 two-hour exposure time frames, but we focused 2 in on this 24-hour averaging time here in that 3 discussion. 4 Now one thing that is different 5 from what Beth talked about for Chapter 2 is 6 that, with the level and form discussion in 7 Chapter 3 we haven't presented or analyzed 8 specific ranges of alternative levels and 9 forms at this point. So what we have is a 10 discussion of the approach we plan to take for 11 the second draft policy assessment. 12 We do have in the form discussion 13 some considerations related to -- general 14 considerations with regard to a short term 15 standard that lead us to essentially 16 emphasizing a 98 percentile form as providing 17 a balance between limiting peak concentrations 18 and providing some regulatory stability. 19 So we have some general discussion 20 on form there, and with level we lay out the 21 general approach that we would identify ranges 22 of potential alternative levels using health202-234-4433 Neal R. Gross & Co., Inc. Page 42 1 studies, the epidemiologic studies. We could 2 lay that out in the second draft in terms of 3 either alternative ranges of PM10 standard

4 levels or PM10-2.5 standard levels or 5 potentially both. 6 What we do in the second draft 7 assessment is going to depend, to some extent, 8 on how our thinking is refined between now and 9 then on the issue of indicator, obviously, and 10 that is going to be, obviously, informed by 11 feedback that we receive on the first draft 12 policy assessment. 13 So I think that Karen's point is 14 well made, that where we go from here is going 15 to depend to a large extent on what kind of 16 feedback we receive on this first draft

17 assessment. 18 So that is all I have on Chapter

19 3. 20 DR. MARTIN: That completed what 21 we wanted to say with regard to overview on 22 the policy assessment, and if there aren't any Page 43 1 questions on that, then perhaps we should move 2 right into opening presentation on the risk

3 assessment. 4 CHAIRMAN SAMET: Well, I think 5 that is what we should do, probably. I think 6 we all received the document on Monday. There 7 is time set aside tomorrow for discussion. 8 We have just received the charge 9 questions on the policy assessment, and I 10 think we should probably reserve discussion 11 for tomorrow, and I think we will be probably 12 better informed and ready to do that. 13 I will say, I think I read most of 14 it. It is a long way from the West Coast to

15 the East Coast. 16 DR. MARTIN: You will note that 17 the word draft is on top of those charge 18 questions. We thought, since we had the 19 opportunity to have some conversation with you 20 about them here that we would leave open those 21 questions and refine them, if appropriate, 22 following that discussion. Page 44

1 CHAIRMAN SAMET: Sure. And I 2 think we should have, hopefully, ample time to 3 discuss the documents. Is that a hand?

4 Sverre? 5 DR. VEDAL: Yes. I just wanted to 6 ask a clarifying question on the coarse 7 standard and see if I understood the point 8 right about using a PM10 standard to cover 9 coarse or a specific coarse standard in the 10 urban versus non-urban sort of setting. 11 I think I understood that, in 12 essence, a PM10 standard really tightens the 13 coarse standard in essence in urban areas

14 versus non-urban areas. Is that correct? 15 DR. JENKINS: That is right, and I 16 did sort of gloss over that issue here just in 17 the interest of time, but there is a pretty 18 extensive discussion of the rationale that was 19 used to justify that PM10 standard in the last 20 review, and it is in this policy assessment 21 that you have. But you are correct in your 22 basic premise. Page 45

1 DR. VEDAL: And the underlying 2 notion there is in favor then of a specific 3 coarse standard then to avoid that 4 irregularity? 5 DR. JENKINS: Well, that is an 6 open question. So one of the questions is, 7 does it remain appropriate to retain a 8 standard or maintain a standard that allows 9 that sort of difference in thoracic coarse10 particle concentrations in urban versus non11 urban environments. 12 That will depend on where the 13 evidence pushes us. So that is an open 14 question.

15 CHAIRMAN SAMET: Joe? 16 DR. BRAIN: I have an adjacent 17 question, which really is on the topic of what 18 is our purview and what are we allowed to 19 comment on because, again, you had talked 20 about alternative standards, and I guess, 21 Karen, you even said, you know, is continuing 22 the PM2.5 justified.202-234-4433 Neal R. Gross & Co., Inc. Page 46 1 So I guess my question is are we 2 limited to the data that is available and what 3 is in the ISA or are we also allowed to think 4 of what kind of sampling system, what kind of 5 data would we like 20 years from now? 6 CHAIRMAN SAMET: Well, the 7 question of monitoring, which might be called 8 for in relationship to any standard, I think, 9 is something that would be addressed, in 10 essence, by a subgroup. So I am not sure we 11 need to go there today, Joe, but I agree, 12 there's lots of issues that get opened up, and 13 I was trying not to get them opened up right 14 now, I think. 15 DR. BRAIN: We will never really 16 be able to consider different alternative 17 standards unless we begin to get the kind of 18 data we need to support those alternative 19 standards. So we are sort of stuck in this 20 rut with PM-10. 21 CHAIRMAN SAMET: Well, we just 22 went down this discussion, you remember, with Page 47 1 NO2, in fact, and there the standard will be 2 forcing the monitoring. So, Karen, is there 3 a directive at this point? 4 DR. MARTIN: Yes. I just wanted 5 to say that you will find on the first or 6 second page of the policy assessment a 7 paragraph that notes that there are monitoring 8 issues that are related to our consideration 9 of indicator, in particular, and that the 10 second draft policy assessment will contain 11 some additional information to help in 12 understanding those linkages. So it is sort 13 of a promise of things to come with regard to 14 going down that path a little bit more. 15 CHAIRMAN SAMET: Is that a person 16 with a hand raise? 17 DR. SPEIZER: Yes. Worrying a 18 little bit about getting on the wrong side of 19 you at this point. But is there any 20 possibility that there can be consideration of 21 regional standards for the coarse particles or 22 does the law just prevent us from doing that? Page 48

1 DR. MARTIN: The standards we are 2 talking about are national standards. So we 3 are not in the business of using the mandate 4 of the Clean Air Act for setting national 5 ambient quality standards to set regional

6 standards. 7 Having said that, I would note 8 that, in the context of other NAAQS reviews, 9 in particular the review ongoing with regard 10 to secondary standards for nitrogen and 11 sulphur, NOx-SOx secondary standards, we are 12 looking at a standard that would incorporate 13 the complexities of recognizing that differing 14 ecosystems respond to the same deposition of 15 nitrogen and sulphur in different ways, and 16 how do you build that type of complexity? 17 How do you build in spatially 18 varying elements into the definition of a 19 standard to allow for a national standard that 20 incorporates the scientific realities of 21 differing impacts in different places? 22 So it is not beyond the realm of Page 49 1 reason that one can incorporate those factors 2 in, but in the end we still need to end up 3 with a nationally defined standard. 4 CHAIRMAN SAMET: Actually, I am 5 going to cut this off, Bob. I'm sorry. I am 6 actually reminded, Rogene, of Governor Bruce 7 King of New Mexico recently died, who talked 8 about opening the box of Pandora's, and I 9 don't want to open it now, because I think I 10 just want to get us through our business for 11 the day before we -- Bob, we will definitely 12 have a chance to come back. I just see that 13 the bad behavior that originated with Sverre 14 will just continue, and we will be here off 15 target forever. So, Frank, you are on my 16 wrong side. 17 Let's see. So why don't we go on 18 -- sorry -- to deal with the quantitative 19 health risk assessment. I am sure we are 20 going to have plenty of time to come back and 21 discuss the policy assessment. I think, 22 again, everybody has tonight perhaps to get a202-234-4433 Neal R. Gross & Co., Inc. Page 50

1 little more familiar with what is in the 2 charge questions.

3 DR. STALLWORTH: Let me also ask 4 if anyone is on the phone, and can you hear

5 us? 6 MS. NUGENT: Holly, this is Angela 7 Nugent calling in at your request, just to let 8 you know that the audio is fine, at least for

9 me. 10 DR. STALLWORTH: Thank you. 11 MR. CORRALES: The sound quality 12 has been good at times and very bad at times. 13 It helps if the microphone is closer.

14 DR. STALLWORTH: Who was that 15 speaking? 16 MR. CORRALES: This is Mark 17 Corrales in OPI. 18 DR. STALLWORTH: Okay. Could you 19 spell your name for the record, please?

20 MR. CORRALES: C-o-r-r-a-l-e-s. 21 DR. STALLWORTH: Thank you. Do we 22 have any public comments? Page 51

1 CHAIRMAN SAMET: We are actually - 2 - we are going to do the health risk 3 assessment. Then we will come to the public

4 comments. 5 Okay, so let me turn then back to

6 Zach. 7 DR. PEKAR: Thank you. I wanted 8 to -- I am Zach Pekar, and I think everybody 9 knows that I have been the lead on the risk 10 analysis and for the second draft here. So I 11 wanted to start just by thanking folks for 12 taking the time to, I think, already give it 13 a pretty intensive read and review. 14 The preliminary comments that we 15 have gotten clearly reflected a lot of thought 16 from folks, and I really wanted to thank you

17 all for that. 18 So my goal really right here is, 19 now we have talked about before with the first 20 draft, is not to provide an exhaustive 21 detailed technical overview of the analysis, 22 but really first just to highlight some of Page 52 1 the key enhancements and changes that we have 2 made since the first draft, and then to walk 3 through the charge questions very quickly, 4 just to make sure that we are all sort of 5 grounded in those before we start the more 6 detailed discussions later on. 7 I managed to catch my kids' cold 8 finally. So if I am sniffling up here, I will 9 try to do that away from the mic. 10 So just beginning with a quick 11 overview of the enhancements, one of the main 12 things that we have done is, I think, in 13 response to what we felt we needed to do, but 14 also really also responding to suggestions

15 from CASAC. 16 We have included now this 17 integrative Chapter 6, which is really our 18 effort to pull together the risk estimates 19 from the urban study areas, together with 20 consideration for the qualitative and 21 quantitative treatment of uncertainty and 22 variability and the National Skill Analyses, Page 53 1 pull all that together into one chapter that 2 would sort of bridge the risk assessment to 3 the policy analysis. That is a major 4 modification to this version of the document. 5 We have also included expanded 6 discussion of our rationale for a number of 7 points, technical points in the analysis, 8 including what Scott talked about, which was 9 our reasoning for not conducting a 10 quantitative assessment of risk for thoracic 11 coarse, providing enhanced explanation for our 12 selection of modeling elements for the core 13 risk assessment, and once again we completed 14 a set of core risk estimates which are 15 estimates that we have greater overall 16 confidence in, and those are supplemented by 17 the sensitivity analysis results which we also 18 have confidence in, and together those two 19 sets of estimates, hopefully, provide some 20 treatment of uncertainty. 21 Then we have also just given a 22 little more explanation for how we202-234-4433 Neal R. Gross & Co., Inc. Page 54 1 qualitatively assessed uncertainty and 2 variability in the analysis, the procedure

3 that we used for that. 4 The sensitivity analysis: The 5 quantitative sensitivity analysis has also 6 been expanded. We have now included 7 consideration for lag effects in the Bell 8 study. That was a suggestion that was made, 9 I think, by CASAC, and we have done that. 10 We have also compared and 11 contrasted the results of the sensitivity 12 analysis with the confidence intervals that we 13 get form considering the statistical fit of 14 the concentration response function. 15 So those were two quantitative 16 pieces of information we had presented last 17 time, but we hadn't compared and contrasted

18 those. So we have done that now. 19 I think another important 20 enhancement to this analysis is the inclusion 21 of consideration for peak shaving. So in the 22 past when we were simulating alternate Page 55

1 standard levels, we have to simulate the 2 reduction of -- the spatial pattern of 3 reduction in the PM2.5; monitor levels. 4 We used proportional rollback in 5 the past. We also considered a hybrid 6 rollback, which sort of was a fusion of a more 7 localized reduction and a more regional 8 reduction. Now for this version, we have also 9 included peak shaving, which is really a 10 focused look at a very localized reduction in 11 the pattern of ambient PM2.5 under alternate

12 standard levels. 13 I am not going to get into a lot 14 of detail on that second bullet. Essentially, 15 time has been so intense with this that, when 16 we looked at peak shaving, we couldn't 17 actually run -- rerun the full risk assessment 18 with peak shaving rollback.

19 What we had to do was look at 20 composite monitor averages and compare 21 composite monitor averages, which is sort of 22 the annual average value for a study area once Page 56 1 you have rolled back the levels. 2 If you compare a composite monitor 3 annual average that you got for peak shaving 4 to what you got, for instance, from 5 proportional rollback or the hybrid rollback, 6 you could compare those to an essentially look 7 at how long term mortality will differ, 8 because long term mortality will differ, 9 because long term mortality reductions are 10 really tied to reductions in the annual 11 average at the composite monitor.

12 So all the second bullet -- and I 13 think our explanation wasn't so clear on this, 14 and I am seeing some looks. Maybe we can talk 15 about this later. 16 I can give a little more 17 clarification, but essentially all we mean by 18 this is that, rather than generating full 19 quantitative long term mortality risk 20 estimates with the peak shaving rollbacks, we 21 stopped short of that and compared and 22 contrasted the degree of reduction in the Page 57 1 composite annual average PM2.5 levels as a way 2 of looking at what we think would happen with 3 mortality, long term mortality. I can provide 4 additional discussion of that, if we need. 5 Then the third bullet: For this 6 analysis, we provided, I think, a more 7 expanded or more intense consideration of the 8 sensitivity analysis results for informing our 9 overall confidence in the core risk estimates. 10 So when we considered the results of the 11 sensitivity analysis in that integrative 12 Chapter 6, how can we really use those to make 13 more solid statements about our overall 14 confidence in the risk estimates? So we have

15 done that. 16 Then finally, in number 4, for the 17 integrative discussion chapter in particular, 18 we have -- Beth was alluding to this, too -- 19 we have really talked more about the way the 20 24-hour -- Excuse me. I am getting ahead of 21 myself here. You can consider the pattern, 22 the relationship between design values, 24-202-234-4433 Neal R. Gross & Co., Inc. Page 58 1 hour in annual design values, at the 15 urban 2 study areas and contrast the way that 3 relationship between design values in our 4 study areas compares to the pattern of design 5 values you see across the broader set of urban 6 areas in the U.S. 7 That allows us to get a sense for 8 how representative our 15 urban study areas 9 are of the broader set of urban areas in the 10 U.S. So it was another way of helping us to 11 interpret our risk estimates and put them in 12 a national context. 13 So now moving into just an 14 overview quickly of the charge questions, and 15 the charges questions are designed to focus on 16 changes, often, and the enhancements that we 17 have done in the risk assessment. 18 So as I discuss these charge 19 questions, you are going to see that the 20 points I just made are largely repeated here. 21 So I will move through some of these fairly 22 quickly. Page 59

1 So we begin with Chapter 3, which 2 is the methods used in conducting the urban 3 study area risk assessment. So one of the 4 things we discussed is the inclusion of this 5 peak shaving rollback approach and the use of 6 the composite monitors as a surrogate for 7 looking at differences in the long term 8 mortality. It is the point I just made. 9 So we were simply interested in 10 the Panel's feedback on inclusion of this peak 11 shaving rollback method. 12 Selection of model inputs: As I 13 mentioned, we provided a little more 14 discussion of how we selected our core risk 15 assessment, how we designed the core risk 16 assessment, and just getting some feedback

17 from the Panel on that. 18 In terms of addressing uncertainty 19 and variability, as I said, we have clarified 20 the process for how we qualitatively assess 21 uncertainty and variability. We have included 22 coverage for some additional sources of Page 60 1 variability and uncertainty. 2 I mentioned the lag issue. I 3 think including peak shaving is actually 4 looking at a critical source of variability, 5 variability specifically in the way urban 6 areas might respond in terms of reductions, 7 the pattern of reductions in ambient 2.5 8 monitoring levels. I guess that sort of

9 covers that. 10 Then in Chapter 4 when we talk 11 about summarizing the urban case study risk 12 estimates, the results, one of the charge 13 questions spoke to the use of the sensitivity 14 analysis results and informing consideration 15 of uncertainty in the core risk estimates, and 16 then considering the interplay of the design 17 values across the study areas, and 18 interpreting risk estimates. 19 so one of the things we realized 20 is that, when we look at the pattern of risk 21 reductions in the 15 urban study areas, you 22 start to have a lot better understanding of Page 61 1 what is going on if you really look in detail 2 at the interplay between the 24-hour and the 3 annual design values at each of the 15 urban 4 study areas. 5 Some study areas have very peaky 6 ambient 2.5 levels. What I mean by that is 7 that the ratio of the max, of the 98 8 percentile PM2.5 level for the average PM2.5 9 level may be very, very wide, and the way that 10 an alternate standard level may affect risk is 11 really linked to that interplay between the 12 design values.

13 So we found that we were able to 14 sort of classify and see a lot of rational 15 explanation for the patterns of risk 16 reductions that we saw, if we really looked in 17 more detail at that relationship between the 18 24-hour and the annual design values. 19 That is featured pretty 20 prominently in that integrative Chapter 6. So 21 just getting some feedback on our observations 22 and how we thought those factors played202-234-4433 Neal R. Gross & Co., Inc. Page 62 1 together in helping to explain the pattern of 2 risk reductions that we saw. 3 Then finally, in integrative 4 Chapter 6, because this chapter is new and I 5 think everybody would agree that it is pretty 6 important, most of the -- a lot of the charge 7 questions really focused on this integrative 8 Chapter. So let; me just walk through these 9 very quickly. 10 First of all, in the integrative 11 chapter, we laid out a set of policy related 12 questions that we wanted to address in that 13 integrative chapter. So one of the key 14 questions is have we missed any of the key 15 policy related questions? 16 Obviously, that is a question also 17 for the policy analysis. Have we missed 18 something in terms of interpreting the risk 19 estimates in the context of thinking about the 20 NAAQS decision making process? 21 Considering the range of factors 22 and interpreting the core risk estimates: so Page 63

1 we wanted to make sure we understood what we 2 saw happening with the risk estimates, and 3 particularly the degree of risk reduction we 4 saw across the different urban study areas. 5 So we considered a number of different factors 6 that I have already mentioned, to a certain 7 extent: the interplay of the annual and 24- 8 our design values, the peakiness of the PM2.5 9 distributions, the way those two factors 10 interplay, and then finally the role of 11 different rollback procedures. 12 So when we conduced a peak shaving 13 rollback versus a proportional rollback, we 14 saw that that could have a marked impact on 15 the degree of risk reduction, and we tried to 16 provide some explanation for why that was.

17 Then assessment of overall 18 confidence: So we attempted to make a pretty 19 clear statement about our degree of confidence 20 in the core risk estimates, informed by the 21 sensitivity analysis results as well as the 22 qualitative treatment of uncertainty and Page 64 1 variability that we had done. So we certainly 2 would like some feedback from the Panel

3 members on that.

4 Then we conducted a number of 5 supplemental national-scale analyses. Two of 6 these, we have already talked about, I think, 7 in the last review of the first draft. So I 8 won't go into a lot of detail. 9 We conducted a national-scale 10 mortality analysis which had two roles. It 11 provided a mortality estimate, and we have ad 12 some discussion about that. It also allowed 13 us to see how our 15 urban study areas 14 compared to the national distribution of risk. 15 So did our study areas fall toward the 16 national distribution? Did they fall broadly 17 across the distribution? 18 It was useful in terms of getting 19 a perspective for the representativeness of 20 our study areas, which would be a nice little 21 link into the second analysis, which was this 22 representative analysis where we compared and Page 65 1 contrasted our urban study areas to the 2 broader national distribution for a range of 3 a variety of risk related factors, basically 4 on incidence rates and so forth. I think we 5 talked about that in some detail last time. 6 Then I think number 3 is new for 7 this analysis. One thing we did is, given the 8 importance of that 24-hour -- the 24-hour and 9 annual design values in explaining patterns of 10 risk reductions across the 15 urban study 11 areas, we said, well, what if we looked at the 12 interaction and the relationship or the 13 pattern of 24-hour and annual design values 14 across the broader set of 250-some larger -- 15 256, I think -- urban study areas that we 16 have. How would our 15 areas compare to that 17 broader pattern? 18 There are a series of scatter 19 diagrams that are presented at the end of 20 Chapter 4 that gets at this issue, and I think 21 that that was helpful in informing what we 22 think -- how we think our 15 urban study areas202-234-4433 Neal R. Gross & Co., Inc. Page 66 1 fall more broadly into the urban distribution 2 of -- sorry, national distribution of urban

3 areas. 4 Then key observations: So we 5 really had a number of key observations which 6 we provide at the end of Chapter 6 in bulleted 7 form, but I think two that we really wanted to 8 highlight. 9 One is that this -- In looking at 10 the relationship between 24-hour and annual, 11 alternative annual and alternative 24-hour 12 standards, one observation we made is that, 13 generally, the annual standard levels produce 14 a more uniform degree of public health 15 protection. 16 When you set an annual standard in 17 terms of long term mortality, in particular, 18 but also short term endpoints, you see a 19 consistent -- a more consistent level of 20 protection, because you are fixing -- you are 21 achieving a more consistent level of reduction 22 in annual average PM2.5 levels, which are Page 67 1 highly correlated, clearly, with reductions in 2 long term mortality, but even with reductions 3 in short term mortality and morbidity. We can

4 talk more about that. 5 The other interesting point is 6 that often, while more stringent 24-hour 7 standard levels would produce an additional 8 risk reduction, often that happens because you 9 have driven the annual average PM2.5 levels 10 very, very low, down to seven, eight, nine, 11 fairly low levels; whereas, if you have a 12 fixed annual standard, you are looking at the 13 annual levels typically at that level, because 14 that is where you have reduced them down to. 15 So an annual -- alternative annual 16 standard of 12, if you having risk reductions, 17 it is because you have got levels down to 12. 18 If you push the 24-hour standard down, for 19 instance, to 25, you will see that often you 20 have annual average levels, if proportional 21 reductions have gone very low. This may be 22 something we need to talk about in more Page 68

1 detail. 2 The final point, I think, is 3 something we are very interested in. We 4 thought that the risk assessment suggested 5 that there could be additional risk-benefit 6 from considering standards, annual standard

7 levels below 12.

8 In the comments from a number of 9 panel members, we got some -- Those Panel 10 members voiced concerns about uncertainty in 11 looking at alternative annual standard levels

12 below 12. 13 Of course, that is a point that is 14 very interesting to us, and I think one 15 question we had is it would be very 16 informative for us to have a really good 17 discussion on what is meant by that 18 uncertainty, how broad that feeling is felt, 19 and is it that there is a concern regarding 20 the -- whether people have concern with 21 confidence in adverse health effects occurring 22 below 12 or whether it is simply that there is Page 69 1 uncertainty in the form of the concentration 2 response function below 12. 3 These are, obviously, two 4 different takes on that issue, and I think 5 just a little more discussion on that and the 6 rationale behind the Panel's perspectives 7 would be very useful to us. 8 Then the final slide -- and there 9 is not a lot of time; so I won't spend a lot 10 of time on this. But because I think time is 11 so tight and because we thought that the 12 findings of the core risk estimates that we 13 had seen suggested that looking at alternative 14 annual standard levels below 12 might be of 15 interest, we went ahead and did some very 16 preliminary simulations of an alternative 17 annual standard level of 10, just to see what18 that seemed to do in terms of additional risk19 benefit. 20 I want to just reiterate that this 21 is a preliminary run, and that we are very 22 interested in the perspectives that the Panel202-234-4433 Neal R. Gross & Co., Inc. Page 70 1 has on uncertainty associated with this kind 2 of simulation, but just to give a quick 3 overview of some of the results that we saw 4 with this. 5 So what this table does is it 6 compares risk under the 12 -- Let me just stop 7 for a second. This is in terms of IHD -- long 8 term exposure related IHD mortality. So that 9 is what we are talking about here. 10 What it does is it says, if we had 11 risks -- If we had run risks for the 1235 12 scenario -- so that is an alternative annual 13 level of 12; so the lowest alternative annual 14 level we looked at, paired with the current 15 24-hour standard, what additional percentage 16 reduction in risk -- so what is the percentage 17 of additional risk reduction we would see by 18 reducing -- first, if we looked at reducing

19 the 24-hour standard down to 25. 20 So if we had kept the 12 and just 21 reduced the 25, so driven the 24-hour standard 22 level down to 25, what is the percentage Page 71

1 reduction in risk that we would see across the 2 study areas? 3 Alternatively, if we had looked at 4 that alternate annual standard of 10, if we 5 had pushed the annual standard level down to 6 10, what is the degree of risk reduction that 7 we would see? So this is comparing two 8 different ways of reducing risk below the 9 standard level of a 12-35 combination. 10 So let's just -- It is always 11 better, I think, if I take one row and explain 12 what is going on here. 13 So if we look at Dallas, Texas, we 14 see that, relative to this under the 12-35 -- 15 so this is already an alternate standard level 16 -- if we look at 12-25, so we drove the daily 17 down to 25, we would see no additional 18 reduction in risk, and this is because Dallas 19 has a very -- does not have a very peaky PM2.5 20 level, meaning it is 98 percent -- It is daily 21 design value, and its annual design values are 22 not terribly different -- The max to average Page 72 1 peak ratio is not very wide. 2 What that means is reducing the 3 24-hour standard really doesn't do anything to 4 its risk reductions, if you assume a 5 proportional reduction when you reduce the 24-

6 hour. 7 Conversely, for Dallas if you 8 reduce the annual standard level to 10, you do 9 see -- and this is pretty dark -- You do see 10 a notable reduction in risk at 34 percent, and 11 I would add that we are talking about annual 12 average PM2.5 levels at the 12-25 level of 12, 13 meaning we are talking about annual average 14 PM2.5 levels that are around 12 with this 15 simulation. 16 Now let's jump down to sort of the 17 opposite side of what we saw, which is looking 18 down at Salt Lake City. Salt Lake City has an 19 extremely peaky PM2.5 level. What I mean by 20 that is it has very low annual average PM2.5 21 and very high peak 98 percentile 24-hour 22 design values. Page 73

1 What that means is a 24-hour 2 standard is going to have a much greater 3 effect on a place like Salt Lake City than it 4 is on Dallas, and we see that, that in this 5 case if you apply an alternate 24-hour 6 standard of 25, kind of a mouthful, you have 7 essentially -- if you assume proportional 8 reduction -- So in getting and achieving that 9 24-hour standard, you are pushing the annual 10 standard down also. So that is a big 11 assumption. 12 You end up pushing the annual 13 average down to 5.7, which is right around

14 that lowest measured level that we assessed 15 risk at. So we only assessed long term risk 16 down to 5.8. So essentially, you reduce all 17 of your long term mortality to zero, given the 18 range down to which we are assessing risk. 19 The numbers here, I think, are not 20 necessarily so important. It is the fact that 21 this further illustrated the point we already 22 saw with the simulations we had done, the202-234-4433 Neal R. Gross & Co., Inc. Page 74 1 alternate standard levels we had done, which 2 is that targeting the annual standard level 3 can have substantial risk reductions, and 4 those had to be associated with annual average 5 levels that are at a level where you have 6 greater confidence in making risk estimates 7 about. But if we look at an alternate 24-hour 8 standard level, in this case of 25, the lowest 9 level we have looked at, you can see very, 10 very substantial additional risk reduction. 11 You can see this down here with 12 these urban study areas, Fresno, Tacoma, West 13 coast areas with very high daily levels and 14 very -- and relatively lower annual average 15 levels, but those risk reductions are 16 associated with annual average PM2.5 levels 17 that are at a level where we would have, 18 obviously, greater concern about making risk 19 estimates, so greater uncertainty in making 20 risk projections. 21 That is just another -- This is 22 sort of reiterating that observation we made. Page 75 1 Go back for a second, and then I 2 will be done. 3 This observation up here that the 4 annual standard level seemed to give us a 5 greater -- give us risk reductions, but we 6 have a greater degree of confidence in 7 asserting those magnitudes of risk reductions 8 relative to the 24. 9 So that was all I wanted to say. 10 Thank you. 11 CHAIRMAN SAMET: Okay, thank you, 12 Zach. So we are going to go, obviously, in 13 greater detail when we go through the charge 14 questions, but are there clarifying questions 15 related to this presentation? Okay, good. 16 Thank you then. 17 We will move on then to public 18 comments, and I think we have one public 19 commenter, David, representing API.

20 DR. SAX: Hello. 21 CHAIRMAN SAMET: It is on. Just 22 say your name, please. Page 76 1 DR. SAX: Good morning. I see a 2 lot of familiar faces, the Harvard cohort over

3 there. 4 I am Sonja Sax, and thank you for 5 the opportunity to speak today. I am from 6 Gradient, and I am commenting on behalf of 7 American Petroleum Institute. 8 The second draft risk assessment 9 has not changed substantially. There have 10 been some additions, but I will basically 11 summarize some of the major concerns that we 12 discussed in great detail in comments on the 13 original draft. 14 In the last PM NAAQS review, the 15 U.S. EPA Administrator actually concluded that 16 the PM RA at the time was not sufficiently 17 robust to use for determining the level of the 18 PM standard, and many of the issues associated 19 with the previous RA were related to the large 20 uncertainties and variability inherent in the 21 epidemiological studies used for the CR 22 functions. Page 77

1 After reviewing the current draft 2 RA, I conclude that these issues have not been 3 resolved, and the RA should not be used to 4 establish any ranges in the policy assessment, 5 and specifically, some of the issues that have 6 carried over from the previous risk assessment 7 include consideration of health endpoints that 8 are less than causal, that still significant 9 uncertainties and variability in the epi 10 studies used to define the CR functions -- for 11 example, confounding measurement error and 12 heterogeneity in the PM2.5 effects, and then 13 lastly, the generation of multiple very 14 subjective point estimates with no best 15 estimate and a null effect that really cannot

16 be ruled out. 17 The health endpoints considered in 18 the RA were those that were determined to be 19 causal and likely causal in the ISA. However, 20 really, the framework that was used was not 21 consistently applied across the epi studies, 22 and the uncertainties were often understated.202-234-4433 Neal R. Gross & Co., Inc. Page 78 1 So, really, it is inappropriate to use 2 anything that is less than causal. 3 For example, in the RA lung cancer 4 was considered, even though this endpoint is 5 really classified s being suggestive, and 6 several significant uncertainties and 7 variabilities are associated with the 8 epidemiological studies that are the basis of 9 the CR function. 10 Although it may be appropriate to 11 use the latest data that we have, there is 12 really little consistency in results across 13 studies, and this really raises a lot of

14 concern. 15 Some of the major uncertainty, 16 again confounding factors, heterogeneity of 17 PM2.5, and measurement error, still have only 18 been addressed in a very qualitative way. In 19 fact, the sensitivity analysis highlights a 20 lot of this uncertainty, particularly for 21 short term effects, and brings into question 22 the utility of the RA. Page 79

1 Also, the sensitivity analysis 2 gives us a bunch of point estimates without a 3 clear distribution. So what is the best 4 estimate? 5 The issue of confounding continues 6 to permeate, but has largely been ignored. 7 For example, single pollutant models keep 8 being utilized, even though there is evidence 9 of confounding by a number of co-pollutants, 10 and these have been addressed in previous risk

11 assessments. 12 Between CD and between region 13 heterogeneity, I think, it is a very important 14 issue. That has also been ignored. Although 15 it was partially addressed in this risk 16 assessment, I think that really needs to be 17 considered. 18 The long risk estimates are even 19 more uncertain, because they are based on a 20 nationwide CR that then is applied to those

21 urban centers and do not reflect the 22 heterogeneity. Page 80 1 Lastly, measurement error 2 continues to be an important source of bias, 3 particularly for these estimates of long term 4 exposure. 5 In conclusion, the uncertainties 6 and variability apparent in the 2005 risk 7 assessment were not resolved in the current 8 risk assessment, and since U.S. EPA concluded 9 in the last risk assessment that it could not 10 base determining the level of the PM2.5 11 standard on the risk assessment, likewise the 12 current risk assessment should not be used to 13 inform the policy assessment. 14 Thank you very much. 15 CHAIRMAN SAMET: Thank you. 16 Questions for our commenter? Good. Thank 17 you. 18 So the next item on the agenda is 19 now not break, but we will move on to charge 20 question 1. Ted, I think you are in the lead

21 on that one.

22 DR. RUSSELL: Yes. Page 81

1 MS. SHPRENTZ: Excuse me, Jon. 2 This is Debbie Shprentz. I also had public

3 comments. 4 CHAIRMAN SAMET: I'm sorry. 5 DR. STALLWORTH: Okay. 6 CHAIRMAN SAMET: Can you say your 7 name again? 8 MS. SHPRENTZ: Debbie Shprentz. 9 CHAIRMAN SAMET: Okay, fine. 10 MS. SHPRENTZ: Yes. It is Debbie 11 Shprentz from the American Lung Association. 12 Can you hear me? 13 CHAIRMAN SAMET: Yes, we can. 14 DR. STALLWORTH: We can hear you

15 well. 16 MS. SHPRENTZ: Well, thank you for 17 the opportunity. I just wanted to make some 18 brief comments on a couple of points about the 19 draft risk assessment. 20 One is we are very happy to see 21 EPA include for the first time the National 22 Assessment of Mortality Risks, and we believe202-234-4433 Neal R. Gross & Co., Inc. Page 82

1 that the studies that EPA has run the risk 2 functions from are the appropriate studies to

3 look at. 4 Those are the major long term 5 cohort studies which have recently been 6 extended to cover longer exposure periods and 7 encompass a greater number of deaths. So EPA 8 comes up with a lower estimate based on the 9 Krewski 2009 study, and then a somewhat higher 10 estimate based on the ACS extended -- I'm 11 sorry, the six city extended study. 12 We think it is appropriate that 13 EPA has estimated risk down to the lowest 14 measured level in both of these studies, which 15 is 5.8 in the ACS study and 10 in the six city 16 study, and that is consistent with our 17 understanding that there is a linear 18 relationship between exposure and risk with no 19 clear threshold, which has been confirmed by 20 another extended analysis of the six city 21 study, the Schwartz et al. 2008 study. 22 EPA also looked at risk down to Page 83 1 the policy relevant background level, and that 2 is consistent with EPA's approach in the 3 regulatory impact analysis. But we do feel 4 that -- you know, EPA asks should we be 5 evaluating risk below 12. 6 I think, to a certain extent this 7 draft was sort of stuck somewhere in the last 8 review when the policy options that were on 9 the table in the last review, but in the 10 current review the ISA has evaluated a large 11 number of new long term studies, confirming 12 the relationship between PM exposure and 13 premature death, and these studies are 14 reporting the facts at lower concentrations 15 than previously studied. 16 You know, in their preliminary 17 comments, some of the members of the committee 18 seemed to indicate that there is more 19 uncertainty at lower concentration, but when 20 I look at the tables in the ISA and the 21 confidence intervals associated with various 22 risk estimates, I certainly don't see any kind Page 84 1 of pattern suggesting that that would be the 2 case. So I don't see a clear justification 3 for that argument, based on the confidence 4 intervals in both the long term and the annual 5 average concentrations in the short term 6 studies. 7 So we certainly think it is 8 appropriate to, in the risk analysis, look at 9 lower concentrations that are consistent with 10 the lower measured levels in these studies. 11 Certainly, it seems appropriate to look at 11 12 and 10 and perhaps even lower concentrations. 13 We also like to see the 14 exploration of the impacts of alternative 15 standard levels extend to the national 16 assessment as well as the 15 cities, because 17 we feel that this would be very informative 18 and would not take a lot of time and 19 resources, given that this is easily 20 accomplished through use of the BenMAP model. 21 In addition, it might be 22 informative to include other health endpoints Page 85 1 in this national analysis, such as emergency 2 room visits, hospital admissions, et cetera. 3 That concludes my remarks. 4 CHAIRMAN SAMET: Thank you, 5 Debbie. Questions for the commenter? Okay,

6 thanks. 7 I guess we haven't missed any 8 other public commenters, just to make sure? 9 So we are back, Ted, to charge 10 question 1. 11 DR. RUSSELL: Thanks, Jon. And 12 did you want to do Questions A and B at the 13 same time? 14 CHAIRMAN SAMET: Okay. 15 DR. RUSSELL: So the questions had 16 to do -- I am not sure if you got a copy of 17 the charge questions on -- No. I was 18 wondering if you could show them up there. 19 That's fine. 20 As Zach presented, they have added 21 this peak shaving approach, and the question 22 was: Is this useful? I actually found it202-234-4433 Neal R. Gross & Co., Inc. Page 86 1 useful and interesting, and I thought it had - 2 - it was a reasonable way to show the impact 3 of what would happen in this case, which is a 4 nice lower bound. 5 I would say it could be better 6 motivated just what sort of control strategy, 7 where it might be applied, and I think you -- 8 in terms of some of the different comments you 9 got back on this, that shows us that it would 10 really be good to know how this might be 11 pulled off. Is there a real control strategy 12 that would do this? 13 I will also say -- So, yes, it 14 would be nice to have that identified. 15 A general comment, and you saw 16 this from more than just myself, is that 17 whatever approach you have, it should be 18 provided mathematically, just so we can really 19 see what the equation is, because words get a 20 little muddled at times. 21 So if you could do that in the 22 appendices, that would be fine, and I also Page 87 1 thought it might be nice to have an example, 2 just one or two cities, just to show the 3 difference and say, okay, this is really what 4 is happening. 5 Question B is -- They talk about 6 using comparisons of composite monitor annual 7 averages using the various approaches, and 8 they ask if that is reasonable to show the 9 variability. 10 My first comment here is that I 11 thought it would be better to have this all 12 motivated by how the current standards really 13 work in terms of the compositing and how you 14 do the annual averaging from the quarters, et 15 cetera, because that would have, I thought, 16 help motivate how you went about the whole 17 approach in this section; because I think if 18 you understand how the annual standard is done 19 and how compositing is done, that answers a 20 number of the questions. Without that, you 21 have to go and look at them to say, okay, how 22 does this align with current practice. Page 88 1 Then in terms of how you do 2 replacing of missing values, I have written up 3 one of the approaches that I think that we 4 have used very profitably. 5 Bottom line is I wasn't really 6 comfortable with how you replace missing 7 values, in that I think that could lead to 8 biases. I think there's other ways that could 9 be done. I won't go into sort of the 10 mathematical approach that I might have 11 suggested. There's other ones that one could

12 also use. 13 So that part left me somewhat 14 uneasy, and again if you could provide the 15 mathematical equations, either within the text 16 or within the appendices, that would be 17 appreciated. 18 CHAIRMAN SAMET: Okay. Thanks. 19 So, Phil? 20 DR. HOPKE: Yes, I agree that I 21 think there are problems with the approach to 22 imputing the missing values, and there are Page 89 1 better ways of doing it, if you really think 2 you need to impute them in the first place. 3 The other thing I am very 4 uncomfortable with is peak shaving. I don't 5 really see that it is a practical approach in 6 terms of setting up a strategy. 7 So the idea of distorting the 8 distributions by chopping the peaks off 9 doesn't impress me as something that is 10 reasonable and realistic to do, and I think it 11 just provides a false impression of what can 12 be potentially accomplished in terms of an 13 implementation strategy. 14 So I don't see where it provides 15 you with significant improved understanding of 16 the risks under various standard scenarios, 17 because I just don't see how you would achieve 18 in most practical cases anything that would, 19 in fact, shave those peaks off. If it were 20 that easy to shave them off, I suspect state 21 and local agencies would have done so long 22 ago.202-234-4433 Neal R. Gross & Co., Inc. Page 90 1 DR. PEKAR: Could I just offer -- 2 Not to interrupt, but I just want to offer a 3 point of clarification. 4 I think we had some discussion 5 here in writing this up about the language of 6 calling peak shaving. I think there is, 7 clearly, very understandable confusion about 8 what we did, and we didn't include as much 9 mathematical discussion, and when you combine 10 those two things -- So let me just be clear. 11 The way the peak shaving was done 12 is that, if you had an urban study area and 13 you had two monitors that exceeded the current 14 daily standard -- the 98 percentile three-year 15 average -- we would look at the degree, the 16 percentage to which that excedence -- the 17 percentage of that excedence. 18 So let's say that -- That is the 19 wrong way to say it. Let's say that you had 20 a 25 percent reduction and the daily design 21 value to attain the daily standard at that 22 monitor. Okay? So instead of 35, it was at Page 91

1 whatever that would be -- 42. 2 We reduced the entire distribution 3 of daily values at that monitor by that 4 percentage. So it is not that we clipped the 5 top off. What we did is we shifted the whole 6 distribution down. I am not sure if that 7 clarifies. 8 DR. HOPKE: That is entirely 9 unclear. There is no way from reading the 10 document that we can infer that approach. 11 DR. PEKAR: Yes. We totally fall 12 on my sword on that. I totally agree that 13 that is confusing, and it doesn't help. But 14 let me then just make this point, that our 15 goal -- and I think Karen was talking about 16 this. We are not trying to simulate in a 17 sense reality with these. 18 We realize how important the 19 nature -- the spatial pattern of reduction can 20 be from a policy standpoint in terms of 21 influencing the risk reduction. So what we 22 wanted to do is have two bounding scenarios Page 92 1 that we would hope would capture the reality 2 of what might occur. 3 One is proportion reduction where, 4 in attaining the daily standard, you are 5 driving everything down. I think that is a6 very regional kind of response, a hyper7 regional response. 8 Then the alternate example would 9 be this peak shaving which will -- 10 DR. HOPKE: Well, the hybrid 11 approach catches a lot of that already.

12 DR. PEKAR: It does. 13 DR. HOPKE: So it is still not 14 clear to me what -- All right. Then it is not 15 clear to me what the difference between the 16 hybrid, because the hybrid is looking at the 17 local sources which might pull down some of 18 those higher valued results. 19 Again, it is just not clear to me 20 what the distinction then is between the two. 21 Again, squishing the whole distribution down 22 is again hard to see how you would do. You Page 93 1 know, that is almost similar to -- Well. 2 So again, I think you want to have 3 your scenarios be something that has at least 4 some grounding in a practical strategy that 5 one could apply in terms of getting at this. 6 So I am just not sure how easy it is going to 7 be to get distributions down in that sort of 8 way. 9 So I still -- If you are going to 10 put it in, it has got to have a much clearer 11 idea as to what it is that it is potentially 12 telling us that is informative in terms of 13 both the risks and potential implementation 14 strategies that would make sense in providing 15 additional protection, because right now, now 16 I am very confused, because the hybrid gives 17 you at least an implementation strategy. You 18 do your partially on your regional thing, 19 partially on your local. Okay. You know, 20 that is fine. But now to move the whole 21 distribution involves both regional and local, 22 because you have got to move the base as well202-234-4433 Neal R. Gross & Co., Inc. Page 94 1 as the peaks, and I am not sure how you would

2 do that. 3 As I say, it is just not clear why 4 this informs us better in terms of defining 5 the risk structure to get at appropriate

6 standard levels. 7 DR. PEKAR: So a couple of points. 8 I think the hybrid -- The hybrid was intended 9 to be, I think, and is in between the 10 proportional and what we are calling the peak 11 shaving. The hybrid is likely -- I say this 12 waving my hands hugely, because we are not 13 trying to project, but it is likely more 14 representative for those specific local areas,

15 because we looked at sources. 16 So I agree. The reality was the 17 hybrid was not conducted -- was conducted for 18 a more limited set of the study areas, and we 19 knew that the peak -- sorry, that the daily, 20 the 24-hour standard, was controlling for some 21 city areas where we were only looking at 22 proportional rollback strategy, and we wanted Page 95 1 to make sure that we had considered a bounding 2 alternate way of rollback for some of those 3 study areas, but I fully realize that, if you 4 got proportional and you have peak shaving, 5 hybrids in the middle, that that peak -- the 6 degree to which that represents a less than 7 likely bounding scenario -- I think, if there 8 is input from the panel members to really 9 basically characterize uncertainty associated 10 with that, would say that is a very highly 11 rare instance and would like to put some 12 caveats on the use of those estimates, I think 13 that is very useful to us. 14 So I appreciate that, but 15 certainly, the hybrid was more sophisticated 16 and made an effort to be more site specific, 17 and I fully recognize that. They are very 18 different. 19 CHAIRMAN SAMET: So I think to 20 summarize then, I think this discussion -- and 21 we will get to you, Joe. Two problems come 22 up. One is you haven't described well enough Page 96 1 what it is you have done, and I think we were 2 all mystified, and the second, I guess, is 3 what is the most real world relevant of the

4 set. Joe? 5 DR. HELBLE: Thank you, Jon. 6 Since you have already fallen on your sword, 7 Zach, I won't twist it further.

8 I share those comments. I read 9 through this several times, and I first had a 10 hard time understanding the distinction 11 between this and the hybrid approach, and the 12 hybrid approach physically made sense to me, 13 and I would have expected that that was a 14 bounding scenario where you are looking at 15 what you can do locally, allowing local 16 controls to propagate through the system and 17 then saying let's also look at the effect of 18 regional controls and what that does to

19 overall PM levels. 20 Peak shaving -- I found myself 21 somewhat mystified. Now having said that in 22 my comments, I said, if you want to play "what Page 97 1 if" scenarios, sure, it is another one that 2 you can examine, but I didn't see what 3 physical insight it provided in terms of 4 ultimately devising or recommending control 5 strategies. 6 So let me leave it at that and 7 simply say I agree with the other comments

8 that have been made. 9 The other comment with regard to 10 filling out the database and dealing with 11 missing values, I would simply ask a question. 12 In addition to different ways of modeling 13 this, my question was: Did you test the 14 interpolation or imputation scheme that you 15 put forward? 16 You have a few areas where you 17 have robust data. One could randomly delete 18 certain data points, calculate or then impute 19 those values and look at that. So there is 20 your experiment. compare the calculated 21 values to what you actually had as measured 22 and say, is this strategy any good or are202-234-4433 Neal R. Gross & Co., Inc. Page 98 1 others better. So if you haven't done that, 2 I would suggest taking a look at that. 3 DR. PEKAR: I think a split set 4 analysis or some form like that, I think, 5 makes a lot of sense. I think, just to be 6 very honest, you know, there are only a number 7 of diagnostics that we can do, and I think 8 that that is something that we can put in 9 line. 10 I think it would be valuable, and 11 I agree, to see what effect that made. I 12 think my intuition is, because -- intuition is 13 a dangerous thing, I realize. But my 14 hypothesis that would be better supported by 15 actually doing the assessment would be, given 16 that we are talking about annual estimates, I 17 have a little less concern, annual estimates 18 being so critical. 19 I will say this. When we had the 20 alternate way of working with the monitoring 21 data where -- and this has more to do with the 22 weighting of the monitors and the way you Page 99 1 generate the composite monitor average. But 2 when you weighted it based on the end, the 3 sample size, rather than giving each monitor 4 equal weight, that did make a difference at 5 times, because we tend to sample -- oversample 6 areas that have higher -- so we went with this 7 alternate way, but that is different than the 8 imputing issue. 9 The only point I will say about 10 the imputing is we have a localized strategy 11 for imputing clearly. It is very localized. 12 It is a local window approach. 13 My read on the suggestions that 14 were made was there may be a global trend you 15 may want to consider. Maybe that is a fair 16 way of looking at it and, certainly, that is 17 conceptually an alternate and a very viable 18 way of looking at it. 19 I think that sensitivity analysis 20 would have been good to look at. I'm not sure 21 we are going to have time to do that. But it 22 is certainly, I think, one of the research Page 100 1 directions might be, as we step toward the 2 next analysis -- I guess it is sort of a 3 cheesy thing to say is -- to chalk that up as 4 something when we have a little more time to 5 really look at that to make a better, more 6 informed decision on which way to impute. 7 Just trying to be honest about the time. 8 CHAIRMAN SAMET: So this is a 9 comment to sort out these three alternatives. 10 Even if you took data from several 11 representative cities, maybe one, as you say, 12 that is sort of peaky and one that is not, and 13 showed the monitor values, and then the 14 distributions that result from the three 15 different strategies for the city -- I am 16 going from sort of the original to the 17 rollback, the hybrid, the peak shaving, 18 whatever we are going to call it now -- maybe 19 that would help, just to try and understand 20 what you are actually doing and what it looks 21 like under different scenarios. 22 I don't know. Phil, Ted, do you Page 101 1 think something like that would be helpful? 2 DR. RUSSELL: Yes, I thought an 3 example would really help. 4 CHAIRMAN SAMET: Yes, and maybe a 5 couple sort of the bounding cases of 6 distributions might work. Frank? 7 DR. SPEIZER: I actually thought 8 that they did that with Appendix E-49 or F-49, 9 F-50, and I was immensely impressed that there 10 was at least a relatively consistent change in 11 one direction by using what was called peak 12 shaving. 13 Now I don't think I understood 14 what peak shaving meant, and now that you tell 15 me it is the shift of distribution, does that 16 also mean that the peaks come down in that 17 distribution or just the whole distribution 18 shifted one way or the other? I don't know. 19 DR. PEKAR: So, yes. I mean, the 20 peak shaving is essentially -- If you have the 21 distribution, the 98 percentile value is -- 22 the design value, the daily design value is 30202-234-4433 Neal R. Gross & Co., Inc. Page 102 1 percent above the daily standard. You are 2 going to reduce every daily value at that 3 monitor by 30 percent, which will get you in 4 attainment of that daily standard. You will 5 be meeting the simulated daily standard level, 6 but the entire mean for that monitor will also 7 drop by 30 percent. 8 The point that is really important 9 is, compared with proportional, you do nothing 10 to the other monitors. That is the reason 11 that the annual average can be left higher 12 with peak shaving. 13 If we have a daily exceedance and 14 we push all the monitors down by the amount to 15 get that daily monitor into exceedance, you 16 are going to have the entire annual average 17 across all the monitors, if you are using a 18 mortality analysis, driven down by that 19 percentage. 20 With the peak shaving that we are 21 doing, you are targeting the one or two 22 monitors, whatever number that exceeded the Page 103 1 daily, but leaving the other monitors

2 untouched. 3 So the annual average will tend to 4 be higher, and that is why you saw -- from 5 your comments, I think that is why you saw 6 some of those levels higher under peak 7 shaving, because you were leaving the other 8 monitors alone, and the annual average is 9 higher. 10 DR. SPEIZER: But isn't that 11 exactly what the implementers will need to 12 have in the way of information that, in fact, 13 if they can target sources that are causing 14 those peaks, you can bring things down into 15 compliance? I don't know the answer to that. 16 DR. HELBLE: But if I could just 17 ask a follow-up question, isn't that what the 18 hybrid scenario is designed to get at? 19 DR. PEKAR: So the hybrid scenario 20 gets kind of at that. The hybrid scenario has 21 an additional reduction in the monitor that 22 exceeds the daily, such that it now matches Page 104 1 the other -- the nearest monitors around it, 2 and there is a distance to K-effect on the 3 other monitors. So it is a more targeted but 4 -- it is a hybrid. It has got elements of

5 both. 6 The peak shaving is clearly going 7 to be a more bounding example where you are 8 just able to say that monitor and whatever was 9 influencing it needs to be tightened, and we 10 are not expecting any effect on the 11 surrounding monitors. The hybrid has a 12 targeted effect on the exceeding monitor that13 exceeds from the daily, and a distance to K14 effect on the monitors around it. 15 DR. HELBLE: So if I could repeat 16 what you said, just to make sure I understand 17 this for clarity, put it a different way; in 18 the peak shaving scenario, what you are really 19 doing is saying, here is an air quality basin 20 that is treated by this monitoring. Whatever 21 we do in that basin does not have any effect 22 on surrounding regions; whereas in the hybrid Page 105 1 scenario, you acknowledge that the PM comes 2 from a mixture of sources, and so you allow 3 controls implemented here to have some effect 4 on depressing the baseline, decaying with 5 distance as you move away from the localized

6 controls. 7 DR. PEKAR: Right. That is 8 exactly right. So, clearly, that peak shaving 9 is more bounding. I mean, it becomes more 10 bounding as we talk about it in terms of 11 representing that very specialized case. But 12 again, I would just reiterate that, when you 13 conduct the risk assessments -- The way I sort 14 of think about this is, it is sort of a 15 sensitivity analysis on this critical issue of 16 variability in the way reductions may occur. 17 If we saw that the peak shaving 18 didn't really affect degree of risk reduction 19 relative to proportional, we would know that 20 this issue is not very important. 21 What we see is that in certain 22 areas we have very peaky PM2.5 distributions,202-234-4433 Neal R. Gross & Co., Inc. Page 106 1 and where the daily standard is controlling, 2 the way you conduct your rollback or the way 3 monitor levels are reduced can have a pretty 4 substantial effect on risk, and that is 5 information we would want to convey to the 6 Administrator, but I think the point that 7 would be important for us to have is, if folks 8 feel that that peak shaving is highly -- less 9 realistic than the hybrid, for a variety of 10 reasons, I think that is very useful. 11 DR. HOPKE: It is hard to imagine 12 how you could have one monitor not affecting 13 other monitors in the region. So that, again, 14 the disconnect seems to be really problematic. 15 DR. PEKAR: Maybe sort of pushing 16 that a little further, it might have been more 17 optimal if the hybrid were used in a wider 18 number of locations. 19 DR. HOPKE: Exactly.

20 DR. PEKAR: So that -- and I hear 21 you on that. I hear you on that. Yes.

22 CHAIRMAN SAMET: Excuse me.Page 107

1 Whoever is on the line, can you please mute 2 your lines. Sounds like somebody has sheep in 3 the background. Lowell? 4 DR. ASHBAUGH: Yes. Well, I think 5 we have actually gotten the point I wanted to 6 make. I actually thought you described the 7 peak shaving quite well. I did understand it. 8 It was bringing that whole monitor down, but 9 what I thought was so unrealistic is that 10 there were no other monitors affected, just 11 that one, and I just thought that was far too

12 local. 13 I understand the reason why you 14 were doing that is it is a bounding mechanism, 15 but I think that it would be important then to 16 say that this is an unrealistic bounding 17 assumption or bounding scenario where the 18 hybrid is much more realistic, because it does 19 allow for some spillover of any controls that 20 affect a high monitor to also affect other 21 monitors nearby. I think that is much more 22 realistic. Page 108 1 I actually thought you described 2 it pretty well. I understood that you were 3 bringing the entire monitor down that was 4 high. It just didn't seem to be an realistic 5 scenario.

6 CHAIRMAN SAMET: Sverre? 7 DR. VEDAL: I agree with that. I 8 think that you might take it a little further 9 and think the proportional rollback is as 10 unrealistic as a boundary case as peak 11 shaving. In that sense, you know, those two 12 bookends may have some -- there may be some 13 point to showing them both, but I agree that 14 the hybrid is the one they should put the most 15 weight behind.

16 CHAIRMAN SAMET: Other comments? 17 This was charge questions 1a and b. Ted, 18 anything you want to summarize or go back to 19 at this point? 20 DR. RUSSELL: I think, actually, 21 for the most part we are fairly consistent on 22 what our response is going to be, basically Page 109 1 saying make this section more clear. It might 2 be useful to provide an example, and in terms 3 of how they do the averaging, they should test 4 it, just to make sure that it works and 5 doesn't introduce any biases. 6 CHAIRMAN SAMET: I think what we 7 might do -- we are due for break at 10:30. We 8 can either go on to charge question 2, which 9 probably would take a fairly -- you don't 10 think it would take long? Okay, then let us 11 go ahead to charge question 2. 12 DR. LIPPMANN: Charge question 2 13 is pretty simple. I will read it. It is so 14 short: We have expanded and clarified the 15 discussion of our rationale for identifying 16 modeling choices comprising the four risk 17 models, focusing in particular on selection of 18 CR functions. To what extent does the Panel 19 consider this discussion to be clear and the 20 model selections appropriate?

21 We have comments from several 22 other members of the Panel, Wayne, Helen, Bob202-234-4433 Neal R. Gross & Co., Inc. Page 110 1 Phalen. Kent Pinkerton hasn't provided any so 2 far, and Ed Avol provided some, even though he 3 wasn't charged with doing so.

4 We all felt that there wasn't much 5 to pick on in terms of how they proceeded, and 6 the Panel, in my view, should commend the 7 authors for expanding and clarifying their 8 rationale for identifying modeling choices 9 comprising the core risk model in a logical 10 and satisfactory manner. 11 Their model selections were, for 12 the most part, appropriate, and anybody who 13 provided comments who wants to expand on that 14 I have spoken to, but they were largely 15 congratulatory. But I had some specific nits 16 to pick in the section where the thing was 17 discussed and, in particular, the morbidity

18 model. 19 This, I found disconcerting in 20 that I called attention to the fundamental 21 error at the last meeting, and that is the Ito 22 et al study which was the only morbidity thing Page 111 1 analyzed was a study in New York City, and 2 apparently some people don't understand that 3 New York City is not Manhattan. 4 There are five boroughs, and there 5 are five counties, and the county names don't 6 correspond exactly to the borough names. 7 Staten Island is Richmond County, and Brooklyn 8 is Kings County. Queens County is Queens 9 Borough, is the borough also. Manhattan is 10 New York County, and that is historical, 11 because originally the City of New York was in 12 Manhattan. It wasn't until 1898 that Brooklyn 13 and Queens joined under the rubric of New York 14 City, but that is where most of the people 15 live. 16 So not only is it mislabeled, but 17 it apparently is miscalculated, because 18 according to the tables, the population of New 19 York is 1.6 million which, I think, 20 corresponds to Manhattan or New York County. 21 So there clearly needs to be some 22 technical correction in that, not that it is Page 112 1 a big thing, because mortality is driving 2 everything, but if you are going to put a 3 morbidity calculation in there, at least get 4 it right. 5 The other thing, which again is 6 something I keep bringing up but it gets 7 ignored, is in terms of the ACS cohort. Some 8 important information is not brought up in the 9 discussion, and I think it needs to be. 10 That is the ACS cohort is a 11 nonrepresentative population. It was much 12 more middle class and to upper middle class 13 than the national population, and within that 14 study it was clearly demonstrated that the 15 risk was concentrated in the lower SES 16 component of the population. 17 So to use the overall population 18 as a representation of risk without a 19 correction, which could be made mathematically 20 very simply, for what it would be for estimate 21 anyway of what a representative U.S. 22 population risk coefficient would have been, Page 113 1 I think, is a serious deficiency. 2 Also, there is a simple 3 misstatement in there about the size of the 4 population. The size of the ACS cohort was 5 indeed about 1.2 million, but the whole cohort 6 couldn't be used, because it was restricted to 7 those communities which had monitoring, and so 8 the number of communities has changed from the 9 original 53, I think, or 65 or something like 10 that in the latest work-up, but that is just 11 a simple -- a misstatement of the population 12 that was being studied. 13 So those were the only nits I 14 would like to pick on the treatment. I think 15 -- I hope that my concerns are not neglected 16 a second time, but I hope you will get it 17 right. Then in the final version they will be

18 corrected. 19 CHAIRMAN SAMET: Thanks, Mort, and 20 there will be quiz to EPA on the burroughs and 21 counties in New York later in the day. 22 I do think it is a question of the202-234-4433 Neal R. Gross & Co., Inc. Page 114 1 bias direction, which we should probably come 2 back to for more general discussion, the 3 question of whether beyond a qualitative 4 statement about directionality, whether some 5 quantitative adjustment could be made, a 6 complicated question. Wayne? 7 DR. CASCIO: Yes. I agree with 8 Mort's comments, and I appreciate the history

9 lesson. I don't know much about New York 10 City. 11 In general, my comments are very 12 positive, and I thought it was a very well 13 written section and easily understood. I 14 would just like to say that I thought you all 15 did a really good job in going back to the ISA 16 and drawing that information into the 17 discussion of the CR relationships. 18 I was particularly pleased to see

19 that there was an honest assessment of what 20 you know and what you don't -- you know, what 21 the uncertainties are and why you chose not to 22 incorporate certain endpoints or certain Page 115 1 studies. That is really all I have to say.

2 CHAIRMAN SAMET: Helen?

3 DR. SUH: I have not much more to 4 add to that. One thing I thought was really 5 curious -- and I didn't include it in my 6 comments, but I just thought maybe I would 7 ask. There was a statement in the text that 8 said that the endpoint that was considered 9 ended up being the causal and the likely 10 causal, and that was just a by-product of all 11 the other criteria that happened. 12 So then I was wondering, given13 that, is that really -- was really just a by14 product. I don't know if it was -- I wasn't 15 sure if that was really completely the way. 16 It wasn't really just a by-product. So I was 17 wondering. 18 DR. PEKAR: You know, by-product - 19 - I think what we meant to sort of describe is 20 that we had a systematic process for 21 identifying those health endpoints, which 22 included the strength of the association Page 116 1 information, which is really, really 2 important. 3 You know, we may include a lot of 4 things in the policy assessment evidence 5 analysis that we can't include in the risk 6 assessment, because we don't have baseline 7 incidence rates. We don't have well defined 8 concentration response functions or other 9 aspects of that. 10 So once we walk down looking at 11 strength of association, whether we have the 12 information, not just from the epi study -- 13 the baseline incidence rates are often a real 14 issue for specialized populations -- then that 15 gets you a set of endpoints that we model. 16 But the point is that we did go into only 17 looking at causal. 18 If there had been -- there are, 19 obviously, some endpoints we have talked about 20 that we are interested in, frankly, in 21 quantifying from the risk assessment 22 standpoint, but we don't have the information Page 117 1 to quantitatively assess those. Birth

2 outcomes would be one where we don't. 3 Exposure time window, how to deal with that, 4 how to get the right metric is very 5 challenging from a quantitative standpoint, 6 for instance. 7 So we would have -- the point is 8 we are not limiting it to causal. 9 CHAIRMAN SAMET: Okay, anything 10 else, Helen? 11 DR. SUH: No, that is it.

12 CHAIRMAN SAMET: Bob? 13 DR. PHALEN: Well, I apologize in 14 advance, Jon and Karen, but I think I have to

15 make these comments. 16 The document is extremely well 17 written and very well defended, but I feel 18 that the people in EPA have one hand tied 19 behind their back when they are trying to do 20 a risk assessment, and the true talent that 21 exists within the EPA, I don't think, can be 22 expressed.202-234-4433 Neal R. Gross & Co., Inc. Page 118 1 When I say one hand tied behind 2 their back, I am relating to limitations, 3 having to have a mass-based metric. It is 4 clear that the science is going after 5 components and that components are driving the

6 health effects. 7 The second is not being able, by 8 policy, to take into account secondary health

9 effects. Not all of the effects of the 10 decision on abatement are direct. There are 11 indirect health effects, and I am not talking 12 about the cost, although that is significant 13 as well, but I am talking about indirect 14 health effects, so of controls. 15 Also, the risk assessment is very 16 limited, again by policy, and the National 17 Academy has recommended in 2008 that, when you 18 perform a risk assessment, first you look at 19 the reason for the risk assessment and how it 20 will be used. 21 If it is going to be used to make 22 a decision, which in this case it is, then one Page 119

1 needs to look at all the risks associated with 2 the decision, in this case all the health 3 risks. How might health potentially be harmed 4 by a restrictive standard should also be 5 addressed. Maybe it won't be harmed; maybe it 6 will. But that needs to be addressed. 7 Then having to have national 8 standards is a real drawback, because as you 9 look at these cities that have been selected 10 as the cities that form the basis of the risk 11 assessment, you have places like Atlanta, 12 Houston and Dallas with very high PM, and 50- 13 60 percent of the death rate from all of these 14 causes, 50 percent of the national average

15 death rate from all these causes that are

16 looked at. 17 So having to be forced to have a 18 national standard, in my opinion, doesn't 19 allow EPA to do justice to public health. 20 That is it for pontification. 21 CHAIRMAN SAMET: Okay. So you 22 raised a set of issues that are not directly Page 120 1 related to charge question 2, and I think 2 perhaps Karen alluded to some of these 3 comments in her earlier remarks. 4 I think the approach and scope of 5 both the process of setting the NAAQS and risk 6 assessment is constrained in some ways, and I 7 think some of those constraints are given in 8 the introductory chapters to the REA in terms 9 of the context, in the regulatory context and 10 some of the Congressional interpretation of 11 these issues. 12 I wonder perhaps, Karen, if you 13 want to provide any further response. It is 14 true that the risk assessment focuses 15 specifically on health outcomes attributable 16 to particulate matter, and the documents 17 leading up to this, of course, have reviewed 18 the information relevant to components and 19 whether that information, in one way or 20 another, whether it is separating out PM 21 further by ultrafine or by one or other 22 components or season, which in a sense is the Page 121 1 same, actually, the scientific evidence is 2 ready for that. 3 I think the documents say, no, not 4 yet, but I think everyone hopes that 5 eventually we will have more a refined 6 understanding. But I wonder, Karen, if all 7 would like to respond. 8 DR. MARTIN: Yes. I think it is 9 real important to distinguish the issue of the 10 indicator for fine particles from some of the 11 other issues that were raised. We aren't 12 constrained to have an aggregated mass 13 indicator. That is what we have evidence to 14 support, and we specifically look in this 15 review, has enough evidence emerged to 16 consider additionally adding or substituting 17 component based standards. 18 We all can discuss that as we 19 discuss the policy assessment, but that is 20 clearly on the table as a science driven 21 approach to setting the standard. So I don't 22 see that is precluded by anything except lack202-234-4433 Neal R. Gross & Co., Inc. Page 122

1 of sufficient information. 2 The other issues -- what precludes 3 consideration of some of those considerations 4 is the law itself. It is how Congress wrote 5 the Clean Air Act and how they interpret and 6 how the review in courts have interpreted that 7 intent. 8 So issues about unemployment 9 caused by impacts on industries to control to 10 meet standards are not the kind of effect that 11 we are allowed to consider as part of the 12 basis for the standard. 13 If PM in the air had both 14 detrimental and beneficial effects, we would 15 look at both of those things. If breathing PM 16 helps some people and hurt other people, we 17 would look at those both together, but these 18 other tangential effects associated with 19 meeting the standard are all part and parcel 20 of the preclusion of not being able to 21 consider the cost of meeting the standard. 22 That is not what Congress intends us to base Page 123 1 the standard on, and everything else that 2 flows from the cost of meeting the standard is 3 part of that range of things that the standard 4 is not to be based on.

5 CHAIRMAN SAMET: Bob? 6 DR. PHALEN: That is well said, 7 Karen, and I understand. My comments are 8 basically implying that I wish you had a 9 mandate that was a little more holistic and a 10 little more broad, but I certainly don't 11 disagree with your comments. 12 CHAIRMAN SAMET: Well, you can 13 remember that the Clean Air Act was last 14 amended in 1990, and perhaps sometime in our 15 lifetimes there will be further amendments 16 that may change the scope of the Act, but for 17 now we are dealing with the Clean Air Act as 18 amended in 1990. 19 We had some lingering issues. I 20 want to ask others if they want to respond. 21 But I think we do need to come back to the22 point that Mort raised, the questions of nonPage 124 1 representativeness, higher socioeconomic 2 status, implications for treatment of the 3 estimate from the ACS and whether, I guess, 4 you think it might be an underestimate because 5 of the higher socioeconomic status.

6 DR. LIPPMANN: That was what I 7 intended to say, that clearly, it is an 8 underestimate, if it could be corrected to 9 represent the population as a whole. It is a 10 large population. 11 Therefore, you could make some 12 informed speculation about the proportion in 13 different socioeconomic levels and their 14 different risk factors, and apply the overall 15 population information to make such an 16 estimate. 17 It would, of course, bring the 18 risk estimate for the ACS cohort closer -- not 19 all the way, but closer -- to the six cities 20 estimate, and I think that would be a very 21 valid discussion point. 22 The six cities was not a study of Page 125 1 convenience, as the ACS was. That was a study 2 in which data were available on personal risk 3 factors for a cancer population study in 4 relation to diet, and for very modest 5 expenditure, while Arden Pope was on 6 sabbatical at Harvard, was under 15k probably, 7 a very important initial report on that 8 population and its response to mortality and 9 long term exposure was launched. 10 All of the refinements have been 11 mostly in terms of statistical methodology and 12 updating of the cohort, and those are all 13 admirable scientific studies, but it was very 14 early recognized that it wasn't a 15 representative population; whereas, I think 16 Frank can correct me if he wants to, but the 17 intent of Ferris and Speizer in setting up six 18 cities was to pick reasonably representative 19 people, at least in the eastern half of the 20 United States. 21 So it was much closer. it was not 22 a NHANES study, which is strictly a population202-234-4433 Neal R. Gross & Co., Inc. Page 126 1 representation base, but it was a much more 2 clearly reasonably representative 3 socioeconomic cross-section of those six 4 communities. 5 This is all important context, if 6 you are using these particular cohorts. 7 DR. PEKAR: Can I -- Just to give 8 a little feedback? I think, certainly, 9 qualitatively discussing that issue, the SES 10 difference in how that could result an effect 11 estimate that we see that is lower. 12 I think the one point I would want 13 to make on trying to some kind of quantitative 14 adjustment: One of the challenges is that the 15 latest 2009 release of the additional analyses 16 really pushed the statistical model assessment 17 much, much further, but it also didn't include 18 some of the earlier differentiation on SES.

19 That makes some of those 20 adjustments a little more challenging, because 21 in that report -- I'm sort of going off in 22 memory; I haven't looked at it in a little Page 127 1 while, but it didn't have the kind of SES 2 differentiation of those more refined, updated 3 model forms in the additional survey period. 4 So I think my concern would be, if 5 we were going to do an adjustment, it might 6 have to be based on earlier CR functions or 7 analyses done on the earlier version of the 8 dataset and couldn't as easily reflect the 9 latest version. It is a little bit of a 10 limitation. 11 DR. LIPPMANN: Well, I wish 12 attention had been paid earlier, so that it 13 wouldn't be now last minute estimation with a 14 significant effort, but at least, even now, I 15 think much more discussion is warranted.

16 CHAIRMAN SAMET: Helen? 17 DR. SUH: I think this gets a 18 little bit into the charge question 3 about 19 this whole notion about variability, and does 20 touch on SES. You are right, Zach. I thought 21 that was nice, but I think there is an easier 22 way perhaps than trying to adjust the ACS Page 128

1 estimate. 2 It is to include the six cities 3 estimate or something like that as sort of a 4 sensitivity analysis to look at the effects of 5 using a different risk estimate. That was 6 something that we had discussed prior in the 7 previous meeting as a possibility, that not 8 really -- I mean, it was done somewhat here. 9 Right? 10 DR. PEKAR: Yes, because one of 11 the sensitivity analyses was done with the six 12 city estimate. 13 DR. SUH: So I think that it does 14 get somewhat at your point, Mort, not as you 15 had suggested perhaps, but in a different way 16 by using these other estimates from the other 17 studies to look at their effect on the risk. 18 So it is sort of perhaps to 19 address some of your issues about SES, it is 20 one way of kind of looking around at that. 21 CHAIRMAN SAMET: I would say that 22 it is a pretty complicated matter, because you Page 129 1 have the original selection into the ACS 2 cohort and its difference from the population 3 at that time. You have differences in the ACS 4 cohort versus the U.S. population over time. 5 In addition, there is the aging of 6 the ACS cohort and the extent to which the 7 effects will be modified by age. Again, at 8 any point you sort of have these two time 9 period analyses or multiple time period 10 analyses. You have sort of an overall crude 11 estimate that incorporates any inherent effect 12 modification by the aging of the cohort 13 itself. 14 I think it is worthy of more 15 discussion. Whether you want to try and do 16 some sort of quantitative correction or 17 whether you really could do it with enough 18 certainty to even justify it, I think, is a 19 different matter, but I think you are hearing 20 it just needs more discussion. 21 I guess, when we come to charge 22 question 3, maybe we will be back to this202-234-4433 Neal R. Gross & Co., Inc. Page 130 1 again. 2 Other comments on charge question

3 2? Sverre?

4 DR. VEDAL: Yes. I had a comment 5 that came up in my review of Chapter 6, but it 6 is more relevant here in terms of the choice 7 of the concentration response function, and it 8 has to do with the emphasis on ischemic heart 9 disease. 10 I was reading that later, and I 11 was wondering why all this emphasis on 12 ischemic heart disease rather than 13 cardiovascular disease, more generally. It 14 became clear. Then I went back to the Krewski 15 report. It doesn't report cardiovascular 16 endpoints. It is restricted to 17 cardiopulmonary, which is mixed up with 18 pulmonary, for which there is no effect, and 19 it is obviously an underestimate, if one is 20 looking at cardiovascular, and then focuses 21 and ischemic heart disease, which there is 22 justification for, but not to the exclusion of Page 131

1 cardiovascular disease. 2 I think that argument is based on 3 Pope's 2004 paper where looking at the ACS 4 cohort, there seemed to be an elevated 5 relative risk for ischemic heart disease 6 versus other types of heart disease. I don't 7 find that all that convincing. I think there 8 is a lot of evidence of noise in those types 9 of subclassifications. 10 So it draws a little bit of a -- 11 It introduces a little bit of question in 12 terms of the Krewski effect estimates then 13 having to be restricted to ischemic heart 14 disease, and it follows through all of the 15 improvements in risk and everything else. It 16 is all based on ischemic heart disease. 17 So I wonder if there are any 18 comments about that. I didn't see that as a 19 potential limitation of including the Krewski 20 study as opposed to other potential sources of 21 the CR function.

22 CHAIRMAN SAMET: Zach? Page 132 1 DR. PEKAR: The point I can make 2 is that I think looking at a subcategory of 3 IHD, I think you can certainly speak to the 4 additional uncertainty that might be 5 associated with that, if there is some of

6 that. 7 It is certainly, as you say -- I 8 mean, the reason we ended up modeling IHD is 9 we had causal association, a strong causal 10 association from ISA based on cardiovascular 11 effects, specifically IHD being a subcategory 12 of that, and because we had the concentration 13 response functions associated with that, for 14 that endpoint. But I think that uncertainties 15 in stratifying and looking at a smaller and 16 more specific endpoint is something we can 17 speak to, certainly qualitatively, in the 18 analysis. 19 DR. VEDAL: Yes. I mean, we have 20 heard a discussion as to why the ACS effect 21 estimates are underestimates based on the 22 socioeconomic distribution, which is valid, Page 133

1 but the focus on ischemic heart disease also 2 gives you a somewhat skewed estimate perhaps 3 in the other direction. 4 DR. SPEIZER: I would agree with

5 those comments. I was concerned about the 6 presentation of Table 311 which is, I believe, 7 the mortality rates that were included in 8 Bell's analysis. 9 DR. PEKAR: What page? 10 DR. SPEIZER: It is page 353. My 11 concern is that the missing-ness in this table 12 probably reflects all you got from Bell. Is 13 that correct? If not, then the table should 14 be completely filled in. 15 DR. PEKAR: Yes. Let me explain 16 this table and I think part of this is 17 greater explanation in parts of the document. 18 The table -- there are multiple entries for 19 the same study area, because depending on the 20 concentration response function, we had to do 21 different -- let me just double-check that I 22 am getting this right.202-234-4433 Neal R. Gross & Co., Inc. Page 134

1 So we would have different 2 combinations of counties for different 3 endpoints and, therefore, you would have 4 different baseline total rates, I believe, for 5 the different endpoints. So it is not that we 6 are missing data. 7 For instance, if you look at the 8 Georgia-1 row there, that is speaking to the 9 short term mortality, and it is giving you the 10 -- and that had a different -- that study had 11 a different assemblage of counties. 12 The next GA-1 -- and it is a 13 different all ages category. Then for GA-1, 14 the next row, greater than 30, that is 15 providing the baseline incidence rates used in 16 the long term mortality analysis. 17 So I think it makes it look like 18 there is missing data, and at times what we 19 should have done is been more clear that we 20 are mapping the concentration response 21 functions for different endpoints. 22 DR. VEDAL: You may just need to Page 135 1 change the title of the table.

2 DR. PEKAR: Yes. 3 DR. VEDAL: Baseline mortality 4 rate, to leave out COPD, it doesn't make any 5 sense. So you really need to redefine what 6 you've got here. 7 DR. PEKAR: And to be honest, I 8 think this could go more in the appendix, and 9 we could describe conceptually where we have 10 coverage. I think that would better. 11 CHAIRMAN SAMET: Anything else on 12 Charge Question 2? So then why don't we break 13 for roughly -- Let's break all the way to 14 eleven, I think, and then get back together. 15 (Whereupon, the above-entitled

16 matter went off the record at 10:37 a.m. and 17 resumed at 11:08 a.m.) 18 CHAIRMAN SAMET: We are back in 19 session, and we are moving on to Charge 20 Question 3, which actually -- Helen, it is 21 you. 22 DR. SUH: Let me say first, Marcia Page 136 1 is photocopying the committee members' 2 comments on visibility and will bring a copy 3 to everyone. I thought they were in the 4 packets, but they are not.

5 CHAIRMAN SAMET: Just for 6 planning, we are moving along pretty well. 7 One thing we may do, and I think, actually, 8 Phil and Joe both, are you both leaving 9 tonight. So we may -- I think, if things go 10 well, we are going to try and get visibility 11 in this afternoon, which I think would work 12 well. I have you both here. Okay, good. 13 DR. FREY: Well, this is Charge 14 Question 3, which is on addressing uncertainty 15 and variability, and the main question is: To 16 what extent does the Panel consider these 17 discussions to be clear and appropriate? 18 I was jokingly going to say 19 unacceptable; do it over. But, no, I think it 20 was excellent. It was very well done. So 21 congratulations on a great job. I think the 22 comments I have are probably just minor Page 137 1 comments, but I will go ahead and share them 2 anyway. 3 Let's see. There is a footnote 4 that explains the basis for identifying key 5 sources of variability. It would be helpful 6 to have some similar discussion on identifying 7 key sources of uncertainty, at least on the 8 qualitative aspects of that. 9 A very minor comment: I think EPA 10 did respond to comments from CASAC about11 adding material about variability due to co12 pollutant concentrations. 13 There is some discussion in the 14 related text about housing and the age of 15 housing having to do with air conditioning, 16 and then that has to do with infiltration 17 rate. I think that is maybe not quite the 18 whole story, that newer homes are typically 19 tighter than older homes, and even if they 20 have the same kind of AC system, older homes 21 would have typically higher infiltration. It 22 is a minor point.202-234-4433 Neal R. Gross & Co., Inc. Page 138

1 Another issue would be climate 2 zones. For example, housing in Boston, maybe 3 there is less central AC than there is in 4 Atlanta, Georgia. 5 Okay, let's see. Another comment: 6 There were comments on the first draft REA, at 7 least in the detailed comments, that exposure 8 modeling is something that we would like to 9 see, and I think we understand that there is 10 time and resource issues and that that is not 11 going to happen on this version of the PM 12 standard revisions. 13 I just wanted to say that we would 14 like to see that in the future, and I think we

15 have all talked about that at the last 16 meeting. 17 I think there were lots of good 18 things in this revision. I think it is worth 19 making positive comments, not just negative 20 comments. So I think the treatment of the 21 concentration response function and looking at 22 different forms of the function and looking at Page 139 1 single versus multi-city studies -- I think 2 that is all really nice stuff. So I am really 3 happy to see that. 4 In response to CASAC comments, EPA 5 did explain what was meant by low, medium and 6 high, I think as far as what the range or 7 magnitude of uncertainty is. I think, when we 8 made the comment, we recognized that those 9 definitions would probably be a little vague 10 and fuzzy, and they are a little vague and 11 fuzzy, but I think that is probably to be 12 expected, and I am not sure that that can 13 really be improved on. 14 There is one statement, and I 15 think this is on page 363 or maybe the 16 previous page -- I didn't actually write the 17 page number. It just says that high sources 18 of uncertainty are likely to influence the 19 interpretation of risk, and then there is a 20 parenthetical note "(if those sources of 21 uncertainty are reduced or more fully 22 characterized)." I didn't really understand Page 140 1 why that parenthetical note was there or what 2 it meant. 3 It seemed to me, if you had high 4 uncertainty, you have high uncertainty, and 5 then talking about reducing uncertainty might 6 be something downstream of that. Maybe that 7 is an action you take by collecting more data, 8 but it wasn't clear what that had to do with 9 the definition of high. So I found that a 10 little confusing. 11 I think it is appropriate -- the 12 definitions of low, medium and high seemed 13 okay. There was some discussion that those 14 are based on a staff consensus, and I sort of 15 hesitate to make this comment, because I don't 16 want to make too big an issue of this, but it 17 did raise in my mind that, you know, this sort 18 of begs the question of what was the process 19 by which that consensus was reached. 20 You know, if one considers -- and 21 I know this was not a formal expert 22 elicitation, but if one looks at kind of the Page 141 1 literature and the schools of thought on 2 elicitation, there are some concerns about 3 group processes, and it wasn't clear if this 4 was a group process, was this a staff meeting? 5 Was it everybody got to write down their 6 comments, and then somehow there was 7 discussion later? If it was a meeting, you 8 know, sometimes you can have strong 9 personalities, and they carry the day, even if 10 they don't have the most meritorious 11 viewpoint. 12 So I am not sure this is a big 13 issue, but maybe a little bit of explanation 14 of the process by which the consensus was 15 achieved could be helpful. 16 On the other hand, I didn't 17 personally see anything glaringly wrong with 18 any of the characterizations in the summary 19 tables of what are the uncertainties, and what 20 is the knowledge base, and the bias and the 21 magnitude and so on. 22 So I think, if anyone in CASAC has202-234-4433 Neal R. Gross & Co., Inc. Page 142 1 a concern about any of the conclusions that 2 EPA reached, we could address those, and in 3 the absence of CASAC saying that there is 4 anything particularly wrong with it, I think 5 that implicitly would imply that we agree with

6 what was the outcome of those staff 7 discussions. 8 I thought it was great that the -- 9 I really liked the discussion of the 10 dependencies among pair-wise combinations of 11 sources of uncertainty, especially for the 12 concentration response functions. I was very 13 happy to see that. 14 I think this cuts across both 15 Chapter 3 and Chapter 6. It seems obvious 16 that the core estimates of risk come out at 17 the low end when you consider all the 18 plausible alternative estimates of risk that 19 come out of the sensitivity analysis, and the 20 sensitivity analysis is kind of couched in 21 terms of that it is addressing uncertainty in 22 a quantitative but not a probabilistic way, Page 143 1 which I think is appropriate. 2 On the other hand, I think it is 3 still appropriate to -- when all the analyses 4 are done, is to maybe have some step back from 5 what the quantitative results are, and then 6 tie back in the qualitative assessment of 7 uncertainty. I am not sure I saw that in 8 Chapter 6. Maybe that is where it would 9 belong.

10 That was a comment that had been 11 made in our previous meeting, was that some 12 assessment, not only of the -- you know, the 13 implications of what can be quantified in the 14 sensitivity analysis, but things that maybe 15 can't be quantified, such as exposure 16 misclassification or some of the what seemed 17 to be known issues with using ambient data as 18 a metric for exposure in epi studies instead 19 of personalized exposure numbers, and how that 20 might bias the estimates. 21 That may tend to support the view 22 that, not only do the quantitative plausible Page 144 1 alternative numbers come out to be higher than 2 the core estimate, but there is reason to 3 believe, furthermore, that if some of these 4 other sources could be quantified, that we 5 would have yet even higher estimates. 6 I won't make a comment, but I 7 guess there is an obvious perhaps policy 8 implication -- maybe this gets into the policy 9 assessment -- of using core estimates that 10 seem to be at the low end of any other 11 alternative estimate. But I guess that is not 12 really a topic for this document. 13 I just have a couple of other 14 really minor comments on Charge Question 3. 15 There is some text on page 356 that refers to 16 the true values of parameters of the model, 17 and that is followed immediately by text that 18 mentions that a model is -- you know, whether 19 it is an accurate representation of the 20 underlying physical system. 21 I think, just the term "true 22 values of the parameters" troubled me a little Page 145 1 bit, because parameters are conditional on the 2 model, and the model may or may not be a good 3 representation of the real system. 4 So t he parameters, in a sense, 5 are an artifact of the model, and to say that 6 there is true values of the parameters almost 7 implies there is a true model. This is sort 8 of just a wording issue. I was trying to 9 think what is a better term than true value. 10 I don't know if optimal value is better. 11 One could think of optimizing the 12 parameter of a model, given conditional on the 13 model that is selected. Maybe that is the 14 concept. 15 I think that is really all I have. 16 I have a few other very minor comments in the 17 written text. I didn't have a chance to 18 iterate with Helen. So I invite you to add 19 your comments. 20 CHAIRMAN SAMET: Okay, Helen. 21 DR. SUH: I second Chris' 22 comments. I thought you guys did a great job202-234-4433 Neal R. Gross & Co., Inc. Page 146 1 responding to the previous comments and 2 incorporating them and putting together 3 something that made a lot of sense. 4 I agree that I thought it was a 5 little -- I don't know what the right word is 6 -- curious or interesting. Usually, when we 7 talk about uncertainty and the sensitivity 8 analyses, when you actually do all your 9 analyses, you usually get estimates that will 10 be in the positive direction and the negative 11 direction, and it seemed that the core was 12 actually very, very conservative compared to 13 all of the other analyses that you conducted, 14 which is slightly not typically how things 15 happen when you do a sensitivity analysis. 16 You usually look at things in both 17 directions. So I thought it would be at least 18 interesting to discuss or think about what 19 that might mean for your core analyses. Maybe 20 is your core analyses too conservative is one 21 way to think about it. 22 The other things, I think you did Page 147 1 a good job of -- I realize a lot of it is 2 qualitative and not quantitative, but it might 3 be good to at least mention seasons -- 4 seasonality as a source of variability, I 5 suppose, because PM peaks at different points 6 in different areas. So it might be good to 7 mention that and what might happen. That's 8 it. 9 CHAIRMAN SAMET: Charge Question 10 3, other comments? 11 DR. HOPKE: Again, I would like us 12 to once again really push the -- In the next 13 round, we should see a much more really moving 14 toward the stochastic uncertainty. We asked 15 for this the last go-round five years ago. We 16 haven't gotten into it now. It is time. 17 CHAIRMAN SAMET: So I think that, 18 too. You know, in part this is almost a sort 19 of, in part, a research recommendation, that 20 you have a platform and framework now for 21 someone to do it. I think probably, Phil, 22 this is always left to development in the Page 148 1 context of these documents, it will not happen 2 with enough time. We are talking about a 3 major effort, I think. 4 Other comments on Charge Question 5 3 and 4, I think, Chris, is you again. 6 DR. FREY: Okay, thank you. I was 7 just adding Phil's comment into my summary

8 here. 9 Charge Question 4: If you don't 10 have it in front of you, this has to do -- Do 11 you have a comment? 12 DR. LIPPMANN: Yes. On 3, a point 13 to page 377, Section 3.5.3. It refers to 14 single and multiple elements as well as single 15 and multiple factors in terms of the 16 sensitivity analysis. 17 There is no definition of what is 18 an element and what is a factor. I had to go 19 to a later chapter to realize that elements 20 refer to modeling elements. So for clarity, 21 I think it should be introduced, the thought 22 of what it is you are referring to. Page 149

1 Elements, it seemed to me, was a 2 poor word, because by next round we will be 3 talking about chemical elements, I hope, and 4 right now it would be a good idea to use some 5 other term for distinguishing between factors 6 and whatever else it is you are talking about. 7 CHAIRMAN SAMET: Okay, Chris. 8 DR. FREY: Thank you. Just for 9 the benefit of those who don't have the charge 10 question in front of them, this deals with 11 sensitivity analysis, and the basic question 12 is: What are the Panel's views on how we have 13 used the sensitivity analysis results to 14 support consideration of uncertainty in the 15 core risk estimates? 16 The general comments are, I think, 17 again very positive, that I think this 18 material was very good and nicely covered a 19 very complex topic. Table 4.3 is a useful 20 summary. There is a classification scheme for 21 small, moderate, moderate large, and large 22 contributions to sensitivity, which I think is202-234-4433 Neal R. Gross & Co., Inc. Page 150 1 helpful. 2 I would comment that, throughout 3 both Chapter 4 and into Chapter 6, that the 4 sensitivity results are referred to sometimes 5 as an uncertainty set or in terms of 6 uncertainty. I think that is not necessarily 7 wrong, but I think it is a little -- It might 8 be a little confusing to some readers, because 9 I think the text is also careful to say that, 10 while these are plausible alternative 11 estimates, they are not a probability sample, 12 and so there is not a probabilistic 13 interpretation. 14 Maybe it is just better to refer 15 to them either as alternative estimates or 16 sensitivity results rather than an uncertainty 17 set. Maybe that is a fine point. it is not 18 a huge big deal. 19 I think -- let's see. The 20 evaluation of alternative model structure that 21 was part of the sensitivity analysis, I want 22 to strongly endorse that, and that is a very Page 151 1 positive revision. 2 It is clear that comparing 3 different model structures is an important 4 source of variation from one estimate to 5 another, and particularly the random effects, 6 log log model, compared to the fixed effects 7 log linear model seemed to be a big 8 difference. Just generally, there is more 9 thorough treatment of model choices in 10 alternative functions, which I think is very 11 helpful. 12 There was a comment, which I think 13 was addressed in part but maybe not fully. So 14 in the last meeting, one of our comments was 15 to look at the range of uncertainty associated 16 with confidence intervals for a given 17 concentration response function. I think 18 that is accounted for in these Tables 4.1 and 19 4.2 in Chapter 4, that you do see confidence 20 intervals on the effects estimate.

21 Another comment we had made was 22 just to compare those confidence ranges to the Page 152 1 ranges of outcomes you get by looking at 2 different functional forms of the model, just 3 to get an idea of, you know, is random error 4 for a given model -- say, for the core model - 5 - a more or less important source of potential 6 uncertainty than comparing alternative model

7 forms? 8 So in other words, just comparing 9 the magnitude of the confidence intervals from 10 the core CR models to the range of values 11 obtained from different alternative functional 12 forms, I personally think would be 13 informative. 14 The sensitivity analysis tends to 15 focus on looking at percent changes in risk, 16 and sensitivity analysis often focuses on 17 variation, like how much does variation in an 18 input affect variation in the answer. 19 So I think, from that perspective, 20 looking at percent differences is a natural 21 thing to do, but I think in terms of the 22 framework of the NAAQS, risk reduction is not Page 153 1 really the policy goal, and the NAAQS are 2 intended to be protective of public health. 3 So, therefore, the more relevant risk metric 4 is probably the number of adverse outcomes. 5 So a specific comment related to 6 that was in -- just looking at some of my 7 notes here. I think it is in Table 4.4 where 8 there are results from the sensitivity 9 analysis, and the column for results says 10 percent difference, but it doesn't actually 11 say percent difference in what. I think it is 12 percent difference in number of adverse

13 outcomes. 14 That could be defined, but I think 15 it would be helpful to just -- even though it 16 is elsewhere in the report, to put the core 17 estimates of the magnitudes in that table or 18 in a footnote, so that when we are looking at 19 percent differences for some of the effects, 20 we know whether this is a percent difference 21 of a big number or a small number. 22 I think that covers basically all202-234-4433 Neal R. Gross & Co., Inc. Page 154 1 of my significant and semi-significant 2 comments. So I will stop there. They are all 3 statistically significant comments, yes.

4 CHAIRMAN SAMET: Helen. 5 DR. SUH: I agree with Chris, and 6 I also have -- and I think this goes into some 7 of the other issues perhaps with some of the 8 other charge questions, one of which is I 9 didn't truly understand this notion of the -- 10 when you did the substantive analysis with the 11 peak shaving and the rollback, and then there 12 was a discussion from there about the 13 peakiness of the cities and the 24-hour versus 14 the annual average and their effect. 15 I found it very confusing, and I 16 wasn't sure of the exact main points that we 17 are trying to get across. I guess the thing 18 I took from it was that maybe there was a 19 regional effect going on. I wasn't really 20 sure of, really, the main points that you 21 wanted to drive home, and I wasn't sure of -- 22 and I guess that there is just some Page 155

1 clarification perhaps needed, because it 2 seemed to be useful on one level, but I just 3 wasn't sure what the main points of it were. 4 The other thing that goes along 5 with that is I couldn't figure out if there 6 was basically some of the premises of the 7 overarching summary conclusions was that there8 are these strong regional or strong city9 specific effects, that maybe they argue 10 against or may complicate the nature of doing 11 national standards, but I guess that is 12 something to consider, so you could carry it 13 forward to the policy analysis when you think 14 about who you are protecting and when the 15 standard may be more or less protective of 16 other groups. 17 So I thought that would be 18 something that would be useful to elaborate or 19 clarify here, so that for the policy analysis 20 it would just follow a little bit better. 21 CHAIRMAN SAMET: Other comments, 22 Charge Question 4, sensitivity analyses? Page 156

1 Mort? 2 DR. LIPPMANN: Just a technical 3 one on pages 445 in this section, 445 and 447. 4 The figure numbers are mislabeled. Change 46 5 to 47. Yes, 46 and 47 to 48, and on page 47, 6 46 and 47, 47 and 48. 7 DR. FREY: And I guess related, I 8 have some other numbering issues. It probably 9 would just be good to proofread Chapter 4 for

10 that. 11 DR. PEKAR: We will proofread it 12 for the first time. 13 CHAIRMAN SAMET: Okay. Other 14 comments? We have sort of been dealing with 15 uncertainty variability in t he sensitivity 16 analyses. Other comments? There is an awful 17 lot of information presented and, clearly, you 18 can always present more, and I think we have 19 some suggestions. I think the bulk of what is 20 needed is there and laid out. 21 Other questions? Sverre? Okay, 22 well, Sverre has nothing. We are moving on to Page 157 1 Charge Question 5, which is you. Go ahead. 2 DR. VEDAL: All right. So Charge 3 Question 5 basically relates to some new 4 information or new ways of presenting data 5 that we have been provided with, and having to 6 do with design values, what is called t he 7 pattern of PM monitoring data or results, and 8 how that relates to some sensitivity analyses, 9 particularly some things that Helen just 10 touched on that we will talk about, as well as 11 information that looks at the role of the 15 12 urban areas selected relative to urban areas 13 in the rest of the country. 14 There were two charge questions. 15 One was: How do we like this new information, 16 how it was presented and the discussion that 17 pertained to that and the points that were 18 gleaned relative to basically two sets of 19 figures, Figures 4-17 through -19 and then a 20 couple of others, 4-20 and -21, that the 21 discussion basically focuses on. 22 Then the second charge question202-234-4433 Neal R. Gross & Co., Inc. Page 158 1 was: Was there anything that was missed that 2 the committee would like to recommend be added 3 in consideration of those five plots? 4 I have had the advantage of being 5 able to see the comments of the other people 6 assigned to these charge questions, and I 7 won't steal their thunder, but will perhaps 8 foreshadow a couple of things. 9 I think the subgroup assigned to 10 this charge question were generally extremely 11 positive with these new displays. I think we 12 regretted not having them five years ago when 13 we had the -- when we were deliberating at 14 that point, and they are extremely useful as 15 a way of focusing our interest on the 16 distribution of cities, certainly, by the two 17 different averaging metrics and design values. 18 With respect to design values, 19 when I first read this, I am not so used to 20 the EPA jargon, and I glossed over the part 21 where this was defined earlier. So I was like 22 trying to figure out what a design value was. Page 159 1 So I went to Google, and it was 2 the first hit, design value, defined it for me 3 there. It was my mistake, because it is 4 defined in Chapter 3, and pretty good, but it 5 might be worth just, rather than jumping right 6 into assuming that is to maybe refer one to 7 another place or an appendix or a definition. 8 The plots are also intended to 9 give us some information and insights into 10 what are called the patterns of PM monitoring 11 data. Zach went over some of that earlier, 12 and I think there are some insights, some good 13 insights, to be gained from that exercise 14 relative to various types of control 15 strategies. 16 I think we were happy to see sort 17 of looking at the effects of an annual

18 standard versus a 24-hour standard and the 19 relative impacts of those. I think that was 20 very insightful. 21 Then, as I alluded to before, this 22 issue of how well the -- and in what way the Page 160 1 15 urban centers reflect what is going on in 2 the rest of the country. 3 We had a lot of discussion earlier 4 on the issue of rollbacks and how they are 5 done and how confusing they are. I don't 6 think there needs to be anymore said about

7 that. 8 There are a lot of points made 9 with respect to rollbacks and how those are a 10 function of the PM monitoring data, and it is 11 tough for me as I get older to try to accept 12 and understand new things, but it is tough to 13 digest it all in a little nutshell in terms of 14 what the points are there. 15 They are all good points, and I 16 like the way of sort of bookending it by 17 cities that have clearly different 18 characteristics, and that is helpful, but it 19 is still a little bit of a tough go. 20 One specific thing that I just do 21 not understand, and there is probably a real 22 simple explanation for it, is this issue of, Page 161 1 if both the 24-hour and the annual design 2 value occur at the same monitor, the peak 3 shaving rollback of 24-hour values has a 4 bigger impact on the annual average design

5 value. 6 I don't understand why it makes a 7 difference that they both occur at the same 8 monitor site. It would seem that would be 9 just a trivial little -- anyway, maybe that is 10 a minor point that someone can explain, but it 11 may not need a general discussion. 12 The issue of peakiness has been 13 brought up before, and I think I understand 14 what you are referring to, the distinction 15 between the design values of the 24-hour 16 average versus the annual average and how 17 different those are. 18 In reading this, I got a couple of 19 mixed messages. I think most of that refers 20 to -- is in the temporal dimension, and so 21 when you have a site or a city where you have 22 a lot of temporal variability in the day to202-234-4433 Neal R. Gross & Co., Inc. Page 162 1 day, 24-hour values, that is going to be 2 reflected in peakiness. 3 There is a point, however, where 4 you are referring to peakiness within a city, 5 and specifically L.A. That is a spatial 6 dimension, a spatial dimension to peakiness. 7 So you get confused as to what you are 8 actually meaning by peakiness. 9 One other person brought up this 10 issue of, I think, what is basically trying to 11 draw too much information from this figure, 12 and it had to do with this issue of Atlanta, 13 Birmingham and Houston, as to where you place 14 them on the plot. 15 There are some mistakes as to 16 where you placed them, and they vary from 17 across the three plots, but for example, if 18 you look right there, Atlanta is right on the 19 border between C and D, and Birmingham is 20 right on the border between B and C, but you 21 know, the discussion makes it appear that they 22 are in one or the other, and it goes on from Page 163 1 there. So I think you are taking liberties 2 with what the actual plot is showing. 3 DR. PEKAR: Well, these 4 discussions -- and just to be clear, and I 5 think we should definitely clarify this. The 6 discussions in terms of controlling standard 7 levels and other aspects of that are going to 8 be based on the actual numerical value, and 9 the plot can be a little fuzzy, just because 10 of -- So you may be -- 11 DR. VEDAL: Yes, I understand 12 that, but basically it means that it is just 13 a smidgen over the border, and one can 14 actually run for a touchdown with that kind of 15 distinction. I'm not sure. 16 DR. PEKAR: Well, that may be. 17 That is a good point. 18 DR. VEDAL: I think the other 19 Panel members -- and I will let them talk 20 about some recommendations that they made with 21 respect to the plots that, I think, are -- we 22 may not all agree on, but are interesting to Page 164

1 consider. 2 Before I get to some 3 recommendations, just what is Pitt Liberty? 4 Why is that 16th point? Maybe it was 5 presented earlier in that thing, and I missed 6 it somewhere, but what is it? 7 DR. PEKAR: So in Pittsburgh we 8 have two different -- we treated Pittsburgh as 9 two different locations. In the risk 10 assessment they become one, but in terms of 11 conducting rollback we had two -- we divided 12 that city and its monitors into two different, 13 essentially, attainment zones that are very, 14 very different, and we conducted the 15 adjustment, the rollback, separately for those 16 two zones and then had one composite monitor 17 that was generated from both. 18 I think an artifact that we should 19 take care of is either deciding can we provide 20 the composite equivalent of this, if we can, 21 which may not be doable, or at least be clear 22 about why we have 16 instead of 15 red stars Page 165 1 and what those -- So, yes. 2 DR. VEDAL: Right. All right. In 3 terms of recommendations for insight, my first 4 impression when I looked at that was it would 5 appear that, if you lowered -- if you just did 6 the 24-hour, brought everyone into compliance 7 with the 24-hour standard, you would pretty 8 much do what you wanted to do. Right? And if 9 instead you did the annual standard and 10 brought everyone in compliance with the annual 11 standard, you would still have some residual 12 number of cities that were still out of 13 compliance with the other standard. 14 Now as you go further along in 15 making your arguments, obviously, the case is 16 that you are going to prefer the annual 17 standard and all that, but the impression that 18 you get from looking at this is exactly the 19 opposite. 20 So you are almost going to have to 21 address this kind of initial impression that 22 you get on looking at this to counter that202-234-4433 Neal R. Gross & Co., Inc. Page 166 1 sort of approach. I am still going to make 2 arguments in favor of the 24-hour, but 3 nevertheless, the impression here is that, 4 gee, all the oomph is in the 24-hour standard, 5 not in the annual standard. 6 At the end of the day when you get 7 to your integrative summary, you know, it is 8 all coming down in argument in favor of more 9 emphasis on the annual standard. 10 DR. PEKAR: Well, I think one 11 point that we didn't clarify in the diagram, 12 really -- I mean, we clarified it in words, 13 but I think the diagram really offers this up 14 -- is that you got a lot of locations that 15 certainly exceed the daily, but if we think -- 16 this presentation of this as skipping the X 17 axis down here, but this is 15. Right? So we 18 are looking at annual averages below that. 19 A lot of these guys have annual 20 average levels, if we are talking about 21 estimating mortality risk, well, we are going 22 to be less confident about making thosePage 167

1 estimates in this and the nature of the 2 relationship, not that it is not -- that we 3 don't believe health affects aren't occurring 4 there, but I guess my point is, if we were to 5 try to predict risk estimates for a lot of 6 these, they may be exceeding the daily and, if 7 we force them down, you are still dealing with 8 annual average estimates that are lower. It 9 is a good point. 10 DR. VEDAL: Good point. Anyway, I 11 just thought it might be helpful just to 12 counter that initial impression, if you are 13 really going to go to bat for the annual

14 standard. 15 DR. PEKAR: Right, and I think 16 something -- Another point that was made 17 earlier -- if I can just give a little more

18 broader feedback. 19 We provide a lot of discussion of 20 these diagnostics and sort of explaining what 21 we see, and I think we had some observations 22 that are pretty strong, but I think there are Page 168

1 some other sort of intermediate observations 2 that could be more clearly made, and I think 3 that has been pulled out by folks, that we 4 don't really have the take-home message on 5 some of these. I think we could sharpen that 6 up a little bit. 7 DR. VEDAL: Yes. I think that is 8 pretty much it.

9 I won't talk about some of the 10 points that others made, and allow them to 11 elaborate on them in terms of other 12 recommendations and ways that you could go 13 that we could perhaps have discussion on. 14 I'll finish.

15 CHAIRMAN SAMET: Thanks. Ted? 16 DR. RUSSELL: Right. As Sverre 17 said, I was very positively taken with this. 18 The one thing that I could add was 19 that -- I think it is in 52-4 -- you break the 20 country up into different regions, and you 21 don't have any region from, I think it is, the 22 Upper Midwest. Page 169

1 You could make up a figure very 2 similar to this that actually shows how the 3 various cities that you currently all in green 4 dots are assigned to each region, and that way 5 you would get an idea of what is the 6 distribution within regions, and you could 7 even make -- sort of have your domain of 8 possible standards and see is there places in 9 the upper Midwest or anywhere that sort of 10 falls in or outside. I just thought that 11 might be instructive. 12 CHAIRMAN SAMET: So anything else 13 that -- That is your A and B comments?

14 DR. RUSSELL: That was A and B 15 together. 16 CHAIRMAN SAMET: All right.

17 Frank? 18 DR. SPEIZER: I don't have any 19 comments on this. 20 CHAIRMAN SAMET: Okay. Rogene? 21 DR. HENDERSON: Well, my comments 22 are very similar to Sverre's. I was very202-234-4433 Neal R. Gross & Co., Inc. Page 170 1 pleased with these figures. I am kind of a 2 visual person, and this helped me a lot in 3 putting everything in perspective. 4 When it came to discussing the 5 rollback method, we had a very good discussion 6 this morning, and that helped me tremendously 7 to see what they were talking about. 8 My confusion here was why would 9 you think you had to roll back the annual and 10 the daily the same amount, because I think the 11 relationship between the exposures -- the peak 12 exposures and the average annual exposures is 13 very complicated and very dependent on where 14 you are and what the source of the high peaks 15 are, et cetera. 16 So I didn't understand why you

17 would want to roll them back to the same 18 degree. It just didn't seem to make sense, 19 but we have had that discussion, and I talked 20 to Zach, and he helped me -- explained to me 21 over the break, but one thing, Zach, that you 22 said that bothered me a little was maybe we Page 171 1 can't do it this time, but next time we will 2 be sure and use the hybrid method, or 3 emphasize it. 4 If we can possibly do that this 5 time, I think it is something that should be 6 done, because it puts us -- EPA wants to be 7 clear and transparent, and it is a defensible 8 method. So to me, it would be worth it to 9 reconsider and emphasize the hybrid rollback

10 method. 11 Now, as far as how I would improve 12 this, when I saw this and the light dawned and 13 I said, oh, I see what they are talking about, 14 I would like to have a third dimension in 15 which the health effects, at least for the 15 16 cities where we have those health effects, 17 could be projected, maybe little peaks coming 18 out from the surface there. But that is a 19 kind of a pipe dream, in a way, but this shows 20 so clearly the exposure situation. it would 21 be nice to have -- Do the health effects march 22 along with it or do they match? So that was Page 172 1 my only comment. 2 CHAIRMAN SAMET: So you want to 3 borrow technology from Avatar and have -- 4 DR. HENDERSON: Yes, that's right. 5 CHAIRMAN SAMET: -- videographs? 6 MR. AVOL: That being all of my 7 other colleagues' comments, I think it was 8 very well done. I do have two questions for 9 you, and one other comment. 10 With regard to your previous note 11 about proofreading, at the risk of being 12 slightly obsessive, I gave you a whole list of 13 proofreading. So I won't go through those, 14 but I did have two questions. 15 One is there is a comment made 16 about Philadelphia and Los Angeles sort of 17 being representative, and it wasn't completely 18 clear to me why that was so. Los Angeles has 19 been called a lot of things, but 20 representative of all western cities is not 21 one of them. So I wasn't quite sure what the 22 basis for that was. Page 173

1 DR. PEKAR: I think -- There was 2 no assessment to determine representativeness 3 on that. I think we needed -- To keep 4 ourselves from being overburdened in 5 conducting the sensitivity analysis, we needed 6 to select an eastern and western city. 7 I think the better way to put it 8 is L.A. is certainly of great interest to us 9 and Philly represents maybe a reasonable 10 eastern, larger eastern city, but I think we 11 overreached with that term. 12 I was reading your comments, and I 13 think we need to pull back from that and just 14 be a little more clear that there wasn't any 15 kind of --If we had -- And for some of the 16 sensitive analyses, we did model it. We did 17 include every city when we could, but -- Yes. 18 MR. AVOL: I think it was great to 19 go to the 15 city approach, and I just 20 couldn't quite follow why those two were 21 representative. So either a comment about 22 that or backing off on that would be fine.202-234-4433 Neal R. Gross & Co., Inc. Page 174 1 The other question is just to 2 follow up on your previous explanation of Pitt 3 and Pitt Liberty for Sverre. I don't quite 4 still follow why there is two, because 5 arguably, for example, in Los Angeles you 6 could separate out the western portion of the 7 Basin and the eastern part, and you get very 8 different plots, and you could also say, well, 9 that would be a couple. 10 So I am not sure why you need to 11 identify Pittsburgh as a place that needs two

12 of them. 13 DR. PEKAR: So in Pittsburgh there 14 was -- I guess a way to put that is that, in 15 talking to our air quality folks, when they 16 were gathering the monitoring data for us 17 basically in the counties of each city, in 18 Pittsburgh there is one monitor that is 19 extremely high, just very, very, very high. 20 If you group that and you do 21 rollbacks, particularly proportional rollback 22 based on getting that to a T, you have massive Page 175 1 reductions in everything. When they looked 2 into that, they found that there was this 3 definition. 4 So I think the way I would say it 5 is we were able in Pittsburgh relatively 6 easily to come up with maybe a more 7 representative -- I don't want to say 8 realistic, but more site specific approach 9 toward what we think is going on there, and in 10 L.A., you are right. I think more 11 researchers, more time looking at that and 12 dividing it up is definitely doable. 13 If I can interpret what you are 14 saying, the criticism is really, hey, if you 15 did it there, there are other places -- 16 MR. AVOL: I am not arguing 17 strongly for doing it there. I am just saying 18 that one can make the same argument that you 19 just made for Mira Loma compared to parts of 20 the Basin. 21 DR. PEKAR: Sure. Maybe one way 22 to think about that is it is a very poor Page 176 1 sensitivity analysis, but we do know the 2 difference it made in Pittsburgh. That shows 3 that this can have substantial effects. So to 4 consider that maybe as a source of uncertainty 5 in some of our other urban analyses, I mean, 6 would we have driven things higher or lower if 7 we parsed out these attainment zones and did 8 rollbacks separately. What do we think would 9 happen? 10 I don't think, just to be -- I try 11 to be honest about these things. I think at 12 the time, we probably won't be able to do 13 something more refined in L.A. for this 14 assessment, but I think, to acknowledge that.

15 MR. AVOL: I would sort of be 16 inclined to put Pitt Liberty and Pittsburgh 17 together. 18 DR. PEKAR: All right. Well, just 19 to be clear, we did reaggregate them, and we 20 conducted the analysis. It was just the 21 rollback that was done differently.

22 CHAIRMAN SAMET: Frank? Page 177

1 DR. SPEIZER: Yes. Thank you for 2 coming back to me. I did have some comments 3 that I probably ought to raise.

4 One was related to the Table 4.1 5 in which you mix both mortality and 6 hospitalizations, and it is not clear to me 7 that the denominators for those tables would 8 be the same. I just need to clarify whether 9 or not they are, because generally, the 10 hospitalizations are per 10,000, and the 11 mortality is per 100,000, and those are really 12 very different numbers, if that is the case.

13 I don't know. 14 DR. PEKAR: I mean, the incidence 15 estimates -- The baseline mortality rates 16 might be expressed in different terms, but we

17 would have corrected for that. So these are 18 annualized estimates for short term mortality 19 versus long term mortality. 20 You know, certainly, working 21 backwards you would see that individual cities 22 might have been modeled in terms of the202-234-4433 Neal R. Gross & Co., Inc. Page 178 1 groupings of counties slightly differently, 2 depending on the underlying epi study, but 3 these are essentially representing the same 4 effort to characterize annual incidence for 5 the two different types of effects, categories 6 of effects, short term versus long term 7 mortality.

8 DR. SPEIZER: But what about the 9 hospitalizations? Aren't they in that table? 10 DR. PEKAR: Oh, sorry. Those 11 would have been total incidence estimates. It 12 is the underlying baseline mortality rates. 13 Maybe I am misunderstanding your question. It 14 certainly would have differed, but the 15 estimate is an annual estimate of the actual

16 effect. 17 DR. SPEIZER: Well, yes. I 18 understand that. They are all relative, but 19 the question is what is -- The denominators 20 for mortality and hospitalizations might be 21 very different. I just don't know if they

22 are. Page 179

1 DR. PEKAR: Oh, yes, yes. I would 2 need to -- Off the top of my head, I don't 3 remember exactly the age group to the two 4 effects. One is going to be based on 5 Schwartz. One is going to be based on Bell. 6 I would need to look at the age groups. 7 We could be more specific for the 8 population that is reflected in these. 9 DR. SPEIZER: You just need to 10 clarify. Maybe a footnote will do it. 11 DR. PEKAR: Okay.

12 DR. SPEIZER: The other -- back to 13 this issue of Los Angeles versus Philadelphia. 14 I had trouble interpreting what Figures 4.7 15 and 4a are showing, and in my interpretation 16 of it, it looked to me like the change in L.A. 17 went from five to 10 percent to as high as 19 18 percent. In Philadelphia, it was, I guess, 10 19 to 15 percent up to 23 percent, and for total 20 mortality -- That was, I guess, for heart 21 disease. then for total mortality, the 22 changes were much lower. Page 180 1 Now if 40 to 50 percent of the 2 total mortality is heart disease, I am not 3 sure I understand why the deltas are so 4 different. 5 DR. PEKAR: I'm sorry. I am a 6 little bit behind on the figures. 7 DR. SPEIZER: Well, you may just 8 want to look and see my general comment, just 9 review them to see whether it makes any sense, 10 but I was concerned about the differences in 11 the deltas that you have. 12 DR. PEKAR: Right. 13 DR. SPEIZER: I think that is all,

14 Jon. 15 CHAIRMAN SAMET: Okay. So other 16 comments, Charge Question 5? Okay, then on to 17 Six, I guess, maybe before lunchtime. 18 DR. SPEIZER: Yes. Now there is 19 no Charge Question for Chapter 5, as far as I 20 can tell, and a number of issues were raised 21 by a number of the reviewers that this looked 22 rather tentative, and maybe whether it belongs Page 181 1 here or belongs in an appendix, that is one 2 issue. 3 Perhaps more importantly, does it 4 make sense to have done a national estimate on 5 the basis of the 15 cities and where we know 6 or it is reported in the text that the 15 7 cities probably represent an upper exposure 8 level for the country. 9 There are a couple of statements 10 that come up in Chapter 6 that reflect on how 11 you do this analysis for the national, and one 12 place it says three to nine percent excess 13 risk for half the country, zero to three 14 percent for the other half of the country. 15 I am just concerned that this has 16 got -- this number that gets generated for the 17 whole country may be too big. Also, I am 18 concerned that it is taking it down, I guess, 19 to the Krewski 2009 5.8 level, I think. That 20 has to be clarified for me. 21 So I thought we ought to have a 22 more general discussion about that before we202-234-4433 Neal R. Gross & Co., Inc. Page 182 1 get into the specifics of Chapter 6. 2 CHAIRMAN SAMET: So let's pause 3 there. So your posing that we discuss 4 essentially whether Chapter 5 should be 5 included as a main chapter?

6 DR. SPEIZER: Yes. Others have 7 raised that question as well. 8 CHAIRMAN SAMET: Zach, do you want 9 to make some explanation of the national? 10 DR. PEKAR: Yes, and I will give a 11 brief explanation. Basically, it was a grid 12 cell based analysis. So one thing, just to be 13 clear, and I think we talk about comparing the 14 15 urban study areas doing a representative 15 comparison based on the national morality 16 estimate. I think that can introduce a little 17 bit of confusion into this. 18 The national mortality analysis 19 was a separate analysis. We didn't scale up 20 the 15 urban study areas at all. We conducted 21 a grid cell based CMAQ hybrid monitor based 22 characterization of ambient air levels, but Page 183 1 basically did a BenMAP. That is an incidence 2 estimation model. 3 So we modeled at the grid cell the 4 mortality for each grid cell in the U.S. So 5 that is the basis for the analysis. it is not 6 a scaled up. We didn't scale up the 15 urban 7 study areas. 8 Another thing is it is for recent 9 conditions. So just to be clear about that, 10 the reason the mortality estimate seems like 11 it is maybe a little higher than might e 12 expected is it is for recent conditions. It 13 is not assuming attainment of the current 14 NAAQS, but your correct that it does model 15 estimate. It does estimate risk all the way

16 down to the elemental 5.8. 17 So if you look at that, that is 18 assessing risk for, in some instances, greater 19 than a 10 microgram per meter cubed spread of 20 annual estimates. So you are going to get a 21 fairly larger estimate of incidence. 22 So I am not sure if that at least Page 184 1 clarifies the approach that was used, and I am 2 not sure if Brian wants to add anything. 3 CHAIRMAN SAMET: Frank, what is 4 your recommendation beyond wanting us to 5 discuss it? 6 DR. SPEIZER: Well, whether this - 7 - I think it is important we clarify that this 8 was not part of the 15 city part of the 9 analysis. 10 My concern is that putting this in 11 here and suggesting that we are going to save 12 88,000 lives anticipates -- I'm trying to 13 think of the right word here -- an 14 expectation, really, that is not going to be 15 met, I mean, in 20 years. 16 So you are going to -- You are 17 sort of saying -- You are sort of suggesting 18 that we are going to accomplish something that 19 isn't going to be accomplished by sort of 20 focusing on this national estimate of a number

21 down to -- 22 DR. PEKAR: Well, let me just be Page 185 1 clear. You know, we certainly are not saying 2 that we are going to save all 88,000. That is 3 saying that is the public health impact of 4 current PM2.5 exposures. 5 You know, clearly, if we did model 6 alternate standard levels, we are not going to 7 pull the entire country down to 5.8. If you 8 could reduce the entire country to the LML, 9 then that is essentially what it is saying, 10 but clearly, if we are talking about a 11 standard level of ranges of 10, 12, 13, 14, 12 whatever, then you are leaving a huge chunk of 13 that 88,000 on the table. So just to be 14 clear, and we should certainly clarify that is

15 not the case.

16 CHAIRMAN SAMET: So do others want 17 to comment on this? I am going to take your - 18 - I think Frank's concern is that you are 19 putting out a big target that is not really 20 the ultimate goal -- I suppose it is the 21 ultimate goal for prevention, but not through 22 the strategies that are going to be laid out202-234-4433 Neal R. Gross & Co., Inc. Page 186 1 in the policy analysis, and there the analysis 2 is based on the findings of the 15 cities. 3 So I guess your question -- this 4 is sort of background motivation and such? 5 DR. SPEIZER: Yes, and I guess I 6 am concerned how this number is going to get 7 used. By putting it out there this way, it is 8 anticipating an unrealistic expectation.

9 CHAIRMAN SAMET: Yes. Joe? 10 DR. BRAIN: I share that concern, 11 and it seems to me it invites criticism and 12 attack. For me, it echoes a little bit some 13 of the things in Chapter 6 where it talks 14 about scenarios which produce 100 percent 15 reduction in risk. You know, I guess I do 16 sort of see where that came from, but it 17 really implies a threshold, a nonlinear dose 18 response curve which I think will be open to 19 criticism. 20 You know, whether these modest 21 reductions can really produce 100 percent 22 reduction in risk -- you know, I find that Page 187 1 sort of troubling, and it seems to me it is 2 sort of putting up this sign on your back, 3 "Kick me." I worry about it like Frank does. 4 CHAIRMAN SAMET: Well, it lacks 5 the careful uncertainty analyses that come 6 along with the rest of the risk assessment as 7 well. So I guess the question is: Putting 8 this in, in a somewhat -- 9 DR. PEKAR: Can I just add 10 something about that 100 percent risk 11 reduction? I think this is a terminology 12 issue that we -- in the way that we describe

13 what was done. 14 All that is happening is we are 15 simulating the reduction in ambient air levels

16 down to 5.8. 17 CHAIRMAN SAMET: Right. 18 DR. PEKAR: Which is that LML. So 19 because we only calculated risk down to that 20 LML, if you get the annual average for a city 21 down to 5.8, you have zero calculated risk 22 within the range that we were calculating Page 188 1 risk. But, clearly, I think, as I think about 2 it, it is ridiculous to have that statement of 3 100 percent reduction. You know, we are no 4 longer -- We are down to a level, annual 5 average level, below which we didn't calculate 6 risk for the purposes of this analysis, but it 7 is clearly not a 100 percent risk reduction, 8 and I think -- 9 DR. BRAIN: No, I think you have 10 explained it well, and even in the document I 11 understood that reasoning, but I came with the 12 same conclusion, is that it is very dangerous 13 to talk about 100 percent reduction. It 14 really implies a kind of biology, epidemiology 15 that hardly anybody -- that no one would agree 16 with. 17 DR. PEKAR: My energy is aimed at 18 ourselves here, myself. That is something we 19 should, I think, have foreseen maybe a little 20 bit. So, yes. 21 DR. HUBBELL: So let me just 22 clarify a little bit about the need for doing Page 189 1 a national scale assessment, because I think 2 that is one of the questions. 3 We certainly pushed on this, 4 because we are getting -- We get a lot of 5 comments and a lot of questions about, well, 6 what is the national burden of risk associated 7 with PM, not with the standard but with PM as 8 it exists today, and we get a question of how 9 well the studies -- or the cities that we have 10 looked at in the risk assessment represent the 11 overall burden of risk throughout the country. 12 So there's two purposes in doing 13 this. One was to get an idea of the overall 14 context of risk within a nationwide risk 15 attribution study. 16 The second was to be able to place 17 the individual cities and identify whether 18 those cities fall within the upper range or 19 the lower range of mortality estimates by 20 looking at the distribution of mortality 21 throughout the U.S. 22 I think we accomplished both of202-234-4433 Neal R. Gross & Co., Inc. Page 190 1 those things, but let me point out that, 2 again, all we are doing when we provide that 3 88,000 number is we are applying what the ISA4 says, which is look at a log linear non5 threshold relationship as the best 6 representation of PM risk, and we are applying 7 that to existing concentrations that we have 8 modeled throughout the country. 9 So if there is a problem with the 10 ISA and its interpretation of what the 11 concentration-response relationship looks 12 like, that is where your comments should go, 13 not to the application and national risk

14 context. 15 This is simply the logical 16 extension of those results, and this is 17 consistent with what is being done in a number 18 of other situations, such as the Global Burden 19 of Disease Study that is being put out by WHO. 20 It is what is done in a number of other risk 21 attribution studies where in Europe and other 22 places where people have gone down to lowest Page 191

1 observed levels in studies. 2 So it is just a natural extension 3 of the risk literature, and it is not really 4 doing anything different than what you are 5 seeing in other studies. The numbers look 6 big, but guess what. People are exposed to a 7 lot of PM, and that is the implication. 8 Again, these studies -- the ACS 9 study and the Harvard study, they estimate 10 somewhere between six and 15 percent risk per 11 -- I mean odds ratios of 1.06 to 1.15 for a 10 12 microgram change. If you are exposing people 13 to a 10 micrograms of PM, that is the percent 14 of mortality you would expect. 15 So these numbers are big, but they 16 are not relatively big compared to what the 17 studies are telling you.

18 CHAIRMAN SAMET: Helen? 19 DR. SUH: Actually, I do see the 20 benefit of something like this. I guess, for 21 me, there was a little bit of a disconnect, 22 because it does rely on CMAQ as well as a lot Page 192 1 of modeling, where a lot of the premise that 2 was set up in the Chapter 3 for how decisions

3 were made are based on measurement and based

4 on actual data. 5 One of the things that is great 6 about this figure, for example, that is shown 7 is that it is based on measurements of what 8 the numbers might be. So in some ways, this 9 chapter is a little bit of a sidestep or it 10 seems to go against some of the requirements 11 set up earlier, which are really -- they

12 should be measurement based. 13 So I guess that is what I -- 14 originally what I thought what Frank was 15 getting at, is that it seems a little bit not 16 really fitting with the overall concept of the 17 risk assessment. 18 So I understand the purpose of it 19 and everything. I guess the problem is that 20 it doesn't really fit in this document, which 21 was set up, really, based on actual numbers, 22 not that the models have different numbers, Page 193 1 but it is set up in a totally different way 2 than the whole prelude to it. 3 So I guess that is why I thought 4 maybe Frank was setting it up sort of as an 5 appendices or something like that rather than 6 the Chapter 5, but I understand now that we 7 are slightly coming at it from different 8 vantage points, but I guess I thought it 9 didn't really quite fit with the whole prelude 10 to it. 11 CHAIRMAN SAMET: So I think there 12 are a few points. One is, obviously, you can 13 make these calculations, and you have done it. 14 Second, in terms of, for example, the global 15 burden of disease and allergy, what I see here 16 is a sort of a counter-factual of convenience 17 that doesn't make any particular sense. It 18 happens to be the lowest measured level in a 19 set of studies, and I am not sure why you 20 would necessarily even pick that. 21 There is substantial more reason 22 in the global burden about what is, in fact,202-234-4433 Neal R. Gross & Co., Inc. Page 194 1 the basis for the comparison. 2 I think probably -- I think, more 3 importantly, is whether this presentation of 4 the model, which I think departs substantially 5 from the care and rigor with everything that 6 has been presented before, in a way detracts 7 from both the risk exposure assessment itself 8 and from Chapter 5. 9 Then probably the last point, most 10 importantly, is these numbers analyses are not 11 going to figure into the policy analysis, if 12 I understand the general approach. I am

13 correct on that. 14 So then in terms of sort of 15 setting out the process by which you are going 16 to go from sort of the ISA on to and into the 17 policy analysis and the recommendations to the 18 Administrator, maybe Frank has, I think, 19 wisely directed us to think carefully about 20 whether this helps or not, because it sort of 21 doesn't belong in the flow of logic and 22 analysis that is going to go from scientific Page 195 1 evidence in the end to policy recommendations, 2 except to say, gee, there is a really big 3 target out there, however you go after trying 4 to assess it. 5 DR. MARTIN: If I could just make 6 a point about the relevance of this, the 7 information in this chapter, to thinking about 8 the policy assessment. 9 One thing -- and Bryan talked 10 about two purposes that this analysis serves. 11 The second purpose served the purpose of 12 addressing the question: When we look at the 13 risks estimated in the 15 cities we selected 14 to do more rigorous analysis of, where do 15 those risk estimates fit in the context of 16 risks that likely are occurring in cities 17 across the U.S.? Are we truly capturing the 18 high end of risk cities? Are we distributed 19 across the upper part of the distribution, et 20 cetera, et cetera? 21 And that does help provide 22 context. Even if the comparison is only made Page 196

1 for risk associated with current levels of air 2 quality, it still provides a frame of 3 reference, and from that point of view, I 4 think it does help inform from a policy 5 assessment perspective our interpretation of 6 have we truly captured the upper end of the 7 distribution of cities with relative to PM 8 risk levels. 9 For that purpose, however, the 10 analysis could, of course, be put in an 11 appendix, and that aspect of it could be drawn 12 out in conjunction with the other types of 13 representativeness analyses that are presented 14 for different kinds of risk related 15 parameters. 16 DR. SUH: Alternatively, you could 17 also just use the measurements data for other 18 cities, and use that instead of using the 19 modeling data. 20 DR. MARTIN: You could use air 21 quality measurement data. 22 DR. SUH: Right. Page 197

1 DR. MARTIN: But that doesn't give 2 you the translation into a risk metric at all.

3 DR. HUBBELL: Let me be clear on 4 that. The reason why you can't simply do that 5 is that some of the differences were driven 6 not by the PM levels but by differences in 7 baseline mortality rates, by the composition 8 of the population, and by other factors that 9 affect the risk metric. 10 DR. SUH: But that is true, 11 regardless of the CMAQ model. That holds for 12 everything. 13 DR. HUBBELL: Right, but -- 14 DR. SUH: I mean, I think using 15 the measurement, the models have the same 16 problems -- right? -- in terms of trying to 17 extrapolate to the -- I mean, even comparing 18 one of the 15 cities to another of the 15 19 cities, there are the same problems. Right? 20 Because you have different demographics, 21 different attribution, different -- 22 DR. HUBBELL: But you can build202-234-4433 Neal R. Gross & Co., Inc. Page 198 1 those in, and that is what -- These risk 2 estimates build all that in. They use 3 databases that have county level mortality 4 baseline incidence rates, different 5 demographics for the different grid cells 6 throughout the U.S. So it combines that 7 information to come up with overall risk 8 estimates. 9 In other words, not just where the 10 PM is, but is the PM in the same places where 11 centers of populations exist? 12 DR. MARTIN: And that is what you 13 are getting from applying the BenMAP model. 14 DR. HUBBELL: It is not just CMAQ 15 air quality data. It is combined with the 16 other information.

17 CHAIRMAN SAMET: Ed? 18 MR. AVOL: I think that this -- I 19 think particularly this figure was useful and 20 contributed. The other parts, I thought, were 21 a little thin around it in terms of how it was 22 integrated into where the direction of the Page 199 1 risk assessment was going in terms of building 2 the case. But this, I think, is important to 3 being a part of why those 15 cities do fit in. 4 I could go either way. I could 5 say that this could be pulled back into 6 appendix, but this maybe ought to be a part of 7 the previous chapter justification as to how 8 you get these 15 cities and what you do with

9 them. 10 CHAIRMAN SAMET: Frank, I am going 11 to go back to you. We have discussed this. 12 If you were going to write your comments to 13 Charge Question 5.5 or whatever, what would 14 you say right now, or recommend? 15 DR. SPEIZER: I guess I still 16 would think this probably belongs in appendix 17 or an accompanying document that talks about 18 what this risk assessment does when you apply 19 it to a global burden or global burden of the 20 U.S., almost a separate document rather than 21 having it in this one. 22 CHAIRMAN SAMET: And, I guess, Page 200 1 back to Ed, Figure 5.4 in a sense would be 2 useful as part of the discussion in Chapter 6, 3 for example -- could be. I think that was 4 your point, and maybe if that is really what 5 you want, that a way to do this would be 6 perhaps to relegate the limited analysis of 7 Chapter 5 to an appendix and have that figure 8 or something you want to pull forward, just to 9 present these communities in some sort of risk 10 distribution, which seems to be what you want

11 to do. 12 Then I think the -- and then you 13 can say that you have done it for that 14 purpose, and not to do a more detailed risk 15 assessment that, I think, would require, if we 16 are really going to be representative, a 17 probably more complete analysis of 18 uncertainty. 19 I am sure you could do some of 20 that, but there is not probably time or 21 necessity, if the main purpose is to have 22 something like Figure 5.4. I think that is Page 201 1 what you said you wanted. I think, Karen, 2 that was your comment.

3 DR. MARTIN: Yes.

4 DR. HUBBELL: If we are to move 5 forward on this as we develop the final 6 version, the question becomes --There was some 7 question about we haven't done as many 8 sensitivity analyses, all these other things. 9 One part of it is that, obviously, 10 the air quality modeling or the exposure 11 surface part -- It is as simple to do a 12 sensitivity like a peak shaving versus a 13 proportional, because we are not looking at 14 rollbacks. We are just looking at ways of 15 integrating air quality data. So that 16 particular aspect of it is harder to work 17 with.

18 In terms of the one based on CR 19 functions, the implications are going to be 20 exactly the same as for the 15 cities. So 21 doing those would be possible, but I am not 22 sure what insights you would gain.202-234-4433 Neal R. Gross & Co., Inc. Page 202 1 CHAIRMAN SAMET: Well, I think, if 2 your major point is to have something like 3 Figure 5.4, this can be relatively invariant 4 with choice of -- It will be invariant with 5 choice of concentration response function, I 6 think. 7 Then probably what you could do is 8 take Chapter 5 and put it there and say, look, 9 we wanted to be able to line up these 10 communities in a broader distribution of risk. 11 We have done an analysis using BenMAP. Our 12 purpose was not to fully characterize 13 uncertainties, et cetera, and end up with 14 something like Figure 5.4, and then pull that 15 into Chapter 6 maybe. Then Frank will be 16 happy. Sverre won't. 17 DR. VEDAL: I'm happy. 18 CHAIRMAN SAMET: He is happy. 19 Okay. 20 DR. HUBBELL: So in that case, we 21 wouldn't necessarily need to expand that 22 appendix to cover these other aspects. Page 203

1 CHAIRMAN SAMET: I don't think so. 2 I don't think so. No, I don't think so at

3 all. 4 DR. HUBBELL: I am just making 5 sure that is not an expectation.

6 CHAIRMAN SAMET: No. Just when 7 you do it, then we will say it was an 8 expectation. No. Just say very carefully why 9 you are not doing it, and that to your purpose 10 it is not important. 11 Maybe we shouldn't launch into 12 Charge Question 6, because that will take a 13 while, and perhaps break for lunch. 14 Frank, will you write up something 15 additional for Chapter 5. Between the two of 16 us, we can do it. 17 What I would suggest is that we 18 break for lunch, maybe get back about quarter 19 after one, and then we could do Charge 20 Question 6, and I think then what we would 21 probably like to do is go to the visibility, 22 if you could have your full team here then. Page 204 1 DR. MARTIN: Probably around 2:30? 2 CHAIRMAN SAMET: I think it will 3 take us probably at least 45 minutes to 4 discuss Charge Question 6. So let's maybe aim 5 for two, and then see. Okay

6 So let's break for about an hour 7 now until 1:15. 8 (Whereupon, the above-entitled 9 matter went off the record at 12:18 p.m. and 10 resumed a 1:21 p.m.)

11

12

13

14

15

16

17

18

19

20

21

22 Page 205

1 A F T E R N O O N S E S S I O N 2 (1:21 p.m.) 3 CHAIRMAN SAMET: We are going to 4 get started. We are back to Charge Question 5 6, and Frank, this is you as the leader. So 6 Charge 6a-e. 7 DR. SPEIZER: All right. Well, 8 several people have commented on this, and I 9 will hope that they will chime in and fight 10 with me, if I say some things crazy. 11 In general, I thought this was a 12 very good chapter, and indeed I think it 13 pulled together things reasonably well. I

14 would have liked to have seen it more 15 conclusively coming to billets, but maybe that

16 is what the PA is for.

17 I think it -- There was concern 18 going in that I thought ought to be somewhere 19 in this document, which was a even tentative 20 risk assessment on PM10-2.5, rather than just 21 a promissory note that it would be discussed

22 in the next document.202-234-4433 Neal R. Gross & Co., Inc. Page 206 1 Fortunately, with the discussion 2 this morning, we did see that there is a 3 chapter in the coming PA document. I just 4 wanted to be sure that there was something 5 that made sure we got there. 6 So several of my comments 7 throughout my review sort of was pushing on 8 the issue that they should do something with 9 10-2.5, and hopefully, they have now done it.

10 I haven't had a chance to read the new 11 document, but hopefully, it is there, because 12 I think it is important that there be a pretty 13 thorough, even though qualitative, discussion 14 of it. 15 With regard to the specific charge 16 questions, I guess the only comment that I 17 have for the first one, a, with regard to the 18 key policy issue, was to make sure that coarse 19 particles got included. So I guess that is

20 taken care of. 21 With regard to b, the relative 22 contributions of the 24-hour design value and Page 207 1 the peak, we have discussed this at some level 2 already, and I think I understand -- I 3 understand, certainly, better what the peak 4 rollback is doing now. It may need some more 5 general text added as to how the distributions 6 were determined. 7 There is concern, I think, about

8 the -- at least some concern about the uneven 9 distributions between the 15 urban sites. Now 10 maybe somewhere along the way there needs to 11 be a baseline figure -- a baseline level of 12 what the mortality rates are in these cities 13 from which you are working from, because they 14 really are quite variable. 15 I know that gets incorporated in 16 the analysis, but intuitively it is hard to 17 see when you see these ranges of percent 18 changes whether you are dealing from very low 19 rates or very high rates, and potentially what 20 the variability is around those rates.

21 So I am not sure how that should 22 be handled, but the estimates are really quite Page 208 1 different from some of the different places, 2 and I think there might be some way to 3 incorporate that better. 4 In general, though, I think the 5 uncertainties and variability has been dealt 6 with reasonably well. Zach has explained to 7 me now that there is a difference between how 8 the national rates are calculated, from what 9 source. I guess the 15 urban sites are not 10 being used for that. So that is helpful, I 11 think. 12 I thought the key observations are 13 presented in a balanced and fair way. In 14 general, I think that the entire document -- 15 chapter -- reads rather well, and does what it 16 is supposed to do, and I am glad it has been 17 added for this document. So I don't have a 18 lot more to say than that. Others may have

19 some comments. 20 CHAIRMAN SAMET: Okay. Sverre? 21 DR. VEDAL: I thought this was a 22 welcome chapter to have. I thought -- one Page 209 1 observation was I thought that for an 2 integrative summary, I thought it was a little 3 what I described as formulaic in some way 4 because, you know, you go through the parallel 5 description of what happened with just meeting 6 the standard and the parallel description of 7 what happened in the alternative standard. 8 It wasn't repetitive, because it 9 was new information, but it was pretty 10 formulaic for what is billed as an integrative 11 summary that is supposed to be a little bit, 12 perhaps, more thoughtful. Nevertheless, you 13 can get over that. 14 I made mention of the ischemic 15 heart disease issue as it relates to the 16 confidence section, which really, because of 17 the emphasis on ischemic heart disease, is 18 going to have to be -- that is going to have 19 to answer those confidence issue aspect, 20 because I think there is some less confidence 21 in that relative to cardiovascular mortality 22 totally, and that is not reflected in this.202-234-4433 Neal R. Gross & Co., Inc. Page 210 1 There is -- I hope it is a mistake 2 in one section where you are talking about PM 3 attributable effect of long term exposure on 4 total mortality. I think that should be -- 5 and I have itemized it here -- I think that 6 should be IHD mortality? Oh, okay. 7 DR. PEKAR: That is right, yes.

8 DR. VEDAL: Yes. It sort of threw 9 me there for a while, and I was having 10 nightmares, but it turns out okay. It is a 11 problem that is easily rectified. 12 I think you are trying too hard to 13 justify the emphasis on long term means, and 14 by that I mean you have one section, and it is 15 on page 6.5, starts on line 3, where you have 16 this statement that short term exposure 17 related risk is also driven by changes in long 18 term average PM concentrations. I don't think 19 that is true. 20 I don't think it has anything to 21 do with long term PM concentrations. In fact, 22 it has been shown that they are pretty Page 211

1 independent and, in fact, that the 2 concentration-response relationship may be 3 steeper at the lower -- for short term effects 4 may be lower at the -- I mean higher at the 5 lower long term concentration end. 6 So I wouldn't make that argument. 7 You don't need to, and I think it is wrong. 8 DR. PEKAR: So if I can just close 9 that a little bit, you know, we are not saying 10 that there is an association between -- let me 11 approach it this way. 12 What we are essentially saying is, 13 given that the concentration response 14 functions we have for short term mortality or 15 morbidity, those are working on differences 16 and working on a reduction in risk. 17 It is a delta for each day, and 18 really then the aggregate change that you see 19 if you add up the reduction exposure for each 20 day and you are applying the CR function to 21 each one of those little increments, the 22 greatest change in risk when you have a Page 212 1 reduction -- when you reduce PM levels, PM2.5 2 levels, it is really the overall shift in the 3 distribution that has the greatest effect on 4 overall mortality, short term mortality, not 5 simply the shift in the highest peaks.6 Now if there is strong non7 linearity, I would say that high -- 8 DR. VEDAL: That is a different 9 point. That is saying that most of the oomph 10 of short term effects occurs lower down in the 11 distribution where most of the data is, and 12 there is no doubt about that. That is 13 happening. 14 You can cut off all the high 15 concentration values, and you are still left 16 with a substantial effect, but it is still due 17 to these little changes. It has nothing to do 18 with the annual average and the long term 19 average where that is at. 20 So those are two entirely 21 different points. Do you see that? 22 DR. PEKAR: I do, but if you are Page 213 1 talking about sort of an area -- the shift in 2 the curve, and if you take all those little 3 additional little small pieces we are talking 4 about, those equate to a shift in the mean, I 5 believe, in the way I am thinking about it.

6 DR. VEDAL: Whether those 7 perturbations are occurring at an annual 8 average of five versus 15 makes absolutely no 9 difference. 10 DR. PEKAR: That is true, so if 11 you talk about this -- 12 DR. VEDAL: It is not driven by 13 long term average issues at all. 14 DR. PEKAR: So I guess what I am 15 trying -- I agree with you that the absolute - 16 - where that annual average is sitting in an 17 absolute sense has no bearing on it at all. 18 DR. VEDAL: Right.

19 DR. PEKAR: But the delta of the 20 annual average will track the change in those 21 little snippets, meaning if you go from, as an 22 annual average, 13 to 14 -- sorry from 14 to202-234-4433 Neal R. Gross & Co., Inc. Page 214 1 13 or from eight to seven, you can have that 2 same change in the annual mean, and you can 3 have that reflected in a series of snippets -- 4 I am going to stop. 5 I guess what I am saying is I 6 think you can consider a shift, an incremental 7 shift in the mean as being related to the 8 change in those individual daily deltas. 9 DR. VEDAL: My only point is -- I 10 mean, I wouldn't -- just wouldn't say it. 11 DR. PEKAR: I agree. 12 DR. VEDAL: You don't need to say 13 it -- 14 DR. PEKAR: I agree. 15 DR. VEDAL: -- in making your

16 case. 17 A couple of other small things, 18 and maybe just one other small thing. It had 19 to do with this national context thing. I 20 think it may be an error as well. 21 It is this issue of what are the 22 15 cities supposed to -- what distribution is Page 215 1 it supposed to reflect? 2 I think, if I understand the tone 3 of the rest of the document, they are supposed 4 to reflect basically cities out of compliance 5 or of the higher end of the air pollution 6 concentration spectrum. 7 I think, from that perspective, 8 they do, and that is clear from the figures 9 that we see, but there is a little place that 10 I itemized here where it says that where the 11 15 cities capture the overall distribution of 12 risk for the nation, which is not true. 13 Right? 14 DR. PEKAR: Well, and I think that 15 is again a rating issue that we could work on. 16 I think the national scale analysis that Bryan 17 was talking about earlier where we plotted the 18 counties in the nation, the mortality 19 estimates for the counties in the nation, and 20 then saw where the counties we had modeled 21 fell across those, and you could see that our 22 counties that we modeled fit the 15 urban Page 216 1 study areas which are roughly 30-some 2 counties, in the 15 cities 30-some counties 3 fall across the upper end of that national 4 distribution of risk. I think that is where

5 that statement comes from. 6 It certainly represents areas with 7 higher ambient levels, but then when you 8 consider baseline incidence and other things 9 that are nicely considered in that BenMAP 10 modeling of risk, and you see where our 11 counties fall across, that is where that

12 statement comes from. 13 DR. VEDAL: Maybe just a little 14 bit of something to make that point a little 15 clearer, because it just sort of set off a 16 little alarm bell for me. I mean, that wasn't 17 the intent. I think the way I interpreted it 18 was clearly not what the intent of the way 19 those cities were used. 20 DR. PEKAR: Right. Well, we can 21 certainly -- if I make a statement -- if we 22 make a statement like that, what are we Page 217 1 pointing to and basing that on -- sort of 2 generally. 3 DR. VEDAL: And then I cut bait 4 on the issue of whether I thought there was 5 any utility in estimating below 12, and I said 6 no. That's it. 7 DR. PEKAR: We are definitely 8 hoping -- well, that will come up later, I 9 guess, but just that we have a little more 10 discussion on that very point, I think. 11 CHAIRMAN SAMET: We will certainly 12 be discussing that in the policy analysis.

13 Let's see. Joe? 14 DR. BRAIN: I also had a positive 15 response to it, and answered yes to many of 16 the charge questions rather easily. 17 I still found the whole chapter, 18 number one, very important, but dense and 19 complex. I mean, there were just so many 20 sentences that I had to sort of read twice. 21 There were long, lots of qualifying clauses. 22 So I think somehow, if it could be made202-234-4433 Neal R. Gross & Co., Inc. Page 218 1 clearer and tighter and shorter, it would be 2 a good idea. 3 It also seemed to me it dealt more 4 with the caveats with the uncertainties and 5 the sensitivity analysis. So you were very 6 clear about the uncertainties. I would like 7 you to be clearer about the certainties and 8 sort of highlight what are some of the clear 9 conclusions. 10 Again, adjacent to what you just 11 heard, I was also interested in the 15 cities, 12 and we have talked about clarifying what they 13 represent and where they are in the 14 distribution, and it did sort of raise the 15 question, in addition to clarity about where 16 these 15 cities are, should you sometime look 17 at 15 cities that do represent the 18 demographics of the United States. 19 Is it important -- so this is, I 20 guess, the question: Is it important to know 21 how different standards would affect the 22 cities in the bottom half? Is that important Page 219 1 to know, if you were making a decision. So I 2 guess that is kind of a real question: Do we 3 need to understand better the cities that are 4 more in compliance, and is that part of the 5 overall assessment for the whole country? 6 Then I think I have already 7 mentioned my concern about the conclusion of 8 100 percent reduction in risk, which raises 9 issues. 10 It sounds like it really reflects 11 the absence of data, which shouldn't be 12 equated to the absence of effects, because on 13 the one hand, we are also saying we are not 14 really aware of any threshold, again given the 15 broad range of sensitivity, that there are 16 some people that are so sensitive that it is 17 hard to find a level of PM that will not 18 affect them. So it seems to me, somehow that 19 needs to be fixed, because that sort of, to 20 me, raises flags and raises questions about 21 the credibility of the whole analysis. 22 Otherwise, I think it is really Page 220 1 coming along, and lots of things to applaud. 2 CHAIRMAN SAMET: Thanks, Joe.

3 Mort? 4 DR. LIPPMANN: I haven't got much 5 to add. I agree that it is a little 6 formulaic, but I mean, that is what we are 7 supposed to be looking at, whether each of 8 these pieces makes sense on its own; and 9 considering the amount of work overall that 10 had to be done, polishing it and making it 11 shorter is probably the hardest part, and 12 there wasn't time to do that. 13 I had one -- I read it carefully, 14 but I couldn't find much to complain about 15 except on page 618 there are statements on 16 line 15 and again on 21 that estimates were 17 also developed for other effects. But I 18 didn't see that such estimates were described. 19 So I wonder why -- I mean, either 20 you cite where such estimates can be found for 21 the curious, which is me, or don't say it at 22 all. Were these estimates in the document Page 221 1 anyplace? 2 DR. PEKAR: Yes. So part of what 3 we were facing -- the last version of the 4 document, one of the criticisms, and I think 5 it was a good one, was that the summary of the 6 risk results -- let me start with an answer to 7 your question. 8 Yes, we did run them, and they are 9 available, but in the last version in full 10 when we described the risk results, we 11 included summaries of all of the categories of 12 mortality modeled, and that description was 13 really, really long and very, very tough for 14 all of us to wade through. 15 So for this analysis, we shortened 16 it and focused it on the cardiovascular 17 categories. In the appendix you still have

18 all of the results for all of these. So -- 19 DR. LIPPMANN: Then all it needs 20 is a reference to where in the appendix we can 21 find it, so that if anybody does want it, it 22 is available. But looking at this document,202-234-4433 Neal R. Gross & Co., Inc. Page 222 1 stand-alone, I didn't know where it was. 2 DR. PEKAR: In 4, we point that 3 out pretty clearly, but I think it has been a 4 while by the time you get to this, since 4. 5 So I think having some footnotes to clarify 6 that -- we can definitely do that. 7 DR. LIPPMANN: Okay. Then 8 apparently we have some difference of opinion 9 emerging already among the Panel about your 10 question, should we be looking below 12 11 micrograms per cubic meter. 12 Here, I wonder, in taking a sneak 13 peek at the draft policy assessment, about the 14 statement that alternative levels at -- what? 15 -- 10 or 11 will be considered. That is not 16 in this document, this risk assessment 17 document, but the policy document presumably 18 is going to extrapolate and extract 19 conclusions from the risk assessment document. 20 So perhaps it is not kosher to 21 talk about the policy assessment document yet, 22 but it certainly sent up a flag to me when I Page 223 1 saw that levels below 12 were going to be 2 considered in a policy sense. So it makes it 3 important for CASAC to say what it chooses to 4 say about extrapolating below 12 from a 5 scientifically justifiable perspective. 6 Now again it is a little bit 7 beyond the framework we are supposed to be 8 acting, but I can certainly sympathize, as I 9 think people listening to my comments over 10 this document and over the years about PM long 11 term concentration NAAQS. 12 I was pretty vigorous in saying 13 that 15 wasn't protective and that it had to 14 go lower, and last round we hoped it would be 15 13 or 14, and it wasn't, and there is a lot 16 more justification for considering 12. 17 I don't see how everything that is 18 in the ISA and the risk assessment justifies 19 talking about something lower than 12, even if 20 it was scientifically warranted. Furthermore, 21 being an engineer by background, I can't 22 imagine changing -- that is complying with Page 224 1 something below 12 for the foreseeable 2 decades, let alone years, unless we radically 3 change everything about the way we do 4 business. 5 So why should we at this point for 6 this round be considering something below 12? 7 Twelve would be not fully protective, I grant 8 you, from my understanding of the science not 9 fully protective, but on the other hand, 10 something lower than that is beyond the realm 11 of physical possibility, as far as I can see. 12 So I wonder how we are supposed to 13 respond to that last charge question. 14 DR. BRAIN: In fact, I would just 15 add, you know, the last sentence in the last 16 charge question is quite clear. Do you 17 believe there is utility in estimating risk 18 for standards below 12 micrograms, and if you 19 looked at all the individual comments, 20 everybody said no. So I would just add that. 21 I guess we are not supposed to 22 consider whether it is achievable or not. Page 225 1 Right? 2 DR. LIPPMANN: We are not supposed 3 to, but we live in the real world. 4 DR. MARTIN: Well, but we also put 5 forward -- the agency puts forward its 6 decisions on standard in the real world, too, 7 and the real world requires that we be able to 8 legally defend the basis for our decisions; 9 and if the science takes us somewhere that we 10 all have intuitions would be very difficult to 11 meet, the Act doesn't give us any leave to 12 consider that. 13 So as part of living in our real 14 world as a regulatory agency, we can only base 15 decisions on what we are allowed to base 16 decisions on. I would encourage you to think 17 about, as you read the policy assessment in 18 the days and weeks to come and ponder over 19 what is there, to the extent that when we meet 20 in April we have discussions that highlight 21 reasons why translating the science into 22 standard levels doesn't get you to that lower202-234-4433 Neal R. Gross & Co., Inc. Page 226 1 range, then I think that is a really valid 2 discussion for us to have. But I encourage 3 you to think about the reasons for your views 4 on this in terms other than considering when 5 in the future we might possibly be able to get

6 there. 7 It just won't work for us as a 8 rationale, and we need to think about how to 9 interpret the science in an objective way

10 here. 11 DR. LIPPMANN: Well, science is a 12 very broad term. Science includes technology, 13 as far as I am concerned, and if there is no 14 technology to take us where we would like to 15 go in terms of public health protection, that 16 is a realistic concern, and of course, I think 17 it leaves the agency vulnerable to serious 18 criticism in Congress and other venues when 19 something that is extremely unlikely to be 20 achievable is put into a regulation. 21 So we presumably will be back in 22 five years, now that the schedule is getting Page 227 1 closer to the intention of the Act, and it 2 used to be 10 years between standard setting, 3 and it is approaching something closer to the 4 target of five years. By that time, in my 5 view, with the agency doing what it needs to 6 do with improved monitoring we may be able -- 7 we should be able to get the past mass-based 8 NAAQS. 9 Then in terms of achieving a 10 public health protection, putting in standards 11 based on the more toxic components is more 12 achievable within what the country can afford. 13 So I think we are getting into a situation 14 where mass-based standards are being taken far 15 beyond where they are reasonable to go when we 16 talk about something below 12.

17 DR. MARTIN: I do feel a need to 18 just react again. If your premise is that the 19 uncertainties associated with the potential 20 for differential toxicity in components in the 21 aggregate mass of fine particles is such that 22 you think it is unjustified to put weight on Page 228 1 studies that look only at aggregated PM2.5 2 mass at these lower levels, that is a line of 3 reasoning I can understand and relate to an 4 interpretation of the science. 5 If your point of view is we 6 shouldn't go there, because we don't think we 7 can attain it, that is a line of reasoning 8 that simply cannot be part of the rationale 9 that the agency considers. 10 Without belaboring the point, and 11 I am sure I am belaboring the point, that is 12 the only way I know how to characterize my 13 reaction to the nature of the discussion here. 14 DR. LIPPMANN: As always, Karen 15 comes up with exactly the right wording. So 16 what I was leading to was that alternative, 17 that taking mass beyond where it should go is 18 not scientifically justifiable, and that by 19 setting up what needs to be done in the next 20 few years so as to get us past that point so 21 that scientific justifications can be made 22 with more confidence based on something other Page 229 1 than mass, then the uncertainty is the handle 2 on which to go there. 3 I think I have said enough.

4 CHAIRMAN SAMET: Frank? 5 DR. SPEIZER: Well, I wanted to 6 sort of again belabor the point a little bit, 7 because I actually was convinced by Karen that 8 we do have to do what she is suggesting, which 9 changes my conclusion originally, too, which 10 was that we wouldn't go there; because indeed, 11 if we take the sort of three components, no 12 threshold as far as we know, a linear dose 13 response, and a potential for a margin of 14 safety, I think she has to go down. She has 15 to go down, and she has to make these 16 calculations. 17 Now once they are made, then I 18 think the argument comes up as to we have to 19 deal with the uncertainties of what that 20 measurement is telling us. It is at that 21 point where I think your argument comes in. 22 But I think she is, by law -- we are, by law,202-234-4433 Neal R. Gross & Co., Inc. Page 230 1 having to look at those numbers, because that 2 is what the law tells us we are supposed to

3 do. 4 DR. LIPPMANN: You are perfectly 5 logical, Frank, and the question is can we 6 finalize this risk assessment when there is no 7 extrapolation below 12, and the policy 8 document may be dealing with numbers that low. 9 DR. PEKAR: Can I just clarify one 10 point very quickly? In the introduction I 11 presented those preliminary analyses for 10. 12 So we have conducted risk -- I mean, they are 13 preliminary. We haven't QA'ed them fully and 14 everything, but those runs are there. 15 In a sense, at least my personal 16 opinion was we were really wanting to get 17 CASAC's feedback on confidence in running 18 those estimates, but those analyses have been 19 run. So we have analyses. They are not 20 summarized in the risk assessment, but they 21 would be available for the final version. So 22 just to be clear. Page 231

1 DR. LIPPMANN: The final version 2 of the risk assessment?

3 DR. PEKAR: Yes. 4 CHAIRMAN SAMET: But just as a 5 comment -- and this does come up in the 6 discussion in the policy assessment. The 7 question of uncertainty does come in as you go 8 lower, and that is, in fact, discussed in the 9 policy assessment. 10 I think, in some ways, with an 11 argument that I don't particularly like that 12 we had seen before, and that is that, of 13 course, as you go to lower and lower end on 14 the concentration response curve, confidence 15 intervals widen and we are less sure about 16 what we have got. 17 Well, it is a necessarily trivial 18 observation, and I am not sure that is exactly 19 the reason you don't go lower, but if we say 20 and make the guidance that, in fact, as you go 21 lower, there is mounting uncertainty about

22 what both the form and nature of the Page 232 1 relationship is, it does become reasonable on 2 a scientific ground to say, well, at some 3 point it is not justifiable to continue to 4 extend the straight line or whatever 5 alternatives there are, because we are not 6 sufficiently certain. 7 I think we could make the 8 argument, Frank, going back that, for now, the 9 science justifies going down to, you know, 10 somewhere in the range, and we would have to 11 decide what points that is, of exposures at 12 which these concentration response functions 13 have been estimated and say, well, it is 14 reasonable to estimate down, putting aside 15 sort of whether it is issues of control 16 practicality or just sounding silly, that 17 since we have assumed a linear dose response 18 relationship; therefore, if you are going to 19 protect public health, you have to go to as 20 low as you can go. 21 That becomes -- I mean, I 22 understand how you can get there, and I think Page 233 1 that was sort of in your logic. 2 DR. SPEIZER: Yes. The question 3 is can we come up with a set of criteria where 4 it becomes irrational to go any lower, and 5 irrational from a scientific perspective. 6 Now if we go to 10, that is one 7 thing, but maybe you should go to seven and a 8 half. I don't know what the answer is. The 9 question is: Is there a way of looking at the 10 data that is generated to look at what the 11 deltas are at these lower levels, taking into 12 account the confidence intervals, and at some 13 point saying this is just noise? 14 CHAIRMAN SAMET: Yes, or we don't 15 know where we are, and these are questions 16 that have to be addressed by an understanding 17 of mechanisms and trying to then determine 18 what is the right concentration response 19 function; because we know that epidemiological 20 observations are not going to sort this out 21 with any precision or certainty as we go lower

22 and lower. Sverre?202-234-4433 Neal R. Gross & Co., Inc. Page 234

1 DR. VEDAL: Yes. I have a bit of 2 a different perspective on the annual 3 standard, which may help, may not. 4 Last time when we argued this and 5 we were arguing about whether we needed 6 something in addition to a more stringent 24- 7 hour standard, Mort made the argument -- it 8 was a good argument -- that we have cities 9 that fly under the radar with not a lot of 10 peakiness, annual averages that are low, and 11 yet and aren't captured by the 24-hour

12 standard. 13 That is not the case now. If you 14 look at those really nice figures that we 15 have, when you lower the 24-hour standard, 16 there ain't nothing left, no cities around, 17 that you gain anything by lowering the annual

18 standard. 19 In addition to that, we have no 20 evidence that outside of 24-hour average peaks 21 and such -- we have no evidence that a 22 constant level of, say, 13 or 12 without any Page 235 1 fluctuations has any adverse effect at all.

2 We don't know that. 3 So you have to kind of make this 4 leap that there is something above and beyond 5 what you do to the 24-hour standard that has 6 any meaning, and we are on very, very thin 7 ice, very thin evidence, to say that. 8 DR. LIPPMANN: Well, that is a 9 different issue, whether you are talking about

10 -- 11 DR. VEDAL: Well, that is a 12 science based issue, Mort. That has nothing 13 to do with feasibility. 14 DR. LIPPMANN: No, but the annual 15 average is a means of allowing variation from 16 day to day, and then the epi says at these 17 annual averages you are getting a linear 18 response in terms of mortality. 19 DR. VEDAL: Well, that is just a 20 measure of exposure of convenience that we use 21 an annual average -- 22 DR. LIPPMANN: Well, sure. Page 236

1 DR. VEDAL: For an observational 2 study like that. That says nothing about what 3 the concentrations of relevance are in terms 4 of time averaging. 5 DR. LIPPMANN: Right, but I mean, 6 it came up before earlier that there is a 7 difference roughly between east and west, much 8 higher coefficients in the east than in the 9 west, maybe Los Angeles excluded, but by and 10 large. 11 We are dealing not only in 12 assuming that annual average is important. We 13 are assuming that particle mass is important. 14 We are assuming that there is a distribution 15 of sensitivities. We are assuming a lot of 16 things. We are dealing with the need, faced 17 with probably the largest public health impact 18 of anything EPA deals with. 19 I think we all agree that there is 20 excess mortality associated with PM. The

21 standards need to be set based on the 22 available scientific information we have, and Page 237 1 so we don't have any choice but, seems to me, 2 to assist EPA in determining whether or not 3 the science they cite is credible and helping 4 them make administrative decisions on

5 standards. 6 Yes, you are raising a point that 7 we wish we had better information, but we wish 8 we had better information on a lot of things.

9 CHAIRMAN SAMET: Let's see. 10 Rogene, you are our last commenter. I am 11 waiting for you to provide the wisdom that 12 will get us out of where we are. 13 DR. HENDERSON: No. When I first 14 read this, I confess to a knee jerk response 15 of, goodness, no, we don't know enough to go 16 below 12. But in thinking about it and all 17 this good discussion, what it is, we are in 18 this conundrum with a no threshold effect, and 19 so how low do you go? 20 If you can't say this is a no 21 effect level, it is really puzzling, and what 22 has been suggested is that, well, it gets too202-234-4433 Neal R. Gross & Co., Inc. Page 238 1 uncertain below that. But that is just kind

2 of a buzz word. We don't know. 3 So what I think we need to do is 4 advise the agency on how to develop criteria 5 by which you stop considerations of lower 6 levels, and those criteria should be 7 scientifically objective and reasonable 8 criteria. 9 I am picturing a graph. You know, 10 on the X axis we have these different levels 11 going lower and lower, and then we could have 12 some measure of our knowledge or lack of 13 knowledge plotted against that, so that it was 14 obvious, well, we don't need to go below this

15 level. 16 I was fearing that we have no 17 evidence that I know -- correct me if -- of 18 health effects at these lower levels, and if 19 we don't have that evidence and we are just 20 basing on extrapolations, I get queasy about 21 it. I don't feel as comfortable. 22 If there is some evidence that Page 239 1 under certain circumstances we can go to these 2 low levels and have an effect, well, that is 3 certainly one of the criteria. But I would 4 vote for developing criteria for where you go 5 and a threshold effects level response. 6 CHAIRMAN SAMET: So maybe -- and I 7 think I see the problem, and maybe there is 8 some -- the logic is reversed here, and Zach, 9 you can tell me if I am getting this wrong. 10 I see language that, in effect, 11 you are saying here are some alternative 12 scenarios for the NAAQS; therefore, we modeled 13 these alternative scenarios. I mean, that is, 14 in essence, how it reads now. 15 There is even sort of language to 16 that effect. So I think, regardless of 17 whether you go -- where you go, the stated 18 rationale for where you take the risk exposure 19 analysis should probably not be what you think 20 the suite of standards will be that you 21 propose, and maybe that -- and Rogene and 22 others have commented that you have some other Page 240 1 rationale, whether it is, hopefully, 2 scientifically based and should be based in 3 how you have assessed the certainty of the 4 evidence available. 5 You know, you could have 100 6 percent confidence in a linear exposure 7 response relationship, and then you go as low 8 as you go. On the other hand, I think we 9 would all say that there is some doubt as you 10 go lower and lower, and increasing doubt as to 11 the nature of that relationship, and 12 uncertainty would become very important. 13 Don't ask me exactly where, but -- certainly, 14 there is more at eight or nine than at 16 or

15 17. 16 So maybe the problem is how you 17 have stated it. I can understand why you have 18 done it, but it doesn't -- you don't have a 19 stopping rule, I guess. I think that is the 20 problem. Helen? 21 DR. SUH: You know, I come at 22 this a little bit different from my background Page 241 1 and exposure. I guess I thought, because of 2 the uncertainty or the lack of knowledge or 3 however it wants to be phrased about the 4 concentration response function, I guess I 5 thought the easy way to do it would be to go 6 and take the annual averages for the cities 7 and take some values for where you see the 10 8 percent annual average for the country, and 9 use that; because there is certainty in that. 10 There is certainty, much more 11 certainty, in terms of what the levels are 12 around the country, and use that as sort of 13 your low/how low do you go kind of level. 14 So what are the annual averages 15 and take some percentage, the 10th percentile 16 or I don't know what you want to choose, and 17 use that to say that is as low as you go, 18 because that is where you have the 19 information, and that is where you have all 20 the measurements. So you could use that to 21 ground that discussion. 22 DR. MARTIN: Well, I will note202-234-4433 Neal R. Gross & Co., Inc. Page 242 1 that that is the type of discussion that 2 appears in the policy assessment in terms of 3 looking directly at the studies, the air 4 quality information in the studies and 5 thinking about what kind of statistics might 6 we draw from that to provide some guidance as 7 to where we might go from the perspective of 8 translating that evidence into the basis for

9 a standard. 10 I also think it is a very 11 separable question to ask the question, if you 12 are going to do a quantitative risk 13 assessment, how low is it reasonable to 14 project estimates of risk in terms of how far 15 are you prepared to move a distribution of air 16 quality and still have confidence that the 17 risks you estimate for it are sufficiently 18 credible that they are worth doing. 19 I really think that is a related 20 but a separate kind of question. We are 21 asking both of them, of course. 22 CHAIRMAN SAMET: And what we said Page 243 1 is the technology should not be the stopping

2 rule. That was sort of back to the Mort 3 discussion. 4 I think I would say that probably 5 your idea of what set of standards might be 6 proposed is probably not the right point for 7 stopping either, and that ideally I think 8 science's confidence in the risk estimates and 9 the general approach -- I mean the level of 10 confidence in the model should be what drives 11 things. 12 You know, initially it puts all 13 our assumptions to the test, you know, your 14 nice descriptors of mild, moderate, whatever, 15 degrees of uncertainty and so on, and I don't 16 know whether we should stop at this point and 17 say you need a better way to do it, and maybe 18 you want to go lower once you develop your 19 stopping -- your justification for stopping at 20 a particular point, or we can give you some 21 further guidance on what we think that point 22 might be. Page 244 1 I suspect, if we sort of did a 2 quick poll, you would get varying opinions 3 with some saying 12 is low enough, and some 4 saying, you know, go lower. I don't know 5 where people would stop. Rogene? 6 DR. HENDERSON: To me, a 7 scientist, our contribution is to help them 8 figure out how to develop these criteria for 9 stopping, but where they stop is a policy 10 judgment, because -- 11 CHAIRMAN SAMET: Well, I would 12 actually say where they stop is not the policy 13 judgment. Where they stop should be the 14 scientific judgment, because that is where you 15 saying there is sufficient uncertainty that 16 you can't inform the policy with the risk 17 assessment anymore, that you have crossed some 18 sort of boundary. 19 So I think, if you say I voted for 20 the linear model and continue to extend it to 21 lower, I mean, you are in a sense moving right 22 into -- directly into the policy implications. Page 245 1 If you say, well, you know, we have confidence 2 in the linear relationship within this range, 3 because it has been observed empirically in a 4 set of studies bracketing that range, then I 5 think you are okay.

6 DR. HENDERSON: But someone has -- 7 in a non-threshold system, somebody has to 8 decide, okay, this is what we will accept. We 9 are accepting some risk. We don't want zero - 10 - I mean, we don't expect zero risk. 11 CHAIRMAN SAMET: Right. 12 DR. HENDERSON: Isn't that policy? 13 CHAIRMAN SAMET: But that is the 14 policy side. 15 DR. HOPKE: The policy judgment is 16 what is the adequate margin of safety. 17 CHAIRMAN SAMET: Right. 18 DR. HOPKE: And when is the margin 19 of safety adequate? 20 CHAIRMAN SAMET: But I think you 21 stop the risk assessment at the point where 22 you are no longer sufficiently certain on that202-234-4433 Neal R. Gross & Co., Inc. Page 246 1 because you feel like you can't inform the 2 policy further. 3 I think, in a sense, the stopping 4 rule lies in your trying to make that judgment 5 as to where confidence is lacking, but there 6 has to be, I think, in some way some sentences 7 in here that say it, and that is, I think, the 8 challenge to all of us to say how would you 9 write those sentences. How would we advise 10 you to write those sentences? Bob? 11 DR. PHALEN: A quick comment. 12 From a dosimetry point of view, I am always 13 shocked at what these levels mean for lifetime 14 exposure, and if I crank in 10 micrograms per 15 cubic meter times 10 cubic meters a day -- and 16 if you want to double that to 20, go ahead -- 17 for 80 years, I get something like 18 approximately a gram deposited in the lung 19 over 80 years with 20 percent deposition. 20 I don't remember what a cigarette 21 deposits. What is it? On the order of a 22 gram, something like that? So we are talking Page 247 1 about extraordinarily low doses. 2 So from a dosimetric point of 3 view, it kind of drives me nuts to think that 4 we have a scientific basis for demanding less 5 than a gram of deposition of material in the 6 lungs over 80 years.

7 DR. SPEIZER: I am not sure that 8 argument can be extended. I mean, you know, 9 bumble bees can't fly because their wings are 10 too small, and giraffes can't have hydrostatic 11 pressures too high in their heads. So you 12 can't get there from -- that way. 13 I'd like to go back to Rogene's 14 comment, and I don't know whether we can do 15 this or whether you can ask staff to do this, 16 but the idea of whether there are real studies 17 out there that at least suggest an effect at 18 those levels might be a way of determining a 19 stopping rule in the sense that, if indeed -- 20 and I don't know whether it is one study or 21 two studies or five studies; it may be only 22 one or two studies at a given level that show Page 248 1 an effect that is acceptable to the scientific 2 community by having been peer reviewed, et 3 cetera, et cetera, might be a way of picking 4 a stopping level for this. 5 Now how easy that is to do or how 6 difficult it is, I don't know. 7 DR. BRAIN: I also think, in 8 addition to more studies at that level showing 9 the risks of those, what we really need are 10 stopping studies. I mean, I am impressed with 11 now the beginning of more and more studies 12 which show the benefits of reducing air 13 pollution. It seems to me that would even be 14 more compelling to reduce it from ten to five 15 and document a benefit.

16 DR. SPEIZER: Yes. There aren't 17 many studies like that that show the impact. 18 CHAIRMAN SAMET: Phil. 19 DR. HOPKE: But as you get to 20 those lower, you are going to wind up with 21 more and more publication bias because you are 22 not going to see anything. You are going to Page 249 1 have questionable power. 2 DR. SPEIZER: Well, I am not sure 3 you have to actually use those in a power way. 4 They just, they become a -- yes, there are 5 effects at 7 1/2; therefore, that is as far as 6 we can go. 7 CHAIRMAN SAMET: Rich? 8 MR. POIROT: Oh, I was just 9 thinking that, at least to a certain extent, 10 some of this -- this issue is maybe 11 exacerbated by this new process. I don't 12 recall putting anywhere near this much 13 attention on the risk assessment in the last 14 review cycle. 15 It is a logical process from the 16 integrated science assessment to the risk 17 assessment to the policy assessment, but I 18 certainly know, for example, on the urban 19 visibility assessment, many of our comments on 20 the risk assessment are imagining what you are 21 going to do with it in terms of setting the 22 policy, and I think that is kind of what some202-234-4433 Neal R. Gross & Co., Inc. Page 250 1 of our comments right now are reflecting as 2 well, is that up until this point, other than 3 the airplane ride, we had no idea exactly 4 where you were going in terms of policy, and 5 there might be certain policy modifications to 6 things that were in the risk assessment, 7 cutting things off at 12 for other good

8 reasons. 9 So anyway, I am kind of blaming 10 the system, and think that there is an element 11 of that in there, and there was something 12 useful about the old process of being able to 13 see the staff paper and the policy 14 implications of it inching along a little bit 15 lagged with the science aspects, and just 16 thought was a beneficial aspect of the old 17 process, was my comment. 18 CHAIRMAN SAMET: I mean, in 19 theory, in the new process these are sort of, 20 quote, "independent steps" as you have 21 outlines of going from the left and your 22 research workshop over here to a NAAQS, and Page 251 1 that, in fact, there is this integration of 2 the scientific evidence. There is the 3 development of a risk model that could be used 4 for looking at any number of scenarios or 5 levels of air quality. 6 In fact, the way you are doing the 7 risk and exposure assessment, it is inherently 8 looking forward to what the NAAQS might be and 9 thinking about it. We had this discussion 10 about the rollback. I mean, those are 11 different sort of control scenarios. 12 So it is a little bit mixed up, 13 but I would suggest that you may not -- I 14 don't think you are going to get quantitative 15 wisdom from us today. I don't think I am 16 going to poll and say eight, nine, ten, and 17 ask the group to vote because that is not the 18 right way to do it either. 19 I think the right way for you to 20 do this is, though, to say that, you know, 21 independent of policy options, in your view, 22 the uncertainty is sufficient in the range of Page 252 1 blank to blank, that you did not go any lower. 2 I think making that kind of statement, I think 3 you can justify it from what is there. 4 You reviewed all the evidence 5 there is. You know what range it has covered. 6 You know what happens as you go lower, and you 7 also know that there is probably more and more 8 mechanistic uncertainty. You know, 9 considerations of dose become relevant. 10 Considerations of clearance, repair processes,

11 and so on. 12 So I think you could write a 13 paragraph that says we went to blank, and 14 leave it at that. I think that is probably 15 the best advice you are going to get out of 16 this group at 2:15 on Wednesday. 17 DR. PEKAR: You know, I think that 18 makes some sense. I think there are just a 19 couple of things, in listening to folks, that 20 I want to mention. 21 I think, in my mind, there might 22 be a difference. I am not saying there is, Page 253 1 but I am still tossing this around in my mind, 2 between guidance on alternate standards that 3 you consider and how far down you are willing 4 to model risk. 5 So do you have a really clear 6 sense of being able to characterize the 7 magnitude or the presence of an effect to a 8 certain level? But then realizing that when 9 we model risk below that, if we stop modeling 10 risk at a certain point, we are essentially 11 assuming from the standpoint of calculating 12 risk that there is zero risk. I mean, you are

13 not -- 14 CHAIRMAN SAMET: No, you are not 15 saying that. You are saying you don't know. 16 You are saying you don't know, and I think 17 that is an important distinction. 18 DR. PEKAR: I agree, but from the 19 numerical standpoint, if you say we don't 20 know, so we are not going to calculate below 21 this, and you give me a total incidence 22 estimate -- I am just putting this on the202-234-4433 Neal R. Gross & Co., Inc. Page 254 1 table as a point to keep in mind. If we only 2 model down to a certain point and we have that 3 incidence estimate, we stop at LML or we stop 4 at nine or we go down to PRB, those have 5 pretty radical implications for the total 6 incidence estimates. 7 Just keep in mind that, if we stop 8 at a certain point because we are uncertain, 9 if you stop at that point, you are saying we 10 are uncertain, and we are not going to assess 11 anything. So rather than saying it could be 12 a linear, it could be -- I guess my point is 13 that in a sense, from a numerical standpoint, 14 you then are looking at a numerical threshold 15 in terms of your calculation. 16 That is just something to keep in 17 mind, and that may be a different point, the 18 nature of how far the CR function goes down 19 versus looking at a standard level of 12 and 20 calculating some risks below that, or 10 and 21 calculating risks below that. 22 In my mind, those might be too -- Page 255 1 that might be a nuance. It might be 2 important. I am not sure, but just to put

3 that -- 4 CHAIRMAN SAMET: You know, you 5 are far enough into the policy analysis to 6 know what you need. I think what you need 7 here is a reasoned basis for setting the lower 8 limit of where you are going.

9 DR. PEKAR: Yes. 10 CHAIRMAN SAMET: And I think that 11 is really what you need. So maybe -- I think 12 this has been a useful discussion. I think it 13 has helped us all realize how challenging this 14 is. Are there other comments on Charge 15 Question 6, integrative -- I will say that I 16 think a number of people made comments about 17 the writing, and I don't know who drafted 18 this, but since this will be so much read and 19 it is so important, it just really needs some 20 attention. 21 There is some masterfully bad 22 writing in it. Sorry. And I think that it Page 256 1 just needs to be carefully fixed, because this 2 is what people will look at. So I just urge 3 you to do that. I think others have the same 4 view. Ed? 5 MR. AVOL: Yes. Just to follow up 6 on that point, I think this chapter, which we 7 all asked for and which you provided, I think, 8 is terrific in concept. It is what everybody 9 is going to look at. 10 I think that, if you believe that 11 you have made your points in the previous five 12 chapters, you just need to clearly summarize 13 that here. It was frustrating, frankly, to 14 read in some of these sections, because there 15 were so many qualifiers and side issues and so 16 forth that you sort of lost the point. 17 So I think, if you believe you 18 have made the point before, make it here and 19 underscore it, and if you are going to use 20 bullets, which is fine, a bullet ought to be 21 a sign to what the issue succinctly is, and 22 then the follow-on text afterward can say it. Page 257 1 For some reason -- I am not quite 2 sure exactly why, although we have heard some 3 discussion -- you still seem to be sort of 4 defensive about these 15 cities. So 5 periodically, throughout this discussion, it 6 sort of comes up, well, there's other cities, 7 but we are using these 15, but don't believe 8 that other cities beyond the 15 don't have 9 health effects, as well. 10 Well, I mean, that is all sort of 11 taken care of up front. I think, if you are 12 going to bring that -- and it is appropriate 13 to bring that into the last chapter -- that 14 should be sort of right up front in the first 15 couple of pages, and then you are done with 16 it, because everything else follows from, this 17 is all based on this 15-city analysis. 18 I think you would be clear about - 19 - you know, if you believe what you have done, 20 just be clear about it, and go forward. Don't

21 be so --

22 DR. PEKAR: Karen and I were202-234-4433 Neal R. Gross & Co., Inc. Page 258 1 talking about this, and I won't say who is 2 responsible for writing it, but I think the 3 one thing to make -- I think we ended up doing 4 additional diagnostic work in that chapter 5 that should be pushed -- possibly pushed to 6 earlier chapters so that it can be more 7 summary, because there are points and 8 observations made in that chapter that were 9 not made earlier on. 10 So a lot of explanation, which

11 could be clearer -- but a lot of the 12 explanation, I think, comes from the fact that 13 we are starting to pull some of these 14 observations out here. I think those could 15 have been presented with a little more detail 16 in earlier chapters, and then this could 17 really be summary. 18 So that may help a little bit, but 19 I think there was fresh, interpretive work 20 done here which probably should have been in 21 earlier chapters. 22 DR. LIPPMANN: On this issue of Page 259 1 the 15 cities, I think you don't need to be so 2 defensive about it. You had little choice in 3 the sense that a city could only be handled if 4 there were suitable sets of data for it. 5 If you take three out and put 6 another three in, it is not going to change 7 very much because it is an aggregate that you 8 are coming up with. So just say these are 9 reasonably representative of urban populations 10 in the U.S. They are not perfect, but 11 constrained by available data, these are the 12 cities you used, and you can make a statement. 13 I think it is extremely unlikely 14 that adding three or five more would 15 materially change the analysis. But then you 16 get back to this issue of you have evidence 17 that they are not -- that the coefficients in 18 these cities are quite different from each 19 other, which is attributable, at least in 20 part, to the compositional differences, which 21 you can't deal with in this round, but needs 22 to be keyed up for future research. Page 260 1 I mean, everything is changing, 2 and risks are changing. What is the 3 consequence of the ever-increasing obesity in 4 the U.S. population? There is emerging 5 evidence that this adds risks to particle 6 exposure for obese people. So the population 7 is changing. 8 So there is all kinds of things 9 that you can't control or analyze at this 10 stage, but you are doing a credible, 11 defensible analysis, and don't be so defensive 12 about it. 13 You are making the best that can 14 be done with current knowledge and integrating 15 it perhaps more elegantly in the final 16 chapter, moving some detail back in chapters 17 earlier so it can be summarized in a bullet in 18 the last chapter, but I think you deserve a 19 lot of credit for a really good-faith and 20 competent, more than competent, effort at 21 getting us to this point.

22 Now that leaves a lot of Page 261

1 unresolved issues, but you are obligated to do 2 the best you can with what you have. 3 DR. BRAIN: The other thing that I 4 really liked was -- from the 15 cities was you 5 clearly showed us how some cities benefit from 6 knocking the peaks, but the annual average 7 didn't make much of a difference, and the 8 reverse. I thought that was the sort of clear 9 lesson, conclusion, and hopefully that will 10 end up in Chapter 6, too, but I think that was 11 a really very positive illustration of that 12 principle. 13 CHAIRMAN SAMET: Well, I was 14 hoping we were feeling better about things,

15 but Sverre has a comment. 16 DR. VEDAL: One of the main 17 punchlines of this chapter that comes out in 18 your key points as well, and it is an 19 important one, is the emphasis on the annual 20 average, and as I take it, the argument is 21 that there is better consistency of health 22 protection when you use an annual standard as202-234-4433 Neal R. Gross & Co., Inc. Page 262 1 your controlling metric. 2 Health protection is a function of 3 long-term concentrations because that is where 4 the health impact is, not on the changes, not 5 on the acute effect changes. 6 So I personally don't think that 7 is enough. I think it may be enough. It is 8 a pretty strong argument to base it on health 9 protection, but I just wonder whether there 10 are some other pillars you can build that 11 argument on than that observation. It is 12 really an important punchline, and it is going 13 to play a big role when we are in the policy 14 game. 15 So I don't know. I mean, I don't 16 know -- it is not enough for me and you will 17 hear that, but I just would encourage you 18 maybe to -- you may have to just shore up that 19 case more strongly than you have. 20 CHAIRMAN SAMET: Okay. Any last 21 things on Chapter 6? I think we have covered 22 some very important issues for the whole Page 263 1 document in discussing this and I think in 2 general I think we found it a very important, 3 useful framework and starting point and, I 4 think, discussed some of the issues. So 5 anything else that you want to go over? No. 6 Okay. So anything last before we 7 move off the risk assessment, anything else, 8 anybody, because we are going to move on to 9 visibility? All right. 10 DR. SPEIZER: Can I just ask, we 11 won't see this document again officially. 12 Will it be part of -- will there be some 13 components of it that we can discuss at the 14 discussion of the PA, whatever changes? 15 CHAIRMAN SAMET: Actually, Karen, 16 I can't quite remember the timetable for 17 finalizing the -- it was probably in your 18 slides. It was in your handout, the schedule. 19 Here it is. 20 The final is April 2010. Is that 21 before or after our phone call? 22 DR. MARTIN: For finalizing? Page 264

1 CHAIRMAN SAMET: The RA. 2 DR. MARTIN: For finalizing both 3 of the risk documents is the end of April. 4 CHAIRMAN SAMET: End of April. 5 So I guess, Frank, the only thing I can tell 6 you is that, even though it is final, we can 7 still comment. So I think that is -- we 8 should just reserve that possibility when we 9 see it. 10 Okay. Then we are going to move 11 on to visibility, which means that you have to 12 pretend it is Thursday morning, and turn the

13 schedule over.

14 DR. MARTIN: I was unable to

15 reschedule the conference call. 16 CHAIRMAN SAMET: Okay. So public 17 commenter, Holly could not -- there are two. 18 Okay. We will hear from them tomorrow. 19 So I think at this point then we 20 will have the overview, the revision.

21 DR. STALLWORTH: Tomorrow we have 22 public comments on the visibility assessment Page 265

1 from Dave Heinold of the American Petroleum 2 Institute, and Anne Smith on behalf of UARG, 3 the Utility Air Regulatory Group. 4 DR. MARTIN: Okay. To begin here, 5 the agenda had lined out that we would start 6 by doing a very brief overview of the two 7 chapters in the policy assessment that relate 8 to welfare effects, visibility and other 9 effects on the secondary standard side and 10 then do a brief presentation on the visibility 11 assessment as a prelude to your discussion of

12 that document. 13 So if we are okay with that -- the 14 people here at the table with me: Vicki 15 Sandiford is the lead author on the visibility 16 chapter in the policy assessment and Meredith 17 Lassiter, the lead author on Chapter 5 on

18 other welfare effects. We also have Marc 19 Pitchford who is going to join us and make a 20 presentation on the visibility assessment 21 itself. 22 So if we can just quickly give a202-234-4433 Neal R. Gross & Co., Inc. Page 266 1 sense of the scope of these chapters in the 2 policy assessment. 3 MS. SANDIFORD: Sure, and there is 4 a handout. They just went around with the 5 slides that I am speaking from -- and Meredith 6 will be speaking from. 7 So Chapter 4 was focused on 8 looking at the adequacy of the secondary 9 standards for visibility-related effects and 10 we looked at the current suite of PM2.5 11 standards, and we had overarching questions 12 that we were seeking to address. 13 The first one of those was, does 14 the currently available scientific evidence 15 and visibility impact information as reflected 16 in the ISA and UFVA, Urban-Focused Visibility 17 Assessment, support or call into question the 18 adequacy of the visibility protection afforded 19 by the current suite of fine particle

20 standards? 21 In trying to address that 22 question, we looked at the newly available Page 267

1 evidence as it was discussed in the ISA. That 2 included new air quality information from the 3 regional planning organizations and other data 4 collected through our own PM networks and then 5 looked at the temporal nature of the 6 visibility effect, the aspects of that 7 including what periods of time during the day 8 were people most impacted by visibility, 9 looking at the length of the exposure periods 10 and other atmospheric conditions that impacted

11 that effect.

12 We also looked at the levels that 13 should be considered in terms of what was 14 important from visibility impairment. We had 15 done a lot of evaluation, as you know, in the 16 UFVA on the preference studies and took that 17 into account. 18 We looked at were current air 19 quality conditions, and then did rollback to 20 just meet the PM2.5 standards. 21 We then went on to consider to 22 what extent information provided in those Page 268 1 previous two documents and in the section of 2 the chapter looking at adequacy of the current 3 standards: was there support for considering 4 different pollutant indicators, averaging 5 times, forms and levels, to replace or 6 supplement the PM2.5 mass-based standards?

7 We looked at what we knew about 8 indicators, what might be appropriate 9 alternative indicators, considered the role of 10 coarse particles. 11 We moved on to averaging time and

12 looked at the nature of the effect and the 13 nature of the exposure; what were the time 14 periods of most interest. Then we looked at 15 a range of levels for both alternatives that 16 we considered. We looked at different 17 percentile forms. 18 As you know, we followed your 19 advice and looked at the relative humidity 20 screen and a range of levels for different 21 forms -- indicators associated with the 22 standards that we were looking at. Page 269

1 So that is the information that is 2 included in the current draft policy 3 assessment and hopefully, you have had a 4 chance to glance at that and it will provide 5 some context for the UFVA review. 6 MS. LASSITER: In Chapter 5 we are 7 considering the non-visibility welfare effects 8 and the overarching question for that chapter 9 was: does the currently available scientific 10 evidence as reflected in the ISA support or 11 call into question the adequacy of protection 12 afforded by the current suite of secondary

13 standards? 14 So in this chapter, each of the 15 non-visibility welfare effects is considered 16 separately. We first considered climate, and 17 asked ourselves what new techniques are 18 available to assess aerosol-related effects on 19 climate. What is the nature and the magnitude 20 of these climate responses to PM? 21 In that particular consideration, 22 we looked at both direct and indirect effects202-234-4433 Neal R. Gross & Co., Inc. Page 270 1 of aerosols, and then looked at global and 2 regional climate impacts. 3 Finally, in consideration of 4 climate, we looked at the evidence of specific 5 PM-related constituents such as black carbon, 6 organic carbon and sulfates and their effects 7 on climate. 8 Considering the ecological effects 9 of PM, I do want to point out here that the 10 NOx SOx secondary assessment considers some of 11 the depositional effects and then the 12 remaining effects of PM are considered in 13 Chapter 5 of the PM policy assessment and 14 there is a section at the beginning of the 15 chapter on the scope of the document. 16 So we looked at key scientific 17 evidence as available to improve our 18 understanding of PM effects on the soils and 19 nutrient cycling of wildlife, on water and on 20 plants, and also considered in this section 21 ecosystem sources associated with PM and 22 considered new techniques that were available Page 271 1 for assessing PM-related effects on 2 ecosystems. 3 Finally, we considered what are 4 linkages that show ambient levels of PM to 5 effects on ecosystem components. 6 The final effect on welfare 7 considered materials effects. So in this 8 section, we review any new and existing 9 evidence for PM-related effects on materials 10 damage and soil link. 11 DR. MARTIN: Okay. If there are 12 no questions about the scope of the issues 13 covered in these last two chapters in the 14 policy assessment, we can move on and do our 15 presentation on the visibility assessment. 16 DR. PITCHFORD: I am going to give 17 you something to look at while I talk. You

18 don't have to look at me. 19 Okay. We have got a very short20 presentation here, talking about the Urban21 Focused Visibility Assessment. Basically, 22 let's just go right to the second slide there. Page 272

1 Let's talk about what we did 2 different in this second review draft compared 3 to the first review draft. Almost in every 4 case, I think, this was the result of 5 suggestions and comments from CASAC. 6 We conducted a logit regression 7 analysis on the response curve for the four 8 urban area preference studies. this allowed

9 us to see more than the dots on the curves and 10 I think it worked out very well. 11 We also did and sent out to you in 12 a supplemental document logit analysis 13 comparing the various components of the 14 Washington, D.C. focus studies. There was two 15 separate studies and in the second study there 16 were a number of substudies or separate groups 17 looking at different sets or subsets of the 18 slides. 19 We also replaced the 95 percent 20 relative humidity cap, which was similar to 21 what had been done for the regional haze rule, 22 with a 90 percent relative humidity screen. Page 273 1 In other words, we did not consider hours of 2 the day, daylight hours with relative humidity 3 above 90 percent in any of the assessments, 4 and we included an assessment of the effect 5 that having that screen had in terms of hours 6 of the day for the different cities that had 7 fog or precipitation or other impacts on 8 visibility. 9 By the way, we have got a 10 supplemental analysis that expands. As you 11 may recall, we only had Met data at the time 12 for 11 of the 15 cities. We have expanded to 13 all the 15 cities, and we had another 14 refinement. When we did the original, we were 15 looking at all hours of the day and some of us 16 didn't know that. So we refined it, and now 17 we've got just the daylight hours. In fact, 18 it doesn't change any of our overall 19 conclusions, but all the numbers change a 20 little bit. 21 We added the 98th percentile to 22 the 90th and 95th percentile forms that we had202-234-4433 Neal R. Gross & Co., Inc. Page 274

1 looked at before and we also added to the -- 2 what we looked at before was the maximum 3 daylight -- or daily daylight hour for the 4 scenarios. We have added to that all hours 5 during daylight. So that is in there. 6 Then finally, we -- in order to 7 better display or to be able to show people 8 what the hourly daylight values for light 9 extinction look like, we incorporated some 10 what are called tile plots, which are very 11 colorful ways to show you what category of 12 visibility we are in for each of the 15 cities 13 for the three years and so forth. 14 Let's go on. In terms of what we 15 are asking in terms of feedback from CASAC, 16 basically sort of a generic question here: 17 how well did we do or did we do well in 18 organizing and making new information that is 19 in the report.

20 Then the next three bullets are 21 basically asking about this new information 22 that I have just discussed: the logit Page 275

1 analysis, adding values just for relative 2 humidity at or below 90 percent and also how 3 that effects the incidence of fog or 4 precipitation. 5 Then finally, the addition to the 6 national areas that considered all daylight 7 hours in the 98th percentile, giving you a 8 total of, I believe, 18 different scenarios to

9 look at. 10 So that is the essence of it, and 11 that is the last slide I have to show. 12 CHAIRMAN SAMET: Okay. Thank you, 13 Marc. Are there general questions before we 14 move to the charge questions? No? Okay. 15 So let's go to the first charge 16 question. I guess, Lowell, you are the lead

17 on that one. 18 DR. ASHBAUGH: Charge question 1 19 basically asks if the Panel agreed that the 20 level of detail provided is adequate and good. 21 Yes, I think the report contains a 22 lot of detail that is presented quite well. Page 276 1 Really, I think it is written very well. I 2 wouldn't recommend deleting any of it. The 3 organization is good, and I had a few comments 4 on some of the presentations later on. 5 My comments on charge question 1 6 are very short. It looks excellent. I don't 7 really have any negative comments on it. So 8 I am going to go on to the next. 9 CHAIRMAN SAMET: Okay. So I guess 10 other commenters? Phil? 11 DR. HOPKE: Again, I think my 12 comment was that it is long and somewhat dry, 13 but completeness of detail and useful given 14 that we are looking at a very different 15 indicator for a secondary standard. So if we 16 are going to provide the basis for making 17 significant change, then the i's have to be 18 dotted and the t's crossed, and I think this 19 does it. 20 CHAIRMAN SAMET: Okay, Dave? 21 DR. GRANTZ: And I agree with Phil 22 and Lowell. It is an excellent job and it Page 277 1 was informative to read. I really liked it. 2 Having said that, in my comments I 3 say with very few exceptions. So I will 4 emphasize the exceptions. 5 I think Chapter 2 contains a 6 little bit more information in the body in 7 terms of the preference studies in the 8 individual cities. I don't think that much 9 detail needs to be in the body of the report. 10 A lot of that can be moved to the appendix 11 without losing much of anything, I think. 12 Similarly I thought, if you were 13 going to create an appendix for that 14 information, the separate document for the 15 statistical analysis could be rolled in with 16 that, I think. There is no reason to keep 17 that separate where no one will see it. 18 Then there were a couple of 19 discontinuities, I thought. I thought Section 20 4.1.3, which introduces considerations for 21 monitoring sites, was -- maybe I missed the 22 flow, but it just seemed completely out of202-234-4433 Neal R. Gross & Co., Inc. Page 278 1 place to me. Maybe it makes more sense if I 2 read it a different way, but I couldn't make 3 sense out of that logic. 4 Then Section 4.1.4 has a lot of 5 detail on modeling methods and the background 6 of the modeling approach. I think that also 7 could be moved to an appendix for enhanced 8 readability. 9 Other than that, I thought it was 10 really a very nice job. 11 CHAIRMAN SAMET: Okay. Other 12 comments on this charge question, this 13 document in general? Yes, Rich? 14 MR. POIROT: I agree with what 15 everyone else has said so far, and I think a 16 lot of the detail has to do with the inherent 17 complexity of the calculations, and also with 18 the number of options that are being 19 considered, many of which are our fault. We 20 kept asking for more and you gave it to us and 21 we shouldn't complain that there is too much 22 detail. Page 279

1 I also thought that it was -- you 2 know, I was very interested in seeing how you 3 did the analysis and so, for the interested 4 reader, it was an excellent exposition of 5 exactly how you did it. I felt like I 6 understood what you did very clearly. 7 Then for the disinterested reader, 8 there were some nice, very concise summary 9 tables at the end that, if you just want to 10 see, here is the whole thing on one page. So 11 I thought it was a nice blend of those two 12 aspects. 13 CHAIRMAN SAMET: Yes, Joe? 14 DR. HELBLE: I just wanted to echo 15 Rich's comments, but focusing specifically on 16 Section 2. So perhaps I am disagreeing with 17 Dave, and it may be because we drove this 18 through questions we asked at the last CASAC 19 review, but I found Section 2 to be well 20 written and clear and I found it particularly 21 useful and I thought it was nicely summarized 22 at the end of that chapter. Page 280 1 So I guess my take on it was, if 2 somebody wanted to skim it quickly and cut to 3 the chase, there was a very nice summary at 4 the end but I thought that detail was useful 5 to have in the body of the report. 6 DR. GRANTZ: Well, just to defend 7 my comment, I think a lot of detail is useful 8 and a lot of it is already in the appendices. 9 I just thought, consistent with other 10 appendices, a lot of that material could have 11 been moved, because a lot of people are going 12 to read this document. 13 A lot of people are going to read 14 through it and try to get the gist and I 15 thought there was just a little bit too much 16 noodling in the details that didn't really 17 contribute to the thrust of the document. 18 CHAIRMAN SAMET: Well, can't make 19 all the readers happy all the time. 20 Other comments? So, Chris, I 21 think we are on to you. 22 DR. FREY: Yes, thank you. So Page 281 1 charge question 2 is regarding the logit 2 analysis that is in Chapter 2. 3 One question is, does this 4 analysis add value to the urban visibility 5 preference study analysis. I think the answer 6 is yes. 7 Does it provide adequate -- or 8 provide additional support for combining and 9 comparing results from the four cities as 10 shown in Figure 216? I think the answer is 11 yes. 12 What are the Panel's views on the 13 clarity and adequacy of the descriptions, dot 14 dot dot. I thought that that section was very 15 clear and well written and made a lot of sense 16 to me and it seemed that it was adequate, too, 17 that it addressed all the issues that I would 18 think of that need to be addressed there. 19 The last question is: provide 20 comments on any additional insights that might 21 be drawn. I don't have any specific 22 suggestions in that regard.202-234-4433 Neal R. Gross & Co., Inc. Page 282

1 I think overall it was a well 2 motivated section. I think it was an 3 appropriate discussion of the limitations of 4 the data. It was a clearly presented model. 5 The model fit to the data was shown visually 6 in Figure 216. The parameter estimates are 7 given in Table 23 along with the T ratios. 8 So I think there was a good basis 9 for critiquing the model fit to the data, and 10 also for the qualitative discussion of the 11 issues with the underlying data. 12 I thought it was nice that there 13 was discussion of what the sort of physical 14 meaning of the coefficients were when 15 comparing the different cities. 16 I think the supporting memo that 17 is referenced in the charge question provides 18 some detail on a model that, I guess -- I 19 guess it is referred to, maybe, as more 20 integrated or less integrated. There is a 21 model 1 and a model 2 in that memo, and the 22 model that is in the report is model 2. Page 283

1 I think model 1 and model 2 didn't 2 give dramatically different results, so they 3 were slightly different formulations. So I 4 think model 2 seems like the appropriate one 5 to bring forward to the main report and the 6 fact that you got similar results with 7 slightly different parameterization is some 8 indication of the robustness. 9 So I think overall it was very 10 good. I don't really have anything else to 11 say about it. 12 CHAIRMAN SAMET: Thank you. We 13 are going to give Tom a chance to comment

14 tomorrow. Lowell? 15 DR. ASHBAUGH: I agree, too, that 16 I like the logit analysis. I thought it is 17 important to the assessment, provides a common 18 basis to analyze the results. 19 One question I had was, and it 20 kind of just keeps coming up because of the 21 sparsity of studies, on page 231 you suggest 22 that additional studies would be useful to Page 284 1 gain further understanding. Do you have plans 2 for such studies? Do you have an outline of 3 them? Is there perhaps a list of studies that 4 could be used for discussion or if funding 5 becomes available? 6 That was just something I was 7 thinking about as I was reading it. It would 8 be useful to have plans ready in case we can 9 do something like that. 10 CHAIRMAN SAMET: Okay. Phil? 11 DR. HOPKE: One of the things that 12 struck me is that light extinction goes as C 13 times L, concentration times distance. The 14 fact that we see apparently a shift in the 50 15 percent point from the places where there is 16 the greatest distance to the shortest distance 17 struck me as a way of -- something that ought18 to be looked at, and whether in fact, the mid19 points on those derived logit curves could be 20 correlated with the inverse distance to the 21 furthest visible feature in these figures and 22 whether that might then give us a better Page 285 1 understanding of why we are seeing the kind of 2 variability, basically, between 20.3 and 29.4 3 or something like that, simply because in 4 Washington, D.C., there's no hills in the 5 background. 6 I mean, Capitol Hill is not a big 7 deal, except to those who live there. But 8 there is no significant distance object which

9 would become obscured at a lower level because 10 of the distance. 11 So I think that would be very 12 informative if we could start to look at how 13 that most distant object effected the 14 perception as reflected in those different 50 15 percent points. 16 CHAIRMAN SAMET: Okay. 17 DR. ASHBAUGH: I commented in my 18 written material, too, that there might be

19 other factors as well that effect these 20 results and why they shift from place to place 21 just due to who is living in those places or 22 how attuned they are to what is out there and202-234-4433 Neal R. Gross & Co., Inc. Page 286 1 just having the ability to see something far 2 away. That may explain it as well. I think 3 there might be other things as well that could 4 explain it. 5 CHAIRMAN SAMET: So there is 6 really an agenda here for designed experiments 7 with building all these factors in in sort of 8 the factorial approach to try and sort this 9 out, I think. Dave? 10 DR. GRANTZ: Well, I think you 11 have just summarized what I was going to say. 12 We clearly need further studies on this. 13 My concern with the way it stands 14 is that I think the data that are available to 15 us indicate there are true regional 16 differences. So to draw a mid-point or some 17 central tendency through things that really 18 represent different groups, I am not sure is 19 going to be defensible. That worries me. 20 CHAIRMAN SAMET: To be discussed 21 in the policy analysis. 22 DR. GRANTZ: Yes. Well, I mean, Page 287 1 the caveats keep growing with each document 2 and I have read the policy analysis and it is 3 appropriately flagged as uncertain. 4 Nevertheless, we are plowing ahead. 5 CHAIRMAN SAMET: Phil pointed out 6 that there are people differences, and then 7 Phil's comment about the vistas and the 8 geographic differences which are there. After 9 looking at Albuquerque for years where you 10 would go, well, gee, can I see Mount Taylor, 11 which happened to be 70 miles away, or not -- 12 you know, as your benchmark. 13 DR. GRANTZ: Well, it may be the 14 people that choose to live there, and it may 15 be the comment that I made, that it is what 16 you become accustomed to wherever you live. 17 They showed in one of those 18 studies that the range of slides that you 19 present skews the answer. So I think we just 20 don't know very much about that, and it is way 21 premature to judge why that is going on, but 22 I think to find a central tendency and stick Page 288 1 to it is a little bit dangerous.

2 I wrote that before I read the 3 policy assessment, but now that it is clear 4 that we are really heading directly into a 5 visibility-type standard, that becomes really 6 important, far more important than if it was 7 a mass standard reflected in visibility. 8 CHAIRMAN SAMET: Okay. Other 9 comments on charge question 2? Then, Rich, 10 you can handle 3? 11 MR. POIROT: Well, I would just 12 like to maybe raise the counterpoint to 13 David's point, and that is, I thought -- well, 14 first of all, the logit analysis, to me, was 15 quite helpful and really nicely done. I 16 thought it helped in also supporting some of 17 the consideration of alternative hypotheses 18 for why there were differences in responses

19 across the areas. 20 I certainly felt that with that 21 addition you have squeezed pretty much all of 22 the information you are going to squeeze out Page 289

1 of those studies. It is a little bit of a 2 game, you know, when you say to contrast, on 3 one hand, this is sufficient information, but 4 on the other hand, we need more of this kind 5 of work. It is a balancing act. 6 To me, I came down more on the 7 side of looking at the relative convergence 8 across the different studies, especially when 9 we get into slightly different, other than the 10 50th percentile response of unacceptable and 11 so forth. To me, I saw more lines that 12 converged rather than lines that diverged. 13 So I think you could see it that 14 way as well. To me, it provided what I 15 thought would be a sufficient basis to go 16 forward with selecting a specific level from 17 within that range as justifiable without 18 diminishing the need for more studies like 19 this. 20 CHAIRMAN SAMET: Okay. Should we 21 go on to Charge Question 3?

22 MR. POIROT: Do I have 3?202-234-4433 Neal R. Gross & Co., Inc. Page 290 1 CHAIRMAN SAMET: Well, it was you 2 or Tom, so I was hoping that you could. 3 MR. POIROT: Tom will, hopefully, 4 be able -- possibly later this afternoon or 5 possibly tomorrow morning, maybe. 6 CHAIRMAN SAMET: Do you want to 7 leave this one for now? That is fine. 8 MR. POIROT: Not necessarily. I 9 can say what I have to say about it. 10 CHAIRMAN SAMET: Okay, go ahead. 11 MR. POIROT: You know, I 12 personally thought that the 90 percent RH 13 screen was a good idea. I recommended it last 14 time, as did others. This gets back to a 15 point I made earlier. 16 I think it is a good idea for use 17 in this assessment, but I also think it is a 18 good idea for use in terms of a metric for a 19 standard, which are two completely different 20 things. 21 I thought that the analysis 22 presented in terms of what effect this 90 Page 291 1 percent RH screen would have on eliminating 2 incidences of naturally occurring weather: 3 fog, rain, snow, sleet, et cetera, was very 4 well done, especially as it has been repaired 5 or redone in the memo that we only just became

6 aware of.

7 I note that Lowell has some 8 specific comments on that that point out some 9 miswording that is very important that I will 10 let him get to in a minute. 11 I believe that there are other 12 good reasons, aside from just the elimination 13 of weather to employ that kind of an RH 14 screen, especially as we look toward a 15 standard. I think that it would -- we want to 16 avoid any possible public perception that EPA 17 is regulating the weather, and I think it 18 really would help to do that quite a bit by 19 not considering those higher levels of 20 humidity. 21 I think also, in terms of making 22 the measurements there, if you let these very Page 292 1 large fog droplets into your nephelometers, 2 you gum them up, and you have to take them

3 down and clean them and so forth. 4 So in terms of how you make the 5 measurements, there are benefits to perhaps 6 adding a smart heater or something that would 7 eliminate the high RH levels as you are making 8 the measurements. You can make essentially 9 better quality measurements of everything

10 else. 11 There are also issues, of course, 12 of the variability of the humidity and the 13 weather along different aspects of the sight 14 path. It is one thing to say, well, RH above 15 90 percent and it is also foggy at this 16 monitor, but what about RH above 90 percent? 17 Is it foggy anywhere within your sight path of 18 your urban area? 19 I am not suggesting that needs to 20 be done, but that would be a way to ask and 21 answer that question, would be to look at the 22 variability, co-variability of RH and weather Page 293 1 conditions across a wider region. 2 I don't think you need to do it. 3 I think what you have done is very adequate. 4 It makes an eloquent demonstration that this 5 RH screen eliminates a small percentage of the 6 hours from consideration and gets rid of 7 almost all of the weather effects, and I think 8 that was the main point. You have done that 9 very well. 10 So that is, I think, about all 11 that I had to say about it. 12 CHAIRMAN SAMET: Okay. Others?

13 Lowell? 14 DR. ASHBAUGH: I guess I will just 15 add my comments, too, now. I am actually 16 surprised that I didn't notice that the number 17 of hours in the column labeled number of 18 daylight hours actually added up to nearly the 19 number of hours in the three-year period which 20 is what was corrected in this memo we got 21 about 15 minutes ago. 22 So I think I had made the comment202-234-4433 Neal R. Gross & Co., Inc. Page 294 1 that one of the statements in the report just 2 wasn't correct, because there were other sites 3 that were greater than six percent of the 4 hours, but that was based on all hours. 5 I think, after reviewing the 6 latest report, I think the numbers are closer, 7 and certainly on average it is less than six 8 percent, but a few sites do have more. But I 9 think you have done an excellent analysis, and 10 it is appropriate to do it. So regardless of 11 the error, which I think has been corrected 12 now, I think it is a good analysis, and should

13 be done. 14 CHAIRMAN SAMET: Anything else on 15 -- this is charge question 3 still.

16 MR. POIROT: Could I add one 17 thing? I ended up sending my draft comments 18 around to some of the folks, including Bill 19 Mullen who wrote back a quick e-mail saying 20 that he agreed that the use of the 90 percent 21 screen was appropriate. 22 He also indicated you might -- in Page 295 1 his experience, you might go up in terms of 2 making the measurements as high as 95 percent 3 with something like an nephelometer, but 4 warning that determining the RH is not always 5 that easy. So when you are measuring 95 6 percent, unless you are really carefully 7 watching your calibration, it could be at much 8 higher levels. 9 So he, who I think, was generally 10 supportive also of the 90 percent RH 11 screening, and also kind of like mentioning 12 the idea of using a Smart Heater to eliminate

13 those hours. 14 Again, none of this reflects 15 actually on the risk assessment document. It 16 is more onto the next stage. 17 CHAIRMAN SAMET: So do you want to 18 go on to 4? I think that's you. 19 MR. POIROT: I guess we don't have 20 the charge questions written up there. Should 21 I read the charge question? 22 In response to CASAC Page 296 1 recommendations, description of current 2 conditions and results of just meeting NAAQS 3 scenarios that considered all daylight hours 4 were added to those based on maximum daily 5 one-hour indicators. The 98th percentile form 6 was also included along with the 90th and 95th 7 percentiles. Tile plots of hourly PM light 8 extinction, Figure 3-12, and composition bar 9 graphs for the top 10 percent of the days for 10 maximum daily one-hour and aggregated 11 individual daylight hours, Figure 3-13, were 12 shown in part to help illuminate the 13 similarities and differences between these 14 various indicators with respect to current 15 conditions. 16 Similarly, additional figures and 17 table entries were generated to illustrate the 18 characteristics of various PM light extinction 19 NAAQS scenario forms, Tables 4-2, 4-5 and 4-7, 20 Figures 4-1 through 4-3. 21 Now comes the question: What are 22 the Panel's views regarding EPA's staffPage 297

1 interpretations of these displays included in 2 the text or the supplemental or alternative 3 interpretations? Are there supplemental or 4 alternative interpretations the Panel would 5 suggest? Are there additional approaches the 6 Panel would suggest regarding ways to 7 summarize, display or assess the results of 8 these analyses, including similarities and 9 differences between the various scenarios? 10 That's the question.

11 CHAIRMAN SAMET: And the answer 12 is? 13 MR. POIROT: I guess I thought all 14 of the additions were useful, and very 15 responsive, I might add, to our requests from 16 last time around. We asked for it, and they 17 gave it to us. 18 I thought the tile plots -- I had 19 actually never seen those kinds of graphics 20 before -- were not only very informative and 21 interesting, but I would like to have a couple 22 to frame and put in my living room. They are202-234-4433 Neal R. Gross & Co., Inc. Page 298 1 very lovely, some of them, and they really 2 illustrate some details, if you sit there -- 3 for the interested the reader, sit there and 4 stare at some of them for a while. 5 For example, they really helped me 6 to see how in some of the eastern urban areas 7 the 90 percent RH screen really would kick out 8 a number of those early morning very humid 9 hours, which is something that I have been a 10 little bit worried about. So I thought they 11 were -- yes, they were very effective and 12 useful graphics, in my view. 13 The plots that show the maximum 14 hourly extinction on the top 10 percent of the 15 days, and also compare those with the top 16 percentile extinction using all of the 17 available daylight hours -- for the rest of 18 you, to kind of tee this issue up a little 19 bit, what was initially suggested was to 20 calculate a percentile based on first 21 calculating a single worst daylight hour of 22 the day, and then you would calculate what is Page 299 1 the 90th or 95th or other percentile of that. 2 The alternative approach that we 3 had suggested was to consider using all the 4 available hours of the day and throughout the 5 year, and calculating your percentiles based 6 on all of the available daylight hours rather 7 than just the single worst hour of the day. 8 So they considered that, and so 9 the question is: Are our graphics in which we 10 compare the results of these two things, these 11 two approaches on high percentiles -- do they 12 clearly communicate the similarities and 13 differences? 14 I thought they were interesting to 15 look at, but I guess I wasn't quite convinced 16 that they really displayed apples and apples. 17 On the one hand, when you are using only the 18 worst hour of the day, there is sufficiently 19 few of those that you can show all of them in 20 terms of bar graphs for each day. But when 21 you are looking at the 95th or the 90th 22 percentile of all of the daylight hours, there Page 300 1 are way too many of those to show them in the 2 same way. 3 So I guess I think it would be 4 useful to really get a better sense of what is 5 the real difference between those two 6 approaches. You know, one of my concerns was 7 that the single worst daylight hour seemed to 8 be somewhat of an illogical thing to combine 9 with a very low percentile, like the 90th 10 percentile. 11 It seemed illogical, because it is 12 saying, on the one hand, one bad hour ruins my 13 entire day; and on the other hand, I am mad as 14 heck about it, but I am not going to get mad 15 until it happens on the 37th day of the year. 16 It just seemed like it was taking kind of an 17 extreme thing on one end, and mixing it with 18 an opposite extreme on the other end. So it 19 just made me a little squishy there. 20 Watch out for me, because I am 21 going to try to dry this thing out a little 22 bit, and so I am coming a little bit from that Page 301 1 perspective of I think there would be benefits 2 to having somewhat of a drier extinction 3 standard in terms of reducing east-west 4 differences, but I don't feel real strongly 5 about that. But it was one of the reasons why 6 I preferred using all the daylight hours 7 rather than a single worst hour, was that I 8 thought the single worst hour would be tending 9 to occur just after dawn, and I think it often 10 does, not always, but it often does, and it 11 would be a very high humidity condition and so

12 forth. 13 So I might add that Bill Mullen 14 also commented back on the same thing, 15 although he has the opposite view, I think. 16 He kind of likes the single worst hour, but 17 had sort of the same comment that I did, is 18 what we would really like to do is be able to 19 see what is the difference between those two 20 approaches. What is the real difference in 21 the chemistry and, by inference, the kinds of 22 sources that you would be going after if you202-234-4433 Neal R. Gross & Co., Inc. Page 302

1 used the one metric versus the other. So that 2 is kind of the advice we have. 3 I actually think you could get to 4 that in the policy assessment. It doesn't 5 necessarily need to be illustrated here, but 6 just to know what the difference is would be

7 useful. 8 What else? So my last point about 9 that was the -- well, maybe it is obvious. I 10 don't know that we have many one-hour control 11 strategies that we could -- especially for 12 sulphate and nitrate -- that we are going to 13 be able to implement to fix that one hour 14 uniquely, on the one hand; and on the other 15 hand, just to me, a day where it is hazy all 16 day is more objectionable to one where it is 17 briefly hazy. 18 Those are policy kinds of 19 considerations. The point, though, is that it 20 would be useful to really see what is the 21 difference somewhat more clearly. 22 That is all I have to say about Page 303

1 it.

2 CHAIRMAN SAMET: Lowell? 3 DR. ASHBAUGH: Okay. I really 4 like the tile plots as well. I don't know if 5 I would want them on my wall, but they are an 6 excellent way to show the data and show what 7 we are looking at. I like that a lot. 8 One thing I would like to see 9 different in them is to have them scaled such 10 that you can compare one year to the next, 11 just straight across without having to jump 12 around, and especially for, like, Houston and 13 Phoenix where there is nine months. Scale it 14 so that they apply to just the nine months of 15 the year, so it is easier to see on the page. 16 I also like the staff 17 interpretation of the plots, but I do think 18 that in Section 3.4.4 you can have more of a 19 discussion like you have in 3.4.5. I have 20 some examples in my written material. I won't 21 read them all, but it is just a little bit 22 more of a description of each city and what is Page 304 1 going on in it. I think it would be useful to 2 have it there. 3 I do like the extinction budgets 4 shown in the top 10 percent of days and the 5 top individual 10 percent daylight hours, and 6 the interpretation of those plots is good

7 also. 8 I am very concerned about the high 9 contribution of light extinction at L.A., for 10 example, from the coarse PM on those two days. 11 I have done an awful lot of data validation 12 for improved data. Even though I know there 13 can be high hours and extremely high hours of 14 PM-10 mass, I also know there can be errors in

15 the data. 16 I just question data that looks 17 that way. So on the one hand, I like seeing 18 both those displays, but on the other, I tend 19 to question some of those extreme outliers, 20 and they, in fact, be wrong. It may just be

21 an error. 22 So I would like to have some Page 305 1 comment about that or at least acknowledge 2 that that is a possibility, because it happens 3 all the time, I think far more than people 4 realize when they are looking at monitoring

5 data. 6 Also, the explanation that coarse 7 PM is determined in part from measurements at 8 Victorville could well explain problematic 9 measurements or problematic values at Los 10 Angeles and Riverdale. Victorville is 11 completely separate from those others. 12 A couple of other details: Sea 13 salt is an important constituent at coastal 14 sites, and I have a little problem with the 15 sandwich approach relying on mass closure to 16 determine hourly organic carbon, because that 17 presumes that all the other species are 18 accurately determined. Especially if you are 19 not including sea salt at some locations, that 20 could be a real problem. 21 I know Warren White has done some 22 work on that showing the prevalence of sea202-234-4433 Neal R. Gross & Co., Inc. Page 306 1 salt, especially in coastal areas. 2 Also, setting negative values to 3 zero -- this is on page 3-26 -- what I'm 4 looking at, the coarse PM introduces a bias in 5 the results. There are times when there is 6 not much coarse material there. It is all 7 fine, and there is enough uncertainty in both 8 the PM2.5 and the PM-10 measurements that you 9 can have a difference that is negative, just 10 because of the uncertainty. 11 By setting negative results to 12 zero, you introduce a bias into the results, 13 and that shouldn't be. That is -- it is real 14 important, I think, to retain negative values 15 as a measure of uncertainty during those low 16 mass periods. 17 I will just say it one more time. 18 I have grave doubts about those outlier 19 concentrations at L.A. and St. Louis. I would 20 like to see those looked at long and hard. 21 I have a few editorial changes 22 that I put in the written material, but I Page 307 1 won't go through those here now. And that is 2 it. 3 CHAIRMAN SAMET: This is charge 4 1uestion 4. Other comments? Yes? Phil? 5 DR. HOPKE: One thing that I would 6 like to suggest is considering looking at 7 visibility based on PM2.5 only. Right now, 8 all of the things have included coarse, and 9 there are some significant measurement issues 10 that arise if coarse gets mixed in, some of 11 which were discussed two weeks ago at the 12 Ambient Monitoring Subcommittee meeting. 13 The key question is whether -- you 14 know, again, given the heterogeneity of the 15 coarse and other sorts of things, it might 16 make more sense -- again, originally, the last 17 go-round, it had been proposed to use fine PM 18 mass as the indicator for urban visibility. 19 The vast majority of the urban 20 visibility degradation is fine particles, and 21 I think it would be helpful to understand a 22 little better as to how much of a difficulty Page 308 1 would really there based on the data if we 2 were to only base it on a fine particle 3 visibility data standard rather than a PM-10 4 or a PSP visibility standard. It would 5 simplify things considerably to have a fine 6 particle visibility standard. 7 CHAIRMAN SAMET: Okay, other 8 comments on charge question 4? Ted, do you 9 have anything you want to add on this? 10 DR. RUSSELL: Actually, just if 11 you look at the schedule originally, there was 12 thought that I might talk a little bit about 13 what did occur, as Phil said, about two weeks 14 ago. 15 We had the Ambient Air Monitoring 16 and Methods meeting that was in response to a 17 white paper that EPA put together that asked 18 us to lend our advice on the approaches that 19 they were going to do to use to measure light 20 extinction directly in response to having a 21 light extinction standard. 22 So that meeting was about two Page 309 1 weeks ago where we looked at some of the 2 methods, as Phil and Rich discussed, in terms 3 of instrument limitations or problems, looking 4 at coarse particles. 5 The thought was that, again, it 6 might be best, it might be much more 7 technically feasible if we looked at smaller 8 particles essentially limiting it to the 9 PM2.5, and we probably would not be losing a 10 significant fraction of the visibility 11 extinction, also the issue associated with 12 relative humidity. 13 So that meeting -- we really 14 looked at the type of methods that were 15 available and basically came to the conclusion 16 that the methods are available. I mean, right 17 now they might be looking at a combined 18 nephelometer-ethylometer pair, one measuring 19 scattering, the other one absorption, but 20 there are some interesting methods that could 21 become available shortly that would do it all

22 at once.202-234-4433 Neal R. Gross & Co., Inc. Page 310 1 So it is technically feasible, if 2 you want. There is the report from that 3 meeting where we addressed the white paper 4 that they developed and their charge 5 questions. It is available, if you want to 6 see it. Holly could get you a copy. I think, 7 actually, it is posted, isn't it? 8 We basically came down to say that 9 it is technically feasible to do it. There 10 are -- the instruments are available. It 11 would probably be better to look at just PM2.5 12 and to keep it at 90 percent or below relative 13 humidity. 14 That was it.

15 CHAIRMAN SAMET: Thanks. Can I 16 ask a general question for the visibility 17 mavens or whatever you are. 18 DR. HOPKE: The key also is to 19 note that, just like economists, we need only 20 one-handed visibility people. 21 CHAIRMAN SAMET: The question, 22 though: With the risk assessment we asked for Page 311 1 this Chapter 6, this integrative chapter, and 2 this one sort of just comes to a halt in its 3 specifics, and without sort of pulling things, 4 in a way, together. 5 Does this need an integrative 6 chapter as well? I don't know. For those of 7 you who looked at it, would that be helpful, 8 in part for communicating more generally about 9 the findings. There is a lot of detail, and 10 this is important. This is going to represent 11 a new step forward, if we end up with a 12 visibility standard. 13 So I guess I would question, when 14 you look at the structure of the document, 15 would it benefit from -- I guess it would be 16 a Chapter 4 -- or 5? Five. Sort of an 17 integrative summary kind of chapter. 18 DR. HOPKE: Or an executive 19 summary. 20 CHAIRMAN SAMET: Yes, or something 21 to bring it together. 22 DR. ASHBAUGH: I do think an Page 312 1 executive summary, something to tie it all 2 together, would be useful. 3 CHAIRMAN SAMET: Yes, and whether 4 it comes at the beginning or the end, but just 5 -- It is good. There is a lot of detail. 6 There is an awful lot to bring together. 7 DR. ASHBAUGH: And, actually, I 8 wouldn't mind seeing it at the beginning to 9 tie it together, and then everything else in 10 the back. That would be good as well.

11 CHAIRMAN SAMET: So some form of 12 summary somewhere, I think. Other comments on 13 this, visibility? Well, here we have public 14 comments tomorrow. So remember that, and then 15 we also will hear from Tom, I guess, if he 16 comes on later today or tomorrow morning. I 17 think Rich and Joe. 18 MR. POIROT: Just to follow up on 19 Lowell's point about the, in some cases, 20 apparently illogical high coarse 21 contributions. I, in my written comments, 22 include an example from St. Louis that I Page 313 1 thought helped show that those seemed 2 illogical, plus talking to the locals on the 3 ground there, they say, oh, don't use that

4 data. 5 On the other hand, what I would 6 like to -- well, let me set this up a little 7 bit. You know, I was a strong advocate in the 8 last review cycle for the use of the sub-daily 9 secondary fine particle standard as a 10 visibility protective standard. 11 I thought it was a good idea then. 12 I still think it is a viable idea. So I've 13 got that bias and that pre-stated position, 14 and a lot of energy went into it and so on and 15 so forth. However, I really like this new 16 proposed PM light extinction indicator. 17 Frankly, I didn't know that the lawyers would 18 let us do this last time around. We didn't 19 know it was an option. 20 I would like to really emphasize 21 what I think is its strength. We have got the 22 opportunity here to have an indicator that is202-234-4433 Neal R. Gross & Co., Inc. Page 314 1 the effect, that is very close to being the 2 effect. Imagine, all you health guys, if you 3 could have some incidence of cardiac 4 mortality, and that would be the indicator of 5 whether or not we were attaining the standard. 6 Well, that is about how strong 7 this relationship is. So because of that, I 8 think it would be desirable to try to keep 9 this indicator actually as pure and direct as 10 we can. Light extinction and even PM light 11 extinction is an actual physical 12 characteristic of the atmosphere. It exists. 13 It preexists, it's out there. 14 There are some issues about 15 whether and how we can best measure it 16 properly, but it is a real thing. So I would 17 like to kind of -- even though I have been 18 critical of some of the coarse particle 19 calculations and it is difficult and the 20 instruments don't respond well and all those 21 kinds of things, I would like to keep on the 22 table and keep advocating for the keeping of Page 315 1 the indicator as being PM light extinction. 2 Even if we are not currently able, 3 for example, to measure every aspect of it 4 with great satisfaction, I think, rather than 5 compromise the indicator, I would like to 6 advocate in the other way. Let's keep this as 7 a good conceptual indicator, and count on 8 being able to quantify it better and refine it 9 better in the future.

10 CHAIRMAN SAMET: Joe. 11 DR. BRAIN: It sounds like opening 12 another box of Pandora's, but I guess my 13 question was: You know, this is a little bit 14 like sophisticated, ever more sophisticated 15 measurements of blood pressure or BMI without 16 thinking about the consequences. 17 So do you want to include anywhere 18 in here, somewhere, you know, what are the 19 consequences of poor visibility? What effect 20 does it have on mood, traffic accidents, 21 housing values? We have seasonal affective 22 disorder. Is there PVAD, poor visibility Page 316

1 affective disorder? Is it related to 2 exercise? 3 Seems to me it is sort of missing 4 -- even if it is highly speculative, what is 5 the human impact of poor visibility? Where is 6 that? Where do we deal with that? 7 CHAIRMAN SAMET: You know, we did 8 have some of that kind of discussion last 9 time, and I think that had to do with sort of 10 this as a -- the visibility as an indicator 11 and its human implications. I guess maybe 12 none of that is written. 13 DR. STALLWORTH: Bill Mullen made 14 the point that these questionnaires that ask15 people to think about visibility only in non16 health terms are flawed, because he thinks 17 visibility does have health impacts, and it 18 should be incorporated in the questionnaire. 19 That is what he said in his comments. 20 DR. BRAIN: Then maybe we need 21 someone on the Panel who is really a 22 psychologist or -- people exercise less when Page 317 1 the visibility is poor. That would have a 2 real health impact. I don't know. Seems to 3 me, there are lots of potential implications. 4 We all know we feel -- you know, you said how 5 great it is when you look out your window and 6 you can measure the visibility. Are you much 7 easier to get along with and a better boss 8 when the air is clean? We will have to ask 9 your wife about this. 10 CHAIRMAN SAMET: It is not about 11 me. But I think it is almost implicit, but it 12 is not explicit. The definitions of health 13 all incorporate well-being or the WHO charter 14 definition 1948 does. 15 This question of what constitutes 16 an adverse health effect, if you go back to 17 the most recent -- I guess it is the 2000 ATS 18 statement -- these notions are there. Maybe 19 this at least needs to be some preamble 20 discussion of these issues. 21 DR. BRAIN: I think so. The 22 silence seems conspicuous, that it doesn't202-234-4433 Neal R. Gross & Co., Inc. Page 318

1 mention -- 2 CHAIRMAN SAMET: Maybe it goes 3 back to -- I do remember this discussion at 4 our last meeting, and maybe Bill had some of 5 this in his comments. I just don't recall. 6 MS. SANDIFORD: We are going to 7 address that a little bit more in the policy 8 assessment. There was discussion in the ISA,

9 and we are not sure how much of that 10 information is useful in informing a selection 11 of different aspects of standards, but we 12 definitely will be looking at that a little 13 bit more. 14 DR. BRAIN: I mean, in the absence 15 of that, how do you use the information, if it 16 has no impact on anything or anybody? So 17 what? It seems to me, without that, where are 18 you? 19 MS. SANDIFORD: Well, the public 20 welfare effect that we are looking at is not 21 only the aesthetics of visibility impairment22 but the impact on a person's sense of wellPage 319 1 being. That is part of the definition. So it 2 is part of the scope of what we look at. 3 What we are not clear about is 4 what kind of information on exposure duration 5 is in those studies, and we will need to go

6 back and look at that some more.

7 CHAIRMAN SAMET: Are there other -

8 - 9 DR. GRANTZ: I have a question. 10 Are there any other data besides those four 11 city preference studies that you can use for 12 that purpose?

13 MS. SANDIFORD: For the 14 psychological impacts? Yes. Oh, I think so. 15 The ISA list -- Chris is over there in the 16 audience, but there are a number of different 17 studies that were identified where they made 18 this link between the visual air quality and 19 the effect on a person's sense of well-being, 20 but we have not investigated those to the 21 degree that we know what it says about the 22 duration of those exposures, whether they are Page 320 1 explicit about that or not. 2 DR. GRANTZ: But in terms of 3 magnitude for whatever period, they are 4 explicit?

5 MS. SANDIFORD: Yes. 6 CHAIRMAN SAMET: Okay. It is 3:30 7 on Wednesday. We have done the risk 8 assessment. I think we have finished the 9 visibility. I think the question of what we 10 do next is something to discuss. 11 We still will hear from Tom when 12 we can hear from Tom about the visibility 13 document. We have two public commenters. I 14 am tempted -- and I think this is something we 15 can discuss. 16 We are ready to maybe take a 17 break, and then possibly move on to discuss 18 the charge questions for the policy 19 assessment, to take a look at those, and then 20 we can revisit this tomorrow after people have 21 had a chance to sort of at least perhaps skim 22 through the policy assessment document.Page 321

1 I think the other alternative for 2 the committee is to take a break and then come 3 back here and work. Notice I didn't say like, 4 you know, go for a walk or anything like that.

5 So come back and work on comments. 6 I think going through the charge 7 questions might be useful and perhaps frame 8 what we want to look at, particularly, tonight 9 in the policy assessment, so we can come back 10 tomorrow with perhaps a sharper discussion 11 than it would be. So would that work for EPA? 12 Do we have the right people around? 13 DR. MARTIN: Well, no. 14 CHAIRMAN SAMET: Well, Lydia is 15 not here in any case. Right?

16 DR. MARTIN: Pardon me? 17 CHAIRMAN SAMET: Lydia was not 18 coming. Right?

19 DR. MARTIN: Some of the other 20 staff -- well, okay. Beth is here. We can go 21 through sort of walking through those charge 22 questions now, and then come back in the202-234-4433 Neal R. Gross & Co., Inc. Page 322 1 morning and get further thinking on them. 2 CHAIRMAN SAMET: I think that is 3 what I was thinking. We can take a break, and 4 then reconvene. Is that okay with everybody 5 to do that? Okay, all right. 6 DR. SPEIZER: Jon, I think we will 7 work tonight to create the comments for the 8 Administrative letter. 9 CHAIRMAN SAMET: I think, ideally, 10 we would get some writing done tonight, too, 11 yes. 12 DR. PHALEN: A couple of questions 13 regarding travel. When do you see us 14 finishing up tomorrow? 15 CHAIRMAN SAMET: I actually really 16 don't know. Presumably, we are going to 17 finish earlier, but I think it is a little

18 hard to -- 19 DR. PHALEN: Too early to start 20 finding earlier flights home?

21 CHAIRMAN SAMET: We could start at 22 7:00 a.m. and get done quite early, but we Page 323 1 probably won't. I think let's come back to 2 that, Bob, and see. I think, in part, one 3 reason to look at the charge questions is to 4 try and sort of maybe frame our discussions 5 for tomorrow and how big they are going to be. 6 So let's take a 15-20 minute 7 break. Phil, good trip back. 8 (Whereupon, the above-entitled 9 matter went off the record at 3:31 p.m. and 10 resumed at 4:02 p.m.) 11 CHAIRMAN SAMET: I think what we 12 will do is spend some time going through these 13 so we can, if nothing else, preview what we 14 are going to be asked to do in looking at this 15 draft and again, I think, maybe sharpen our

16 comments.

17 Remember that we do have a -- 18 right now it is two half-days scheduled for 19 discussion of the first draft. So there is a 20 fine dividing line between talking about the 21 charge questions and what we are going to be 22 doing and how we are going to approach it and Page 324 1 talking about the first draft. So let's try 2 and stay on the charge question side of the 3 divide, wherever it may be. 4 Who is going to lead this -- the 5 presentations? 6 MS. HASSETT-SIPPLE: So in 7 thinking about these charge questions, we 8 initially thought that we had overarching 9 questions that applied to the four substantive 10 chapters. You have a chance to look at the

11 document. 12 Chapter 1 is just introduction, 13 organization, a little bit of PM history, but 14 we really tried to focus on more details for 15 the history, and recent litigation, the 16 chapters where it was most applicable, so in 17 Chapters 2, 3 and 4. 18 Looking overall, we felt that 19 there was some general questions that applied 20 to all of the substantive chapters, 2 through 21 5, on organization and technical content. So 22 the first charge questions that you see relate Page 325 1 to those four chapters. 2 So I don't know if you want to 3 take the time -- I don't know if you have had 4 a chance to read through them or if you want 5 to take a few minutes to look through them. 6 DR. LIPPMANN: Without knowing 7 what is in it, we can only ask some questions. 8 I would be curious about how margin of safety 9 will be dealt with, because that's, I think, 10 clearly a policy relevant issue. And with PM, 11 one can argue that there is no margin of 12 safety as long as we assume linearity and some 13 response as adverse, if mortality is an 14 adverse response at any level.

15 So how can a document address 16 setting a margin of safety under that kind of 17 constraint? 18 MS. HASSETT-SIPPLE: Well, 19 remember that this document is to inform the 20 Administrator's decision. So ultimately, it 21 is the Administrator's judgment on how she 22 views setting standards with an adequate202-234-4433 Neal R. Gross & Co., Inc. Page 326 1 margin of safety. 2 What we have tried to do in 3 Chapter 2 -- and I can't speak as much about 4 Chapter 4 and 5, and Chapter 3 is almost a 5 promise to come, because we really need to get 6 some feedback from CASAC and the public. But 7 in Chapter 2 on fine particles and the primary 8 standards, what we have put together with our 9 preliminary draft conclusions are to talk 10 about, if we put more weight on different 11 parts of the evidence, where it leads us 12 toward as far as level, and it's really -- 13 this first draft is really all about level 14 with some initial discussion of form for the 15 annual standard, but the preliminary 16 conclusions we reached on indicator, and I 17 don't know if you've had a chance to look at 18 it, basically are reaffirming what we have 19 done in the past, recognizing that there is 20 new information to look at, to inform our 21 understanding of alternative indicators, but 22 not enough at this point in our preliminary Page 3271 conclusions to reach us to move beyond a mass2 based standard. 3 The same with the averaging time, 4 that where we see still evidence to support 5 the 24-hour and annual standard, that the 6 other averaging times that we have considered, 7 we felt that the information at this point was 8 insufficient to support alternative averaging 9 times. 10 So you will see the discussion 11 later on really focus on level, and what we 12 have tried to do is to outline our approach 13 with how we address the evidence based and the 14 risk-based considerations and discuss, if we 15 put more weight on this part of the 16 information, it would lead us toward this 17 conclusion; if we put more weight on this part 18 of the evidence or the risk information, it 19 would lead us to this conclusion. 20 So we have tried to balance that 21 discussion out. Namely, I think you will see 22 that in Section 2.3.6 where we talk about the Page 328 1 interplay between the two standards. 2 DR. MARTIN: If I could just add

3 to that. 4 DR. LIPPMANN: An artful way of 5 not answering my question. 6 DR. MARTIN: I do want to speak 7 more specifically to your question about 8 margin of safety. 9 What you will not find in this 10 first draft discussion is a lot of discussion 11 about margin of safety. There is certainly 12 not going to be any formula, and there never 13 will be and, if you look at past history, we

14 all know there never has been. 15 What the past decisions -- the 16 approach used in past decisions and likely the 17 approach used in this decision is a balancing 18 of where do I see the strongest evidence, at 19 what point do that evidence become uncertain, 20 do I see concerns about susceptible 21 populations that might not be part of the 22 strongest body of evidence, do I have reason Page 329 1 to think I need to go somewhat lower in a 2 level of a standard in order to provide 3 protection for susceptible populations? 4 There are shades of gray issues, 5 but in this first pass through, what we were 6 trying to do was outline different ways of 7 thinking about the information from the 8 studies and how that might translate into a 9 basis for a standard; and based on, hopefully, 10 input from you all to shed further thinking on 11 those approaches, in the second draft I think 12 we can be more sharp, more sharply address the 13 question of over what ranges do we think we 14 have a stronger basis, and then to what extent 15 might one go beyond, and what would be the 16 additional protections that you are trying to 17 create this margin of safety to protect 18 against? Is it simply to protect against 19 generalized uncertainties or is it to protect 20 against specific types of effects for which we 21 might have far less information, but 22 continuing concerns that may be setting202-234-4433 Neal R. Gross & Co., Inc. Page 330 1 something at the same level as mortality 2 protection wouldn't be equally protective. 3 So I think we need to take a first 4 pass through and get your feedback on this 5 first draft, and then sharpen that discussion 6 in the second draft. 7 DR. LIPPMANN: Well, I hope then 8 that, perhaps with the meeting, the 9 teleconference meeting where we discuss this 10 first draft, we could tee up the remaining 11 policy related issues before you get too 12 serious a final writing on it. 13 CHAIRMAN SAMET: But I think the 14 point about when we see some of the really 15 heart of the matter issues in the first draft, 16 second draft, I think, is critical, because 17 this is our first draft, and we know there are 18 things left to be done, some of which are the 19 most critical. 20 So in effect, we will have one 21 shot at commenting on them. I do have a 22 question about sort of these charge questions Page 331 1 1 and 2, and you might just -- in helping us 2 think about addressing them, when you read the 3 document, as you said, it is way too long just 4 to get it in, and how much of the text -- in 5 other words, you have gone back and pulled 6 material on specific studies you found 7 particularly relevant. 8 Just in terms of our understanding 9 of where that text comes from, are you just 10 sort of lifting it out of the ISA or the risk 11 assessment or is this -- because there are 12 segments of text that are quite detailed about 13 individual studies and so on. Is this sort of 14 stuff we have already seen? Is it a rewrite? 15 MS. HASSETT-SIPPLE: Well, where 16 it is exactly lifted from, the ISA or the risk 17 assessment document, it should be in 18 quotations. So that gives you a clue. But we 19 have not brought in any new evidence beyond 20 what is discussed in the integrated science 21 assessment or the additional analyses that 22 have been done or air quality analyses, not Page 332 1 additional risk-based analyses. 2 So the evidence really builds upon 3 the information in the ISA and the information 4 that we put into Chapters 2, 3, 4 and 5, 5 actually. It is just enough to help us tell 6 the story, to be able to present enough 7 information to support the rationale. 8 CHAIRMAN SAMET: Okay. So for 9 example, I am looking at page 239 where there10 is a whole paragraph on a number of new multi11 city and single-city studies, and lists out 12 all these studies. Some of them even come 13 from Harvard. But I guess my point is, none 14 of this is in quotes, and so I just really 15 want to know -- you know, this looks like the 16 kind of material we have been reading a lot of 17 in the other documents. 18 Are you sort of reformulating all 19 this or, when you come to these sort of 20 descriptive things -- and same is true for the 21 risk assessment -- are you just sort of moving 22 things over that are particularly important, Page 333 1 and then, if so, how do you decide? 2 I think that would be something

3 useful as we look at the structure and

4 content. 5 MS. HASSETT-SIPPLE: So I think, 6 particularly in this section which is 7 associations with short-term PM2.5 exposures, 8 and there is an analogous section for 9 associations with long-term PM2.5 exposures 10 that begins on 2.2-34, what we are really 11 looking at here was, in addition to the 12 information from the ISA, to bring in more 13 information about what we knew about the air 14 quality distributions evaluating those 15 studies. 16 So I mentioned this morning that 17 in CNO EPS -- and CNO used information 18 reported in the studies, air quality 19 information reported in studies. We needed 20 more information on the air quality 21 distributions, and we sent out a request for 22 information to a number of study authors,202-234-4433 Neal R. Gross & Co., Inc. Page 334 1 including many people in this room. 2 We used that information. Some of 3 that was reported in the ISA, and then we are 4 bringing in even additional information here 5 on the air quality levels associated with the 6 studies. 7 CHAIRMAN SAMET: Let me just pose 8 a question to the Panel. If you look, for 9 example, at Charge Question 2 -- and we have 10 been working with EPA staff to try and bring 11 as much specificity as possible to these 12 charge questions. 13 So these kinds of questions: To 14 what extent does the Panel find that the first 15 draft PA accurately reflects and clearly16 communicates the currently available -- blah17 blah-blah, including important uncertainties 18 as characterized in the final ISA? 19 So I know what you mean, I think, 20 but do we want more specificity in these kinds 21 of charge questions? We had pushed back a 22 little bit to say give us a little more about Page 335 1 what you mean, and part of this does relate to 2 how much information should get moved over the 3 length and content. 4 So if we are left with this, is 5 our question could there be better 6 communication, should there be shorter 7 communication, should there be more something

8 else? 9 So let me just throw that out for 10 discussion, because we are going to end up 11 answering this in some way. The answer is not 12 45 percent or 85 percent. It is something, 13 you know, of some different sort from that. 14 So this is a chance to bring some greater 15 specificity here. 16 MS. HASSETT-SIPPLE: And I think, 17 as Karen said this morning, we recognize that 18 this is longer than you wanted. I think Dr. 19 Brain actually said 100 pages, and this 20 chapter alone is 100 pages. 21 So where you can give us guidance 22 on what is most useful and what is information Page 336 1 that you feel is not useful for the policy 2 assessment that might be extracted or 3 tightened up quite a bit, that would be very 4 helpful feedback for us. 5 CHAIRMAN SAMET: Rogene, do you 6 want to comment on this sort of charge 7 question? You have seen a few of these in 8 your day.

9 DR. HENDERSON: Some of the 10 questions -- they are so broad. It is like a 11 motherhood statement, and so it is easy to 12 say, it was all right, you know, without 13 taking it seriously. 14 So I think the more specific your 15 question can be about the information you 16 really want from us, the better our answers 17 will be, because -- I mean, that is my -- 18 CHAIRMAN SAMET: Yes. I think 19 here, for example, you could certainly say, 20 you know, is the right amount of information 21 being transferred? What is the right amount 22 of information? Are we too much, too little? Page 337 1 Maybe we could just try and bring some 2 specificity here. 3 I think this would be helpful to 4 us as we write our review, so we write as 5 focused as possible, I think. 6 DR. HENDERSON: It depends on what 7 you want from us. If you want specific 8 information, you had better ask it. 9 DR. MARTIN: If we think about 10 Chapter 2, where you have pointed us to a 11 page, and we think about this kind of 12 technical content question, one way to make 13 the questions more specific is to say: In the 14 discussion of long term exposure studies, does 15 the Panel agree that appropriate -- that this 16 is an appropriate set of studies to call out 17 for specific attention? Would the Panel 18 recommend -- you know, what are the Panel's 19 views about giving equal weight to these 20 studies? Are there some studies that should 21 be given greater emphasis for whatever reason, 22 other studies given less emphasis?202-234-4433 Neal R. Gross & Co., Inc. Page 338 1 One could be specific in that way 2 and, if that is more helpful to you to help -- 3 CHAIRMAN SAMET: Well, I think 4 that the question is how to -- 5 (Simultaneous speaking.) 6 CHAIRMAN SAMET: -- and I think 7 again, I think we have been able to work 8 together to try and get these sharper. I 9 think these are -- I think this will come up 10 as we turn the page and look at some of the

11 others. Mort? 12 DR. LIPPMANN: Two things. One, 13 we have less than a month, almost a month but 14 less than a month, before we get into a review 15 session. What can we as individuals do in 16 that time to facilitate the formal meeting? 17 If some of us want to go through 18 this paragraph by paragraph, even line by 19 line, and make very specific comments, how 20 would Jonathan then organize integrating that 21 in time? 22 Should we be circulating our Page 339

1 individual comments to each other in advance? 2 of course, that has not been the recommended 3 procedure for most meetings. I have ignored 4 that and done it sometimes. 5 Considering that we have under a 6 month to get ready for this meeting, what 7 should we be doing? 8 CHAIRMAN SAMET: So now we will 9 certainly come back to have a discussion about 10 our approach and getting some assignments 11 made, because you are right. The time is 12 short. This is, of course, our first view of 13 both the draft and these charge questions, and 14 I think by the time we leave tomorrow, we 15 should be at a point where we have identified 16 who will be taking on what. 17 I am sure it would be helpful to 18 EPA that, as we all read through this, if we 19 have specific comments to make, we should and, 20 I am sure, will make those comments, 21 regardless of who is charged with specific 22 responses. Page 340

1 I think what I am most concerned 2 about now is trying to make sure we have some 3 sharp enough questions for this document that 4 we can really answer them. I think, if you 5 look at number 3, approach, 3a: Example, what 6 are the Panel's views with regard to general 7 approach of considering and integrating 8 evidence base and risk-based considerations? 9 You know, taken at its most 10 general level, if we gave you our views, we 11 would answer the question. I think you really 12 want to know sort of have you taken the right 13 general approaches as set out, as described. 14 I think, in part, because this is 15 a public process and we are responding to 16 questions from you, that it should be clear 17 about what is going on. 18 We have answered these kinds of 19 questions enough to be able to understand 20 probably what you are really getting at, but 21 I don't think the question exactly says. It 22 is sort of code, and I think we should try and Page 341

1 do a little better than that. 2 If you ask the Panel's views, you 3 could probably get two views from each person, 4 if you wanted. So I think, again, it is just 5 a question of what do you want out of this? 6 DR. LIPPMANN: Jon, one follow-up, 7 specifically to what Karen just said. The 8 risk assessment document has already decided 9 which studies are appropriate for risk 10 assessment. So are we to say some of these 11 are not influential on policy choices? Are we 12 locked into having the relevant studies being 13 those that were relied on in the risk

14 assessment? 15 MS. HASSETT-SIPPLE: I think that 16 you will see that the studies are important 17 for the quantitative risk assessment are 18 studies that are also important for the other 19 space considerations. 20 We are also looking at a broader 21 range of studies that for -- in the criteria 22 to select studies that would be useful for the202-234-4433 Neal R. Gross & Co., Inc. Page 342 1 quantitative risk assessment, some of those 2 studies might have fallen out, one, because we 3 didn't have good baseline incidence data for 4 the population being studied. 5 You know, there may be a number of 6 different reasons, but the evidence-based 7 considerations in the policy assessment takes 8 a broader look at the body of scientific 9 evidence, but it would include all of the 10 studies that were considered in the 11 quantitative risk assessment. 12 DR. LIPPMANN: In other words, we 13 start as a given with those that have been 14 vetted and used in the risk assessment, but 15 can suggest that other studies can be also 16 considered vital to the policy assessment.

17 CHAIRMAN SAMET: Joe. 18 DR. BRAIN: I guess my question is 19 -- and maybe this should be a charge question 20 -- does the document do what it is intended to 21 do? So the question is, you know, what is it 22 intended to do? I guess it is a summary for Page 343 1 the Administrator and for the public to read 2 and help them make a decision. 3 So it seems to me it is now so 4 long and complicated. I mean, do we really 5 expect the Administrator to look through all 6 this and come to a conclusion? 7 I mean, shouldn't it be 15 or 20 8 pages, and shouldn't it clearly -- seems to me 9 somewhere there should be a clear statement, 10 what is the current standard; what are the 11 alternative standards that you should 12 consider, and then a really very brief summary 13 of the evidence supporting these different 14 alternatives where, if she wants more, the 15 public wants more, then point them back to the

16 ISA or to the REA. 17 I am just sort of -- I guess 18 another question is, you know, is there an 19 ideal format for what has really worked in the 20 past for the Administrator to make a decision? 21 What do Administrators like? 22 This seems too long, too detailed, Page 344 1 not sharp and pointed enough to really serve 2 those purposes, or am I wrong? I don't know. 3 DR. MARTIN: I would like to speak 4 to that. The audience for this document right 5 now is not the Administrator. I mean, this is 6 a set of preliminary staff conclusions that we 7 have documented that lays out different ways 8 of thinking about the information, and a full 9 array of information, and we are coming to you 10 and the public to solicit your input. This 11 isn't for the Administrator. 12 DR. BRAIN: Then there is 13 something later?

14 DR. MARTIN: We wouldn't talk to 15 her until we have a final document that has 16 been through the review of this committee, 17 that has then received public comment, so that 18 we can have a more well-informed set of views 19 to present to her, and to present to her your 20 views on this. 21 So I wouldn't think about this 22 document as the set-up for the Administrator Page 345 1 making the decision. This document is to set 2 up different approaches to creating a 3 rationale for going from the science, the 4 risks, to standards, and that we need, in our 5 opinion, to lay it out fully enough to get 6 well informed input from you all. 7 If all we did was present summary 8 conclusions, what we would then have to share 9 with you is just do you agree with our summary 10 conclusions, and it becomes more a matter of 11 intuition than a matter of a well-reasoned 12 approach to going from the underlying studies 13 to the basis for a standard. 14 So we feel pretty strongly that we 15 need to put enough in this document so that we 16 can reasonably elicit from you your thinking 17 on rationales, and to create a very summary 18 form, in the end, of course, that is what gets 19 communicated to her. But it will be based on 20 having gone through this process first so that 21 we have a better understanding of what it is 22 we feel is most appropriate to communicate.202-234-4433 Neal R. Gross & Co., Inc. Page 346 1 DR. BRAIN: So I am learning. So 2 there is a fourth document that will follow 3 this? 4 DR. MARTIN: There are internal --

5 DR. BRAIN: Because that sounds 6 like the really important one. I could also 7 imagine you would look at that document and 8 say, you know, how do I then marshal evidence 9 to support the final recommendations that does 10 go to Lisa Jackson. 11 DR. MARTIN: What will go to her 12 will be reflective -- obviously, she will get 13 a copy of the document, but we go through an 14 internal decision making process that includes 15 a series of briefings that goes up through 16 agency management, through the head of the Air 17 Office, through the Administrator, and those 18 briefing packages do a lot like what you are 19 describing: Here is what the standard is. 20 This is what we said before. This is the new 21 evidence. This is how we have thought about 22 the evidence. These are our -- a range of Page 347 1 considered opinions. This is what CASAC has 2 to say. This is what the public has said. 3 It provides that kind of overview, 4 but it has this as the underpinnings for it. 5 So that when we get questions about, well, why 6 did you say that, why did they advise that, we 7 can point to the basis for it and ground it in 8 the information that we have. That is how the 9 process works. 10 DR. BRAIN: But even there, 11 wouldn't it be helpful to have a simple 12 statement somewhere in here at the beginning 13 or the end of the current standard, you know, 14 which -- and then the alternative standards. 15 I mean, I guess I also sort of missed the 16 sunset chapter that sort of pulled the whole 17 document together. That maybe didn't prejudge 18 the next level that you are talking about, but 19 at least sort of laid out -- you know, it 20 didn't give the final -- how the novel ended, 21 but it said, you know, here are three or four 22 possible ways the novel will end. Page 348

1 It is hard to see the forest for 2 the trees still. 3 DR. MARTIN: And it also -- 4 obviously, you need the opportunity to really 5 read through the document to better understand 6 what is here. Each chapter ends with a very 7 succinct summary of what preliminary 8 conclusions we have reached with regard to 9 adequacy, indicator, time level and form. 10 We see that the primary standards 11 for fine particles, standards for thoracic 12 coarse particles, visibility standards as 13 being discrete decisions that need to be made, 14 but each of those chapters ends with a very 15 succinct summary of the preliminary 16 conclusions presented in that chapter. 17 DR. HENDERSON: I have a question. 18 I think what Joe is describing is what we used 19 to see in the staff paper, and I have seen -- 20 I have heard the policy assessment described 21 as the new staff paper. In some ways, I see 22 that in here. Page 349

1 There are a lot of things that I 2 heard Joe asking for that are here. You know, 3 the very good introduction gives you the past 4 history, et cetera, and gives you the approach 5 for choosing a primary standard, and adequacy

6 of current standard. 7 I like the organization, but if 8 this -- this isn't the staff paper. You 9 sounded as if there was going to be another 10 document after this that would summarize it 11 more like the staff paper where the last 12 chapter says, you know, these are the 13 alternatives that we are recommending for 14 consideration by the Administrator. 15 So if this isn't that, we don't 16 get to see what is being recommended. 17 DR. MARTIN: But, in fact, this is 18 that, and each chapter does end with a 19 succinct summary, just as the staff papers 20 did. 21 DR. HENDERSON: Okay. So there 22 were summaries. I had a chance to look it202-234-4433 Neal R. Gross & Co., Inc. Page 350 1 over, speed read it, and I like the 2 organization of it. 3 DR. MARTIN: And if, in the end, 4 by the time we make this document final, it is 5 a very simple matter to create a two-page 6 overarching summary of the conclusions on each 7 of the four areas and put that right up front. 8 That is simply copying the bottom lines from 9 each chapter, and pulling them together. That 10 is a very easy thing to do. 11 DR. HENDERSON: I think that --

12 DR. MARTIN: That we have to 13 decide based on input, what the appropriate 14 the conclusions are. 15 DR. HENDERSON: Okay. Well, I 16 think that would be good. It would be a good 17 summary to have. But I did like the 18 organization of it. 19 CHAIRMAN SAMET: As I said, there 20 is a fine line between discussing the charge 21 questions and discussing the document. 22 DR. HENDERSON: I'm so sorry. Page 351

1 CHAIRMAN SAMET: No, no, no. That 2 is all right. We haven't really gotten to the 3 charge questions yet, because I think there 4 are some thing is here we should discuss, 5 because actually, the more I look at them, the 6 more I am concerned that they are a bit too 7 vague, too long, too rambling, and that some 8 of them -- really, I think it would be helpful 9 for you to do some sharpening, and we have 10 done this in the past, again, in back and

11 forth. 12 We haven't done that yet, but as I 13 look at them -- and again, I think part of 14 this is just to say that here was the question 15 that we set out to answer, and here is our 16 answer. And if you can't quite tell what the 17 question is about, I think it is a process 18 issue and one where, I think -- and again, I 19 am just sort of staring at 4b. 20 If everybody can just look at 4b, 21 now I think there are some important ideas in 22 there, but even Sverre could not ferret that Page 352 1 question out. 2 DR. VEDAL: Well, it would be a 3 long answer.

4 CHAIRMAN SAMET: It would be a 5 long answer. But again, we are getting to a 6 very important set of issues there, but I 7 think, if you are asking us, you know, has the 8 quantitative -- what are you asking us in 9 that? I am not going to try and translate it. 10 DR. MARTIN: In your view, does 11 the quantitative risk assessment provide 12 support for the conclusion that the current 13 set of standards is not adequately health 14 protective. 15 CHAIRMAN SAMET: But that is not 16 what this question says -- "can reasonably be 17 judged to be" -- 18 DR. MARTIN: That is what the 19 first half of this says. 20 CHAIRMAN SAMET: Well, there is 21 another one almost like what you said that 22 comes earlier. That is the only reason I am - Page 353 1 - so I do think we should probably focus a 2 little bit on this. Chris, you had a comment? 3 DR. FREY: Well, it was in the 4 vein of this previous conversation, just a 5 sort of process question that might help me s 6 I review this tonight. 7 What is the relationship, if any, 8 between the policy assessment document and the 9 text that ultimately goes into the Federal 10 Register Notice of the proposed rulemaking? 11 Is it closely related or totally different? 12 DR. MARTIN: It is often very 13 closely related. 14 DR. FREY: Okay. So we should 15 think of the audience -- 16 DR. MARTIN: If you look at past 17 final staff papers and then read the Federal 18 Register proposal, you will find a whole lot 19 of stuff that is there, and that is one of the 20 reasons why we want to keep enough detail in 21 here so that, when we prepare a rulemaking 22 package, we aren't going out with untested202-234-4433 Neal R. Gross & Co., Inc. Page 354 1 presentations of information. 2 We are drawing from something that 3 truly has been vetted through this Panel and 4 with the public, and you will see a lot of 5 similarities. 6 DR. FREY: So you want -- so I am 7 interpreting, you want something that is kind 8 of self-supporting with enough information 9 that a member of the public could read that, 10 and it is a coherent story, and I think some 11 of the push-back you are hearing from our 12 group is, well, a lot of this is in ISA or the 13 REA, why not just cite those and then move on. 14 DR. MARTIN: I think that -- I am 15 really glad you asked that question, because 16 part of our motivation for doing the document 17 this way is we are looking for a vehicle to 18 then create a proposed rulemaking document, 19 and that rulemaking document has to convey in 20 one document for the public where have we 21 been, where are we now, what did we look at, 22 what was this based on, what is the basis for Page 355 1 the decision, what did CASAC say about it. 2 All those things are in it, and if 3 we make this document so thin because you all 4 have read the other underlying documents, 5 therefore, you don't need the repetition, now 6 then we have to create yet a new document when 7 we go to move forward with a proposal package, 8 and that won't have had the benefit of your 9 insights on how it was put together. 10 So that is what do. I should have 11 thought to say that more clearly earlier on. 12 That might have been more helpful. So I am 13 glad you asked that question.

14 CHAIRMAN SAMET: Helen. 15 DR. SUH: I have to say I am 16 confused still, partly because of my 17 preconceived notions prior to this what this 18 was meant to be. Now I am rethinking it a 19 little bit. 20 Partly, I think it is that the 21 PM2.5 body of evidence -- it has been carried 22 through quite a bit, because it is also in the Page 356 1 risk assessment. So there is quite a lot of 2 discussion about what were the studies that 3 were the most important and how everything was 4 carried through. 5 There is a little bit more of a 6 story where I could see the summary might be 7 more informative. But for the coarse 8 particles, quite a few things got tabled. It 9 was not included in the risk assessment. So 10 they got tabled, and they said in the risk 11 assessment supposedly that they would be 12 discussed in the policy assessment. 13 So I was expecting in some way a 14 little bit more analysis, so that the thoracic 15 coarse part would be treated a little 16 differently than the fine part. So it seemed 17 -- and I am trying to put that together with 18 what you have just talked about and how that 19 really fits, because then the -- because I was 20 expecting a little bit more, and now this has 21 the PM-10 as an indicator, for example, and 22 takes the scientific evidence perhaps one step Page 357

1 farther. 2 I don't know if that belongs here 3 or that doesn't belong here or -- because then 4 that is not really just a summary or a drawing 5 forth of certain information. It is really a 6 new something here.

7 DR. JENKINS: So let me ask a 8 clarifying question. Are you saying it is 9 because you have looked at the Chapter 3 and 10 think that there needs to be more there or are 11 you just saying -- 12 DR. SUH: No, I am just not sure. 13 I am just asking you. 14 DR. JENKINS: Okay. Well, I think 15 that with your example of the indicator, I 16 think we do have a relatively extensive 17 discussion of the PM-10 indicator and its 18 appropriateness for a standard that is meant 19 to protect against thoracic coarse particles. 20 DR. SUH: In the policy

21 assessment? 22 DR. JENKINS: Yes. Yes, in the202-234-4433 Neal R. Gross & Co., Inc. Page 358 1 policy assessment. That sort of discussion is 2 not in there. So I mean, one thing from my 3 perspective on the thoracic coarse particles, 4 you know, all the information, the basic 5 information, of course, is contained in the 6 ISA, but it is oftentimes not brought together 7 in the most policy relevant context. 8 So one of the things that we try 9 to do in this document is to bring those -- 10 all those studies together so that they can 11 inform specific policy questions. 12 So even though there may be some 13 restatement of -- re-description of some of 14 the studies, we are trying to bring them 15 together within the context of answering the 16 specific policy questions. 17 So I think -- well, I mean, we can 18 wait and see your reaction after you have had 19 a chance to look at it, but I think there is 20 some amount of detail in there, in my opinion, 21 enough detail at least to draw some informed 22 conclusions. Page 359

1 DR. SUH: The reason I asked this 2 is in one of the charge questions -- it was 3 like 9b -- it says -- basically, it says that 4 overall -- in considering the air quality 5 information as well as the overall body of 6 evidence and associated uncertainties, staff's 7 preliminary conclusion is that, depending on 8 the emphasis placed on the different aspects 9 of the evidence and uncertainties, it could be 10 judged that the PM standard does or does not 11 provide adequate protection. 12 DR. JENKINS: Is that not specific 13 enough for you? 14 DR. SUH: But then it says what do 15 you view this preliminary conclusion -- which 16 is, to me, not really a conclusion. I don't

17 know. 18 DR. JENKINS: Well, I think -- 19 DR. SUH: I guess I am trying to 20 figure out how -- you know, from the ISA how 21 can you take that information, bring it 22 forward, and then even -- I guess I am not Page 360 1 sure how you can provide -- do you understand 2 what I am saying? 3 DR. JENKINS: Yes, and I think 4 what you have just pointed out is due in part 5 to the fact that this is a first draft 6 document, and we have initial preliminary 7 conclusions. We haven't drawn final 8 conclusions, and we are looking for feedback 9 from the committee to help us inform those 10 final conclusions that will be included in the 11 final document, but what that part of the 12 chapter lays out that you just highlighted 13 there is it lays out the evidence and the air 14 quality information and its associated 15 uncertainties and basically says that, to the 16 extent that you emphasize X, Y and Z, well, if 17 the Administrator is going to place emphasis 18 on these considerations, then a justifiable 19 conclusion would be that the current standard 20 does not provide adequate health protection. 21 On the other hand, you know, we 22 have all these uncertainties associated with Page 361

1 the evidence. So to the extent those 2 uncertainties are emphasized, a different 3 conclusion could be reached. 4 So one of the things we are 5 looking for is feedback on, well, you know, 6 what is the appropriate weight to place on 7 some of these uncertainties, which are brought 8 out in the document itself, versus the 9 evidence, which is again discussed a bit in 10 the document itself, and in the context of 11 those air quality concentrations, which again 12 are included in the document? 13 So I think that part of that is -- 14 I think part of this -- I think the 15 conclusions of the document will be sharpened 16 as we go through the process, the iterative 17 process of revising it through multiple 18 drafts. So I would fully expect us to have a 19 sharper conclusion in the final document that 20 we have in the first draft document, and I 21 think that final conclusion will be informed 22 in large part by what we hear from this group.202-234-4433 Neal R. Gross & Co., Inc. Page 362 1 DR. SUH: So are you asking the 2 Panel to say which way we think you should go 3 or is it -- 4 DR. JENKINS: I think that, you 5 know, if you -- sort of the last sentence on 6 that question: What are the Panel's views on 7 this preliminary conclusion on the alternative 8 approaches to considering the evidence and its 9 uncertainties, and on the adequacy of the

10 current PM-10 standard? 11 So, I think we would be looking

12 for feedback on all of that. 13 CHAIRMAN SAMET: So just a 14 comment, and then I will get to you, Frank. 15 We do have a month to go before reviewing 16 this. The quality of the review -- we have 17 used the charge questions as a guide to our 18 reviews. 19 In my mind, again, taking a look 20 at them now, the charge questions themselves 21 are not yet in an acceptable form, in that 22 they need to be sharpened and focused, and Page 363 1 that that would need to be done very quickly. 2 I am not sure that we can sit here 3 as a group and rewrite the charge questions 4 with you or for you, but I would like to see - 5 - and I think it is urgent that we have a far 6 sharper group of charge questions by the

7 week's end. 8 I will be happy to provide you 9 with my comments, as I have done previously, 10 and I think the kinds of comments that I made 11 before about the lack of specificity in the 12 charge questions, all the issues that have 13 provoked those comments are sitting in this 14 batch that you have given us now. 15 So I do think they really do need 16 to be sharpened. I think we as a group need 17 to think about whether all the key issues are

18 addressed. 19 I think it is helpful to help us 20 better understand how this new document fits 21 in and what it is supposed to do, because I 22 think that purpose -- and I think Joe and Page 364 1 others have talked about that -- is important, 2 but I do think -- I think these comments do 3 need some back and forth, and there is 4 probably no reason to pick on one or another. 5 I will give you some general feedback. Frank? 6 DR. SPEIZER: Well, I wanted to 7 raise a point that will lead to frustration 8 unless it is fixed. It looks to me as though 9 the charge questions were almost written by 10 somebody who has not read the preliminary

11 draft. 12 Charge Question 8 talks about 13 Sections 2.4.1 and 2.5.1 and 2.4.2 and 2.5.2. 14 Those sections are not written yet in the 15 preliminary document. So I don't know. You 16 are going to make a lot of frustration if you 17 leave it the way it is. 18 MS. HASSETT-SIPPLE: Actually, Dr. 19 Speizer, that is actually a mistake. That 20 should be 2.3.4.1, 2.3.5.1. So those are the 21 evidence-based considerations. 22 DR. SPEIZER: Okay. All right. Page 365 1 But then I come to the specifics of the 2 question. I mean, first of all, that has got 3 to be cleaned up. 4 The question says: what are the 5 Panel's views on the insights that can be 6 gained from -- well, I think we are beyond 7 that. We want to know what insights you have 8 used, and we will critique those. But for us 9 to at this stage try to give you insights as 10 to what you ought to be thinking about seems 11 like you are slowing the process down. 12 We want to read what you have done 13 and comment on it, not at this stage give you 14 new insights to think about. Again, this is 15 just another example of how we have to sharpen 16 the questions.

17 CHAIRMAN SAMET: Yes. Joe? 18 DR. BRAIN: Just one other thing 19 that I would like, thinking of the public or 20 potentially the Administrator, is, can you 21 succinctly somewhere at the beginning or the 22 end tell me what has changed in the last five202-234-4433 Neal R. Gross & Co., Inc. Page 366 1 years, so we are looking at this -- so what is 2 the greater clarity about? What uncertainties

3 have been reduced? 4 It is sort of scattered in there, 5 but it seems to me a nice -- I am looking for 6 some macroscopic, high-level summaries of what 7 is there and that seems to me one example. 8 You know, do you carefully, clearly lay out 9 where are we now in terms of how that 10 contrasts to where we were five years ago or 11 ten years ago when some of these standards 12 were initially promulgated? 13 DR. HENDERSON: I am wondering if 14 you couldn't simply -- I am just looking at 15 how you simplify these, and you could almost 16 just use the outline and say, you know, what 17 is the -- because the Panel agreed with the 18 reasoning on the adequacy for the current 19 suite of standards, but sort of getting that 20 whole -- maybe see -- and do you agree with 21 the indicators, because that is what people 22 are thinking about. But when you have all of Page 367 1 these words in there, you kind of get lost.

2 CHAIRMAN SAMET: Mort? 3 DR. LIPPMANN: We are coming to 4 grips with the level of the standard and the 5 impact of the alternate standard. I don't 6 think we have come to grips with the ranges of 7 responses that are indicated in the 8 literature. 9 For the short-term standard, we 10 have huge differences in the indicated 11 response from city to city among 15 cities. 12 In the long-term standard related to 13 mortality, we have coefficients that range 14 over a very wide range of coefficient, with 15 the one -- sort of the index standard being an 16 outlier, so to speak, among the various 17 studies. 18 So what is the Administrator 19 supposed to decide about protecting the public 20 in terms of the magnitude of effect? If the 21 ACS cohort is the basis, how then does she 22 deal with the fact that all of the other Page 368 1 cohorts indicate significantly bigger impact? 2 It is hard to talk about margin of 3 safety in that context when you are taking the 4 one with the least impact. Is the six cities 5 a low-ball estimate because of things we 6 discussed earlier or is it really the best one 7 available and what is wrong then with the 8 others which we have judged to be 9 scientifically credible but give different

10 results? 11 I think this is where the rubber 12 is going to hit the road in terms of the 13 adequacy of the standards, and I don't see any 14 questions that we are supposed to comment on 15 with regard to what coefficients should be

16 used.

17 CHAIRMAN SAMET: Bob? 18 DR. PHALEN: Rogene's point is too 19 important to ignore. I just wanted to comment 20 on Rogene's point because as I look at this, 21 I was intimidated by it. I don't have time to 22 unscramble all of these words, and if we look Page 369 1 at technical comment 2a, that can be reduced 2 to two lines. 3 We didn't have to say the 4 discussion of the health and welfare effects 5 evidence draws from the currently available 6 scientific so-and-so presented in the ISA, 7 which included consideration of information 8 from previous reviews. 9 We know that. Eliminate that, and 10 I would eliminate to what extent, and I would 11 just ask the question: does the Panel find 12 that this first draft PA accurately reflects 13 and clearly communicates available health and 14 welfare effects evidence from PM, including 15 important uncertainties, period, because now 16 it says as characterized in the final ISA. 17 So before we make a comment, I 18 guess we would have to go back and re-read the 19 ISA, which is what, 500 pages? So I would 20 reduce that, too, to one sentence, and then it 21 is more clear, it is less ambiguous, and it 22 easy to deal with.202-234-4433 Neal R. Gross & Co., Inc. Page 370 1 CHAIRMAN SAMET: I mean, I think 2 you are highlighting one example of the many 3 problems with the charge questions and I think 4 both for our efficiency and our ability to be 5 helpful, I think it is important to sharpen

6 these. 7 I think, if we could just agree on 8 how that is going to get done quickly, I think 9 that is what is critical. Ed? 10 MR. AVOL: I have a question to 11 follow up on the comment that both Beth and 12 Karen made earlier. That was on this notion 13 that in the policy assessment you have not 14 only -- you were looking at other -- a broader 15 wealth of evidence, I think was the way you 16 might have phrased it or something to that 17 extent, to draw in to inform for what was in

18 the document.

19 I haven't looked at the document 20 yet. So I have to plead ignorant here, but it 21 seems to me that the -- I mean, my 22 understanding was the policy assessment was Page 371 1 sort of built upon the former documents. 2 That is, it sort of took the hard 3 work and the long time and the careful 4 scrutiny that was put into the risk 5 assessments and the judgments from that about 6 what were the appropriate studies to use and 7 which ones had validity and what the decisions 8 were for those, and those would be carried

9 forward. 10 It sounds like you are willing to 11 consider other -- the other evidence that may 12 or may not have been in the previous documents 13 or that you may reorder or change what is in 14 the previous documents. 15 So my question is: as you go 16 through that, are you going to highlight or 17 comment on why you chose to do what you did? 18 CHAIRMAN SAMET: Actually, don't 19 answer yet. Whoever is on the line, can you 20 mute -- can you hear us? If you could mute it 21 or just disconnect? 22 DR. STALLWORTH: Is there anyone Page 372 1 on the line? Okay, let's just hang up. Let's 2 just hang up the conference line. 3 MS. HASSETT-SIPPLE: So, Ed, just 4 to clarify that, when I made the comment that 5 we take a broader look at the body of 6 scientific evidence, it is all information 7 that is contained in Integrated Science 8 Assessment but may not, for a variety of 9 reasons, have been considered in the 10 quantitative risk assessment. 11 So if we focus on the long-term 12 exposure studies -- for example, the 13 quantitative risk assessment focused on the 14 ACS, the most recent ACS study, Krewski 2009, 15 and the sensitivity analysis with the most 16 recent Harvard Six Cities Study late in 2006. 17 But here we also look at information from the 18 Women's Health Initiative study in early 2007. 19 We looked at the information on the Children's 20 Health -- the Southern California Children's 21 Health study. We looked at information on the 22 cystic fibrosis study that Goss did in 2004. Page 373 1 So a much broader range of information to 2 inform our understanding of whether or not the 3 current standards provide adequate protection 4 and, if not, what alternative standards should

5 be looked at. 6 So I think, if you can look at -- 7 If you can take the time to look at how we 8 have laid out the approach for fine particles 9 in Section 2.1.3, and the different types of 10 information on how we consider them and how we 11 integrate the evidence and the risk-based 12 information, and follow it all the way 13 through, but then in Section 2.3.6, where we 14 come back and integrate the risks, because we 15 look at the evidence and we look at the risks 16 to inform our understanding of adequacy and 17 alternative standards, and then we pull them 18 back together again. 19 So if you can just be patient and 20 look and give us feedback on how we pulled it 21 together. You know, it is probably not 22 perfect. It is a first draft, and it is long.202-234-4433 Neal R. Gross & Co., Inc. Page 374 1 I think we have tried to take a very broad 2 look at the evidence and expect that we will 3 be narrowing and tightening and sharpening our 4 approach as we get feedback from you and from 5 the public, but the first cut is a broad look. 6 But as far as the body of scientific evidence, 7 we are not bringing in anything that wasn't 8 discussed in the Integrated Science

9 Assessment. 10 DR. HENDERSON: Jon, I was 11 wondering. An idea struck me. I think we are 12 commiserating over too -- I am wondering. 13 When anyone reads this -- a policy assessment 14 document, they will be burning to put down 15 certain points, and they can write those down. 16 It is an extra burden then to try 17 to fit those comments under your charge 18 question. Is it of any benefit to you for us 19 to just write down what our concerns are about 20 the document without fitting them into the 21 structure of your charge document -- charge 22 questions? Page 375

1 DR. MARTIN: Let me just respond 2 to that. There is clearly a tension between 3 trying to frame questions -- since you do use 4 these questions to organize your discussion, 5 there is clearly the tension between trying to 6 frame them open-endedly enough so that you are 7 encouraged to comment on those things that you 8 thought were important to comment on, which we 9 might not, in all cases, be able to know in 10 advance what that might be versus providing 11 enough specificity that you can give 12 meaningful comments. 13 What we are hearing from you now 14 is that more specificity would be better. 15 That is clear, and more simple expressions of 16 the questions would be better. But in the 17 end, we don't want to preclude your commenting 18 more broadly to the extent you feel it's 19 appropriate.

20 CHAIRMAN SAMET: And I am sure 21 individuals will and the group will. I can't 22 imagine being concerned about it. Page 376

1 DR. SPEIZER: I would be concerned 2 if your questions became so specific that we 3 could answer them yes or no, because that 4 would not help you at all. 5 DR. MARTIN: I don't want yes or 6 no answers, which is one reason why you see 7 things like to what extent. I don't want to 8 say is this a -- do you agree and just get a 9 yes or no answer. That is honestly not 10 particularly useful if you don't opine as to 11 the reasoning behind your answer.

12 CHAIRMAN SAMET: But we do want 13 answerable questions and I think that is where 14 you need to get. 15 Let's take the remaining four 16 minutes and discuss a process that is going to 17 lead to better questions and quickly, because 18 we are going to have to make assignments and 19 I think you got a sense for some of the 20 general concerns. 21 I actually don't think it is 22 necessarily our job to sort of re-craft these Page 377 1 questions. I will be happy to -- 2 DR. MARTIN: Of course, it isn't. 3 To the extent you have very specific 4 suggestions, we are perfectly prepared to go 5 away and spend the time tomorrow that we would 6 have otherwise been here, since we have 7 completed this Panel's discussions much more 8 quickly, to rework these questions. I have no 9 problem going back and making them more 10 specific. 11 I do want to leave them open-ended 12 enough to encourage broader thinking. One 13 question I have for you. Right now we have 14 framed some general questions that cut across 15 all the chapters, and then more specific 16 questions within each chapter. Otherwise, we 17 will end up repeating some of the questions 18 for each chapter -- 19 CHAIRMAN SAMET: That is fine. 20 DR. MARTIN: So if you are 21 agreeable with that, we will -- 22 CHAIRMAN SAMET: That is fine.202-234-4433 Neal R. Gross & Co., Inc. Page 378

1 Sure. 2 DR. MARTIN: -- retain the format 3 of asking some questions that will apply 4 across the board. Questions about level of 5 detail are important, and we are only going to 6 ask it once, but we are asking it with regard 7 to each chapter, those kinds of things. 8 Then we will look to be more 9 specific in teasing out the issues about our 10 views on adequacy, indicator averaging, and 11 high-level and form for each of the three 12 chapters and try to craft more specific 13 questions. 14 CHAIRMAN SAMET: Yes, Mort? 15 DR. LIPPMANN: There is nothing 16 here about biological plausibility as could be 17 informed by the toxicological literature. 18 Isn't that related to the policy issue? 19 DR. MARTIN: I would say that 20 falls into the issues that we would judge to 21 have been settled in the ISA. The ISA dealt 22 with those issues in reaching their causal Page 379

1 inferences and we take those causal inferences 2 as givens here and, therefore, we are not 3 revisiting the underlying basis for those 4 judgments.

5 DR. LIPPMANN: But shouldn't there 6 be some summary of what you just said in the 7 policy document? 8 DR. MARTIN: There is a summary in 9 each chapter of what the ISA had to say about 10 the causal nature of the relationships. We 11 start the discussions with that as the 12 predicate for what follows. 13 DR. LIPPMANN: But in the 14 questions we are being asked, shouldn't we 15 comment on the adequacy of that summary for 16 this purpose? 17 DR. MARTIN: Well, that is part of 18 technical content. Did we adequately capture 19 the technical content, and if you think we 20 didn't do justice to describing the causal 21 inference, then that is a question that you 22 can speak to that point. Page 380

1 CHAIRMAN SAMET: But I do think 2 the information on toxicology and mechanisms 3 will come both in the causal inferences and 4 considerations of uncertainty, for example, 5 and extrapolation of concentration response 6 relationships or susceptibility; I mean, some 7 of the issues that will emerge as important 8 considerations. 9 So I think it should be there in a 10 general way, even if not -- as an 11 underpinning, if not mentioned specifically. 12 Let me -- so I do think -- I think 13 we can probably -- if there are editor -- 14 burning comments that you would like to make 15 or changes to these charge questions, I 16 suggest that you make them, Ed, tonight, and 17 we will give them to the EPA staff tomorrow. 18 Let's talk a little bit about 19 tomorrow and what we need to get done. So we 20 have two public commenters on visibility. We 21 have Tom. I think we need to take some time 22 and talk about our way forward with this Page 381 1 review, and how to divide up the work because 2 there is a lot of it and this is a new kind of 3 document and I think this is a very important 4 review for the Panel.

5 So we want to talk about that and 6 I think, to the extent that we can make some 7 progress on our reviews to the risk assessment 8 and the visibility document, that that would

9 also be useful.

10 So we have our leaders for the 11 charge questions, if they could begin to 12 assemble. I don't think -- I think this is 13 probably pretty straightforward in general. 14 There is some new text, Frank, that we sort of 15 committed to here around Chapter 5 and some 16 other things, so we will have to remember to

17 do that. 18 So that looks to me like our job 19 tomorrow. A different question is, can we 20 finish early or will we. I guess probably the 21 answer is, I don't know, and for those of you 22 eager to possibly leave early, let's see what202-234-4433 Neal R. Gross & Co., Inc. Page 382 1 happens. 2 So I think with that -- and I 3 don't know if -- I am certainly game to start 4 a half-hour earlier if others are, with the 5 possibility of finishing earlier. So that 6 would mean we would start at eight instead of 7 8:30. I think generally we have all been down 8 by then. 9 DR. STALLWORTH: Jon, one way we 10 could use time tomorrow, if you want to, is 11 have subgroup leaders e-mail me their 12 consensus responses tonight, and I put 13 together those two letters and we go over them 14 tomorrow. That would move things along, and 15 then on April 8th we can just address the 16 policy assessment and any finishing touches. 17 MR. POIROT: Will we have EPA 18 staff working on earlier plane reservations

19 for all of us? 20 DR. STALLWORTH: I'm optimistic I 21 can make a twelve o'clock flight, and when I 22 get there, I just go to the kiosk of American Page 383 1 Airlines and I choose the earlier flight. 2 CHAIRMAN SAMET: Yes. I think we 3 could be done. Again, it depends on how 4 broadly we reach tomorrow. I think the 5 business we have to do, which is to hear from 6 Tom and our public commenters, I think, and 7 decide how we are going to -- I think deciding 8 how to approach the review will take some 9 discussion and designating people to take

10 leads. 11 I think this is an opportunity for 12 you to sort of step forward and say this is 13 what I want to work on, as well. So take a 14 look at the -- I would suggest that another 15 piece of homework for tonight would be to look 16 again at these charge questions and take a 17 look at the policy assessment. 18 DR. STALLWORTH: I think we can 19 commit to ending by eleven, 11:30? 20 CHAIRMAN SAMET: We can try. Yes. 21 So let's see where we are. I mean, we are 22 certainly aiming to get done earlier to the Page 384 1 extent possible. 2 DR. HENDERSON: But we will have 3 lunch available? 4 DR. STALLWORTH: Yes, that is the 5 problem. If you want to stay around for 6 lunch, you can. 7 DR. HENDERSON: I do. Did you 8 settle on whether people were going to draft 9 their parts of the letter tonight and then -- 10 CHAIRMAN SAMET: Well, that is 11 what Holly said. 12 DR. STALLWORTH: Is that agreeable 13 to subgroup leaders? It does help move things 14 along. It is going to be a very imperfect 15 letter tomorrow. We have struggled with this 16 in the past. We will probably continue 17 struggling with it, but if I have a first 18 draft that everybody, then, can later comment 19 on, it really saves tons of e-mail traffic and 20 we are that much further along. 21 Is that agreeable with the 22 subgroup leaders, to e-mail me a consensus Page 385 1 response to your question tonight and if I 2 don't hear from you, I am going to use 3 whatever I have already got from you. 4 DR. FREY: Just so you recognize 5 it is a response. It might not be the fully 6 vetted, two-sentence -- 7 DR. STALLWORTH: Right, and then 8 tomorrow we can talk about it, but it gives us 9 something to start with. Thank you. 10 CHAIRMAN SAMET: Okay, good. So 11 anything else -- 12 DR. STALLWORTH: I go to bed at 13 ten o'clock. So I need to start putting it 14 together at nine. 15 CHAIRMAN SAMET: Okay. So 16 anything else anyone wants to bring up? So we 17 have done a lot today. We have wandered here 18 and there but I think we have gotten the job

19 done that we needed to on these two documents. 20 I think this discussion of the 21 policy assessment was very valuable, and I 22 think we are, obviously, still probably all202-234-4433 Neal R. Gross & Co., Inc. Page 386 1 learning exactly what it is going to turn out 2 to be and I think, again as I said, our 3 guidance here will be important. 4 So I think we are done at five 5 after five. 6 DR. PHALEN: Jon, do you want a 7 show of hands on eight o'clock versus 8:30?

8 CHAIRMAN SAMET: Show of hands on 9 eight o'clock versus 8:30? All in favor of 10 eight o'clock? Two hands. 11 DR. HENDERSON: Eight-thirty? 12 Five hands.

13 CHAIRMAN SAMET: You western 14 wimps. 15 DR. PHALEN: And it will save 16 time, you know, if we come mentally capable. 17 CHAIRMAN SAMET: I think, if you 18 want to try and have a stab at getting out 19 earlier, I would suggest eight. 20 MR. POIROT: Can I just ask if we 21 know what time are the visibility commenters

22 and Tom? Page 387

1 DR. STALLWORTH: Well, it is on 2 the schedule for 9:10 tomorrow morning, so I 3 think that is when they will call in.

4 MR. POIROT: So we would be 5 working on something else for the first part. 6 CHAIRMAN SAMET: Well, I think if 7 we had a solid block from eight to nine, that 8 would probably be useful. Come on, Sverre, 9 you can do it. Eight o'clock, voluntary? 10 DR. SUH: We can have it at eight 11 o'clock in Sverre's room.

12 CHAIRMAN SAMET: So how about a 13 grudging eight? All right. Thanks, everyone. 14 (Whereupon, the above-entitled 15 matter went off the record at 5:06 p.m.)

16

17

18

19

20

21

22

### 202-234-4433 Neal R. Gross & Co., Inc. Page 388

A abatement 118:10 ABC 1:13 ability 18:12 286:1

370:4 able 8:16,18 9:13 11:16 19:1 26:4 46:16 61:13 104:8 118:7 122:20 158:5 175:5 176:12 189:16 202:9 225:7 226:5 227:6,7 250:12 253:6 274:7 290:4 301:18 302:13 315:2,8 332:6 338:7 340:19 375:9 above-entitled 135:15 204:8 323:8 387:14 absence 142:3

219:11,12 318:14 absolute 213:15,17 absolutely 213:8 absorption 309:19 AC 137:20 138:3 Academy 118:17 accept 160:11

245:8 acceptable 248:1

362:21 accepting 245:9 accidents 315:20 accompanying 199:17 accomplish 184:18 accomplished

84:20 89:12 184:19 189:22 account 16:8 118:8

233:12 267:17 accounted 151:18 accurate 144:19 accurately 305:18

334:15 369:12 accustomed 287:16 achievable 224:22 226:20 227:12 achieve 89:17 achieved 141:15 achieving 66:21

73:8 227:9 acknowledge 105:1 176:14 305:1 ACS 82:10,15 112:7,10 113:4 124:3,18 125:1 127:22 129:1,3,6 131:3 132:20 191:8 367:21 372:14,14 act4:14 15:16,17

48:4 122:5 123:13 123:16,17 225:11 227:1 289:5 acting 223:8 action 140:7 actual 163:2,8 178:15 192:4,21 314:11 acute 262:5 ad 64:11 add72:11 115:4 145:18 168:18 184:2 187:9 211:19 220:5 224:15,20 281:4 293:15 294:16 297:15 301:13 308:9 328:2 added 85:20 158:2

207:5 208:17 273:21 274:1,4 293:18 296:4 adding 121:16 137:11 148:7 259:14 275:1 292:6 addition 84:21

97:12 129:5 218:15 234:6,19 248:8 275:5 288:21 333:11 additional 27:6,22 29:16 31:14 32:2 32:18,20 47:11 57:4 59:22 67:7 68:5 69:18 70:15 70:17 71:17 74:10 93:15 103:21 126:15 127:3 132:4 203:15 213:3 258:4 281:8 281:20 283:22 296:16 297:5 329:16 331:21 332:1 334:4 additionally 121:16 additions 76:10

297:14 address 62:12 128:19 142:2 165:21 266:12,21 318:7 325:15 327:13 329:12 382:15 addressed46:9

78:18 79:10,15 119:5,6 151:13 233:16 281:17,18 310:3 363:18 addressing 35:15

59:18 136:14 142:21 195:12 331:2 adds 260:5 adequacy 13:4,14 14:15 24:8 26:10 26:15 34:13,22 35:5,7,12 36:12 36:20 38:9 266:8 266:18 268:2 269:11 281:13 348:9 349:5 362:9 366:18 368:13 373:16 378:10 379:15 adequate 15:20

20:3 21:3,6 37:12 245:16,19 275:20 281:7,16 293:3 325:22 359:11 360:20 373:3 adequately 352:13

379:18 adjacent45:16

218:10 Adjourn 3:22 adjust 127:22 adjustment 114:5 126:14 127:5 164:15 adjustments 126:20 administrative

237:4 322:8 Administrator

4:16 17:21 18:16 76:15 106:6 194:18 343:1,5,20 344:5,11,22 346:17 349:14 360:17 365:20 367:18 Administrators

343:21 Administrator's

325:20,21 admirable 125:13 admissions 85:2 advance4:20 117:14 339:1 375:10 advantage 158:4 adverse 15:22

40:11 68:21 153:4 153:12 235:1 317:16 325:13,14 advice4:16 16:4,6 18:1,11,17 19:12 21:4 22:4 252:15 268:19 302:2 308:18 advise 238:4 246:9

347:6 Advisory 1:3 2:18

4:9,12,14,15 advocate 313:7 315:6 advocating 314:22 aerosols 270:1 aerosol-related

269:18 aesthetics 318:21 affect 61:10 105:18 107:20,20 152:18 197:9 218:21 219:18 affective 315:21

316:1 afford 227:12 afforded 26:16

266:18 269:12 afternoon 136:11

290:4 afterward 256:22 age 129:7 137:14 179:3,6 agencies 5:1 89:21 agency 1:1 11:13 16:4,7 18:21 23:18 24:4 225:5 225:14 226:17 227:5 228:9 238:4 346:16 agency's 15:8 agenda 3:1,4 7:20

9:16 80:18 265:5 286:6 ages 134:13 aggregate 211:18

227:21 259:7 aggregated 121:12

228:1 296:10 aging 129:5,12 ago 89:22 147:15 158:12 293:21 307:11 308:14 309:1 366:10,11 agree46:11 62:5

88:20 91:12 94:16 97:7 98:11 108:7 108:13 114:7 133:4 142:5 146:4

### 202-234-4433 Neal R. Gross & Co., Inc. Page 389

154:5 163:22 188:15 213:15 214:11,14 220:5 236:19 253:18 276:21 278:14 283:15 337:15 345:9 366:20 370:7 376:8 agreeable 377:21

384:12,21 agreed 275:19

294:20 366:17 ahead4:4 57:20

69:15 109:11 137:1 157:1 246:16 287:4 290:10 aim 204:4 aimed 188:17 aiming 383:22 ain't234:16 air 1:3 4:8,14 27:10

27:13,19,22 28:2 31:14 32:3,16,21 36:22 38:2 48:4 104:19 122:5,13 123:13,17 137:15 174:15 182:22 187:15 196:1,20 198:15 201:10,15 215:5 242:3,15 248:12 251:5 265:3 267:2,18 308:15 317:8 319:18 331:22 333:13,18,20 334:5 346:16 359:4 360:13 361:11 airline4:6 Airlines 383:1 airplane 250:3 al 82:21 110:22 alarm 216:16 Albuquerque

287:9 align 87:22 allergy 193:15 allow 39:6 48:19 105:2 107:19 119:19 168:10 allowed 39:13

45:18 46:3 64:12 122:11 225:15 272:8 allowing 96:15

235:15 allows 31:8,9 39:20

45:8 58:7 alluded 120:2 159:21 alluding 57:18 alternate 54:22

55:11 61:10 71:4 71:15 73:5 74:1,7 92:8 95:2 98:20 99:7,17 185:6 253:2 367:5 alternative 13:5,16 14:15 15:13 28:22 29:1 30:17 31:1 31:20 33:9,10,19 34:20 35:2 38:17 41:8,22 42:3 45:20 46:16,18 66:11,11 67:15 68:11 69:13,16 70:12,13 84:14 142:18 144:1,11 150:10,15,20 151:10 152:6,11 209:7 222:14 228:16 239:11,13 268:9 288:17 297:2,4 299:2 321:1 326:21 327:8 343:11 347:14 362:7 373:4,17 Alternatively 71:3 196:16 alternatives 100:9

232:5 268:15 343:14 349:13 ambient 32:5 48:5 55:11 60:7 61:6 143:17 182:22 187:15 216:7 271:4 307:12 308:15 ambiguous 369:21 ambitious 11:15 amended 123:14 123:18 amendments 123:15 American 5:9 76:7

81:11 265:1 382:22 amount24:3 102:14 170:10 220:9 336:20,21 358:20 ample44:2 analogous 333:8 analyses 31:15

32:20 52:22 64:5 126:15 127:7 128:11 129:9,10 143:3 146:8,9,13 146:19,20 155:22 156:16 157:8 173:16 176:5 187:5 194:10 196:13 201:8 230:11,18,19 297:8 331:21,22 332:1 analysis 32:3 51:10

51:21 53:3,7,17 54:2,4,5,12,20 57:6,8,11 60:14 62:17 63:21 64:10 64:21,22 65:7 78:19 79:1 82:20 83:3 84:8 85:1 98:4 99:19 100:2 102:18 105:15 116:5 128:4 132:18 133:8 134:16 142:19,20 143:14 146:15 148:16 149:11,13 150:21 152:14,16 153:9 154:10 155:13,19 173:5 176:1,20 181:11 182:12,18,19 183:5 184:9 186:1 186:1 188:6 194:11,17,22 195:10,14 196:10 200:6,17 202:11 207:16 215:16 217:12 218:5 219:21 221:15 239:19 255:5 257:17 259:15 260:11 272:7,12 273:10 275:1 277:15 279:3 281:2,4,5 283:16 286:21 287:2 288:14 290:21 294:9,12 356:14 372:15 analyze20:14

260:9 283:18 analyzed41:7 111:1 Angela 50:6 Angeles 172:16,18 174:5 179:13 236:9 305:10 Anne 265:2 annual 23:4 24:13

28:12 30:14 31:7 31:8,17 33:14 55:22 56:3,10 57:1 58:1 61:3,18 63:7 65:9,13 66:10,11,13,16,22 67:9,12,13,15,15 67:20 68:6,11 69:14,17 70:12,13 71:4,5,21 72:8,11 72:13,20 73:9,12 74:2,4,14,16 75:4 84:4 87:6,14,18 98:16,17 102:11 102:16 103:3,8 154:14 159:17 161:1,4,16 165:9 165:10,16 166:5,9 166:18,19 167:8 167:13 170:9,12 178:4,15 183:20 187:20 188:4 212:18 213:7,16 213:20,22 214:2 234:2,10,17 235:14,17,21 236:12 241:6,8,14 261:6,19,22 326:15 327:5 annualized 177:18 answer 19:3 25:13 103:15 152:18 209:19 221:6 233:8 281:5,10 287:19 292:21 297:11 335:11 340:4,11 351:15 351:16 352:3,5 371:19 376:3,9,11 381:21 answerable 376:13 answered 217:15

340:18 answering 328:5

335:11 358:15 answers 87:19

336:16 376:6 anticipated 15:22 anticipates 184:12 anticipating 186:8 anybody 110:12 188:15 221:21 263:8 318:16 anymore 160:6

244:17 anyplace 221:1 anyway 112:21 137:2 161:9 167:10 250:9

### 202-234-4433 Neal R. Gross & Co., Inc. Page 390

API75:19 apologize 117:13 apparent 80:6 apparently 111:2 111:17 222:8 284:14 312:20 appear 162:21 165:5 appearance 5:14 appears 242:2 appendices 86:22

88:16 193:5 280:8 280:10 appendix 101:8 135:8 159:7 181:1 196:11 199:6,16 200:7 202:22 221:17,20 277:10 277:13 278:7 applaud 220:1 apples 299:16,16 applicable 324:16 application 190:13 applied 77:21

79:20 86:7 324:9 324:19 apply73:5 93:5 124:14 199:18 303:14 378:3 applying 190:3,6 198:13 211:20 appreciate 9:8

95:14 114:8 appreciated 88:17 approach 23:13

41:10,21 59:5 83:2 85:21 86:17 87:17 88:10,21 89:5 91:10 92:11 96:11,12 99:12 120:4 121:21 166:1 173:19 175:8 184:1 194:12 211:11 243:9 278:6 286:8 299:2 305:15 323:22 327:12 328:16,17 339:10 340:5,7 345:12 349:4 373:8 374:4 383:8 approaches 22:18

33:18 38:7,13 87:7 88:3 297:5 299:11 300:6 301:20 308:18 329:11 340:13 345:2 362:8 approaching 227:3 appropriate 13:5,6 13:17 31:16 39:15 39:19 43:21 45:7 78:10 82:2,12 84:8,11 94:5 109:20 110:12 136:17 140:11 143:1,3 257:12 268:8 282:3 283:4 294:10,21 337:15 337:16 341:9 345:22 350:13 361:6 371:6 375:19 appropriately

24:20 287:3 appropriateness

35:9 357:18 approximately

246:18 April 10:19 11:4

225:20 263:20 264:3,4 382:15 Arden 125:5 area 55:22 59:3

90:12 133:19 213:1 272:8 292:18 areas 27:16 28:19

32:22 39:8,9,13 39:13,22,22 44:13 44:14 52:19 58:2 58:4,6,8,9 60:6,17 60:21 61:4,5 63:4 64:13,15,20 65:1 65:11,15,16,22 66:3 71:2 74:12 74:13 94:14,18,21 95:3 97:16 99:6 105:22 147:6 157:12,12 182:14 182:20 183:7 216:1,6 275:6 288:19 298:6 306:1 350:7 arguably 174:5 argue 155:9 325:11 argued 234:4 arguing 175:16

234:5 argument 84:3 131:2 166:8 175:18 211:6 229:18,21 231:11 232:8 234:7,8 247:8 261:20 262:8,11 arguments 165:15 166:2 ARMISTEAD 1:21 array 344:9 artful 328:4 articulate 17:19 19:5 articulating 23:13 artifact 145:5 164:18 Ashbaugh 2:2 6:10

6:10 107:4 275:18 283:15 285:17 293:14 303:3 311:22 312:7 aside43:7 232:14

291:12 asked 9:19 27:18 147:14 256:7 269:17 279:18 297:16 308:17 310:22 323:14 354:15 355:13 359:1 379:14 asking 242:21 274:15,21 278:20 349:2 352:7,8 357:13 362:1 378:3,6 asks 83:4 275:19 aspect 196:11

201:16 209:19 250:16 315:3 aspects 116:9 137:8 163:7 202:22 250:15 267:6 279:12 292:13 318:11 359:8 assemblage 134:11 assemble 381:12 asserting 75:7 assess 59:20 117:1 195:4 254:10 269:18 297:7 assessed 54:1 73:14

73:15 240:3 assessing73:18 183:18 271:1 assessment 3:6,8,9

3:10,12,14,20 7:21 8:1,3,5,12,18 9:20 10:6,7,12,21 10:22 11:20 12:19 13:10,11,19 20:15 21:5,7,16 26:3,14 28:17 29:5 32:4 33:12 35:19 37:11 41:11 42:7,12,17 42:22 43:3,9 44:20 47:6,10 49:19,21 51:3 53:2,10,13 55:17 58:17 59:3,15,16 63:17 68:4 76:8 77:4,6 79:16 80:7 80:8,9,11,12,13 81:19,22 84:16 98:15 114:19 116:4,6,21 117:20 118:15,18,19 119:11 120:6,14 121:19 126:16 143:6,12 144:9 164:10 173:2 176:14 187:6 189:1,10 192:17 194:7 195:8 196:5 199:1,18 200:15 205:20 219:5 222:13,16,19,21 223:18 225:17 230:6,20 231:2,6 231:9 242:2,13 244:17 245:21 249:13,16,17,17 249:19,20 250:6 251:7 263:7 264:22 265:7,11 265:16,20 266:2 266:17 269:3 270:10,13 271:14 271:15,21 273:4 283:17 288:3 290:17 295:15 302:4 310:22 318:8 320:8,19,22 321:9 331:11,17 331:21 332:21 336:2 341:8,10,14 341:17 342:1,7,11 342:14,16 348:20 352:11 353:8 356:1,9,11,12 357:21 358:1 370:13,22 372:8 372:10,13 374:9 374:13 381:7 382:16 383:17 385:21 assessments 13:9

23:22 24:6 79:11 105:13 273:3 371:5 assigned 158:6,9 169:4 assignments

339:10 376:18 assist237:2 associated 10:11

### 202-234-4433 Neal R. Gross & Co., Inc. Page 391

27:12 29:8 35:10 37:13 38:5 40:16 70:1 74:4,16 76:18 78:7 83:21 95:9 119:1 122:18 132:5,13 151:15 189:6 196:1 227:19 236:20 268:21 270:21 309:11 334:5 359:6 360:14,22 association 27:9

81:11 115:22 116:11 132:9,10 211:10 associations 26:22

27:11 37:6 333:7 333:9 assume72:4 73:7

325:12 assumed 232:17 assuming 159:6 183:13 236:12,13 236:14,15 253:11 assumption73:11 107:17 assumptions

243:13 Atlanta 119:11 138:4 162:12,18 atmosphere 314:12 atmospheric

267:10 ATS 317:17 attack 186:12 attain 15:13 90:21

228:7 attainability 15:2 attainable 20:20 attaining 15:1 92:4

314:5 attainment 102:4 164:13 176:7 183:13 attempted 63:18 attention 110:20 127:12 249:13 255:20 337:17 attributable 120:15

210:3 259:19 attribution 189:15 190:21 197:21 attuned 285:22 audience 23:8

319:16 344:4 353:15 audio 50:8 author 12:12,14

34:6 265:15,17 authors 28:1 32:19 110:7 333:22 available 5:6 14:18

22:20 26:12,20 29:3,10,14,21 30:11 35:16 36:2 36:3,11 39:17 46:2 125:2 221:9 221:22 230:21 236:22 240:4 259:11 266:14,22 269:9,18 270:17 270:22 284:5 286:14 298:17 299:4,6 309:15,16 309:21 310:5,10 334:16 368:7 369:5,13 384:3 Avatar 172:3 average 31:8 40:19

55:22 56:3,11 57:1 61:8 66:22 67:9,20 71:22 72:12,13,20 73:13 74:4,14,16 84:5 90:15 99:1 102:11 102:16 103:3,8 119:14 154:14 161:4,16,16 166:20 167:8 170:12 187:20 188:5 210:18 212:18,19 213:8 213:13,16,20,22 234:20 235:15,21 236:12 241:8 261:6,20 294:7 averages 55:20,21

87:7 166:18 234:10 235:17 241:6,14 averaging 30:10,13

30:17,18 31:9,11 31:17 35:3 38:20 40:5,6 41:2 87:14 109:3 158:17 236:4 268:4,11 327:3,6,8 378:10 avoid45:3 291:16 Avol 2:2 6:8,8 110:2 172:6 173:18 175:16 176:15 198:18 256:5 370:10 aware 35:18 36:5

219:14 291:6 awful 156:16

304:11 312:6 axis 166:17 238:10 a.m 1:14 4:2 135:16,17 322:22 B b 85:12 87:5 108:17 162:20 169:13,14 206:21 back 36:16,17 40:7

49:12,20 51:5 56:1 75:1 85:9 86:9 108:18 114:2 114:15 117:19 118:2 123:21 129:22 130:14 135:14,18 143:4,6 170:9,17 173:13 177:2 179:12 187:2 199:5,11 200:1 203:18 205:4 226:21 232:8 243:2 247:13 259:16 260:16 290:14 294:19 301:14 312:10 317:16 318:3 319:6 321:3 321:5,9,22 323:1 323:7 331:5 334:21 339:9 343:15 351:10 364:3 369:18 373:14,18 377:9 background 23:15

83:1 107:3 186:4 223:21 240:22 278:5 285:5 Background/Sch...

3:5 backing 173:22 backwards 177:21 bad49:13 50:12

255:21 300:12 bait217:3 balance41:17

327:20 balanced 208:13 balancing 289:5

328:17 bar296:8 299:20 base 35:21 80:10

93:22 122:22 126:1 141:20 225:14,15 262:8 308:2 340:8 based 14:17 17:8 18:17 29:4 32:15 79:19 82:8,10 84:3 99:2 121:17 123:4 127:6 131:2 131:16 132:10,21 140:14 163:8 174:22 179:4,5 182:12,15,21,21 186:2 192:3,3,7 192:12,21 201:18 227:11 228:22 235:12 236:21 240:2,2 257:17 294:4 296:4 298:20 299:5 307:7 308:1 327:2 327:13 329:9 345:19 350:13 354:22 baseline 105:4 116:6,13 134:4,15 135:3 177:15 178:12 197:7 198:4 207:11,11 216:8 342:3 basic44:22 149:11

358:4 basically 38:4 65:3

76:10 95:9 108:22 123:8 153:22 155:6 157:3,18,21 162:10 163:12 174:17 182:11 183:1 215:4 271:21 274:16,21 275:19 285:2 309:15 310:8 326:18 359:3 360:15 basin 104:19,21 174:7 175:20 basing 217:1

238:20 basis 13:3,14 15:7 19:4 20:8 23:16 78:8 119:10 122:12 137:4 172:22 181:5 183:5 194:1 225:8 242:8 247:4 255:7 276:16 282:8 283:18 289:15 329:9,14 345:13 347:7 354:22 367:21 379:3 bat 167:13 batch 363:14 bearing 213:17 bed 385:12 bees 247:9 beginning 22:17

25:11 52:10

### 202-234-4433 Neal R. Gross & Co., Inc. Page 392

248:11 270:14 312:4,8 347:12 365:21 begins 333:10 begs 140:18 behalf76:6 265:2 behavior49:13 belabor229:6 belaboring 228:10

228:11 believe 33:19 81:22 133:6 134:4 144:3 167:3 213:5 224:17 256:10,17 257:7,19 275:8 291:11 bell 54:7 133:12 179:5 216:16 Bell's 133:8 belong 143:9 194:21 357:3 belongs 180:22 181:1 199:16 357:2 benchmark 287:12 beneficial 122:14

250:16 benefit 19:22 69:19 149:9 191:20 248:15 261:5 311:15 355:8 374:18 benefits 248:12

292:5 301:1 BenMAP 84:20 183:1 198:13 202:11 216:9 best 16:19 77:14

79:3 190:5 252:15 260:13 261:2 309:6 314:15 368:6 Beth 12:10 25:5,17

33:22 34:10 35:4 41:5 57:18 321:20 370:11 better 25:12 28:1 32:4,21 33:3 43:12 60:22 71:11 86:5 87:11 89:1 94:4 98:1,14 100:5 135:10 145:9,10 150:14 155:20 173:7 207:3 208:3 219:3 237:7,8 243:17 261:14,21 274:7 284:22 292:9 300:4 307:22 310:11 315:8,9 317:7 335:5 336:16 337:8 341:1 345:21 348:5 363:20 375:14,16 376:17 beyond48:22 114:3 184:4 223:7 224:10 227:15 228:17 235:4 257:8 327:1 329:15 331:19 365:6 bias 80:2 114:1 141:20 143:20 248:21 306:4,12 313:13 biases 88:8 109:5 big 22:3 24:9 73:10 112:1 140:16 141:12 150:18 151:7 153:21 181:17 185:19 191:6,15,16 195:2 262:13 285:6 323:5 bigger 161:4 368:1 Bill 294:18 301:13

316:13 318:4 billed 209:10 billets 205:15 biological 378:16 biology 188:14 Birmingham 162:13,19 Birth 117:1 bit47:14,18 127:9 127:18 131:10,11 141:13 145:1 155:20 160:19 168:6 180:6 182:17 186:12 188:20,22 191:21 192:9,15 209:11 211:9 216:14 223:6 229:6 234:1 240:22 250:14 251:12 258:18 273:20 277:6 280:15 288:1 289:1 291:18 298:10,19 300:22 300:22 303:21 308:12 313:7 315:13 318:7,13 324:13 334:22 336:3 351:6 353:2 355:19,22 356:5 356:14,20 361:9 380:18 black 10:10 270:5 blah 334:16 blah-blah 334:17 blaming 250:9 blank 252:1,1,13 blend 279:11 block 387:7 blood 315:15 blue 10:20 BMI 315:15 board 2:18 378:4 Bob 7:5 49:5,11 109:22 117:12 123:5 246:10 323:2 368:17 body 277:6,9 280:5

328:22 342:8 355:21 359:5 372:5 374:6 bookending 160:16 bookends 108:12 border 162:19,20 163:13 borough 111:6,9,9 boroughs 111:4 borrow 172:3 boss 317:7 Boston 138:2 bothered 170:22 bottom 19:2 22:8

88:5 218:22 350:8 bound 86:4 boundary 108:10

244:18 bounding 91:22

95:1,7 96:14 101:5 104:7 105:9 105:10 107:14,16 107:17 box49:8 315:12 bracketing 245:4 Brain 1:19 6:22

45:16 46:15 186:10 188:9 217:14 224:14 248:7 261:3 315:11 316:20 317:21 318:14 335:19 342:18 344:12 346:1,5 347:10 365:18 break 80:19 109:7 135:12,13 168:19 170:21 203:13,18 204:6 320:17 321:2 322:3 323:7 breathing 122:15 Brian 6:22 184:2 bridge 53:2 brief81:18 182:11

265:6,10 343:12 briefing 346:18 briefings 346:15 briefly 14:4 302:17 bring 36:22 103:14 124:17 136:2 257:12,13 283:5 311:21 312:6 333:12 334:10 335:14 337:1 358:9,14 359:21 385:16 bringing 107:8 108:3 112:6 334:4 374:7 brings 23:20 78:21 broad 68:18 123:10

219:15 226:12 336:10 374:1,5 broader58:5,9

65:2,14,17 167:18 202:10 341:20 342:8 370:14 372:5 373:1 377:12 broadly 64:16 66:1

375:18 383:4 broken 38:18,19 Brooklyn 111:7,12 brought 112:8 161:13 162:9 165:6,10 331:19 358:6 361:7 Bruce49:6 Bryan 195:9

215:16 budgets 304:3 build48:16,17 197:22 198:2 262:10 building 199:1

286:7 builds 332:2 built 371:1 bulk 156:19 bullet55:14 56:12

57:5 256:20 260:17 bulleted 66:6 bullets 256:20

274:20 bumble 247:9 bunch79:2 burden 189:6,11 190:18 193:15,22 199:19,19 374:16

### 202-234-4433 Neal R. Gross & Co., Inc. Page 393 burning 374:14

380:14 burroughs 113:20 business 48:3 49:10

224:4 383:5 buzz238:2 by-product 115:10 115:16,18 C C 4:1 162:19,20

284:12 calculate 97:18 188:5 253:20 298:20,22 calculated 97:20 187:19,21 208:8 calculating 187:22

253:11 254:20,21 298:21 299:5 calculation 112:3

254:15 calculations 193:13

229:16 278:17 314:19 calibration 295:7 California 6:3,9,11

6:19 7:6 372:20 call 26:15,21 100:18 263:21 264:15 266:17 269:11 337:16 387:3 called46:7 101:11 110:20 157:6 159:10 172:19 274:10 calling 50:7 90:6

94:10 Canada 36:6 cancer78:3 125:3 cap 272:20 capable 386:16 Capitol 285:6 capture 92:1

215:11 379:18 captured 196:6 234:11 capturing 195:17 carbon 270:5,6

305:16 cardiac 314:3 cardiopulmonary 130:17 cardiovascular 130:13,15,20 131:1 132:10 209:21 221:16 care 164:19 194:5

206:20 257:11 careful 150:9 187:5

371:3 carefully 194:19

203:8 220:13 256:1 295:6 366:8 Carolina 1:14 6:5

7:4 carried 77:6

355:21 356:4 371:8 carry 141:9 155:12 CASAC 1:16 5:19

8:15 20:2 21:6 52:15 54:9 137:10 139:4 141:22 142:3 223:3 272:5 274:15 279:18 295:22 326:6 347:1 355:1 CASAC's 21:3

230:17 Cascio 2:3 7:3,3

114:7 case 20:15 24:17

28:19 34:15 60:11 73:5 74:8 84:2 86:3 105:11 108:10 118:22 119:2 165:15 177:12 185:15 199:2 202:20 214:16 234:13 262:19 272:4 284:8 321:15 cases 89:18 101:5 312:19 375:9 catch 52:7 catches 92:11 categories 178:5

221:11,17 category 30:2 134:13 274:11 causal40:14 77:8

77:19,19 78:2 115:9,10 116:17 117:8 132:9,9 378:22 379:1,10 379:20 380:3 caused 122:9 causes 119:14,15 causing 103:13 caveats 95:12

218:4 287:1 CD 79:12 cell 182:12,21 183:3,4 cells 198:5 centers 79:21 160:1 198:11 central 138:3

286:17 287:22 certain 63:6 83:6

97:18 105:21 114:22,22 232:6 239:1 245:22 249:9 250:5 253:8 253:10 254:2,8 357:5 374:15 certainly 17:6 64:1

83:22 84:7,11 95:15 99:16,22 123:10 126:8 132:3,7,17 158:16 166:15 173:8 177:20 178:14 185:1,14 189:3 207:3 216:6,21 217:11 222:22 223:8 239:3 240:13 249:18 288:20 294:7 328:11 336:19 339:9 382:3 383:22 certainties 218:7 certainty 129:18

233:21 240:3 241:9,10,11 cetera 85:2 87:15 170:15 195:20,20 202:13 248:3,3 291:3 349:4 Chairman 1:14,18

5:18 7:13 16:17 43:4 44:1 45:15 46:6,21 47:15 49:4 51:1 75:11 75:21 80:15 81:4 81:6,9,13 85:4,14 88:18 95:19 100:8 101:4 106:22 108:6,16 109:6 113:19 115:2 117:9,12 119:21 123:5,12 127:16 128:21 131:22 135:11,18 136:5 145:20 147:9,17 149:7 154:4 155:21 156:13 168:15 169:12,16 169:20 172:2,5 176:22 180:15 182:2,8 184:3 185:16 186:9 187:4,17 191:18 193:11 198:17 199:10,22 202:1 202:18 203:1,6 204:2 205:3 208:20 217:11 220:2 229:4 231:4 233:14 237:9 239:6 242:22 244:11 245:11,13 245:17,20 248:18 249:7 250:18 253:14 255:4,10 261:13 262:20 263:15 264:1,4,16 275:12 276:9,20 278:11 279:13 280:18 283:12 284:10 285:16 286:5,20 287:5 288:8 289:20 290:1,6,10 293:12 294:14 295:17 297:11 303:2 307:3 308:7 310:15,21 311:20 312:3,11 315:10 316:7 317:10 318:2 319:7 320:6 321:14,17 322:2,9 322:15,21 323:11 330:13 332:8 334:7 336:5,18 338:3,6 339:8 342:17 350:19 351:1 352:4,15,20 355:14 362:13 365:17 367:2 368:17 370:1 371:18 375:20 376:12 377:19,22 378:14 380:1 383:2,20 384:10 385:10,15 386:8 386:13,17 387:6 387:12 chalk 100:3 challenge 246:8 challenges 126:14 challenging 117:5 126:20 255:13 chance 10:2 49:12 145:17 206:10 269:4 283:13 320:21 324:10 325:4 326:17 335:14 349:22 358:19 change 101:10 123:16 135:1

### 202-234-4433 Neal R. Gross & Co., Inc. Page 394

156:4 179:16 191:12 211:18,22 213:20 214:2,8 224:3 259:6,15 273:18,19 276:17 371:13 changed76:9 113:8

365:22 changes 52:1 58:16 152:15 179:22 207:18 210:17 212:17 229:9 262:4,5 263:14 306:21 380:15 changing 223:22

260:1,2,7 chapter 12:7,8,13 12:14 31:4 34:2,7 34:10,11 36:14,16 36:17,19 38:3,19 41:5,7 42:18 52:17 53:1 57:12 57:17 59:1 60:10 61:20 62:4,4,8,11 62:13 65:20 66:6 130:5 142:15,15 143:8 148:19 150:3,3 151:19 156:9 159:4 180:19 181:10 182:1,4,5 186:13 192:2,9 193:6 194:8 195:7 199:7 200:2,7 202:8,15 203:15 205:12 206:3 208:15,22 217:17 256:6 257:13 258:4,8 260:16,18 261:10 261:17 262:21 265:16,17 266:7 268:2 269:6,8,14 270:13,15 277:5 279:22 281:2 311:1,1,6,16,17 324:12 326:3,4,4 326:7 335:20 337:10 347:16 348:6,16 349:12 349:18 350:9 357:9 360:12 377:16,18 378:7 379:9 381:15 chapters 3:6,12 12:5,15 22:14,18 120:8 256:12 258:6,16,21 260:16 265:7 266:1 271:13 324:10,16,17,20 325:1 332:4 348:14 377:15 378:12 characteristic

314:12 characteristics 160:18 296:18 characterization 182:22 characterizations 141:18 characterize 24:20

95:9 178:4 202:12 228:12 253:6 characterized 139:22 334:18 369:16 charge 3:10,15,18

43:8,17 50:2 52:3 58:14,18 60:12 62:6 75:13 80:19 85:9,17 108:17 109:8,11,12 120:1 127:18 129:21 130:2 135:12,19 136:13 144:14 147:9 148:4,9 149:9 154:8 155:22 157:1,2,14 157:22 158:6,10 180:16,19 199:13 203:12,19 204:4 205:4,6 206:15 217:16 224:13,16 255:14 275:14,15 275:18 276:5 278:12 281:1 282:17 288:9 289:21 294:15 295:20,21 307:3 308:8 310:4 320:18 321:6,21 323:3,21 324:2,7 324:22 330:22 334:9,12,21 336:6 339:13 342:19 350:20 351:3 359:2 362:17,20 363:3,6,12 364:9 364:12 370:3 374:17,21,21 380:15 381:11 383:16 charged 110:3

339:21 charges 58:15 charter 317:13 chase 280:3 cheesy 100:3 chemical 149:3 chemistry 301:21 Children's 372:19

372:20 chime 205:9 choice 130:6 202:4

202:5 237:1 259:2 choices 109:16 110:8 151:9 341:11 choose 241:16

287:14 383:1 chooses 223:3 choosing 349:5 chopping 89:8 chose 114:21

371:17 Chris 6:4 145:21

148:5 149:7 154:5 280:20 319:15 353:2 CHRISTOPHER 1:20 chunk 185:12 cigarette 246:20 circulating 338:22 circumstances

239:1 cite 220:20 237:3

354:13 cities 84:16 87:2 100:11 119:9,10 124:19,22 125:18 128:2 154:13 158:16 160:17 165:12 169:3 171:16 172:20 177:21 181:5,7 186:2 189:9,17,18 195:13,16,18 196:7,18 197:18 197:19 199:3,8 201:20 207:12 214:22 215:4,11 216:2,19 218:11 218:16,17,22 219:3 234:8,16 241:6 257:4,6,8 259:1,12,18 261:4 261:5 273:6,12,13 274:12 277:8 281:9 282:15 367:11 368:4 372:16 city 72:18,18 73:3

82:11,15,20 94:21 100:15 111:1,3,11 111:14 114:10 128:12 155:8 161:21 162:4 164:12 173:6,10 173:17,19 174:17 184:8 187:20 259:3 303:22 319:11 332:11 367:11,11 clarification 56:17

90:3 155:1 clarified 59:19 109:14 166:12 181:20 clarifies 91:7 184:1 clarify 155:19 163:5 166:11 177:8 179:10 184:7 185:14 188:22 222:5 230:9 372:4 clarifying44:6

75:14 110:7 218:12 357:8 clarity 104:17 148:20 218:15 281:13 366:2 Clarkson7:9 class 112:12,12 classification 149:20 classified 78:5 classify 61:14 clauses 217:21 clean 1:3 4:8,14

48:4 122:5 123:13 123:17 292:3 317:8 cleaned 365:3 clear 56:13 63:19

79:3 82:19 84:2 90:10 92:14,15,19 94:3 109:1,19 118:4 130:14 134:19 136:17 140:8 141:3 151:2 163:4 164:21 171:7 172:18 173:14 176:19 177:6 182:13 183:9 185:1,14 197:3 215:8 218:6 218:8 224:16 230:22 253:5 257:18,20 261:8 279:20 281:15 288:3 319:3 340:16 343:9 369:21 375:15

### 202-234-4433 Neal R. Gross & Co., Inc. Page 395

clearance 252:10 clearer 93:10

216:15 218:1,7 258:11 clearly 19:5 51:15

67:1 90:7 99:11 104:6 105:8 111:21 112:14 121:20 124:7 126:2 156:17 160:17 168:2 171:20 185:5,10 188:1,7 216:18 222:3 256:12 261:5 279:6 282:4 286:12 299:12 302:21 325:10 334:15 343:8 355:11 366:8 369:13 375:2,5 climate 138:1

269:16,19,20 270:2,4,7 clipped 91:4 close 211:8 314:1 closely 353:11,13 closer 50:13 124:18 124:19 125:21 227:1,3 294:6 closure 305:15 clue 331:18 CMAQ 182:21

191:22 197:11 198:14 CNO 333:17,17 coarse 12:9 34:8,15

34:18,21 35:11,20 37:14,16 38:18 39:3,7,12,21 40:2 40:11,20 44:6,9,9 44:13 45:3,9 47:21 53:11 206:18 268:10 304:10 305:6 306:4,6 307:8,10 307:15 309:4 312:20 314:18 348:12 356:7,15 357:19 358:3 coast43:14,15

74:13 coastal 305:13

306:1 code 340:22 coefficient 112:22

367:14 coefficients 236:8

259:17 282:14 367:13 368:15 coherent 354:10 cohort76:2 82:5 112:7,10 113:4,5 124:18 125:12 129:2,4,6,12 131:4 367:21 cohorts 126:6

368:1 cold 52:7 colleagues 172:7 collected 267:4 collecting 140:7 College7:12 colorful 274:11 column 153:9

293:17 combination71:9 combinations 134:2 142:10 combine 90:9

300:8 combined 198:15

309:17 combines 198:6 combining 281:8 come47:13 49:12

49:20 51:3 95:21 101:16 114:1 123:21 129:21 142:16,19 144:1 175:6 181:10 187:5 198:7 217:8 225:18 231:5,7 233:3 240:21 321:2,5,9,22 323:1 326:5 332:12,19 338:9 339:9 343:6 365:1 367:6 373:14 380:3 386:16 387:8 comes 8:7 13:22

82:8 105:1 216:5 216:12 228:15 229:18,21 257:6 258:12 261:17 296:21 311:2 312:4,16 331:9 352:22 comfortable 88:6

238:21 coming7:18 9:18 166:8 171:17 177:2 193:7 205:15 206:3 220:1 259:8 283:20 300:22 321:18 344:9 367:3 commend 110:6 comment5:7,9

45:19 86:15 87:10 97:9 100:9 130:4 137:9 138:5 139:8 140:15 143:10 144:6 148:7,11 150:2 151:12,21 153:5 172:1,9,15 173:21 180:8 185:17 201:2 206:16 231:5 246:11 247:14 250:17 261:15 264:7 276:12 280:7 283:13 287:7,15 293:22 301:17 305:1 336:6 344:17 353:2 362:14 365:13 368:14,19 369:1,17 370:11 371:17 372:4 375:7,8 379:15 384:18 commented 205:8

239:22 285:17 301:14 commenter75:19

80:16 85:5 237:10 264:17 commenters 85:8

276:10 320:13 380:20 383:6 386:21 commenting 76:6

330:21 375:17 comments 3:9 8:21 11:21 20:11,16 26:1 50:22 51:4 51:14 68:8 75:18 76:12 81:3,18 83:17 86:8 96:8 96:22 97:7 103:5 108:16 109:21 110:13 114:8,11 115:6 117:15 120:3 123:7,11 130:2 131:18 133:5 136:2,22 137:1,10 138:6,7 138:19,20 139:4 141:6 144:14 145:16,19,22 146:1 147:10 148:4 149:16 151:14 154:2,3 155:21 156:14,16 158:5 169:13,19 169:21 172:7 173:12 177:2 180:16 189:5 190:12 199:12 206:6 208:19 223:9 224:19 249:19 250:1 255:14,16 264:22 272:5 276:3,5,7 277:2 278:12 279:15 280:20 281:20 288:9 291:8 293:15 294:17 307:4 308:8 312:12,14 312:21 316:19 318:5 321:5 322:7 323:16 338:19 339:1,19,20 363:9 363:10,13 364:2 374:17 375:12 380:14 commiserating

374:12 commit 383:19 commitments 9:13 committed 381:15 committee 1:3 4:12

4:14,15 6:1 8:10 83:17 136:1 158:2 321:2 344:16 360:9 Committee's 21:10 common 283:17 communicate

299:12 345:22 communicated

345:19 communicates

334:16 369:13 communicating

311:8 communication

335:6,7 communities 113:7 113:8 126:4 200:9 202:10 community 248:2 compare 55:20

56:2,6 65:16 97:20 151:22 298:15 299:10 303:10 compared 54:10,17

56:21 64:14,22 102:9 146:12 151:6 175:19 191:16 272:2

### 202-234-4433 Neal R. Gross & Co., Inc. Page 396

compares 58:4

70:6 comparing71:7 151:2 152:6,8 182:13 197:17 272:13 281:9 282:15 comparison 182:15 194:1 195:22 comparisons 87:6 compelling 248:14 competent260:20

260:20 complain 220:14

278:21 complete 200:17 completed 10:12

42:20 53:13 377:7 completely 115:15 133:14 172:17 277:22 290:19 305:11 completeness

276:13 complex 149:19

217:19 complexities 48:13 complexity48:16

278:17 compliance 103:15 165:6,10,13 215:4 219:4 complicate 155:10 complicated 114:6 128:22 170:13 343:4 complying 223:22 component 30:1,5 112:16 121:17 components 29:19 118:5,5 120:18,22 227:11,20 229:11 263:13 271:5 272:13 composite 55:20,21

56:2,11 57:1 59:6 87:6 99:1 164:16 164:20 compositing 87:13

87:19 composition 197:7

296:8 compositional

259:20 comprising 109:16 110:9 compromise 315:5 concentrated 112:15 concentration

24:15 54:14 69:1 83:19 116:8 130:7 132:12 133:20 134:20 138:21 142:12 151:17 202:5 211:5,13 212:15 215:6 223:11 231:14 232:12 233:18 241:4 284:13 380:5 concentrations

27:11,14 32:5 37:8,9 39:7,8,10 39:12,21 40:20 41:17 45:10 83:14 84:5,9,12 137:12 190:7 210:18,21 236:3 262:3 306:19 361:11 concentration-re... 190:11 211:2 concept 145:14 192:16 256:8 conceptual 315:7 conceptually 99:17 135:9 concern 68:19,20

74:18 78:14 98:17 127:4 133:11 142:1 184:10 185:18 186:10 205:17 207:7,8 219:7 226:16 286:13 concerned 133:5 180:10 181:15,18 186:6 226:13 304:8 340:1 351:6 375:22 376:1 concerns 68:10

76:11 113:15 141:2 300:6 328:20 329:22 374:19 376:20 concise 279:8 conclude 18:18

77:2 concluded40:13

76:15 80:8 concludes 85:3 conclusion 80:5 188:12 219:7 229:9 261:9 309:15 327:17,19 343:6 352:12 359:7,15,16 360:19 361:3,19 361:21 362:7 conclusions 17:5 17:12,20,22 18:8 18:11,16 22:8 33:18 38:15 40:8 40:9 142:1 155:7 218:9 222:19 273:19 326:9,16 327:1 344:6 345:8 345:10 348:8,16 350:6,14 358:22 360:7,8,10 361:15 conclusively

205:15 condition 301:11 conditional 145:1 145:12 conditioning 137:15 conditions 183:9 183:12 267:10,19 293:1 296:2,15 conduced 63:12 conduct 31:14 32:2 105:13 106:2 conducted4:19 5:1

33:1 36:7 37:21 64:4,9 94:17,17 146:13 164:14 176:20 182:20 230:12 272:6 conducting 53:9

59:2 164:11 173:5 conference 264:15

372:2 confess 237:14 confidence 28:10

53:16,18 54:12 57:9,14 63:18,19 68:21 74:6 75:6 83:21 84:3 151:16 151:19,22 152:9 209:16,19,20 228:22 230:17 231:14 233:12 240:6 242:16 243:8,10 245:1 246:5 confident 166:22 confirmed 82:19 confirming 83:11 conflict5:14 confounding 36:9

77:11 78:16 79:5 79:9 confused 93:16 162:7 355:16 confusing 91:13 140:10 150:8 154:15 160:5 confusion 90:7 170:8 182:17 congratulations 136:21 congratulatory 110:15 Congress 122:4,22

226:18 Congressional

120:10 conjunction 31:6 31:21 196:12 consensus 21:11 140:14,19 141:14 382:12 384:22 consequence 39:5

260:3 consequences

315:16,19 conservative 146:12,20 consider 10:2 11:6 14:16 16:1 24:1 46:16 57:21 99:15 109:19 121:16 122:11,21 136:16 142:17 155:12 164:1 176:4 214:6 216:8 224:22 225:12 253:3 267:21 273:1 299:3 343:12 371:11 373:10 considerably 308:5 consideration 13:7 13:17 14:1 15:15 17:9 18:1 21:21 33:5 37:16 39:16 47:8,20 52:20 54:7,21 57:7 60:14 77:7 122:3 149:14 158:3 269:21 270:3 288:17 293:6 349:14 369:7 considerations

32:8,11,15 35:21 38:13 41:13,14 122:3 238:5 252:9 252:10 277:20 302:19 327:14 340:8 341:19 342:7 360:18 364:21 380:4,8 considered 22:1

30:7 31:19 55:5 57:10 63:5 77:17

### 202-234-4433 Neal R. Gross & Co., Inc. Page 397

78:4 79:17 95:1 115:8 216:9 222:15 223:2 267:13 268:9,16 269:15,16 270:12 270:20,22 271:3,7 275:6 278:19 296:3 299:8 327:6 342:10,16 347:1 372:9 considering 12:6 13:4 14:19 23:14 29:15,22 34:19 35:5,22 36:20 38:5,7 54:13 60:16 62:21 68:6 220:9 223:16 224:6 226:4 268:3 269:7 270:8 291:19 307:6 339:5 340:7 359:4 362:8 considers 140:20

228:9 270:10 consistency 78:12

261:21 consistent4:17

66:19,19,21 82:16 83:2 84:9 101:10 108:21 190:17 280:9 consistently 77:21 conspicuous

317:22 constant234:22 constituent 305:13 constituents 270:5 constitutes 317:15 constrained 120:6 121:12 259:11 constraint 325:17 constraints 31:11 120:7 contain47:10 contained 33:10,11

358:5 372:7 contains 275:21 277:5 content 324:21

333:4 335:3 337:12 379:18,19 CONTENTS 3:1 context 10:5 48:8

58:12 62:19 120:9 120:9 126:5 148:1 189:14 190:14 195:15,22 214:19 269:5 358:7,15 361:10 368:3 continue 17:7

24:17,21 30:12 31:16 49:14 232:3 244:20 384:16 continued 29:11 continues 79:5 80:2 continuing45:21

329:22 contrast58:2 289:2 contrasted 54:11

54:17 56:22 65:1 contrasts 366:10 contribute 280:17 contributed 198:20 contribution 244:7

304:9 contributions 149:22 206:22 312:21 control 86:6,11

97:4 122:9 159:14 232:15 251:11 260:9 302:10 controlled40:21 controlling 94:20 106:1 163:6 262:1 controls 96:16,18 105:3,6 107:19 118:14 conundrum 237:18 Convene 3:2 convened 1:13 convenience 125:1 193:16 235:20 converged 289:12 convergence 289:7 conversation43:19

353:4 Conversely72:7 convey 106:5

354:19 convinced 229:7

299:15 convincing 131:7 COPD 135:4 copy 85:16 136:2

310:6 346:13 copying 350:8 core 53:12,14 57:9

59:14,15 60:15 62:22 63:20 69:12 110:9 142:16 144:2,9 146:11,19 146:20 149:15 152:4,10 153:16 Corrales 50:11,16

50:17,20 correct5:10 44:14

44:21 125:16 133:13 183:14 194:13 238:17 294:2 corrected 113:18 124:8 177:17 293:20 294:11 correction 111:22 112:19 129:16 correlate 39:11 correlated 67:1

284:20 correspond 111:6 corresponds 111:20 cost 15:1 118:12 122:21 123:2 couched 142:20 count 315:7 counter 165:22 167:12 counterpoint

288:12 counter-factual 193:16 counties 111:5 113:21 134:2,11 174:17 178:1 215:18,19,20,22 216:2,2,11 country 157:13 160:2 168:20 181:8,13,14,17 185:7,8 189:11 190:8 219:5 227:12 241:8,12 county 111:5,7,8,8 111:10,20 198:3 couple40:21 81:18

94:7 101:5 144:13 157:20 158:8 161:18 174:9 181:9 214:17 252:19 257:15 277:18 297:21 305:12 322:12 course 21:13 39:3

68:13 120:17 124:17 196:10 226:16 231:13 242:21 292:11 339:2,12 345:18 358:5 377:2 court23:1,3,11 courts 122:6 cover25:2 44:8

82:6 202:22 coverage 59:22 135:10 covered 149:18

252:5 262:21 271:13 covers 60:9 153:22 co-pollutant 37:20 co-pollutants 79:9 co-variability

292:22 CR76:21 77:10

78:9 79:20 109:18 114:17 127:6 131:21 152:10 201:18 211:20 254:18 craft 378:12 crank 246:14 crazy205:10 create 15:18 19:15

277:13 322:7 329:17 345:17 350:5 354:18 355:6 creating 345:2 credibility 219:21 credible 237:3

242:18 260:10 368:9 credit260:19 criteria 115:11

233:3 238:4,6,8 239:3,4 244:8 341:21 critical 60:4 98:18 105:15 314:18 330:16,19 370:9 criticism 175:14 186:11,19 226:18 criticisms 221:4 critique 365:8 critiquing 282:9 crossed 244:17

276:18 cross-section 126:3 crude 129:10 cubed 183:19 cubic 222:11

246:15,15 curious 115:5 146:6 220:21 325:8 current 13:4,15

24:8 26:10,16 27:17 28:9 31:22 33:20 34:14 35:7 35:14 37:10,11 38:9 70:14 77:1 80:7,12 83:10 87:12,22 90:13 183:13 185:4

### 202-234-4433 Neal R. Gross & Co., Inc. Page 398

196:1 260:14 266:10,19 267:18 268:2 269:2,12 296:1,14 343:10 347:13 349:6 352:12 360:19 362:10 366:18 373:3 currently 26:12

27:16 29:2,10,14 29:21 30:11,13 33:11 39:1 169:3 266:14 269:9 315:2 334:16 369:5 curve 186:18 213:2

231:14 272:7 curves 272:9

284:19 cut49:5 212:14

217:3 280:2 374:5 377:14 cuts 142:14 cutting 250:7 cycle 249:14 313:8 cycling 270:19 cystic 372:22 C-o-r-r-a-l-e-s

50:20 D D4:1 162:19 daily71:16,20

74:13 90:14,20,21 91:3 92:4 94:19 101:22 102:1,2,4 102:5,13,15 103:1 103:22 104:13 106:1 166:15 167:6 170:10 214:8 274:3 296:4 296:10 Dallas 71:13,18

72:7 73:4 119:12 damage 271:10 dangerous 98:13 188:12 288:1 dark72:9 Dartmouth 7:11 data 32:16 36:22

46:2,5,18 78:11 97:17,18 98:21 100:10 125:2 134:6,18 140:7 143:17 157:4,7 159:11 160:10 174:16 192:4 196:17,19,21 198:15 201:15 212:11 219:11 233:10 259:4,11 267:3 273:11 282:4,5,9,11 286:14 303:6 304:11,12,15,16 305:5 308:1,3 313:4 319:10 342:3 database 97:10 databases 198:3 dataset 127:8 Dave 265:1 276:20

279:17 286:9 David 2:5 6:18

75:19 David's 288:13 Davis 6:11 dawn 301:9 dawned 171:12 day 8:7,13 49:11 113:21 141:9 161:22 162:1 166:6 211:17,20 235:16,16 246:15 267:7 273:2,6,15 298:22 299:4,7,18 299:20 300:13,15 302:15,16 336:8 daylight273:2,17

274:3,3,5,8 275:6 293:18 296:3,11 298:17,21 299:6 299:22 300:7 301:6 304:5 days 225:18 296:9 298:15 304:4,10 deal 34:13 49:18 117:3 150:18 229:19 259:21 285:7 316:6 367:22 369:22 dealing 97:10 123:17 156:14 167:7 207:18 230:8 236:11,16 deals 10:4 149:10

236:18 dealt 35:6 208:5

218:3 325:9 378:21 death 83:13 119:13 119:15 deaths 82:7 Debbie 81:2,8,10

85:5 decades 224:2 decaying 105:4 decide 232:11

245:8 333:1 350:13 367:19 383:7 decided 341:8 deciding 18:5 164:19 383:7 decision 14:20

31:10 62:20 100:6 118:10,22 119:2 219:1 325:20 328:17 343:2,20 345:1 346:14 355:1 decisions 15:9 18:13,22 20:12 22:20 23:1,2,4,5 24:5 36:12 38:9 192:2 225:6,8,15 225:16 237:4 328:15,16 348:13 371:7 defend 19:1,1

225:8 280:6 defended 117:17 defending 19:2 defense 16:19 defensible 171:7

260:11 286:19 defensive 257:4

259:2 260:11 deficiency 113:1 define77:10 defined49:3 116:7 153:14 158:21 159:2,4 defining 94:4 definitely49:11 163:5 175:12 217:7 222:6 318:12 definition48:18 140:9 148:17 159:7 175:3 317:14 319:1 definitions 139:9 140:12 317:12 degradation

307:20 degree 56:22 63:3

63:15,19 66:14 71:6 75:6 90:15 95:6 105:18 170:18 319:21 degrees 243:15 delete 97:17 deleting 276:2 deliberating 158:13 deliberations 4:18

4:21 delta 211:17

213:19 deltas 180:3,11

214:8 233:11 demanding 247:4 demographics 197:20 198:5 218:18 demonstrated 112:14 demonstration 293:4 denominators 177:7 178:19 dense 217:18 departs 194:4 depend42:7,15

45:12 dependencies 142:10 dependent 170:13 depending 133:19 178:2 359:7 depends 337:6

383:3 deposited 246:18 deposition48:14

246:19 247:5 depositional

270:11 deposits 246:21 depressing 105:4 derivable 13:2 derive 25:9 derived 284:19 describe 115:19 135:9 187:12 described 95:22 107:6 108:1 209:3 220:18 221:10 340:13 348:20 describing 346:19

348:18 379:20 description 209:5,6

221:12 296:1 303:22 descriptions

281:13 descriptive 332:20 descriptors 243:14 deserve 260:18 deserves 38:22 deserving 18:6 design 57:22 58:1,3

58:4 60:16 61:3 61:12,18 63:8 65:9,13 71:21,21

### 202-234-4433 Neal R. Gross & Co., Inc. Page 399

72:22 90:20 101:22,22 157:6 158:17,18,22 159:2 161:1,4,15 206:22 Designated 2:21

4:8 designating 383:9 designed 58:15

59:15 103:18 286:6 desirable 314:8 detail 20:7 55:14

61:1,17 64:8 65:5 68:1 75:13 76:12 258:15 260:16 275:20,22 276:13 277:9 278:5,16,22 280:4,7 282:18 311:9 312:5 353:20 358:20,21 378:5 detailed 17:2 51:21

52:6 138:7 200:14 331:12 343:22 details 280:16

298:2 305:12 324:14 determine 173:2

233:17 305:16 determined 5:13

77:18 207:6 305:7 305:18 determining 76:17

80:10 237:2 247:18 295:4 detracts 194:6 detrimental 122:14 develop 19:17

201:5 238:4 243:18 244:8 developed 220:17

310:4 developing 239:4 development 147:22 251:3 devising 97:4 DFO 5:2 diagnostic 258:4 diagnostics 98:7 167:20 diagram 166:11,13 diagrams 65:19 died49:7 diet 125:4 differ 56:7,8 differed 178:14 difference45:9

87:3 92:15 99:4 126:10 129:2 151:8 153:10,11 153:12,20 161:7 176:2 208:7 213:9 222:8 236:7 252:22 261:7 300:5 301:19,20 302:6,21 306:9 differences 59:7 129:3 152:20 153:19 180:10 197:5,6 211:15 259:20 286:16 287:6,8 288:18 296:13 297:9 299:13 301:4 367:10 different20:19

21:15,18 24:11,12 25:20 36:8 38:4,6 38:8 41:4 46:16 48:15,21 63:4,5 63:11 69:4 71:8 71:22 86:8 95:18 97:12 99:7 100:15 100:21 104:17 124:13,14 128:5 128:15 129:19 133:21 134:1,2,4 134:5,10,11,13,21 138:22 147:5,6 151:3 152:2,11 158:17 160:17 161:17 164:8,9,12 164:14 168:20 174:8 177:12,16 178:5,21 180:4 191:4 192:22 193:1,7 196:14 197:20,21,21 198:4,5 208:1,1 212:8,21 218:21 234:2 235:9 238:10 240:22 251:11 254:17 259:18 268:4,16 268:20 272:2,17 273:6 275:8 276:14 278:2 282:15 283:2,3,7 285:14 286:18 289:8,9 290:19 292:13 303:9 318:11 319:16 326:10 329:6 335:13 342:6 343:13 344:7 345:2 353:11 359:8 361:2 368:9 373:9 381:19 differential 227:20 differentiation 126:18 127:2 differently 27:3 176:21 178:1 356:16 differing48:13,21 difficult225:10

248:6 314:19 difficulty 307:22 digest 160:13 dimension 161:20 162:6,6 171:14 diminishing 289:18 direct 118:10

269:22 314:9 directed 194:19 direction 101:11 114:1 133:3 146:10,11 198:22 directionality 114:4 directions 100:1 146:17 directive47:3 directly 13:18 24:2 119:22 242:3 244:22 288:4 308:20 disagree 123:11 disagreeing 279:16 discernible 24:16 disclosures 5:12 discomfort21:22 disconcerting 110:19 disconnect 106:14 191:21 371:21 discontinuities

277:19 discrete 348:13 discuss 8:14 44:3

49:21 58:18 121:18,19 146:18 182:3 184:5 204:4 263:13 320:10,15 320:17 327:14 330:9 351:4 376:16 discussed 31:5,13

31:21 59:4 76:12 110:17 128:6 199:11 205:21 207:1 231:8 263:4 267:1 274:22 286:20 307:11 309:2 331:20 356:12 361:9 368:6 374:8 discussing 8:2,11 126:9 170:4 217:12 263:1 350:20,21 discussion 3:10,18 15:11 25:7 36:15 36:17 38:20 40:4 41:3,6,10,12,19 43:7,10,22 44:18 46:22 53:6 57:4 57:17 59:14 64:12 68:17 69:5 90:4,9 95:20 109:15,19 112:9 114:2,17 124:21 127:15 129:15,20 132:20 137:6,13 140:13 141:7 142:9 154:12 157:16,21 160:3 161:11 162:21 167:19 168:13 170:5,19 181:22 200:2 206:1,13 217:10 226:2 228:13 231:6 237:17 241:21 242:1 243:3 251:9 255:12 257:3,5 263:14 265:11 282:3,10,13 284:4 303:19 316:8 317:20 318:3,8 321:10 323:19 326:14 327:10,21 328:10,10 330:5 335:10 337:14 339:9 356:2 357:17 358:1 369:4 375:4 383:9 385:20 discussions 4:21

9:2 10:6 52:6 136:17 142:7 163:4,6 225:20 323:4 377:7 379:11 disease 130:9,12,13 130:21 131:1,5,6 131:14,16 133:1 179:21 180:2 190:19 193:15 209:15,17 disinterested 279:7 disorder 315:22

316:1 display 274:7 297:7

### 202-234-4433 Neal R. Gross & Co., Inc. Page 400 displayed 299:16 displays 158:11

297:1 304:18 dissertation 21:4 distance 104:2,13 105:5 284:13,16 284:16,20 285:8 285:10 distant285:13 distinction 92:20

96:10 161:14 163:15 253:17 distinguish 121:9 distinguishing 149:5 distorting 89:7 distributed 195:18 distribution 64:14

64:16,17 65:2 66:1,2 79:3 91:2,6 92:21 93:21 101:15,17,17,21 132:22 158:16 169:6 189:20 195:19 196:7 200:10 202:10 212:3,11 214:22 215:11 216:4 218:14 236:14 242:15 distributions 28:2

32:22 63:9 89:8 93:7 100:14 101:6 105:22 207:5,9 333:14,21 diverged 289:12 divide 324:3 381:1 divided 164:11 dividing 175:12

323:20 doable 164:21 175:12 document9:20,21 11:5,8 12:18,19 16:10,16 17:7,15 19:15 21:8,8,16 22:4,13 23:8,19 25:4,7,19 26:2,3 29:5 43:6 53:4 91:10 117:16 133:17 144:12 188:10 192:20 199:17,20 205:19 205:22 206:3,11 208:14,17 215:3 220:22 221:4,22 222:16,17,17,19 222:21 223:10 230:8 248:15 263:1,11 265:12 270:15 272:12 277:14 278:13 280:12,17 287:1 295:15 311:14 320:13,22 324:11 325:15,19 331:3 331:17 340:3 341:8 342:20 344:4,15,22 345:1 345:15 346:2,7,13 347:17 348:5 349:10 350:4,21 353:8 354:16,18 354:19,20 355:3,6 358:9 360:6,11 361:8,10,12,15,19 361:20 363:20 364:15 370:18,19 374:14,20,21 379:7 381:3,8 documented 344:7 documents 10:13 10:18,19 12:2 13:20 14:5 15:11 44:3 120:16 121:3 148:1 264:3 268:1 332:17 355:4 371:1,12,14 385:19 doing7:20 13:18 13:21 47:22 89:1 98:15 100:20 102:21 104:19 107:14 110:3 155:10 175:17 182:14 188:22 189:12 190:2 191:4 201:21 203:9 207:4 227:5 242:18 251:6 258:3 260:10 265:6 323:22 339:7 354:16 domain 169:7 dose 186:17 229:12

232:17 252:9 doses 247:1 dosimetric 247:2 dosimetry 246:12 dot281:13,14,14 dots 169:4 272:9 dotted 276:18 double 246:16 double-check 133:21 doubt212:12 240:9

240:10 doubts 306:18 downstream 140:6 Dr4:3 5:17 6:4,6

6:10,14,16,18,20 6:22 7:1,3,5,7,9 7:11 9:6 16:19 19:21 20:4 21:1,9 21:12,14 22:11,12 33:22 34:3,5 42:20 43:16 44:5 44:15 45:1,5,16 46:15 47:4,17 48:1 50:3,10,14 50:18,21 51:7 75:20 76:1 80:22 81:5,14 85:11,15 88:20 90:1 91:8 91:11 92:10,12,13 94:7 96:5 98:3 101:2,7,19 103:10 103:16,19 104:15 105:7 106:11,15 106:19,20 107:4 108:7,20 109:12 114:7 115:3,18 117:11,13 121:8 123:6 124:6 126:7 127:11,17 128:10 128:13 130:4 132:1,19 133:4,9 133:10,15 134:22 135:2,3,7,22 136:13 145:21 147:11 148:6,12 149:8 154:5 156:2 156:7,11 157:2 163:3,11,16,18 164:7 165:2 166:10 167:10,15 168:7,16 169:14 169:18,21 172:4 173:1 174:13 175:21 176:18 177:1,14 178:8,10 178:17 179:1,9,11 179:12 180:5,7,12 180:13,18 182:6 182:10 184:6,22 186:5,10 187:9,18 188:9,17,21 191:19 195:5 196:16,20,22 197:1,3,10,13,14 197:22 198:12,14 199:15 201:3,4 202:17,20 203:4 204:1 205:7 208:21 210:7,8 211:8 212:8,22 213:6,10,12,14,18 213:19 214:9,11 214:12,14,15 215:14 216:13,20 217:3,7,14 220:4 221:2,19 222:2,7 224:14 225:2,4 226:11 227:17 228:14 229:5 230:4,9 231:1,3 233:2 234:1 235:8 235:11,14,19,22 236:1,5 237:13 240:21 241:22 244:6 245:6,12,15 245:18 246:11 247:7 248:7,16,19 249:2 252:17 253:18 255:9 257:22 258:22 261:3,16 263:10 263:22 264:2,14 264:21 265:4 271:11,16 275:18 276:11,21 279:14 280:6,22 283:15 284:11 285:17 286:10,22 287:13 293:14 303:3 307:5 308:10 310:18 311:18,22 312:7 315:11 316:13,20 317:21 318:14 319:9 320:2 321:13,16 321:19 322:6,12 322:19 325:6 328:2,4,6 330:7 335:18 336:9 337:6,9 338:12 341:6 342:12,18 344:3,12,14 346:1 346:4,5,11 347:10 348:3,17 349:17 349:21 350:3,11 350:12,15,22 352:2,10,18 353:3 353:12,14,16 354:6,14 355:15 357:7,12,14,20,22 359:1,12,14,18,19 360:3 362:1,4 364:6,18,22 365:18 366:13 367:3 368:18 371:22 374:10 375:1 376:1,5 377:2,20 378:2,15 378:19 379:5,8,13

### 202-234-4433 Neal R. Gross & Co., Inc. Page 401 379:17 382:9,20 383:18 384:2,4,7 384:12 385:4,7,12 386:6,11,15 387:1 387:10 draft7:21 8:3,4,17

9:21 10:22 11:8 11:10 16:10,22 25:21 26:14 28:17 31:19 32:4 33:11 41:11 42:2,6,11 42:16 43:17 47:10 51:10,20 52:2 64:7 76:8,13 77:1 81:19 83:7 138:6 222:13 269:2 272:2,3 294:17 323:15,19 324:1 326:9,13 328:10 329:11 330:5,6,10 330:15,16,17 334:15 339:13 360:5 361:20 364:11 369:12 373:22 384:8,18 drafted 255:17 drafts 10:13 21:16

361:18 dramatically 283:2 draw 13:18 17:5

40:9 162:11 242:6 286:16 358:21 370:17 drawback 119:8 drawing 114:16

354:2 357:4 drawn 196:11

281:21 360:7 draws 12:1 131:10

369:5 dream 171:19 drier 301:2 drive 1:14 154:21 driven 67:9 70:21 102:18 121:20 176:6 197:5 210:17 213:12 drives 243:10 247:3 driving 92:5 112:1 118:5 drop 102:7 droplets 292:1 drove71:16 279:17 dry 276:12 300:21 dubbed 8:2 due 37:18,19 109:7 137:11 212:16 285:21 360:4 duration 319:4,22 Durham 1:14 D.C 272:14 285:4 E e4:1,1 183:11

205:1,1 eager 381:22 earlier 120:3 126:18 127:6,7,12 158:21 159:11 160:3 164:5 167:17 192:11 215:17 236:6 258:6,9,16,21 260:17 290:15 322:17,20 352:22 355:11 368:6 370:12 382:4,5,18 383:1,22 386:19 early 10:15 125:14

298:8 322:19,22 372:18 381:20,22 easier 127:21

303:15 317:7 easily 84:19 114:13 127:8 175:6 210:11 217:16 east7:3 43:15

236:7,8 eastern 125:19 173:6,10,10 174:7 298:6 east-west 301:3 easy 89:20 93:6 241:5 248:5 295:5 336:11 350:10 369:22 echo 279:14 echoes 186:12 ecological 270:8 economists 310:19 ecosystem 270:21

271:5 ecosystems 48:14

271:2 Ed 2:2 6:8 110:2 198:17 200:1 256:4 370:9 372:3 380:16 editor 380:13 editorial 306:21 effect73:3 77:15

96:17 98:11 104:10,12,14,21 105:3 106:4 122:10 126:10 128:17 129:11 130:18 131:12 132:20 154:14,19 178:16 210:3 212:3,16 235:1 237:18,21 239:2 239:10,16 247:17 248:1 253:7 262:5 267:6,11 268:12 271:6 273:4 285:19 290:22 314:1,2 315:19 317:16 318:20 319:19 330:20 367:20 effected 285:13 effective 298:11 effects 12:16 15:22

22:16 27:1,16 35:10 37:13 40:12 40:16 54:7 68:21 77:12 78:21 118:6 118:9,9,11,14 122:14,18 128:4 129:7 132:11 151:5,6,20 153:19 155:9 159:17 171:15,16,21 176:3 178:5,6 179:4 211:3 212:10 219:12 220:17 238:18 239:5 249:5 257:9 265:8,9,18 266:9 269:7,15,18,22 270:6,8,11,12,18 271:1,5,7,9 275:3 293:7 329:20 369:4,14 efficiency 8:22

370:4 effort52:18 95:16 127:14 148:3 178:4 260:20 eight 67:10 214:1

240:14 251:16 382:6 386:7,9,10 386:19 387:7,9,10 387:13 Eight-thirty 386:11 either42:3 88:15 109:8 150:15 164:19 173:21 199:4 220:19 243:7 251:18 elaborate 155:18 168:11 elegantly 260:15 element 30:9 31:3 148:18 250:10 elemental 183:16 elements 11:19

29:6 35:2 48:18 53:12 104:4 148:14,19,20 149:1,3 elevated 131:4 eleven 135:14

383:19 elicit 345:16 elicitation 140:22 141:2 eliminate 292:7 295:12 369:9,10 eliminates 293:5 eliminating 30:5

291:1 elimination 291:12 eloquent293:4 emerge 380:7 emerged 121:15 emergency 85:1 emerging 222:9

260:4 emphasis 130:8,11 166:9 209:17 210:13 261:19 337:21,22 359:8 360:17 emphasize 171:3,9

277:4 313:20 360:16 emphasized 361:2 emphasizing41:16 empirically 245:3 employ 291:13 empowered4:15 encompass 82:7 encourage 16:2

225:16 226:2 262:17 377:12 encouraged 375:7 ended 115:9 132:8

258:3 294:17 347:20 endorse 150:22 endpoint78:4 115:8 132:14,16 endpoints 66:18

77:7,17 84:22 114:22 115:21 116:15,19 130:16 134:3,5,21 ends 348:6,14 energy 188:17

313:14 engineer 223:21 enhanced 53:11

278:7

### 202-234-4433 Neal R. Gross & Co., Inc. Page 402

enhancement

54:20 enhancements 52:1

52:11 58:16 ensure 5:2 entire 91:2 102:6 102:16 108:3 185:7,8 208:14 300:13 entirely 91:8

212:20 entries 133:18

296:17 ENVIRONMEN... 1:1 environments

45:11 EPA4:18 9:5 76:15

80:8 81:21 82:1,7 82:13,22 83:4 113:20 117:18,21 119:19 137:9 139:4 142:2 158:20 171:6 236:18 237:2 291:16 308:17 321:11 334:10 339:18 380:17 382:17 EPA's 83:2 296:22 epi 32:17,22 37:1,3

40:18 77:9,21 116:12 143:18 178:2 235:16 epidemiologic 36:5

37:4 42:1 epidemiological

76:21 78:8 233:19 epidemiology 188:14 EPS 333:17 equal 99:4 337:19 equally 22:9 330:2 equate 213:4 equated 219:12 equation 86:19 equations 88:15 equivalent 164:20 error77:11 78:17

80:1 110:21 152:3 214:20 294:11 304:21 errors 304:14 especially 142:11

289:8 291:4,14 302:11 303:12 305:18 306:1 essence44:12,13

46:10 239:14 275:10 essentially 39:20

41:15 55:14 56:6 56:17 73:7,16 101:20 164:13 178:3 182:4 185:9 211:12 253:10 292:8 309:8 establish 77:4 estimate 24:17

64:11 77:15 79:4 82:8,10 112:20 124:3,16,18,20 126:11 128:1,3,5 128:12 129:11 133:2 144:2,11 151:4,20 178:15 178:15 181:4 182:16 183:10,15 183:15,21 184:20 191:9 232:14 242:17 253:22 254:3 368:5 estimated 82:13 195:13 232:13 estimates 28:10,18

52:18 53:14,15,19 56:20 57:9,14 58:11 60:12,15,18 62:19,22 63:2,20 69:12 74:6,19 77:14 79:2,18 80:3 83:22 95:12 98:16,17 128:16 131:12 132:21 142:16,18 143:20 144:5,9 146:9 149:15 150:11,15 153:17 167:1,5,8 177:15,18 178:11 183:20 189:19 195:15 198:2,8 207:22 215:19 220:16,18,20,22 230:18 242:14 243:8 254:6 282:6 estimating 166:21

217:5 224:17 estimation 127:13 183:2 et 82:21 85:2 87:14 110:22 170:15 195:19,20 202:13 248:2,3 291:3 349:4 Europe 36:7 190:21 evaluated 28:2

83:10 evaluating 13:14

83:5 333:14 evaluation 150:20

267:15 evening 11:1 eventually 121:5 everybody4:11

7:19 49:22 51:8 62:5 141:5 224:20 256:8 322:4 351:20 384:18 ever-increasing

260:3 evidence 13:1 19:6

22:1,20 23:20 24:2 26:12,18,21 26:22 27:9 29:11 32:8,10,11,15 33:20 35:17,21 36:1,2 37:22 38:2 38:5,7 39:17 40:9 40:13,18,19 45:13 79:8 116:4 121:1 121:13,15 131:8 195:1 234:20,21 235:7 238:17,19 238:22 240:4 242:8 251:2 252:4 259:16 260:5 266:14 267:1 269:10 270:4,17 271:9 326:11 327:4,13,18 328:18,19,22 331:19 332:2 340:8 342:9 343:13 346:8,21 346:22 355:21 356:22 359:6,9 360:13 361:1,9 362:8 369:5,14 370:15 371:11 372:6 373:11,15 374:2,6 evidence-based

342:6 364:21 exacerbated

249:11 exact 154:16 exactly 103:11 105:8 106:19 111:6 165:18 179:3 201:20 228:15 231:18 240:13 250:3 257:2 279:5 331:16 340:21 386:1 examine 97:2 example 20:5 77:11

78:3 79:7 87:1 92:8 101:3 104:7 109:2 138:2 162:17 174:5 192:6 193:14 200:3 249:18 298:5 304:10 312:22 315:3 332:9 334:9 336:19 340:5 356:21 357:15 365:15 366:7 370:2 372:12 380:4 examples 20:1

303:20 excedence 90:16,17 exceed 166:15 exceedance 102:13 102:15 exceeded 90:13 102:22 exceeding 104:12 167:6 exceeds 103:22 104:13 excellent 136:20

276:6,22 279:4 294:9 303:6 exceptions 277:3,4 excess 181:12

236:20 excluded 236:9 exclusion 130:22 Excuse 57:20 81:1 106:22 executive 311:18

312:1 exercise 159:13

316:2,22 exhaustive 51:20 exhibit24:15 exist 198:11 existing 190:7

271:8 exists 117:21 189:8

314:12 expand 110:13

202:21 expanded 53:5

54:6 57:7 109:14 273:12 expanding 110:7 expands 273:10 expect 32:2 191:14

245:10 343:5 361:18 374:2

### 202-234-4433 Neal R. Gross & Co., Inc. Page 403 expectation 184:14 186:8 203:5,8 expected 96:13 139:12 183:12 expecting 104:10

356:13,20 expenditure 125:5 experience 295:1 experiment97:20 experimental 37:22 experiments 286:6 expert 140:21 explain 62:1 71:11 133:15 139:5 161:10 286:2,4 305:8 explained 170:20 188:10 208:6 explaining 65:9 167:20 explains 137:4 explanation 53:11

53:22 56:13 61:15 63:16 133:17 141:13 160:22 174:2 182:9,11 258:10,12 305:6 explicit 317:12

320:1,4 exploration 84:14 exposed 191:6 exposing 191:12 exposition 279:4 expository 25:14 exposure 28:7,7

33:8 40:22 41:1 70:8 80:4 82:6,18 83:12 117:3 125:9 138:7 143:15,18 143:19 171:20 181:7 194:7 201:10 210:3,16 211:19 235:20 239:18 240:6 241:1 246:14 251:7 260:6 267:9 268:13 319:4 337:14 372:12 exposures 30:19,20

30:21 32:13 34:17 35:11 40:10,16,17 170:11,12,12 185:4 232:11 319:22 333:7,9 expressed 117:22 177:16 expressions 375:15 expressly 19:15 extend 9:11 84:15

232:4 244:20 extended 82:6,10

82:11,20 247:8 extension 190:16 191:2 extensive44:18

357:16 extent20:17 26:20

42:7,15 63:7 83:6 109:18 129:6 136:16 225:19 249:9 267:22 329:14 334:14 360:16 361:1 369:10 370:17 375:18 376:7 377:3 381:6 384:1 extinction 274:9

284:12 296:8,18 298:14,16 301:2 304:3,9 308:20,21 309:11 313:16 314:10,11 315:1 extra 374:16 extract222:18 extracted 336:2 extraordinarily

247:1 extrapolate 197:17

222:18 extrapolating

223:4 extrapolation

230:7 380:5 extrapolations 238:20 extreme 300:17,18

304:19 extremely 24:6

72:19 117:16 158:10,14 174:19 226:19 259:13 304:13 e-mail 294:19

382:11 384:19,22 E-49 101:8 F F 205:1 FACA4:18 5:3 faced 236:16 faces 76:2 facilitate 22:4

338:16 facing 221:3 fact47:1 73:20

78:19 89:19 103:12 193:22 210:21 211:1 224:14 231:8,20 251:1,6 258:12 273:17 283:6 284:14,18 304:20 349:17 360:5 367:22 factor 148:18 factorial 286:8 factors 14:1,19,21 15:7 16:6,7 49:1 61:22 62:21 63:5 63:9 65:3 78:16 124:14 125:3 148:15 149:5 197:8 285:19 286:7 facts 83:14 fair 16:18 99:15

208:13 fairly 58:21 67:11 108:21 109:9 183:21 fall 64:15,16 66:1 91:11 189:18 216:3,11 fallen 96:6 342:2 falls 169:10 378:20 false 89:11 familiar 50:1 76:2 far 18:12 107:11 110:2 139:6 171:11 180:19 224:11 226:13 227:14 229:12 242:14 249:5 253:3 254:18 255:5 278:15 286:1 288:6 305:3 326:12 329:21 363:5 374:6 farther 357:1 fault278:19 favor45:2 166:2,8

386:9 fearing 238:16 feasibility 235:13 feasible 309:7

310:1,9 feature 284:21 featured 61:19 February 10:15 Federal 2:22 4:8,12

4:13 353:9,17 feedback 38:12

42:11,16 59:10,16 61:21 64:2 126:8 167:18 230:17 274:15 326:6 330:4 336:4 360:8 361:5 362:12 364:5 373:20 374:4 feel 17:18 32:9 83:3

84:17 106:8 117:17 227:17 238:21 246:1 301:4 317:4 336:1 345:14,22 375:18 feeling 68:18

261:14 fell 215:21 felt52:13 68:18 110:4 279:5 288:20 324:18 327:7 ferret 351:22 Ferris 125:17 fibrosis 372:22 fight205:9 figure 155:5 156:4 158:22 162:11 169:1 192:6 194:11 198:19 200:1,7,22 202:3 202:14 207:11 244:8 281:10 282:6 296:8,11 359:20 figures 157:19,19 170:1 179:14 180:6 215:8 234:14 284:21 296:16,20 fill 9:2 filled 133:14 filling 97:10 final 11:14 18:21

20:6 23:2 68:2 69:8 113:17 201:5 230:21 231:1 260:15 263:20 264:6 271:6 330:12 334:18 344:15 346:9 347:20 350:4 353:17 360:7,10 360:11 361:19,21 369:16 finalize 10:18 11:11 230:6 finalizing 263:17

263:22 264:2 finally 52:8 57:16

62:3 63:10 270:3 271:3 274:6 275:5 financial 5:12 find 17:11 47:5

### 202-234-4433 Neal R. Gross & Co., Inc. Page 404

131:7 186:22 219:17 220:14 221:21 287:22 328:9 334:14 353:18 369:11 finding 322:20 findings 69:12 186:2 311:9 fine 12:7 26:10,22

29:13,20 30:2,6,6 31:4 39:4,8,9 50:8 81:9 85:19 86:22 93:20 121:10 150:17 173:22 227:21 256:20 266:19 290:7 306:7 307:17,20 308:2,5 313:9 323:20 326:7 348:11 350:20 356:16 373:8 377:19,22 finish 168:14

322:17 381:20 finished 320:8 finishing 322:14

382:5,16 first 8:3 9:7,16,20 10:9,22 12:17 16:10,21 18:14,20 31:19 34:13,22 35:5,8 42:11,16 47:5 51:19,22 52:2 62:10 64:7 70:18 81:21 87:10 89:2 96:9 118:18 135:22 138:6 156:12 158:19 159:2 165:3 206:17 237:13 257:14 266:13 269:16 272:3 275:15 288:14 298:20 323:19 324:1,22 326:13 328:10 329:5 330:3,5,10,15,17 334:14 339:12 345:20 352:19 360:5 361:20 365:2 369:12 373:22 374:5 384:17 387:5 fit54:13 192:20 193:9 195:15 199:3 215:22 282:5,9 374:17 fits 356:19 363:20 fitting 192:16

374:20 five 111:4,5 147:15 158:3,12 179:17 213:8 226:22 227:4 247:21 248:14 256:11 259:14 311:16 365:22 366:10 386:4,5,12 fix 302:13 fixed 67:12 151:6

219:19 256:1 364:8 fixing 66:20 flag 222:22 flagged 287:3 flags 219:20 flawed 316:16 flight 382:21 383:1 flights 322:20 flip 30:3 flow 194:21 277:22 flows 123:2 fluctuations 235:1 fly 234:9 247:9 focus 12:5 25:12

29:7 31:1 58:15 133:1 152:15 272:14 324:14 327:11 353:1 372:11 focused 8:1 26:1

40:6,15 41:1 55:10 62:7 221:16 266:7 271:21 337:5 362:22 372:13 focuses 23:19 120:14 130:20 152:16 157:21 focusing 11:18

29:17 109:17 158:15 184:20 279:15 fog 273:7 275:3

291:3 292:1 foggy 292:15,17 folks 51:11,16 106:7 168:3 174:15 252:19 294:18 follow 155:20 173:20 174:2,4 256:5 312:18 346:2 370:11 373:12 followed 144:17

268:18 following 10:16

43:22 follows 131:14

257:16 379:12 follow-on 256:22 follow-up 103:17

341:6 footnote 137:3 153:18 179:10 footnotes 222:5 force 167:7 forced 119:17 forcing 15:17 47:2 foreseeable 224:1 foreseen 188:19 foreshadow 158:8 forest 348:1 forever49:15 form 31:4,5,22

32:1 35:4 38:21 41:6,12,16,20 54:13 66:7 69:1 98:4 119:10 231:22 296:5 312:11 326:14 345:18 348:9 362:21 378:11 formal 140:21

338:16 formally 11:4 format 343:19

378:2 former 12:20 371:1 forms 31:20 41:9 127:3 138:22 152:2,7,12 268:5 268:17,21 273:22 296:19 formula 328:12 formulaic 209:3,10

220:6 formulations 283:3 forth 65:4 256:16

274:13 289:11 292:3 301:12 313:15 351:11 357:5 364:3 Fortunately 206:1 forward 17:8,21 18:21 97:15 155:13 200:8 201:5 225:5,5 251:8 257:20 283:5 289:16 311:11 355:7 359:22 371:9 380:22 383:12 found 17:10 21:20

21:22 61:13 85:22 96:20 110:19 140:9 154:15 175:2 217:17 220:20 263:2 279:19,20 331:6 four29:6 109:16

272:7 281:9 319:10 324:9 325:1 347:21 350:7 376:15 fourth 346:2 fraction 309:10 fractions 29:16 frame 25:6 196:2

297:22 321:7 323:4 375:3,6 framed 25:11

377:14 frames 41:1 framework77:20 147:20 152:22 223:7 263:3 Frank 2:14 7:1

49:15 101:6 125:16 169:17 176:22 184:3 187:3 192:14 193:4 194:18 199:10 202:15 203:14 205:5 229:4 230:5 232:8 264:5 362:14 364:5 381:14 frankly 116:20

256:13 313:17 Frank's 185:18 fresh 258:19 Fresno 74:12 Frey 1:20 6:4,4 136:13 148:6 149:8 156:7 280:22 353:3,14 354:6 385:4 Friday 11:1 front 148:10 149:10 257:11,14 350:7 frustrating 256:13 frustration 364:7

364:16 full 55:17 56:18

203:22 221:9 344:8 fully 17:19 95:3,17 139:21 151:13 202:12 224:7,9 230:13 345:5 361:18 385:5 function 54:14 69:2

### 202-234-4433 Neal R. Gross & Co., Inc. Page 405

78:9 130:7 131:21 133:20 138:21,22 151:17 160:10 202:5 211:20 233:19 241:4 254:18 262:2 functional 152:2,11 functions 24:15

76:22 77:10 82:2 109:18 116:8 127:6 132:13 134:21 142:12 151:10 201:19 211:14 232:12 fundamental 110:20 funding 284:4 further 21:21

73:21 96:7 106:16 108:8 120:13,21 123:15 126:17 165:14 243:21 246:2 284:1 286:12 322:1 329:10 384:20 furthermore 144:3

223:20 furthest284:21 fusion 55:6 future 138:14

226:5 259:22 315:9 fuzzy 139:10,11 163:9 F-49 101:8 F-50 101:9 G G4:1 gain 201:22 234:17

284:1 gained 159:13

365:6 game 262:14 289:2

382:3 gathered 32:19 gathering 174:16 GA-1 134:12,13 gee 166:4 195:2

287:10 general 11:20 23:8

25:3 41:13,19,21 86:15 114:2,11 149:16 161:11 180:8 181:22 194:12 205:11 207:5 208:4,14 243:9 263:2 275:13 278:13 310:16 324:19 340:6,10,13 364:5 376:20 377:14 380:10 381:13 generalized 329:19 generally 66:13

130:13 151:8 158:10 177:9 217:2 295:9 311:8 382:7 generate 99:1 generated 28:19

164:17 181:16 233:10 296:17 generating 56:18 generation77:13 generic 274:16 geographic 287:8 Georgia 6:16 138:4 Georgia-1 134:8 germane 17:4

20:12 getting 19:4 47:18

57:20 59:16 61:21 64:18 73:8 93:5 133:22 174:22 189:4 192:15 198:13 226:22 227:13 235:17 239:9 260:21 339:10 340:20 352:5 366:19 386:18 giraffes 247:10 gist280:14 give 20:1 21:17 23:15 51:12 56:16 70:2 75:4,5 126:7 159:9 167:17 182:10 197:1 225:11 243:20 253:21 265:22 271:16 283:2,13 284:22 334:22 335:21 347:20 364:5 365:9,13 368:9 373:20 375:11 380:17 given4:21 53:21

65:7 73:17 84:19 98:15 115:12 120:7 145:12 151:16 152:4 211:13 219:14 247:22 276:13 282:7 307:14 337:21,22 342:13 363:14 givens 379:2 gives 79:2 93:16

133:2 331:18 349:3,4 385:8 giving 19:18 22:6

99:3 134:9 275:7 337:19 glad 208:16 354:15

355:13 glance 269:4 glaringly 141:17 gleaned 157:18 global 99:14

190:18 193:14,22 199:19,19 270:1 gloss 44:16 glossed 158:20 go4:3 8:8 17:2 20:7

21:18 42:14 46:11 49:17 64:8 75:1 75:12,13 87:21 88:9 108:18 109:8 109:11 116:16 135:8 136:9 137:1 148:18 157:1 160:19 165:14 167:13 168:12 172:13 173:19 190:12 192:10 194:16,22 195:3 199:4,11 203:21 209:4 213:21 223:14 226:15 227:15 228:6,17 229:2,10,14,15 231:7,13,19,20 232:19,20 233:4,6 233:7,21 237:15 237:19 238:14 239:1,4,17,17 240:7,8,10 241:5 241:13,17 242:7 243:18 244:4 246:16 247:13 249:6 252:1,6 254:4 257:20 263:5 271:22 274:14 275:15 276:8 287:10 289:15,21 290:10 295:1,18 307:1 317:16 319:5 321:4,20 329:1,15 338:17 346:10,11 346:13 355:7 361:16 362:2,15 369:18 371:15 377:4 382:13,22 385:12 goal 11:11 17:6

51:18 91:15 153:1 185:20,21 goes 154:6 155:4

162:22 254:18 284:12 318:2 346:15 353:9 going4:3 8:19 34:1

39:6 42:7,10,14 47:14 49:5,20 51:2 55:13 58:19 61:1 71:12 73:2 75:12 93:6,9 99:21 100:16,18 102:2,16 104:6 108:22 112:2 114:15 118:4,21 126:21 127:5 136:10,18 138:11 154:19 160:1 162:1 163:7 165:16,20 166:1 166:21 167:13 175:9 179:4,5 183:20 184:11,14 184:16,18,19 185:2,6,17,22 186:6 194:11,15 194:22 199:1,10 199:12 200:16 201:19 205:3,18 209:18,18 214:4 222:18 223:1 232:8,9,18 233:20 238:11 242:12 248:20,22,22 249:21 250:4,21 251:14,16 252:15 253:20 254:10 255:8 256:9,19 257:12 259:6 262:12 263:8 264:10 265:19 271:16 276:8,16 277:13 280:11,13 283:13 286:11,19 287:21 288:22 300:14,21 301:22 302:12 304:1 308:19 311:10 318:6 321:6 322:16 323:5,12 323:14,21,22 324:4 328:12 335:10 340:17 345:3,12 349:9 352:9 353:22 360:17 364:16 368:12 370:8

### 202-234-4433 Neal R. Gross & Co., Inc. Page 406 371:16 376:16,18 377:9 378:5 383:7 384:8,14 385:2 386:1 good 5:18 7:13 9:6

16:20 50:12 68:16 75:15 76:1 80:16 86:10 97:22 99:20 114:15 136:12 138:17 145:2 147:1,3,6 149:4 149:18 156:9 159:4,12 160:15 163:17 167:9,10 170:5 205:12 218:2 221:5 234:8 237:17 250:7 275:20 276:3 282:8 283:10 290:13,16,18 291:12 294:12 304:6 312:5,10 313:11 315:7 323:7 342:3 349:3 350:16,16 385:10 goodness 237:15 good-faith 260:19 Google 159:1 Goss 372:22 gotten 11:2 51:15

107:5 147:16 351:2 385:18 Governor49:6 go-round 147:15

307:17 Gradient76:6 gram 246:18,22

247:5 grant224:7 Grantz 2:5 6:18,18

276:21 280:6 286:10,22 287:13 319:9 320:2 graph 238:9 graphics 297:19

298:12 299:9 graphs 296:9 299:20 grave 306:18 gray 329:4 great76:12 136:21

142:8 145:22 173:8,18 192:5 315:4 317:5 greater 53:15 73:2

74:6,18,19 75:5,6 75:13 82:7 133:17 134:14 183:18 294:3 335:14 337:21 366:2 greatest211:22

212:3 284:16 green 169:3 greetings 9:11 grid 182:11,21

183:3,4 198:5 grips 367:4,6 ground 16:4,5

232:2 241:21 313:3 347:7 grounded 20:18

52:5 grounding 93:4 group 141:3,4

174:20 179:3 251:17 252:16 265:3 354:12 361:22 363:3,6,16 375:21 groupings 178:1 groups 155:16

179:6 272:16 286:18 growing287:1 grudging 387:13 guarantee 18:14 Guardian 1:14 guess 7:17 8:2

45:20 46:1 60:8 85:7 96:2 100:2 124:3 129:21 144:7,11 154:17 154:22 155:11 156:7 167:4 174:14 179:18,20 180:17 181:18 186:3,5,15 187:7 191:6,20 192:13 192:19 193:3,8 199:15,22 206:16 206:19 208:9 213:14 214:5 217:9 218:20 219:2 224:21 240:19 241:1,4 254:12 264:5 275:16 276:9 280:1 282:18,19 293:14 295:19 297:13 299:15 300:3 311:13,15 312:15 315:12 316:11 317:17 332:13 342:18,22 343:17 347:15 359:19,22 369:18 381:20 guidance 231:20

242:6 243:21 253:2 335:21 386:3 guide 362:17 gum 292:2 guys 145:22 166:19

314:2 H H 1:20 half 125:19 181:13 181:14 218:22 233:8 352:19 half-days 323:18 half-hour 382:4 halt 311:2 hand44:3 47:16 117:18 118:1 141:16 143:2 219:13 224:9 240:8 289:3,4 299:17 300:12,13 302:14,15 304:17 313:5 360:21 handle 229:1

288:10 handled 207:22

259:3 handout263:18

266:4 hands 94:12 386:7

386:8,10,12 hang 372:1,2 happen 57:2 86:3 138:11 146:15 147:7 148:1 176:9 happened 115:11

209:5,7 287:11 happening 63:2

87:4 187:14 212:13 happens 9:2 67:8 193:18 252:6 300:15 305:2 382:1 happy 81:20 139:3 142:13 159:16 202:16,17,18 280:19 363:8 377:1 hard 92:22 96:10 106:11 207:16 210:12 219:17 306:20 322:18 348:1 368:2 371:2 harder201:16 hardest220:11 harmed 119:3,5 Harvard 6:20,22

7:2,14,15,16,17 76:2 125:6 191:9 332:13 372:16 Hassett 12:10 HASSETT-SIPP...

25:18 324:6 325:18 331:15 333:5 335:16 341:15 364:18 372:3 haze272:21 hazy 302:15,17 head 179:2 346:16 heading 288:4 heads 247:11 health 3:8,9,10

6:21 7:16,21 8:17 10:6 13:10 15:19 27:1,16 28:14 33:16 35:13 37:12 40:12 41:22 49:19 51:2 66:14 68:21 77:7,17 84:22 115:21 118:6,8,11 118:14 119:2,3,19 120:15 153:2 167:3 171:15,16 171:21 185:3 226:15 227:10 232:19 236:17 238:18 257:9 261:21 262:2,4,8 314:2 316:16,17 317:2,12,16 352:13 360:20 369:4,13 372:18 372:20,21 hear50:4 81:12,14 106:20,21 262:17 264:18 312:15 320:11,12 361:22 371:20 383:5 385:2 heard 15:5,6 132:20 218:11 257:2 348:20 349:2 hearing 129:19

354:11 375:13 heart 130:8,12,21 131:5,6,13,16 133:1 179:20 180:2 209:15,17 330:15 heater 292:6

295:12 heck 300:14 Heinold 265:1

### 202-234-4433 Neal R. Gross & Co., Inc. Page 407

Helble 2:6 7:11,11

96:5 103:16 104:15 279:14 Helen 1:22 6:20 109:22 115:2 117:10 127:16 135:20 145:18,20 154:4 157:9 191:18 240:20 355:14 Hello 75:20 help 10:5 22:4

23:12 24:10 25:12 47:11 87:16 91:13 100:19 101:3 195:21 196:4 234:3 244:7 258:18 291:18 296:12 332:5 338:2 343:2 353:5 360:9 363:19 376:4 384:13 helped 25:13 170:2 170:6,20 255:13 288:16 298:5 313:1 helpful 17:12 65:21 101:1 137:5 141:15 150:1 151:11 153:15 160:18 167:11 208:10 288:15 307:21 311:7 336:4 337:3 338:2 339:17 347:11 351:8 355:12 363:19 370:5 helping 24:7,11

58:10 62:1 237:3 331:1 helps 50:13 122:16 194:20 Henderson 2:7

6:14,14 169:21 172:4 237:13 244:6 245:6,12 336:9 337:6 348:17 349:21 350:11,15,22 366:13 374:10 384:2,7 386:11 hesitate 140:15 heterogeneity

77:12 78:16 79:13 79:22 307:14 hey 175:14 high 39:8 72:21

74:13 107:20 108:4 119:12 139:6,17 140:3,4 140:9,12 170:14 174:19,19 179:17 195:18 207:19 212:7,14 247:11 292:7 295:2 299:11 301:11 304:8,13,13 312:20 higher 82:9 92:18

99:6 102:11 103:4 103:6,9 124:1,5 137:21 144:1,5 176:6 183:11 211:4 215:5 216:7 236:8 291:19 295:8 highest212:5 highlight51:22

66:8 218:8 225:20 371:16 highlighted 360:12 highlighting 370:2 highlights 3:8,13

3:18 10:20 78:19 highly 67:1 95:10 106:8 316:4 high-level 366:6

378:11 Hill 285:6 hills 285:4 historical 111:10 history 114:8

324:13,15 328:13 349:4 hit 159:2 368:12 holds 197:11 holistic 123:9 Holly 2:21 4:7 50:6

264:17 310:6 384:11 home 154:21

322:20 homes 137:18,19 137:20 homework 383:15 honest98:6 100:7 114:19 135:7 176:11 honestly 376:9 hope 92:1 113:15 113:16 149:3 205:9 210:1 330:7 hoped 223:14 hopefully 11:1,16 19:9 23:22 26:4 44:2 53:19 206:9 206:11 240:1 261:9 269:3 290:3 329:9 hopes 121:4 hoping 16:12 217:8

261:14 290:2 Hopke 2:8 7:9,9

88:20 91:8 92:10 92:13 106:11,19 147:11 245:15,18 248:19 276:11 284:11 307:5 310:18 311:18 hospital 85:2 hospitalizations 177:6,10 178:9,20 hour28:13 58:1

72:6 204:6 234:7 274:3 298:21 299:7,18 300:7,12 301:7,8,16 302:13 hourly 274:8 296:7

298:14 305:16 hours 30:19 273:1

273:2,5,15,17 274:4 275:7 293:6 293:17,18,19 294:4,4 295:13 296:3,11 298:9,17 299:4,6,22 301:6 304:5,13,13 hour's 8:10 housing 137:14,15 138:2 315:21 Houston 119:12 162:13 303:12 HUBBELL 188:21 197:3,13,22 198:14 201:4 202:20 203:4 huge 150:18 185:12

367:10 hugely 94:12 human40:22 316:5

316:11 humid 298:8 humidity 268:19

272:20,22 273:2 275:2 291:20 292:12 301:11 309:12 310:13 hurt 122:16 hybrid 55:5 56:5

92:10,16,16 93:16 94:8,8,11,17 95:15 96:11,12 100:17 103:18,19 103:20 104:4,11 104:22 106:9,17 107:18 108:14 171:2,9 182:21 hybrids 95:5 hydrostatic 247:10 hyper92:6 hypotheses 288:17 hypothesis 98:14 I ice 235:7 idea 11:9 89:7

93:11 149:4 152:3 169:5 189:13 218:2 243:5 247:16 250:3 290:13,16,18 295:12 313:11,12 374:11 ideal 343:19 ideally 17:17 243:7

322:9 ideas 351:21 identified 27:5,7

86:14 319:17 339:15 identify 13:15

41:21 174:11 189:17 identifying 15:14 109:15 110:8 115:21 137:4,6 ignorant 370:20 ignore 368:19 ignored 79:6,14 112:7 339:3 IHD 70:7,8 132:3,8 132:11 210:6 illogical 300:8,11

312:20 313:2 illuminate 296:12 illustrate 296:17

298:2 illustrated 73:21

302:5 illustration 261:11 imagine 106:11

223:22 314:2 346:7 375:22 imagining 249:20 immediately 144:17 immensely 101:9 impact 18:13 63:14

83:3 86:2 161:4 185:3 236:17 248:17 262:4 266:15 316:5 317:2 318:16,22 367:5 368:1,4 impacted 267:8,10

### 202-234-4433 Neal R. Gross & Co., Inc. Page 408 impacts 33:4 48:21

84:14 122:9 159:19 270:2 273:7 316:17 319:14 impairment267:14

318:21 impartiality 5:15 imperfect 384:14 implement 302:13 implementation

89:13 93:13,17 implemented 105:3 implementers 103:11 implication 144:8 191:7 implications 124:2 143:13 201:19 244:22 250:14 254:5 316:11 317:3 implicit 317:11 implicitly 142:5 implies 145:7 186:17 188:14 imply 142:5 implying 123:8 importance 19:11

65:8 important 14:4 18:2 22:10 37:15 54:19 62:6 73:20 79:13 80:2 91:18 102:8 105:20 106:7 107:15 112:8 116:2 121:9 125:7 126:5 151:3 152:5 184:7 199:2 203:10 206:12 217:18 218:19,20 218:22 223:3 236:12,13 240:12 253:17 255:2,19 261:19 262:12,22 263:2 267:14 283:17 288:6,6 291:9 305:13 306:14 311:10 332:22 334:17 341:16,18 346:6 351:21 352:6 356:3 364:1 368:19 369:15 370:5 375:8 378:5 380:7 381:3 386:3 importantly 33:13 181:3 194:3,10 impress 89:9 impressed 101:9

248:10 impression 89:11 165:4,17,21 166:3 167:12 improve 19:22 171:11 270:17 improved 89:15 139:13 227:6 304:12 improvements 131:15 imputation 97:14 impute 89:2 97:18 100:6 imputing 88:22

99:8,10,11 inappropriate78:1 inching 250:14 incidence 65:4 116:7,13 134:15 177:14 178:4,11 183:1,21 198:4 216:8 253:21 254:3,6 275:3 314:3 342:3 incidences 291:2 inclined 176:16 include 5:4 14:6

37:17 77:7 81:21 84:22 90:8 115:5 116:3,5 126:17 128:2 173:17 312:22 315:17 342:9 included 52:16 53:5 54:6 55:9 59:21 115:22 133:7 182:5 206:19 221:11 267:2 269:2 273:4 296:6 297:1 307:8 356:9 360:10 361:12 369:7 includes 12:1 36:4

39:3 226:12 346:14 including 18:1 53:8

60:3 131:19 267:7 294:18 297:8 305:19 334:1,17 369:14 inclusion 54:20

59:4,10 incorporate48:12

49:1 114:22 208:3 317:13 incorporated 23:12

207:15 274:9 316:18 incorporates 48:20 129:11 incorporating 146:2 increases 24:21 increasing 240:10 incremental 214:6 increments 211:21 independent211:1

250:20 251:21 index 367:15 indicate 83:18

286:15 368:1 indicated 294:22

367:7,10 indicates 10:16 indication 283:8 indicator 29:9,12

29:13,15,22 30:8 35:3 38:20,22 39:2,16 40:3 42:9 47:9 121:10,13 276:15 307:18 313:16,22 314:4,9 315:1,5,7 316:10 326:16 348:9 356:21 357:15,17 378:10 indicators 268:4,8

268:9,21 296:5,14 326:21 366:21 indirect 118:11,13

269:22 individual 20:10 177:21 189:17 214:8 224:19 277:8 296:11 304:5 331:13 339:1 individuals 338:15

375:21 industries 122:9 infer 91:10 inference 301:21

379:21 inferences 379:1,1

380:3 infiltration 137:16 137:21 influence 139:18 influencing 91:21 104:9 influential 341:11 inform 23:13 31:15

32:3,9 36:11 80:13 196:4 244:16 246:1 325:19 326:20 358:11 360:9 370:17 373:2,16 information 11:22 13:2,12,13,22 14:8,9,17 17:10 17:22 18:4,5,8,15 18:19 19:7,8 21:19 22:2 23:21 24:3 25:13 26:2 26:13 27:19,22 29:3,4,14,21 30:4 30:12,16,21,22 32:18 33:2,21 38:3 47:11 54:16 103:12 106:5 112:8 114:16 116:1,12,22 120:18,19 122:1 124:15 156:17 157:4,11,15 159:9 162:11 195:7 198:7,16 209:9 236:22 237:7,8 241:19 242:4 266:15 267:2,22 269:1 274:18,21 277:6,14 288:22 289:3 318:10,15 319:4 326:20 327:7,16,18 329:7 329:21 332:3,3,7 333:12,13,17,19 333:20,22 334:2,4 335:2,22 336:15 336:20,22 337:8 344:8,9 347:8 354:1,8 357:5 358:4,5 359:5,21 360:14 369:7 372:6,17,19,21 373:1,10,12 380:2 informative 68:16

84:17,22 93:12 152:13 277:1 285:12 297:20 356:7 informed42:10

43:12 63:20 100:6 124:12 345:6 358:21 361:21 378:17 informing 33:18

57:8 60:14 65:21 318:10 informs 94:4 inherent76:20 129:11 278:16 inherently 251:7

### 202-234-4433 Neal R. Gross & Co., Inc. Page 409 initial 125:7 165:21 167:12 326:14 360:6 initially 243:12

298:19 324:8 366:12 Initiative 372:18 Innovative 10:12 input 17:9 19:19

20:13,21 95:8 152:18 329:10 344:10 345:6 350:13 inputs 59:12 insight97:3 165:3 insightful 159:20 insights 159:9,12 159:13 201:22 281:20 355:9 365:5,7,9,14 instance 56:4 67:19

95:11 117:6 134:7 instances 183:18 Institute 5:10 6:15

76:7 265:2 instructive 169:11 instrument 309:3 instruments 310:10

314:20 insufficient 327:8 integrate 33:17

373:11,14 integrated 13:19

25:10 198:22 249:16 282:20,20 331:20 372:7 374:8 integrating 201:15

260:14 338:20 340:7 integration 251:1 integrative 52:17

57:11,17 61:20 62:3,7,10,13 166:7 209:2,10 255:15 311:1,5,17 intend 11:7 intended 10:8 15:16,17 94:8 124:7 153:2 159:8 342:20,22 intends 122:22 intense 55:15 57:7 intensive 51:13 intent 122:7 125:17

216:17,18 intention 227:1 interaction 65:12 interactions 4:22 interest5:14 8:11

44:17 69:15 158:15 173:8 268:14 interested 59:9

68:3 69:22 116:20 218:11 279:2,3 298:3 interesting 67:5

68:14 86:1 146:6 146:18 163:22 297:21 299:14 309:20 intermediate 168:1 internal 346:4,14 interplay 60:16

61:2,11 63:7,10 328:1 interpolation 97:14 interpret 13:12

58:11 122:5 175:13 226:9 interpretation 120:10 139:19 150:13 179:15 190:10 196:5 228:4 303:17 304:6 interpretations

297:1,3,4 interpreted 122:6

216:17 interpreting 60:18

62:18,22 179:14 354:7 interpretive 258:19 interrupt90:2 intervals 54:12

83:21 84:4 151:16 151:20 152:9 231:15 233:12 intimidated 368:21 introduce 34:3 109:5 182:16 306:12 introduced 148:21 introduces 131:11

277:20 306:4 introduction

230:10 324:12 349:3 introductions 3:4

6:1 introductory 120:8 intuition 20:18

98:12,12 345:11 intuitions 225:10 intuitively 207:16 invariant202:3,4 inverse 284:20 investigated 319:20 invite 145:18 invites 186:11 involves 93:21 irrational 233:4,5 irregularity45:4 Irvine7:6 ISA 26:3,13 29:5

36:17 40:7,8 46:3 77:19 83:10,20 114:15 132:10 190:3,10 194:16 223:18 266:16 267:1 269:10 318:8 319:15 331:10,16 332:3 333:12 334:3,18 343:16 354:12 358:6 359:20 369:6,16,19 378:21,21 379:9 ischemic 130:8,12 130:21 131:5,13 131:16 133:1 209:14,17 Island 111:7 issue 39:18 42:9

44:16 60:2 65:20 69:4 79:5,14 99:8 105:15,20 116:14 121:9 126:9 138:1 140:16 141:13 145:8 159:22 160:4,22 161:12 162:10,12 179:13 181:2 187:12 206:8,18 209:15 209:19 214:21 215:15 217:4 235:9,12 249:10 256:21 258:22 259:16 298:18 309:11 325:10 351:18 378:18 issues 5:15 10:4 14:22 16:1 20:11 35:6 46:12 47:8 76:18 77:2,5 119:22 120:11 121:11 122:2,8 123:19 128:19 138:10 143:17 154:7 156:8 180:20 213:13 219:9 232:15 256:15 261:1 262:22 263:4 271:12 281:17 282:11 292:11 307:9 314:14 317:20 329:4 330:11,15 352:6 363:12,17 378:9 378:20,22 380:7 item 3:1 80:18 itemized 210:5

215:10 items 10:11,21 iterate 145:18 iterative 361:16 Ito 110:21 i's 276:17 J Jackson 346:10 January 10:15 jargon 158:20 Jenkins 12:13

33:22 34:5,6 44:15 45:5 357:7 357:14,22 359:12 359:18 360:3 362:4 jerk 237:14 job 114:15 136:21 145:22 147:1 276:22 278:10 376:22 381:18 385:18 Joe 6:22 7:11 45:15

46:11 95:21 96:4 136:8 186:9 217:13 220:2 279:13 312:17 315:10 342:17 348:18 349:2 363:22 365:17 join 265:19 joined 111:13 jokingly 136:18 Jon 5:17 6:2 81:1

85:11 96:5 117:14 180:14 322:6 341:6 374:10 382:9 386:6 Jonathan 1:14,18

338:20 JOSEPH 1:19 2:6 judge 287:21 378:20 judged 39:14 352:17 359:10 368:8 judgment244:10 244:13,14 245:15 246:4 325:21

### 202-234-4433 Neal R. Gross & Co., Inc. Page 410 judgments 17:4 18:8 23:12 24:7 371:5 379:4 July 11:12,14 jump 72:16 303:11 jumping 159:5 June 11:10 justice 119:19 379:20 justifiable 223:5 228:18 232:3 289:17 360:18 justification 84:2 130:22 199:7 223:16 243:19 justifications 228:21 justified 45:22 justifies 223:18 232:9 justify44:19 129:18 210:13 252:3 K K 104:13 Karen 19:21 25:18

28:11 38:11 45:21 47:2 91:15 117:14 120:2,12 121:6 123:7 201:1 228:14 229:7 257:22 263:15 335:17 341:7 370:12 Karen's 42:13 keep 11:16 79:7 112:6 173:3 254:1 254:7,16 277:16 287:1 310:12 314:8,21,22 315:6 353:20 keeping 314:22 keeps 283:20 Kent 110:1 kept70:20 278:20 key 26:8 52:1 62:13 62:14 66:4,5 137:4,7 206:18 208:12 261:18 270:16 307:13 310:18 363:17 keyed 259:22 kick 187:3 298:7 kids 52:7 kind4:5 20:21

42:15 46:4,4,17 70:1 73:6 83:22 92:6 103:20 122:10 126:13 127:1 128:20 137:20 140:22 142:20 163:14 165:21 170:1 171:19 173:15 188:14 219:2 235:3 238:1 241:13 242:5,20 247:3 249:22 250:9 252:2 283:20 285:1 289:4 291:13 295:11 298:18 300:16 301:16 302:2 311:17 314:17 316:8 319:4 325:16 332:16 337:11 347:3 354:7 367:1 381:2 kinds 196:14 260:8

297:19 301:21 302:18 314:21 334:13,20 340:18 363:10 378:7 King49:7 Kings 111:8 kiosk 382:22 knee 237:14 knew94:19 268:7

333:13 knocking 261:6 know7:17 23:3

27:2 45:21 50:8 83:4,16 86:10 93:1,19 98:6 100:22 101:18 103:15 105:19 108:11 114:9,20 114:20 115:14,18 116:3 140:17,20 140:21 141:8 143:12 144:18 145:10 146:5 147:18 152:3 153:20 162:21 166:7 176:1 177:13,20 178:21 181:5 185:1,5 186:15,20,22 188:3 207:15 209:4 211:9 218:20 219:1 222:1 224:15 228:12 229:12 232:9 233:8,15,19 235:2 237:15 238:2,9,17 240:5 240:21 241:16 243:12,13,16 244:4,4 245:1 247:8,14,20 248:6 249:18 251:20 252:5,6,7,8,17 253:15,16,20 255:4,6,17 257:19 262:15,16 267:15 268:18 273:16 279:2 287:12,20 289:2 290:11 300:6 302:6,10 303:4 304:12,14 305:21 307:14 311:6 313:7,17,19 315:13,18 316:7 317:2,4,4 319:21 321:4 322:16 325:2,3 326:17 328:14 330:17 332:15,15 334:19 335:13 336:12,20 337:18 340:9,12 342:5,21 343:18 344:2 346:8 347:13,19,21 349:2,12 352:7 357:2 358:4 359:17,20 360:21 361:5 362:5 364:15 365:7 366:8,16 369:9 373:21 375:9 381:21 382:3 386:16,21 knowing 325:6 knowledge 141:20

238:12,13 241:2 260:14 known 15:21 143:17 knows 51:9 kosher 222:20 Krewski 82:9

130:14 131:12,19 181:19 372:14 K-effect 104:2 L L 284:13 labeled 293:17 lack 5:14 121:22

238:12 241:2 363:11 lacking 246:5 lacks 187:4 lag 54:7 60:2 lagged 250:15 laid 11:13 38:14

62:11 156:20 185:22 347:19 373:8 Lake72:18,18 73:3 language 90:5

239:10,15 large 36:16 42:15

76:19 83:10 124:10 149:21,21 236:10 292:1 361:22 largely 58:20 79:6 110:14 larger 65:14 173:10 183:21 largest236:17 Lassiter 265:17

269:6 lastly 29:18 33:17

77:13 80:1 late 11:8 372:16 latest78:11 113:10 126:15 127:9 294:6 launch 203:11 launched 125:9 law4:16 47:22 122:4 229:22,22 230:2 lawyers 313:17 lay 22:17 23:9

39:15 41:20 42:2 345:5 366:8 laying 14:13 19:16

22:22 38:16 lays 344:7 360:12

360:13 lead 12:11,12,13

38:8 41:15 51:9 80:20 88:7 265:15 265:17 275:16 324:4 327:16,19 364:7 376:17 leader 205:5 leaders 381:10

382:11 384:13,22 leading 120:17

228:16 leads 326:11

383:10 leap 235:4 learning 346:1

386:1 leave43:20 97:6 135:4 225:11 252:14 290:7 339:14 364:17

### 202-234-4433 Neal R. Gross & Co., Inc. Page 411

377:11 381:22 leaves 226:17

260:22 leaving 103:1,7 136:8 185:12 left 88:13 102:11 147:22 212:15 234:16 250:21 330:18 335:4 legally 225:8 legislative 14:7 lend 308:18 length 16:16 17:15 19:14 21:5 267:9 335:3 lesson 114:9 261:9 letter 8:20 20:2,6

322:8 384:9,15 letters 20:1,7

382:13 let's 9:1 49:17

71:10 72:16 90:18 90:19 96:17 135:13 137:3 138:5 150:19 182:2 204:4,6 217:13 237:9 271:22 272:1 274:14 275:15 315:6 323:1,6 324:1 372:1,1 376:15 380:18 381:22 383:21 level 24:18 31:6,21

32:7 35:4 36:21 38:20 41:6,20 61:8,9,10 66:19 66:21 67:13 69:17 70:13,14,22 71:5 71:9,15,20 72:8 72:12,19 73:14 74:2,5,8,9,17 75:4 76:17 80:10 82:14 83:1 102:5 155:2 181:8,19 185:11 188:4,5 193:18 198:3 207:1,11 219:17 234:22 237:21 238:15 239:5 241:13 243:9 247:22 248:4,8 253:8 254:19 275:20 285:9 289:16 325:14 326:12,13 327:11 329:2 330:1 340:10 347:18 348:9 367:4 378:4 levels 20:14 21:22

24:22 27:10 32:9 41:8,22 42:4,4 55:1,3,12 56:1 57:1 60:8 61:6 66:13,22 67:7,9 67:11,13,17,20 68:7,11 69:14 72:12,14 74:1,5 74:13,15,16 84:10 84:15 94:6 96:19 103:6 106:3 124:13 163:7 166:20 182:22 185:6 187:15 191:1 196:1,8 197:6 212:1,2 216:7 222:14 223:1 225:22 228:2 233:11 238:6,10,18 239:2 241:11 246:13 247:18 251:5 267:12 268:5,15 268:20 271:4 291:19 292:7 295:8 334:5 liberties 163:1 Liberty 164:3 174:3 176:16 lies 246:4 lifetime 246:13 lifetimes 123:15 lifted 331:16 lifting 331:10 light 171:12 274:8 284:12 296:7,18 304:9 308:19,21 313:16 314:10,10 315:1 liked 142:9 205:14

261:4 277:1 likes 301:16 likewise 80:11 limit255:8 limitation 127:10 131:19 limitations 37:18 118:2 282:3 309:3 limited 36:14 37:20

37:21 46:2 94:18 118:16 200:6 limiting41:17 117:8 309:8 line 14:17 19:3 22:8

23:16 88:5 98:9 107:1 202:9 210:15 220:16 228:2,7 232:4 323:20 338:18,19 350:20 371:19 372:1,2 linear 82:17 151:7 190:4 229:12 232:17 235:17 240:6 244:20 245:2 254:12 linearity 212:7

325:12 lined 265:5 lines 19:19 21:15 107:2 289:11,12 350:8 369:2 lingering 123:19 link 64:21 271:10

319:18 linkages 47:12

271:4 linked 61:11 Lippmann 2:9 6:6

6:6 109:12 124:6 127:11 148:12 156:2 220:4 221:19 222:7 225:2 226:11 228:14 230:4 231:1 235:8,14,22 236:5 258:22 325:6 328:4 330:7 338:12 341:6 342:12 367:3 378:15 379:5,13 Lisa 346:10 list 172:12 284:3

319:15 listen4:5 listening 16:13

223:9 252:19 lists 332:11 literature 141:1 191:3 367:8 378:17 litigation 324:15 little47:14,18 50:1

53:22 56:16 59:13 64:20 69:5 78:12 86:20 98:17 100:4 106:16 108:8 123:9,10 126:8,20 126:22 127:9,18 131:10,11 139:9 139:10 140:10 141:13 144:22 146:5 150:7,8 155:20 160:13,19 161:9 163:9 167:17 168:6 170:22 171:17 173:14 180:6 182:16 183:11 186:12 188:19,22 191:21 192:9,15 198:21 209:2,11 211:9,21 212:17 213:2,3,21 215:9 216:13,14,16 217:9 220:5 223:6 229:6 240:22 250:14 251:12 258:15,18 259:2 273:20 277:6 280:15 288:1 289:1 298:10,18 300:19,21,22 303:21 305:14 307:22 308:12 313:6 315:13 318:7,12 322:17 324:13 334:22,22 336:22 341:1 353:2 355:19 356:5,14,15,20 380:18 live 111:15 225:3

285:7 287:14,16 lives 184:12 living 225:13

285:21 297:22 LML 185:8 187:18 187:20 254:3 local 89:21 92:17

93:19,21 94:14 96:15 99:12 107:12 localized 55:7,10

99:10,11 105:5 locally 96:15 locals 313:2 locations 36:8 37:1

37:4,8 106:18 164:9 166:14 305:19 locked 341:12 log 151:6,6,7 190:4 logic 194:21 233:1

239:8 278:3 logical 110:9 190:15 230:5 249:15 logit272:6,12

274:22 281:1 283:16 284:19 288:14 Loma 175:19 long 21:10 24:14

28:6 32:12 33:7

### 202-234-4433 Neal R. Gross & Co., Inc. Page 412

40:10 43:14 56:7 56:8,9,19 57:3 59:7 66:17 67:2 70:7 73:15,17 79:18 80:3 82:4 83:11 84:4 89:21 109:10 125:9 134:16 177:19 178:6 210:3,13,17 210:21 211:5 212:18 213:13 217:21 221:13 223:10 276:12 306:20 325:12 331:3 337:14 343:4,22 351:7 352:3,5 371:3 373:22 longer 16:11 17:16

82:6 188:4 245:22 335:18 long-term 262:3

333:9 367:12 372:11 look 8:6 13:8 24:21

26:9,18 27:19 28:5,6,9,11,17 29:6 30:19 32:7 33:2,6,13 37:3,7 38:1 40:22 55:10 55:19 56:6 60:20 61:1 71:13,16 74:7 82:3 83:20 84:8,11 87:21 90:15 96:17 97:19 98:2 99:20 100:5 118:18 119:1,9 121:14 122:15,17 128:4,17 134:7,17 146:16 151:15 162:18 179:6 180:8 183:17 190:4 191:5 195:12 202:8 218:16 228:1 230:1 233:10 234:14 256:2,9 271:17,18 274:9 275:9 285:12 291:14 292:21 299:15 308:11 310:11 311:14 317:5 319:2,6 320:19 321:8 323:3 324:10 325:5 326:17,20 328:13 333:3 334:8 338:10 340:5 342:8 343:5 346:7 349:22 351:5,13,20 353:16 354:21 358:19 362:19 368:20,22 372:5 372:17 373:6,7,15 373:15,20 374:2,5 378:8 383:14,15 383:17 looked 19:5,6 30:17

30:20 36:1,9 55:16 61:16 65:11 70:14,18 71:3 74:9 82:22 94:15 119:16 126:22 165:4 175:1 179:16 180:21 189:10 224:19 266:10,22 267:5 267:12,18 268:7 268:12,14,16,19 269:22 270:1,4,16 274:1,2 284:18 306:20 309:1,7,14 311:7 357:9 370:19 372:19,21 373:5 looking 12:2,22 18:19 24:2 27:9 27:13 29:8 32:16 38:12 48:12 57:2 59:7 60:4 66:9 67:12 68:11 69:13 72:17 92:16 94:21 96:14 99:16,18 116:10,17 128:20 130:20 131:3 132:2,15 138:21 138:22 152:1,15 152:20 153:6,18 159:17 165:18,22 166:18 175:11 189:20 201:13,14 220:7 221:22 222:10 233:9 242:3 251:4,8 254:14,19 266:8 267:9 268:2,22 272:17 273:15 276:14 287:9 289:7 299:21 303:7 305:4 306:4 307:6 309:3,17 318:12,20 323:14 324:18 332:9 333:11 341:20 354:17 360:8 361:5 362:11 366:1,5,14 370:14 looks 8:13 56:14 100:20 140:22 157:11 190:11 276:6 304:16 332:15 364:8 381:18 Los 172:16,18 174:5 179:13 236:9 305:9 losing 277:11 309:9 lost256:16 367:1 lot23:19,21 24:10

36:8 51:15 55:13 60:22 61:14 62:6 64:8 69:9,9 76:2 78:13,20 84:18 92:11 98:5 116:3 131:8 146:3 147:1 156:17 160:3,8 161:22 166:14,19 167:5,19 170:2 172:19 189:4,5 191:7,22 192:1 208:18 223:15 234:9 236:15 237:8 258:10,11 260:19,22 267:15 275:22 277:10 278:4,16 280:7,8 280:10,11,13 281:15 303:7 304:11 311:9 312:5,6 313:14 328:10 332:16 346:18 349:1 353:18 354:4,12 356:1 364:16 381:2 385:17 lots 46:12 138:17

217:21 220:1 317:3 Louis 306:19

312:22 Lovelace 6:15 lovely 298:1 low 39:9 67:10,11

67:21 72:20 139:5 140:12 142:17 144:10 207:18 230:8 232:20 234:10 237:19 239:2 240:7 241:13,17 242:13 244:3 247:1 300:9 306:15 Lowell 2:2 6:10 107:3 275:16 276:22 283:14 291:7 293:13 303:2 Lowell's 312:19 lower24:21,22

27:14 39:6,11,20 74:14 82:8 83:14 83:19 84:9,10,12 86:4 112:15 126:11 167:8 176:6 179:22 189:19 211:3,4,5 212:10 223:14,19 224:10 225:22 228:2 231:8,13,13 231:19,21 233:4 233:11,21,22 234:15 238:5,11 238:11,18 240:10 240:10 243:18 244:4,21 248:20 252:1,6 255:7 285:9 329:1 lowered 165:5 lowering 234:17 lowest24:18 70:13

73:14 74:8 82:13 190:22 193:18 low-ball 368:5 low/how241:13 lunch 203:13,18

384:3,6 lunchtime 180:17 lung78:3 81:11

246:18 lungs 247:6 Lydia9:12 321:14

321:17 L.A 162:5 173:8 175:10 176:13 179:16 304:9 306:19M macroscopic 366:6 mad 300:13,14 magnitude 28:6

33:6 139:7 141:21 152:9 253:7 269:19 320:3 367:20 magnitudes 75:7 153:17 main 35:6 52:11 136:15 154:16,20 155:3 182:5 200:21 261:16 283:5 293:8 maintain45:8 maintaining 5:5

### 202-234-4433 Neal R. Gross & Co., Inc. Page 413

35:9 major 53:3 76:11

78:15 82:4 148:3 202:2 majority40:17

307:19 making 5:5 9:9

62:20 74:6,18,19 138:19 165:15 166:22 203:4 214:15 219:1 220:10 252:2 260:13 274:18 276:16 291:21 292:7 295:2 345:1 346:14 377:9 managed 52:7 management

346:16 mandate 16:3 48:3 123:9 Manhattan 111:3,9 111:12,20 manner 110:10 mapping 134:20 Marc 265:18

275:13 march 1:10 171:21 Marcia 135:22 margin 15:20

229:13 245:16,18 325:8,11,16 326:1 328:8,11 329:17 368:2 Mark 50:16 marked 63:14 Marriott 1:13 marshal 346:8 MARTIN 9:6 16:19 20:4 21:9 21:14 22:12 42:20 43:16 47:4 48:1 121:8 195:5 196:20 197:1 198:12 201:3 204:1 225:4 227:17 241:22 263:22 264:2,14 265:4 271:11 321:13,16,19 328:2,6 337:9 344:3,14 346:4,11 348:3 349:17 350:3,12 352:10 352:18 353:12,16 354:14 375:1 376:5 377:2,20 378:2,19 379:8,17 mass 121:12

227:21 228:2,17 229:1 236:13 288:7 304:14 305:15 306:16 307:18 327:1 massive 174:22 mass-based 29:12

30:8 118:3 227:7 227:14 268:6 masterfully 255:21 match 171:22 matches 103:22 material 137:11 149:18 247:5 280:10 285:18 303:20 306:6,22 331:6 332:16 materially 259:15 materials 271:7,9 mathematical

88:10,15 90:9 mathematically

86:18 112:19 matter 1:4 4:9 120:16 128:22 129:19 135:16 204:9 323:9 330:15 345:10,11 350:5 387:15 mavens 310:17 max 61:7 71:22 maximum 274:2

296:4,10 298:13 mean 32:5 56:17

61:6 72:19 101:16 101:19 102:6 105:9 128:8 132:8 132:19 146:19 166:12 176:5 177:14 184:15 191:11 197:14,17 210:14 211:4 213:4 214:2,7,10 216:16 217:19 220:6,19 230:12 232:21 236:5 239:13 243:9 244:21 245:10 246:13 247:8 248:10 250:18 251:10 253:12 257:10 260:1 262:15 285:6 286:22 309:16 318:14 334:19 335:1 336:17 343:4,7 344:5 347:15 358:2,17 365:2 370:1,21 380:6 382:6 383:21 meaning71:20

72:13 162:8 213:21 235:6 282:14 meaningful 375:12 means 16:22 72:2

73:1 163:12 210:13 235:15 264:11 meant 34:17 68:17 101:14 115:19 139:5 140:2 355:18 357:18 measure40:1

235:20 238:12 306:15 308:19 314:15 315:3 317:6 measured 73:14

82:14 84:10 97:21 193:18 measurement 77:11 78:17 80:1 192:3,12 196:21 197:15 229:20 307:9 measurements 192:7 196:17 241:20 291:22 292:5,8,9 295:2 305:7,9 306:8 315:15 measures 15:12 measuring 295:5

309:18 mechanism 107:14 mechanisms

233:17 380:2 mechanistic 252:8 medium 139:5 140:12 meet27:17 122:10

225:11,19 267:20 meeting 1:6,13 3:2

5:16,20 8:5 9:14 10:17 11:3 13:21 28:8 102:5 110:21 122:19,21 123:2 128:7 138:16 141:4,7 143:11 151:14 209:5 296:2 307:12 308:16,22 309:13 310:3 318:4 330:8 330:9 338:16 339:6 meetings 4:20 5:5,6

339:3 member 354:9 members 1:16 2:1

64:3 68:9,10 83:17 95:8 109:22 136:1 163:19 memo 282:16,21

291:5 293:20 memory 126:22 mentally 386:16 mention 39:1 147:3 147:7 209:14 252:20 318:1 mentioned 28:11

38:11,18 39:2 59:13 60:2 63:6 219:7 333:16 380:11 mentioning 295:11 mentions 144:18 Meredith 265:16

266:5 meriting 21:21 meritorious 141:10 message 168:4 messages 161:19 met5:3 184:15

273:11 meter 183:19

222:11 246:15 meters 246:15 method 59:11 170:5 171:2,8,10 methodology 125:11 methods 59:2

278:5 308:16 309:2,14,16,20 metric 117:4 118:3 143:18 153:3 197:2,9 262:1 290:18 302:1 metrics 158:17 Mexico49:7 mic 52:9 microgram 183:19 191:12 micrograms 191:13 222:11 224:18 246:14 microphone 50:13 mid 284:18 middle 95:5 112:12 112:12 Midwest 168:22 169:9 mid-point286:16 mild 243:14

### 202-234-4433 Neal R. Gross & Co., Inc. Page 414 miles 287:11 million 111:19 113:5 mind 140:17

252:21 253:1 254:1,7,17,22 312:8 362:19 minor 136:22 137:9,22 144:14 145:16 161:10 minute 127:13

291:10 323:6 minutes 204:3

293:21 325:5 376:16 Mira 175:19 miscalculated 111:17 misclassification 143:16 mislabeled 111:16 156:4 missed 62:14,17

85:7 158:1 164:5 277:21 347:15 missing 9:14 88:2,6

88:22 97:11 134:6 134:18 316:3 missing-ness 133:11 misstatement 113:3,11 mistake 159:3

210:1 364:19 mistakes 162:15 misunderstanding 178:13 miswording 291:9 mix 30:6 177:5 mixed 130:17 161:19 251:12 307:10 mixing 300:17 mixture 105:2 model 59:12 84:20 109:20 110:9,11 110:18 116:15 126:16 127:3 144:16,18 145:2,2 145:5,7,12,13 150:20 151:3,6,7 151:9 152:2,4,4,6 173:16 183:2,14 185:5 194:4 197:11 198:13 243:10 244:20 251:3 253:4,9 254:2 282:4,5,9 282:18,21,21,22 282:22 283:1,1,4 modeled 177:22 183:3 190:8 215:20,22 221:12 239:12 modeling 53:12

97:12 109:16 110:8 132:8 138:8 148:20 192:1 196:19 201:10 216:10 253:9 278:5,6 models 79:7 109:17 152:10 192:22 197:15 moderate 149:21 149:21 243:14 modest 125:4 186:20 modification 53:4 129:12 modifications

250:5 modified 129:7 Monday43:6 monitor 55:3,20,21

56:2,11 87:6 90:22 91:3 99:1,3 100:13 102:3,6,15 103:21 104:8,12 106:3,12 107:8,20 108:3 161:2,8 164:16 174:18 182:21 292:16 monitoring 37:19 46:7 47:2,7 60:8 98:20 104:20 113:7 157:7 159:10 160:10 174:16 227:6 277:21 305:4 307:12 308:15 monitors 59:6

90:13 98:22 102:10,14,17,22 103:1,8 104:1,3 104:11,14 106:13 107:10,21 164:12 month 338:13,13

338:14 339:6 362:15 months 303:13,14 mood 315:20 morality 182:15 morbidity 37:7

67:3 110:17,22 112:3 211:15 morning 5:18 9:6

76:1 170:6 206:2 264:12 290:5 298:8 312:16 322:1 333:16 335:17 387:2 Mort 6:6 113:19 123:22 128:14 156:1 220:3 234:7 235:12 243:2 338:11 367:2 378:14 mortality 37:7 56:7

56:8,9,19 57:3,3 59:8 64:10,11 66:17 67:2,3 70:8 73:17 81:22 102:18 112:1 125:8 133:7 134:9 134:16 135:3 166:21 177:5,11 177:15,18,19 178:7,12,20 179:20,21 180:2 182:18 183:4,10 189:19,20 191:14 197:7 198:3 207:12 209:21 210:4,6 211:14 212:4,4 215:18 221:12 235:18 236:20 314:4 325:13 330:1 367:13 MORTON 2:9 Mort's 114:8 motherhood

336:11 motivate 87:16 motivated 86:6

87:12 282:2 motivation 186:4

354:16 Mount287:10 mounting 231:21 mouthful 73:6 move 8:16 17:8

43:1 58:21 75:17 80:19 93:20,22 105:5 201:4 242:15 263:7,8 264:10 271:14 275:14 320:17 327:1 354:13 355:7 382:14 384:13 moved 268:11

277:10 278:7 280:11 335:2 moving 58:13 135:19 136:6 147:13 156:22 244:21 260:16 332:21 muddled 86:20 Mullen 294:19

301:13 316:13 multi 332:10 multiple 77:13 129:9 133:18 148:14,15 361:17 multi-city 36:6 139:1 mute 107:1 371:20

371:20 mystified 96:2,21 N N4:1 205:1,1,1 NAAQS 3:5 48:8

62:20 76:14 120:5 152:22 153:1 183:14 223:11 227:8 239:12 250:22 251:8 296:2,19 name 50:19 75:22

81:7 names 111:5,6 narrowed 31:10 narrowing 374:3 nation 215:12,18

215:19 national 28:20 48:2

48:4,19 52:22 58:12 64:14,16 65:2 66:2 81:21 84:15 85:1 112:13 118:16 119:7,14 119:18 155:11 181:4,11 182:9,15 182:18 184:20 189:1,6 190:13 208:8 214:19 215:16 216:3 275:6 nationally49:3 national-scale 64:5

64:9 nationwide79:20 189:14 natural 152:20 191:2 naturally 291:2 nature 25:15,15

28:6 33:6 37:21 91:19 155:10 167:1 228:13 231:22 240:11

### 202-234-4433 Neal R. Gross & Co., Inc. Page 415

254:18 267:5 268:12,13 269:19 379:10 near249:12 nearby 107:21 nearest 104:1 nearly 293:18 necessarily 17:16

73:20 150:6 193:20 202:21 231:17 290:8 302:5 376:22 necessary 17:2 necessity 200:21 need 17:4,5,13,18 18:9,22 19:22 46:11,18 49:2 57:4 67:22 89:2 103:11 123:21 134:22 135:5 161:11 173:13 174:10 177:8 179:2,6,9 188:22 202:21 207:4 211:7 214:12 219:3 226:8 227:17 236:16,21 238:3,14 243:17 248:9 255:6,6,11 256:12 259:1 281:18 286:12 289:4,18 293:2 302:5 310:19 311:5 316:20 319:5 326:5 329:1 330:3 345:4,15 348:4,13 355:5 362:22 363:1,15 363:16 364:3 376:14 380:19,21 385:13 needed 52:13 155:1 156:20 173:3,5 234:5 333:19 385:19 needs 79:16 104:9 111:21 112:9 119:1,6 129:20 160:6 174:11 207:10 219:19 221:19 227:5 228:19 255:19 256:1 259:21 277:9 292:19 317:19 357:10 negative 138:19 146:10 276:7 306:2,9,11,14 neglected 113:15 nephelometer

295:3 nephelometers

292:1 nephelometer-et...

309:18 network 37:19 networks 267:4 never46:15 297:19

328:12,14 nevertheless 166:3

209:12 287:4 new 6:6 36:5,10

49:7 62:4 65:6 83:11 111:1,3,10 111:11,13,18,20 113:21 114:9 157:3,4,15 158:11 160:12 206:10 209:9 249:11 250:19 267:2 269:17 270:22 271:8 274:18,21 311:11 313:15 326:20 331:19 332:10 346:20 348:21 355:6 357:6 363:20 365:14 381:2,14 newer 137:18 newly 26:20 266:22 NHANES 125:22 nice 64:20 86:4,14

87:1 127:21 139:2 171:21 234:14 243:14 278:10 279:8,11 280:3 282:12 366:5 nicely 149:18 216:9

279:21 288:15 nightmares 210:10 nine 67:10 181:12

240:14 251:16 254:4 303:13,14 385:14 387:7 nitrate 302:12 nitrogen48:10,15 nits 110:15 113:13 noise 131:8 233:13 non45:10 123:22 190:4 212:6 316:15 nonlinear 186:17 nonrepresentative 112:11 non-threshold

245:7 non-urban 39:22

44:10,14 non-visibility 269:7

269:15 noodling 280:16 North 1:14 6:5 notable72:10 note 14:22 20:6,10

32:14 43:16 48:7 139:20 140:1 172:10 205:21 241:22 291:7 310:19 notes 47:7 153:7 notice4:20 7:13

293:16 321:3 353:10 notion45:2 127:19 154:9 370:12 notions 317:18

355:17 novel 347:20,22 November 11:14 NOx 270:10 NOx-SOx48:11 NO247:1 nuance 255:1 Nugent50:6,7 null 77:15 number 36:5,10

37:20 53:6 57:16 63:5 64:4 65:6 66:5 68:8 79:9 82:7 83:11 87:20 98:6 102:22 106:18 113:8 139:17 153:4,12 153:21,21 165:12 180:20,21 181:16 184:20 186:6 190:3,17,20 217:18 251:4 255:16 272:16 278:18 293:16,17 293:19 298:8 319:16 332:10 333:22 340:5 342:5 numbering 156:8 numbers 73:19 143:19 144:1 156:4 177:12 191:5,15 192:8,21 192:22 194:10 230:1,8 273:19 294:6 numerical 163:8

253:19 254:13,14 nutrient270:19 nuts 247:3 nutshell 160:13 O O 4:1 205:1,1,1 OAQPS 27:21 obese 260:6 obesity 260:3 object285:8,13 objectionable

302:16 objective 226:9

238:7 obligated 261:1 obscured 285:9 observation 16:9 16:14,21 17:14 66:12 74:22 75:3 209:1 231:18 262:11 observational

236:1 observations 61:21

66:4,5 167:21 168:1 208:12 233:20 258:8,14 observed 191:1

245:3 obsessive 172:12 obtained 152:11 obvious 142:15 144:7 238:14 302:9 obviously 10:1

38:12 42:9,10 62:16 69:3 74:18 75:12 116:19 130:19 165:15 193:12 201:9 346:12 348:4 385:22 occur92:2 105:16 161:2,7 301:9 308:13 occurring 68:21 167:3 195:16 213:7 291:2 occurs 212:10 odds 191:11 offense 16:20 offer 16:14 17:22 18:1 20:9 90:1,2 offers 166:13 office 5:12 346:17 Officer 2:22 4:8 officially 263:11 oftentimes 358:6 oh 34:5 171:13 178:10 179:1 210:6 249:8 313:3

### 202-234-4433 Neal R. Gross & Co., Inc. Page 416

319:14 okay 50:18 51:5

75:11,15 81:5,9 85:5,14 87:3,21 88:18 90:22 93:19 109:10 117:9 119:21 136:12 138:5 140:13 145:20 148:6 149:7 156:13,21 169:20 179:11 180:15,16 202:19 204:5 208:20 210:6,10 222:7 245:5,8 262:20 263:6 264:10,16 264:18 265:4,13 271:11,19 275:12 275:14 276:9,20 278:11 284:10 285:16 288:8 289:20 290:10 293:12 303:3 308:7 320:6 321:20 322:4,5 332:8 349:21 350:15 353:14 357:14 364:22 372:1 385:10,15 old 250:12,16 older 137:19,20 160:11 once 53:13 55:22 116:10 147:12 229:17 243:18 309:22 378:6 ones 88:11 371:7 one-handed 310:20 one-hour296:5,10

302:10 ongoing48:9 oomph 166:4 212:9 open 5:5 43:20 45:6

45:13 49:9 186:18 opened46:12,13 opening4:4 43:2

49:8 315:11 open-ended 377:11 open-endedly

375:6 operates 4:13 OPI 50:17 opine 376:10 opinion 119:18

222:8 230:16 345:5 358:20 opinions 244:2

347:1 opportunities 5:7 opportunity 19:18

21:17 22:6 43:19 76:5 81:17 313:22 348:4 383:11 opposed 131:20 opposite72:17 165:19 300:18 301:15 optimal 106:17 145:10 optimistic 382:20 optimizing 145:11 option 313:19 options 83:8

251:21 278:18 order22:19 246:21

274:6 329:2 organic 270:6

305:16 organization 34:9

276:3 324:13,21 349:7 350:2,18 organizations

267:3 organize 338:20

375:4 organized 10:4 12:3 22:13 34:11 organizing274:18 original 76:13 100:16 113:9 129:1 273:14 originally 25:9 111:11 192:14 229:9 307:16 308:11 originated49:13 ought20:14 177:3 181:21 199:6 205:18 256:20 284:17 365:10 outcome 142:6 outcomes 117:2 120:15 152:1 153:4,13 outlier 306:18

367:16 outliers 304:19 outline 284:2

327:12 329:6 366:16 outlined 9:15 outlines 14:6

250:21 outside 169:10

234:20 overall 12:11 53:15

57:9,13 63:17 96:19 112:17 124:14 129:10 189:11,13 192:16 198:7 212:2,4 215:11 219:5 220:9 273:18 282:1 283:9 324:18 359:4,5 overarching 26:6,7

29:1 155:7 266:11 269:8 324:8 350:6 overburdened 173:4 overreached 173:11 oversample 99:5 overview 3:6,12

9:19 10:3 15:5 25:3 34:2 42:21 51:21 52:11 58:14 70:3 264:20 265:6 347:3 o'clock 382:21

385:13 386:7,9,10 387:9,11 P P4:1 PA 205:16 206:3

263:14 334:15 369:12 package 353:22

355:7 packaged 19:12 packages 346:18 packets 136:4 page 3:1 47:6 133:9 133:10 139:15,16 139:17 144:15 148:13 156:5 210:15 220:15 279:10 283:21 303:15 306:3 332:9 337:11 338:10 pages 156:3 257:15

335:19,20 343:8 369:19 paid 127:12 pair 309:18 paired 70:14 pair-wise 142:10 Pandora's49:8

315:12 panel 1:4 2:1 3:10

4:9,10,19,22 59:17 64:2 68:9,9 69:22 95:8 109:18 109:22 110:6 136:16 163:19 222:9 275:19 297:4,6 316:21 334:8,14 337:15 337:17 354:3 362:2 366:17 369:11 381:4 Panel's 59:10 69:6 149:12 281:12 296:22 337:18 340:6 341:2 362:6 365:5 377:7 paper 131:3 250:13 308:17 310:3 348:19,21 349:8 349:11 papers 12:20

349:19 353:17 paragraph47:7

252:13 332:10 338:18,18 parallel 209:4,6 parameter 145:12

282:6 parameterization

283:7 parameters 144:16 144:22 145:1,4,6 196:15 parcel 122:19 Pardon 321:16 parenthetical 139:20 140:1 Park 1:13 parsed 176:7 part 8:7 16:3,7 17:8 18:2 19:14 36:16 38:3 88:13 108:21 110:12 122:11,19 123:3 133:16 147:18,19 150:21 151:13 158:20 174:7 184:8,8 195:19 199:3,6 200:2 201:9,11 219:4 220:11 221:2 225:13 228:8 259:20 263:12 296:12 305:7 311:8 319:1,2 323:2 327:15,17 328:21 335:1 340:14 351:13 354:16 356:15,16 360:4,11 361:13 361:14,22 379:17 387:5 partially 79:15

### 202-234-4433 Neal R. Gross & Co., Inc. Page 417 93:18,19 participate 9:10 particle 12:7 26:10

37:16 39:8,9,12 40:20 45:10 236:13 260:5 266:19 308:2,6 313:9 314:18 particles 12:9 27:1

29:13,17,20 30:2 30:6,6 31:5 34:8 34:16,18,21 35:11 35:20 37:14 38:18 39:4,4,7,21 40:3 40:11 47:21 121:10 206:19 227:21 268:10 307:20 309:4,8 326:7 348:11,12 356:8 357:19 358:3 373:8 particular 27:20

29:16 30:1 33:4 47:9 48:9 57:17 66:17 109:17 110:17 126:6 193:17 201:16 243:20 269:21 particularly 17:11

20:20 63:3 78:20 80:3 114:18 142:4 151:5 157:9 174:21 198:19 231:11 279:20 321:8 331:7 332:22 333:6 376:10 particulate 1:4 4:9 120:16 partly 355:16,20 parts 133:17 175:19 198:20 326:11 384:9 pass 329:5 330:4 path47:14 292:14

292:17 patience 19:10 patient 373:19 pattern 55:2,11

57:21 58:4 60:7 60:20 62:1 65:13 65:17 84:1 91:19 157:7 patterns 61:15 65:9 159:10 pause 182:2 peak 32:5 41:17

54:21 55:9,16,18 56:3,20 59:5,10 60:3 63:12 72:1 72:21 85:21 89:4 90:6,11 92:9 94:10,19 95:4,5 96:20 100:17 101:11,14,20 102:12,20 103:6 104:6,18 105:8,17 106:8 107:7 108:10 154:11 161:2 170:11 201:12 207:1,3 peakiness 63:8 154:13 161:12 162:2,4,6,8 234:10 peaks 89:8,19 94:1 101:16 103:14 147:5 170:14 171:17 212:5 234:20 261:6 peaky 61:5 71:19

72:19 100:12 105:22 peek 222:13 peer248:2 Pekar 51:7,8 90:1

91:11 92:12 94:7 98:3 101:19 103:19 105:7 106:15,20 115:18 126:7 128:10 132:1 133:9,15 135:2,7 156:11 163:3,16 164:7 166:10 167:15 173:1 174:13 175:21 176:18 177:14 178:10 179:1,11 180:5,12 182:10 184:22 187:9,18 188:17 210:7 211:8 212:22 213:10,14 213:19 214:11,14 215:14 216:20 217:7 221:2 222:2 230:9 231:3 252:17 253:18 255:9 257:22 people 15:12 68:20 111:2,14 117:18 122:16,16 125:19 158:5 190:22 191:6,12 205:8 219:16 223:9 244:5 255:16 256:2 260:6 265:14 267:8 274:7 280:11,13 287:6,14 305:3 310:20 316:15,22 320:20 321:12 334:1 366:21 383:9 384:8 percent71:20

72:10 90:20 102:1 102:3,7 119:13,14 152:15,20 153:10 153:11,12,19,20 179:17,18,19,19 180:1 181:12,14 186:14,21 187:10 188:3,7,13 191:10 191:13 207:17 219:8 240:6 241:8 246:19 272:19,22 273:3 275:2 284:15 285:15 290:12 291:1 292:15,16 294:3,8 294:20 295:2,6,10 296:9 298:7,14 304:4,5 310:12 335:12,12 percentage70:15

70:16,22 90:16,17 91:4 102:19 241:15 293:5 percentile 32:1

41:16 61:8 72:21 90:14 101:21 241:15 268:17 273:21,22 275:7 289:10 296:5 298:16,20 299:1 299:22 300:9,10 percentiles 296:7

299:5,11 perception 285:14

291:16 perfect259:10

373:22 perfectly 230:4

377:4 perform 118:18 period 127:3 129:9 129:9 293:19 320:3 369:15 periodically 257:5 periods 82:6 267:7

267:9 268:14 306:16 permeate 79:6 permissible 15:7 permitted 16:1

37:10 person47:15 162:9 170:2 341:3 personal 125:2

230:15 personalities 141:9 personalized 143:19 personally 141:17 152:12 262:6 290:12 person's 318:22

319:19 perspective 28:20 64:19 152:19 170:3 196:5 215:7 223:5 233:5 234:2 242:7 301:1 358:3 perspectives 69:6

69:22 pertained 157:17 pertinent 17:4 perturbations

213:7 Petroleum 5:9 76:7

265:1 Phalen 2:11 7:5,5 110:1 117:13 123:6 246:11 322:12,19 368:18 386:6,15 Phil 7:9 88:19 100:22 136:8 147:21 248:18 276:10,21 284:10 287:5 307:4 308:13 309:2 323:7 Philadelphia 172:16 179:13,18 PHILIP2:8 Philly 173:9 Phil's 148:7 287:7 Phoenix 303:13 phone 50:4 263:21 photocopying 136:1 phrased 241:3

370:16 physical 97:3 144:20 224:11 282:13 314:11 physically 96:12 pick 110:5,16 113:14 125:18 193:20 364:4 picking 248:3 picturing 238:9 piece 383:15 pieces 18:2 54:16

### 202-234-4433 Neal R. Gross & Co., Inc. Page 418

213:3 220:8 pillars 262:10 Pinkerton 110:1 pipe 171:19 Pitchford 265:19

271:16 Pitt 164:3 174:2,3 176:16 Pittsburgh 164:7,8 174:11,13,18 175:5 176:2,16 place 14:16 73:3

89:2 159:7 162:13 174:11 181:12 189:16 215:9 278:1 285:20,20 360:17 361:6 placed 162:16

359:8 places 48:21 119:11 169:8 175:15 190:22 198:10 208:1 284:15 285:21 placing 18:4,6 plan 25:10 41:10 plane 382:18 planned 31:14 planning 10:18 136:6 267:3 plans 284:1,8 plants 270:20 platform 147:20 plausibility 378:16 plausible 142:18 143:22 150:10 play 24:13 96:22

262:13 played 61:22 plead 370:20 please 50:19 75:22 107:1 pleased 114:18 170:1 plenty49:20 plot 162:14 163:2,9 plots 158:3 159:8 162:17 163:21 174:8 274:10 296:7 297:18 298:13 303:4,17 304:6 plotted 215:17

238:13 plowing287:4 plus 313:2 PM 12:12 76:14,16

76:18 83:12 96:19 105:1 119:12 120:20 122:13,15 138:11 147:5 157:7 159:10 160:10 189:7,7 190:6 191:7,13 196:7 197:6 198:10,10 210:2 210:18,21 212:1 219:17 223:10 236:20 267:4 269:20 270:9,12 270:13,18,21 271:4 296:7,18 304:10 305:7 306:4 307:17 313:16 314:10 315:1 324:13 325:10 359:10 369:14 PM-related 270:5

271:1,9 PM-1046:20

304:14 306:8 308:3 356:21 357:17 362:10 PM10 34:16 35:8

35:14 36:12,21,22 37:8,10,11 39:2,3 39:5,20 42:3 44:8 44:12,19 PM10-2.5 37:6

42:4 205:20 PM2.5 29:12,19

30:7 32:6 45:22 55:3,11 57:1 61:8 61:8 63:8 66:22 67:9 71:19 72:12 72:14,19,20 74:16 77:12 78:17 80:10 105:22 185:4 212:1 228:1 266:10 267:20 268:6 306:8 307:7 309:9 310:11 333:7,9 355:21 point 14:3 20:11

25:22 35:17 41:9 42:13 44:7 47:3 47:19 59:8 67:5 68:2,13 73:21 77:14 79:2 90:3 91:14 99:9 102:8 106:6 107:5 108:13,19 116:16 117:7 123:22 124:21 126:12 128:14 129:8 132:1 137:22 148:12 150:17 158:14 161:10 162:3 163:17 164:4 166:11 167:4,9,10,16 190:1 194:9 195:6 196:3 200:4 202:2 212:9 214:9 216:14 217:10 222:2 224:5 228:5 228:10,11,20 229:6,21 230:10 232:3 233:13 237:6 243:6,16,20 243:21 245:21 246:12 247:2 250:2 253:10 254:1,2,8,9,12,17 256:6,16,18 260:21 263:3 264:19 270:9 284:15 288:13 290:15 291:8 293:8 302:8,19 312:19 316:14 326:22 327:7 328:19 330:14 332:13 339:15 343:15 347:7 364:7 368:18,20 379:22 pointed 287:5

337:10 344:1 360:4 pointing 20:5 217:1 points 16:15 25:2

26:8 53:7,7 58:20 81:18 94:7 97:18 147:5 154:16,20 155:3 157:17 160:8,14,15 168:10 193:8,12 212:21 232:11 256:11 258:7 261:18 284:19 285:15 374:15 Poirot2:13 6:12,12

249:8 278:14 288:11 289:22 290:3,8,11 294:16 295:19 297:13 312:18 382:17 386:20 387:4 policy 3:6,12,19

4:18 8:3,5,11 9:20 10:21 11:19 12:19 19:15 21:5,7,16 26:1,8 32:4 34:12 41:11 42:12,22 43:9 44:20 47:6 47:10 49:21 53:3 62:11,15,17 77:4 80:13 83:1,8 91:20 116:4 118:8 118:16 121:19 144:7,8 153:1 155:13,19 186:1 194:11,17 195:1,8 196:4 206:18 217:12 222:13,17 222:21 223:2 225:17 230:7 231:6,9 242:2 244:9,12,16,22 245:12,14,15 246:2 249:17,22 250:4,5,13 251:21 255:5 262:13 265:7,16 266:2 269:2 270:13 271:14 286:21 287:2 288:3 302:4 302:18 318:7 320:18,22 321:9 325:10 330:11 336:1 341:11 342:7,16 348:20 353:8 356:12 357:20 358:1,7,11 358:16 370:13,22 374:13 378:18 379:7 382:16 383:17 385:21 polishing 220:10 poll 244:2 251:16 pollutant79:7 137:12 268:4 pollutants 36:10 pollution 215:5

248:13 ponder225:18 pontification 119:20 poor 149:2 175:22

315:19,22 316:5 317:1 Pope 125:5 Pope's 131:3 population 111:18 112:11,13,16,17 112:22 113:4,11 124:9,10,15 125:3 125:8,15,22 129:2 129:4 179:8 197:8 260:4,6 342:4 populations 27:4,4

27:6 33:3,5 36:15 36:19 116:14

### 202-234-4433 Neal R. Gross & Co., Inc. Page 419

198:11 259:9 328:21 329:3 portion 174:6 pose 334:7 posed 25:8 posing 182:3 position 313:13 positive 37:5 114:12 138:19 146:10 149:17 151:1 158:11 217:14 261:11 positively 168:17 possibility47:20 128:7 224:11 264:8 305:2 382:5 possible 8:21 169:8

201:21 291:16 334:11 337:5 347:22 384:1 possibly 171:4

226:5 258:5 290:4 290:5 320:17 381:22 posted 310:7 potential 13:16 19:16 34:20 35:1 38:14,17 41:22 93:13 131:19,20 152:5 227:19 229:13 317:3 potentially42:5

89:12 93:11 119:3 207:19 365:20 power249:1,3 practical 89:5,18

93:4 practicality 232:16 practice 87:22 PRB 254:4 preamble 317:19 precipitation 273:7

275:4 precision 233:21 preclude 375:17 precluded 121:22 precludes 122:2 preclusion 122:20 preconceived

355:17 predicate 379:12 predict 167:5 preexists 314:13 prefer 165:16 preference 267:16

272:8 277:7 281:5 319:11 preferred 301:6 prejudge 347:17 preliminary 20:16

25:21 51:14 69:16 69:21 83:16 230:11,13 326:9 326:15,22 344:6 348:7,15 359:7,15 360:6 362:7 364:10,15 prelude 193:2,9

265:11 premature 83:13

287:21 premise44:22 192:1 227:18 premises 155:6 prepare 353:21 prepared 242:15

377:4 presence 253:7 present 1:16 2:1

5:2 15:11 18:15 156:18 200:9 287:19 332:6 344:19,19 345:7 presentation43:2

75:15 133:6 166:16 194:3 265:10,20 271:15 271:20 presentations 15:5

276:4 324:5 354:1 presented 16:11

25:10 41:7 54:16 65:19 85:20 156:17 157:16 164:5 194:6 196:13 208:13 230:11 258:15 275:22 282:4 290:22 348:16 369:6 presenting 22:5

25:12 157:4 presents 38:4 presiding 1:15 pressure 315:15 pressures 247:11 presumably 222:17

226:21 322:16 presumes 305:17 pretend 264:12 pretty44:17 51:13

61:19 62:5 63:18 72:9 106:3 108:2 109:13 128:22 136:6 159:4 165:7 167:22 168:8 206:12 209:9 210:22 222:3 223:12 254:5 262:8 288:21 345:14 381:13 prevalence 305:22 prevent47:22 prevention 185:21 preview 323:13 previous 22:19

27:5 76:19 77:6 79:10 128:7 139:16 143:11 146:1 172:10 174:2 199:7 256:11 268:1 353:4 369:8 371:12,14 previously 27:15

83:15 363:9 pre-stated 313:13 primary 12:6 22:15

23:4 34:6,7 326:7 348:10 349:5 principle 261:12 prior9:12 128:6 355:17 probabilistic 142:22 150:12 probability 150:11 probably 8:9 43:5

43:10,11 109:9 114:1 125:6 133:12 136:22 139:9,11 147:21 153:4 156:8 160:21 176:12 177:3 181:7 194:2 194:9 199:16 200:17,20 202:7 203:21 204:1,3 220:11 236:17 239:19 243:4,6 252:7,14 258:20 263:17 309:9 310:11 323:1 340:20 341:3 353:1 364:4 373:21 380:13 381:13,20 384:16 385:22 387:8 problem 190:9 192:19 210:11 239:7 240:16,20 305:14,20 377:9 384:5 problematic 106:14 305:8,9 problems 88:21

95:21 197:16,19 309:3 370:3 procedure 54:2

339:3 procedures 63:11 proceeded 110:5 process 14:9,11

22:9 59:20 62:20 115:20 120:5 140:18 141:4,14 194:15 249:11,15 250:12,17,19 340:15 345:20 346:14 347:9 351:17 353:5 361:16,17 365:11 376:16 processes 141:3

252:10 produce 14:6 66:13

67:7 186:14,21 product 115:14 profitably 88:4 progress 381:7 project 12:11 94:13

242:14 projected 171:17 projections 74:20 prominently 61:20 promise47:13

326:5 promissory 205:21 promulgated

366:12 proofread 156:9,11 proofreading 172:11,13 propagate 96:16 properly 314:16 proportion 92:3 124:12 proportional 55:4

56:5 63:13 67:20 72:5 73:7 94:10 94:22 95:4 102:9 105:19 108:9 174:21 201:13 proposal 353:18

355:7 propose 239:21 proposed 11:13

243:6 307:17 313:16 353:10 354:18 protect 15:19,21

34:17 35:10 232:19 329:17,18 329:19 357:19 protecting 34:21 155:14 367:19

### 202-234-4433 Neal R. Gross & Co., Inc. Page 420 protection 1:1

26:16 28:14 32:12 33:16 35:12,13 36:21 37:13 40:2 66:15,20 93:15 226:15 227:10 261:22 262:2,9 266:18 269:11 329:3 330:2 359:11 360:20 373:3 protections 329:16 protective 153:2 155:15 223:13 224:7,9 313:10 330:2 352:14 provide4:16 9:19 10:5 16:6 19:19 20:2 21:20 24:2 28:14 29:11,14 30:4,12 32:12 33:15 51:20 53:19 57:3 63:16 66:6 88:14 109:2 120:13 164:19 167:19 190:2 195:21 237:11 242:6 269:4 276:16 281:7,8,19 329:2 352:11 359:11 360:1,20 363:8 373:3 provided 32:10

35:14 36:21 57:6 59:13 64:11 86:18 97:3 110:1,2,13 157:5 256:7 267:22 275:20 289:14 provides 29:21

89:11,14 196:2 282:17 283:17 347:3 providing 5:7 10:3 16:3 37:12 40:1 41:16,18 53:11 93:14 134:15 375:10 provisions 31:17 provoked 363:13 PSP 308:4 psychological

319:14 psychologist

316:22 public 1:6 3:9 4:19

4:20,22 5:1,6,7,9 6:1,21 7:15 15:19 15:21 28:14 33:15 35:13 37:12 50:22 51:3 66:14 75:17 75:18 81:2 85:8 119:19 153:2 185:3 226:15 227:10 232:19 236:17 264:16,22 291:16 312:13 318:19 320:13 326:6 340:15 343:1,15 344:10 344:17 347:2 354:4,9,20 365:19 367:19 374:5 380:20 383:6 publication 248:21 pull 52:18 53:1

92:17 173:13 185:7 200:8 202:14 258:13 373:17 pulled 86:11 168:3 199:5 205:13 331:5 347:16 373:20 pulling 311:3 350:9 pulmonary 130:18 punchline 262:12 punchlines 261:17 pure 314:9 purpose 11:21 12:18,18,22 19:16 19:18 192:18 195:11,11 196:9 200:14,21 202:12 203:9 319:12 363:22 379:16 purposes 20:3 22:3 188:6 189:12 195:10 344:2 pursue 21:15 pursuing 19:20 purview45:18 push 67:18 102:14 147:12 pushed71:5 126:16 189:3 258:5,5 334:21 pushes45:13 pushing73:9,12 106:15 206:7 push-back 354:11 put 14:16 17:21

58:11 93:10 95:11 97:15 98:8 104:17 108:14 112:2 153:16 173:7 174:14 176:16 190:19 196:10 202:8 225:4 226:20 227:22 255:2 259:5 297:22 306:22 308:17 326:8,10 327:15,17 332:4 345:15 350:7 355:9 356:17 371:4 374:14 382:12 puts 18:21 171:6

225:5 243:12 putting 146:2 170:3 184:10 185:19 186:7 187:2,7 227:10 232:14 249:12 253:22 385:13 puzzling237:21 PVAD 315:22 p.m 204:9,10 205:2

323:9,10 387:15 Q QA'ed 230:13 qualifiers 256:15 qualifying 217:21 qualitative 52:20

63:22 78:18 114:3 137:8 143:6 147:2 206:13 282:10 qualitatively 54:1

59:20 126:9 132:17 quality 27:10,13,19

27:22 28:2 31:14 32:3,16,21 36:22 38:2 48:5 50:11 104:19 174:15 196:2,21 198:15 201:10,15 242:4 242:16 251:5 267:2,19 292:9 319:18 331:22 333:14,18,20 334:5 359:4 360:14 361:11 362:16 quantified 143:13

143:15 144:4 quantify 315:8 quantifying 116:21 quantitative 8:17

13:9 23:21 24:6 35:19 49:18 52:21 53:10 54:5,15 56:19 114:5 117:5 126:13 129:16 142:22 143:5,22 147:2 242:12 251:14 341:17 342:1,11 352:8,11 372:10,13 quantitatively

117:1 quarter203:18 quarters 87:14 queasy 238:20 Queens 111:8,8,13 question 13:11 18:15,20 24:19 26:11,15,21 27:8 29:1 30:3 44:6 45:6,14,17 46:1,7 62:16 68:15 78:21 80:20 85:10,21 87:5 97:11,13 103:17 109:8,11 109:12 113:22 114:3,6 120:1 127:18 129:22 130:2 131:11 135:12,20 136:14 136:15 140:18 144:14 147:9 148:4,9 149:10,11 155:22 157:1,3,22 158:10 174:1 178:13,19 180:16 180:19 182:7 186:3 187:7 189:8 195:12 199:13 201:6,7 203:12,20 204:4 205:4 218:15,20 219:2 221:7 222:10 224:13,16 230:5 231:7 233:2,9 242:11,11,20 255:15 266:17,22 269:8,11 274:16 275:16,18 276:5 278:12 281:1,3,19 282:17 283:19 288:9 289:21 292:21 294:15 295:21 296:21 297:10 299:9 304:16,19 307:13 308:8 310:16,21 311:13 315:13 317:15 319:9 320:9 324:2 328:5 328:7 329:13 330:22 334:8,9 335:5 336:7,15 337:12 338:4

### 202-234-4433 Neal R. Gross & Co., Inc. Page 421 340:11,21 341:5 342:18,19,21 343:18 348:17 351:14,17 352:1 352:16 353:5 354:15 355:13 357:8 362:6 364:12 365:2,4 369:11 370:10 371:15 374:18 377:13 379:21 381:19 385:1 questionable 249:1 questionnaire

316:18 questionnaires

316:14 questions 3:10,15

3:19 25:6,8,9,14 26:6,7,7,19 28:5 29:8,9,19 30:11 30:15 34:12 35:16 43:1,9,18,21 45:6 50:2 52:3 58:14 58:15,19 60:13 62:7,12,14,15 75:14,14 80:16 85:5,12,15,17 87:20 108:17 123:22 154:8 156:21 157:14 158:6 172:8,14 189:2,5 206:16 217:16 219:20 233:15 266:11 271:12 275:13,14 279:18 295:20 310:5 320:18 321:7,22 322:12 323:3,21 324:7,9 324:19,22 325:7 330:22 334:12,13 334:21 336:10 337:13 339:13 340:3,16,19 347:5 350:21 351:3 358:11,16 359:2 362:17,20 363:3,6 363:12 364:9 365:16 368:14 370:3 374:22 375:3,4,16 376:2 376:13,17 377:1,8 377:14,16,17 378:3,4,13 379:14 380:15 381:11 383:16 quick 5:22 52:10

70:2 244:2 246:11 294:19 quicker 8:8 quickly 8:16 11:7

34:1 52:3 58:14 58:22 62:9 230:10 265:22 280:2 363:1 370:8 376:17 377:8 quite 11:7 107:7

137:17 172:21 173:20 174:3 193:9 207:14,22 224:16 257:1 259:18 263:16 275:22 288:15 291:18 299:15 322:22 331:12 336:3 351:16 355:22 356:1,8 quiz 113:20 quotations 331:18 quote 250:20 quotes 332:14 R R4:1 205:1 RA 76:16,19 77:2,3

77:18 78:3,22 264:1 radar234:9 radical 254:5 radically 224:2 rain 291:3 raise47:16 140:17

177:3 218:14 288:12 364:7 raised 119:22

121:11 123:22 180:20 182:7 raises 24:19 78:13

219:8,20,20 raising 237:6 rambling 351:7 random 151:5

152:3 randomly 97:17 range 15:13 20:13

32:9 62:21 65:2 73:18 123:3 139:6 151:15 152:10 187:22 189:18,19 219:15 226:1 232:10 245:2,4 251:22 252:5 268:15,20 287:18 289:17 341:21 346:22 367:13,14 373:1 ranges 20:19 41:8

41:21 42:3 77:4 151:22 152:1 185:11 207:17 329:13 367:6 rare 95:11 rate 119:13,15

135:4 137:17 rates 65:4 116:7,13

133:7 134:4,15 177:15 178:12 197:7 198:4 207:12,19,19,20 208:8 rating 215:15 ratio 61:7 72:1 rational 61:14 rationale 14:13

18:3 19:13 44:18 53:6 69:6 109:15 110:8 226:8 228:8 239:18 240:1 332:7 345:3 rationales 345:17 ratios 191:11 282:7 REA 120:8 138:6

343:16 354:13 reach 17:13 327:1

383:4 reached 19:3 22:8

23:1 38:15 40:8 140:19 142:2 326:16 348:8 361:3 reaching 17:12,20

24:5 378:22 react227:18 reaction 228:13

358:18 read 10:2 11:6

43:13 51:13 96:8 99:13 109:13 158:19 206:10 217:20 220:13 225:17 237:14 255:18 256:14 277:1 278:2 280:12,13 287:2 288:2 295:21 303:21 325:4 331:2 339:18 343:1 348:5 350:1 353:17 354:9 355:4 364:10 365:12 readability 278:8 reader279:4,7

298:3 readers 150:8

280:19 reading 91:9

130:10 161:18 173:12 284:7 332:16 reads 208:15

239:14 374:13 ready43:12 121:2

284:8 320:16 339:6 reaffirming 326:18 reaggregate 176:19 real 86:11 96:3 116:13 119:8 121:9 145:3 160:21 219:2 225:3,6,7,13 247:16 300:5 301:4,20 305:20 306:13 314:16 317:2 realistic 89:10

106:9 107:18,22 108:4 175:8 226:16 realities 48:20 reality 91:17 92:1

94:16 realize 91:18 95:3

98:13 147:1 148:19 255:13 305:4 realized 60:19 realizing 253:8 really 34:1 36:14

39:16 40:6 44:12 45:17 46:15 51:16 51:18,22 52:14,17 55:9 56:10 57:12 57:19 61:1,11,16 62:7 66:5,7 68:16 72:3 77:15,20 78:1,5,12,13 79:16 86:10,18 87:3,12 88:5 89:1 89:5 95:8 100:5 101:3 102:8 104:18 105:18 106:14 114:15 115:1,4,13,13,15 115:16 116:1,1 126:16 128:8 129:17 135:5 139:2,2,13,22 142:9 144:12,14 145:15 147:12,13 153:1 154:19,20 166:12,13 167:13 168:4 175:14

### 202-234-4433 Neal R. Gross & Co., Inc. Page 422

177:11 184:14 185:19 186:17,21 188:14 191:3 192:11,16,20,21 193:9 195:2 200:4 200:16 207:14,22 209:16 211:18 212:2 219:10,14 219:22 221:13,13 226:1 230:16 234:14 237:21 242:19 248:9 253:5 255:11,19 258:17 260:19 261:4,11 262:12 276:1,7 277:1 278:10 280:16 283:10 286:6,17 288:4,5,15 291:18 295:6 298:1,5,7 299:16 300:4 301:18 302:20 303:3 308:1 309:13 313:15,20 316:21 322:15 324:14 326:5,12 326:13 327:11 330:14 332:2,14 333:10 336:16 340:4,11,20 343:4 343:12,19 344:1 346:6 348:4 351:2 351:8 354:15 356:19 357:4,5 359:16 363:15 368:6 384:19 realm48:22 224:10 reason 18:7 22:19

49:1 102:10 107:13 118:19 132:8 144:2 183:10 193:21 197:4 231:19 257:1 277:16 323:3 328:22 337:21 352:22 359:1 364:4 376:6 reasonable 86:2 87:8 89:10 173:9 227:15 232:1,14 238:7 242:13 reasonably 125:18

126:2 205:13 208:6 259:9 345:16 352:16 reasoned 19:4

255:7 reasoning 14:13,17

15:8 16:5 17:19 18:3,10 19:13,17 19:20 20:3 21:3 21:15 22:1,6 23:9 23:11,16 53:9 188:11 228:3,7 366:18 376:11 reasons 21:7,14

106:10 225:21 226:3 250:8 291:12 301:5 342:6 353:20 372:9 recall40:7 249:12

273:11 318:5 receive42:11,16 received 9:22 43:6

43:8 344:17 receiving 19:12 recognize 95:17

335:17 385:4 recognized 125:14

139:8 recognizing48:13

326:19 recommend 158:2

199:14 276:2 337:18 recommendation

147:19 184:4 recommendations

18:12 20:9 163:20 164:3 165:3 168:12 194:17 195:1 296:1 346:9 recommended 118:17 290:13 339:2 349:16 recommending

97:4 349:13 reconsider 171:9 reconsiders 39:17 reconvene 322:4 record 50:19

135:16 204:9 323:9 387:15 records 5:5 rectified 210:11 red 164:22 redefine 135:5 redone 291:5 reduce72:5,8

73:16 102:2 185:8 212:1 248:14 369:20 reduced 67:14

70:21 91:2 106:3 139:21 366:3 369:1 reducing70:18,18

71:8 72:2 140:5 248:12 301:3 reduction 55:2,3,7

55:8,10 56:22 63:3,15 66:21 67:8 70:16,17 71:1,6,18 72:5,10 73:8 74:10 90:20 91:19,21 92:3 103:21 105:18 152:22 186:15,22 187:11,15 188:3,7 188:13 211:16,19 212:1 219:8 reductions 56:9,10

60:6,7,21 61:16 62:2 65:10 67:1,2 67:16,21 72:4 74:3,15 75:5,7 105:16 175:1 186:21 refer 36:16 148:20

150:14 159:6 reference 196:3 221:20 referenced 282:17 referred 150:4

282:19 referring 148:22

161:14 162:4 refers 144:15

148:13 161:19 refine43:21 315:8 refined42:8 121:5

127:2 176:13 273:16 refinement273:14 refinements 125:10 reflect79:21 127:8

160:1 181:10 215:1,4 reflected 26:13

29:4 51:15 162:2 179:8 209:22 214:3 266:15 269:10 285:14 288:7 reflecting 20:13

250:1 reflective 346:12 reflects 21:10

133:12 219:10 295:14 334:15 369:12 reformulating

332:18 regard 14:14 20:8

35:7 40:5,12 41:14 42:21 47:13 48:9 97:9 172:10 206:15,17,21 281:22 340:6 348:8 368:15 378:6 regarding40:9

68:19 281:1 296:22 297:6 322:13 regardless 197:11

239:16 294:10 339:21 region79:12

106:13 168:21 169:4 293:1 regional47:21 48:5

55:7 92:6,7 93:18 93:21 96:18 154:19 155:8 267:3 270:2 272:21 286:15 regions 104:22

168:20 169:6 Register 353:10,18 regression 272:6 regretted 158:12 regulating 291:17 regulation 226:20 regulatory41:18

83:3 120:9 225:14 265:3 reincarnation

12:20 reiterate 26:2

69:20 105:12 reiterating 74:22 relate 12:6,15

228:3 265:7 324:22 335:1 related 10:21 15:1

28:7 33:8,8 41:13 47:8 62:11,15 65:3 70:8 75:15 76:19 120:1 137:14 153:5 156:7 177:4 196:14 210:17 214:7 242:19 316:1 330:11 353:11,13 367:12 378:18 relates 157:3,8

209:15 relating 118:2 relation 125:4 relationship 40:10

40:14 46:8 57:22 58:3 61:17 65:12

### 202-234-4433 Neal R. Gross & Co., Inc. Page 423

66:10 82:18 83:12 167:2 170:11 190:5,11 211:2 232:1,18 240:7,11 245:2 314:7 353:7 relationships

114:17 379:10 380:6 relative 71:14 75:8

105:19 131:5 157:12,18 159:14 159:19 178:18 196:7 206:21 209:21 268:19 272:20,22 273:2 275:1 289:7 309:12 310:12 relatively 8:16

74:14 101:10 175:5 191:16 202:3 357:16 release 126:15 released 10:14 11:1 relegate 200:6 relevance 195:6

236:3 relevant 14:1,8,10

14:19 15:15 17:3 17:10 26:8 34:12 83:1 96:3 120:18 130:6 153:3 252:9 325:10 331:7 341:12 358:7 relied 341:13 rely 191:22 relying 35:20

305:15 remain 39:19 45:7 remaining 24:9

28:8 270:12 330:10 376:15 remanded 23:3,5 remarks 85:3

120:3 remember46:22

123:13 179:3 246:20 263:16 312:14 318:3 323:17 325:19 381:16 remind4:11 5:21

7:19 34:14 reminded49:6 reminding 8:3 reorder 371:13 repair 252:10 repaired 291:4 repeat 36:18

104:15 repeated 58:20 repeating 377:17 repetition 355:5 repetitive 209:8 replace 88:6 268:5 replaced 272:19 replacing 88:2 report 125:7

126:21 130:15,15 153:16 274:19 275:21 277:9 280:5 282:22 283:5 294:1,6 310:2 reported 27:12,15

27:20 32:17 37:5 181:6 333:18,19 334:3 reporting 83:14 represent 124:9

181:7 189:10 218:13,17 286:18 311:10 representation

112:18 126:1 144:19 145:3 190:6 representative

28:18 58:8 64:22 94:14 100:11 112:21 125:15,18 126:2 172:17,20 173:21 175:7 182:14 200:16 259:9 representativeness 64:19 124:1 173:2 196:13 represented 15:18 representing 75:19

105:11 178:3 represents 95:6

173:9 216:6 request5:8 50:7

333:21 requests 297:15 require 200:15 requirements 4:13

4:17 5:3,4 14:7 192:10 requires 225:7 rerun 55:17 reschedule 264:15 research 1:13 6:15

99:22 147:19 250:22 259:22 researchers 175:11 reservations

382:18 reserve43:10

264:8 residual 165:11 resolved 77:3 80:7 resource 138:10 resources 84:19 respect 158:18

160:9 163:21 296:14 Respiratory 6:15 respond 25:22

48:14 60:6 121:7 123:20 137:10 224:13 314:20 375:1 responding 52:14

146:1 340:15 response 24:15

52:13 54:14 69:2 92:6,7 108:22 116:8 120:13 125:8 130:7 132:13 133:20 134:20 138:21 139:4 142:12 151:17 186:18 202:5 211:13 217:15 229:13 231:14 232:12,17 233:18 235:18 237:14 239:5 240:7 241:4 272:7 289:10 295:22 308:16,20 325:13 325:14 367:11 380:5 385:1,5 responses 269:20

288:18 339:22 367:7 382:12 responsible 258:2 responsive 297:15 rest 157:13 160:2

187:6 215:3 298:17 restatement 358:13 restricted 113:6

130:16 131:13 restrictive 119:4 result 100:14

126:10 272:4 results 53:17 54:11

57:8,10 60:12,14 63:21 70:3 78:12 92:18 143:5 149:13 150:4,16 153:8,9 157:7 190:16 221:6,10 221:18 281:9 283:2,6,18 285:20 296:2 297:7 299:10 306:5,11 306:12 368:10 resumed 135:17

204:10 323:10 retain 31:16 45:7

306:14 378:2 rethinking 355:18 reverse 261:8 reversed 239:8 review 1:4 3:4,5,19 4:10 8:17 9:18 11:4,9 12:12 23:2 23:14 25:10,11 27:7 28:16 36:2,4 39:14 44:20 48:9 51:13 64:7 76:14 83:8,9,10 121:15 122:6 130:5 180:9 206:7 249:14 269:5 271:8 272:2 272:3 279:19 313:8 337:4 338:14 344:16 353:6 362:16 381:1,4 383:8 reviewed 5:12

120:17 248:2 252:4 reviewers 180:21 reviewing7:20

10:14 13:20 23:10 77:1 294:5 362:15 reviews 22:19,21

27:5 48:8 362:18 369:8 381:7 revising 361:17 revision 138:18

151:1 264:20 revisions 138:12 revisit 320:20 revisiting 379:3 rework 377:8 rewrite 331:14

363:3 re-craft 376:22 re-description

358:13 re-read 369:18 RH 290:12 291:1

291:13 292:7,14 292:16,22 293:5 295:4,10 298:7 Rich 2:13 6:12

249:7 278:13 288:9 309:2 312:17 Richmond 111:7

### 202-234-4433 Neal R. Gross & Co., Inc. Page 424 Rich's 279:15 rid 293:6 ride 250:3 ridiculous 188:2 right21:1 25:19

33:19 43:2 44:8 44:15 46:13 51:18 73:13 92:14 93:15 105:7,8 112:4 113:17 117:4 127:20 128:9 133:22 146:5 149:4 157:2 159:5 162:18,18,20 165:2,2,8 166:17 167:15 168:16 169:16 172:4 175:10 176:18 180:12 184:13 187:17 196:22 197:13,16,19 199:14 205:7 210:7 213:18 215:13 216:20 225:1 228:15 233:18 236:5 243:6 244:21 245:11,17 250:1 251:18,19 257:14 263:9 271:22 307:7 309:16 321:12,15,18 322:5 323:18 336:12,20,21 339:11 340:12 344:4 350:7 351:2 364:22 377:13 385:7 387:13 rigor 194:5 rigorous 195:14 risk 3:8,9,10 7:21

8:18 10:6 13:10 19:6 22:1 24:9,18 26:14 28:10,17,18 29:3,5 33:12 43:2 49:19 51:2,9 52:18 53:2,10,13 53:14 55:17 56:19 57:9,14 58:11,17 59:3,14,15 60:11 60:15,18,20 61:10 61:15 62:2,18,22 63:2,3,15,20 64:14 65:3,10 67:8,16 68:4 69:12,18 70:6,16 70:17 71:1,6,8,18 72:4,10 73:15,15 73:18 74:3,6,10 74:15,18,20 75:5 75:7 76:8 77:6 79:10,15,18 80:6 80:8,9,11,12 81:19 82:1,13,18 82:22 83:5,22 84:8 91:21 94:5 105:13,18 106:4 109:16 110:9 112:15,18,22 116:5,21 117:20 118:15,18,19 119:10 120:5,14 124:14,18 125:2 128:5,17 131:5,15 139:19 142:16,18 149:15 152:15,22 153:3 164:9 166:21 167:5 172:11 181:13 183:15,18 186:15 186:22 187:6,10 187:19,21 188:1,6 188:7 189:6,10,11 189:14,14 190:6 190:13,20 191:3 191:10 192:17 194:7 195:15,18 196:1,8,14 197:2 197:9 198:1,7 199:1,18 200:9,14 202:10 205:20 210:17 211:16,22 215:12 216:4,10 219:8 221:6,10 222:16,19 223:18 224:17 230:6,12 230:20 231:2 239:18 242:12,14 243:8 244:16 245:9,10,21 249:13,16,20 250:6 251:3,7 253:4,9,10,12,12 263:7 264:3 295:15 310:22 320:7 327:18 331:10,16 332:21 341:8,9,13,17 342:1,11,14 352:11 356:1,9,10 371:4 372:10,13 381:7 risks 28:8 33:8

35:19 70:11,11 81:22 89:16 93:13 119:1,3 195:13,16 242:17 248:9 254:20,21 260:2,5 345:4 373:14,15 risk-based 26:13

28:4 32:8,11 33:5 33:21 327:14 332:1 340:8 373:11 risk-benefit 68:5 Riverdale 305:10 Riverside 6:19 road 368:12 ROBERT 2:11 robust76:17 97:17 robustness 283:8 Rogene 2:7 6:14

49:6 169:20 237:10 239:21 244:5 336:5 Rogene's 247:13

368:18,20 role 28:12 63:10

157:11 262:13 268:9 roles 24:11 33:14 64:10 roll 170:9,17 rollback 55:4,6,18

56:5,5 59:5,11 63:11,13,13 94:22 95:2 100:17 106:2 108:9 154:11 161:3 164:11,15 170:5 171:9 174:21 176:21 207:4 251:10 267:19 rollbacks 56:20

160:4,9 174:21 176:8 201:14 rolled 56:1 277:15 room 85:2 297:22

334:1 387:11 roughly 135:13

216:1 236:7 round 5:22 147:13

149:2 223:14 224:6 259:21 row7:15 71:11

134:8,14 rubber 368:11 rubric 111:13 ruins 300:12 rule 11:13,14

240:19 243:2 246:4 247:19 272:21 ruled 77:16 rulemaking 11:12

353:10,21 354:18 354:19 run 55:17 69:21

70:11 82:1 163:14 221:8 230:19 running 230:17 runs 230:14 rural 39:13,22 Russell 1:21 6:16

6:16 80:22 85:11 85:15 101:2 108:20 168:16 169:14 308:10 rut46:20S s4:1 78:5 205:1,1,1

353:5 sabbatical 125:6 safety 15:20 229:14

245:16,19 325:8 325:12,16 326:1 328:8,11 329:17 368:3 Salon 1:13 salt72:18,18 73:3

305:13,19 306:1 Samet 1:14,18 5:17

5:18 6:2 7:13 16:17 43:4 44:1 45:15 46:6,21 47:15 49:4 51:1 75:11,21 80:15 81:4,6,9,13 85:4 85:14 88:18 95:19 100:8 101:4 106:22 108:6,16 109:6 113:19 115:2 117:9,12 119:21 123:5,12 127:16 128:21 131:22 135:11,18 136:5 145:20 147:9,17 149:7 154:4 155:21 156:13 168:15 169:12,16,20 172:2,5 176:22 180:15 182:2,8 184:3 185:16 186:9 187:4,17 191:18 193:11 198:17 199:10,22 202:1,18 203:1,6 204:2 205:3 208:20 217:11 220:2 229:4 231:4 233:14 237:9 239:6 242:22 244:11 245:11,13

### 202-234-4433 Neal R. Gross & Co., Inc. Page 425 245:17,20 248:18 249:7 250:18 253:14 255:4,10 261:13 262:20 263:15 264:1,4,16 275:12 276:9,20 278:11 279:13 280:18 283:12 284:10 285:16 286:5,20 287:5 288:8 289:20 290:1,6,10 293:12 294:14 295:17 297:11 303:2 307:3 308:7 310:15,21 311:20 312:3,11 315:10 316:7 317:10 318:2 319:7 320:6 321:14,17 322:2,9 322:15,21 323:11 330:13 332:8 334:7 336:5,18 338:3,6 339:8 342:17 350:19 351:1 352:4,15,20 355:14 362:13 365:17 367:2 368:17 370:1 371:18 375:20 376:12 377:19,22 378:14 380:1 383:2,20 384:10 385:10,15 386:8 386:13,17 387:6 387:12 sample 99:3,5 150:11 sampling46:4 Sandiford 265:15

266:3 318:6,19 319:13 320:5 sandwich 305:15 satisfaction 315:4 satisfactory 110:10 save 184:11 185:2

386:15 saves 384:19 saw 8:4 25:21

61:16 62:2 63:2,4 63:14 70:3 72:17 73:22 86:15 103:4 103:5 105:17 143:7 171:12 215:20 223:1 289:11 Sax75:20 76:1,4 saying 96:17 104:19 109:1 142:3 175:14,17 184:17 185:1,3,9 211:9,12 212:9 214:5 219:13 223:12 233:13 239:11 244:3,4,15 252:22 253:15,15 253:16 254:9,11 294:19 300:12 357:8,11 360:2 says 70:10 139:17 153:9 181:12 190:4 215:10 235:16 236:2 252:13 319:21 340:21 349:12 352:16,19 359:3,3 359:14 360:15 365:4 369:16 scale 182:19 183:6 189:1 215:16 303:13 scaled 183:6 303:9 scatter 65:18 scattered 366:4 scattering 309:19 scenario 70:12 95:7

96:14 103:18,19 103:20 104:18 105:1 107:17 108:5 296:19 scenarios 89:16

91:22 93:3 97:1 100:21 186:14 239:12,13 251:4 251:11 274:4 275:8 296:3 297:9 schedule 8:7 9:17

9:18 10:10,11 11:12,15 226:22 263:18 264:13 308:11 387:2 scheduled 11:3

323:18 schedules 9:9 scheme 97:14 149:20 School 6:21 7:15 schools 141:1 Schwartz 82:21

179:5 science 2:18 10:12 13:19 118:4 121:20 224:8 225:9,21 226:9,11 226:12 228:4 232:9 235:12 237:3 249:16 250:15 331:20 345:3 372:7 374:8 science's 243:8 scientific 1:3 4:9,15 12:22 13:3 26:12 26:20 29:3 35:17 48:20 121:1 125:13 194:22 228:21 232:2 233:5 236:22 244:14 247:4 248:1 251:2 266:14 269:9 270:16 342:8 356:22 369:6 372:6 374:6 scientifically 223:5

223:20 228:18 238:7 240:2 368:9 scientist244:7 scope 120:4 123:16

266:1 270:15 271:12 319:2 Scott 12:13 25:5 34:6 53:8 screen 268:20

272:22 273:5 290:13 291:1,14 293:5 294:21 298:7 screening 295:11 scrutiny 371:4 sea 305:12,19,22 season 120:22 seasonal 30:21

315:21 seasonality 147:4 seasons 31:2 147:3 second7:21 8:17 10:13 11:7,10 26:14 27:2 28:17 28:22 30:9 32:3 33:11 35:12 41:11 42:2,6 47:6,10 51:10 55:14 56:12 64:21 70:7 75:1 76:8 96:2 113:16 118:7 145:21 157:22 189:16 193:14 195:11 271:22 272:2,15 329:11 330:6,16 secondary 22:16

48:10,11 118:8 265:9 266:8 269:12 270:10 276:15 313:9 Secondly 29:13 section 14:6 39:16

87:17 109:1 110:16 114:13 148:13 156:3 209:16 210:2,14 268:1 270:14,20 271:8 277:19 278:4 279:16,19 281:14 282:2 303:18 327:22 333:6,8 373:9,13 sections 256:14

364:13,14 see 8:12 9:1 15:10 19:14,18 23:22 27:14,15 28:15 30:21 44:7 49:12 49:17 58:5,19 61:14 64:13 66:18 67:19 69:17 70:17 71:1,7,14,17 72:9 72:9 73:4 74:9,11 76:1 81:20 83:22 84:2,13 86:19 89:5,14,17 92:22 97:2 98:11 105:21 114:18 121:22 126:11 131:18 137:3 138:5,9,14 139:3 141:17 142:13 147:13 150:19 151:19 158:5 159:16 167:21 169:8 170:7 171:13 177:21 180:8,9 186:16 191:19 193:15 204:5 206:2 207:17,17 211:18 212:21 215:9,21 216:10 217:13 220:18 223:17 224:11 237:9 239:7,10 241:7 248:22 250:13 263:11 264:9 272:9 277:17 279:10 284:14 286:1 287:10 289:13 298:6 301:19 302:20 303:8,15 306:20 310:6 322:13 323:2 324:22 327:4,10 327:21 328:18,20 330:14 341:16 348:1,10,19,21 349:16 354:4 356:6 358:18

### 202-234-4433 Neal R. Gross & Co., Inc. Page 426

363:4 366:20 368:13 376:6 381:22 383:21 seeing 56:14 191:5

279:2 285:1 304:17 312:8 seeking 266:12 seen 12:21 69:13

205:14 231:12 297:19 331:14 336:7 348:19 segments 331:12 select 173:6 341:22 selected 59:14 119:9 145:13 157:12 195:13 selecting 289:16 selection 53:12

59:12 109:17 129:1 318:10 selections 109:20 110:11 self-supporting

354:8 semi-significant 154:1 sending 294:17 sense 10:3 21:2

58:7 91:17 93:14 96:12 98:5 108:11 120:22 135:5 145:4 146:3 170:18 180:9 181:4 193:17 200:1 213:17 220:8 223:2 230:15 244:21 246:3 247:19 252:18 253:6 254:13 259:3 266:1 278:1,3 281:15 300:4 307:16 318:22 319:19 376:19 sensitive 173:16

219:16 sensitivities 236:15 sensitivity 53:17 54:4,5,11 57:8,11 60:13 63:21 78:19 79:1 99:19 105:15 128:4,11 142:19 142:20 143:14 146:7,15 148:16 149:11,13,22 150:4,16,21 152:14,16 153:8 155:22 156:15 157:8 173:5 176:1 201:8,12 218:5 219:15 372:15 sent222:22 272:11

333:21 sentence 224:15

362:5 369:20 sentences 217:20

246:6,9,10 separable 242:11 separate 29:15,22 174:6 182:19 199:20 242:20 272:15,16 277:14 277:17 305:11 separately 164:15 176:8 269:16 separating 120:20 September 25:21 series 22:14 25:6,8

26:6,19 29:9,18 30:10,15 34:12 65:18 214:3 346:15 serious 113:1

226:17 330:12 seriously 336:13 serve 12:22 344:1 served 195:11 serves 195:10 SES 112:15 126:9

126:18 127:1,20 128:19 session 135:19

338:15 sessions 5:2 set43:7 48:5 53:14 58:5,9 62:11 65:14 66:16 94:18 96:4 98:3 116:15 119:22 150:5,17 192:2,11,21 193:1 193:19 216:15 233:3 236:21 243:5 245:4 313:6 337:16 340:13 344:6,18 345:1 351:15 352:6,13 sets 53:19 157:18

259:4 272:17 setting 14:9,10 15:9

44:10 48:4 89:6 120:5 121:21 125:17 193:4 194:15 227:2 228:19 249:21 255:7 306:2,11 325:16,22 329:22 settle 384:8 settled 378:21 set-up 344:22 seven 67:10 214:1

233:7 shades 329:4 share 96:8 137:1 186:10 345:8 sharp 329:12 340:3

344:1 sharpen 168:5

323:15 330:5 365:15 370:5 sharpened 361:15

362:22 363:16 sharpening 351:9

374:3 sharper 321:10

338:8 361:19 363:6 sharply 329:12 shave 89:19,20 shaving 54:21 55:9

55:16,18 56:3,20 59:5,11 60:3 63:12 85:21 89:4 90:6,11 92:9 94:11 95:4 96:20 100:17 101:12,14 101:20 102:12,20 103:7 104:6,18 105:8,17 106:8 107:7 108:11 154:11 161:3 201:12 shed 329:10 sheep 107:2 shift 101:15 212:2

212:5 213:1,4 214:6,7 284:14 285:20 shifted 91:5 101:18 shocked 246:13 shore 262:18 short21:4 28:7

32:12 33:7 40:12 40:15,16 41:14 56:21 66:18 67:3 78:21 84:5 109:14 134:9 177:18 178:6 210:16 211:3,14 212:4,10 271:19 276:6 339:12 shortened 221:15 shorter 218:1

220:11 335:6 shortest284:16 shortly 309:21 short-term 333:7

367:9 shot 330:21 show 85:18 86:2

87:2,8 247:22 248:12,17 271:4 274:7,11 275:11 298:13 299:19 300:1 303:6,6 313:1 386:7,8 showed 100:13

261:5 287:17 showing 108:13 163:2 179:15 248:8 305:22 shown 192:6

210:22 281:10 282:5 296:12 304:4 shows 86:9 169:2 171:19 176:2 Shprentz 81:1,2,8,8

81:10,11,16 side 28:4 30:3

47:18 49:16 72:17 245:14 256:15 265:9 289:7 324:2 sidestep 192:9 sight292:13,17 sign 187:2 256:21 significant 37:6

77:8 78:6 89:15 118:12 127:14 154:1,3 276:17 285:8 307:9 309:10 significantly 368:1 silence 317:22 silly 232:16 similar 34:9 93:1 137:6 169:2,22 272:20 283:6 similarities 296:13

297:8 299:12 354:5 Similarly 277:12

296:16 simple 109:13 113:2,11 160:22 201:11 347:11 350:5 375:15 simplify 308:5

366:15 simply 15:7 20:21

59:9 68:22 97:7 97:11 112:20 190:15 197:4 212:5 228:8 285:3 329:18 350:8 366:14

### 202-234-4433 Neal R. Gross & Co., Inc. Page 427 simulate 55:1

91:16 simulated 102:5 simulating 28:8

54:22 187:15 simulation 70:2

72:15 simulations 33:8

69:16 73:22 Simultaneous

338:5 single 79:7 139:1 148:14,14 298:21 299:7 300:7 301:7 301:8,16 single-city 332:11 Sipple 12:11 sit298:2,3 363:2 site 95:16 161:8,21 175:8 sites 207:9 208:9

277:21 294:2,8 305:14 sitting 7:14 213:16

363:13 situation 171:20

227:13 situations 190:18 six 82:11,15,20 124:19,22 125:17 126:3 128:2,11 180:17 191:10 294:3,7 368:4 372:16 size 29:16 99:3 113:3,4 skewed 133:2 skews 287:19 Skill 52:22 skim 280:2 320:21 skipping 166:16 sleet291:3 slide 35:1 69:8

271:22 275:11 slides 263:18 266:5

272:18 287:18 slightly 146:14 172:12 178:1 193:7 283:3,7 289:9 slowing 365:11 small 24:9 149:21 153:21 213:3 214:17,18 247:10 293:5 smaller 132:15

309:7 smart292:6 295:12 smidgen 163:13 Smith 265:2 sneak 222:12 sniffling 52:8 snippets 213:21

214:3 snow291:3 socioeconomic 124:1,5,13 126:3 132:22 soil 271:10 soils 270:18 solicit 344:10 solid 57:13 387:7 somebody 107:2

245:7 280:2 364:10 somewhat 82:9

88:13 96:21 128:8 128:14 133:2 187:8 276:12 300:8 301:2 302:21 329:1 Sonja76:4 sophisticated 95:15

315:14,14 sorry 9:14 34:5

49:5,18 66:2 81:4 82:11 94:19 178:10 180:5 213:22 255:22 350:22 sort 8:12 12:19

24:11 25:2 44:10 44:16 45:9 46:19 47:12 52:4 53:2 55:6,21 60:8 61:14 72:16 74:22 83:7 86:6 88:9 93:7 100:2,9,12 100:16 101:5 105:13,14 106:15 115:19 126:21 128:3,18 129:8,10 129:16 140:14,17 145:7 147:18 156:14 159:16 160:16 166:1 167:20 168:1 169:7,9 172:16 176:15 184:17,17 184:19 186:4,16 187:1,2 193:4,16 194:14,16,20 200:9 206:7 210:8 213:1 216:15 217:1,20 218:8,14 219:19 229:6,11 232:15 233:1,20 239:15 241:12 243:2 244:1,18 250:19 251:11 256:16 257:3,6,10 257:14 261:8 274:16 282:13 286:7,8 301:17 311:2,3,16 316:3 316:9 320:21 321:21 323:4 330:22 331:10,13 332:18,19,21 335:13 336:6 340:12,22 343:17 347:15,16,19 351:19 353:5 358:1 362:5 366:4 366:19 367:15 371:1,2 376:22 381:14 383:12 sorts 307:15 sound 50:11 sounded 349:9 sounding 232:16 sounds 107:2 219:10 315:11 346:5 371:10 source 30:1 60:4

80:2 147:4 151:4 152:5 170:14 176:4 208:9 sources 29:19 30:5

59:22 92:17 94:15 103:13 105:2 131:20 137:5,7 139:17,20 142:11 144:4 270:21 301:22 Southern 6:2,9

372:20 SOx 270:10 so-and-so 369:6 space 341:19 sparsity 283:21 spatial 31:8,11,16

55:2 91:19 162:5 162:6 spatially48:17 speak76:5 132:3 132:17 326:3 328:6 344:3 367:16 379:22 speaking 50:15 134:8 266:5,6 338:5 special 38:22 specialized 105:11 116:14 species 305:17 specific 11:19 15:12 28:5 41:8 44:9 45:2 94:14 95:16 110:15 132:16 153:5 155:9 160:20 175:8 179:7 206:15 270:4 281:21 289:16 291:8 329:20 331:6 336:14 337:7,13,17 338:1 338:19 339:19,21 358:11,16 359:12 376:2 377:3,10,15 378:9,12 specifically 12:6 14:22 26:9 28:15 29:10 30:18,19 40:17,18 60:5 77:5 120:15 121:14 132:11 162:5 279:15 328:7 341:7 380:11 specificity 334:11

334:20 335:15 337:2 363:11 375:11,14 specifics 182:1

311:3 365:1 spectrum 215:6 speculation 124:12 speculative 316:4 speed 350:1 Speizer2:14 7:1,1

47:17 101:7 103:10 125:17 133:4,10 169:18 177:1 178:8,17 179:9,12 180:7,13 180:18 182:6 184:6 186:5 199:15 205:7 229:5 233:2 247:7 248:16 249:2 263:10 322:6 364:6,19,22 376:1 spell 50:19 spend 22:22 69:9

323:12 377:5 spillover 107:19 split98:3 spoke 60:13 spoken 110:14 spread 183:19 squeeze288:22 squeezed 288:21 squishing 92:21

### 202-234-4433 Neal R. Gross & Co., Inc. Page 428 squishy 300:19 St 306:19 312:22 stab 386:18 stability41:18 staff2:18 12:20 140:14 141:4 142:6 247:15 250:13 296:22 303:16 321:20 334:10 344:6 348:19,21 349:8 349:11,19 353:17 380:17 382:18 staff's 359:6 stage 260:10

295:16 365:9,13 Stallworth 2:21 4:3

4:7 19:21 21:1,12 22:11 34:3 50:3 50:10,14,18,21 81:5,14 264:21 316:13 371:22 382:9,20 383:18 384:4,12 385:7,12 387:1 standard 14:9,10

23:4 24:5,8 28:12 28:13 30:9,14,14 31:1,3,6,7,7,17,18 31:20 34:7,14,16 34:19 35:3,8,9,14 35:22 36:13,22 37:10,12,16 38:10 39:6,20 41:15 42:3,4 44:7,8,9,12 44:13,19 45:3,8,8 46:8 47:1 48:12 48:19,19 49:3 55:1,12 61:10 66:13,16 67:7,12 67:16,18 68:6,11 69:14,17 70:15,19 70:21 71:4,5,9,15 72:3,8 73:2,6,9,10 74:1,2,8 75:4 76:18 80:11 84:15 87:18 89:16 90:14 90:21 92:4 94:6 94:20 102:1,4,5 106:1 119:4,18 121:21 122:12,19 122:21 123:1,2,3 138:12 155:15 159:18,18 163:6 165:7,9,11,13,17 166:4,5,9 167:14 185:6,11 189:7 209:6,7 225:6,22 227:2 234:3,7,12 234:15,18 235:5 242:9 254:19 261:22 265:9 276:15 288:5,7 290:19 291:15 301:3 308:3,4,6 308:21 311:12 313:9,10 314:5 326:15 327:2,5 329:2,9 343:10 345:13 346:19 347:13 349:5,6 357:18 359:10 360:19 362:10 367:4,5,9,12,15 standards 12:7,8,8 13:5,6,15,16 14:2 14:15,16 15:1,2,9 15:14,18 18:22 20:14,19 21:19 22:15,16 23:6 24:12,13,14 26:11 26:17 27:17 28:9 28:22 29:2,7 33:9 33:14,19 34:20 35:2 38:17 45:20 46:17,19 47:21 48:1,2,5,6,10,11 66:12 68:6 87:12 119:8 121:17 122:10 155:11 169:8 218:21 224:18 227:10,14 236:21 237:5 239:20 243:5 253:2 266:9,11,20 267:20 268:3,6,22 269:13 318:11 325:22 326:8 328:1 343:11 345:4 347:14 348:10,11,12 352:13 366:11,19 368:13 373:3,4,17 standpoint91:20 116:22 117:5 253:11,19 254:13 stands 286:13 stand-alone 222:1 stare 298:4 staring 351:19 stars 164:22 start4:4 51:11 52:5

60:22 221:6 265:5 285:12 322:19,21 342:13 379:11 382:3,6 385:9,13 started 205:4 starting 258:13

263:3 starts 210:15 state 6:5,12 89:20 stated 21:6,7

239:17 240:17 statement4:4 15:6

63:19 114:4 115:7 139:14 188:2 210:16 216:5,12 216:21,22 222:14 252:2 259:12 317:18 336:11 343:9 347:12 statements 57:13 181:9 220:15 294:1 Staten 111:7 States 125:20

218:18 statistical 54:13 125:11 126:16 277:15 statistically 37:5 154:3 statistics 242:5 status 124:2,5 stay 324:2 384:5 steal 158:7 steeper 211:3 step 100:1 143:4

311:11 356:22 383:12 steps 250:20 stewardess4:6 stick 287:22 stochastic 147:14 stop 70:6 154:2

214:4 238:5 243:16 244:5,9,12 244:13 245:21 253:9 254:3,3,7,9 stopped 56:21 stopping 240:19

243:1,7,19,19 244:9 246:3 247:19 248:4,10 story 137:18 332:6

354:10 356:6 straight232:4

303:11 straightforward

381:13 strategies 93:14

97:5 100:15 159:15 185:22 302:11 strategy 16:18 86:6

86:11 89:6,13 93:4,17 94:22 97:22 99:10 stratifying 132:15 streamline 17:7 strength 115:22 116:11 313:21 strengthen 26:21 strictly 125:22 stringent 67:6

234:6 strong 132:9 141:8 155:8,8 167:22 212:6 262:8 313:7 314:6 stronger 329:14 strongest 328:18

328:22 strongly 17:18 150:22 175:17 262:19 301:4 345:14 struck 284:12,17

374:11 structure 25:19,20

94:5 150:20 311:14 333:3 374:21 structures 151:3 struggled 384:15 struggling 384:17 stuck46:19 83:7 studied 83:15 113:12 342:4 studies 13:3 27:20

28:3 32:17 33:1 36:5,6,7,9,11 37:4 37:20 40:22 42:1 42:1 76:21 77:10 77:21 78:8,13 82:1,2,5,14 83:11 83:13 84:6,10 115:1 125:13 128:17 139:1 143:18 189:9 190:21 191:1,5,8 191:17 193:19 228:1 242:3,4 245:4 247:16,21 247:21,22 248:8 248:10,11,17 267:16 272:8,14 272:15 277:7 283:21,22 284:2,3 286:12 287:18 289:1,8,18 319:5 319:11,17 329:8 331:6,13 332:11 332:12 333:15,18 333:19 334:6

### 202-234-4433 Neal R. Gross & Co., Inc. Page 429 337:14,16,20,20 337:22 341:9,12 341:16,18,21,22 342:2,10,15 345:12 356:2 358:10,14 367:17 371:6 372:12 study 27:22 28:19

32:19 37:1,3,3 52:19 54:8 55:22 58:2,4,8 59:3 60:11,17,21 61:4 61:5 63:4 64:13 64:15,20 65:1,10 65:15,22 71:2 74:12 82:9,11,15 82:16,21,21 90:12 94:18 95:3 110:22 111:1 112:14 116:12 124:22 125:1,3,22 131:20 133:19 134:10 178:2 182:14,20 183:7 189:15 190:19 191:9,9 216:1 236:2 247:20 272:15 281:5 333:22 372:14,16,18,21 372:22 stuff 139:2 331:14

353:19 subcategory 132:2 132:11 subclassifications 131:9 Subcommittee

307:12 subgroup46:10 158:9 382:11 384:13,22 subjective 77:14 subsets 272:17 substantial 24:3

74:3,10 106:4 176:3 193:21 212:16 substantially 76:9 194:4 substantive 154:10

324:9,20 substituting 121:16 substudies 272:16 sub-daily 30:20

313:8 succinct 348:7,15

349:19 succinctly 256:21

365:21 sufficient 11:5 122:1 244:15 251:22 289:3,15 sufficiently 76:16

232:6 242:17 245:22 299:18 suggest 8:19 23:18

98:2 203:17 247:17 251:13 283:21 297:5,6 307:6 342:15 380:16 383:14 386:19 suggested 68:4

69:13 88:11 128:15 237:22 298:19 299:3 suggesting 84:1 184:11,17 229:8 292:19 suggestion 54:8 suggestions 52:14

99:13 156:19 272:5 281:22 377:4 suggestive40:13,14

78:5 Suh 1:22 6:20,20

115:3 117:11 127:17 128:13 135:22 145:21 154:5 191:19 196:16,22 197:10 197:14 240:21 355:15 357:12,20 359:1,14,19 362:1 387:10 suitable 259:4 suite 24:12 26:16

239:20 266:10,19 269:12 366:19 suites 29:1 33:9,10 sulfates 270:6 sulphate 302:12 sulphur48:11,15 summaries 5:6

221:11 349:22 366:6 summarize76:11

95:20 108:18 256:12 297:7 349:10 summarized

230:20 260:17 279:21 286:11 summarizing 25:15

60:11 summary 141:18 148:7 149:20 155:7 166:7 209:2 209:11 221:5 258:7,17 279:8 280:3 311:17,19 312:1,12 342:22 343:12 345:7,9,17 348:7,15 349:19 350:6,17 356:6 357:4 379:6,8,15 sunset 347:16 supplement268:6 supplemental 64:5

272:12 273:10 297:2,3 supplemented

53:16 support21:3 26:14

29:11,15,22 30:4 30:12,16,22 46:18 121:14 143:21 149:14 266:17 268:3 269:10 281:8 327:4,8 332:7 346:9 352:12 supported 29:2

32:10 33:20 98:14 supporting 37:22

282:16 288:16 343:13 supportive 295:10 suppose 147:5 185:20 supposed 208:16

209:11 214:22 215:1,3 220:7 223:7 224:12,21 225:2 230:2 363:21 367:19 368:14 supposedly 356:11 sure 14:12,18 16:11 16:17 21:12 44:1 46:10 49:19 52:4 63:1 85:8,16 91:6 93:6 94:1 95:1 97:1 99:20 104:16 109:4 115:15 139:12 141:12 143:7 154:16,20 154:21 155:3 163:15 171:2 172:21 174:10 175:21 180:3 183:22 184:2 193:19 200:19 201:22 203:5 206:4,5,18 207:21 228:11 231:15,18 235:22 247:7 249:2 255:2 257:2 266:3 286:18 318:9 339:17,20 340:2 357:12 360:1 363:2 375:20 378:1 Surely 21:9 surface 171:18

201:11 surprised 293:16 surrogate 59:6 surrounding 13:13 104:11,22 survey 127:3 susceptibility 380:6 susceptible 27:3,4

27:6 33:3 36:15 36:19 328:20 329:3 suspect 8:9 9:3

89:20 244:1 Sverre 2:15 7:7

44:4 49:13 108:6 130:3 156:21,22 168:16 174:3 202:16 208:20 233:22 261:15 351:22 387:8 Sverre's 169:22

387:11 sword 91:12 96:6 sympathize 223:8 system46:4 96:16 137:20 144:20 145:3 245:7 250:10 systematic 115:20 T t 145:4 156:15 157:6 174:22 205:1 282:7 table 3:1 70:5 83:9 121:20 133:6,11 133:13,16,18 135:1 149:19 153:7,17 177:4 178:9 185:13 254:1 265:14 282:7 296:17 314:22 tabled 356:8,10 tables 83:20 111:18 141:19 151:18 177:7 279:9 296:19 Tacoma74:12

### 202-234-4433 Neal R. Gross & Co., Inc. Page 430

take 8:20 14:22 15:12 16:7,22 19:9 23:9 41:10 71:11 84:18 108:8 109:9,10 118:8 140:7 164:19 185:17 202:8 203:12 204:3 213:2 226:14 229:11 239:18 241:6,7,15 259:5 261:20 280:1 292:2 320:16,19 321:2 322:3 323:6 325:3,5 330:3 359:21 372:5 373:7 374:1 376:15 379:1 380:21 383:8,9,13 383:16 taken 168:17

206:20 227:14 257:11 340:9,12 takes 69:4 225:9

342:7 356:22 take-home 168:4 talent 117:20 talk 12:15 28:16

34:1 35:1 56:14 60:10 67:4,22 87:5 105:10 146:7 157:10 163:19 168:9 182:13 188:13 213:11 222:21 227:16 271:17 272:1 308:12 326:9 327:22 344:14 368:2 380:18,22 381:5 385:8 talked41:5 45:19

49:7 51:19 53:8 57:19 64:6 65:5 116:19 138:15 170:19 195:9 218:12 356:18 364:1 talking 37:2 48:2 70:9 72:11,13 91:15 98:16 118:11,13 140:5 148:2 149:3,6 166:20 170:7 171:13 174:15 185:10 210:2 213:1,3 215:17 223:19 235:9 246:22 258:1 271:20 313:2 323:20 324:1 347:18 talks 186:13 199:17

364:12 tangential 122:18 tangents 17:3 target 11:11 49:15 103:13 185:19 195:3 227:4 targeted 104:3,12 targeting 30:1 74:2 102:21 Taylor287:10 team 28:16 203:22 teasing 378:9 Tech 6:17 technical 51:21

53:7 111:22 156:2 324:21 337:12 369:1 379:18,19 technically 309:7

310:1,9 techniques 269:17

270:22 technology 15:17 172:3 226:12,14 243:1 Ted 6:16 80:20

85:9 100:22 108:17 168:15 308:8 tee 26:5 298:18

330:10 teleconference

330:9 tell 9:8 16:13 101:14 180:20 239:9 264:5 332:5 351:16 365:22 telling 93:12 191:17 229:20 tells 230:2 temporal 161:20 161:22 267:5 tempted 320:14 ten 248:14 251:16

366:11 385:13 tend 99:5 103:3 143:21 304:18 tendency 286:17

287:22 tending 301:8 tends 39:11 152:14 tension 375:2,5 tentative 180:22

205:19 term 28:7,7 32:13

33:7,7 40:10,12 40:15,16 41:14 56:7,8,9,19 57:3 59:7 66:17,18 67:2,3 70:8 73:15 73:17 78:21 80:3 82:4 83:11 84:4,5 125:9 134:9,16 144:21 145:9 149:5 173:11 177:18,19 178:6,6 210:3,13,16,18,21 211:3,5,14 212:4 212:10,18 213:13 223:11 226:12 337:14 terminology 187:11 terms 14:7 17:12 18:16 21:22 24:7 24:10 35:2 38:16 42:2 59:18 60:6 62:18 64:18 66:17 69:18 70:7 86:8 87:13 88:1 89:6 89:12 91:20 93:5 93:12 94:4 97:3 105:10 109:2 110:5 112:7 120:8 125:11 130:6 131:12 142:21 148:15 150:5 152:21 160:13 163:6 164:10 165:3 168:11 177:16,22 193:14 194:14 197:16 198:21 199:1 201:18 226:4,15 227:9 235:18 236:3 241:11 242:2,14 249:21 250:4 254:15 267:13 273:5 274:14,15 277:7 290:18,22 291:21 292:4 295:1 299:20 301:3 309:2 316:16 320:2 331:8 366:9 367:20 368:12 terribly 71:22 terrific 256:8 test97:13 109:3

243:13 Texas 71:13 text 88:15 115:7 137:14 144:15,17 145:17 150:9 181:6 207:5 256:22 297:2 331:4,9,12 353:9 381:14 thank 22:11 50:10

50:21 51:7,16 75:10,11,16 76:4 80:14,15,16 81:16 85:4 96:5 148:6 149:8 177:1 275:12 280:22 283:12 385:9 thanking 51:11 thanks 33:22 85:6 85:11 88:18 113:19 168:15 220:2 310:15 387:13 theory 250:19 thin 198:21 235:6,7

355:3 thing41:4 65:7

89:3 93:18 98:13 100:3 110:16,22 112:1,5 115:4 136:7 152:21 154:17 155:4 160:20 164:5 168:18 170:21 182:12 183:8 195:9 214:18,19 233:7 258:3 261:3 264:5 279:10 292:14 294:17 300:8,17,21 301:14 303:8 307:5 314:16 350:10 351:4 358:2 365:18 things 9:5 14:5 17:1 47:13 52:12 59:4 60:19 90:10 103:14 116:4 122:15 123:3 136:9 138:18 143:14 146:14,16 146:22 157:9 158:8 160:12 172:19 176:6,11 186:13 190:1 192:5 201:8 205:10,13 214:17 216:8 220:1 236:16 237:8 243:11 250:6,7 252:19 260:8 261:14 262:21 284:11 286:3,17 290:20 299:10 307:8,15 308:5

### 202-234-4433 Neal R. Gross & Co., Inc. Page 431

311:3 314:21 330:18 332:20,22 338:12 349:1 355:2 356:8 358:8 361:4 368:5 375:7 376:7 378:7 381:16 382:14 384:13 think 5:21 8:8,15

8:22 9:4 11:22 13:21 15:4 17:16 19:11 20:4 21:17 23:16 24:4 25:1 42:13 43:4,5,10 43:11,13 44:2,11 46:3,8,14 49:9,21 51:8,12 52:12 54:9,19 56:13 57:2,6 60:3 62:5 64:6 65:4,6,15,20 65:22,22 66:7 68:2,14 69:4,10 71:11 73:19 75:18 79:13,16 80:20 82:12 83:6 84:7 86:7 87:17 88:3,7 88:8,21 89:1,10 90:4,6 91:15 92:5 93:2 94:8,9 95:7 95:12,19,20 96:1 98:3,4,5,7,10,12 99:19,22 101:1,13 103:5 105:14 106:6,10 107:4,15 107:21 108:8,9,20 109:6,10 111:19 112:9 113:1,9,14 113:22 115:19 117:14,21 120:1,4 120:7 121:3,4,8 123:21 124:4,20 125:15 126:8,12 127:4,15,17,21 128:13 129:14,18 129:19 131:2,7 132:2,3,14 133:16 134:17 135:8,10 135:14 136:7,9,11 136:19,21 137:9 137:17 138:9,14 138:17,18,20 139:1,6,7,11,15 140:11 141:22 142:4,14 143:1,2 144:21 145:9,11 145:15 146:18,21 146:22 147:17,21 148:3,5,21 149:16 149:17,22 150:6,7 150:9,19 151:10 151:12,17 152:12 152:19,21 153:7 153:11,14,22 154:6 155:13 156:18,19 158:9 158:11 159:12,16 159:19 160:6 161:13,19 162:10 163:1,5,18,21 164:18 166:10,13 166:15 167:15,21 167:22 168:2,5,7 168:19,21 170:9 170:10 171:5 172:7 173:1,3,7 173:10,13,18 175:4,9,10,22 176:8,10,11,14 180:13 181:19 182:13,16 184:7 184:13 185:18 186:18 187:11 188:1,1,8,9,19 189:1,22 193:11 194:2,2,4,18,19 196:4 197:14 198:18,19 199:2 199:16 200:3,12 200:15,22 201:1 202:1,6 203:1,2,2 203:20 204:2 205:12,17 206:12 207:2,7 208:2,4 208:11,14 209:20 210:4,5,12,18,20 211:7 214:6,20 215:2,7,14,16 216:4,17 217:10 217:22 219:6,22 221:4 222:3,5 223:9 225:16 226:1,3,8,16 227:13,22 228:6 229:3,14,18,21,22 231:10 232:7,22 236:19 238:3 239:7,16,19 240:8 240:19 242:10,19 243:4,7,21 244:19 245:5,20 246:3,6 246:7 247:3 248:7 249:22 250:10 251:14,15,19 252:2,2,12,14,17 252:18,21 253:16 255:6,10,11,12,16 255:22 256:3,6,7 256:10,17 257:11 257:18 258:2,3,12 258:14,19 259:1 259:13 260:18 261:10 262:6,7,21 263:1,2,4 264:7 264:19 272:4,10 275:21 276:1,11 276:18 277:5,8,11 277:16 278:6,15 280:7,21 281:5,10 281:18 282:1,2,8 282:16 283:1,4,9 285:11 286:2,9,10 286:14 287:19,22 289:13 290:16,17 291:15,17,21 293:2,3,7,10,22 294:5,6,9,11,12 295:9,18 300:3 301:1,9,15 302:3 303:17 304:1 305:3 306:14 307:21 310:6 311:22 312:12,17 313:12,21 314:8 315:4 316:9,15 317:11,21 319:14 320:8,9,14 321:1 321:6 322:2,6,9 322:17 323:1,2,11 323:15 325:9 327:21 329:1,11 329:13 330:3,13 330:16 331:2 333:2,5 334:19 335:16,18 336:14 336:18 337:3,5,9 337:11 338:3,6,7 338:9,9 339:14 340:1,4,11,14,21 340:22 341:4,15 344:21 348:18 350:11,16 351:3,8 351:13,17,18,21 352:7 353:1,15 354:10,14 355:20 357:10,14,16 358:17,19 359:18 360:3 361:13,14 361:14,21 362:2,4 362:11 363:5,10 363:15,16,17,19 363:22,22 364:2,2 365:6,14 367:6 368:11 370:1,3,5 370:7,8,15 373:6 374:1,11 376:13 376:19,21 379:19 380:1,9,12,12,21 381:3,6,12,12 382:2,7 383:2,4,6 383:7,11,18 385:18,20,22 386:2,4,17 387:3 387:6 thinking 13:1 16:4

22:5 25:3,7 42:8 62:19 195:7 213:5 237:16 242:5 249:9 251:9 284:7 315:16 322:1,3 324:7 329:7,10 344:8 345:16 365:10,19 366:22 377:12 thinks 316:16 third 31:3 57:5 171:14 Thirdly 27:8 thoracic 12:8 34:8

34:15,18,21 35:11 35:19 37:14,15 38:17 39:3,7,12 39:21 40:2,11,19 45:9 53:10 348:11 356:14 357:19 358:3 thorough 151:9

206:13 thought 16:12 23:7

23:11 43:18 51:15 61:22 68:4 69:11 86:1 87:1,11,15 101:2,7 107:6,9 107:11 108:1 114:12,14 115:4,6 127:20 136:3 141:1 142:8 145:22 146:4,17 148:21 155:17 167:11 169:10 181:21 192:14 193:3,8 198:20 205:11,18 208:12 208:21,22 209:1,2 217:4 241:1,5 250:16 261:8 277:12,19,19 278:9 279:1,11,21 280:4,9,15 281:14 282:12 283:16 288:13,16 289:15 290:12,21 297:13 297:18 298:10 299:14 301:8 308:12 309:5 313:1,11 324:8

### 202-234-4433 Neal R. Gross & Co., Inc. Page 432

346:21 355:11 375:8 thoughtful 209:12 thoughts 21:20 three 28:5 100:9,14 162:17 181:12,13 229:11 259:5,6,14 274:13,20 347:21 378:11 three-year 31:8

90:14 293:19 threshold 82:19 186:17 190:5 219:14 229:12 237:18 239:5 254:14 thresholds 24:16 threw210:8 throw 335:9 thrust280:17 thunder 158:7 Thursday 264:12 tie 143:6 312:1,9 tied 56:10 117:18 118:1 tight 69:11 tightened 104:9

336:3 tightening 31:9

374:3 tightens 44:12 tighter 137:19

218:1 tile 274:10 296:7

297:18 303:4 time 8:20 9:3,9 11:6 16:22 19:9 22:22 23:9 27:3 30:10,18 35:4 38:20 40:1,5,7 41:1,2 43:7 44:2 44:17 49:20 51:12 54:17 55:15 65:5 69:9,10,10 76:16 81:21 84:18 85:13 96:10 99:21 100:4 100:7 113:16 117:3 129:3,4,8,9 138:10 147:16 148:2 156:12 171:1,1,5 175:11 176:12 200:20 220:12 222:4 227:4 234:4 236:4 267:7 268:11,13 273:11 280:19 290:14 297:16 305:3 306:17 313:18 316:9 323:12 325:3 327:3 338:16,21 339:11,14 348:9 350:4 368:21 371:3 373:7 377:5 380:21 382:10 386:16,21 times 30:13,17

50:12,12 86:20 96:9 99:5 134:18 246:15 268:5 284:13,13 306:5 327:6,9 timetable 263:16 title 135:1 today 5:8 7:21 8:21 10:7,14,17 12:5 28:16 46:11 76:5 189:8 251:15 312:16 385:17 Tom 283:13 290:2

290:3 312:15 320:11,12 380:21 383:6 386:22 tomorrow7:22 8:7 10:7,17 12:14 43:7,11 264:18,21 283:14 290:5 312:14,16 320:20 321:10 322:14 323:5 339:14 377:5 380:17,19 381:19 382:10,14 383:4 384:15 385:8 387:2 tone 215:2 tonight49:22 136:9

321:8 322:7,10 353:6 380:16 382:12 383:15 384:9 385:1 tons 384:19 top 43:17 91:5 179:2 296:9 298:14,15 304:4,5 topic45:17 144:12 149:19 tossing 253:1 total 134:4 178:11 179:19,21 180:2 210:4 253:21 254:5 275:8 totally 91:11,12 193:1 209:22 353:11 touch 9:17 12:17 14:3 22:15 127:20 touchdown 163:14 touched 157:10 touches 382:16 tough 160:11,12,19

221:13 toxic 227:11 toxicity 227:20 toxicological

378:17 toxicology 380:2 track 11:17 213:20 traffic 315:20

384:19 transferred 336:21 translate 13:2

329:8 352:9 translating 225:21

242:8 translation 197:2 transparent 171:7 travel 9:12 322:13 treated 104:20 164:8 356:15 treatment 52:21

53:20 63:22 113:14 124:2 138:20 151:9 trees 348:2 tremendously 170:6 trend 99:14 Triangle 1:13 tried 63:15 324:14

326:2 327:12,20 374:1 trio 7:14 trip 323:7 trivial 161:9

231:17 trouble 179:14 troubled 144:22 troubling 187:1 true 117:20 120:14 144:16,21 145:6,7 145:9 197:10 210:19 213:10 215:12 286:15 332:20 truly 15:18 25:13 154:9 195:17 196:6 354:3 try 8:19 14:12 17:7

52:9 100:19 129:15 136:10 160:11 167:5 176:10 280:14 286:8 300:21 314:8 323:4 324:1 334:10 337:1 338:8 340:22 352:9 358:8 365:9 374:16 378:12 383:20 386:18 trying 19:10 21:2

46:13 91:16 94:13 100:7 117:19 126:13 127:22 145:8 154:17 158:22 162:10 184:12 195:3 197:16 210:12 213:15 233:17 246:4 266:21 329:6,16 340:2 356:17 358:14 359:19 375:3,5 turn 5:16 9:4 11:7 12:4 17:3 25:16 28:21 51:5 264:12 338:10 386:1 turning7:22 10:9 turns 210:10 twelve 224:7

382:21 twice 217:20 twist96:7 two 10:19 12:15 16:15 26:5 33:17 35:6,15 53:18 54:15 63:9 64:5 64:10 66:7 69:3 71:7 87:2 90:10 90:13 91:22 92:20 95:21 102:21 108:11 129:8 157:14,18 158:16 164:8,9,11,12,16 172:8,14 173:20 174:4,11 178:5 179:3 189:12 195:10 203:15 204:5 212:20 247:21,22 264:17 265:6 268:1 271:13 272:14 279:11 290:19 299:10,11 300:5 301:19 304:10 307:11 308:13,22 320:13 323:18 328:1 338:12 341:3 369:2 380:20 382:13 385:19 386:10 two-hour41:1 two-page 350:5 two-sentence 385:6 type 10:10,20 48:16

242:1 309:14

### 202-234-4433 Neal R. Gross & Co., Inc. Page 433 types 131:6,8 159:14 178:5 196:12 329:20 373:9 typically 67:13 137:18,21 146:14 t's 276:18 U UARG 265:2 UFVA 266:16

267:16 269:5 ultimate 185:20,21 ultimately 97:4

325:20 353:9 ultrafine 29:17 120:21 unable 264:14 unacceptable 136:19 289:10 uncertain79:19

238:1 254:8,10 287:3 328:19 uncertainties 13:13

33:7 37:17,18,19 38:6,8 76:20 77:9 77:22 78:6 80:5 114:21 132:14 141:19 202:13 208:5 218:4,6 227:19 229:19 329:19 334:17 359:6,9 360:15,22 361:2,7 362:9 366:2 369:15 uncertainty 24:20

52:21 53:20 54:1 59:18,21 60:1,15 63:22 68:10,18 69:1 70:1 74:19 78:15,20 83:19 95:9 132:4 136:14 137:7 139:7,18,21 140:4,4,5 142:11 142:21 143:7 146:7 147:14 149:14 150:5,6,16 151:15 152:6 156:15 176:4 187:5 200:18 229:1 231:7,21 240:12 241:2 243:15 244:15 251:22 252:8 306:7,10,15 380:4 unclear 91:9 uncomfortable

89:4 underestimate 124:4,8 130:19 underestimates 132:21 underlies 16:5 underlines 14:14 15:8 underlying 17:19 18:3,7,10 19:13 19:13 20:8 21:19 23:20 25:5 45:1 144:20 178:2,12 282:11 345:12 355:4 379:3 underpinning

380:11 underpinnings

347:4 underscore 256:19 understand 32:4

32:21 33:4 87:18 100:19 104:16 107:7,13 111:2 123:7 138:9 139:22 154:9 160:12,21 161:6 161:13 163:11 170:16 178:18 180:3 192:18 193:6 194:12 207:2,3 215:2 219:3 228:3 232:22 240:17 307:21 340:19 348:5 360:1 363:20 understandable 90:7 understanding

27:13 28:1 47:12 60:22 82:17 89:15 96:10 121:6 224:8 233:16 270:18 284:1 285:1 326:21 331:8 345:21 370:22 373:2,16 understated 77:22 understood44:7,11

63:1 101:13 108:2 114:13 188:11 279:6 uneasy 88:14 unemployment 122:8 uneven 207:8 uniform 66:14 uniquely 302:14 United 125:20

218:18 University 6:2,5,7

6:8,11,19 7:2,4,6 7:8,10,17 unjustified 227:22 unrealistic 107:9 107:16 108:10 186:8 unresolved 261:1 unscramble 368:22 untested 353:22 untouched 103:2 updated 127:2 updating 125:12 upper 112:12 168:22 169:9 181:7 189:18 195:19 196:6 216:3 urban7:22 28:19

39:13,22 44:10,13 45:10,11 52:19 58:1,5,8,9 59:2 60:5,11,21 61:3 63:4 64:13 65:1 65:10,15,22 66:1 66:2 74:12 79:21 90:12 157:12,12 160:1 176:5 182:14,20 183:6 207:9 208:9 215:22 249:18 259:9 271:20 272:8 281:4 292:18 298:6 307:18,19 Urban-Focused

3:13 266:16 urge 19:10 256:2 urgent 363:5 use 20:22 25:6

29:11 57:12 59:5 60:13 76:17 78:1 78:11 84:20 88:12 95:12 112:17 149:4 171:2 196:17,18,20 198:2 235:20 241:9,12,17,20 249:3 256:19 261:22 290:16,18 294:20 307:17 308:19 313:3,8 318:15 319:11 366:16 371:6 375:3 382:10 385:2 useful 17:10,11

20:20 23:7 24:7 64:18 69:7 85:22 86:1 95:13 106:10 109:2 149:19 155:2,18 158:14 198:19 200:2 250:12 255:12 263:3 276:13 279:21 280:4,7 283:22 284:8 297:14 298:12 300:4 302:7,20 304:1 312:2 318:10 321:7 333:3 335:22 336:1 341:22 376:10 381:9 387:8 usually 146:6,9,16 utility 78:22 217:5

224:17 265:3 utilized79:8 U.S 1:1 36:6 58:6

58:10 76:15 80:8 112:21 129:4 183:4 189:21 195:17 198:6 199:20 259:10 260:4 V vague 139:9,10

351:7 valid 124:21 132:22 226:1 validation 304:11 validity 371:7 valuable 98:10

385:21 value 55:22 71:21

90:21 101:21,22 101:22 102:2 145:9,10 158:22 159:2 161:2,5 163:8 206:22 281:4 valued 92:18 values 57:22 58:1,3

58:5 60:17 61:3 61:12,18 63:8 65:9,13 71:21 72:22 88:2,7,22 91:3 97:11,19,21 100:13 144:16,22 145:6 152:10 157:6 158:17,18 161:3,15 162:1 212:15 241:7 274:8 275:1 305:9 306:2,14 315:21

### 202-234-4433 Neal R. Gross & Co., Inc. Page 434 vantage 193:8 variabilities 78:7 variability 52:22

54:2 59:19,21 60:1,4,5 64:1 76:20 77:9 80:6 87:9 105:16 127:19 136:15 137:5,11 147:4 156:15 161:22 207:20 208:5 285:2 292:12,22 variable 207:14 variation 151:4 152:17,17,18 235:15 variety 27:1 65:3 106:9 372:8 various 83:21 87:7

89:16 159:14 169:3 272:13 296:14,18 297:9 367:16 vary 162:16 varying48:18

244:2 vast40:17 307:19 Vedal 2:15 7:7,7

44:5 45:1 108:7 130:4 132:19 134:22 135:3 157:2 163:11,18 165:2 167:10 168:7 202:17 208:21 210:8 212:8 213:6,12,18 214:9,12,15 216:13 217:3 234:1 235:11,19 236:1 261:16 352:2 vehicle 354:17 vein 353:4 venues 226:18 Vermont 6:13 version 53:4 55:8 113:17 127:7,9 138:11 201:6 221:3,9 230:21 231:1 versus 24:13 44:10

44:14 45:10 63:13 129:4 131:6 139:1 154:13 159:18 161:16 177:19 178:6 179:13 201:12 213:8 254:19 302:1 361:8 375:10 386:7,9 vetted 342:14

354:3 385:6 viable 99:17 313:12 Vicki 265:14 Victorville 305:8

305:10 videographs 172:5 view21:11 110:6 143:21 196:3 227:5 228:5 246:12 247:3 251:21 256:4 298:12 301:15 339:12 352:10 359:15 viewpoint 141:11 views 14:14 20:13

20:17 149:12 226:3 281:12 296:22 325:22 337:19 340:6,10 341:2,3 344:18,20 362:6 365:5 378:10 vigorous 223:12 visibility 3:13 8:1,8 10:7 12:16 13:10 23:5 136:2,10 203:21 249:19 263:9 264:11,22 265:8,10,15,20 266:15,16,18 267:6,8,14 271:15 271:21 273:8 274:12 281:4 288:7 307:7,18,20 308:3,4,6 309:10 310:16,20 311:12 312:13 313:10 315:19,22 316:5 316:10,15,17 317:1,6 318:21 320:9,12 348:12 380:20 381:8 386:21 visibility-related

266:9 visibility-type

288:5 visible 284:21 visits 85:2 vistas 287:7 visual 170:2 319:18 visually 282:5 vital 342:16 voiced 68:10 voluntary 387:9 vote 239:4 251:17 voted 244:19 vulnerable 226:17 W wade221:14 wait358:18 waiting 237:11 walk25:5 34:22

35:16 52:2 62:8 116:10 321:4 walking 34:10

321:21 wall 303:5 wandered 385:17 want4:11 5:11 8:6

8:13 9:11 12:4 14:3,22 23:17 25:4 49:9,10 69:20 85:12 90:2 93:2 96:22 99:15 106:5 108:18 120:13 123:20,20 126:12 129:15 140:16 150:21 170:17 172:2 175:7 180:8 182:8 185:16 200:5,8,10 221:21 241:16 243:18 245:9 246:16 252:20 263:5 270:9 279:9 290:6 291:15 295:17 303:5 308:9 310:2,5 315:17 321:8 325:2,4 328:6 332:15 334:20 336:6,16 337:7,7 338:17 340:12 341:5 353:20 354:6,7 365:7,12 375:17 376:5,7,12 377:11 381:5 382:10 383:13 384:5 386:6,18 wanted 9:15 11:20

25:2 42:21 44:5 47:4 51:7,11,16 62:12 63:1 66:7 75:9 81:17 91:22 94:22 107:5 138:13 154:21 165:8 201:1 202:9 206:4 229:5 279:14 280:2 335:18 341:4 364:6 368:19 wanting 184:4

230:16 wants 110:13 125:16 171:6 184:2 241:3 343:14,15 385:16 warning295:4 warranted 127:15

223:20 Warren 305:21 Washington7:8

272:14 285:4 wasn't9:13 40:8 56:13 88:5 110:3 110:4 111:12 115:14,16 125:14 140:8 141:3 154:16,19,21 155:3 172:17,21 173:14 209:8 216:16 220:12 223:13,15 294:2 299:15 374:7 Watch 300:20 watching295:7 water270:19 waving94:12 way43:14 57:1,19

58:2,10 60:5 61:9 63:9 78:18 86:2 90:11,19 91:9 93:8 95:2 98:20 98:22 99:7,16,18 100:6 101:18 103:12 104:17 105:13,16 106:2,2 115:15 120:19 124:19 127:22 128:15,20 135:13 142:22 146:21 158:15 159:22 160:16 169:4 171:19 173:7 174:14 175:4,21 183:15 186:7 187:12 193:1 194:6 199:4 200:5 207:10 208:2,13 209:3 211:11 213:5 216:17,18 224:3 226:9 228:12 233:9 241:5 243:17 246:6 247:12,18 248:3 249:3 251:6 251:18,19 273:9 278:2 284:17 286:13 287:20 289:14 292:20 300:1,2 303:6

### 202-234-4433 Neal R. Gross & Co., Inc. Page 435

304:17 311:4 315:6 328:4 331:3 335:11 337:12 338:1 354:17 356:13 362:2 364:17 370:15 373:12 380:10,22 382:9 Wayne 2:3 7:3 109:22 114:6 ways 19:16 21:18

38:4 48:15 71:8 88:8 89:1 97:12 120:6 157:4 168:12 192:8 201:14 231:10 274:11 297:6 329:6 344:7 347:22 348:21 wealth 370:15 weather291:2,13

291:17 292:13,22 293:7 Wednesday 1:9

252:16 320:7 weeks 225:18

307:11 308:13 309:1 week's 363:7 Wegman 9:12 weigh 22:7 weighing 22:7 weight 18:4,6 99:4 108:15 227:22 326:10 327:15,17 337:19 361:6 weighted 19:7 99:2 weighting 98:22 welcome 3:2 5:19

9:7 208:22 welfare 12:16 15:21 22:16 265:8 265:18 269:7,15 271:6 318:20 369:4,14 well-being 317:13

319:19 well-informed 344:18 well-reasoned

345:11 went46:22 69:15

87:16 99:6 130:14 135:16 159:1,11 179:17 204:9 252:13 266:4 267:21 313:14 323:9 387:15 west43:14 74:12

236:7,9 western 172:20 173:6 174:6 386:13 we've 273:17 white 305:21

308:17 310:3 wide 61:9 72:1

367:14 widen231:15 wider 106:17 293:1 wife 317:9 wildlife 270:19 willing 253:3

371:10 wimps 386:14 wind 248:20 window99:12 117:3 317:5 wings 247:9 wisdom 237:11

251:15 wisely 194:19 wish 123:8 127:11

237:7,7 Women's 372:18 wonder 120:12 121:6 131:17 220:19 222:12 224:12 262:9 wondering 85:18 115:12,17 130:11 366:13 374:11,12 word43:17 146:5 149:2 184:13 238:2 wording 145:8

228:15 words 86:19 152:8 166:12 198:9 273:1 331:5 342:12 367:1 368:22 work 8:20 23:17

28:13 33:15 87:13 101:6 136:11 201:16 215:15 220:9 226:7 258:4 258:19 289:5 305:22 321:3,5,11 322:7 338:7 371:3 381:1 383:13 worked 272:10

343:19 working 24:14

98:20 177:20 207:13 211:15,16 334:10 382:18 387:5 works 109:4 347:9 workshop 250:22 work-up 113:10 world 96:3 225:3,6

225:7,14 worried 298:10 worries 286:19 worry 187:3 Worrying47:17 worst298:21 299:7

299:18 300:7 301:7,8,16 worth 8:10 138:18 159:5 171:8 242:18 worthy 129:14 wouldn't 127:13

202:21 211:6 214:10,10 229:10 276:2 312:8 330:2 344:14,21 347:11 wrap 22:12 write 139:16 141:5 199:12 203:14 246:9,10 252:12 337:4,4 374:15,19 writing 21:4 90:5

255:17,22 258:2 322:10 330:12 written 88:2 114:13 117:17 145:17 276:1 279:20 281:15 285:18 295:20 303:20 306:22 312:21 316:12 364:9,14 wrong47:18 49:16

90:19 141:17 142:4 150:7 211:7 239:9 304:20 344:2 368:7 wrote 122:4 288:2

294:19X X 166:16 238:10

360:16Y Y 360:16 year 11:15 299:5

300:15 303:10,15 years 12:21 46:5 147:15 158:12 184:15 223:10 224:2 226:22 227:2,4 228:20 246:17,19 247:6 274:13 287:9 366:1,10,11 York 6:7 111:1,3 111:10,11,13,19 111:20 113:21 114:9 Z Z 360:16 Zach28:15 51:6,8

75:12 85:20 96:7 127:20 131:22 159:11 170:20,21 182:8 208:6 239:8 zero73:17 181:13 187:21 245:9,10 253:12 306:3,12 zones 138:2 164:13 164:16 176:7 0 0.2 8:4 1 1 80:20 85:10

275:18 276:5 282:21 283:1 324:12 331:1 1a 108:17 1uestion 307:4 1-3 3:6 1.06 191:11 1.15 191:11 1.2 113:5 1.6 111:19 1/2 249:5 1:15 204:7 1:21 204:10 205:2 10 1:10 69:17 71:4

71:6 72:8 82:15 84:12 179:17,18 183:19 185:11 191:11,13 222:15 227:2 230:11 233:6 241:7 246:14,15 254:20 296:9 298:14 304:4,5 10th 241:15 10,000 177:10 10-2.5 206:9 10:30 109:7 10:37 135:16 100 186:14,21

187:10 188:3,7,13 219:8 240:5 335:19,20 100,000 177:11 11 3:6 84:11 222:15

273:12

### 202-234-4433 Neal R. Gross & Co., Inc. Page 436

11:08 135:17 11:30 383:19 12 67:16,17 68:7,12

68:22 69:2,14 70:6,13,20 72:12 72:14 83:5 185:11 217:5 222:10 223:1,4,16,19 224:1,6,18 227:16 230:7 234:22 237:16 244:3 250:7 254:19 12-25 71:16 72:12 12-35 71:9,14 12:18 204:9 1235 70:11 13 185:11 213:22

214:1 223:15 234:22 14 185:11 213:22

213:22 223:15 15 28:19 58:1,8

60:21 61:3 64:13 65:10,16,22 84:16 157:11 160:1 164:22 166:17 171:15 173:19 179:19 181:5,6 182:14,20 183:6 184:8 186:2 191:10 195:13 197:18,18 199:3,8 201:20 207:9 208:9 213:8 214:22 215:11,22 216:2 218:11,16 218:17 220:16 223:13 257:4,7,8 259:1 261:4 273:12,13 274:12 293:21 343:7 367:11 15k 125:6 15-city 257:17 15-20 323:6 16 164:22 240:14 16th 164:4 17 240:15 18 275:8 1898 111:12 19 157:19 179:17 1948 317:14 1990 123:14,18 2 2 12:7,13 31:4

34:10 36:16,17,19 41:5 109:8,11,12 120:1 130:3 135:12 277:5 279:16,19 281:1,2 282:21,22 283:1,4 288:9 324:17,20 326:3,7 331:1 332:4 334:9 337:10 2a 369:1 2.1.3 373:9 2.2-34 333:10 2.3.4.1 364:20 2.3.5.1 364:20 2.3.6 327:22 373:13 2.4.1 364:13 2.4.2 364:13 2.5 60:7 61:6 2.5.1 364:13 2.5.2 364:13 2:15 252:16 2:30 204:1 2046:5 184:15

246:16,19 343:7 20.3 285:2 2000 317:17 2004 131:3 372:22 2005 80:6 2006 23:2 31:10

372:16 2007 372:18 2008 82:21 118:17 2009 82:9 126:15 181:19 372:14 2010 1:10 263:20 21 157:20 220:16 216 281:10 282:6 23 179:19 282:7 231 283:21 239 332:9 24 28:12 30:19

57:22 63:7 72:5 75:8 234:6 24-hour24:13

30:14 31:18,20 33:14 34:16 35:14 40:6,19 41:2 57:20 61:2,18 65:8,8,13 66:10 66:11 67:6,18 70:15,19,21 72:3 72:21 73:1,5,9 74:7 94:20 154:13 159:18 161:1,3,15 162:1 165:6,7 166:2,4 206:22 234:11,15,20 235:5 327:5 25 3:8 67:19 70:19

70:21,22 71:17 73:6 74:8 90:20 250-some 65:14 256 65:15 266 3:12 271 3:13 275 3:15 29.4 285:2 3 3 12:8,14 34:2,7,11

36:14 38:3,19 41:7 42:19 59:1 65:6 127:18 129:22 135:20 136:14 142:15 144:14 147:10 148:5,12 159:4 192:2 210:15 288:10 289:21,22 294:15 324:17 326:4 332:4 340:5 357:9 3a 340:5 3-12 296:8 3-13 296:11 3-26 306:3 3.4.4 303:18 3.4.5 303:19 3.5.3 148:13 3:30 320:6 3:31 323:9 30 101:22 102:3,7 134:14 30-some 216:1,2 311 133:6 324 3:18 34 72:10 35 90:22 353 133:10 356 144:15 363 139:15 37th 300:15 377 148:13 387 3:22 4 4 3:2 57:16 60:10

65:20 148:5,9 150:3 151:19 155:22 156:9 222:2,4 266:7 295:18 307:4 308:8 311:16 324:17 326:4 332:4 4a 179:15 4b 351:19,20 4-1 296:20 4-17 157:19 4-2 296:19 4-20 157:20 4-3 296:20 4-5 3:12 296:19 4-7 296:19 4.1 151:18 177:4 4.1.3 277:20 4.1.4 278:4 4.2 151:19 4.3 149:19 4.4 153:7 4.7 179:14 4:02 323:10 40 180:1 42 91:1 445 156:3,3 447 156:3 45 204:3 335:12 46 156:4,5,6 47 156:5,5,5,6,6 4700 1:13 48 156:5,6 5 5 3:4 157:1,3 180:16,19 182:4 193:6 194:8 200:7 202:8 203:15 265:17 269:6 270:13 311:16 324:21 326:4 332:4 381:15 5.4 200:1,22 202:3

202:14 5.5 199:13 5.773:13 5.8 73:16 82:15 181:19 183:16 185:7 187:16,21 5:06 387:15 50 119:12,14 180:1

284:14 285:14 50th 289:10 500 369:19 52-4 168:19 53 113:9 6 6 52:17 57:12 61:20

62:4 66:6 130:5 142:15 143:8 150:3 181:10 182:1 186:13 200:2 202:15 203:12,20 204:4 205:5 255:15 261:10 262:21 311:1 6a-e 205:6 6.5 210:15

### 202-234-4433 Neal R. Gross & Co., Inc. Page 437

60 119:13 618 220:15 65 113:9 7 7 249:5 7:00 322:22 70 287:11 75 3:9 8 8 364:12 8th 11:4 382:15 8:30 1:14 4:2 382:7

386:7,9 80 246:17,19 247:6 85 3:10 335:12 88,000 184:12

185:2,13 190:3 9 9 3:5 9b 359:3 9th 11:4 9:10 387:2 90 272:22 273:3

275:2 290:12,22 292:15,16 294:20 295:10 298:7 310:12 90th 273:22 296:6

299:1,21 300:9 95 272:19 295:2,5 95th 273:22 296:6

299:1,21 98 31:22 41:16 61:7

71:20 72:21 90:14 101:21 98th 273:21 275:7

296:5