U.S. ENVIRONMENTAL PROTECTION AGENCY

 10TH CONFERENCE OF AIR QUALITY MODELS

 DAY TWO

 U.S. EPA

 109 T.W. ALEXANDER DRIVE

 RESEARCH TRIANGLE PARK, NC

 MARCH 14, 2012

 8:30 A.M.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 2

1 GUEST SPEAKERS:

2 Bret Anderson, US Forest Service

3 Kirk Baker, US EPA - OAQPS

4 George Bridgers, US EPA - OAQPS

5 Roger Brode, US EPA - OAQPS

6 Dan Dix, All4, Inc.

7 Tyler Fox, US EPA - OAQPS

8 Ryan Gesser, Georgia-Pacific, LLC

9 Steven Hanna, Hanna Consultants

10 Ralph Morris, ENVIRON

11 Robert (Bob) Paine, AECOM

12 Mark Podrez, RTP Environmental Associates

13 Erik Snyder, US EPA - Region 6

14 James Thurman, US EPA - OAQPS

15

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24

25
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 3

1 U.S. ENVIRONMENTAL PROTECTION AGENCY

2 10TH CONFERENCE OF AIR QUALITY MODELS

3 MARCH 14, 2011

4 MR. BRIDGERS: Well, good morning

5 everybody. I think we'll get started here in about 30

6 seconds, so if we could take our seats, it would be

7 appreciated.

8 While everybody is taking their seats, I

9 wanted to just hit a few logistics that we went over

10 yesterday. I know there may be a few new faces in the

11 room. For those that are new this morning, welcome.

12 For everybody who was with us yesterday, I hope you had

13 a pleasant evening and a restful overnight period.

14 I wanted to remind everybody that this

15 is a public hearing. All the presentation material

16 that's on the screen and everything that's spoken is

17 being recorded. Be mindful of that.

18 And to that end, during the Q and A

19 sessions, if you would like to ask a question, we ask

20 that you use the microphones and identify yourself

21 before you ask the question.

22 Also, I guess it goes without saying as

23 you look at the agenda that we've got 20ish, 25

24 presentations; however you want to count it across the

25 course of the day. We've got a full agenda. So, we're
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 4

1 just asking for everybody, especially the presenters,

2 to try to be mindful of the schedule and respectful of

3 the other presentations, but we did pretty good

4 yesterday staying on time. We actually got out a few

5 minutes early.

6 So, I am not going to say anything else

7 because Tyler Fox is my boss and I'm not speaking for

8 him.

9 MR. FOX: Well, welcome back. It looks

10 like everybody decided to come back and then some. As

11 George said, we have a packed agenda for today, but I

12 thought it would be useful for everybody if we went

13 through the process and the scope of what we're talking

14 about in terms of updating Appendix W to provide

15 context for today in particular because today we'll be

16 going through the current draft of the PM2.5 guidance

17 which we still have to release and George will go

18 through that. Our apologies ahead of time for not

19 getting that out, but we needed to finish up some

20 things and get some internal review before we put that

21 out and that will be forthcoming, I believe, either by

22 the end of the month or early in April to facilitate

23 comments as part of this docket and this meeting on

24 that.

25 We'll also be talking about the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 5

1 challenges with respect to the 1-hour and NO2 and SO2,

2 NAAQS, and all of those issues are things that may be

3 ripe for consideration in terms of updating Appendix W

4 and, at the end of the day, we'll be talking about

5 emerging models and techniques.

6 At the 9th Conference, we introduced the

7 source apportionment techniques and other uses of

8 photochemical models to account for single source

9 impacts. Now, we have a commitment based on the grant

10 of the Sierra Club petition by Gina McCarthy in our

11 agreement. It's not as if she's forcing it upon us.

12 That we address chemistry in Appendix W and update

13 Appendix W accordingly to account for those ozone and

14 secondary PM2.5 impacts and that also overlays with the

15 ongoing evaluations that we've been undertaking with

16 the Federal Land Managers on long-range transport

17 models.

18 So, I should have started with a review

19 yesterday in terms of our current regulatory models and

20 status and updates. There are aspects, as you all

21 heard, that would be ripe. So, I want to make sure

22 that we all understand the scope. There may be issues

23 outside of this that people want to comment on, but in

24 terms of updating Appendix W, everything is fair game

25 for the most part and we really need your input in
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 6

1 terms of that process.

2 So, in terms of the current regulatory

3 models, as I said, there may be improvements or updates

4 augmentation to the model formulations. There were a

5 number of issues yesterday, but there may be others to

6 consider that you all have. We'll hear maybe perhaps

7 tomorrow in the public session. There's also the

8 suitability of the current performance evaluations for

9 regulatory purposes that we would welcome input and

10 comment on and perhaps modifying.

11 As we discussed yesterday, the need for

12 field studies or to take existing field studies and add

13 them to the suite of studies that are used to evaluate

14 our models as we go through the regulatory process.

15 And, as we ended yesterday, we talked about the new

16 beta release of the Mesoscale Model Interface Program

17 to facilitate the use of prognostic data for CALPUFF,

18 AERMOD, and SCICHEM. As Brett indicated, we would need

19 to, you know, provide guidance in terms of the use of

20 that model if put into the regulatory arena, but it may

21 be an issue, and likely an issue that we would need to

22 address in terms of updating Appendix W and codifying

23 rulemaking.

24 In terms of the 1-hour NO2 and SO2

25 NAAQS, we've been dealing with that for a year and a
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1 half or so. They'll be presentations about aspects

2 that are relevant to consideration for updating

3 Appendix W.

4 I believe Bob Paine's going to talk

5 about an approach to account for emissions variability

6 given the form of the standard. The averaging time has

7 provided challenges to us. We've issued guidance on

8 March 1st on the treatment of intermittent sources.

9 More clarification or specificity there may be

10 something to consider as we update Appendix W.

11 And then the current three-tiered

12 screening approach which served us very well as we

13 moved into the 1-hour NO2 standard. The first

14 approach, a conservative approach, of full conversion

15 is what it is, but perhaps updates to the ambient ratio

16 method which we'll hear about later today. Perhaps

17 pursuing refined model status for either OLM or PVMRM

18 as techniques within AERMOD and, you know, that gets to

19 both improvements or augmentation of those model

20 formulations as well as doing sufficient evaluation of

21 those techniques to provide the confidence necessary in

22 order to establish it as refined techniques.

23 The benefit of that would be that it

24 wouldn't be a case-by-case justification. There would

25 be things that -- that you could just take and use. Of
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 8

1 course, those techniques, as you all know, in terms of

2 dealing with the standard have a number of additional

3 input requirements that would need to be addressed

4 sufficiently and we have current defaults and always

5 defer to case-by-case local or source specific or area

6 specific type of information in terms of in-stack

7 ratios and the background ozone, but those are things

8 that may also need to be considered as we move forward.

9 And I believe that we'll hear about an evaluation of

10 PVMRM this afternoon as well.

11 In terms of emerging models and

12 techniques, we've been working with FLMs putting forth

13 both the statement of program needs which I'll cover

14 this afternoon, as well as a new evaluation paradigm

15 for the long-range transport models and the chemistry

16 models which we feel is appropriate and more in line

17 with how they're used in the regulatory arena. The fit

18 for purpose type of paradigm in terms of we need to

19 demonstrate the models can do the things that they're

20 required to do under the regulations and make sure that

21 we and you have confidence in their ability to do that.

22 We also have been undertaking a number

23 of model inter-comparisons and evaluations to inform

24 that process of what models are viable and what

25 techniques are viable for assessing single source
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 9

1 impacts on ozone and secondary PM2.5. I'd also say for

2 visibility and deposition purposes, in terms of AQRVs,

3 so we look forward to working with the various model

4 developers in the future as part of this phase three

5 effort as we both provide you the information, as will

6 be discussed in detail this afternoon on our

7 evaluations, and what models and techniques we have

8 been looking at and putting forth to the community for

9 consideration.

10 As we go through the process as was done

11 in the original IWAQM phase one and phase two efforts,

12 starting with that broader landscape and then narrowing

13 it down to those models that are viable and, if

14 necessary, determine what areas or aspects of those

15 models will need further develop, research and

16 development improvement, such that they can meet the

17 needs that we have both at the EPA and for the Federal

18 Land Managers.

19 So, in terms of process, as I introduced

20 yesterday at the first modeling conference that I was

21 part of, the 8th Modeling Conference, that was a month

22 before we actually promulgated AERMOD and that was the

23 last time we updated Appendix W. The focus was to

24 incorporate AERMOD into Appendix W. There are a number

25 of other changes, perhaps housekeeping type of items,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 10

1 that were taken care of, but that is the last time

2 Appendix W was formally updated through notice and

3 comment rulemaking.

4 So, you're all on notice that the next

5 update officially starts now with this 10th Modeling

6 Conference. As I've mentioned, we've committed to

7 doing that in a grant of the petition to Sierra Club

8 and in that, we explicitly stated that we would expect

9 that at the next conference in three years, the 11th

10 Modeling Conference, that we'd be discussing proposed

11 rulemaking and changes that either the EPA is

12 considering in an upcoming proposal or have already

13 proposed and getting feedback in that context.

14 So, I imagine that most people in the

15 room and elsewhere have the question, okay, so what

16 happens between now and then? That's a good question.

17 Hopefully, I have a good answer.

18 So, between now and then, there is no

19 engineering ABC formula for this. Obviously, at the

20 tail end of a promulgation process to get a proposal

21 out, we're going to have to go through our bureaucratic

22 and administrative procedures of review both within our

23 agency -- we'll determine whether or not this

24 rulemaking is significant or not to go through review

25 by OMB. Previously, they have not gone through that
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 11

1 review process, so there's some internal things that

2 we're going to have to work out that will take up time

3 at the tail end of the process in getting a proposal

4 out of the door. Those are things that you don't have

5 to worry about that we have to worry about quite a bit

6 and also they take up time in terms of getting

7 something out of the door.

8 But, for the most part, what we're

9 looking forward to and what we've been stressing

10 throughout and will continue to stress throughout today

11 and tomorrow is that we need to take into account your

12 public comments. So, we need your information both,

13 you know, here and now, as well as in the coming month

14 or so. I believe that we are going to formally extend

15 the comment period to the end of April -- the 30th of

16 April, to allow for comments from you all. That'll

17 allow, not only time to digest the information that

18 we've provided here and the presentations and the

19 background information up to now, hopefully it will

20 allow you time to get the draft PM2.5 guidance and

21 provide comment on it, as well as the two outstanding

22 reports on the AQRV assessment and evaluation, as well

23 as the plume chemistry evaluation and those reports

24 will be out, I believe, in early April.

25 So, hopefully, that provides sufficient
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 12

1 time to look at those things. That said, that doesn't

2 preclude any consideration after that fact, but we

3 really do need your input so that we can then go

4 through a process of identifying those critical or

5 priority items from our standpoint, summarize those

6 public comments and priorities later in 2012, and get

7 that out back to the community and engage with you

8 through the appropriate venues, either through

9 individual trade associations or other types of venues

10 to vet that to make sure that we've both heard you and

11 are, you know, in line intersection in terms of some of

12 the priorities that we have and that you have in the

13 community.

14 And then we'll continue our efforts to

15 inform the process and provide information. We will

16 issue public reports, EPA reports, and the like, either

17 solely by the agency or in collaboration with the FLMs.

18 We'll be submitting journal articles to promote peer

19 review of the evaluations, the models that we may be

20 tweaking, if you will, or updating. And then we'll

21 participate actively in workshops and conferences that

22 are upcoming.

23 At the tail end here, I mentioned

24 suggesting a specialty conference perhaps late in 2012

25 or early 2013. I believe AWMA or other organizations
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 13

1 have regularly or periodically had such conferences and

2 we've actively participated in those and we would

3 welcome that type of venue to have much more detailed

4 discussion and focused discussion on specific items so

5 that we can make progress.

6 And then, as George mentioned, this year

7 we're having a week-long -- our annual workshop with

8 the regional, state, local modelers, is going to be a

9 week-long workshop that involves both the permitting

10 side as well as the SIP side, so we won't be able to

11 facilitate a public session as we did last year, but I

12 would imagine or I would anticipate that in 2013, in

13 the late April, May, early June time frame, we would

14 have our annual workshop and we would, again, extend an

15 extra day or two to have discussion about the items

16 that we're focusing on and working on.

17 Again, we're trying to provide as many

18 avenues as we can to allow for the type of interactions

19 and more full disclosure and transparency in those

20 interactions. And so, the other aspect to help us

21 coordinate that, would be the third bullet there, is

22 George mentioned that we had established a technical

23 coordination workgroup that was really born from the

24 fact that here we would get various meetings with

25 different trade organizations and the like on a
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 14

1 periodic basis and we would meet with certain subsets

2 of the community. In most cases, we're hearing the

3 same thing across the community, but we learn of

4 different efforts that are going on across different

5 parts of the community and one aspect of that was the

6 cross-fertilization across the community of those

7 efforts to make sure that people are aware of what is

8 going on and connecting the dots there and so it makes

9 it easier on us to work with you all, but also to make

10 sure that if there are collaborative efforts that we

11 can take advantage of, that we're aware of those. We

12 can inform that committee of the things that we're

13 doing by meeting on a periodic basis and have those

14 representatives be able to disseminate information and

15 feed that information throughout the community.

16 So, we've established a technical

17 coordination workgroup. They helped and were a

18 tremendous help in getting the agenda solidified, the

19 invited speakers, and the like. So, we greatly

20 appreciate that effort and we really feel that that is

21 a good way to continue coordination as we go through

22 this process. So, we can talk more about that and get

23 your thoughts and ideas. Again, it's one of those

24 things where everybody probably wants to be part of the

25 workgroup. What we stressed in initially forming it is
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 15

1 that we needed people who were going to be active

2 participants and had been, kind of, deemed

3 representatives for a broader group and the like, so

4 they did have a responsibility and an obligation to

5 take that information back to their host organization

6 and so we can make sure that everybody in the community

7 is represented in that fashion, but it's a very

8 effective and efficient way to coordinate, in addition

9 to the other aspects that I mentioned here.

10 So, obviously there's a lot of time and

11 effort that will be taken to try and coordinate and

12 communicate, but we also need to take the time to get

13 the job done in terms of the work that we're doing.

14 Hopefully today, as you see the

15 information that we're providing, you'll gain a better

16 understanding and appreciation of the time and effort

17 that folks here have put forth to put these things in

18 place in addition to dealing with case-by-case

19 situations on permits and the like and dealing with

20 other aspects of the EPA workload.

21 I should mention that this is one aspect

22 of what my group deals with and so we're also dealing

23 with federal rules and other types of things doing the

24 assessments and the work, writing guidance for other

25 purposes, and applying these models in other -- for
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 16

1 other purposes. So, these folks are doing a wonderful

2 job, but they do have quite a bit on their plate. So,

3 what we'd ask is that we try and find an appropriate

4 process that respects both the community and our time

5 and resources so that we can move forward in the best

6 way -- most effective and efficient way possible.

7 So, before I turn it over to George, I

8 guess, what I'd like to do is open it up and make sure

9 that, in terms of the process and the scope,

10 everybody's understanding. If there's any questions, I

11 would welcome them now because I want to make sure as

12 we go into today and go through all the details and

13 more substantive things, that we have an understanding

14 of that process and scope and you all know what we're

15 expecting of you all in that process and if there's any

16 questions or clarification, please let me know now.

17 No questions?

18 AUDIENCE MEMBER: Bob Paine, AECOM.

19 Just a question on the length of time to comment on the

20 PM2.5 guidances becoming smaller and smaller, we were

21 hoping for I would think 60 days, but it looks like it

22 would be less than 30 days, perhaps.

23 MR. FOX: Well, so, in terms of the

24 guidance, I mean, what we really need and want is

25 comments through this formal process, but that doesn't
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 17

1 preclude comments after this formal process. So, I

2 wouldn't think of it as that the door is slamming and

3 then we're going to move on. I would think of it --

4 that, it's to everybody's advantage to get comments in

5 sooner rather than later so that we can take those into

6 account and then, as we modify and update that guidance

7 throughout the year and then finalize it near the end

8 of the year, we can take those considerations and

9 comments into account.

10 So, you know, same thing with those

11 other two reports in terms of the evaluations. They're

12 kind of seeing it as an evolving process and the like.

13 Again, it's to all of our advantage to get comments

14 sooner. If they're put together in this, you know,

15 context, we can deal with them in aggregate and then

16 work through them. If they come in afterwards, that's

17 fine, in terms of the PM2.5 guidance and those reports

18 we'll deal with those, but what we're trying to

19 emphasize is that people use the current process. But

20 by no means do I mean that that shuts the door and then

21 we're not going to consider anybody's thoughts or

22 comments. In fact, you know, we would welcome

23 additional analyses or other type of work that people

24 may do as you hear more on the guidance and as you hear

25 about potential techniques this afternoon, they may
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 18

1 spark some ideas that you could do that could then

2 inform that guidance.

3 And that's very similar to the way in

4 which we have updated the SIP modeling guidance. We

5 put something out there for review and we've gotten,

6 you know, a huge amount of input from the states and

7 others and assessment by the states that were very

8 valuable in coming up with that final guidance.

9 AUDIENCE MEMBER: Is there a schedule

10 for that guidance to come out final?

11 MR. FOX: We, at this point, we'll be

12 talking about that in Chicago in late April with the

13 regional, state, and local modelers. Obviously, we

14 have a new old ozone standard and may have a new PM

15 standard at some point in time. We're trying to align

16 and update to that guidance -- that SIP modeling

17 guidance, with those types of requirements and it's not

18 clear, given the existing level of the ozone standard,

19 whether or not the current guidance is sufficient or

20 not. We're addressing those types of issues and we'll

21 let people know. I wouldn't anticipate any update to

22 that guidance before the end of the year.

23 AUDIENCE MEMBER: That was -- no update

24 to the SO2 guidance before the end of the year?

25 MR. FOX: No -- okay, I thought you were
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 19

1 talking about ozone, PM, regional haze, SIP modeling

2 guidance.

3 AUDIENCE MEMBER: Yeah, I wanted to

4 bring up the issue of the SO2 guidance that came out

5 last fall, I think it was. Is there a schedule for

6 that to become final?

7 MR. FOX: The agency got comments on

8 that overall guidance and right now, we and senior

9 management, and others are determining how to move

10 forward, so I can't give you any answer on that. Any

11 other comments?

12 Welcome, Raj. Raj Rao, everybody.

13 There were a number of people looking for you

14 yesterday, Raj, so I figured that I would.

15 MR. BRIDGERS: As we transition here, I

16 wanted to thank everybody. The creatures of habit in

17 the room that everybody sat almost exactly where you

18 sat yesterday, so I feel very comfortable as I look out

19 and I see the various people in the room.

20 I'm going to go ahead and say please

21 accept my pardons. The presentation you're about to

22 see, there was a lot of midnight oil -- later than

23 midnight oil, later than that midnight oil that was

24 burnt last night. So I had a request in the audience

25 to dim the lights. If I fall asleep while giving the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 20

1 presentation, just nudge me. So, here we go.

2 So, George Bridgers, U.S. EPA, so we got

3 that on the Record. The topic of my talk today, if I'm

4 not doing spell check which makes no difference to the

5 room, is The Lochness Monster, Sasquatch, my Insta6 Blade Brackets, things that everybody's heard about,

7 but nobody has quite seen.

8 So, as was already asked in the question

9 if this will move forward, we had intended to have PM

10 two and half -- the draft PM2.5 permit modeling

11 guidance out last fall in advance of the 10th Modeling

12 Conference that was originally scheduled for October.

13 We intended to have this out in a timely fashion for

14 the review and comment by the state and local agencies

15 and then also by the permit modeling community.

16 One of many aspects as to why we delayed

17 the conferences, obviously, this was not the only one,

18 but it did provide us some more time to work on it.

19 As, like everything else, best laid plans, good

20 intentions, whatever you want to say, we didn't get it

21 out before October and it's not on the website today.

22 Part of the reason for that is, Tyler's

23 already gone through, that we have had a multitude of

24 things that have been ongoing. There's a lot of

25 complexities here with the guidance document that we're
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 21

1 talking about and, to that end, there's been a lot of

2 coordination that has been needed between senior

3 management, the policy division, and OGC. We had

4 actually had hopes -- we were burning some of the oil

5 this last weekend trying to get this document in a

6 final form that we could put out there, but there just

7 wasn't time for the review internally that happened

8 before we could then put it out. As Tyler said, our

9 plans now are to have it out in the very near future,

10 but I did want to make the comment, and this comes back

11 to what Bob asked, is the comments for the PM2.5

12 guidance are in no way tied to this conference. And

13 that we welcome the comments after the docket and we've

14 already mentioned this morning it's going to be

15 extended to April 30th.

16 The thought is that when we release this

17 draft guidance, the states and locals are going to make

18 comments. The modeling community as a whole is going

19 to make comments. There may, in fact, need to be a

20 second version of the draft document that's put out

21 sometime later in the year for more comments. Tyler

22 had mentioned the fact that we may be soliciting other

23 ideas or work from all of you. This is a collaborative

24 approach and it's something that, working together, I

25 think that we can put together a quality document when
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 22

1 it's finalized.

2 The regional, state, and local modelers

3 workshop will be our next opportunity to sit down with

4 the states and have an informed discussion. Keeping to

5 the timeline that Tyler mentioned that we would have it

6 out sometime in April, there probably won't be a

7 tremendous amount of review time by the states and

8 locals by the time they come to the conference, but we

9 will have that as a part of our workshop and we'll also

10 summarize at least what we've heard today, tomorrow,

11 and over the course of the docket with the state,

12 local, and tribal agencies in Chicago. And then, as I

13 said, we're going to put the goal of finalizing this

14 only a year late at the end of 2012.

15 So, for the rest of this presentation, I

16 have to put this disclaimer out there, one that I'm

17 very tired, but two, the slides throughout the

18 remainder of this presentation -- they're glimpses as

19 to what we're doing. They're by no means the final

20 document and that they have not gone through all of the

21 formal internal review processes. So please, we

22 shouldn't take these as sort of the EPA's formal

23 recommendations or particular endorsements. So,

24 caveats as they are.

25 I thought it would be appropriate, most
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 23

1 of the people in the room this is going to be old hat

2 and so, pardon the next five minutes or maybe a little

3 bit less, but I wanted to kind of set the stage for

4 everyone, build a little bit of background so we can

5 understand where we came from to where we've arrived to

6 today.

7 Actually, this kind of dates my history

8 with air quality as well because it was about 1997 when

9 I joined the state agency but, nonetheless, the daily

10 and annual PM2.5 NAAQS was set back in 1997. At that

11 time, there was no monitoring network and there hadn't

12 been any significant modeling done. There really

13 wasn't the techniques or tools in place. So, when this

14 standard was set, just within a few months after it was

15 set -- promulgated, a PM10 surrogate policy was put

16 into effect. And this, for the most part, allowed the

17 status quo to continue with respect to the permit

18 applicants using their PM10 requirements to satisfy

19 that for the PM2.5.

20 Not a lot happened through the early

21 part of the 2000s. We got past the 2000, you know, the

22 bug with the computers and that wasn't an issue, and

23 2011 or 9/11 and what not. We got to 2006 and we

24 decided to revise the standard. We only revised the

25 daily standard, moving it from 65 to 35, but we
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 24

1 retained the annual standard of 15.

2 Things were going along pretty good.

3 2008, though, was the first real nugget that helps us

4 with this guidance document and, subsequently, with the

5 work that we all have to do in showing compliance.

6 It's that we got the PM2.5 NSR implementation rules

7 promulgated and through this, the first vehicle for

8 helping us was put into place and that was the SERs --

9 the significant emission rates. In here, for direct

10 PM, the ten tons was set as the SER there and then

11 there was also a precursor of PM2.5's SERs set for

12 NAAQS and SO2. Both of those were set at 42 tons per

13 year which happened to be equivalent to their SERs for

14 them as major pollutants by themselves. There was also

15 this little quirk put in there about this

16 grandfathering provision that allowed the federal

17 permits to continue relying upon this PM10 surrogate

18 policy. There's a bunch of dates that are involved in

19 that. I didn't want to bring it up in this context,

20 but I encourage anybody, if they want to know that

21 history, to go back and look at this May 16th rule.

22 On or about February 11th, though, of

23 2010, EPA published a rule to repeal this

24 grandfathering provision and also put an early end to

25 the PM10 surrogate policy. Well, with that action on
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 25

1 February 10th of 2011, we came into the world where we

2 knew that we were going to have to deal with PM2.5 from

3 a compliance demonstration standpoint of view and to

4 aid that, there were a couple of actions. I talked to

5 you yesterday with the Model Clearinghouse update that

6 there was a Region 6 clearinghouse action in -- it was

7 in 2010 -- I think it was February 2010, with regards

8 to a couple of things with background monitor and then

9 the calculation or what model value to apply to

10 background compliance demonstration for PM2.5.

11 On the heels of that clearinghouse

12 action and based in some degree to the work that had

13 already been laid down for us by OTAQ, there was a lot

14 of work that already had been done in that arena for

15 PM2.5. There was a memo that was released March 23rd,

16 2010 and it was entitled The Modeling Procedures for

17 Demonstrating Compliance with PM2.5 NAAQS. That's a

18 pretty good name.

19 Somehow, this has affectionately become

20 the Page memo. Now, Steve Page has signed dozens and

21 dozens of memos but from the context of this community,

22 we've just always come back and referred to this as the

23 Page memo. I imagine the next one that fits in this

24 community, we'll call it the Page memo two or whatever

25 we want -- a do-over or whatever. But at any rate,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 26

1 there was a couple of important things here. This memo

2 really was the foundation -- is the foundation for

3 where we are today with respect to being able to show

4 compliance with PM2.5 and it's also the foundation of

5 the draft guidance that you've yet to see.

6 There's a couple of very important

7 things that were addressed here and Roger Brode has

8 showed up and he could probably talk at great length

9 about the aspects that we're now dealing with the

10 probabilistic standard. Previously, it was a

11 deterministic, second high type approach and now we've

12 moved into the world of probabilistic standards and

13 that throws a monkey wrench in the way that the -- the

14 general convention had been for compliance

15 demonstrations.

16 Another thing is, you know, there was

17 all the complexities associated with the ability for

18 existing models to be able to show the secondarily

19 formed PM2.5. These tools and techniques, there were

20 some out there, but they were not promulgated into

21 Appendix W and they are definitely not at this point.

22 And finally, there was the recognition

23 that you need to pay special attention to your

24 background monitoring concentrations because now that

25 takes on a more important role than just characterizing
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 27

1 the primary emissions from your surrounding facilities

2 and the background longer range transport, but you're

3 also catching a bit of the secondary forms locally and

4 remotely. So, you just need to understand the context

5 to which you're doing your compliance demonstration.

6 A few more nuggets that came into place

7 and this was a rather important one, that in October of

8 2010, there was the PM2.5 increment, the significant

9 impact level, and the significant monitoring

10 concentrations were promulgated and so we moved from

11 the world of some interim SILs and SMCs to a world

12 where we had a defined set of tools that we could use

13 in the compliance demonstrations. I put the table in

14 here. It's worth pointing out the annual SIL for PM2.5

15 and class two areas .3 and 1.2 with respect to the 24-

16 hour standard. So, that was for reference.

17 And then that kind of brings us to the

18 modern era. This last year on May 16th, the surrogate

19 policy ended and so, at this point, without question,

20 all the PM2.5 -- or all the PSD compliance

21 demonstrations with respect to PM2.5 had to consider

22 primary and, if applicable, the secondarily formed

23 PM2.5. I said here from precursors but, nonetheless,

24 it's something that needed to be done. It was no

25 longer just an optional thing where you had a policy to
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 28

1 which you could rely upon.

2 And then one more nugget that actually

3 plays into moving forward is that on the 21st of July,

4 Gina McCarthy signed a memo that effectively ended this

5 interpolluting trading ratios that had been set as

6 presumptive ratios in the preamble to the 2008 rule

7 that we talked about on May 16th.

8 I think it's worth pointing out that

9 although we removed the presumptions for applicability

10 across the country, it didn't remove the position of

11 the agency that we felt that these ratios could be set

12 on a specific non-attainment area or a regional basis,

13 but that would require local agencies, state agencies,

14 and the like to work very closely with the regional

15 office, develop a technical justification for those

16 ratios that are set.

17 The memo actually went further and had

18 four different steps as to how one might go through

19 that process and even offered up, I guess, in nine

20 cities around the country that there was at least some

21 data already available to which agencies in those areas

22 may be able to glean information upon. But there is a

23 wealth of regional modeling and other things that are

24 already in place that agencies may be able to rely

25 upon.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 29

1 While all this was going on, I guess

2 Tyler Fox made the request, but there was a request

3 from EPA made to NACAA. It was basically a plea for

4 help with the states to let's work together in a

5 collaborative fashion to tackle some of the issue with

6 regards to modeling PM2.5, especially from a single

7 source perspective.

8 So, early in 2010, a NACAA workgroup was

9 formed. There are others in this room that probably

10 can speak a lot more knowledgeably of this workgroup,

11 but I'll give it my bit of a try here.

12 The objective was to come up with a set

13 of technical recommendations back to the agency with

14 respect to the guidance. It comprised obviously with

15 NACAA you're talking about state modelers, permit

16 engineers, staff that worked with the mission's

17 inventories, and we had also some regional office

18 representation and some representation from here from

19 OAQPS. They worked together. I think initially there

20 was probably 20 or more different items that were put

21 on the table and that coalesced into three main focus

22 areas that are listed here below.

23 Each of the workgroups that were formed

24 had a specific charge or they formed a specific charge

25 and then they worked across the year 2010 to come up
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 30

1 with a set of recommendations.

2 Let's see -- one, two, three, four days

3 after I started with the agency here, a report was

4 delivered to my desk which I appreciated greatly. It

5 was nice reading along with all of the training that I

6 had to go through. That was the culmination -- the

7 compilation of all the recommendations from the three

8 subcommittees. That report is available through NACAA

9 but for the interest of this conference, we have posted

10 it on the SCRAM website specific to the 10th Modeling

11 Conference.

12 I'm not going to read all the names but

13 since this is going into the Record, I thought it was

14 appropriate to have some recognition for all those that

15 worked diligently and provided information back to us.

16 So, I'm going to show each of the workgroups. Briefly,

17 you can read your names and, again, many of the people

18 that helped with these recommendations are sitting in

19 this room. And then I'm going to show kind of the

20 bullet points of what the recommendations were from the

21 workgroup.

22 So, with the Emissions Inventory Sub23 Workgroup, there was a request or a recommendation that

24 there needs to be a new emphasis on the development of

25 reliable PM2.5 emission factors. Until such factors
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 31

1 could be developed, quality assured, and are available,

2 the workgroup recommended to kind of leverage against

3 existing state programmatic work. An example that was

4 given was with CARB.

5 There was also a request or a

6 recommendation that we provide guidance as to what

7 types of emissions sources are required to include with

8 respect to secondary formation in their modeling

9 analysis. The example that was given in their bullet

10 points was for combustion sources.

11 The secondary formation from the project

12 source, this workgroup was made up of a large number of

13 people that I've worked with through the number of

14 years with SIP related modeling and a lot of the

15 recommendations that came out of this workgroup are

16 mimicked in some of the slides that I'll give later

17 that are directly found in our draft guidance. So,

18 I'll give everybody just a second to read all those

19 names.

20 And with respect to the recommendations,

21 at the top level, they had recommended a four-tiered

22 approach to modeling PM2.5 conducting the air quality

23 analysis. They wanted us to reconsider the use of the

24 maximum modeled value for comparison to the NAAQS and

25 that's something that was established in that Model
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 32

1 Clearinghouse memo and reinforced in the March 23rd

2 Page memo.

3 They requested or recommended the

4 development of offset ratios which reflected the

5 geographic and seasonal variations with respect to the

6 single source permitting, that fits in somewhat the

7 context of what was discussed with the McCarthy memo,

8 and then to complete an evaluation of plume models and,

9 if necessary, clarify the guidance for tier three

10 modeling approach. I have a slide of their tiers here

11 in a second.

12 They also said that we should consider

13 adding comprehensive chemistry to AERMOD and if it were

14 to be done, then we would have to look at the tier four

15 and their level and also require an update to Appendix

16 W.

17 I look to Roger Brode, he's typing on

18 his computer. How's that chemistry coming? It's

19 coming. I say that in jest, I apologize for putting

20 Roger on the spot there.

21 And then with respect to the tier four, the

22 photochemical grid models, there were a whole bunch of

23 issues that were brought up and I'll go ahead and say

24 that these issues have been brought forth from the

25 recommendations they sent to us and that we're also
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 33

1 continuing to solicit comment in our draft guidance

2 document when you see it. Such as: what's the best

3 way to apply the models? There's a varying number of

4 sources whether there's DDM or source apportionment or

5 the like. Should we use plume and grid or some type of

6 sub-grid characterization? Absolute or relative

7 modeling? You know, we got into the world of relative

8 modeling with regards to your state implementation

9 plans, but we still did a very straightforward,

10 absolute type modeling with dispersion modeling. Now,

11 we're bridging those communities. What do we do?

12 And then with respect to photochemical model,

13 now that you've got a photochemical model, especially

14 if you're using a sub-grid treatment, do we use it for

15 both the primary and for the secondary form because

16 we'll have that information. Or does it still make

17 sense to use AERMOD for your fence line effects and

18 things that are within the near-field and then somehow

19 use the secondarily formed impacts from the

20 photochemical model?

21 And here's this table. I hope it's a little

22 bit clear. I've tried to make it big so it could be

23 seen from the back of the room, but they followed a

24 typical approach that we had with other NAAQS

25 pollutants where you had a SIL and a cumulative
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 34

1 analysis and then there was four different tiers.

2 Three of the tiers were contained within the cumulative

3 analysis, but in each one of their steps, there was

4 some consideration for the secondary formation from

5 precursor emissions.

6 Then finally, here's the third workgroup or

7 sub-workgroup and this was with respect to the

8 representation of background concentrations. So, just

9 a second to recognize everyone. I see some names and

10 they're in this room. Mike Kiss. John Glass is here.

11 Leigh Bacon.

12 With respect to the recommendations that came

13 out of this workgroup, there was a couple of different

14 paired sum approaches that were recommended with

15 respect to whether it was continuous data or whether it

16 was every one and three day samples. That is a problem

17 that's unique to PM2.5 is that we don't sample every

18 day at all locations. Their recommendation here was to

19 develop an analysis technique to help with whether one

20 or more monitoring sites could be used whether we're

21 talking sort of a creeked field approach. What about -

22 - and that kind of plays into this next one where we

23 talk about the infusion of model predictions with

24 observations. And also there was a request to modify

25 AERMOD to read an hourly background PM2.5
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 35

1 concentrations through a file. And then finally,

2 modify Appendix W to accommodate the recommendations.

3 So, now that we've got the background, how we

4 got to where we are. We've got the Page memo. We've

5 got some other pieces in place. We've got the NACAA

6 recommendations, the charges. Okay EPA, go out and

7 let's get this thing done.

8 I should mention up front, and this was

9 stated in great detail in the Page memo, that there's a

10 screening nature to everything that we've talked about

11 because we don't have an explicit model that is

12 promulgated in Appendix W to do a one for all, for

13 example. So, I can read the bullets word for word,

14 but, you know, given the potential contribution, the

15 secondary formation, it's not explicitly accounted for,

16 the prominent role of background, certain aspects of

17 the standard. There are things that are with this

18 particular criteria pollutant that aren't with others.

19 PSD modeling should be viewed as the

20 screening level analysis and that's analogous with what

21 had been done for NO2 and that's in Section 5, 2, 4 of

22 Appendix W. Do a little more RegText quoting with

23 respect to Section 5, 2, 2, 1 (c). The choice of

24 methods to be used to assess the impacts of individual

25 source depends upon the nature of the source and its
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 36

1 emissions and, therefore, with respect to everything

2 that we've talked about, there's this need, a

3 significant need for consultation with your state and

4 local authorities and with the regional office.

5 I know that the words case-by-case become the

6 most dreaded terms ever, but we're in an arena now that

7 we don't have a wealth of information and so we are

8 going to glean things from every case-by-case

9 situation. What's paramount to this is the modeling

10 protocol. That's the avenue to which the consultation

11 can start and there should be a well-developed modeling

12 protocol that's then been approved by the state and

13 local agency and the regional office before a lot of

14 work gets done because we've seen in several cases that

15 I've actually gone through with PM2.5. I won't say

16 that there were some simpler ways. Simpler makes it

17 sound lesser than it was, but there was probably some

18 more efficient ways that the application could have

19 been done. People kind of get worked up in a frenzy

20 and lose sight of the ultimate goal.

21 I threw the flowchart in here. This is very

22 typical for NSR PSD. I'm not going to spend much time

23 here. The important point of this is that once you

24 fall into the PSD side of the house and you're above

25 the SERs, we're still recommending in our guidance
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 37

1 document that you're going to go through, at a

2 screening level, the significant impact analysis and

3 also a cumulative analysis. The significant impact

4 analysis being one which you're not including

5 background. You're only looking at the emissions and

6 the secondary formation from your project source. And

7 then the cumulative impact analysis is where you're

8 going to look at sources that cause a significant

9 concentration gradient on or about your source and also

10 you're going to take into consideration aspects of your

11 background concentrations.

12 Within that framework, we're proposing four

13 different scenarios and these four different cases, if

14 you will, will help further define how you would step

15 through the SIL or the cumulative analysis. And it may

16 be that in one of the cases or a couple of the cases,

17 you may not step through the SIL or the cumulative

18 analysis.

19 So, really quickly, the first case is -- I

20 won't say it's a no-brainer. We put it in here just

21 for completeness. And this is one where your facility

22 doesn't trigger the SERs. You've got a maybe major

23 facility for CO or what have you, but your net

24 emissions increase or proposed emissions increase from

25 PM2.5 is less than ten tons and with respect to your
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 38

1 precursor pollutants are less than 40 tons. You're

2 done. You don't have to do anything with PM2.5.

3 Celebrate. We can all go have a drink.

4 Case two, and this one is going to be fairly

5 typical, is where you have a facility that is major.

6 It's a major facility -- a PSD facility. And you've

7 triggered for your primary or direct PM2.5 emissions.

8 They're above ten tons per year. And your precursor

9 emissions from NOx and SO2 are less than 40. And here,

10 this should be the easiest case because we've already

11 got the Page memo that's in place. The groundwork has

12 been done. You're going to do a very typical

13 compliance demonstration using AERMOD. And with

14 respect to here, we're saying that you do not have to

15 make any assessment of secondary formation.

16 The third case, and this one is the one, this

17 is the big one. This is going to be the meat of the

18 draft guidance when it's out. And this is one where

19 you've triggered for both PM2.5 and one or both of your

20 precursor emissions. And here, you've got to make

21 compliance demonstration that takes into consideration

22 the direct impacts of primary PM2.5 and also the

23 secondary formation. That being said, this sum

24 assessment that has to be done does not necessarily

25 mean that you have to go all the way to a photochemical
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 39

1 model, meaning that everyone's got to run CAMx or CMAQ

2 or the like or some new model that's yet to be formed.

3 There may be a completely qualitative way

4 that you can go about demonstrating your compliance

5 demonstration, at least from the secondary formation.

6 You're still going to have to go through AERMOD for

7 your primary. But in most cases, there's probably some

8 hybrid qualitative quantitative approach that can be

9 done. And this is where we don't have a wealth of

10 cases and the case-by-case is going to help us. And we

11 really only anticipate in a handful of situations that

12 one would go all the way to the step that a single

13 source itself would be running a photochemical model

14 run. It's not out of the realm of possibilities, but

15 we think it's going to be a rare exception, not the

16 rule.

17 And then the fourth case, and this one may

18 end up being the most controversial, maybe not to the

19 community, but within the legal world, it may. And

20 this is one where you are not triggering for your

21 primary PM2.5, but you are triggering for one of your

22 precursor emissions SERs, so NOx or SO2 have gone above

23 40 tons with respect to the net emissions increase.

24 Here, we're saying, well, sort of like case one, maybe

25 no analysis is needed. Maybe you need to make a few
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 40

1 statements, but the pollutants NO2 and SO2, they're

2 already -- their SERs for their major is already 40

3 tons, so you've already triggered compliance with the

4 1-hour NO2 and or SO2 standard. They're very stringent

5 when, you know -- well, they're adequately stringent.

6 Maybe I should say it that way.

7 Nonetheless, what we are trying to get

8 confirmation from within the agency is saying that

9 compliance -- that facilities compliance with the 1-

10 hour NO2 or SO2 standards is adequately protective of

11 that of the secondary formation for PM2.5. We hope we

12 can get this through. This would be something that

13 would be good for the community. All that being said,

14 let's talk about how the devils are in the details.

15 Let's actually go through a couple of the bullets about

16 how we actually go through the modeling process.

17 I've already said case one we're not doing

18 anything. Case four, we hope we're not doing anything.

19 Maybe there's some discussion there, but at the end of

20 the day we're hoping there is no modeling involved.

21 So, both cases two and three require at least the

22 compliance demonstration for the primary PM2.5. I

23 already said it's your standard fair AERMOD type of

24 exercise. It's the preferred near-field model. You're

25 going to have all your normal considerations. You're
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 41

1 going to have your modeling domain considerations.

2 You're going to have your source impact considerations.

3 You're going to have to think about your meteorological

4 inputs. I could go on and on. We've seen talks

5 yesterday that went into length about the different

6 things that we need to consider.

7 And then when you get to the cumulative

8 impact, that's where we have to start thinking about

9 the inclusion of background and that could be directly

10 from the monitor and you also have to think about how

11 you're going to model explicitly from the other

12 sources. I could spend a week here and still, thanks

13 to James for providing this, but I wanted to put it in

14 the Record. It's just a review, obviously, of the

15 AERMOD system.

16 Some things to think about and since our

17 friends from OTAQ are in the room, we do have some

18 considerations to think about with the receptor grid

19 placement. It's unique. You have to think about it.

20 It's unique to your modeling domain depending upon

21 things such as terrain, sources modeled, various other

22 aspects. They should be placed in areas that are

23 considered an ambient air. There's nothing new there.

24 There are some current provisions, though, that say

25 that PSD source impact analysis with respect to the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 42

1 NAAQS is required only in areas that have an existing

2 PM2.5 monitoring locations as well as locations that

3 are appropriate for comparing predicted PM2.5

4 considerations to the NAAQS based on the PM2.5

5 monitoring sitting requirements and recommendations.

6 That's a mouthful. I'm not going to -- there's a lot

7 of nuances here. There's some caveats at the bottom

8 that this is -- that PSD modeling is not required to

9 include receptors that are located at sites that are

10 not "population oriented" and also it's not required to

11 consider effects, at least from the annual standard

12 perspective, of receptors that are considered micro and

13 middle scale.

14 There's a lot of nuances here and I would say

15 that we can engaged in another venue to talk about some

16 of those nuances, but there's some issues here just to

17 be mindful of specific to PM2.5.

18 Emissions and source characterization, at

19 least from respect of the primary direct PM2.5

20 modeling, you're going to follow Appendix W with

21 respect to Section 8-1. You have to look at Table 8-2

22 with respect to the maximum allowable emissions. With

23 your source characterization, you know, you have to

24 make sure you have all of the source release

25 parameters. I understand that the characterization
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 43

1 actually goes back to the NACAA recommendations. There

2 are a bunch of issues here because this is an area

3 where we don't have as much information, but it's

4 growing with time. In, obviously, things like building

5 locations, urban rule, what have you. Five years of

6 meteorology. I'll just go past that one. For one year

7 on site data. I didn't actually put in this slide --

8 let me back up in all fairness. The one thing that's

9 not on here is that we talked about the MMIF tool

10 yesterday and so that's something that we need to

11 further think about incorporating possibly down the

12 road.

13 Just a few comments on the -- once you've

14 moved to the cumulative impact in case two still where

15 we're just looking at the primary direct PM only. Just

16 a few things to think about with respect to monitored

17 background. You have to consider a few things, you

18 know, should -- the monitoring concentrations should

19 account for the contributions of the secondary

20 formation associated with your existing facility and

21 also those throughout your domain. Consideration

22 should be given to make sure that you're not double

23 counting in your background monitor for sources that

24 you're explicitly modeling.

25 It's likely not going to be quite as
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 44

1 important for secondary contributions -- secondary

2 formation, just because of the lack of spatial and

3 temporal location of that formation and also the

4 uniformity of the background concentrations from the

5 secondary perspective, but you could have some issues

6 if you have a site that's located fairly close to a

7 larger source.

8 You also may have to take into account

9 seasonal variation and this is one, I think I have the

10 caveat here that you may have a facility with fugitive

11 or low level emissions that, at least from the primary

12 perspective, your worst situations are probably going

13 to be in the winter time when you have a lot of stable

14 conditions or longer stable conditions than you would

15 in the summer. Whereas, the maximum levels of the

16 secondary contributions, especially say in the

17 southeast that are sulfate driven, are going to be in

18 the spring and summer. So, in that type of case, you

19 may have to take into consideration the seasonal

20 variation. And then I put a caveat in here also that

21 the relative composition of PM2.5 also might need to be

22 something that needs to be considered, especially in

23 the western part of the country.

24 Now, let's talk about comparison to the

25 NAAQS. We're still in case two. Somewhat, we can
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 45

1 think about case three on, at least on the direct side.

2 We haven't got to the secondary formation aspect yet.

3 At least with respect to the primary PM2.5, it's going

4 to be pretty a straightforward approach. Remember that

5 I said we have to take into consideration the

6 probabilistic form of the standard. To that end, when

7 you combine the monitored and modeled concentration,

8 just because the standard is a 98th percentile and this

9 is what was addressed in that February 2010 Model

10 Clearinghouse action, you don't take the 98th

11 percentile model and add it to the 98th percentile

12 background because that would result in something that

13 is less conservative than that of the NAAQS.

14 So, for the annual, the actual background or

15 the design value concentration that you're going to use

16 is going to be the design value. It's a three-year

17 average of the annual average PM2.5 concentrations.

18 And then for the daily, it's the three-year average of

19 the 98th percentile of the 24-hour average

20 concentrations. It just so happens that it's the

21 eighth highest if you actually had an everyday sampler

22 that had 365 samples, but please reference Appendix N

23 to 40 CFR Part 50. If you had like every three day

24 monitor or a monitor that had a lot of missing data to

25 understand the rank for this specific design value
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 46

1 calculation.

2 With respect to combining the modeled and

3 monitored, though, with respect to the annual PM2.5

4 NAAQS at the SIL level. We're just at the SIL level.

5 We're not combining it with the background. You're

6 going to take the modeled annual average of the highest

7 concentrations from each year that's modeled and

8 average that. Or, if you're only modeling one year

9 based on site specific, it would be the highest annual

10 average from that year and you can compare it against

11 the 15 data.

12 Did I say that right, Roger? Did I read it

13 right, Roger? This is Roger's.

14 MR. BRODE: Well, it's the annual

15 average -- average across the number of years at each

16 receptor to the highest --

17 MR. BRIDGERS: Right, at every receptor

18 and the highest -- so, you would average the highest at

19 each receptor across the five years and you would pick

20 the highest of those, right?

21 MR. BRODE: Right.

22 MR. BRIDGERS: And then when we look

23 at the cumulative analysis, this is where we bring in

24 the background concentrations. So, once again, you're

25 going to use the same step where you're going to look
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 47

1 at the highest annual average at each receptor averaged

2 over five years and pick the maximum of that and then

3 add it to the design value. So, I'll make the

4 corrections there. Again, midnight oil, with respect

5 to NAAQS and SIL.

6 And then when we talk about the daily,

7 here we're going to do the same type thing only we're

8 going to look at it from the model 24-hour average.

9 So, again you're going to have your receptor grid.

10 You're going to find the highest daily PM2.5

11 concentration at a receptor averaged over five years.

12 Pick the maximum out of the entire receptor grid. Then

13 that's what you're going to then compare against the

14 SIL and then, not the NAAQS, sorry. And then with the

15 cumulative, then we add in the background

16 concentrations. The background concentrations here,

17 though, we have a first and second tier. This is also

18 outlined in the Page memo. So, there's already

19 information out there that you could run and look at

20 later this afternoon.

21 So, let me talk about the first tier

22 here. For applications where the impact of the primary

23 emissions aren't really temporally correlated, fairly

24 steady state throughout the year, the first tier

25 modeling analysis would be exactly as advertised. It's
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 48

1 that highest at a receptor for the year, averaged over

2 five years, the highest receptor point then compared to

3 the SIL. But when you have cases that are a little

4 more challenging, where you have seasonal variability,

5 then we have to look at something that's quite

6 different where you might take into consideration a

7 seasonal or quarterly basis. And that would be

8 considered a second tier.

9 It's probably going to be more of an

10 issue with the daily standard than it's going to be

11 with the annual standard, but that doesn't exclude that

12 there may be one or two cases where there is some

13 impact on the annual standard.

14 And then I put down some bullets here

15 with respect to that second tier where you would go

16 through and determine four seasonal background values

17 that then would be combined with the modeled

18 concentrations. Then that would then be compared

19 respectively to the SIL. So, I've got a few edits here

20 to make in the slide.

21 Important thing is the AERMOD now has

22 the capabilities to track all this. So, Roger has done

23 all the dirty work. And there may be some future

24 updates and some post-processing that would need to be

25 done to help a little more inform the PM2.5 analysis.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 49

1 Is that a fair assessment? Okay. Let's

2 switch gears. We talked a lot about case two where we

3 were talking primarily about the direct PM2.5 analysis

4 which was already somewhat straightforward, but now

5 let's talk about the assessment of the secondarily

6 formed PM2.5 and this puts us in case three where we

7 were both triggered the SER for the primary PM2.5 and

8 also for the precursors.

9 Here, some level assessment has to be

10 done. And we've already talked about that. That it

11 could be a completely qualitative, it could be some

12 hybrid quantitative qualitative, or it could be full

13 blown modeling. I can't stress enough the consultation

14 is paramount here. And one of the things I didn't

15 mention about the modeling protocol earlier in the

16 presentation is one of the most paramount things in

17 that modeling protocol is the conceptual description.

18 You really need to understand the nature of PM2.5 and

19 the nature of the environment around your source to be

20 able to effectively develop your strategy for modeling.

21 So, what's a qualitative only approach?

22 There are situations and this is one example. This

23 isn't the only example. One example where your

24 precursor emissions are only marginally higher than the

25 SER, so maybe you've got, I don't know, 100 tons, just
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 50

1 an example. Maybe that's a bad - maybe 85. Let me

2 just throw a number -- 83. That's when the Wolfpack

3 won the last National Championship.

4 AUDIENCE MEMBER: 40. Precursor in

5 terms of 40.

6 MR. BRIDGERS: They are, but I mean,

7 they could be 41. They could be 162, but nonetheless,

8 it's not 10,000. Your background levels are also very

9 low. So that when you look at your -- back in case

10 two, when you look at your direct PM2.5 concentrations,

11 their modeled impact compared in the cumulative sense

12 with background concentrations, you're still halfway to

13 the standard. And so now, you know, the statement is,

14 okay, in reality is 83 tons of NOx going to create in

15 my environment 20 micrograms of PM? And I challenge

16 anybody to go find in any documentation where that's

17 happened. I imagine it has but, nonetheless, that's

18 what we're talking about. You kind of look at things

19 objectively. You see if it even fits a real case and

20 then you make a technical justification for not going

21 further into a full blown photochemical modeling

22 exercise.

23 We have recently dealt with this in

24 Region 10. Now granted, the OCS off in the Arctic

25 Ocean and Beaufort and the Chukchi Sea is unique to the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 51

1 whole country. But, nonetheless, they put together

2 what effectively was a weight of evidence type of

3 discussion that their background was, I forget, it was

4 like 15 and with their model it was, you know, whatever

5 it ended up being, like in the low 20s, and so they

6 were so far removed from the NAAQS. It was ammonia

7 limited, so there wasn't the right chemistry to make a

8 lot of ammonium nitrate and it just wasn't reasonable.

9 And that was the end of the story.

10 That's not going to be the case for most

11 of the country and this is where we get back into this

12 hybrid qualitative quantitative approach and this is

13 probably going to be most situations where we've got

14 higher background concentrations. I'm thinking the

15 industrial midwest. I'm thinking the southeast. Your

16 primary impacts are fairly high. You're pretty close

17 to NAAQS to start off already and so this is where the

18 consultation process really kicks in. It's trying to

19 understand how far are you removed from the standard

20 and what type of analysis would be most appropriate

21 moving forward and what tools do you already have in

22 the war chest? Has the state just recently done a SIP

23 demonstration or was there regional modeling that you

24 could leverage upon to show? Maybe there was some

25 sensitivity modeling that was done that could be
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 52

1 brought into the fray.

2 One of the recommendations that came out

3 of the NACAA workgroup and that we -- although the

4 McCarthy memo said that we removed the presumptive

5 offset ratios, that didn't in any way exclude states

6 from going through and making that type of

7 justification and it may very well be that that's a

8 good policy for states to go through, at least on non9 attainment basis areas or on regional areas that have

10 similar types of emission density and traffic patterns

11 and the like to create offset ratios that then could be

12 applied by the various applicants.

13 So, the modeling demonstration would be

14 your direct PM2.5 with AERMOD. You apply the offset

15 ratios. If everything is below the SIL or, if you have

16 a violation and that combination shows that you don't

17 cause or contribute to that violation, you're done.

18 And then there's going to be a talk

19 later this afternoon that Ralph Morris is going to

20 give. There's something that was -- that was actually

21 a Greg Yarwood presentation that was given at CMAS last

22 year. Now, the presentation specific to ozone, but we

23 really feel that something like that screening tool

24 approach could be done for PM2.5. It does require some

25 agency, some body, to do some level of PM2.5
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 53

1 photochemical modeling, but once the tool is in place,

2 then it can be applied across the region.

3 And then we get to the chemical

4 transport modeling. The middle bullet is probably the

5 most important one. We hope that this is the rare

6 case, especially in light of the compliance

7 requirements with the 1-hour and NO2 and SO2 standards.

8 The NACAA recommendations have this basically as their

9 tier three and tier four cumulative impact assessments.

10 We're, I guess, tier three with them you have

11 Lagrangian models and tier four had the Eulerian

12 models. We think that there's a lot of promise.

13 SCICHEM, for example, may be another solution and

14 that's something that will be discussed this afternoon

15 in the emerging models and techniques session. And

16 also there will be some discussions on the Eulerian

17 models. CAMx, CMAQ, they've been widely used for years

18 for SIP attainment purposes, but now we're starting to

19 look at them from single source specific. Now, they're

20 resource intensive. We fully understand that. But I

21 did want to provide and I think Kirk Baker -- I don't

22 know if Kirk's in the room, but he provided some of the

23 information here as we were putting together this

24 document. He's been helpful here.

25 So, just a few things with respect to
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 54

1 single source. There's various different applications

2 of brute forth method which is the old tried and true

3 zero out. You've got source apportionment techniques

4 that have taken on a more prominent role in recent

5 years, especially with some of the rulemakings that

6 have come past.

7 You've got DDM and there's, somewhere on

8 the horizon, higher water DDM. And then, we talked

9 about a little bit earlier about the sub-grid treatment

10 and if we go down the path of sub-grid treatment, do we

11 get into the realm where we're also thinking about the

12 photochemical model applying to both the primary and

13 the secondarily formed PM2.5.

14 As NACAA did in the sub-workgroup two

15 readout, we also continue to be soliciting from the

16 community various issues. With respect to

17 photochemical models, do we model five years? Is there

18 some episodic type of meteorology that we would use in

19 this criteria? What would be appropriate with respect

20 to secondary formation?

21 Emissions input. Now, earlier I said

22 follow Table 8-2 -- maximum allowables. It makes a lot

23 of sense when you're talking about direct, primary,

24 AERMOD run concentrations, but now we're trying to get

25 a realistic look at the world with the photochemical
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 55

1 model. Does it make sense to put all of your

2 facilities in there at maximum allowable?

3 Horizontal grid resolution. You know,

4 everything until recent for most of the states was

5 running 12 kilometers. A few states were doing four

6 kilometers. Is that appropriate? Is that sufficient?

7 And then that brings up the idea about the sub-grid

8 treatment.

9 I've mentioned earlier absolute versus

10 relative. How do we tackle that? We've got the two

11 worlds of the SIP attainment planning world and the NSR

12 PSD world colliding. Earlier, there was a mention

13 about SO2. That's an area where the two worlds are

14 just exploding together. Additional work is needed to

15 fully understand all of the implications with these

16 various different approaches.

17 We also -- it was mentioned that there's

18 an ozone PM2.5 and regional haze modeling guidance. In

19 addition to this guidance that's being developed here

20 and they are being developed independent, well, to a

21 certain degree, Brian does work in our group and we

22 talk but, nonetheless, they're independent documents

23 right now. We've got issues with defining just in the

24 model the size and chemical speciation of PM2.5.

25 And then the whole world of performance
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 56

1 evaluation. With the SIP world, we spend quite a bit

2 of time on MET performance evaluation and looking at

3 the emissions and recirculating through the

4 photochemical modeling and performance evaluation there

5 and, you know, what's going to be expected and

6 representative for this community? And we're still --

7 probably more questions than answers.

8 As I mentioned, we have this existing

9 guidance document for photochemicals. It may and

10 probably is not totally appropriate in this context.

11 We want to make sure that we don't develop something in

12 this guidance document that goes in difference to that

13 document and we also have aspects of this document that

14 we don't want to do -- that's going to be difference to

15 the qualitative hot spot analysis that OTAQ has formed

16 that we've used many pieces of in this document.

17 There's also these other applications --

18 NEPA, DOJ, and what not that are ongoing. We want to

19 make sure that everything is consistent there as well.

20 So, I put a couple of links up here. That's obviously

21 not -- I didn't actually have the link for the

22 qualitative hot spot analysis, but that is linked

23 through the 10th Modeling Conference website because,

24 like I said, at least with respect to calculating your

25 impact with respect to the NAAQS -- the design value,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 57

1 that's more thoroughly explained, I think, in Section 9

2 of that guidance.

3 And so that ends my talk and I think I'm

4 pretty close to the time that we had set. I should

5 also mention that we, on the schedule, had set aside

6 some time for a panel discussion. Presentations by a

7 state and local representative, a regional office

8 representative, and an industrial representative. All

9 three of the people had agreed to do so with the

10 understanding that we were going to release the PM2.5

11 guidance document so they could make a presentation on

12 it. To that end, two of the speakers respectfully

13 said, well, no guidance document, no presentation.

14 But, they have also graciously offered, at least in the

15 case of Jim Boyle, that he can answer specific

16 questions to the NACAA workgroup recommendations. He

17 was spearheading that. And Randy Robinson was going to

18 be our regional and local -- our regional office

19 representative and he also can answer some questions

20 specific to things that they've dealt with in Region 5.

21 But before we do so, and respect to Ryan

22 Gesser, he had prepared a presentation and it was sort

23 of indifferent whether I got his document or not. So,

24 my appreciation to you Ryan for agreeing to talk.

25 We'll definitely have time for questions and answers
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 58

1 after Ryan. So, it's all you.

2 MR. GESSER: As you're about to see and

3 hear, I can barely get through my opening monologue in

4 ten minutes, so I'm happy to take up a little more time

5 that's been yielded from the other panelists and,

6 hopefully, wrap this up still in about 20 minutes to

7 where we can get back to the schedule and the time for

8 those questions and answers.

9 My name is Ryan Gesser and I'm speaking

10 on behalf of the American Forest and Paper Association

11 which is the AF&PA, the National Trade Association of

12 the Forest Products Industry and the manufacturing

13 operations for those products. And I'm happy to speak

14 on behalf of that group. As a group we're very

15 grateful to be acknowledged and recognized as an

16 important stakeholder in this process in recognition of

17 all that we have at stake and on the line through this

18 process as well as the size of our footprint, not just

19 in terms of the importance of our industry to the

20 American economy, but also in terms of our footprint in

21 being located and operating nationwide and virtually

22 all 50 states and ten EPA regions. This means we're

23 getting a lot of experience very quickly and

24 identifying a number of concerns which is what I'm

25 wanting to share in this forum.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 59

1 As George mentioned, our comments or our

2 participation, I don't remember if we were invited or

3 volunteered, but whichever the case, we did hope to be

4 sharing comments based on what we've seen of the draft

5 guidance and obviously I just saw what everybody just

6 saw for the first time. So, the flavor of my

7 presentation is really based on our experience and what

8 we hope will be addressed and obviously working through

9 a lot of details in that guidance. And so my

10 presentation is very much a user's perspective and so I

11 think it'll be something of a change of pace from a lot

12 of the discussions that we had yesterday which were

13 very technically and detail oriented. This certainly

14 comes from a higher level and reflects real world

15 applications where we have to deal with the constraints

16 that are in the real world which is making the best out

17 of meteorological data, background data, all those

18 things that are available and have issues to work

19 through and constraints to overcome.

20 So, naturally, this talk and the things

21 I'll discuss are a mix of both technical issues, but

22 also policy issues and I know that that can quickly get

23 across the lines of sometimes the narrow scope of what

24 this group and Appendix W is meant to capture. But I

25 think it's important to address those policy issues to
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 60

1 the extent that they reflect challenges in the

2 technical formulation of the models and the way we

3 apply them. So, I am going to address some policy

4 issues and I hope that that's okay and everyone

5 appreciates that perspective.

6 As I shared the content of my

7 presentation today with peers and colleagues and

8 counterparts from the member companies, more than a

9 couple of people said, you're just really going there

10 to complain, aren't you? I said no, no, no. I'm not

11 complaining. I'm articulating our specific concerns.

12 I think that's a very subtle distinction, but one that

13 I hope you appreciate, especially in light of the

14 discussions that we've heard about limitations on

15 budget and needing to set priorities. So, if nothing

16 else, consider this talk my vote for where the

17 priorities go and, you know, where I hope those

18 priorities are set and we can move forward on a

19 relatively quick time frame. So, I'm offering those

20 comments in that context and with that spirit. I hope

21 you accept them that way and we look forward to

22 continuing the dialogue there.

23 To frame up our industry perspective,

24 we're focusing on integrated pulp and paper mills here

25 which are generally a major source in the regulatory
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 61

1 sense of the word and that we're on the PSD list of 28

2 major sources. We are title five major sources in

3 virtually all cases. But as far as an industrial

4 operation goes, we're actually pretty well controlled.

5 We're already subject to a number of regulatory

6 programs that end up having standards to control PM not

7 limited to just the new source performance standards

8 that would apply to a number of our industrial boilers

9 and eventually we'll be subject to Boiler MACT.

10 But also our chemical recovery

11 operations which are sort of the heart of a pulping

12 operation. They're already subject to an existing MACT

13 standard which happens to regulate and set standards

14 for PM as the surrogate for a number of HAPs and beyond

15 that, at the front end and back end of our operations

16 we have wood yards, haul roads, finishing converting

17 operations, all of which can be sources of fugitive

18 dust that are generally subject, at a minimum, to state

19 level requirements for fugitive dust management of some

20 type or another. So, I think we are a relatively well

21 controlled source of PM, albeit being a major source

22 category.

23 And then more generally, beyond PM, you

24 know, we've been a heavily regulated sector. We're

25 still a heavily regulated sector. And we're going to
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 62

1 continue to be a heavily regulated sector. In addition

2 to the NSPS and MACT standards that apply, not just to

3 our combustion sources, but our process sources and,

4 again, we're going to be subject to Boiler MACT. We

5 are a targeted source category under the various

6 regional haze rules, whether it's BART and/or

7 reasonable progress. A number of facilities will be

8 having reductions come about because of that.

9 And also, we're in the process of going

10 through the residual risk and technology review

11 process, so we see a lot of reasons or a lot of things

12 that are going to be coming down the pipeline in the

13 next six years or so that are going to be reducing

14 emissions from our operations even further.

15 So, especially with that in mind of all

16 the changes that are going to be happening in the next

17 six years, you know, we feel like we have a lot of

18 stake, a big stake, in this process.

19 Like many industrial sectors, our

20 industry finds it difficult to demonstrate compliance

21 with the applicable NAAQS following the current EPA

22 guidance. And I'm going to qualify, any time I say

23 demonstrated compliance, I'm going to qualify it with

24 that same statement. If I wanted to be hyperbolic, I

25 would say it's impossible. You just can't demonstrate
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 63

1 compliance under this guidance, but we are on the

2 Record, so I want to be precise and say, let it suffice

3 to say that it's challenging to demonstrate compliance

4 following the guidance. This, obviously, has a number

5 of consequences.

6 New projects can't move forward until

7 these modeling issues are resolved. George alluded to

8 the significant emission rates that trigger this PSD

9 permitting process and the significant emission levels

10 that we test and, you know, the numbers, you know, you

11 should that they're very small, so it's virtually any

12 project ends up in the cumulative analysis process and

13 even -- we find that there are sources that you have to

14 resolve the modeling issues before you can even worry

15 about what the project is.

16 But that's not the only case. There's

17 plenty of situations. A number of states have

18 requirements that require modeling even if you're not

19 worried about PSD. This can come about from minor

20 source permit modifications or operating permit

21 renewals. So, just under the status quo we have a

22 number of facilities in our industry that end up having

23 to do modeling reveals issues that have to be

24 addressed. And the result is ending up with better

25 than BACT or better than MACT levels of control that
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 64

1 might be necessary to resolve a modeling issue and, in

2 some cases, that would require a significant capital

3 investment in new or upgraded controls, but at a

4 minimum, it's probably going to require at least some

5 on paper reductions to permit limits because remember,

6 in this context, we're always modeling maximum

7 allowable emission rates from all the sources and that

8 often corresponds to a reduction in operating

9 flexibility, whether it's just in terms of the

10 operating scenario, alternative fuels, those sorts of

11 considerations. So, there's a lot of this going on.

12 And I had to come up with a way to

13 explain all this with the magnitude of the challenge to

14 our non-modeling staff that have environmental

15 concerns, but don't do modeling or just in operational

16 and management staff that don't, obviously, do

17 modeling. And I found it most convenient to boil it

18 down this way, just what I call the order magnitude

19 axiom where, you know, under the old PM10 24-hour

20 standard of 150 generally was a controlling standard

21 for types of sources like ours that are -- have a lot

22 of PM emissions. And under that standard of 150, it

23 wouldn't be unusual to find a background level of about

24 50, meaning we had 100 micrograms to work with and fit

25 our operations into the model. Now, of course, back in
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 65

1 the PM10 surrogacy days, we were only considering

2 filterable PM in that analysis and that's what the

3 permits most often reflected.

4 So, we know that the PM2.5 24-hour

5 standard is substantially more stringent at the level

6 of 35 and we find typical background concentrations

7 more like 25. So, now we only have ten micrograms of

8 margin to fit our operations into the model. The

9 additional complication, of course, is the PM10

10 surrogacy policy is gone and we are now including

11 condensable PM in our analyses.

12 And so we find ourselves modeling

13 emission rates that are about equal. We rely heavily

14 on emission factor data. Filterable PM2.5 might be, on

15 average, range varies from different sources, maybe

16 about 70 percent of the filterable PM10, but then we

17 add back in the condensable PM which varies wider.

18 Sometimes, it's as little as 25 percent, but could even

19 be 100 percent or more of the filterable PM rate. So,

20 at the end of the day, boiling it all down, when I have

21 to explain why we're having such a hard time to our

22 management, we're modeling an equal or greater emission

23 rate. But what was previously defined in terms as

24 stack layouts, stack heights, et cetera -- what was

25 previously defined to fit within 100 micrograms, now
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 66

1 has to fit within ten. I don't think any of us need a

2 model to actually tell us that this is going to be a

3 challenge and we're left with this question of saying

4 can these emissions fit in this new standard?

5 If we look at the monitoring data, I

6 think we'd be encouraged and we'd say, yeah, I think we

7 could be okay. This -- I have a couple of examples

8 here, no doubt there's other cases around the country

9 that you might be familiar with or have thought of or

10 be aware of, but there's one case we know of with a

11 member company where there's a federal reference to

12 PM2.5 monitor that's located less than two kilometers

13 away from a large integrated tissue mill and wood

14 products operation. And if you look at the state's

15 summary report, what they quantified as the design

16 value, three year average with the 98th percentile,

17 it's 20.8 compared to the standard of 35. That's a

18 good result.

19 There's another case where a state has

20 deployed a special purpose, high concentration PM2.5

21 monitor within 25 kilometers of not just a big tissue

22 mill, but also a refinery, chemical plant, power plant,

23 coal and coke handling operation. You look at that

24 data, you get a design value 98th percentile of 20.7.

25 A -- it's not too different. B -- it's pretty far
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 67

1 under the standard. That case is interesting because

2 you go 120 kilometers upwind to what that state calls a

3 regional scale general background monitor and the

4 result is 18.3. It's not that different. It's a

5 little bit lower. And it's comfortably below the

6 standards.

7 So, we consistently see these monitor

8 design values in a range of about 18 to 26, which is

9 just, roughly, 50 to 75 percent of the NAAQS standard

10 and so we think, great. That's an ample margin

11 relative to the standard. But, it leaves very little

12 room if we take that design value and add it to a

13 conservative model result to show compliance under the

14 standard. So, can the emissions fit? We go back to

15 the modeling and we see the answer is usually no

16 following the current guidance. So, the examples I

17 want to talk about are some PM2.5 modeling analyses

18 following the current guidance, limited to the

19 characteristic sources at an integrated pulp and paper

20 mills which are utility boilers making power and steam

21 and the chemical recovery units which are, again, the

22 sort of unique characteristic source of our industry

23 and a very important part of the process.

24 So, we can look at modeling results for

25 a different -- for the different scenarios of emissions
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 68

1 control. So, we're going to look at some results where

2 a source has already implemented the existing source

3 recovery MACT standards that apply for our industry and

4 assume that they are going to meet or they have their

5 plan to meet the existing Boiler MACT standards that

6 would apply.

7 And then we can also look at a case

8 where -- take the controls a step further and assume

9 that that facility could just put on or achieve the new

10 source MACT levels of control which are about 40 to 85

11 percent lower than the existing and compare the results

12 in those cases. And when we formulate this, you know,

13 we're doing this by applying a typical PM2.5 size

14 distribution, adding back in the condensable PM to make

15 sure all those are accounted for.

16 I would note I've left out the fugitive

17 sources here, which isn't to say they're not important.

18 Obviously, they are and we definitely appreciate the

19 work that we heard about yesterday in getting best

20 practices because that is important, but I'm leaving

21 them out so as not to confound the analyses or distract

22 from, sort of, the fundamental issues here. I'm not

23 including the regional sources. We'll get to them once

24 we've solved our own problems. And secondary impacts

25 aren't included. Under the current guidance they
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 69

1 haven't been yet. We'll obviously come back and think

2 about what that is.

3 So, we'll do this and we'll show the

4 results under the current guidance using the maximum

5 model result added to a 98th percentile background and

6 then go into the other extreme where we hope some

7 guidance might lead us is the more -- the more --

8 approach what we call the paired sums or tier three

9 where we simulate an hourly background concentration in

10 the model. Use that capability in the model to give us

11 the overall 98th percentile or highest eighth high,

12 matching the form of the standard.

13 So, there's a ton of information on this

14 slide. This was a slide designed for a ten-minute

15 presentation. I don't have much more time than that so

16 I'm going to hit some highlights here. What I'll do is

17 focus on what we've just termed Mill A here. We've got

18 three different mills here. I'll just focus on the one

19 because the results are consistent as you look at the

20 different scenarios. What you find in Mill A this is -

21 - this would have emissions of about 100 pounds per

22 hour total from the chemical recovery process. Maybe

23 another 50 pounds an hour of boiler emissions in there

24 once you've got Boiler MACT installed. And this would

25 be the existing technology case and if we use the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 70

1 existing guidance for the maximum model result plus the

2 98th percentile, the result we would get is 82.6.

3 Obviously, that's over the standard. So, if we go to

4 the other extreme and look at the highest eighth high

5 and a paired sums approach, the result we get would be

6 48.5.

7 So, I'll stop right there and address a

8 couple things with that difference. We see substantial

9 differences in the magnitude of impacts at the highest

10 first high and the highest eighth high levels. Of

11 course, accounting for the background and using these

12 different approaches. We suspect and we can, in our

13 comments, look into greater detail that we'll submit.

14 We suspect that what we're seeing is some sensitivity

15 of these very maximum results to the model performance

16 of low wind speeds and that we heard about yesterday

17 and that those low wind speed conditions are more

18 frequent when you have AERMINUTE that is generating

19 these data at a very low wind speed threshold. So, we

20 suspect that's what's going on.

21 The other thing I would note is that,

22 you know, that's a big difference in the high first

23 high and the highest eighth high however you look at

24 it. Just by the magnitude of that case, it's 34

25 micrograms. Another way to look at it would be to say
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 71

1 the highest eighth high is 59 percent of the highest

2 first high. Maybe I should have inverted that to say

3 that the highest first high is 70 percent higher than

4 the highest eighth high. But whichever way you look at

5 that difference, when I look at that number, what we

6 would wonder is is that what EPA intended or expected

7 when saying this is a screening technique. We're

8 concerned we're not accounting for the secondary

9 transformation so, you know, that's the difference we

10 see and that's how much that guidance means in a real

11 world application. And again, I'm focusing on the one,

12 but I think we find those results pretty consistently

13 in the different scenarios we look at.

14 So, if that guidance or if that

15 recommendation comes about because of concerns about

16 secondary -- accounting for the secondary formation

17 impact, then we're left with questions like is it

18 reasonable because we don't necessarily expect those

19 impacts to be at the same time and the same place?

20 George had alluded to the NACAA study

21 which looked at different ways of characterizing that

22 secondary formation impact and someone will correct me,

23 I'm sure in the Q and A if I'm wrong in how I

24 interpreted it, but I saw it in a number of places.

25 So, I felt like I read it right where it said that in
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 72

1 all cases where they looked at secondary formation

2 using photochemical models or various offset

3 approaches, in all cases secondary formation accounts

4 for less than or equal to one microgram of PM2.5. So,

5 if we're trying to protect ourselves for one microgram,

6 you know, here's cases where we're doing it by adding

7 34 micrograms or a substantial amount higher than that

8 amount. So, that's a concern.

9 The second part of this slide that I

10 would highlight is on the bottom where we move to the

11 second scenario of assuming a better level of control

12 and running that through the model and, if we follow

13 the current guidance, we get an impact of 46.8. If we

14 run it through the paired sums and take the eighth high

15 approach, we would get a value of about 31.

16 So, we're doing better, but here we get

17 to the conclusion that's very alarming which is that no

18 scenario we looked at, even with state of the art

19 control, would show attainment following the current

20 guidance. And that is a very significant philosophical

21 milestone to say that at state-of-the-art levels of

22 control, we're following the guidance and, frankly,

23 we're not that close to complying with the standard.

24 So, in the light of all this, we'd ask,

25 you know, is there room to then back -- add back in the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 73

1 fugitive source impacts? We look forward to applying

2 those best practice techniques and, you know, I think

3 we can. Can we fit in regional source impacts if we're

4 located in a dense industrial area? I don't know. Is

5 there room for secondary formation in these impacts

6 once we add them? You know, maybe, maybe not.

7 Obviously, we're going to exercise this new draft

8 guidance and see if it could work using the qualitative

9 or semi-qualitative approaches.

10 I would note that sort of working off of

11 the NACAA recommendations and the offset approaches,

12 what we saw was that we typically expect to add

13 somewhere between, I think it's about 4 to 15 percent

14 to a modeled direct PM2.5 impact based on the

15 characteristic emission rates of PM2.5 and the

16 precursors from these kind of operations. So, at the

17 level of the standard, 15 percent is more than three

18 micrograms. You know, that's a substantial amount if

19 you only have ten to work with, so it's hard to say.

20 And, of course, behind all this is looming a revised

21 PM2.5 standard. So, if it were to happen to go down as

22 30 which is perhaps the lowest we've heard might be the

23 proposal, you know, we see some cases where we would be

24 encouraged that, yeah, it might work. But once you add

25 everything back in, it's still going to be a challenge.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 74

1 How am I doing on time? Can I take

2 another minute or two?

3 I have this slide, it's under the

4 heading of AERMINUTE, but it probably speaks more

5 generally to the concept of model stability. And this

6 example, I'll try to go through it very quickly.

7 In those states that I mentioned where

8 modeling is done frequently for minor permit

9 applications or operating permit renewals creates a

10 really rich history of model results in a regulatory

11 context and all of the data, of course, that went into

12 those models. So, we looked at an experiment where we

13 took a source, assumed it was static, no changes, no

14 growth, which obviously is not what we hope. We want

15 to find opportunities for growth, but let's keep all

16 the model inputs equal and look at the model results

17 over the past 11 years when we've had various versions

18 of the model and certainly various versions of the

19 inputs that go into it in terms of the MET data and the

20 processors that go into it.

21 So, this is a little bit concerning and

22 again, this is obviously just one case. Maybe other

23 people have looked at this sort of thing, maybe not.

24 But a couple of alarming things jumped out here and I'm

25 not saying the answers or this is completely right or
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 75

1 wrong, but I think it is important. The key findings

2 are summarized here looking at the various model tests

3 over time. Starting back in 2001, if we were using the

4 tools available at that time, you get a model result.

5 We normalize that to 100 and did the various exercises

6 with model versions and meteorological data over the

7 years up and to the current day version of AERMOD 12060

8 with AERMET 11059 and AERMINUTE 11325. And the couple

9 of things that stand out here in the results column is

10 there can be significant run to run variability. And

11 obviously, we don't have to go through why that it is,

12 but it really seems to be due to the meteorological

13 data. When you change the meteorological data sets,

14 you're picking up periods of time or episodes that lead

15 to high concentrations in the model and, you know,

16 these pop out in the results.

17 Focus really on just the recent history,

18 we found that introducing AERMINUTE to the results did

19 cause a pretty significant jump of about 25 percent to

20 this 24-hour average model result. And, just in

21 summary, we're at a point now where we've run this

22 model, inputs being equal except for the model version,

23 meteorology, et cetera, and we find our model result is

24 38 percent higher than it was 11 years ago around the

25 vintage of the 7th Modeling Conference and the tools we
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 76

1 had available at that time.

2 Is this right? Is this wrong? I don't

3 know. There's no monitoring data to go along with

4 this. We wouldn't expect a correlation if there was

5 because of the way we run the models in a regulatory

6 context. But again, you know, I think it's important

7 and it speaks to the necessity of having good notice

8 and comment and opportunities to consider consequence

9 analyses of changing, not just model versions and

10 guidance, but even the inputs to the models themselves.

11 So, with that, it's obviously premature to call

12 anything here today a conclusion. But in terms of

13 observations and comments, let that be my summary that

14 AF&PA is concerned about the current EPA guidance being

15 overly conservative in situations where it just can't

16 practically be implemented.

17 So, we appreciate the efforts that we've

18 heard about to develop best practices for fugitive

19 source modeling. That's very important to us. And

20 also identify and correct systematic deficiencies in

21 model performance for things like the low wind speeds

22 that we heard about yesterday. Obviously, we eagerly

23 anticipate getting the draft guidance and working

24 through it. We appreciate the opportunity to comment

25 on it and certainly plan to take advantage of that.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 77

1 We're going to be focused on how the

2 guidance can be applied to develop sound, unbiased

3 estimates of our impacts, including background and

4 secondary formation, emphasizing the consistency on

5 temporal and spatial scales of the background

6 concentrations and secondary impacts. And obviously,

7 we're in favor of reasonable and practical

8 implementation of new standards in modeling guidance.

9 We think it's very important as we've heard many times

10 to critically apply this guidance in practice and

11 promote stability of the modeling, especially during

12 these times of regulatory implementation.

13 So, where we're looking forward over the

14 next decade of having to implement a number of control

15 programs, the prospect of this extrapolating out in our

16 model results continue to change -- is very

17 challenging. It makes our decision making process very

18 difficult. And we suspect that there will be

19 opportunities or we'll need to look at opportunities to

20 revisit some of the traditional approaches that are

21 reflected in the guidance such as the way ambient air

22 is modeled as well as variable emissions which we'll

23 hear about more.

24 So, I'll stop my remarks there. Again,

25 thanks for the time and turn it back over to George for
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 78

1 Q and A.

2 MR. BRIDGERS: I just checked with Jim

3 and he can be a resource if there's some questions

4 specific to the NACAA comments, but -- and Randy, I

5 imagine you're the same, wherever Randy is. He can be

6 a resource for questions, but probably not wanting to

7 say any other remarks. So, at this time, we'll have a

8 real quick Q and A session, unless there's a lot of

9 comments and we can kind of structure the schedule.

10 So, it's open mic time. And in case there aren't any

11 questions or while people are thinking about questions,

12 I want to apologize for the slides on the NAAQS

13 comparison that had it with the NAAQS versus the SIL.

14 We'll have that corrected and uploaded to the web

15 probably later this afternoon.

16 AUDIENCE MEMBER: Bob Paine, AECOM.

17 Just wanted to understand when I heard about adding

18 model monitors to the background. As I recall for NO2

19 and SO2, it's adding the 99th models to the 99th or

20 98th monitor and is it not -- would it be consistent to

21 do that for PM2.5 as well? And why not go to the tier

22 -- three tier approach?

23 MR. BRIDGERS: In all fairness, Bob, I'm

24 going to absolutely defer this to the expert that is

25 not sitting at the table which is Roger Brode. I could
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 79

1 try to answer it, but I know that Roger would have to

2 clean up my mess.

3 MR. BRODE: Well, I think one point is

4 that what's been provided in the guidance is sort of a

5 starting point. This is something that we feel is

6 appropriate for these reasons. There's always the

7 opportunity to propose alternative approaches with

8 adequate justification that can be considered in

9 specific cases.

10 I think it's a legitimate question and I

11 think part of it is the complication introduced for

12 PM2.5 of the secondary component. So, I think as we

13 gain a better understanding of that, it's certainly

14 possible that our guidance may evolve and examples like

15 this will help inform us as to what the issues are and,

16 you know, how to move forward if we can to improve the

17 guidance.

18 I did have a couple of questions

19 specifically about this and I appreciate you

20 articulating your specific concerns for us and

21 actually, I don't mind complaining. It's the whining,

22 maybe, that's more of a problem.

23 MR. GESSER: I hope I didn't do that.

24 MR. BRODE: I'm wondering if you could

25 explain in more detail, in terms of the paired sums,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 80

1 how you actually implemented that for the daily

2 standard? You know, it's a monitor -- a continuous

3 monitor or?

4 MR. GESSER: Yeah. In those cases, I

5 believe in every case, they were continuous monitors.

6 We're mindful and obviously didn't go into details

7 about the difference in the quality of the monitors and

8 we recognize that that's part of a protocol process

9 that would fully evaluate the appropriateness of the

10 models, the quality of the data filling, et cetera. In

11 those cases, what we found in the real world and, of

12 course, it's not every case, is there's usually some

13 continuous monitors upwind and downwind within about

14 100 or so kilometers and that's the data that we have

15 to work with so, we worked with it.

16 We would look at the information

17 complied by states in terms of their network

18 assessments and monitoring plans. Those usually have

19 indicators of quality, purpose of the monitors,

20 settings, and if we can make a comparison, for example,

21 of the county level emissions of where our source is

22 and is the monitor exposed in a similar way. We would

23 make those types of considerations.

24 Looking at those kind of summaries that

25 the states provide, they often show correlations
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 81

1 between the different areas and we try to take

2 advantage of that as well. So, it was continuous

3 monitors implemented in the model with the hourly

4 background files that we knew about.

5 MR. BRODE: I was actually more asking

6 about just mechanically. So, you did generate an

7 hourly background file input to the model that had a

8 constant value for a given day and then --

9 MR. GESSER: Hour. Continuous hourly

10 values.

11 MR. BRODE: They were continuous hourly

12 values. Okay. I just wanted to clarify that. I guess

13 the other question I was going to have is if you had

14 actually looked at how the results you showed there

15 would compare if you had combined the 98th percentile

16 monitored with 98th percentile monitored results.

17 MR. GESSER: Yeah, so, I obviously

18 didn't show that. I guess that would fall in between,

19 sort of, the two extremes I showed.

20 MR. BRODE: Well, that's kind of in

21 response to Bob's question as well.

22 MR. GESSER: I think maybe the way I

23 would answer it is to say when we've looked at the

24 seasonal variability, for example, you know, of course

25 with seasonal variability you can sometimes end up with
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 82

1 seasonal 98th percentiles that are higher than your

2 overall 98th percentile. And wouldn't you know it, the

3 model result always ends up there. So, it ends up

4 being higher or, in many cases, it does. So, I think

5 it seems like going to the extreme of the paired sums,

6 hourly, whether that's implemented with hourly

7 continuous data or the one and three type data which, I

8 think, we've yet to see some details about how to do

9 that. I see us heading toward that approach.

10 MR. BRODE: Did you ever -- did you look

11 at just adding the 98th percentile monitored to a

12 single 98th percentile modeled?

13 MR. GESSER: Yeah. It would still be

14 levels that comparable to what I showed since that 20 -

15 - that 98th percentile background is still usually

16 about 20 to 25. So, adding that to a model result that

17 may be still in the 20s even is going to be above the

18 standard.

19 MR. BRODE: So, that was somewhere

20 between the two?

21 MR. GESSER: It would be somewhere

22 between.

23 MR. BRODE: Okay. Thank you.

24 AUDIENCE MEMBER: I guess I just wanted

25 to add, there we go. Usually I don't have a problem
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 83

1 with talking loud. Meg Patulski, OTAQ. I just wanted

2 to add since so much of your guidance relies on what we

3 worked with you on for a hot spot guidance that we've

4 gotten similar questions about why did you pick 98th

5 percentile? Why not in the 99th percentile? And

6 things like that. And obviously, without -- I guess

7 I'll just say what's unsaid is we had to be consistent

8 with the NAAQS and so a lot of this was something that

9 we, you know, had to develop these tools to be

10 consistent with how the NAAQS itself was created. So,

11 that's why it's the 98th percentile and there are all

12 these hoops that we're walking through. If the NAAQS

13 was different, we would have different calculations.

14 And I was also interested in what the tier two results

15 would have been, so.

16 MR. BRODE: I appreciate you clarifying

17 for the Record that it's OTAQ's fault.

18 MS. PATULSKI: Well, just, you know, it

19 was a several month effort to create Section 9 of our

20 guidance and also the tool that we've created in our

21 training to calculate the tier two -- NAAQS tier two

22 results. So, it was definitely an undertaking, so.

23 MR. BRODE: Well, on a more serious

24 note, I think it's good to acknowledge that interaction

25 that we had with OTAQ was very helpful for us and
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 84

1 timely for us to prepare for what we're dealing with

2 now.

3 MR. FOX: That experience let us know

4 earlier rather than later how complicated this is and

5 how difficult it is, but the bottom line is that we

6 want to have a realistic and representative

7 characterization as if there were a monitor there in

8 terms of these receptors and the like. Not double

9 count, but appropriately account for the contributions

10 of the project source. That project source is the

11 primary emissions. If necessary, that project source

12 is contributions to secondary formation through its

13 precursor emissions as well as the representative

14 background which may be composed of, you know, PM2.5,

15 but also there's an urban increment, so each area is

16 very unique and different, you know, adding up

17 different parts of the distribution can be quite

18 complicated and so we're trying to work through and is

19 evidenced by Roger's questions and we try to do some

20 different simulations.

21 The more information we can have in

22 terms of looking at real situations and seeing how well

23 the different approaches mimic reality I think would

24 help us in terms of coming up with an approach that we

25 feel and you all feel comfortable with.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 85

1 AUDIENCE MEMBER: Mike Kiss, Virginia

2 DEQ. This question is for George. I saw no reference

3 in the guidance with respect to increment consumption

4 and expansion. And what we're seeing in some of our

5 analyses already is that the class one increment, in

6 particular, has already been consumed by direct PM2.5

7 emissions. And we're expecting to see a lot of

8 increment expansion through the transport rule and

9 secondary or precursor reductions sulfates and

10 nitrates. And I think there's going to be some

11 difficulty in class one modeling in pairing the

12 secondary reductions in time and space with the direct

13 PM emissions. For example, from a combined cycle plan.

14 Is there any thought to adding that to your guidance

15 document?

16 MR. BRIDGERS: Since the question was

17 specifically addressed to me, is Ryan Corrales still in

18 the room?

19 In all seriousness, at this time the

20 guidance does not include anything with respect to

21 modeling for increment. Obviously, moving forward,

22 it's a draft document. It's a living document. Even

23 when it goes final, it's a living document just like

24 the other what we affectionately have called for years

25 Brian's guidance, but the photochemical regional haze
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 86

1 PM2.5 modeling guidance. And consideration for

2 increment could very well be added to it. I don't know

3 that there's any plans at the immediate time. Right

4 now, the immediate plan is to get what we've got out,

5 but it would be taken under advisement.

6 AUDIENCE MEMBER: Okay. Thank you.

7 MR. FOX: I think we're, right now,

8 focusing on the NAAQS compliance demonstration and

9 getting that in a firm foundation there, but you're

10 point is well taken in that we need to address

11 increment. There's some nuances there and you

12 mentioned them in terms of increment expansion and

13 other types of things that get into other types of

14 issues in terms of how best to account for that and I

15 think, as George indicated, we're going to have to work

16 with our policy division and both from a policy, legal,

17 and technical standpoint, be able to do things that

18 allow for an appropriate analysis. In that context,

19 it's not something that we're ignoring. It's kind of

20 first things first and we would welcome comments along

21 those lines in terms of specific experiences with

22 details about problems that you're encountering or

23 issues that you foresee. That would allow us then, you

24 know, as we work throughout this year to address that

25 and maybe bring that in - hopefully, bring that in
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 87

1 later on in the year. Once we've got a firmer

2 foundation of how we're doing it -- how we're

3 addressing issues from a NAAQS compliance standpoint

4 because that will give us a foundation to then view

5 increment.

6 AUDIENCE MEMBER: Yeah. I think we

7 expect to see increment expansion with these -- the

8 transport rule, for example. I think the key is going

9 to be trying to pair those reductions in time and space

10 on the secondary side with the direct consumption from

11 natural gas type facilities. Thank you.

12 MR. BRIDGERS: And Mike, your comments,

13 thank you. Actually, Dan Deroeck just walked into the

14 room, so it's good that there are other people within

15 our policy division that are hearing these requests.

16 AUDIENCE MEMBER: Jim Boylan, Georgia

17 EPD. I wanted to follow up on the NACAA

18 recommendations for PM secondary formation. I know

19 George joked with Roger about putting chemistry into

20 AERMOD, but my question is does EPA have any plans to

21 put chemistry into AERMOD?

22 MR. BRIDGERS: Fair question.

23 MR. FOX: No. No, we're, at this point,

24 and you'll see more this afternoon in terms of the

25 emerging models and techniques, there are existing
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 88

1 models that are out there that address secondary

2 formation and are more suitable and have advanced in

3 terms of the science and the like. At this point, we

4 would like to look at and evaluate the existing

5 capabilities and capacity for those models to meet

6 those needs. And hoping, expecting that we'll be able

7 to find in those existing models or modifications to

8 those existing models that capacity or capability so

9 that we're not thinking of adding PM2.5 chemistry or

10 ozone chemistry to AERMOD. And that seems like the

11 best approach for now.

12 AUDIENCE MEMBER: I guess, is it fair to

13 say that it's not completely out of the realm of

14 possibility, but it's unlikely?

15 MR. FOX: Well I -- it just changed. It

16 would be EPA engaging in developing its own new model

17 to account for chemistry that would go through the

18 promulgation and it would probably be more challenging

19 and difficult to take the current construct in AERMOD

20 and add chemistry than to just embrace those more

21 complete, full, elegant characterizations of that in

22 existing models.

23 Now, as George noted, and I think we all

24 understand and appreciate, those types of models then

25 have resource constraints in terms of their ability to
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 89

1 be used in this context. And so, as indicated in the

2 McCarthy petition grant and George reiterated, the

3 pursuit of updating Appendix W to account for ozone

4 impacts and secondary PM2.5 will not necessarily lead

5 us to a model and a model would be used in every and

6 all cases.

7 We, in some ways, see the use of a full

8 scale model as an exception rather than rule in that

9 the vast majority of cases where we're dealing with

10 precursor emissions and the secondary impacts from the

11 project source could be handled through other

12 techniques if they could be developed with a credible

13 basis. Either using photochemical models, or as was

14 eluded to, both in the NACAA guidance and what we'll

15 put forth shortly, credible techniques to base on an

16 area-by-area basis representation of the impacts

17 through offset ratios, you know, sensitivities through,

18 you know, techniques in photochemical models like DDM,

19 and those are things that we're looking at. And as

20 referenced in George's presentation, this afternoon

21 there'll be a presentation on how a similar situation

22 was dealt with in Sydney, Australia for ozone and if

23 the techniques can get there for secondary PM, maybe

24 that's a viable option to be able to pursue to be able

25 to come up with those types of characterization in a
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 90

1 screening tool standpoint, short of full scale

2 modeling. But I think all those things are on the

3 table and we welcome everybody's thoughts and comments

4 on existing capabilities that you're aware of or

5 experiences in dealing with those models. And from an

6 operations standpoint, thinking in the context of their

7 suitability for operation and use in a permit

8 environment as well as any scientific or technical

9 aspects of those models.

10 AUDIENCE MEMBER: George Schewe of

11 Trinity. I've got a question for Ryan. Ryan, the last

12 table there AERMET and AERMOD and all that good stuff.

13 Did you see any differences in any of your results

14 between the different mills or anything? Flat versus

15 complex terrain?

16 MR. GESSER: That's a good point. I

17 probably, if I had more time, would have done a better

18 job of setting the stage of those examples. These

19 really were, I think, exclusively flat terrain

20 situations. It was a pretty simple analysis insofar as

21 just point sources that are unambiguous. Everybody

22 knows how to model them in a flat terrain without any

23 complex situation.

24 MR. FOX: And I would just add that

25 those types of examples are very useful to look at the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 91

1 sensitivity. As you noted, the meteorological data

2 seemed to be driving quite a bit. Actually, if you

3 look at the results, in some ways the models showed

4 some, you know, some stability in terms of use over

5 time which was good. It seemed like some of the inputs

6 and the like were driving those things and so the more

7 that we could, you know, engage and investigate those

8 types of sensitivities would be useful. And,

9 hopefully, as an illustration of that for NO2 and SO2,

10 James Thurman and Erik Snyder will be providing the

11 AIWG results that tried to get at those types of issues

12 and perhaps we should consider something similar for

13 PM2.5 down the road.

14 AUDIENCE MEMBER: John Gill, EnviroMet.

15 As a follow-up to George's question with regard to the

16 same table. As a fellow modeler, you mentioned that

17 you kept the MET data the same. Did that include how

18 you were more concerned about your sectoring,

19 especially with the implementation changes to the

20 sector guidance a few years ago and were you using on21 site data for that as well?

22 MR. GESSER: No. If I gave that

23 impression, I should clarify. The MET data was not the

24 same. It was -- the sources were constant throughout

25 that and this was a situation where the state would
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 92

1 provide data or suggest the data to be used, arguably

2 without thoroughly reviewing the consequences or what

3 might be lurking in that data or just legitimately

4 explain big differences like that you see in the model

5 results. And I mean, they could have been legitimate.

6 There just wasn't any consideration of that.

7 MR. BRIDGERS: Maybe one last question

8 and then we're going to have to break.

9 AUDIENCE MEMBER: Hello. This is Julie

10 Mitchell with URS Corporation and I have a question.

11 San Joaquin Valley APCD has put out a guidance document

12 on how to model PM2.5. It's got a couple tiers, but

13 the tier two approach does talk about secondary

14 formation and including that in your AERMOD analyses.

15 Have you reviewed through that and what is your

16 findings or feelings on the appropriateness of that

17 analysis?

18 MR. BRIDGERS: If I'm not mistaken,

19 Leland had shared that with us at some point in the

20 past. I'm going to say that it's something that's not

21 been a focal point as of now, but it's something that

22 he, at least, extended our way, but in the myriad of

23 everything that we -- I haven't specifically reviewed

24 the document. No.

25 MR. FOX: If I recall correctly, I think
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 93

1 they were using the same type of approach that the

2 NACAA recommendations had with offset ratios and the

3 like and so, again, as was indicated, those types of

4 things would have to be approved on a case-by-case

5 basis in terms of their appropriateness for use in that

6 area representing the type of chemical regime and the

7 like that exists. So, you know, at this point in time,

8 we haven't reviewed it in detail, but I believe it's

9 following something similar to what was recommended in

10 the NACAA. Run AERMOD for the primary and then have

11 some offset ratio account for the secondary

12 contribution from the project source.

13 MS. MITCHELL: Right. And he was

14 advising to run AERMOD for the SO2 and NO2 and then do

15 a post-processing with taking your -- an offset ratio

16 that was appropriate so that you could determine what

17 the impacts of the secondary SO2 and NO2 could be and

18 then combine that with the PM2.5. Thanks.

19 MR. BRIDGERS: Thanks. With that, I

20 think we need to move on to our break. Since we're a

21 little bit over, let's go ahead and take till 10:40 --

22 to 10:40. And as everybody is leaving, I also want to

23 thank again the volunteer effort by Jim Boylan and

24 Randy Robinson and also Ryan Gesser for the panel this

25 morning.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 94

1 (WHEREUPON, a brief recess was taken.)

2 MR. BRIDGERS: Okay, if we could have

3 everybody start taking their seats it would be greatly

4 appreciated. Thank you.

5 I feel like I'm standing in front of the

6 television camera and they're doing this, you know,

7 extend it out and it's that cross talk across the

8 banker desk that is just gibberish. So, hopefully,

9 Tyler will be back in here soon because he had some

10 points he wanted to make here as we kick off the 1-hour

11 NO2 and SO2 and speaking of the devil, here's the

12 Michigan man right here.

13 MR. FOX: And for the Record, this is

14 water, not coffee so when you see me gulping it I'm not

15 drinking coffee. I already had my dose. All right.

16 So, I'm going to go through what we've been doing to

17 try and address some of the challenges that we're all

18 recognizing in modeling compliance under the new 1-hour

19 NO2 and SO2 NAAQS. I'll also try and get us back on

20 schedule and try and provide more time for James and

21 Erik because I think those type of results and the like

22 are more important and provide more real information

23 for everybody to chew on.

24 So, we all are very familiar with the

25 new 1-hour NO2 standard of 100 ppb based on a 98th
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 95

1 percentile annual distribution of daily maximum 1-hour

2 values and that was effective April 12th, 2010. That

3 was followed shortly afterwards with a new 1-hour SO2

4 standard of 75 ppb based on the 99th percentile of that

5 same distribution and that was effective in August.

6 As part of those rules, as of the

7 effective date, PSD requirements came into effect and

8 so we followed up with clarification memos for the NO2

9 standard and the SO2 standard and I'll go through those

10 briefly. But I'll try and focus most of the discussion

11 on the March 1st guidance memo and aspects there that I

12 think or at least we thought were very critical to get

13 out there to the community and allow for flexibility to

14 address issues that we have become aware of. And I

15 have to stress the fact that we understand and feel

16 your pain. We are dealing with these issues as best we

17 can. That March guidance was issued as fast as we

18 could to address and to provide much needed flexibility

19 under these standards and I recognize that some folks

20 would have liked review of those, but that would have

21 just dragged that process out. So, we're facing the

22 trade-off between going through longer review processes

23 versus getting things out that we think are valuable

24 and flexible and allow for more appropriate analyses

25 for compliance.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 96

1 But that said, these are guidance, so

2 review and follow-up and additional guidance can always

3 come afterwards to address certain issues and I think

4 it's very valuable to have real examples as James and

5 Erik and others, Ryan, provided earlier to inform the

6 development of that future guidance and that AIWG

7 effort was intended to evaluate the existing guidance

8 including flexibilities in the March guidance as well

9 as give us some understanding of what new guidance may

10 be necessary. And this just provides the two

11 clarification notes in terms of the applicability of

12 Appendix W under those two new 1-hour standards as well

13 as the additional clarification that we provided in

14 March of last year.

15 So, as I said, the NAAQS for NO2 was

16 revised in 2010. The monitoring guidance, design

17 values, were based on three year averages, but it's

18 important to note that that does not preempt or alter

19 the Appendix W requirement for use of one year of site

20 specific data which is preferred or the use of five

21 years National Weather Service data.

22 For NO2, the guidance was more

23 exhaustive, I guess, or detailed than for SO2. We

24 established that under Appendix W, AERMOD is the

25 preferred model for estimating NO2 impacts in the near-
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 97

1 field and we saw the existing three-tiered screening

2 approach in Appendix W as applicable to this new 1-hour

3 standard with some different considerations. And I'll

4 go through the details in terms of those three tiers.

5 Thankfully, we had that tiered approach and screening

6 techniques available as part of the third tier for use

7 here.

8 So, in terms of the applicability of

9 these three tiers, tier one is obviously a conservative

10 test of full conversion and it can be used without any

11 justification just as it was before. And under the

12 annual standard, we understand and acknowledge that

13 most applicants were able to use either the first tier

14 or the second tier in demonstrating compliance. And

15 that we have not had as much experience with the third

16 tier and these detailed screening methods, but under

17 the 1-hour standard, we certainly have and will and

18 need to.

19 The second tier is applicable in many

20 cases, but needs to have additional consideration given

21 the nature of how that ambient ratio method applies and

22 given the default ratio of .75, at least at the time of

23 this clarification memo, as being representative of

24 area wide quasi equilibrium conditions. So, in there

25 that was designed more specifically to the annual
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 98

1 standards so we thought it was applicable but, again,

2 additional considerations need to be there and we'll

3 hear later this afternoon about an approach to

4 potentially modify that -- that has been brought to us.

5 Tier three represents more formal

6 modeling using the existing techniques of ozone

7 limiting method or the plume volume molar ratio method

8 within AERMOD. And it can be used on a case-by-case

9 basis. These are not refined methods. As I mentioned

10 at the beginning of the day, perhaps one aspect of

11 updating Appendix W is doing what we need to to allow

12 these to be seen and used as refined methods. But with

13 these techniques come greater requirements to inform

14 the model appropriately and that gets at the

15 representativeness of the background ozone data as well

16 as the in-stack ratios. And those are obviously much

17 more important as you analyze and assess the 1-hour

18 NAAQS.

19 So, in the memo we went into detail

20 about the tier three approaches, recognizing that given

21 our lack of direct application and use of these

22 techniques, there wasn't as much understanding across

23 the full community. We did the best we could to try

24 and provide information that we thought was very

25 relevant and will continue to do so.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 99

1 OLM is specifically referenced in

2 Appendix W and PVMRM is also considered in that

3 category until more robust evaluations can be done.

4 Both of them are available as non-regulatory default

5 options in AERMOD which requires a justification and

6 approval from the regional office on a case-by-case

7 basis.

8 One note here is that as part of the

9 March guidance, we did provide a number of evaluations

10 of these tools and I believe Roger may go through some

11 of those in the next discussion. And we did feel as if

12 some of the work that we did could be used as the

13 justification to try and reduce the burden or hurdle of

14 demonstrating their applicability on a case-by-case

15 basis. That's not to mean that you don't have to work

16 with the regional authority or the regional office to

17 get approval, but you can reference the information

18 that we put together and do maybe a little bit more

19 work to provide sufficient justification. But we did

20 do, we think, a good job of providing some information

21 that can be leveraged in that case.

22 We also noted that applications of OLM

23 in AERMOD should routinely use the OLMGROUP ALL for

24 combining plumes. There had been some confusion there

25 and now that the treatment is within AERMOD rather than
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 100

1 outside of AERMOD, some of the past issues have been

2 resolved and we feel more comfortable using that

3 OLMGROUP ALL in those circumstances.

4 We have several documents listed there.

5 As I noted, Roger did some additional evaluations which

6 we believe showed encouraging results, but we do

7 recognize that there is a sparsity of information and

8 data. It's very limited in order to move forward in

9 the next step but, hopefully, we can work towards that

10 in terms of considering PVMRM as a refined method in

11 the future. And in regard to those evaluations, we

12 extended those and updated them for predicting hourly

13 NO2 concentrations.

14 For the SO2 NAAQS, again, the same holds

15 even though we have a three-year averaging time for the

16 NAAQS design values that does not preempt or alter

17 Appendix W's requirement for use of one year on site

18 MET data and the five years of National Weather Service

19 data.

20 In terms of the clarification, we did

21 put forth the fact that we believe that the current

22 guidance in Appendix W that was done in the context of

23 the previous twenty-four, annual, and the three-hour

24 secondary SO2 NAAQS were generally applicable to the 1-

25 hour standard and that AERMOD, just as in the case of
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 101

1 NO2, was the preferred model for estimating these

2 impacts in the near-field.

3 So, then we had heard from a number of

4 folks through both the state and local tribal agencies,

5 the regional offices in terms of specific permits and

6 experiences, and also directly from you all, the

7 stakeholder community. Either directly through memos

8 or other information provided to either me or Chet

9 Wayland or our upper management, there were a number of

10 concerns. I think it was mentioned earlier, the

11 impossibility of demonstrating compliance here and

12 particular issues rose to the top of the list in terms

13 of priorities for us.

14 So, in March we issued after probably

15 three or four months of work on these issues, a number

16 of things that we felt were necessary to assist you all

17 in demonstrating compliance here. I should say that

18 even though the memo itself was referenced specifically

19 for NO2, the options and treatment here that are put

20 forth are relevant for SO2 except, of course, the

21 recommendations related to the tiered approach for NO2.

22 So, we clarified the procedures for

23 analyzing results given the new form of the NAAQS. We

24 modified the recommendations in terms of the tier two

25 ambient ratio and the in-stack ratio defaults for the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 102

1 tier three options. Again, we modified those national

2 defaults that can be used without any further

3 justification. We always prefer and would look for

4 more appropriate source area specific information for

5 use in that situation and so those defaults in no way,

6 shape, or form preempt folks from informing the model

7 in a more appropriate way and we encourage you to do

8 so.

9 Thirdly, we addressed the treatment of

10 intermittent emissions. A prime example would be

11 emergency generators. As we were finding that in a

12 number of situations, more and more the previous way of

13 treating these sources under the annual NO2 NAAQS and

14 even under the pre-existing SO2 NAAQS, they were not

15 the controlling scenarios and now, under the hourly

16 NAAQS, they were becoming the controlling scenarios in

17 terms of determining whether or not one would get a

18 permit or not.

19 Given the nature of the standard and the

20 nature of those sources, we put forth some flexibility

21 there in terms of treating those sources under these

22 two NAAQS. Then we talked about recommendations

23 regarding combining nearby background sources and the

24 modeling there with the monitored contributions in the

25 case of cumulative analyses. So, in terms of the form
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 103

1 of the standard, we suggest that or recommend that for

2 comparison of the SIL that the impact from your source

3 be based on a multi-year average of the highest 1-hour

4 concentrations at each receptor. And that's consistent

5 with the maximum contribution that a source could make

6 at that receptor. And then, in terms of the cumulative

7 impact analysis in determining whether or not you're

8 causing or contributing to a violation, we would ask

9 that you examine whether the project contributes

10 significantly to model violations paired in time and

11 space, including all cases where the cumulative impact

12 exceeds the NAAQS, at or below the 98th percentile for

13 NO2 or the 99th percentile for SO2.

14 And to support that, I believe within a

15 month or so of release of this guidance, after a lot of

16 work by Roger and James, we modified AERMOD to be able

17 to post-process that information and support those

18 analyses. We recognized there was a lot of angst about

19 the post-processing requirements and the needs in order

20 to come up with the correct values for comparison to

21 the SIL and the NAAQS here. And we worked diligently

22 to try and incorporate those. We considered having a

23 separate post-processor and air post program, if you

24 will. In the end, we decided to put these types of

25 post-processing routines in AERMOD to support these
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 104

1 analyses and so we hope that that was helpful to you

2 all because we know that there was a burden imposed

3 initially there.

4 We also addressed treatment of

5 intermittent emissions. This has come up in a number

6 of contexts. Given the form of the standard, we

7 highlight the fact that there is a concern that

8 assuming continuous operations for these types of

9 sources would effectively impose an additional level of

10 stringency beyond what was intended in the level of the

11 standard itself. And as a result, we recommended that

12 the compliance demonstrations be based on emissions

13 scenarios that can be logically assumed to be

14 relatively continuous or which occur frequently enough

15 to contribute significantly to the annual distribution

16 of daily maximum 1-hour concentrations.

17 And I know Roger said that yesterday and

18 we didn't expand on that too much. I think one aspect

19 here is that we need to know the specifics of

20 situations and cases. We're trying to address

21 situations where emergency generators or other types of

22 emissions scenarios that are either -- can't be

23 projected or can't be controlled, like an emergency

24 generator coming online and that occur fairly

25 infrequently in terms of that situation. Those are the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 105

1 types of situations that we intended to address here.

2 If there are planned downtimes or other

3 types of situations where emissions spike, if those are

4 planned, that would be an aspect where they may or may

5 not fall in here. That's something that you would have

6 to work with the regional office to work with. And, as

7 noted there, routine testing and operations may be one

8 of those things that would not be considered an

9 intermittent source.

10 We've been getting questions, as an

11 aside, related to this at both right before the

12 conference and then yesterday as part of the

13 conference. We -- I wrote a letter of concurrence to

14 Region 2 based on a request from Region 2 in the

15 context of a situation involving hydro-fracking in

16 Region 2. And the specific request was from New York

17 DC and Region 2 was asking for our concurrence in their

18 review of that. And what we said was that given the

19 information that was provided to us, that the sources

20 in question could not be treated as an intermittent

21 source and, therefore, could not be eliminated from the

22 compliance demonstration or not accounted for.

23 There are ways in which you can account

24 for those types of sources. If they're moving within

25 an area, I believe that in the work that Region 10 did
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 106

1 in the OSC permits, their situation where rigs are

2 moving and they actually try to account for that

3 spatial dynamic of moving over the year, but that's a

4 planned activity and it's known. So, that did not seem

5 to comply with the spirit of what we were talking about

6 here in terms of intermittent sources. So, work with

7 the regional office and others in terms of how best to

8 treat those types of sources in that situation. But

9 that letter was just a concurrence and was part of the

10 public record to submit as a public comment in that

11 context that we concurred with their assessment in

12 terms of saying that those sources were not -- could

13 not be treated under this guidance as intermittent

14 sources and, therefore, not included at all. So,

15 hopefully that helps clarify a little bit of that and

16 if there are questions, we can deal with those

17 separately.

18 In terms of determining background

19 concentrations, cumulative analyses will be required,

20 as you know, if the emissions exceed the interim SIL.

21 Those were established as part of the memos that went

22 out soon after the standards were set. What we try to

23 do is address the components of the cumulative impact

24 analysis including identification of the nearby sources

25 to include in the modeling inventory. So, you're
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 107

1 explicitly modeling them and then how to combine those

2 results with the monitored background. And what we

3 stressed here, as well as in the previous memos in

4 regards to the NO2 and SO2 standards, is that we

5 advised and cautioned against the literal and

6 uncritical application of very prescriptive procedures,

7 particularly those that one will find in the 1990 draft

8 NSR workshop manual. In some of those cases, following

9 that manual will result in overly conservative types of

10 assessments. And the challenge here is to find a

11 proper balance between balancing those factors that are

12 appropriate to account for versus those that, under the

13 context of this new 1-hour standard, would be

14 appropriate.

15 So, for example, the straightforward

16 application of 50 kilometers and then going out another

17 50 kilometers in terms of your area of influence and

18 the sources that you would include in your modeling

19 analysis, frankly, is not something that we see as

20 necessary and should be reconsidered. Again,

21 everything should be viewed in the case or the context

22 of the situation at hand. But blindly following the

23 workshop manual in that regard, I believe as Roger said

24 yesterday and we have said elsewhere, if one wants to

25 go down that road and do an obviously overly
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 108

1 conservative analysis, we won't stop you. But these

2 guidance -- this guidance was intended to provide

3 flexibility and, again, caution not to do that.

4 And so, I'll give an example at the end

5 that we've seen recently that illustrates some of the

6 pitfalls of that, but we urge you and the community to

7 take advantage of the flexibilities that we're

8 providing here. Work with the regional office to

9 understand the particulars of how to work in that. But

10 we've got to move away from the very prescriptive

11 nature of things that were in the workshop that were

12 done under the previous NAAQS.

13 I guess I shouldn't say this for the

14 public Record, but I'll go ahead and say it anyway. At

15 some point, perhaps in the future, we'll have a

16 bonfire. A well-controlled one. We will have all of

17 our permits in place before doing that.

18 So, but joking aside, really, we do need

19 you all in the community to work effectively with us,

20 with this guidance to take advantage of the

21 flexibility. If you, you know, hold onto these old

22 techniques of 20, 30 years ago, we acknowledge and

23 admit you're going to have problems and you're going to

24 have issues. So, we hope that we can work together.

25 As an illustration of that, in Appendix W we have the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 109

1 concept of a significant concentration gradient. And

2 we identify a significant concentration gradient in the

3 vicinity of the source as the sole criterion for

4 identifying which nearby sources to model.

5 Now, there's aspects of that that can be

6 somewhat complicated, but it's not impossible to define

7 that. It may be that, as we move forward in updating

8 Appendix W, we can work towards having a more concrete

9 understanding and example of how we define that

10 significant concentration gradient, what it means, and

11 how best to put in practice an approach to identifying

12 in a more, I guess, prescriptive way what nearby

13 sources to model. But right now, we need to work with

14 what we've got.

15 So, we did not comprehensively define

16 the term given the uniqueness of each modeling

17 situation, but if we can get an understanding of these

18 situations in the context of these standards,

19 hopefully, we can provide more information and refine

20 the guidance and ultimately perhaps update Appendix W.

21 So, these gradients in the vicinity of

22 the source imply that nearby sources' potential

23 interaction with the proposed source impacts will not

24 be represented well by monitored concentrations at a

25 specific location. So, there's a feedback mechanism
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 110

1 between the monitored background that you're going to

2 use and the nearby sources and that requires some best

3 professional judgment and assessment in terms of making

4 sure you're not double counting and making sure that

5 you're properly accounting for the concentrations

6 gradients in and about the project source.

7 We hear about the nominal 50 kilometer

8 distance and other things. I know in the workshop last

9 year there were questions that, well now EPA, you're

10 suggesting that the focus on nearby sources is within

11 about 10 kilometers. We're trying to provide

12 information that's helpful to you all, but there is no

13 bright line. Obviously, with a 1-hour standard, we

14 want to make sure that you're focusing on sources in

15 closer proximity to the project source or those that

16 are, you know, going to be important to account for in

17 terms of potential violations and contribution of the

18 new project source to those violations. And it does

19 suggest that you need to look in a tighter domain.

20 What specifically that domain is, you need to work with

21 your regional office and the like to design that on a

22 case-by-case basis and as we do more modeling and

23 understand better the nature of NO2 and SO2 in that

24 context, we can provide more concrete examples. And

25 then, perhaps, refine that type of information.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 111

1 And then, one of the more popular topics

2 is how we combined the monitored to modeled

3 concentrations. In the interest of time, I'm going to

4 move down to the next slide where in the March 1st memo

5 -- well, in June, we identified the overall highest 1-

6 hour monitored background as a first tier. So, in a

7 tiered approach, that's a conservative approach. In

8 the March memo, we suggested a new first tier and that

9 being the monitored design value and expressed that

10 that should be acceptable as a less conservative first

11 tier. And then given the form of the standards both

12 for NO2 and SO2, we suggested then looking at the

13 diurnal and seasonal patterns of those concentrations

14 to then look and see whether or not more refined

15 combination of those monitored concentrations is

16 appropriate.

17 And so, again, I don't want to read this

18 stuff but, basically, based on the guidance and

19 Appendix W, again, all the things that we're putting

20 forth are rooted in Appendix W. That we suggested that

21 the use of the multi-year averages of the 98th

22 percentile of the available background concentrations

23 by season and hour of day is an appropriate methodology

24 for the 1-hour standard.

25 So, we've provided three tiers here. A
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 112

1 first tier, admittedly conservative. A second tier

2 that is fairly easy to implement, but still may have

3 some nature of conservatism to it. But then a third

4 tier, here, that allows one to take advantage in an

5 appropriate way the combination of those data on a

6 seasonal basis.

7 And here for Salt Lake City, you can

8 see, I believe this was the same example provided in

9 the guidance. You've got the 1-hour design value

10 there. You've got the standard level up here. And

11 each of the different colors are the 98th percentile

12 for winter, spring, summer, and fall. And by hour of

13 day. So, that information can be provided by the

14 background monitor and we've suggested a more refined

15 approach to combining those that we feel both provides

16 more appropriateness and reality to a particular

17 situation and it's firmly rooted in the data that are

18 available. And appropriate for combining in a way

19 that, at least at this point in time, we feel

20 comfortable with people using.

21 So, that really covers the guidance that

22 we put out in March. Again, we've gotten comments in

23 terms of the sufficiency of that guidance and

24 additional issues. Maybe not going far enough in

25 certain areas and the like and we welcome those types
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 113

1 of comments to push us to better address your issues.

2 I guess two things that I want to

3 mention before I get to this slide and finish up. When

4 we first started this process and these NAAQS came out,

5 the first call was, you know, hey it's impossible. It

6 can't be done. Not, you know, Dana Carvey, not going

7 to do it. It's just -- it can't be done. Well, at the

8 end of last year, our policy division polled the

9 regional offices and I will get the firm numbers and

10 we'll submit it to the docket for everybody to see, but

11 based on that polling of the regions, we had, I

12 believe, 27 final permits that had demonstrated

13 compliance with the NO2 standard. Ten of those permits

14 had used either the tier one or tier two approach to

15 demonstrate compliance. Seventeen used the tier three

16 approach -- either OLM or PVMRM. From what we

17 understand, out of those 17, three used OLM and 14 used

18 PVMRM. So, we understand and hear what you're saying

19 in terms of the difficulty and challenges here. But we

20 do see -- and in certain situations and this was across

21 the entire regions. You know, there were some regions

22 that may have had more, but every region had a final

23 permit that had successfully demonstrated compliance

24 with the NO2 standard. And I didn't pull the SO2

25 results. That doesn't seem to be as much of an issue,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 114

1 but it was, and I believe the number was closer to the

2 -- in the handful.

3 But I did want to stress, and again,

4 we'll compile this information and put it in the docket

5 for you to see, that people are applying the guidance.

6 People are successfully completing their compliance

7 demonstration. And we appreciate and kudos to those

8 who are moving forward and using these tools and

9 techniques and guidance successfully.

10 With that said, as you all know through

11 the Clearinghouse process and other venues or avenues,

12 we get pulled into situations where there are issues in

13 terms of demonstrating compliance. So, I don't want to

14 discount or diminish the fact that there are serious

15 challenges here. But I want to provide an example and

16 I don't want to say any names and I'll try to be very

17 generic in characterizing this which, I think from our

18 standpoint, puts a burden and obligation on you all in

19 terms of applying the guidance appropriately and taking

20 advantage of the things that we're providing and

21 working hard to provide to you.

22 So, we had a situation -- a source -- a

23 combustion source was demonstrating compliance with the

24 SO2 NAAQS under PSD. The timing of it was right around

25 the issuance of the March guidance and then the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 115

1 subsequent release of AERMOD. So, when we first heard

2 of this application, our understanding is that they

3 were having difficulty demonstrating compliance. In

4 fact, they had modeled the fact that they exceeded the

5 SIL, that they were going through the cumulative impact

6 analysis and that they were significantly contributing

7 to violations. And so, what was put forth was a novel

8 approach, acknowledging that there were violations, but

9 they were small in number. A probabilistic approach,

10 so to speak.

11 Appreciate the creativity there and the

12 like, but under the current guidance, those types of

13 approaches you've demonstrated that there is a

14 violation. You've got to address those types of

15 things. We engaged through the regional office and

16 others and early on in that process, pointed to the

17 fact that we had March guidance. We had a new version

18 of AERMOD. Perhaps one could run that through. They

19 had previously post-process without the benefit of the

20 tools that we had provided in AERMOD and so, we didn't

21 hear anything for a little bit. Then, we -- probably

22 in late summer or so, start hearing back about this

23 issue and then get more details about what's going on.

24 Still having an issue in terms of demonstrating

25 compliance. We understood that the individuals
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 116

1 involved pushed back on re-running through AERMOD which

2 we understand would have taken hours, days perhaps to

3 do and to take advantage of the new capabilities in

4 AERMOD. We also then got more details on the modeling

5 and found out that they were going out 90 kilometers

6 away and modeling an SO2 source -- 90 kilometers away

7 and they were following the puzzle book. They were

8 going out and modeling all of these sources.

9 We looked to our guidance and sent the

10 message back that, well, it looks like the domain is

11 much more expansive than we would suggest. To make

12 matters even worse, we then looked and saw that they're

13 between the project source and the source 90 kilometers

14 away. It was a large SO2 source. I'll give them that.

15 But there was a monitor between those two sources. And

16 so why one wouldn't use that monitor as --

17 MR. BRODE: They did use the monitor.

18 MR. FOX: Oh? They double counted.

19 MR. BRODE: That was the monitor they

20 used.

21 MR. FOX: Oh. Thank you.

22 So, to make matters even worse, you

23 know. So then, what we did or what Roger and James and

24 others did, is that we took the -- we got the input

25 files and ran it through the new version of AERMOD and
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 117

1 found that, yes, they were above the SIL, but when you

2 looked at the violations that they were modeling at the

3 nearby sources -- the explicit sources that they had

4 modeled, yes there were violations above the NAAQS

5 level, but this source was contributing nothing.

6 Nothing to those sources. Way -- I mean, it wasn't

7 even close to the SIL. It was, literally, nothing.

8 So, long story short, you know, we provided the

9 information. I hope that the information found its way

10 to the right places and that ultimately gets resolved.

11 But, you know, we need you to work with

12 us in this context. We need you to apply the guidance.

13 We need you to apply the tools. We need you to engage

14 with the regional offices. This is a totally avoidable

15 situation. And before those types of situations get to

16 a point where they're political or other types of

17 things where that's not the most constructive way to

18 work these things out, we really need to, you know,

19 engage and work better together as a community.

20 I'm not saying that this is what we see

21 most often. It isn't. It's an exception to the rule

22 and it's becoming more of an exception to the rule.

23 But I use it as an example just to say let's critically

24 evaluate the way in which we did things in the past.

25 Let's embrace the fact that we do have to demonstrate
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 118

1 compliance under these, you know, 1-hour standards and

2 the PM standards and the like. And let's work together

3 to find credible, technically credible, legally

4 defensible ways to demonstrate compliance such that you

5 can get your permits and that we can move forward with

6 the types of environmental protection that we need to.

7 So, with that -- there's again these

8 outreach efforts that we've been engaging in to try and

9 provide information and answer questions. As a lead10 in, we've got the AERMOD Implementation Workgroup that

11 James and Erik will cover and provide more examples.

12 And that was a key thing in dealing with the types of

13 issues that were being brought to us, you know, I know

14 I said yesterday that the devil is in the details and

15 I'll repeat it. The devil is in the details in a lot

16 of these situations. So, the more information we have

17 in terms of the particulars of what you're challenged

18 with or facing, the better we can help diagnose and

19 understand and either provide a case-by-case solution

20 to that problem and through that build better guidance

21 or address a real issue in our guidance and provide

22 that in timely fashion so that not only that project

23 source but other sources facing that same type of

24 challenge can benefit from that.

25 So, I'll end now and turn it over, I
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 119

1 believe, to Roger to go through.

2 MR. BRODE: So, I'll try to do this

3 fast. I guess a number of concerns that have been

4 raised about the ability of the model to predict

5 impacts, ambient impacts, and its accuracy. You hear

6 over and over again that the AERMOD is overly

7 conservative. We acknowledge that in some respects how

8 the model is applied has conservatism built into it in

9 terms of modeling with maximum allowable emissions, for

10 example. But the model itself is not designed to be

11 conservative. It's designed to be unbiased. And even

12 within OAQPS people are asking, well, can AERMOD even

13 calculate 1-hour averages -- failing to understand that

14 that's the basic time step in the model.

15 So, I'm just going to try to go through

16 briefly a lot of this stuff you've probably seen

17 before. AERMOD was very extensively evaluated before

18 it was promulgated. A total of 17 databases for use

19 which is far more than any other model had gone through

20 before. It was in two phases. A developmental phase

21 where you're actually changing the model as you

22 evaluate it and then independent evaluations covered a

23 range of scenarios. It looked at short term intensive

24 field studies. Long term studies from operating plants

25 and so on.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 120

1 They're very different. The one on the

2 left, Prairie Grass that's sort of intensive field

3 Tracer study where you had a large number of receptors

4 oriented in arcs like that. So, removing the

5 uncertainty due to wind direction which can be

6 important. Here's the Lovett power plant example.

7 Operating a power plant and far fewer monitors, but

8 located on a critical impact area - a hill nearby.

9 A number of methods were used in that

10 process. We were comparing AERMOD performance to the

11 then preferred models that AERMOD would replace. For

12 example, for ISC, for non-downwash, non-complex drain

13 cases CTDM Plus was a preferred model for complex

14 drain. And ISC-PRIME came along sort of in the middle

15 of that process for downwashes. So, overall, AERMOD,

16 you know, did pretty well against the models that it

17 was sort of competing against or replacing.

18 So, I'm just going to go through these.

19 QQ Plots, a number of people raised concerns. Well, a

20 QQ Plot, you know, there's a lot more to it, but at

21 least it gives you some sort of a quick visual

22 understanding of how well the model works and I think

23 what we're interested in in the model, especially for

24 these hourly standards, is how well is the model going

25 to predict the peak of the concentration distribution
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 121

1 because that's what's going to be compared to the

2 NAAQS. We don't necessarily care did it get this value

3 right for that hour at that receptor. We'd like it to

4 but, you know, the level of skill involved to do that

5 is much greater than we're expecting.

6 So, this is complex drain -- Lovett.

7 AERMOD is the red curve so that line that it's almost

8 right on is the one-to-one line. So, the unpaired

9 distribution observed in AERMOD agreed very well. And

10 also, to put it in perspective with these new

11 standards, the model that it was replacing was CTDM

12 Plus which was about a factor of two higher in that

13 case and it actually required very robust, site

14 specific MET monitoring in order just to run CTDM Plus.

15 We haven't imposed that high level of standard in terms

16 of collecting site specific data to apply AERMOD even

17 in complex terrain situations. So, we've made a

18 significant step forward, I think, in terms of the

19 ability of the modeling to handle these challenges.

20 ISC was about a factor of ten higher so, yes, it was

21 clearly conservative for that, but it was also not a

22 refined model for complex terrain.

23 This is another complex terrain. Again,

24 AERMOD did quite well against the other models. This

25 is a downwash case in Alaska, where a prime downwash
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 122

1 was involved so we had AERMOD versus ISC-PRIME and then

2 ISC before PRIME. And even with PRIME in there, AERMOD

3 actually did better than ISC-PRIME and performed very

4 well.

5 Another downwash case and, let's see,

6 there's an urban case, tall stack in Indianapolis,

7 comparing AERMOD versus ISC. ISC has some biased over8 predict it looks like, but AERMOD is pretty much

9 unbiased.

10 One thing to point out is that there's

11 some caveats here. These performance evaluations

12 typically involved some fairly robust site specific MET

13 data that was collected as part of the field study.

14 You typically have hourly actual emissions or at least

15 pretty good estimates of the emissions in order to

16 remove as much uncertainty or bias associated with

17 those key model inputs as possible.

18 So, you know, we're not -- this doesn't

19 necessarily translate into the, sort of, practical

20 world where I'm modeling my maximum allowable

21 emissions. Maybe I'm using the nearest representative

22 airport. It's representative enough, but it's

23 certainly not necessarily going to achieve comparable

24 results as you see here for those various reasons.

25 And also, you know, even -- well, I've
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 123

1 seen this case yesterday where we had one monitor, but

2 even in the long-term field studies, there was

3 typically about eight or ten or maybe more monitors

4 that were being compared to, not a single monitor, to

5 remove some of the uncertainty associated with the

6 wind. The direction's a little bit off and then you

7 either miss the monitor or you don't and that can

8 result in significant differences.

9 So, just to caution that comparing PSD

10 permit modeling results to observed concentrations at a

11 single monitor or some monitor nearby is subject to

12 possible misinterpretation and not necessarily a good

13 indicator of the performance of the model. I mean, it

14 may be useful information. We're interested in seeing

15 that. We shared an example yesterday for the Portland

16 Plant where there was one monitor downwind of the

17 source. It was actually reasonably well sited. In

18 that case, it actually did match up reasonably well,

19 but if we looked hour by hour, it certainly wouldn't

20 have looked as good.

21 So, now I'm going to talk about NO2.

22 So, that kind of was a general evaluation of AERMOD

23 dispersion. No chemistry involved. NO2 adds a new

24 dimension. I do think, you know, we need to

25 acknowledge you said that not many people needed the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 124

1 tier three options in the past. At least maybe we were

2 thankful that at least Region 10 did. And the State of

3 Alaska because I think we're very fortunate that they

4 basically sponsored the implementation of the OLM and

5 PVMRM options within AERMOD several years ago due to

6 that. And I think we're in better shape now that they

7 did that than if they hadn't. So, I just want to

8 acknowledge, Herman Wong and the State of Alaska for

9 those efforts.

10 So, a lot of this has been documented in

11 the documents that are referenced on SCRAM and in, you

12 know, one of the clarification memos. So, this was

13 probably the evaluation that was done for ISC, PVMRM,

14 and basically these are power plant plumes showing --

15 comparing the ratios, you know, with distance from the

16 source and overall, it's not perfect, but it actually

17 for a convective case, it picks up the fact that it

18 converts to NO2 pretty quickly. In stable cases, it

19 recognizes that there's not much conversion because

20 there's a very narrow, small plume. Not much

21 entrainment of ozone.

22 We only had two longer term field

23 studies and I think that that's still the case that

24 we've looked at for NO2 to evaluate these options. One

25 in Hawaii -- Palau, Hawaii where there's one monitor.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 125

1 And then New Mexico. Empire Abo, where there's two

2 monitors. And this just sort of summarizes the ratio

3 of the robust highest concentrations and the average

4 ratio of predicted observed for AERMOD with PVMRM --

5 PVMRM is about 1.5. There's some bias to over-predict

6 it looks like, mostly for that case. OLM with OLMGROUP

7 ALL actually does much better in terms of the average

8 bias. OLM without OLMGROUP ALL certainly shows over9 prediction as well as full conversion. We expect that.

10 And that is, just again, to make the

11 point that we have not stated anywhere that PVMRM is a

12 better algorithm or approach than OLM in any given

13 situation. We don't know. I mean it may depend on the

14 circumstances. There are aspects of PVMRM that are

15 more refined. That might make it more appropriate in

16 some cases than others but, you know, OLM certainly

17 does much better in a few cases here. So, we hope to

18 learn more about that and provide better guidance.

19 We've provided some ideas. Some indicated some cases

20 where PVMRM may not be as appropriate for low level

21 sources, for example.

22 These are -- but that was, yeah, that

23 was 1-hour. The previous evaluations that have been

24 done for this were just focused on the annual

25 standards, so this is what we updated more recently.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 126

1 These are the QQ plots. That again shows the OLMGROUP

2 ALL working pretty good. You know, it's not perfect,

3 but it matches pretty well with observations. PVMRM at

4 least, kind of near, but not quite as well, but

5 certainly highlights the conservatism of the full

6 conversion and OLM without OLMGROUP ALL.

7 That was the one monitor at New Mexico.

8 This is another monitor where AERMOD does show with

9 PVMRM more tendency to over-predict. That was

10 highlighted on the table whereas OLMGROUP ALL was

11 looking better. Palau is where AERMOD PVMRM actually

12 does pretty well in matching. Again, these are the NO2

13 concentrations -- hourly NO2 concentrations. So,

14 OLMGROUP ALL certainly much better than the other

15 cases.

16 We had AERMOD -- a question came up

17 about mobile sources and can the model -- how much

18 confidence do we have in the model to predict

19 concentrations for mobile sources. AERMOD was actually

20 applied as part of the risk and exposure assessment for

21 the Atlanta area as part of the most recent NO2 NAAQS

22 review that resulted in the new hourly standard. And

23 it was certainly focused on hourly impacts. A majority

24 of the impacts were attributable to mobile sources and

25 there's a longer story there which I'll kind of skip
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 127

1 over. But when it came to us, that's the kind of

2 monitor to monitor comparisons we were seeing that

3 AERMOD was, you know, grossly over-predicting the

4 monitors. There were a number of factors there -- the

5 MET data. They were using OLM. The roadways as area

6 sources with OLM but without OLMGROUP ALL, so that was

7 where we actually looked at that and realized that

8 OLMGROUP ALL may actually be a pretty good idea.

9 But there are other issues, so that was

10 kind of the before slide. This is actually a time

11 series of NO2 concentration model versus monitored at a

12 particular monitor for, I think, about a month. This

13 is sort of after. So, much better agreement. It's not

14 perfect, but in this case there's actually quite a bit

15 of under-prediction of the observed. Well, that's

16 because the wind is calm, so there actually are high

17 concentrations under light wind conditions. In this

18 case, the model grossly under-predicted those

19 concentrations because it thought the wind was calm and

20 so missing.

21 But given the level of detail and

22 uncertainties in the emissions for the mobile sources,

23 I think that's pretty good agreement. This is another

24 monitor -- sort of a very similar picture there. So,

25 that's -- and this is just overall looking at the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 128

1 ranked distribution. One of the things we realized is

2 that the emissions input to the model were defined by

3 season and hour of day, but did not account for any

4 kind of day of week component. And that showed up

5 clearly here. If you compare during the weekday, the

6 model actually agreed much better with predictions than

7 on the weekends. So, some of that over-prediction

8 actually was due -- occurred on the weekends. But more

9 recently, we've had some interaction I want to sort of

10 preempt later talks, but maybe feed into later talks on

11 the NOx options and NO2 options in AERMOD, but sort of

12 revisited this and had a range of conversation.

13 At New Mexico, there were two monitors.

14 Kind of one north and one south that had NO2, NOx, and

15 so forth, and ozone. And the original modeling

16 evaluation had been done before we got involved. Just

17 paired the north monitor -- ozone monitor and evaluated

18 the north NO2 monitor which may be not the best idea.

19 So, we looked at reversing them. So, we used the south

20 monitor for ozone to predict impacts at the north

21 monitor and actually did maybe improve the results a

22 little bit in some cases.

23 But we also -- some concerns were

24 raised. You'll hear more about it later. And wish we

25 could have coordinated the timing on this a little bit
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 129

1 better, but about -- one aspect of PVMRM in AERMOD in

2 terms of using relative dispersion coefficients may

3 tend to over-estimate the volume of the plume under

4 stable conditions because the relative dispersion

5 coefficients really aren't applicable. The way they

6 were formulated weren't necessarily applicable to

7 stable conditions.

8 So, I actually started looking at what

9 if we implemented PVMRM in AERMOD like it was for ISC

10 originally by Pat Hanrahan using just total dispersion,

11 but using a smaller portion of the plume predicted by

12 total dispersion. And so I've done some tests with

13 this which actually are kind of encouraging. And,

14 yeah, so this is the new PVMRM results for the south

15 monitor. Again, there was some sensitivity on whether

16 you used the same monitor for the ozone or the other

17 monitor. But it actually performs much better than

18 with the current implementation. And this is the other

19 monitor, and I can't even see what I'm looking at, of

20 the same sort of thing. I don't have time to go

21 through details because I want to leave time.

22 And then the other comment had been made

23 is there's some, you know, disagreement on whether the

24 way it's implemented now should actually use four

25 sigmas to define the volume of the plume versus what
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 130

1 Pat Hanrahan has used with total dispersion which was

2 1.28. And this is just showing the difference. If I

3 run the model as it's currently designed for one of the

4 monitors, that's what PVMRM gives. And this is what it

5 would give if you used the 1.282 to define the plume

6 volume. It actually agrees much better in that case.

7 But if you go to the other monitor, again, that's not

8 perfect, but at least you're in the ballpark for the

9 peak values. Now, you're seeing -- introducing some

10 under-prediction by reducing the volume of the plume.

11 And for Palau where we actually did

12 pretty good with the current implementation. If we

13 just change the sigma -- the number of sigma Zs from

14 what's in the model now, four, which gives you pretty

15 good agreement, to the 1.282, clearly a biased under16 predict, which is kind of what we would expect. And

17 this is total -- using total dispersion, again, as a

18 something to investigate for Palau and it worked pretty

19 well before that and it still works pretty good. So,

20 at least it didn't compromise the performance there.

21 Just to put it in perspective, finally,

22 I thought I would throw in for Palau, where AERMOD with

23 PVMRM does pretty well. ISC PVMRM actually gave you

24 that. So, you know, same chemistry, same approach,

25 except for the total. I think this is actually both
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 131

1 using total dispersion and the same number of sigmas,

2 so the only difference is the dispersion model. And

3 it's interesting to see how much improvement we get

4 using AERMOD versus ISC with the same chemistry option.

5 So, that was kind of an interesting point.

6 And that's where I'll stop.

7 MR. BRIDGERS: After a very on schedule

8 day yesterday, we have slipped. And so, in the

9 interest of time, I think what we'll do is we'll go

10 ahead now and let's break for lunch and then when we

11 come back from lunch, we'll pick back up on the

12 schedule that we were going to keep. But to be fair to

13 Erik and James, I don't want them to try to launch into

14 a 45-minute presentation that a lot of people have come

15 to see in less than 20 minutes. So, let's break for

16 lunch and that means be back at 12:40 and we'll kick it

17 back off.

18 So, thank you and see you after lunch.

19 (WHEREUPON, a lunch break was taken.)

20 MR. BRIDGERS: Okay, if we could take

21 our seats. I see a lot already have. They're

22 watching the clock better than I am. We're already

23 four minutes over the schedule I had set.

24 So, as we start the afternoon session,

25 obviously we've still got leftovers from the morning
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 132

1 session. Just a little bit of logistics and making

2 sure that we can stay somewhat on time with the

3 afternoon session and still get our question and answer

4 sessions in because that's an important aspect of this

5 conference is the dialogue between all of us.

6 What we're going to do, as I had

7 mentioned before lunch, is we're going to have the

8 AIWG, the AERMOD Implementation Workgroup talk by Erik

9 Snyder and James Thurman now, but the talk that was

10 originally schedule for one to one-fifteen that Roger

11 Brode was going to deliver, we're going to scratch that

12 from the agenda.

13 But that being said, a lot of the

14 information that is included in that presentation can

15 be found on the SCRAM website at the 10th Modeling

16 Conference under the TSD for the New Jersey 126 and

17 Roger and I are in conversations that we most likely

18 will still go ahead and post the presentation under the

19 conference presentations on the SCRAM website. I don't

20 know that we would actually formally submit it to the

21 docket, but nonetheless, it will be in there and there

22 is additional information in the TSD.

23 Just in the interests of time, we want

24 to hear from all of our invited speakers, so without

25 further ado, let me turn the agenda or the podium over
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 133

1 to, I guess, James Thurman and Erik Snyder.

2 MR. SNYDER: Hopefully, everybody's not

3 too full and I'll try to go through the 200 slides

4 pretty quickly. I think we've got 50 or thereabouts.

5 I'm at Region 6 in Dallas and we formed

6 the AIWG group to focus on NO2 and SO2 modeling.

7 Anyway, we formed this last February, I think it was,

8 or so. And we did some work last year and some follow9 up work this year. Doing some cumulative.

10 Mainly, we formed the workgroup

11 initially with these new standards and at the Region 6

12 we had a fair amount of experience already with some of

13 the new standards and issues with modeling with some

14 permits and so really, it's just focused on some one

15 hour NO2 and SO2 as the workgroup and analyzing and

16 trying out different things and trying to build up some

17 community knowledge level in modeling.

18 It's comprised, I think, there's about

19 27 or 28 states that have members. I think we have six

20 or seven regional offices and OAQPS involvement, so

21 it's a good crowd of people.

22 The stage one of it was basically to --

23 we worked with our members that we had and we came up

24 with specific facilities and then we kind of made them

25 generic so that we wouldn't be modeling a specific
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 134

1 source so to speak. But we based these generic

2 facilities on real facilities and just modified stack

3 parameters and locations somewhat, but well within the

4 tolerances of what we would see.

5 We completed this with all the single

6 facility modeling. It was a non-cumulative last June

7 at the regional state local workshop in Atlanta. And

8 then we continued the AIWG process and we did some

9 source grouping analysis, individual source

10 culpability, and some cumulative analysis as well. We

11 still have some additional work we'll be working on on

12 these issues and then other issues as far as adding

13 background and some of the other things as we work on

14 the NO2 modeling and the future SO2 as well.

15 I'll kind of give an overview. We

16 started with 12 typical industrial facilities that

17 required the modeling and then we kind of expanded

18 based on the workgroup. We had four more that we added

19 to it and so overall, I mean, we worked to review it

20 and, like I said, because of the concerns, especially

21 with SO2 modeling that nobody wanted to have SO2

22 modeling at their own facility. We figured ahead of

23 time before the actual modeling for the maintenance

24 SIPS or whatever.

25 So, we did take these generic scenarios
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 135

1 to try to give that and it also had some generic

2 property boundaries in lot of cases. We created base

3 scenarios and then we also did a combination of stack

4 heights and emission control combinations to see, when

5 we modeled exceedances, what in the base level if we

6 could fix it with additional, feasible things that you

7 could do.

8 The way we kind of divided up the work

9 in the regions and the states was, basically, the

10 people that worked with that industry a lot did the

11 work and the modeling so they were most familiar with

12 it as well. And, of course, we did the normal five

13 years met data and building downwash when we had the

14 downwash data.

15 The caveats on the initial modeling we

16 did and even on this modeling -- it's not in

17 cumulative, I mean, even for the cumulative runs that

18 we've done, we only put some sources in there. It's

19 not the full level of what it might within the modeling

20 domain of a 10, 20, 30 kilometers whatever you were

21 looking at for your area of concern.

22 We didn't include the background

23 monitoring values because that's going to vary a lot

24 over -- so we know that there's also background

25 monitoring to add in on these things.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 136

1 As again, this was done not necessarily

2 for one specific task other than to really get some

3 experience doing this in the states and initially these

4 standards were really being talked about in really

5 difficult to attain and stuff and so we thought we'd

6 form this group to really get some basic knowledge of

7 is it plausible to work it out and show attainment or

8 not.

9 I guess I'll turn it over to James for

10 this part here.

11 MR. THURMAN: This is just a summary

12 table of NO2 results. I'm not going to go very more in

13 detail. This is also in the draft summary that we put

14 out on SCRAM last week and that's going to be updated

15 in the next couple of weeks as we finish adding maps.

16 Basically, what we have here is each

17 facility we modeled base emissions or uncontrolled

18 emissions the sales in yellow are where we had

19 violations and it gives the maximum design value in

20 micrograms and ppb. And also a percent of the

21 receptors in the grid that violate. The ones in green

22 are those where we passed -- didn't have any problems

23 with the NAAQS.

24 So, these are all the NO2 facilities

25 like the steel mill, for example, in the base case
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 137

1 there were violations of OLM and PVMRM, but when you go

2 to a controlled scenario, the OLM case passes, but the

3 PVMRM still had exceedances with one receptor. So, I

4 mean that shows some sensitivity OLM or PVMRM.

5 Again, these are just summaries of NO2

6 and continuing with the summaries of NO2 on the

7 refineries are actually very borderline. It could be

8 depending upon how you convert from micrograms ppb.

9 So, you can look at this on your own in

10 the report or the presentation was posted on SCRAM.

11 SO2, same thing. We had some similar

12 facilities. One thing I'd like to point out about the

13 ethanol plant is initially you see a violation of 296

14 and then control strategy is still 296. It wasn't the

15 stack that you would -- the main-stack wasn't the

16 problem, but actually what the modelers also did it.

17 This is with a 50 meter fence line. They actually

18 modeled with a 300 meter fence line and that receptor

19 was inside the facility property so then you didn't

20 have to have an exceedance. So this was a good case of

21 showing how the distance to ambient air is important.

22 And then we have some more scenarios

23 down more in the table and a few more. And like I

24 said, more details can be found in this draft summary.

25 We've got all the inputs in the summary right now. Bar
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 138

1 charts for all the facilities and maps for all the

2 facilities that had exceedances and next week, I'll

3 fill in the rest of the facilities. I thought we'd put

4 in maps even for those that didn't have exceedances

5 just so you could see the spatial profiles.

6 So, we're just going to go through a

7 couple single source examples. If you were at the

8 workshop last year, you'll remember we did all of them,

9 but we don't have that much time today so we're just

10 going to do a couple.

11 And I also want to acknowledge we have a

12 few people in here that are a part of AIWG. I just

13 want to thank them for all their efforts. It's been a

14 lot of work. They had, you know, busy schedules, but

15 they still took time out to help us with this so we

16 really appreciate it and they did all the work here.

17 The first example for NO2 is the ethanol

18 plant, actually. This was Dawn Froning from Missouri

19 and Jen Krzak from Iowa. They were the modelers. So,

20 we actually had states working together.

21 They evaluated four scenarios. These

22 four scenarios with four different meteorological data

23 states. All I'm going to cover are results for one

24 today. But the four scenarios were our base case, you

25 know, starting out with 1100 tons. Then, scenarios two
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 139

1 and three changed stack heights to 65 meters for this

2 one point C004 and also for scenario three we added --

3 they added controls to get the emissions down to 381

4 tons. And in scenario four they added more controls to

5 get down to 172 and, just to let you know, volume

6 source remained unchanged in all of these.

7 They also had descriptions of what these

8 sources are. C01 and two are flares. The third one is

9 an emergency fire pump. And the fourth one is the

10 source you would think of in an ethanol plant, the

11 regenerative thermal oxidizer. I don't what that does,

12 but it sounds important. That's probably a fancy word

13 for a moonshine still, I don't know.

14 So, these are the emissions in grams per

15 second down here. We highlighted the one source that

16 we changed in yellow. So, we start out with a 32 grams

17 per second, 43 meters stack height. Then, we changed

18 stack height up to 65 meters and reduced emissions down

19 to nine grams per second and then three grams per

20 second.

21 They also modeled the, like I said,

22 distance to ambient air. They modeled a 300 meter

23 versus 50 meter fence line and then they also modeled

24 stack ratios. First, they did .1 for all sources and

25 then one -- .05 for the main source. They were looking
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 140

1 at C4 and then .1 for all others. And then a .25 for

2 all sources.

3 And this is just an outline of the

4 facility, how the 50 meter versus 300 meter fence lines

5 and the spatial relationship of all the sources.

6 Here's that main source that will be controlled. The

7 red line is 300 meters. The blue line is 50 meters

8 fence line.

9 These are bar charts of the maximum

10 design value for each scenario in micrograms per meter

11 cubed. Solid black line is the 188, 189 microgram

12 level which is the NAAQS. These are all scenarios

13 based on increasing the stack height and then with

14 controls and more controls. And then different color

15 bars are those NO2 stack ratio sensitivities and fence

16 line.

17 Now, one thing you'll see is you don't

18 see a lot of difference between the different scenarios

19 when you control that one stack. And actually you

20 don't really see any difference in when you change the

21 NO2 stack ratio for that one source, the red and blue

22 bars and the green and -- for the 300 meter you don't

23 see a lot of difference. For the 50 meter fence line,

24 you do see when you change that ratio for that one

25 source, you get a difference because it's outside the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 141

1 facility so its impacts are probably, you can see them

2 better.

3 So, there's some spatial plots. This is

4 where we're going to show the base case. The first one

5 is for the .1 ratio at 300 meter fence line and the

6 star represents the maximum design value. It's right

7 on the fence line. You see the exceedances are

8 basically kind of the orangy colors.

9 Or is this SO2? Okay. This is NO2, but

10 I think I used a different -- it should have been

11 stopped at 188. It should have had a break there, but

12 anyway, the kind of orangy colors are the violations.

13 50 meter fence line, you get a max of 810. Still on

14 the property line. And then changing the stack ratios

15 for 300 meter fence line, you don't see any difference

16 for max values. For 50 meters it goes up to 930

17 micrograms, but the overall spatial doesn't change a

18 whole lot.

19 At .25, obviously you're changing that

20 ratio a lot. It goes up to 1,000. And then .5, the

21 default that's talked about in the NO2 guidance gives

22 you a lot higher.

23 So, we had some findings initially. Our

24 results are sensitive to distance and ambient air, and

25 the stack ratios. The maximum design values did not
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 142

1 change based on changing that stack height or applying

2 controls to that stack of interest. And we actually

3 went back and did a few reruns to get a source

4 contribution and it looks like the maximum design

5 values were driven by the emergency fire pump. Now, we

6 modeled that at continuous emissions so there may be --

7 in the permit you may want to take permit limits or

8 something to help with that.

9 This shows is that problematic emissions

10 may not always due to the stacks thought to be causing

11 the problem. You know, you think I'm going to control

12 that big stack and we find, well, not necessarily.

13 The next one we're going to talk about

14 is for SO2. This is a coal-fired EGU. I did some runs

15 myself and Erik Milligan from Oklahoma did some runs.

16 I modeled with Charleston, South Carolina met data and

17 he modeled with Springfield, Missouri met data.

18 We modeled six scenarios of a baseline,

19 increasing stack height and controls. A combination of

20 stack height increasing controls and you can see how

21 the emissions change. More stack height increases and

22 then another stack height increase with controls and a

23 higher exit velocity and the actual inputs we'll show

24 in a second.

25 And also, I did a run for two of the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 143

1 stacks where we modeled a 65 meter stack height. Their

2 original stack heights are 150 meters. So we thought

3 what if you had the model at 65 for some reason. Also,

4 I modeled with uncontrolled emissions at 10,000 tons.

5 I believe this was based on a proposed EGU with

6 controls already in place, so it looked like it was

7 already fairly well controlled maybe.

8 These are the parameters. C1 and C2 are

9 the two emission points we'll be adding stack heights

10 and controls to. C1 is a 780 megawatt boiler. C2 is

11 an auxiliary boiler. C3 is a diesel generator and four

12 and five are fire pumps.

13 The uncontrolled emissions I modeled

14 with for C1 are 290 grams per second. Controlled, I

15 modeled 57 grams per second. Oklahoma modeled 112

16 grams per second.

17 Now, these are the bar charts broken

18 out. Springfield -- so these are the Oklahoma runs

19 here. These are the runs that I did here. As you can

20 see, this red bar and this blue bar -- this white blue

21 bar are the uncontrolled emissions and the base

22 emissions using a 65 meter stack height. When their

23 initial stack heights are 150 and 100, so I modeled

24 them at 65 each. So you can see that makes a huge

25 difference in the concentrations. We did model with
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 144

1 downwash. But except for those two cases, there's no

2 NAAQS exceedances. So, if you were to model as-is,

3 they would have passed.

4 Just some spatial plots for the

5 uncontrolled case with the base parameters, but at 65

6 meter stack height, we get a 905 microgram. Right off

7 the facility property, less than 500 meters. If we

8 model with the base emissions and a 65 meter stack

9 height, we go down to 445 micrograms and you still have

10 a maximum design value of just off the property. But

11 then once you raise the stack height to, you know,

12 original stack height, it goes down to 65 micrograms

13 and the max is actually farther out, but you have no

14 exceedances. And eventually -- this is with

15 uncontrolled emissions and this is with the base

16 emissions and base stack parameters. Nice green

17 background. So 33 micrograms.

18 So, our findings from the EGU are we

19 didn't have any NAAQS exceedances unless we changed

20 stack heights to 65 meters. For the base case and the

21 controlled cases where we didn't change stack height,

22 the maximum design value was driven by the diesel

23 generator.

24 For the uncontrolled cases and for all

25 the 65 meter stack height cases base or uncontrolled,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 145

1 the maximum design value was driven by that big boiler

2 which makes sense.

3 The Springfield case that Oklahoma ran

4 seemed to be more sensitive to changes in-stack height

5 than the Charleston case and that could be a

6 combination of the meteorology and terrain.

7 These results may not be indicative of

8 all EGUs, especially older ones that may not be as well

9 controlled or have different stack combinations and

10 terrain combinations.

11 So, those are our single source. We

12 have three cumulative scenarios that I'll talk about.

13 I'll talk about the first two for NO2 and SO2 and then

14 Erik will come in with the natural gas compressors.

15 The first one is an ethanol plant, fuel

16 or asphalt plant based on AIWG facilities we ran. So,

17 we put them together. It didn't have any NAAQS

18 exceedances.

19 The second one was an NO2. This was

20 from a PSD scenario in Minnesota. They told me it was

21 okay to use it. One receptor exceeded the NAAQS for

22 NO2, but was located on facility property. And the

23 design value was driven by that facility, so -- and it

24 was not the facility of interest. And then, like I

25 said, Erik will talk about natural gas compressors.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 146

1 The first one, the ethanol plant, the

2 turbine and asphalt plant, the receptor grid is

3 centered over the ethanol plant. Here's the fuel

4 turbine and the asphalt plant is out here. I can't

5 remember the exact distances we put these at, so this

6 is probably about -- this should be about 5 kilometers.

7 I think this might be 15 from here to here.

8 Here are the results. No violations.

9 The maximum is somewhere around the ethanol plant.

10 What's interesting here is that here's a fuel turbine.

11 When we ran this in single source mode for the initial

12 AIWG stuff, Hadar from Tennessee modeled it and he

13 modeled in complex terrain and he had violations. You

14 can see those in the summary report, but they were less

15 -- they were maybe about 500 to a kilometer away and

16 you could tell it was driving by terrain. This is

17 relatively flat terrain and the fuel turbine doesn't

18 really cause any issues here. It's kind of an

19 interesting thing to see that the terrain for the fuel

20 turbine did have an effect, but here there's really no

21 problem.

22 This is the one from Minnesota. This is

23 the facility they were modeling for. Their facility of

24 interest. You can see, you know, the dense network of

25 receptors. You know, the fence line receptors around
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 147

1 the different facilities. These arcs down here are

2 part of a polar grid, but then you have a Cartesian

3 grid.

4 Now, the maximum design value is 189 and

5 it was somewhere in here, but it was driven by this

6 facility. So, technically, you wouldn't consider this

7 facility's effect on itself, so I don't think there was

8 a problem there.

9 I'm going to let Erik take over now and

10 talk about natural gas compressors.

11 MR. SNYDER: Okay. This is, we

12 presented this material last summer, but it's one of

13 the first cumulative runs and I think it's kind of

14 pertinent because there's a lot of oil, gas, shale clay

15 stuff going on. That was one of the reasons we looked

16 into this originally.

17 We did a scenario that's basically four

18 compressor stations, assimilating in the DFW area. In

19 the non-attainment area, we've got some rules on NOx,

20 but Texas has put them in most of East Texas for their

21 engines -- put these rules in place to control NOx.

22 And so even outside the non-attainment area to help

23 with the ozone levels.

24 But we've had a large shale clay. One

25 of the first ones in the country was in the Dallas-Fort
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 148

1 Worth area west of Fort Worth and into Fort Worth and

2 Arlington area. So, we're pretty familiar with this

3 type of set-up.

4 We had facilities -- we had four

5 facilities. We had scenario one that was a baseline.

6 We had a small compressor at 28 tons per year. Another

7 facility was 230 tons per year. 165 tons per year.

8 And 135 tons per year. And these were in -- I'll show

9 a map in a minute.

10 In the second scenario we raised, on the

11 engines, we raised the height up to about 17 meters

12 which, from the research we had done and the

13 experience, that was kind of the upper limit of maybe

14 stacks without too much back pressure on the engines.

15 In the third scenario was going to,

16 would incorporate a 35 meters stack height on the

17 engines from a standpoint of you'd actually have to do

18 some induced fan probably to do that.

19 In scenario four was to look at, okay,

20 what if you had one of these facilities that was maybe

21 built in the 80s or something. There are a number of

22 facilities close together like this that was built in

23 the 80s and maybe they had an air field ratio controls

24 was all they really had on the unit. And so you might

25 have a 6 gram per horsepower hour type emission rate.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 149

1 We modeled all four facilities. We

2 looked at 100 percent, 80 percent conversion. So, tier

3 one, tier two, looked at OLM and PVMRM and looked at

4 different in-stack ratios of .1, .25, .5 and used a .9

5 equilibrium ratio.

6 We also did model a lesser controlled

7 facility just using the same thing other than we didn't

8 do the OLM on it, I guess. And short stacks on that

9 unit. So, it's basically any existing facility you

10 might come across initially. This was done with a

11 couple other modelers; Chu Phong and Ashley Moore

12 at Region 6.

13 This is a bar graph and, again, this

14 scenario one is the -- this first scenario here and I

15 do note the scale and nobody's jaw dropped, at least

16 not too far anyway. But this is in micrograms per

17 meter cubed, so this is definitely showing a problem

18 here, especially this last scenario, this SC5, is for

19 maybe one of the existing 80s type facilities that

20 you've run into. Or 70s or something like that.

21 Okay, looking at this from the

22 standpoint of existing facilities that you might run

23 into and if they have to do a project or another

24 facility comes in nearby and constructs and they show

25 we got this other facility that's doing this type or a
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 150

1 combination of facilities are generating this.

2 Can we solve this problem is one of

3 those open questions. So, that's kind of the construct

4 of this scenario set-up.

5 So, we have the baseline scenario and

6 we're still up over 1,000 micrograms with a lot of

7 these scenarios and, just left to right, the blue is

8 100 percent so that's full conversion tier one. Tier

9 two, 80 percent ARM is red. And the yellowish, dirty

10 yellow, is using the .1 PVMRM. .25 is next. .5 and

11 then OLM .1, .25, and .5.

12 And so, but you can see that as we go,

13 even SC2 gets us, I mean, this black line is the NAAQS

14 down here. It gets us below the NAAQS even just

15 increasing the stack heights without doing anything

16 else on controls if you have a well-controlled facility

17 already.

18 As far as if you back off on the

19 controls, it does cause a problem, even if you have the

20 increased stack height. This is scenario four.

21 This is just to bring a scale now. I

22 pulled SC5 out so the historical non-controlled

23 facility or not very well-controlled facility, low

24 stacks, is out of the picture. So, this gives a little

25 bit more context. Okay, with baseline, 100 percent,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 151

1 you know, you run the AERMOD, get the outputs and don't

2 do any slicing of it at all. 1,800 plus micrograms.

3 Using 80 percent, we drop it some. Then looking at

4 PVMRMs, we had three cases, and it's still up over

5 1,000 for the baseline. OLM in this case was quite a

6 bit lower.

7 Roger, you spoke earlier about the

8 differences between OLM and PVMRM and the way they

9 react with low level facilities and so, in some cases,

10 PVMRM might not be the best scenario to go at. It

11 should be -- consider looking at both. We haven't

12 really developed an opinion on which one is the best

13 under each situation. So, we're open to looking at

14 both of them.

15 MR. BRODE: The best one is the one that

16 gives you the best answer.

17 MR. SNYDER: Yes, whatever the best

18 answer is. That may be user-defined.

19 But, again, the increased stack height,

20 the only one that -- 100 percent was a little bit above

21 here, but looking at this, even going to any of the

22 three tier threes we had here you show, and this is

23 just PVMRM runs on the stack height one with 17 meters

24 increases, all these are showing fine as far as you can

25 add the background monitoring and still be in
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 152

1 compliance or most likely. And again, if you add, it

2 shows you can get it done further on the impact levels.

3 Here's some spatial plots. Okay, this

4 is the 100 percent conversion so straight out of the

5 box for scenario one baseline. No stack height

6 increases and the 1838. And, as you would expect, the

7 concentrations are really targeted right around the

8 facilities. And this is -- kind of give you the

9 spatial plot of the facilities. We had one here. We

10 had another one right here on the edge. That was the

11 small one. Another here and then another one down

12 here. And these distances are a couple of kilometers -

13 - zero to two kilometers, so the spacing on these

14 facilities is a little over two kilometers here in

15 direction. So, fairly close knit package of sources.

16 So, we see a fair amount of sources like

17 this with the shale clay where you get a lot of these

18 units fairly close potentially. So, it may be

19 conservative compared to what you run into in a lot of

20 cases, but it may be realistic in some cases. I mean,

21 we know it's realistic in some cases from the stuff

22 we've seen in Texas.

23 This is the cumulative using the PVMRM,

24 so again and I didn't point out on this, but even the

25 scale over here on the right, really, once in the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 153

1 yellows, you have to get up in the mid-yellows to be

2 above the exceedance level, almost into the oranges.

3 Really, it drops off really quick around these

4 facilities. If you had a lot larger footprint on the

5 facility than what we modeled, again, the distance to

6 ambient air, you may not have a problem at all.

7 And this is doing a stage three PVMRM

8 .1. In the analysis and the data sets that we've

9 collected, I mean, .1 may be on the low side for

10 natural gas units. We've seen them lower than .1 and

11 we've seen them higher. .2 levels. .25 on some of the

12 data that I've gotten through the region and some

13 protocols and stuff. So, but again, I mean getting in14 stack ratios, doing that test, to me, is not difficult

15 if you've got existing facilities to do that and then

16 use that data to help drive the modeling. Again, the

17 max is still over 1,000, but your non-attainment zone

18 where your actual modeling exceedance is really small.

19 This is the stage two or scenario two,

20 excuse me. So this is with increasing the stack height

21 to 17 meters and 100 percent conversion. So this is,

22 you know, you almost get there with doing 100 percent

23 conversion, not even doing a tier two, and I don't have

24 that plot, but you would get there depending upon what

25 your background is. But if your background NOx levels
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 154

1 are high enough, you might have to go to tier three,

2 but it looks doable on these short stack, fairly, I

3 mean, these facilities -- if they're fairly well4 controlled, it seems they can pass in a lot of cases.

5 This is, again, this was increases in6 stacks. 17, less controls and, of course, the spatial

7 plot grows a little bit, but still you have everything

8 is in attainment with the 17 meters and using PVMRM,

9 even with the less controlled level than the range of

10 emissions rates we had. I think we backed that one

11 off, though. I think it was in the two to three grams

12 per horsepower hour emissions rate. I'd have to

13 double-check on that exact number.

14 And then this is the cumulative 100

15 percent ratio with the higher stacks, so you can, with

16 dispersion and raising the stacks on these units, it's

17 not a huge tonnage of emissions so it drops it off real

18 quick.

19 And this is the scenario four. This is

20 the stacks with less controls and 35 meters stacks.

21 So, raising stacks along may not solve the situation.

22 From the analysis on this type of industry anyway, it

23 looked like controls were the first thing, but if you

24 had a lot of sources around, but you just have to model

25 and see what works best for the facility.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 155

1 And just for grins, this is what the

2 facility looks like without, you know, a 1980s vintage

3 facility and it does show a huge area of non4 attainment, but the non-attainment levels are basically

5 the dark yellows and you can see the contour line here

6 of the standard and a distance of six kilometers, six

7 and eight kilometers on the bottom.

8 So, you know, we know there's going to

9 be situations where some of these might model this way

10 with PVMRM, but the question is can they be solved or

11 is it not a doable situation? In general, the NOx

12 controls for this industry are fairly reasonable cost

13 compared to some of the other point source controls.

14 Lessons learned, James, jump it at any

15 time. Again, you need to evaluate both controls and

16 stack heights within GEP. Evaluate for low stacks.

17 Small property footprints are still the main problem

18 and so it's not surprising. And I think out of this,

19 again, the stacks that drive it aren't the starch

20 dryers or the ethanol plant or the boilers. It was the

21 emergency unit, but that's fixable with permit

22 restrictions and stuff.

23 Another thing is this is one of those

24 things that it really helps to spend some time getting

25 the facility information together as far as the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 156

1 emissions rates and any information on in-stack ratios,

2 downwash, and getting your property lines. Make sure

3 you've got good property definition for the facility as

4 far as what's ambient air or the start point is.

5 We know a number of states have started

6 working on collecting this as part of the AIWG process

7 as well. And also for the SO2. People are pulling

8 information together out of permits and stuff. Again,

9 emergency units are solvable. And again, NAAQS

10 exceedance is not just tied to emissions levels, but

11 that is an important thing.

12 This last bullet, from the perspective

13 of Region 6 and some of the feedback, I haven't heard

14 from the region other than one facility that they were

15 facing new controls to go forward with and they decided

16 to pull the plug on a project. Other than that, I

17 haven't heard of any SO2 projects being pulled. We

18 have had successful demonstrations in the region and

19 the state. It is a tight standard. We all realize

20 that, but it seems that in our case it's workable. I

21 guess we would support any information and if there's

22 examples and work through those things. Work with the

23 regions.

24 With NO2, again, not necessarily tied to

25 the emissions levels or the predominant sources, but
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 157

1 again, in-stack ratios, getting it for other facilities

2 may be problematic. I've run into that on one project

3 already, but it's important to work with the other

4 facilities and try to get that information and also do

5 the research necessary.

6 The NO2 modeling is sensitive to the in7 stack ratios and, of course, there's differences

8 between the two tier three options and the background

9 data has an impact as well.

10 I just emphasize the importance of this

11 as we move forward as a group, a community. We really

12 need to work together on pulling as much information on

13 NO2 and in-stack ratio data for all types of

14 facilities. Parameters and controls.

15 As far as AIWG, we're continuing the

16 process and we'll be looking more on the cumulative and

17 looking at some other scenarios and stuff as we move

18 forward. Of course, the workgroup is also involved in

19 local permitting, so it depends on their workloads as

20 well. We look to loop in additional experiences in the

21 RSL workshop coming up in another month, month and a

22 half, but that's not that far away. So I'm not sure

23 how much extra it will -- we may have some extra

24 anyway.

25 I think we've got the draft report
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 158

1 created, but it's kind of rough right now, but I think

2 we'll be working on that to really populate it further

3 than what we've done so far in document what we've done

4 so far and I guess that's kind of how I'll wrap it up.

5 I appreciate any input from people on

6 scenarios when they have problems demonstrating. Work

7 with the regions, work with us, and it will get to AIWG

8 as well and we're interested in trying to help tackle

9 those things and work forward.

10 MR. BRIDGERS: What I will say is, you

11 know, James said that there would be some updates made

12 as more maps and more charts are made for the AIWG

13 report. I'm the one who actually uploads the stuff to

14 SCRAM, so as that goes up, we'll make sure that under

15 the recent addition, there will be some note that the

16 report had been updated. Otherwise, it would not.

17 So, somewhat getting this train back on

18 the right schedule, if there is a right schedule, we're

19 going to shift gears a little bit and head into a

20 couple of invited or a slew of invited talks and the

21 first up is something that a lot of us have heard quite

22 a bit about over the last year.

23 So Bob, if I can find your presentation

24 here, we can --

25 MR. PAINE: Hopefully, you have it.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 159

1 MR. BRIDGERS: -- yeah, it should be on

2 here.

3 All yours.

4 MR. PAINE: Okay, this was first

5 introduced last June at the regional workshop and we've

6 made a lot of progress. Unfortunately, the version I

7 have has fellow authors and I would like to acknowledge

8 the contributions of AECOM authors and staff members

9 Dave Heidle and Rich Hamel and EPRI members in the

10 back of the room there. Eladio Nipping and Naresh

11 Kumar. And, of course, they'll answer all your

12 questions.

13 I'm going to talk about the guideline

14 procedures that effect what emission rates you have to

15 use in modeling and how we would deal with variable

16 emission distributions. And a description of a

17 procedure for a Monte Carlo type of approach, which

18 we're calling EMVAP which stands for the Emissions

19 Variability Processor.

20 We've got the code working and have

21 evaluated three of the AERMOD evaluation databases with

22 this procedure and I'm going to report those results.

23 I'll talk a little bit about how the results are

24 sensitive to the number of simulated years going from

25 50 to maybe 5,000 simulated years and conclusions.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 160

1 This is a famous table in Appendix W

2 which says for short term averages you have to model

3 your maximum emission rate, design capacity, and assume

4 continuous operation. And obviously, some sources

5 don't operate at their maximum emissions limit

6 continuously and that's definitely a problem.

7 Some sources may be able to accommodate

8 more than one emission rate which they could assign a

9 probability to and this procedure is designed to try to

10 give a source credit for being able to do that. Some

11 intermittent sources would be, for example, emergency

12 backup engines, but sometimes bypass stacks which

13 operate infrequently but have much higher emission

14 rates during those operations. They present modeling

15 challenges and so assuming a fixed peak one hour

16 emission rater continuously will certainly result in

17 unrealistic model results when compared to a monitor.

18 So, this approach would be to assume a

19 prescribed distribution of emission rates and so this

20 processor which I'm going to describe uses this

21 information to develop alternative ways to come up with

22 a compliance rather than just using one emission value.

23 Here's an example of a time surge over

24 8,760 hours and this is emission rate on the y-axis.

25 You can see that the peak hourly emission rate is about
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 161

1 133 grams per second, but the average is more like

2 about a third of that.

3 So, how do we use this information? Say

4 this was a typical operation for a source. One way to

5 do this is to put this into a cumulative frequency

6 distribution and you can see that about a quarter of

7 the time the source is off. And maybe two percent of

8 the time it's pretty high and to model this, if a

9 source wanted to permit this type of emission rate,

10 they would have to say well, I'm going to model it at

11 maybe 140 grams per second to be safe. Well, that's

12 clearly going to overstate the emission rate for most

13 of the time.

14 One way to do this in our emission

15 variability processor is to come up with a few cases

16 and to put boxes around those cases. As long as we can

17 envelope this cumulative distribution with these sets

18 of boxes and this is just an example. In this case we

19 say well, we'll never operate more than 140 grams per

20 second emission rate, but 98 percent of the time we'll

21 be no more than 100 grams per second and 89 percent of

22 the time no more than 65 grams per second and so on.

23 The average emission rate, the weight

24 average, would be a third of the peak emission rate.

25 In some cases I'm going to describe later, we can
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 162

1 divide this probability distribution in maybe five,

2 ten, or even 20 divisions. Or another way is you can

3 divide this range of emissions by emission rate into

4 five, ten, or 20 divisions. Obviously, the more

5 divisions you have, the closer you come to fitting this

6 curve and therefore, the more closely you will come to

7 actual emissions. And we're going to show how the

8 procedure is sensitive to that type of set-up.

9 So, this approach would be to create an

10 emissions frequency distribution I just showed you an

11 example of. And then model the source with unit

12 emissions for each case. In this past slide I had six

13 different cases, but you could have up to 20 cases in

14 the way the procedure is set up now.

15 Each case is modeled with unit emissions

16 with its own exhaust parameters and even if you add a

17 bypass stack that we even at a different location, you

18 can model that because that's a separate AERMOD run.

19 And then you would create hundreds,

20 thousands of simulated annual realizations of the

21 concentrations distribution by basically rolling the

22 dice, taking the probability, and then applying the

23 emission rate that corresponds to the percentage that

24 comes up.

25 Then we, basically, as a post-processor,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 163

1 there is no change in AERMOD. We compile many, many

2 simulated years of concentrations and then post-process

3 these with a look alike to the AERMOD post-processor.

4 We have basically replicated the AERMOD software to

5 create the right design concentrations.

6 So, how do we do this random selection?

7 Well, in one case -- in some cases, that is, peak

8 emissions might occur in groups of hours, but since the

9 form of the standards of the one hour NO2 and SO2

10 involve only the highest concentrations an hour in any

11 given day, a group of hours in a day only count as one,

12 you know, basically one day's maximum concentrations.

13 So, it's conservative to basically spread these high

14 concentrations out among as many days as possible.

15 That makes the process simpler, but somewhat

16 conservative, so that's what we do.

17 But we also have a procedure where if

18 two different sources operate together, we can use the

19 same sequence of random numbers to make high emissions

20 occur in the same time and if they're not in tandem, we

21 give them different initial random numbers.

22 So, the purpose and definition of this

23 system is to do a probabilistic post-processor for a

24 range of emission rates. We have three different

25 modules in addition to, of course, AERMOD itself.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 164

1 EMDIST is used to look at hourly emissions and decide

2 how to set-up your cases for running AERMOD. Then you

3 run AERMOD with unit emissions. Then you use the EMVAP

4 probabilistic emissions simulator to take the

5 probabilities you've set up, select an emission rate

6 for each hour, apply it to your output concentration

7 for each receptor for each hour of the year up to five

8 years of normal AERMOD runs, and come up with output

9 files which are then fed into the EMPOST post-processor

10 which gives you the output design concentration

11 predictions.

12 I'm not going to dwell much on EMDIST

13 except that it can take into its -- input the, let's

14 say, several years of hourly emissions data, come up

15 with very useful statistics. You also want to do for

16 each case a realistic hourly stack exit velocity

17 emission and exhaust velocity.

18 Let's go to EMVAP. EMVAP is an

19 interesting part of this whole procedure because it

20 takes the number and lists of the years included in the

21 analysis and the number of Monte Carlo simulated years

22 to perform. Obviously, each run would use the same

23 receptors. Each of the cases would be run with AERMOD

24 with identical receptors, but the stacks can actually

25 move. If you're using a bypass stack in some cases.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 165

1 You then use a random, sort of a non2 random random number file, but you can start with

3 different starting points in that file to get sources

4 that are uncorrelated. But sources that are linked can

5 use the common sequences of random numbers for up to

6 ten source data sets, one of which can be -- by the

7 way, you can run AERMOD for a group of sources the

8 traditional way. Combine them with running AERMOD with

9 sources that have variable emissions and have a hybrid

10 approach and even have a concentration file of just

11 background. So, you can add in sources run the old

12 fashioned way and sources run the EMVAP way.

13 Okay, EMPOST will then take the results

14 of EMVAP and give you the required output. You'll want

15 to know how many years you're using, file names, et

16 cetera. The number of modeling iterations that were

17 performed and it will give you the statistics to

18 report.

19 Now, let's go into the evaluation of

20 EMVAP. We developed a working code. We decided to,

21 besides trying it out on prototype examples to see if

22 our own staff could figure out how to run it, we

23 actually tried it on three AERMOD databases that were

24 previously evaluated with actual emissions.

25 But we ran AERMOD with both actual and
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 166

1 peak emissions for those data sets just to see how the

2 evaluation might change if you were required to use

3 what Appendix W says you have to do to compare a model

4 to a monitor. And then we ran EMVAP to see if we got a

5 more realistic result from running the peak emissions.

6 And we ran it over 1,000 simulated years and we would

7 expect that the EMVAP result would be more conservative

8 that the actual emissions because we have this buffer.

9 We're covering the cumulative distribution with a set

10 of cases, but certainly less conservative than using

11 peak allowable emissions.

12 We used the Lovett Generating Station.

13 We've seen that mentioned before. Clifty Creek and

14 Kincaid. So, these are all electric generating

15 stations, all of which had hourly emissions very well

16 documented, and different terrain settings from complex

17 to rolling to flat.

18 Here's Lovett showing the stack here and

19 the hill with the monitors. And this is the frequency

20 distribution of those emissions. Pretty steep. And

21 you would expect that EMVAP might do something about

22 this because obviously modeling this emission rate all

23 year has a lot of conservatism.

24 If you chose, for example, six cases.

25 We chose five, ten, and 20 in different slices, but you
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 167

1 can see that the exit velocity as a function of

2 emission rate will, as you might expect, go up as you

3 get from minimum load to full load and that is

4 reflected in the inputs to various discreet cases.

5 Now, I'm going to explain what this is

6 all about. These are the results of the -- this is the

7 design concentration and these are concentration bar

8 charts here. These are the observed at the controlling

9 monitor. This is the AERMOD with the actual emissions,

10 so this is slightly under-predicting actually. AERMOD

11 with the maximum emissions we're probably over12 predicting by a factor of two. As you can imagine with

13 this emission distribution, the average emission is

14 about half the peak, so no surprise that the peak

15 emission rate run through AERMOD would be something on

16 the order of twice what you got for the actual

17 emissions.

18 Now, here's EMVAP with five, ten, and 20

19 cases with the vertical slices that go along the

20 probability x-axis and then five, ten, and 20 slices

21 doing the emission rates, cutting the emission rates

22 into various sections. As you can imagine, as you go

23 into more and more cases, you get more toward the

24 actual emission condition. In all cases, we are more

25 conservative than the observed and more conservative
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 168

1 than modeling with actual emissions, but much less

2 conservative than with maximum emissions. So, this

3 procedure is giving us tremendous benefit in this case

4 and performing as expected.

5 The next one, Clifty Creek, with six

6 monitors in various directions between Kentucky and

7 Ohio--

8 AUDIENCE MEMBER: Indiana.

9 MR. PAINE: Sorry. Thank you. My

10 geography -- but it's close to Ohio.

11 Okay. We have here a fairly shallow

12 drop off of emissions. So, you might expect in this

13 case, EMVAP might be less helpful and will still see

14 that it is, indeed, the case.

15 We again did five, ten, and 20 slices

16 with vertical slices here and then five, ten, and 20

17 divisions of the emission rate from the peak emissions

18 down here. And this is, by the way, there's three

19 units so I'm just going to show you that they're all

20 similar, sort of flat drop off of emission rates. And

21 then we see, as expected, similar set up here observed

22 design concentration over those six monitors.

23 AERMOD actually over-predicted somewhat

24 with actual emissions and with peak emissions. Over25 predicted some more and you can see that with EMVAP we
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 169

1 got the expected -- not a huge benefit because of the

2 flat drop off, but with more and more slices we got a

3 little bit lower over-prediction here and always higher

4 than AERMOD with actual emissions. Lower with AERMOD

5 with peak emissions.

6 The last case would be Kincaid. Flat

7 terrain. 28 monitors. Lots to choose from here. In

8 this case, sort of a peak, a few percentage pretty high

9 up, so obviously if you modeled with this one you would

10 maybe over-predict by a factor of two and, indeed, we

11 are seeing that is the case. Actual versus maximum, a

12 factor of two difference in just running AERMOD.

13 Actual emissions actually under-predicted slightly.

14 We're seeing again a similar trend.

15 EMVAP again always greater than AERMOD with actual

16 emissions. So, we're getting expected and beneficial

17 results with EMVAP.

18 Sensitivity analysis. You can get not

19 only -- we're using the 50 percentile statistic out of

20 EMVAP. That is, you take 1,000 simulations and you

21 rank them. You take the 50th percentile, but you can

22 take other percentiles and we decided to say okay,

23 depending upon how many iterations I run EMVAP through,

24 how fast does this solution converge? The 50th

25 percentile convergence is pretty fast. After 500
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 170

1 iterations it's pretty flat. In fact, after 50 it's

2 close to converging.

3 Obviously, as you go higher and higher

4 you get more extremes and this keeps going up as you

5 add more iterations because you can get more different

6 selections of random numbers because each iteration

7 starts with a different random number. Although,

8 sources in tandem will start with the same random

9 number. But you can see, even the 90th converges

10 pretty fast and it turns out that for these evaluations

11 we used the 50th and the 50th works out well.

12 Current limits in this code. There's no

13 really effective limit to the number of receptors. We

14 have tested it with 10,000 receptors and the computer

15 hasn't exploded.

16 Source groups to be combined. You can

17 combine ten different source groups currently. Some of

18 those can be groups with constant emissions run the

19 current way or background. Low cases per source group

20 up to 20 with 5,000 simulated years up to five years of

21 real modeling and the typical run time was surprisingly

22 fast. You might think you have to have, well, it used

23 to be a Cray but Crays are old fashioned. Let's say

24 a Linux cluster, but you don't need a Linux cluster.

25 You can do it on your laptop. A few minutes to an hour
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 171

1 maybe. Maybe the time it takes to run AERMOD or maybe

2 five years of AERMOD. Not too bad.

3 And so, conclusions and status.

4 Currently operational. EPRI is beta testing this.

5 Considering implementation approaches.

6 We have found against field data that we like the

7 results. As predicted, we are between actual emission

8 results and especially for peaky types of emission

9 distributions, we are much better using peak emissions.

10 And you can imagine for sources that have very rare,

11 but very high emissions, that could be a much bigger

12 benefit, so if the source can see their way to

13 accepting different emission limits for different

14 probabilities, this may be a way to go. EPRI is still

15 testing this procedure.

16 And I think that concludes my talk.

17 MR. BRIDGERS: Moving right along. That

18 is much easier this time. I turn the floor over to

19 Mark.

20 MR. PODREZ: Thank you.

21 Hello, I'm Mark Podrez from RTP

22 Environmental Associates and I'd like to thank George

23 and Tyler for making time today for this presentation

24 which is on an update ambient ratio method or ARM for

25 performing one hour NAAQS analyses.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 172

1 In EPA's March 2011 guidance, they make

2 the statement that given the stringency of the new one

3 hour NO2 standard, many permit applicants may find it

4 necessary to use the less conservative tier two or

5 three approach for their analyses.

6 Usually, it's the PVMRM or OLM tier

7 three methods that must be used because the current one

8 hour ARM guidance is very conservative. I think as

9 Tyler noted this morning that out of the 26 PSD permits

10 that were issued that had to deal with perform analyses

11 for the one hour NO2 NAAQS, 17 ended up having to use

12 one of the tier three methods.

13 EPA has also stated that at this point

14 there's no preference for any of these methodologies at

15 this time. The test evaluations conducted to date

16 have been somewhat limited and have shown that under

17 different circumstances, one method or the other may

18 indicate better performance or shall I say give the

19 best answer.

20 Now, ARM was originally developed by Chu

21 and Meyers in 1991 and in the report they noted that

22 the plume mixing and near-field NOx chemistry processes

23 can be accounted for through the empirical use of

24 ambient monitoring data. They compile annual average

25 NO2 concentration divided by total NOx concentration
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 173

1 ratios, the ambient ratios, from a large number of

2 ambient monitors and they recommended looking at the

3 90th percentile value of 0.75 as a reasonable upper

4 bound estimate for the annual ambient ratio. And in

5 this ARM method, you simply take the modeled total NOx

6 concentration and multiply it by the ambient ratio to

7 determine the final NO2 concentration.

8 Now, in the more current one hour AMR

9 guidance, the EPA has cited two more recent studies to

10 support the current recommendation of a fixed ratio of

11 .8 for one hour analyses. However, both of these

12 studies as well as monitoring data evaluations

13 demonstrate that the ratios are really variable as a

14 function of time or distance from the emission source

15 and the current fixed value method may be overly

16 conservative, especially when your monitoring very

17 near-field fence line concentrations.

18 One of the studies was the Wang Study of

19 NOx near roadways for short-term monitoring tests.

20 You'll see that the background concentrations for NOx

21 and ozone were very low. Even the maximum measured NO2

22 impacts were really quite low, less than the ambient

23 ozone concentration. So in this case, there is no

24 ozone limiting occurring. In effect, this study is

25 based on such low measured impacts, it may not be
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 174

1 indicative of the processes that may be occurring from

2 higher impacts form point sources where the entrainment

3 of ambient ozone into coherent plumes may be more

4 important as compared to well-mixed roadway emission

5 sources and where ambient ozone concentrations may

6 limit the conversion.

7 Some of the study results. Certainly,

8 some of the ambient ratios measured were variable and

9 EPA, you know, focused on using the highest measured

10 ambient ratio as a conservative fixed ratio for their

11 one hour ARM recommendation.

12 I'd like to present a couple plots that

13 show the variation of the measured ambient ratios.

14 This is first as a function of the inverse of distance.

15 So these are closer in. These are farther data points.

16 Note that the closer in points generally have the lower

17 ratio. Again, this is consistent with the simple

18 conceptual mechanism of ozone being entrained and then

19 subsequently oxidizing to NO2 which really dominates

20 the near-field plume chemistry and as there is more

21 time for dispersion, more time for entrainment, there

22 is more conversion occurring.

23 You may ask why the inverse of distance

24 is plotted here. Here is another plot where the ratio

25 is plotted as a function of NOx, total NOx
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 175

1 concentration. You can see that these have very

2 similar looking graphs and this really kind of

3 illustrates how the NOx concentration can somewhat be

4 thought of as maybe a bit of a surrogate for the amount

5 of time, distance, dilution, reaction that has been

6 occurring.

7 So, a variable ratio ARM method, calling

8 it ARM-2, could be less conservative than the current

9 fixed ratio, more conservative than refined tier three

10 methods, it could fill gap, and if it's based on a

11 large enough set of one hour ambient monitoring data,

12 again it implicitly or empirically addresses a wide

13 range of the processes occurring in the near-field.

14 What would the benefits be? Well, these

15 are really the same benefits that the original ARM

16 technique afforded. It's a simplified screening

17 approach that's easy to implement either in spreadsheet

18 templates or it could be coded rather easily into

19 AERMOD or a post-processor. It does not require

20 detailed in-stack ratio data. It does not require

21 representative ozone data which also avoids having to

22 look further and make decisions about issues of

23 potential ozone scavenging in the data sets. It

24 doesn't really introduce complex offsetting errors

25 between complicated modules in the model. And, you
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 176

1 know, ultimately it would reduce both applicant time

2 for preparing these analyses and agency for reviewing

3 them.

4 So, we looked at a large data set of

5 ambient one hour NOx data. We looked at the AQS

6 database from the last decade of all NOx sites in the

7 U.S. We looked at various subsets of that AQS

8 database. We also looked at some of these data sets

9 that have been used in tier three testing of PVMRM and

10 OLM. The Empire Abo. The Wainwright data set, a new

11 data set that Steve Hanna will talk about more in the

12 next presentation. New Mexico Environmental Department

13 had a big database. And then we got one from the

14 Canadian Oil Sands monitoring network.

15 We have plotted all of these data sets

16 by the observed ambient ratio as a function of NOx

17 concentration. They all show very similar

18 relationships. I'm going to go through them quickly in

19 the interests of time.

20 Empire Abo and the Wainwright data sets,

21 the New Mexico State data set, the Canadian Oil Sands

22 data set, this is a network of six monitors. Numerous

23 IC engines all around them. You will notice that the

24 x-axis on here is larger. There are higher impacts

25 being measured here. Anybody who is looking at this
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 177

1 little tail here, that tail is most likely an artifact

2 of the NOx analyzer over-ranging on the NOx channel and

3 therefore, that is not real world data. And then here

4 are the plot for all rural and AQS monitoring

5 stations.

6 So, in all these plots you see the same

7 trend of lower ratios being observed at higher NOx

8 concentrations. Certainly, there is a wide spread of

9 ratios at the lower NOx concentrations. Well, that's

10 because a low NOx concentration could indicate either a

11 smaller nearby source that has less time for

12 entrainment and conversion and therefore, a lower

13 ratio. Or, a larger, more distant source that has more

14 dilution and more time for entrainment and conversion.

15 So, we try to take some of these data

16 plots and develop a variable ratio curve that could be

17 used for ARM-2. Because of the large number of data

18 points, we took the data and sorted them into bins. We

19 tried to get a reasonable upper bound for each bin. We

20 selected the 98th percentile not because it's related

21 to the form or the standard, it's just a good indicator

22 of upper level ratios in that bin. And this is what

23 such a graph would look like. This is, again, that

24 same kind of rural and suburban data. Each of the

25 diamond points is the 98th percentile for that bin
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 178

1 fitted to a curve. You'll notice that the curve does

2 start dropping off at the highest NOx levels. We did

3 limit these curves then to .15, somewhat arbitrary, but

4 a .15 ratio to kind of represent an in-stack ratio

5 average and so that does agree well with what we end up

6 seeing here at the tail end of the distribution.

7 These are some of the actual ratios then

8 calculated both for the rural suburban data subset.

9 Here's the urban city center subset. You'll see that

10 they're really quite consistent. The urban city center

11 are a little higher, but for example, at 300 ppb total

12 NOx, the two ratios would be .21 and .23.

13 We also looked at various geographical

14 subsets. Northeast, southeast, midwest, mountain

15 states. Southwest. And with the exception of the

16 mountain states which had a much lower number of data

17 points, things are very consistent. I think for the

18 mountain states, when you start getting to bins that

19 have less than 100 or 50 data points, you're basically

20 taking the highest observed ratio. But again, for

21 example, 300 ppb total NOx, the ratios vary from about

22 .2 to .23. This is all sites compiled together and

23 that ratio would be .24.

24 So, we took this curve, we developed the

25 curve for all data sites in the AQS database and we
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 179

1 wanted to do some performance testing in comparison to

2 PVMRM and OLM using the available data sets. The

3 Empire Abo North we looked at. Palaau and the New

4 Wainwright data sets. Basically ran AERMOD to

5 calculate the total NOx and applied the ARM to variable

6 curve ratio. And we ran AERMOD with PVMRM and OLM.

7 One note used on our assumptions from

8 Empire Abu North Site. As Roger was talking about

9 today, we also wanted to address ozone scavenging at

10 the North Site. It's not appropriate to use that ozone

11 data because when you're having impacts, there's less

12 ozone because it's being used to convert to NO2 and so

13 that artificially lowers the ozone values. So, we used

14 the higher of the north and south monitors. And we

15 also used an in-stack ratio of 0.2 instead of the 0.1

16 used in EPA's original modeling.

17 You know, most of these sources at

18 Empire Abo are IC engines and .2 is closer to the

19 current typical guidance. You know, just those two

20 little changes resulted in the highest PVMRM modeled

21 NO2 concentrations being about 30 ppb higher. Almost

22 30 percent higher than the results originally presented

23 by EPA in 2011. So, you know, again, obviously PVMRM

24 is very sensitive to the ozone and in-stack ratio

25 assumptions being used.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 180

1 So, we plotted the performance in Q-Q

2 plots for these different data sets. First Empire Abo,

3 a monitoring station about 1.6 kilometers from the

4 source. I averaged the ten highest monitored

5 concentrations to give you a feel for the impacts. 375

6 for NOx. 91 for NO2. About half of the NAAQS.

7 This is the original reported

8 performance by the EPA in their March 2011 memo and,

9 again, you can see that both PVMRM and OLM Group are

10 clustering pretty close to the one-one line at the high

11 concentrations. OLM is a little low and PVMRM is a

12 little high.

13 This is an updated one. And here, total

14 NOx is blue. 100 percent conversion. The PVMRM is

15 green. OLM Group ALL is yellow. And ARM two is red.

16 So, you can again see that with these

17 updated assumptions that PVMRM and OLM now are about a

18 factor of 1.5 higher, but you will notice that ARM-2 is

19 performing roughly the same for this data set.

20 The Palaau data set. Here the

21 monitoring site is only located 200 meters away from

22 the source. You know, very little time for

23 entrainment, mixing, and reaction to be taking place.

24 This actually might be a better site for assessing in25 stack ratios than for determining how well these
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 181

1 methods address the atmospheric conversion processes.

2 We do see some very high monitored NOx impacts. But

3 not very high NO2. You know, the average ratios for

4 the high concentrations is only about .12.

5 Here's the Q-Q plot for this source.

6 Again, in this case we see PVMRM, OLM, and ARM-2 all

7 performing about the same. They're all over8 predicting, whereas full conversion drastically over9 predicts the highest concentrations.

10 The Wainwright data set is 500 meters

11 from the source. Similar maximum concentrations as

12 Palaau. Similar Q-Q plot.

13 So, I think the conclusions is that,

14 well, a couple conclusions. First of all, the relative

15 performance between PVMRM and OLM, you know, it can

16 vary depending upon the data set.

17 Again, the tier three results can be

18 very sensitive to ozone data and in-stack ratio and

19 really any continued performance tests really should be

20 using the same assumptions as the current guidance for

21 permit modeling.

22 The ARM-2 method is more conservative at

23 lower concentrations. It's assuming very high

24 conversion because it's based on that upper bound of

25 observed conversion at low concentrations. But at the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 182

1 higher concentrations, it really performs comparable to

2 the tier three methods.

3 Finally, we just wanted to do a little

4 sensitivity testing to compare the methods. Again, we

5 used the same data sets that were in the MACTEC PVMRM

6 sensitivity analysis. We did not use the data sets

7 that had downwash because we did not have building

8 structures in the downwash parameters and, in addition,

9 we did a couple of hypothetical source configurations.

10 Really, the conclusions are kind of

11 similar depending upon the data set in one source or

12 one method or the other might give you the best answer.

13 For the EPA original data sets, PVMRM

14 generally predicts the lowest NO2 concentration,

15 although them ARM-2 are similar, except for that last

16 case. The gas turbine and complex terrain where here,

17 ARM-2 is predicting the lowest concentration.

18 Here are some project examples. Again,

19 there is variability which one of these methods ends up

20 predicting the lowest NO2 concentration.

21 So, in summary, ARM-2 is a simple method

22 that's easy to implement. It's straightforward to

23 review. It is more conservative that PVMRM and OLM at

24 low concentration ranges, but at the higher

25 concentration ranges it performs comparably. It could
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 183

1 fill a gap in between the current, more conservative

2 and not as useful one hour ARM and the refined tier

3 three methods. And we believe that the ARM method for

4 one hour modeling should be revised. I think, also,

5 that this points out that additional data sets with

6 higher NO2 impacts at or above the level of the NAAQS

7 are still really needed along with concurrent source

8 data, emission data, are needed to better evaluate

9 these various conversion options.

10 So, I would just ask you to please

11 provide your comments or suggestions as part of the

12 record of these hearings.

13 Thank you very much.

14 MR. BRIDGERS: Outstanding job there,

15 Mark.

16 Humming right along, I turn the floor

17 over to Steve.

18 MR. HANNA: Okay, thank you.

19 The previous talk by Mark Podrez and

20 this talk are both sponsored by the American Petroleum

21 Institute and we've been working -- Mark and I have

22 been working in tandem on this and we -- most of the

23 work that I'll be describing here on this very specific

24 evaluation of PVMRM and OLM was actually carried out by

25 Liz Hendrick and Vin Tino of Epsilon and they were the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 184

1 project managers actually. So, Bruce Egan and I were

2 more of the science people and planning the evaluations

3 and interpreting them. And I also wanted to note that

4 we've been really pleased with the collaboration with

5 the EPA OAQPS that has taken place over the course of

6 this study. We've had, you know, several information

7 exchanges. A couple of face-to-face meetings. And

8 some of the technical suggestions we've made, Roger

9 Brode tested with some of the runs that he reported

10 earlier today. So, we think this is a good example of

11 a collaborative exercise between industry and the

12 agency.

13 Well, there's already been a lot of talk

14 about PVMRM and OLM and Mark just talked about the

15 ambient ratio method. This morning, Roger showed many

16 examples of the evaluations and there's this tier one,

17 tier two, and tier three and so on. And the PVMRM, you

18 might say, is an intermediate it, I would consider it

19 an intermediate approach. It's a very simplified

20 chemistry approach that just looks at the amount of

21 entrained ozone into the plume and then figures out how

22 much NO2 is going to be produced as a result of that.

23 And there are more detailed plume models available like

24 SCICHEM and RPM that include a little more detailed

25 chemistry. So, there are several categories that are
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 185

1 available.

2 And OLM, I guess you might say, is in

3 between the ambient ratio method and PVMRM in terms of

4 complexity. It does what it says it does. It limits

5 the amount of ozone that can be mixed in. So, if

6 there's not enough ozone to thoroughly react the

7 available NO, then it recognizes that and doesn't

8 proceed.

9 Well, what we were trying to do with

10 this specific study is find a new data set. As we say

11 from Mark's presentation and Roger's presentation,

12 there is just a few databases. They only have one or

13 two monitors. There's the power plant plume study in

14 the Netherlands that's included.

15 So, we looked around first trying to

16 identify a better data set and the API is more

17 interested in low level sources and not so much in the

18 power plant plume and the aircrafts flying through

19 them. So, we ended up identifying a particular area in

20 Alaska and it's, as you all in this room recognize,

21 it's often hard to get people to give up their data

22 because it's not too good in some cases to be

23 identified here as a source and plastered across a

24 screen. So, we were very fortunate to have these data.

25 It's what we call the Wainwright data
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 186

1 set, Alaska, and Mark already described most of this.

2 There's a picture of the site. It's just a little town

3 and the power plant is on the edge of town. One

4 monitoring station 500 meters away and then there's a

5 local ASOS met station. But the monitoring station has

6 meteorological data that it's measuring also.

7 There's these five diesel fired

8 generators and stacks. And the picture of the building

9 is here, so you can count five stacks and there were

10 logs, operating logs, of which unit was operating when.

11 Then for the emissions, temperatures, and so on, we

12 just used the manufacturers design criteria for those

13 generators.

14 We tested a few versions of AERMOD and I

15 should stress that OLM and PVMRM have one missions

16 here; to calculate the ratio of NO2 to NOx. So, AERMOD

17 is calculating the NOx concentration and then you just

18 multiply by whatever OLM or PVMRM gets.

19 Roger reviewed this morning some of the

20 scientific considerations that are being considered,

21 like the relative dispersion parameters and whether

22 they should be larger than the continuing dispersion

23 parameters. The use of this N sub Z which is how wide

24 you assume the plume to be when it entrains ozone.

25 Some other things like downwash and whether the model,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 187

1 the multiple plumes, how they're combined. If you have

2 several plumes that combine, they are now, actually,

3 have a smaller total size so there's less ozone

4 entrained into the combine plumes than there would be

5 if they stayed the same.

6 So, we did a few of these model runs.

7 The downwash was done assuming, what you see here,

8 there's the building itself on the upper with a peaked

9 roof and then a couple of storage tanks and a shop

10 building were included in the downwash considerations.

11 There's the local monitoring station

12 that we used about a year's worth of data. Because of

13 the land, air, surface data not being available, we

14 just assumed it was desert shrub land in one direction

15 and water in the other.

16 And in order to narrow things down a

17 little more, we only considered hours in which the wind

18 direction was in a 60 degree sector containing the

19 monitor and we applied about boot software.

20 It was a little difficult to do an

21 apples-to-apples comparison here and defining the

22 thresholds and how the background was going to be added

23 and the minimum value that we would consider. So that

24 was all carefully worked out.

25 So here's the first example, just of the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 188

1 prediction of NO2 to NOx ratio. And we thought we'd

2 see what would happen with stable versus unstable

3 conditions and for different monitored concentrations

4 and none of them seemed to make much difference. And

5 the key thing here, you'll notice, is that most of the

6 observed and predicted numbers are between 0.2 and 0.4.

7 Incidentally, we assumed an initial in-stack 0.2 ratio.

8 So, there's not really much happening here in

9 Wainwright, Alaska. It's only a little bit of the NOx

10 is converted to NO2 in that plume in this location.

11 OLM, on the other hand, because it

12 converts more to the NO2, somewhat over-predicted the

13 ratios. And just looking at the statistics, the

14 highest NO2 value once you link it with AERMOD was

15 over-predicted by about a factor of two, perhaps a

16 little bit more, by OLM. PVMRM, the mean was better

17 predicted by PVMRM than by OLM. And then we have the

18 fractional mean bias and I included a little asterisk

19 there about not significantly different from zero with

20 a 95 percent confidence limit for PVMRM.

21 Now, just doing a couple of Q-Q plots,

22 as I said, AERMOD calculates NOx concentration as if it

23 was an inert substance, so here's what we got and

24 there's just a few of the highest values that are

25 predicted high, so even though this is, yeah, this is
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 189

1 just the straight NOx, so we're predicting maybe a

2 factor or two high, but once you get down to the lower

3 values, it's under-predicting.

4 Now, when you combine it with PVMRM and

5 OLM, and now you're looking at NO2 Q-Q plot and Roger

6 showed these for other places and Mark showed them

7 also. You're a little bit closer with PVMRM than with

8 OLM. The blue curve is full conversion, so that's

9 obviously way, way over-predicting, but was we showed,

10 there's really not that much conversion at this site.

11 These two, this slide is for the

12 original AERMOD assumption of this N sub Z equal to

13 four. And then we change it to 1.28 as Roger described

14 in some of his tests and that didn't make all that much

15 difference here because of the lack of much conversion.

16 So, the limitations, just to summarize

17 now, is just that we don't really have the emissions,

18 we're just sort of estimating them from operating logs

19 and performance data.

20 There's only one monitoring location, so

21 we don't really know what the upwind ozone is and we're

22 selecting only the hours when the wind is blowing

23 towards the monitor, so the ozone that we're using may

24 have been affected. Although, we did do a test of that

25 on one of the sensitivity runs and because it's out in
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 190

1 the middle of nowhere, the ambient NOx and NO2

2 concentrations are lo anyway. So, it's not like you're

3 in a big industrialized area.

4 So, just at this site, PVMRM is doing

5 well. OLM is over-predicting. Both of them, sort of,

6 over-predict the high end concentration, but to be

7 really fair, when you're doing a comparison or trying

8 to evaluation a model, you should look at the entire

9 set of data sets in different locations. Because as

10 we've seen from Roger's presentation and this one,

11 sometimes PVMRM is better. Sometimes OLM is better.

12 It may be due to just AERMOD itself under-predicting,

13 so this is just one piece of information.

14 And finally, what we've been saying all

15 along is why don't we do a real field experiment and

16 get to the bottom of this instead of just dealing with

17 all these data sets with one monitor and listing

18 caveats?

19 Thank you.

20 MR. BRIDGERS: Right on time. All

21 right. Thank you, Steve.

22 We have one more talk before we launch

23 into our Q and A. I will yield the podium to Dan.

24 MR. DIX: Thank you, Roger.

25 We've heard a lot of information today
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 191

1 about how important the OLM method is becoming now that

2 we have a new one hour NO2 NAAQS in place.

3 I'm going to go through a case study

4 that kind of shows how easy it might be to collect this

5 NO2 NOx ratio information.

6 You know, we're collecting this NO2 NOx

7 information for PVMRM and OLM for the one hour NOx

8 modeling and many facilities out there are already

9 collecting NOx data using continuous emission

10 monitoring systems for a variety of either state of

11 Federal programs. These monitors out there, a lot of

12 them are the same and they're collecting the NOx data

13 through the chemiluminescence process. And that's also

14 measuring NO and NO2 information.

15 So, kind of a brief agenda. What I'm

16 going to go over is I'm going to talk a little bit

17 about the project that I brought this up. I'll talk

18 about the current set-up of what the facility had

19 there. Then, I'll also talk about the equipment in

20 place, the equipment that I used to collect this

21 information. Then, some conclusions and additional

22 considerations that came out of the project.

23 You know, we were conducting some

24 exploratory one hour NO2 and SO2 NAAQS modeling for a

25 cement facility. It wasn't for any PSD project. This
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 192

1 particular client was being proactive and wanted to

2 assess their status with the new hour NO2 and SO2

3 NAAQS, you know, for future planning. So, they wanted

4 to see where they fell out.

5 When we did the NO2 NAAQS modeling, we

6 went straight to the tier three and we used the OLM

7 method and we used the default .5 ratio for the NO2 NOx

8 in-stack ratio. You know, this was a cement kiln and

9 what we knew from other studies out there was the ratio

10 was probably more in the ten percent range. So, we

11 decided that we would use their existing NOx monitors

12 to collect some of this information so that we had some

13 site specific NO2 NOx ratio information.

14 So, the current set-up of the facility

15 is that they had a Thermo Scientific 42y

16 chemiluminescence NO NO2 NOx monitor, which is a fairly

17 common monitor set-up to collect NOx readings. The

18 system currently collected just NOx data on their data

19 acquisition handling system and their project logic

20 controllers.

21 Just a little brief background on the

22 chemiluminescence process. You know, what's taking

23 place is that NO is being introduced to O3 and when

24 it's converted, it gives off a luminescence and the

25 luminescence is linearly proportional to the NO
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 193

1 concentrations. So, that's kind of how the monitor is

2 working.

3 A little bit more in detail of the

4 monitor. This is a 42y, flow schematic and what we

5 have is a sample coming in here and there's basically a

6 solenoid here and it goes into two different modes.

7 Basically, you have your NO mode here which is just a

8 pass through. Essentially, all of the sample gas is

9 coming through and going into the reaction chamber

10 here. An O3 converter is introducing ozone into this

11 reaction and then when the NO converts to NO2, that

12 luminescence is then the NO concentration.

13 This solenoid then switches back and

14 forth, usually every ten seconds, and it goes into this

15 other mode known as the NOx mode and this is where the

16 NO2 is converted to NO and now that is sent to the

17 reaction chamber and the ozone is introduced. And now

18 when that NO is converted to NO2 and luminescence is

19 given off, that measured amount is now my NOx amount.

20 And when I take my delta between the NOx and the NO,

21 now I'm getting our NO2 reading. So, this particular

22 type of monitor is measuring NO, NO2, and NOx.

23 So, what we decided was it was

24 definitely easier and more cost effective to install a

25 temporary data logger than it was to set-up NO2
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 194

1 readings in their current data system for a couple of

2 reasons. They were collecting this NOx data, you know,

3 as a permit requirement so they didn't really want us

4 tinkering with that monitor at all. Also, if they were

5 to set-up NO2 readings on our PLC system, it would have

6 required the purchase of new cards as well.

7 So, we decided it would be simple to

8 install our Campbell Scientific SEAR-1000 data logger

9 and we hooked it up to the unused ports on the 42y and

10 we programmed the SEAR-1000 to take one minute average

11 readings of the NO, the NO2, and NOx.

12 This is just some pictures of the set13 up. This is SEAR-1000 here. In this particular

14 picture there's a lot of wires because I set-up to a

15 met tower. I had only three wires coming out of the

16 SEAR-1000 and we hooked them up to these empty slots

17 here on the back of the 42y and the wire that is

18 existing here goes to their data system, so we didn't

19 have to mess with any of these wires or anything. We

20 simply were able to hook up to some existing ports

21 here. One was reading NO, NOx, and NO2 and we were

22 lucky in that the analyzer -- usually these analyzers

23 come set-up to export to these particular ports here,

24 but it's easy to set-up. You can see how many empty

25 slots are on the back of these analyzers.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 195

1 So, here are the results. This is

2 basically a snapshot of the one minute averages. We

3 have the NO NOx readings on top here and then the NO2

4 and I've scaled this logarithmically so we could see

5 the NO2 NOx ratio. Basically, we found what we thought

6 we would see. We weren't above 50 which was good. We

7 were in that six to ten percent range for the in-stack

8 NO2 NOx ratio. So, we felt confident that we could go

9 forward with using ten percent in the OLM method.

10 When we did ten percent and we modeled

11 it for this facility, that was enough to get us below

12 the NO2 one hour NAAQS.

13 So, just some conclusions and

14 considerations that came out of the project. You know,

15 we really found this to be a, you know, cost effective

16 and simple way to collect this information, you know,

17 for use in NO2 modeling for AERMOD's OLM and PVMRM

18 options.

19 You know, a lot facilities operate these

20 exact type of NOXCEMS under a variety of different

21 programs. There's a lot of them out there that could

22 be potentially collecting this info.

23 You know, it was easy for us to set up a

24 data logger. We set it up. We left it there for a

25 three month period and while it was there, we had the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 196

1 facility run the kiln through all the different

2 operating conditions so we could get a good, robust

3 asset.

4 That being said, there's no reason why

5 they couldn't have hooked up their data system to

6 collect this data as well, but for this particular

7 case, it was just easier to come in and put in an

8 external data logger.

9 What we're recommending to our clients

10 for facilities that may not have any NOXCEMS in place

11 is a lot of facilities are also required to do NOx

12 testing every year. So, we're recommending that they

13 talk to their stack testing company and request that

14 they also do NO2 testing during that. I mean, you're

15 only going to get a three hour data set there, so if

16 it's a fairly continuous type of operation, it might be

17 useful. If you're doing RATA testing, you know, it

18 could be a 12 hour period, but you're not going to get

19 as much as if you had NOXCEMS in place.

20 Like I said before, this was a purely

21 exploratory type modeling we were doing. It wasn't for

22 a PSD, but our thinkings are that, you know, if this

23 were to go to a PSD modeling project, that we would

24 include the set-up of all this in a protocol, outline

25 exactly what we did, how we collected the data, what we
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 197

1 used in the protocol so it's out there in front of the

2 stage to see how we collected it.

3 You know, I just took one minute average

4 concentration values from the data logger. I'd be

5 curious if anybody had any other opinions on other

6 different statistical approaches to take for that in7 stack ratio information.

8 As far as QA QC considerations on this,

9 you know, the NOx monitor is being calibrated per

10 requirements. There's a couple of other things you

11 could do. You could send a known amount of NO2

12 cylinder gas to the analyzer to see how efficiently the

13 NO2 converter is working.

14 So, that's pretty much it. You know,

15 this was just a very cost effective, easy way to

16 collect this information and I feel like a lot of

17 facilities could potentially benefit from this so we

18 could collect this information and get it in some type

19 of database so that we have a place to go to to get the

20 in-stack NO2 information.

21 With that, I'll turn it back over to

22 George.

23 MR. BRIDGERS: That's a great talk, Dan,

24 absolutely.

25 Actually, I should have told you don't
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 198

1 run away because we're going to do our Q and A and this

2 will actually span, let's see, Erik and James and Bob

3 and Steve and Mark, so everybody who's talked since our

4 last Q and A, the hot seats are up here in front.

5 The floor is open.

6 MR. PAINE: I have a question of Erik,

7 actually.

8 You did a lot of stack ratio. Now, did

9 you use the version of AERMOD where it extends the

10 downwash above the formula height?

11 MR. SNYDER: We used the standard

12 version, so most of the work -- I think some of the

13 cumulative work, and James you could answer on them

14 other runs you did, but a lot of the oil and gas work

15 we did last summer which is when we did it.

16 MR. PAINE: So you have to raise the

17 stack higher?

18 MR. SNYDER: Yes.

19 MR. THURMAN: Yeah, I think everybody's

20 runs used version 11059.

21 MR. PAINE: You may have to update your

22 conclusions.

23 MR. BRODE: They used the version that

24 does extend it above that.

25 MR. PAINE: Oh, okay. They used the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 199

1 current version. Okay. You're sure?

2 MR. THURMAN: We used the 11059 which

3 introduced that.

4 MR. PAINE: Okay.

5 MR. THURMAN: That change was introduced

6 in version 11059 and 11059 is the version that they

7 used.

8 MR. PAINE: Okay.

9 MR. THURMAN: But it's not the most

10 recent version because that was recently updated.

11 MR. PAINE: What would that do?

12 MR. THURMAN: That would have no effect

13 on these results.

14 MR. PAINE: Good. Okay.

15 AUDIENCE MEMBER: This is Dana Wood with

16 BP.

17 I just wanted to make one clarifying

18 statement, particularly about the Empire Abo data set.

19 It was collected by Doug Bluett with Amoco at the time.

20 It's a BP data set. And during that data collection,

21 there was no actual emissions data that was collected.

22 What was used in the New Mexico evaluation was the

23 potential to emit emissions and so, really, to add to

24 the statement that Steve Hanna made, we really need to

25 get out there and do a field study where we collect
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 200

1 actual, you know, actual emissions data at the same

2 time we're collecting the monitoring data to truly

3 evaluate these models. And I know that's going to be

4 an expensive undertaking and I hope that the EPA would

5 be willing to collaborate with industry and other

6 groups in order to do that.

7 MR. FOX: I think we would agree and

8 echo that same sentiment as follow-up to the conference

9 as to how we might be able to set those things up.

10 Again, as I mentioned earlier in terms of the budget

11 constraints and the like, but I think working together

12 in terms of developing the protocol for the field study

13 and the like and if there are funding sources that

14 could get it done, it would certainly serve a valuable

15 -- it would be of great value to the community in

16 getting that done.

17 AUDIENCE MEMBER: Mike Anderson, TRC.

18 This is a question for Roger.

19 Roger, did the model performance

20 evaluation results that you presented this morning take

21 into account the calculations of downwash from stacks

22 higher that GEP formula height or the use of the

23 AERMINUTE low wind speed data?

24 MR. BRODE: I think downwash was

25 present. I don't know that there were any stacks that
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 201

1 were above formula height, but I'd have to think about

2 the most recent version I ran for those, but both of

3 those cases did have site specific data, so airport

4 data were not relied upon.

5 I mean, that might be a good question to

6 look at if there's a nearby -- near enough by airport

7 that would be more or less adequately representative,

8 but I guess, yeah. The one man data would not be

9 available for those time periods.

10 MR. ANDERSON: Okay. So, the point I

11 was making is that the issue of the effect of the

12 calculation of downwash for stacks above the formula

13 height wasn't addressed there and the low wind speed

14 case hasn't specifically been addressed in those

15 studies?

16 MR. BRODE: Those questions were not

17 paramount in doing those evaluations. I don't know

18 that either of those questions really played a very

19 significant role, but it's something that maybe I'll

20 take a look at.

21 AUDIENCE MEMBER: This question is for

22 Bob Paine.

23 In your EMVAP paper, in a permitting

24 environment, any permit must be enforceable. How would

25 you enforce a distribution of emissions in a way that
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 202

1 the plant managers would be happy with?

2 MR. PAINE: Okay. I'm not going to

3 speak for EPRI because EPRI is developing the tool and

4 not the policy, but I'll put on my consultant's hat and

5 I would say that this would be averaged over a five

6 year period. Let's say. Because the modeling would be

7 over a five year period and you would make sure that

8 your distribution of emissions was within the envelope

9 of what you modeled.

10 So, it could be -- if you had an

11 exceptionally odd year, you could average it with the

12 four other years you're using in your distribution.

13 That's the best possible approach.

14 I don't know if EPA is going to think

15 about that, but that -- okay.

16 MR. BRODE: That was actually my

17 question to Bob the last time he presented it to us.

18 MR. PAINE: I'd like to give you an

19 answer.

20 MR. BRODE: I mean, if you have an

21 operating plant, I could see where you could base it on

22 existing data, but you know, we're open to some ideas

23 on how that might be done. I think that's one of the

24 key questions in being able to move forward with

25 implementing an approach like that.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 203

1 One thing is, the form of the standard,

2 the fact that it's a multi-year average is something

3 that should be taken into consideration. It may give a

4 little bit more flexibility such that if you determine

5 what the, you know, you take a permit condition on a

6 particular emission distribution, you can track it over

7 time and if you tend to overshoot, at least you can

8 maybe make plans to come back into compliance over the

9 multi-year period. That's something that could be

10 considered as part of that.

11 MR. FOX: This is Tyler. I had the same

12 question and it is something for EPA, not the modeling

13 folks, to consider because that would be an important

14 aspect in how you actually put it in a permit limit and

15 monitor it and manage it as such.

16 AUDIENCE MEMBER: Eladio Knipping, EPRI.

17 Actually, Tyler, you're sort of

18 expressing the same type of comment that I was going to

19 make that this is something that is going to require,

20 first, an understanding of the emissions distributions

21 of existing units and understanding how different units

22 of different named clay capacities, capacity factors,

23 control technologies, how they operate, and what are,

24 you know, what types of emission distributions would be

25 acceptable to include in such an exercise.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 204

1 And this is really a discussion that EPA

2 will really have to take the lead on, but we will be

3 providing information and analysis of that type of

4 information so that people can understand how different

5 power plants and the different operating environments

6 and under different operating conditions what their

7 emissions distributions are and what their stack

8 parameters relevant to those emissions distributions

9 are as well.

10 MR. FOX: This is Tyler Fox, OAQPS.

11 That would be very helpful. I think, in

12 addition, to understanding the operating conditions

13 and, I guess, the value or usefulness of that approach

14 in those different conditions. That would be useful

15 for others in the community who have maybe an

16 understanding absent the same type of continuous

17 emissions monitoring information of the nature of the

18 distribution of their emissions or operating cycles to

19 then understand whether or not it would be a valuable

20 approach for them.

21 I guess, along those lines, I'd have a

22 question that -- two fronts. One is, how do you take

23 an approach that is designed for a certain type of

24 source and has data that may not be readily available

25 to other sources and use it across the different
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 205

1 situations for permitting and how do you, you know, tie

2 a new source's distribution that doesn't yet exist to

3 an existing source or a set of existing sources'

4 profiles?

5 I think those are questions that we

6 would like and something to consider in comments from

7 the community on as they review the report and

8 understand this method. As Eladio said, and then try

9 to translate it to what it would mean to them in terms

10 of how they operate.

11 MR. KNIPPING: I just want to say that

12 first the -- with respect to your first comment on how

13 industries or other sources may implement this tool

14 without the appropriate knowledge, well I can't really

15 speak to that, but that should not preclude the use of

16 the tool for this particular application of power

17 plants where we do have that information.

18 Second, as to how an emissions

19 distribution is assigned to a new source based upon the

20 amount of data that we're gathering over many years for

21 1,000 plus units, that is a discussion that we're

22 really going to have to have with the information

23 available. Without that information available, we're

24 really just speculating at this moment and speculating

25 is dangerous.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 206

1 So, let's first wait for the data to

2 tell us what and inform us how we move forward.

3 MR. FOX: Thank you. I appreciate that.

4 I guess my first comment was really to

5 the community at large, for them to think about this

6 approach and relate it to their experience in terms of

7 understanding their operations so that they can then

8 have an understanding and express an opinion through

9 the public comment period for this conference to EPA in

10 terms of the value that they see in this approach.

11 AUDIENCE MEMBER: John Glass, South

12 Carolina DHEC.

13 I think this question is probably either

14 for Roger or Tyler. I wanted to go back to that March

15 memo of last year. The Clarification Memo. We

16 discussed the screening of background sources and I

17 wanted to make sure that I was interpreting that

18 correctly. Some others might be interpreting it the

19 same was as I am and I want to clarify that.

20 You cautioned against the prescriptive

21 application of the old 1990 guidance, you know, causing

22 overly conservative results. Part of that prescriptive

23 guidance would be, you know, you form a significant

24 impact area. You would include all of the sources

25 within that significant impact area. And then you
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 207

1 would screen from 50 kilometers there out.

2 I think the intent of your memo would be

3 certainly that you could apply that significant

4 concentration gradient on the screening area, 50

5 kilometers out from the significant impact area, but I

6 think your memo also implies that you could apply that

7 within the significant impact area.

8 Is that a correct interpretation?

9 MR. BRODE: I think that's consistent

10 with my understanding of Appendix W and that's kind of

11 the main point. It was to focus what Appendix W

12 actually says regarding nearby sources should be

13 considered for inclusion and the significant

14 concentration gradient is the only criterion that it

15 provides. And it goes on to say something like except

16 in rare cases there will be relatively few sources. I

17 forget the exact wording, but yeah, I don't -- I think

18 it's for the whole domain and certainly the closer the

19 source, a background source, is to your source, the

20 more likely it would be causing significant

21 concentration gradients.

22 But also it tried to make a point in

23 that memo that that question, should that source be

24 included or not, is not an isolated question. It has

25 to be looked at from the perspective of what ambient
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 208

1 background concentrations I'm also going to be

2 including and the cumulative impact analysis and make

3 sure that all the different pieces fit together in a

4 way that makes sense for that application.

5 So, that's something I hope people will

6 keep in mind is look at each of those pieces, not

7 independently, but as a whole. Make sure they make

8 sense together.

9 MR. GLASS: Right. And I think that

10 memo also talks about there are certain situations

11 where you would not include any background sources and

12 probably the only way you're going to get at that is

13 using that significant concentration gradient. I

14 think.

15 There may be another way to exclude

16 those within the significant impact area, but I'm

17 thinking that's the only way you're probably going to

18 get at that.

19 MR. BRODE: Well, every case is

20 different and I think that question then gets to, not

21 just where the monitor is, but what metric you take

22 from the monitor to represent background.

23 So, if you take the first highest value

24 across three years or five years of monitoring, that

25 might allow you to justify eliminating more sources
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 209

1 from the inventory than if you used something -- a

2 metric that provided a lower background.

3 MR. GLASS: Right. Thank you.

4 AUDIENCE MEMBER: This is Steve Hanna.

5 I hear the words significant concentration gradient.

6 There's always concentration gradients. How do you

7 know or define when they're significant?

8 MR. BRODE: Appendix W does not define

9 significant in any more detail than that. So, I think

10 that's a good question and that's part of it.

11 MR. HANNA: You'll know it when you see

12 it.

13 MR. BRODE: Well, exactly. I mean, we

14 actually -- one thing is people, we actually provided

15 some examples in, I think, our webinar. I don't know

16 if they've shown it here.

17 We took a source, actually a taller

18 stack and a shorter stack and calculated the

19 concentrations and actually calculated gradients. And

20 one of the things that our March memo points out is

21 that Appendix W just says significant concentration

22 gradient. It doesn't say a gradient in which

23 direction, so there's a longitudinal gradient. Along

24 the path of the plume, there's a lateral gradient and,

25 in my view, maybe the lateral gradient should be given
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 210

1 more weight, in fact, especially for an hourly

2 standard. Because one of the issues is if there is a

3 strong lateral gradient, it means that that plume's

4 impacts may not be adequately captured by a monitor.

5 I think that's, I mean, Appendix W

6 doesn't go on to say why that's the one criterion, but

7 if you think about it, I think that makes sense. If

8 there is significant concentration gradient, then an

9 ambient monitor may not adequately capture that

10 source's contribution.

11 But, you know, we actually did some

12 plots and it was kind of interesting. We might try to

13 do some more. We actually talked about maybe modifying

14 AERSCREEN to output the concentration gradient versus

15 distance or something.

16 There's lots of idea, but there's just

17 too many of them and not enough time to do them, but I

18 think it's a fair question. I don't think that there's

19 a clear answer for it, but we're welcome to hear other

20 feedback or comments or suggestions along those lines.

21 MR. BRIDGERS: And Roger I was just

22 going to add the fact that in addition to SCRAM being

23 the place for MCHISRS and the Clearinghouse actions and

24 Clarification Memorandums and everything else. The two

25 webinars are actually posted on SCRAM. They're linked
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 211

1 under the recent additions, so there's a webinar that

2 was given back a couple of months after the March memo

3 came out for NO2 and then one for SO2 that was given

4 later in the summer.

5 So, if you didn't have a chance to

6 participate, you can go review the slides by going to

7 SCRAM under recent additions.

8 MR. BRODE: I think they actually have

9 the audio with that, too.

10 MR. BRIDGERS: I don't have that link,

11 but it's posted.

12 MR. BRODE: I think it was June 2011 NO2

13 webinar where we included those gradient plots.

14 MR. BRIDGERS: We do have time for more

15 questions.

16 AUDIENCE MEMBER: Hi. My name is Sam

17 Sampieri, Department of Energy and Environmental

18 Protection in Connecticut and, of course, our old

19 Commissioner is Gina McCarthy.

20 My question to you guys -- by the way,

21 Bob, that was a great analysis you did. It would be

22 great for one of our EGUs in Connecticut to use.

23 However, we have a problem with all

24 that, with our SO2 SIP modeling. And the question I

25 have basically for EPA is we have a year and a few
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 212

1 months to submit modeling for our SO2 SIP. We're not

2 going to submit non-attainment. Well, of course,

3 without any further ado to it, we could have some non4 attainment issues.

5 My question is, you would have to change

6 Appendix W to use your type of analysis to put in our

7 emission rates for these big EGU coal plants. That

8 could work to be in attainment, but you would have to

9 change Appendix W and 8.1 or 8.2 table where you would

10 have to use the maximum allowable emission rate and

11 we're going to be looking at pound per hour maximum

12 hourly emission rate for the hourly standard to be in

13 compliance by 2017.

14 So, you would have lean this process to

15 change Appendix W in time so all the SIP modeling can

16 be submitted.

17 Am I off base?

18 Plus, are we going to open up a can of

19 worms if we start letting EGUs doing that kind of

20 analyses and, you know, we're going to start talking

21 about we're getting away from the maximum allowables

22 and we're going to start modeling with actuals or your

23 Monte Carlo analysis.

24 MR. FOX: Tyler Fox, OAQPS.

25 I think, as we asked before, the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 213

1 critical question is whether or not you could actually

2 implement permit limits based on that approach. I

3 think a lot of the questions that we asked in terms of

4 that approach from a technical standpoint as well as

5 actually how you would implement that would have to be

6 resolved.

7 To the point of modifying Appendix W,

8 right now I would say yes. In order to use that type

9 of approach, it's conceivable that it would require a

10 modification to Appendix W and yes, in terms of

11 updating Appendix W in that time frame, that would be

12 an extremely difficult task.

13 That being said, we issued guidance in

14 March that provided some flexibility. So again,

15 internally we would have to work with our management

16 and also the folks in the policy division and the OGC

17 to see whether or not there's existing flexibility to

18 interpret things in a way that would allow that to be

19 done without Appendix W.

20 But again, that would have to be

21 something that we have both the information that Bob

22 and Eladio referred to, have an understanding of how it

23 would actually be implemented in a permit limit, and

24 then work the chain within EPA to see whether or not

25 that's a feasible approach.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 214

1 We're understanding and trying to be as

2 flexible as possible, you know, working through

3 guidance rather than through rulemaking. You guys know

4 very much, as you've indicated, the rulemaking process

5 sometimes does not work with the existing time frames

6 for other compliance purposes.

7 MR. SAMPIERI: It's one thing to talk

8 about new source permitting, but it's another thing now

9 with the SIP modeling that's going to come due fairly

10 shortly and, you know, do we have the time to do all

11 that and so, is that one of these things where we'd be

12 doing a case-by-case basis? But that's going to be in

13 a SIP, you know, and each state, of course, has its own

14 issues.

15 Thank you.

16 MR. FOX: Thank you for that question.

17 AUDIENCE MEMBER: Dick Perry, Stinger.

18 I was interested to see Monte Carlo

19 raising its head again because about , oh, eight or ten

20 years ago, John Chadwick and I did a study of running

21 of looking at heavy metal variability running through a

22 kiln, a cement kiln processor and in that Monte Carlo

23 analysis and something you may want to look at, we did

24 one further thing after we had run this. I got the

25 statistics. We looked at the likelihood of that
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 215

1 maximum potential to emit ever occurring and found that

2 it was out at 33 standard deviations from the center

3 line which, you do the math, and it's an unimaginable

4 number of years.

5 So, the point there is that between 33

6 standard deviations and what you're likely to see,

7 you've got a lot of room for maneuver to make things

8 more reasonable and still give yourself a comfortable

9 margin of conservatism on how you structure the

10 emission variability.

11 MR. FOX: I have one more question for

12 Bob. This is Tyler.

13 In terms of the distribution that you

14 showed, do you understand or know what's causing that

15 high end of the distribution? Are those peaking units

16 or, you know, start-up shut-down emergency generator

17 type of use that may actually be remedied through the

18 treatment of intermittent sources or are those, you

19 know, base units operating at their allowable levels

20 just in an -- infrequently?

21 MR. PAINE: Well, for the AERMOD

22 evaluation data sets and Roger, you may have as much

23 understanding as I do, these were back intersection he

24 1970s and 80s. It could be just a slug of coal with

25 high sulfur emissions. I don't think it was a bypass
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 216

1 stack operation or anything like that. It was just a

2 delivery of coal or maybe -- because I don't think that

3 they had any controls at these sources. So, it was the

4 old fashioned sulfur variability.

5 MR. FOX: Okay.

6 MR. BRIDGERS: I know we're running just

7 a few minutes long on the session, but we'll entertain

8 a few more questions. We had a lot packed into this

9 session and with the remainder of the conference, we

10 only have one more Q and A time before we get to the

11 public presentations tomorrow.

12 MR. HANNA: This is Steve Hanna. I have

13 another question about this Monte Carlo.

14 If you have to model multiple sources in

15 the domain, do you account for correlations between the

16 emissions of the different sources?

17 MR. PAINE: Well, you can by having the

18 same random number starting point for sources that

19 would be correlated and if they're not, you can add

20 different random numbers starting point. So, you can

21 accommodate that.

22 And of course if sources are not to be

23 run with variable emission distribution, you can run

24 them normally, save those concentration files and just

25 add them into the EMPOST processor. So, you can
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 217

1 accommodate variable sources and constant sources in

2 the whole process.

3 MR. FOX: This is Tyler.

4 That actually is a very good question,

5 especially in the context of a SIP situation where it

6 may be a multi-source area. So, how to handle that

7 would be a useful thing to maybe put some information

8 out there on. Or thoughts.

9 MR. BRIDGERS: Okay, we'll do this like

10 the auctioneer.

11 Questions going once? Ah, we'll just

12 cut to the chase. Sold.

13 I appreciate everybody being patient

14 through the session, if we could give our speakers

15 another round of applause.

16 Let's go ahead and take until five

17 minutes after three. That's just a few minutes under

18 15, but that will hopefully keep us on schedule to get

19 out of here.

20 So, thank you.

21 (WHEREUPON, a brief recess was taken.)

22 MR. BRIDGERS: I guess I will call Tyler

23 Fox to the microphone. Paging Tyler Fox.

24 MR. FOX: All right. The room doesn't

25 seem as full. I guess everybody is still taking their
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 218

1 break.

2 We're going to go ahead and start

3 introducing this last session for the day. Very much

4 appreciate the presentations that we've had up to this

5 point. They've been very valuable and very productive

6 in terms of informing us and hopefully the community

7 and we look forward to further engagement on those new

8 methods, techniques, and the like.

9 Similarly, we're going to be introducing

10 some of the work that EPA has done jointly with the

11 FLMs and try and engage similarly with the community

12 and get your thoughts and comments on this work and how

13 we're approaching handling the issues that we see in

14 terms of potentially updating Appendix W to meet

15 ongoing emerging needs.

16 So, over the past years, at least three

17 or four, we've been engaging with the FLMs and we've

18 had various meetings in terms of the issues related to

19 the long range transport applications and issues

20 related to chemistry.

21 It's clear, over time and particularly

22 now, that our interests and needs are overlapping

23 across a number of multiple programs and regulatory

24 responsibilities that we have.

25 Examples include the NEPA air quality
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 219

1 analyses for energy development on Federal lands. We

2 went through a process with EPA, Department of

3 Interior, and the Department of Agriculture to sign an

4 MOU on going forward with consistent and credible

5 analyses under NEPA and that took a huge step forward

6 in terms of a number of issues related to long range

7 transport and chemistry and it shows the commitment of

8 the Federal partners to work together and try and make

9 things both more technically credible, but also respect

10 the resources that are required to conduct these types

11 of analyses.

12 And most recently, I've mentioned in

13 previous talks EPA has granted the Sierra Club petition

14 on ozone and secondary PM2.5 models and we are

15 considering in engaging in a process starting with this

16 conference and potentially updated Appendix W to bring

17 those types of models in.

18 I will say that we will be following the

19 IWAQM process for conducting the necessary evaluations

20 and reporting of those evaluations and engage with the

21 community in the same manner in which we did previously

22 and that resulted in the 2003 promulgation of CALPUFF.

23 In addition to the Federal partners

24 viewing, you know, the needs and the like, we have

25 comments from the most recent modeling conference from
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 220

1 the ALMA AB-3 committee that also provide information

2 and support for moving in this direction.

3 The comment areas included the need for

4 regional models, Eulerian models, best uses for

5 Lagrangian and Eulerian models, and where does EPA go

6 from here.

7 So, I just wanted to pull some of these

8 comments. In terms of needs for regional models, they

9 identified the fact that ozone and PM are pollutants

10 involving precursors and trans-chemistry in transport

11 and that our current Appendix W do not address these

12 needs.

13 Just for everybody's clarification, we

14 addressed the use of photochemical models in a separate

15 guidance document. The SIP modeling guidance for

16 ozone, PM, and regional haze SIPS and attainment

17 demonstrations which does link to Appendix W and

18 follows the same type of approach in terms of an

19 alternative model and applies that and, as you all

20 know, models such as CAMX and CMAQ are the workhorse

21 models in the SIP context. And that EPA needs to

22 provide procedures for modeling PM2.5 and I think that

23 we recognize that. We're working on draft guidance in

24 the permitting context that gets at single source

25 issues and so we certainly would agree with those
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 221

1 comments.

2 In terms of the Lagrangian models, they

3 are suitable for individual sources, but there could be

4 a resource penalty in terms of running them. And the

5 chemistry, at that point, was limited. Ozone is not

6 modeled and PM2.5 can be modeled in certain degrees of

7 sophistication.

8 In terms of Eulerian models, there's

9 issues in terms of, you know, conserving mass and the

10 like with respect to these models. I think a number of

11 the analyses that we have done or are doing understand

12 those points and are trying to address those points.

13 And obviously we're dealing with situations where we

14 have complex, non-linear chemical conversions and at

15 sometimes long distances for regulatory purposes.

16 And in terms of approved use of these

17 models, there have been evaluation studies, but EPA

18 should have system to determine acceptable criteria for

19 approving these models. And the models accuracy is

20 good in some areas and poor in others. Continue to

21 work on consistent evaluation approaches recommended,

22 similar to short range models.

23 Obviously, looking at those comments and

24 looking at the direction that we're moving. We believe

25 that we're responding in line with those comments and
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 222

1 our needs, as I mentioned, we've been discussing with

2 the Federal Land Managers and other Federal partners.

3 We -- this is a slide from a previous,

4 earlier presentation. As I mentioned, we'll follow the

5 same type of IWAQM effort. We've defined it as phase

6 three and we're focusing in on that next generation of

7 model to meet those needs such as single source ozone

8 and secondary PM2.5 and AQRVs.

9 And our program needs and commitments,

10 again, have made it clear that we need to address the

11 long range transport and chemistry issues and have made

12 that a high priority for us.

13 In terms of meeting with the FLMs, we

14 initially talked about the needs and attributes for

15 models as part of the process and previously under

16 IWAQM, the group put out a models attribute study or

17 report. We would feel as if we would follow suit and

18 provide that type of document in the near future.

19 These are some of the needs and attributes that were

20 discussed as part of those interactions with the

21 Federal Land Managers.

22 As we move forward, as has been pointed

23 out throughout the conference thus far, evaluation is

24 at the heart of our ability to move forward and have

25 the confidence necessary to use and, in this case,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 223

1 promulgate new models and techniques.

2 There are a number of historical efforts

3 that looked at these things. EPA's 1986 eight model

4 study based on the Savannah River and Oklahoma

5 mesoscale experiments and then the use of those

6 throughout these different efforts that ultimately led

7 to the promulgation of CALPUFF were critical in that

8 process.

9 However, there was some lessons learned

10 in terms of these evaluation efforts. There is no EPA

11 recommended methodology for evaluation of long range

12 transport models and there's no consistent approach

13 between the efforts in the 80s and 90s.

14 The evaluation methodology used all

15 published AMS metrics and data organization strategies.

16 This did not take into consideration the regulatory use

17 of the long range transport models and the schemes for

18 weighting those may not have been appropriate.

19 We observed high sensitivity of these

20 models to meteorological inputs. And we see the need

21 for more objective meteorological performance

22 evaluation measures as part of moving in the future.

23 And there were no data sets available to

24 evaluation chemical transformation mechanisms with the

25 long range transport models.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 224

1 So, in terms of working forward and, as

2 mentioned previously, a fit for purpose type of

3 paradigm, we feel compelled to define our performance

4 objectives by starting with the regulatory use of the

5 models. And our current use of these models include

6 PSD class one NAAQS and increment analyses. The AQRV

7 analyses. A visibility and deposition going on. And,

8 in terms of potential future uses, the single source

9 ozone and single source PM2.5 analyses.

10 So, in terms of an evaluation framework

11 moving on, we believe that the evaluation of these long

12 range transport models within their defined regulatory

13 niche requires an evaluation of these three independent

14 components of the system; the meteorological

15 component, the advection and diffusion component, as

16 well as the chemical transformation.

17 In terms of the inter-comparisons, the

18 modeling comparisons, the long range transport models

19 performance is going to inherently be linked to the

20 suitability of the meteorological inputs, so you get

21 out what you put in and so we need to make sure that we

22 address those and that we use a common source of

23 meteorological data across these modeling systems when

24 we evaluate them to reduce potential contribution of

25 the differences put into those analyses by the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 225

1 meteorology. And that was one of the motivating

2 factors for the beta release of MMIF, the Mesoscale

3 Model and Interface tool. Program. Sorry.

4 At least I didn't say BARF, right Bret?

5 And, you know, the meteorological model

6 performance is necessary and is integral to any part of

7 an evaluation framework and, as we talked about, we put

8 forth the MMIFSTAT tool to facilitate those types of

9 evaluations and the visualization tools to facilitate

10 the use of that information for visual inspection of

11 these data in a number of different frameworks that

12 exist in different parts of the community.

13 In terms of the single source chemistry

14 evaluations, application of these models for chemistry

15 usually only involved in individual or a small set of

16 sources and traditionally, photochemical model

17 evaluation techniques the chemistry evaluation are

18 combined with the inert Tracer evaluation to evaluate

19 the suitability of a model. The best performing

20 chemistry model will only be as good as its ability to

21 treat advection and diffusion appropriately.

22 So, we are taking these things into

23 account and trying our best, as you'll see in the

24 following presentations, to put forth an evaluation

25 paradigm and put the models that are currently
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 226

1 available to the community through their paces

2 consistent with the regulatory applications that we see

3 these fulfilling under the guideline and then letting

4 that information go to you in the community and getting

5 your feedback on that.

6 So, with that, I'll turn it over to Kirk

7 Baker.

8 MR. BAKER: Thanks, guys. Thanks,

9 Tyler.

10 I'm here, basically, to provide an

11 additional layer of overview to Tyler's overview and to

12 fill in some of the gaps between Roger's talks.

13 So, I'm going to talk a little bit today

14 about some of the work that we've been doing to get a

15 better idea for what types of tools we have and how

16 well they work and in terms of looking at the secondary

17 impacts from single sources, so a lot of this is a work

18 in progress. So, as we go through this talk I'm going

19 to show and the talks after this, just keep in mind

20 that this is a work in progress. All of this is

21 evolving. So, if you see something that looks really

22 cool, I wouldn't get too excited or I wouldn't get too

23 upset about stuff you're seeing because it's likely to

24 change as we go through this process of understanding

25 these things better.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 227

1 So, what I've got here that's probably

2 kind of hard to see in the back, I just got a chart

3 showing some of the different scales of modeling and

4 types of modeling we do. We do single source modeling

5 assessments at fence lines, urban scales. We do all

6 sources, types of assessments for the purposes of

7 projecting design values for model attainment

8 demonstrations of the NAAQS.

9 And where I've got some of the yellow

10 areas that we're trying to focus on learning more about

11 single source impacts of secondary PM2.5 and ozone near

12 the fence line for permit applications, urban scale

13 types of impacts, and long range transport assessments

14 for things like ozone.

15 So, I'm going to talk first a little bit

16 about some of the work that we're doing for looking at

17 long range transport of ozone, PM2.5, and deposition at

18 class one areas and then talk a little bit more in19 depth about some of the single source modeling we're

20 doing looking at fence line and urban scale types of

21 evaluations.

22 So, for the long range transport, this

23 is important to do primarily for NEPA type of

24 assessments and we're looking at a lot of existing and

25 alternative types of modeling systems for PM and ozone
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 228

1 in combination. A big step for this was Brett having

2 the foresight to develop the MMIF tool that he talked

3 about yesterday, so now we've got a program that can

4 translate prognostic meteorological model output into a

5 common platform for a variety of dispersion,

6 Lagrangian, and photochemical models. So, when we

7 start comparing all these types of modeling systems,

8 we're starting from as consistent a place as possible

9 with the meteorology and that's very helpful.

10 So, with that tool in this beta form is

11 currently available on SCRAM and we're actively

12 supporting that internally in our group and with

13 Environ.

14 So, a lot of the work that has been done

15 is currently being done through contract with Environ

16 and this is going to be talked in more detail in

17 subsequent presentations.

18 So, one thing that Tyler touched on was

19 how well do these modeling systems just transport mass

20 around a large domain between source and receptor? So,

21 one useful way of trying to evaluate that is comparing

22 modeled -- the modeling system predictions for Tracer

23 release experiments.

24 So, we have a big report where we've

25 compared a variety of different Lagrangian and
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 229

1 photochemical modeling systems to several different

2 Tracer release experiments like ETEX in Europe. The

3 Great Plains Tracer experiment and CAPTEX in 1983 that

4 people are pretty familiar with. And as part of that

5 evaluation we looked at SCIPUFF, CAMX, HYSPLIT,

6 FLEXPART and CALPUFF in different combinations for

7 different field experiments.

8 In addition to that, there's a second

9 report that Environ is working on which is more of a

10 consequence analysis and Bret's going to talk more

11 about that later.

12 Notwithstanding how they compare with

13 Tracer experiments, it's just how consistently do these

14 models predict air quality and air quality related

15 values like deposition at class one areas?

16 So, what the plot is on the bottom is

17 some results from that report that show CAMX as the two

18 bars on the left for total nitrogen deposition at one

19 particular -- at a few different class one areas and

20 CALPUFF total deposition at a few areas on the right

21 and we can see that they're different. We're not

22 trying to say that one is better than the other, but we

23 just want to understand what these differences are.

24 And I think in the end, when we apply models as

25 consistently as possible and against each other, we're
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 230

1 probably going to learn things about both of them and

2 make both of these types of systems better and have a

3 better comfort level when we try to apply them for

4 these types of purposes.

5 So, switching gears back to the urban

6 scale, we're looking at the single source impacts of

7 PM2.5 and ozone for PSD NSR purposes. Tyler talked

8 about the Sierra Club petition, so now we've made an

9 agreement to come up with some modeling guidance to put

10 some information out there to provide some steps for

11 people on how to do these types of assessments.

12 So, we're going to build off things that

13 are already out there like the NACAA recommendations

14 for doing single source assessments for PM2.5 for the

15 purposes of PSD NSR. Evaluate some of those approaches

16 and see how well we think that's going to work for

17 these purposes.

18 In addition to that, we think a lot of

19 the work as we go through this process is going to

20 inform and help improve technical basis for

21 establishing inter-pollutant trading ratios for PM2.5.

22 So, NACAA recommended a multi-tier

23 approach and, in general, the approach just kind of

24 goes from a more simple approach to a more complex.

25 So, they start off with just applying AERMOD and taking
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 231

1 the primary PM2.5 estimates and adding offset ratios to

2 estimate secondary impacts at receptors near the

3 source. One issue with that is it can be kind of

4 complicated to come up with really good offset ratios

5 that are location and site specific.

6 So, moving forward from that, there are

7 more complicated approaches like using Lagrangian puff

8 or particle models that have secondary chemistry or

9 using a photochemical modeling system. They

10 recommended all three of these things as a possibility.

11 And so when we get to something like a

12 photochemical model simulation, there are a lot of

13 different ways to track the single source contribution.

14 We need to do a lot of work and we're just starting to

15 do that work to try and understand which approach is

16 going to be the best or are there a bunch of comparable

17 approaches that are going to be equally appropriate for

18 these types of purposes.

19 So, we're working with contractors,

20 other Federal agencies to try and figure these things

21 out.

22 One thing that we're thinking about is

23 the feasibility and utility of a screening level tool

24 that would provide quick, reasonable, and credible

25 single source secondary impacts before you got to more
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 232

1 complicated modeling systems. And just thinking along

2 those lines, where do you draw the line between using a

3 screening level tool and more complicated tools like

4 Lagrangian puff models and photochemical models?

5 And then when we get to these places

6 like a photochemical model, like I said before, how do

7 you apply it in a way that's going to be appropriate

8 for these purposes?

9 Ralph's going to talk a little bit later

10 about a possible screening level tool. At CMAS last

11 October, Environ presented a reduced form single source

12 screening model that estimates ozone impacts from VOC

13 and or NOx emissions based on CAMX hired order DDM

14 modeling that was done for the City of Sydney. So,

15 they wanted to come up with a screening level approach

16 for their permitting purposes before they required

17 sources to do a full scale photochemical model

18 assessment.

19 So, we think this approach may

20 potentially fill a need for a technically sound

21 screening tool to allow us to efficiently evaluate what

22 sources would be required to do more rigorous types of

23 assessments and hopefully this type of tool could also

24 help improve or give us a better inter-pollutant

25 trading ratios as we go forward.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 233

1 For single source modeling, we've

2 currently got three reports being compiled by Environ.

3 I've touched on the top two already. We're hoping

4 that, even though they're geared more towards long

5 range transport assessments, but I think a lot of the

6 lessons we've learned and the information there is

7 going to be helpful on an urban scale, too.

8 The third bullet on the top, the

9 evaluation of chemical dispersion models using

10 atmospheric plume measurements from field experiments,

11 that's the one I have not touched on.

12 So, for an urban scale in the near13 field, how well do these models perform and one way to

14 try to assess that is to compare the model estimates

15 from a particular source to field measurements that are

16 made.

17 There have been many different field

18 campaigns over recent years where helicopters or

19 airplanes have flown through the plume of a particular

20 source, so we're hoping to use some of those

21 experiments as an approach to try to get an idea of how

22 well some of these models are performing for these

23 purposes.

24 The report is focused on two different

25 field campaigns. Tennessee Valley Authority
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 234

1 measurements in 1999 from Cumberland and measurements

2 made as part of TEXAX 2006 around Oakley Union Plant.

3 So, those are the two field experiments that are being

4 evaluated at and the models that we're evaluating there

5 are SCICHEM, CAMX, and CALPUFF. Both the regulatory

6 version of CALPUFF and the newer version six series of

7 CALPUFF.

8 So, we expect that report to be

9 available in early April and that's going to be talked

10 about in a little more detail later.

11 Jim Kelly is going to talk a little bit

12 about some of the SCICHEM work we're doing in-house.

13 We've evaluated against the 1999 TVA field experiment.

14 We're learning a lot about the application of SCICHEM

15 and we're hoping that work is going to lead us towards

16 better understanding the model and finding the best

17 approach to ramping it up to use it in a regulatory

18 fashion where we would apply the model for multiple

19 years.

20 The other thing that we're doing is

21 we're comparing a variety of different photochemical

22 modeling systems, single source approaches, to the 1999

23 TVA field experiment. And this is just a plot of SO2

24 on the top and NO2 at the bottom that Jim put together

25 for the TVA case from SCICHEM.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 235

1 The photochemical modeling that I just

2 mentioned, there's a variety of different approaches

3 that we're using in the photochemical models and a

4 variety of photochemical models. We're starting off

5 simple using CAMX and CMAQ and a brute force approach

6 where we just zero out the facility.

7 And we've also got an additional

8 sensitivity approach where we're using higher order DDM

9 where available, DDM to track the emission sensitivity

10 from that particular source to see what its

11 contributions are. Just -- all of these techniques are

12 just a way to isolate the contributions of a particular

13 source and we're comparing these to the field campaign

14 measurements.

15 In addition, we're looking at CAMX

16 source apportionment. When it becomes available, we're

17 going to be adding in CMAQ source apportionment.

18 When we've got issues with proximity, a

19 lot of times the fence line and the source are a lot

20 closer to each other than the grid resolution that

21 we're using in the photochemical model.

22 So what do we do in those situations?

23 So, we want to look into using ultra24 high resolution fine grid nests around the source

25 looking at sub-grid and plume treatment and sub-grid
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 236

1 and plume sampling to see how well that's going to work

2 for these types of purposes.

3 And, as we move forward, we're hoping to

4 fold into that the new CMAQ sub-grid plume treatment,

5 APT, that's being developed by EPRI.

6 The modeling set-up that we're using for

7 this evaluation, it looked pretty good on my screen up

8 in my office, but not so good here. So, on the right

9 is the photochemical modeling domains. We've got a 36

10 kilometers continental U.S. domain and we're nesting

11 down with a 12 kilometers domain which is about the

12 size of the big box. And then it's kind of hard to

13 see, but around this green dot we've got a smaller,

14 four kilometers domain, and that's what I'll be showing

15 results from the four kilometers domain today.

16 Preliminary results.

17 So, for the TVA case in 1999, we're

18 using 1999 CEM emissions for NOx and SO2 from

19 Cumberland. In addition to that, we're using 1999

20 biogenics and the other anthropogenic emissions are

21 based on the 2001 NEI.

22 WRF 3.3 was used to feed met inputs to

23 CMAQ 4.7.1 and CAMX 5.40.

24 So, just to kind of show comparability

25 in estimates in a photochemical model from a single
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 237

1 source, there's kind of a lot of things going on here,

2 so I'll walk you through what each of these tiles are.

3 What you're looking at, in each tile is the two week

4 episode maximum of NOx from this particular source.

5 So, on the top row we've got CMAQ

6 estimates and on the bottom row are CAMX estimates.

7 On the top left is the CMAQ brute force

8 simulation, so just from zeroing out emissions from the

9 facility, this is the two week maximum contribution

10 that we're seeing.

11 In the top center is the contribution

12 based on DDM where we did not do any brute force

13 sensitivities. We just used DDM, the approach, to

14 track the contribution from that source through the

15 photochemical model and you can see that those two

16 approaches come up with pretty consistent answers.

17 On the bottom we've got the same types

18 of things with CAMX.

19 On the far left is the brute force zero

20 out run.

21 In the center, using higher order DDM.

22 On the right is source apportionment.

23 Again, they're pretty consistent, but

24 there are differences and as we move forward, we want

25 to better understand these differences and figure out
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 238

1 what the reasons are, if they're important differences

2 or not when we start using this modeling for permits.

3 On the top right, we're continuing to

4 work with ORD to implement source apportionment into

5 CMAQ and once we have that, we'll have the sixth tile

6 on this plot.

7 Looking at the same thing, but this time

8 this is the two week episode maximum impact from

9 primary elemental carbon from this source. So, the

10 tiles are the same thing here on the top. You see the

11 CMAQ brute force zero out of elemental carbon compared

12 to DDM and they're pretty comparable in their

13 estimates.

14 On the bottom, you've got CAMXs results.

15 We don't have -- the most recent version of CAMX does

16 not have DDM for PM2.5 species, so we don't have any

17 information on that, but the source apportionment on

18 the right and the brute force on the far left are

19 pretty comparable. The differences you see in some of

20 the spatial extent of the plumes are due to differences

21 in the horizontal and vertical advection schemes in

22 these two models and it's also a function of the color

23 scheme that we're using. That tail to the right of the

24 plume is there in CMAQ, too. It's just not quite as

25 pronounced to show up as different colors on this plot,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 239

1 the lighter blue.

2 As I mentioned before, there's going to

3 be times we've got a source receptor proximity issue

4 and this just shows an example of that. If you're in

5 the front couple of rows and can see it. The yellow

6 boxes are one kilometer grid cells and the source in

7 question is outlined in green. At the monitor -- the

8 impacts are being assessed at is the letter A. The B

9 monitor is a meteorological monitor. You can see

10 they're all in pretty close proximity to each other and

11 the one kilometer modeling cells do not really

12 adequately represent this proximity relationship.

13 So, we want to look at a variety of

14 different approaches and this is kind of the next step.

15 We have been able -- we don't have a whole lot of

16 results for you yet, but we're going to evaluate the

17 sub-grid and plume treatment and looking at finer

18 nesting to look at and see how we can resolve these

19 relationships when we've come into these types of

20 problems.

21 So, in the end, our goal is to have a

22 really sound and useful modeling guidance for people to

23 go out with when we start doing single source

24 assessments for PM2.5 and ozone and so I think that all

25 this work that you're going to be hearing about after
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 240

1 me is going to feed into that, including the things

2 that I just showed you.

3 MR. BRIDGERS: All right, Ralph.

4 MR. MORRIS: Thanks, George. I'm Ralph

5 Morris with Environ. I think I've heard my name

6 several times here, so it's good to be up here

7 defending myself.

8 Anyway, I'm going to talk about the long

9 range transport evaluation using the Tracer test. This

10 report has been up in SCRAM for over a month. It's

11 only 260 pages, so I'm sure you've all read it by now.

12 I did provide a summary. The 35 pages is actually the

13 summary, so if you want to read that. Anyway, it's

14 been up there for a while. It documents dispersion

15 modeling that EPA and mainly Bret Anderson has been

16 doing over the last three or four years. It's hard to

17 say. Anyway, it's a lot of work. We got all the

18 results and QAed them and documented them and did some

19 interpretations, but we didn't do any of the runs. I

20 usually don't like to do this, because it's hard enough

21 to explain the stuff that I do and also I probably

22 would have done things a little differently. But we

23 did QA the results and document them in this tome.

24 So, in their Tracer tests, and with this

25 crowd I probably don't need to do this, but you release
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 241

1 a known amount of inert material that has a zero

2 background or as close to zero background as possible

3 from the source and then you measure it downwind. So,

4 we're measuring the transport and dispersion. There's

5 no chemistry. There's no deposition. So, you're

6 actually evaluating the long range transport model's

7 ability to treat this transport dispersion.

8 In the early 60s and 70s, we had lots of

9 great dispersion tests using radioactive materials and

10 that's how we did all of our -- a lot of our dispersion

11 curves. That's frowned upon these days. It was good

12 while it lasted.

13 So, an example of the past Tracer

14 experiments are as the 1986 eight model study. I think

15 Tyler mentioned that and that's a study Argonne

16 National Lab did. I was one of the reviewers of that

17 study. I was only 12 at the time, but I was very into

18 reviewing.

19 Rocky Mountain was one I worked on and

20 then this 1988 EPA evaluation was the CALPUFF using two

21 of the experiments and that actually fed into the IWAQM

22 recommendation of CALPUFF as long range transport

23 model.

24 1994 European Tracer experiment is the

25 one that I'll be talking about. That was, they had a
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 242

1 real time experiment where they used Tracers and people

2 got forecast deals and tried to simulate and then they

3 had a retrospective ATMES two where they went back and

4 did it again and that experiment was actually spawned

5 by another experiment called Chernobyl which they

6 weren't ready for.

7 So, CALMET using the 1988 experiment

8 because part of the objective here was to see how

9 CALPUFF had changed from the 1998 Tracer test

10 evaluation. They used the Oklahoma Great Plains 1980

11 database that had ARCs at 100 kilometers and 600

12 kilometers and the Savannah River Lab SO6 database that

13 had an ARC at 100 kilometers. So, these are ARCs or

14 receptors. And at that time, the 1998 study and this

15 study using the new version of CALPUFF used a fitted

16 Gaussian plume evaluation technique where you take the

17 observations along the ARC of receptors. They'd be

18 like 20 observations and fit a Gaussian plume on there

19 and you look at things like the maximum ops on the ARC

20 or the maximum fitted plume center line because the

21 receptor may not have picked up the maximum

22 concentration. And you can look at plume widths and

23 cross wind integrated. And then you can do the same

24 thing with the model predictions where you may have 100

25 receptors. And you can also look at timing statistics
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 243

1 of how long is the Tracer on the ARC and when did it

2 get there and when did it leave.

3 One of the findings that we had here was

4 that for this long wind distance, like a 600 kilometers

5 ARC, assuming that the Tracer has a Gaussian plume

6 along the ARC may not be true because there's wind

7 shear and stuff and so we found out, at least in the

8 Savannah River Lab that that was a poor fit and it

9 could be misleading.

10 So, I think you need to look at this.

11 Look at how the fit and the observations occur and then

12 see whether it's working because this is not -- it

13 doesn't always work.

14 So, we revised the Tracer test

15 evaluations that we documented. It was for the two

16 historical Savannah River Lab, a GP 80 that had ARC

17 receptors experiments, but also the 1983 Cross

18 Appalachian Tracer experiment. They had five releases

19 and I'll talk more and then the 1994 European Tracer

20 experiment. That had more, lots of receptors out

21 there.

22 And the idea, at least for the Savannah

23 River Lab GP 80 was to compare the current version of

24 CALPUFF, which was 5.8, with the older version, CALPUFF

25 four and what's changed and whether some new techniques
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 244

1 like puff splitting has improved CALPUFF's performance.

2 And then for some experiments, maybe the

3 CAPTEX and the ETEX compared multiple model

4 configurations like I have on the right.

5 Originally, my talk was going to try to

6 talk about everything, but people want to leave here

7 before eight, so I'm just going to talk about CTEX3 and

8 everything is in the report. So, it's been up there

9 for a while.

10 So, the CAPTEX experiment had five

11 releases during 1983 and they were released from either

12 Dayton, Ohio and Sudbury. Actually, EPA modeled two of

13 them. CTEX3, October 2nd, 1983 from Dayton, Ohio. And

14 then CTEX5 from Sudbury. And Dayton, Ohio is where my

15 Cal Bears play tonight in the play-in game for the

16 NCAA, so that brings a little relevance.

17 So, in doing this we did some met or a

18 met model was evaluated first like we talked about

19 yesterday and so there are multiple CALMET

20 configurations. We'll talk about that.

21 And one of the things, EPA's objective

22 for doing multiple model evaluation is identify the

23 best performing CALMET configurations that kind of fed

24 into their EPA OFM recommendations. And then CTEX5

25 also had the MM5 evaluated in there. And then for
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 245

1 CAPTEX, we also evaluated multiple models and I'll talk

2 about that at the end.

3 Okay. For CTEX3, there are 31 CALMET

4 sensitivity tests and what was varied were things like

5 the MM5 model that was put into CALMET where there was

6 80 kilometers. Actually, it was MM4 for CTEX3. 36

7 kilometers MM5 or 12 kilometers MM5. That was put in.

8 And then how the MM5 was used by CALMET because you

9 could use it different ways. As a first guess field

10 which is the usual way of using it. As a step one wind

11 fields which means you skip the diagnostic effects and

12 feed into the CALMET step two procedures that does an

13 objective analysis of the observations. CALMET was

14 also ran with no MM5. CALMET was also run with

15 different grid resolutions; 18, 12, and 4 kilometers.

16 And there was how did the observations

17 get blended into the step one wind fields and that's

18 controlled partly by this RMAX R1 procedures where

19 there a three A, B, and C different values for RMAX R1

20 would be the 100 kilometers for the surface winds and

21 200 kilometers winds. That's the distances that are

22 used to blend into the field being the EPA FLM

23 recommended settings from their August 2009

24 Clarification Memo. And then the D is not to use any

25 observations.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 246

1 We also had three MMIF sensitivity

2 tests. So, where the MM5 data at the 36, 12, and 4

3 kilometers are processed with a minor interpolation

4 from the -- to get from C to D or B to C way. That has

5 to be done just because of the way the models are

6 formulated.

7 And then CALMET STAT was used to

8 evaluation wind speed and direction and for the CALMET

9 runs we just did it for winds.

10 And then down the bottom which you guys

11 can't see are some benchmarks that we typically compare

12 our mesoscale model performance with.

13 So, this is the wind speed direction

14 bias. I'm just showing one. There's 31 tests --

15 CALMET tests. Three MMIF tests. So this is just

16 showing the ones with the MM5 that was run with 12

17 kilometer because we didn't seem to see, if you get to

18 finer MM5 fields, this is up to 12 kilometers, we got

19 better wind performance which is a good thing because

20 we're paying a lot of money for something.

21 And this one uses a CALMET resolution of

22 12 and four, so the experiments fours are 12 kilometers

23 resolution as experiment six are four kilometers

24 resolutions.

25 And then the one on the far right is the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 247

1 MMIF 12 kilometers met which -- MMIF mostly doing a

2 pass through, for the most part, of MM5 winds. It's

3 kind of the MM5 performance.

4 And if you look at the top left at the

5 wind speed bias, you can see that the ones that are

6 shaded are the ones that are the B series. The RMAX1

7 RMAX2 which is what the EPA FLM recommended. And you

8 can see for the wind speed bias there, they also under9 prediction, but the under-prediction for the B series,

10 quote B series, EPA FLM recommended has the lowest

11 bias. Yay. Did something right.

12 And then on the wind directions on the

13 right, the bias, again, the lowest bias, the highest

14 bias is from the MMIF, i.e. the MM5 data coming in and

15 then the use of CALMET actually reduced the bias in the

16 wind direction with the lowest wind direction bias

17 being the A and the B series.

18 And then on the bottom is the error. I

19 know it's hard to see, I was sitting in the back. It's

20 hard to see and you've got all these smart people

21 sitting up front and they got big heads and it's hard

22 to see through them.

23 You can't see the bottom, but the B

24 series is performing the best and if you look at the

25 wind direction bias, the MMIF or rather the MM5 model
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 248

1 actually exceeds the benchmark at 30 degrees. It's not

2 a pass-fail grade, but it's, you know, the CALPUFF is

3 doing it's intended thing of making the winds match at

4 the observation sites better than what it got from MM5.

5 So, conclusions from the CTEX CALMET

6 model performance, I skipped a lot of runs here, but

7 the recommended settings that EPA Federal Land Managers

8 came up with in the May 2009 Clarification Memo

9 produced the best wind model performance. And then

10 using a four kilometers grid resolution, CALMET tended

11 to produce better wind performance than using the 12 or

12 18 kilometers. I can't say anything about less than

13 four kilometers because we didn't run anything, a

14 model, on that.

15 And then using MM5 data with higher

16 resolution, we didn't see a huge difference between 12

17 and 36, but that is definitely better than the 80

18 kilometers MM4 data.

19 And then this is CTEX4. CTEX5 also

20 found that the EPA recommended RMAX1 RMAX2 settings did

21 produce the best wind performance.

22 But I do want to caveat in a way at the

23 bottom which no one can see, but this is not an

24 independent evaluation. The same wind data that goes

25 in the evaluation database is also used as input to
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 249

1 CALPUFF. Excuse me, CALMET. So, it's not an

2 independent evaluation.

3 Moving onto the CALPUFF CALMET

4 sensitivity tests. In this case, CALPUFF was evaluated

5 using 25 CALMET sensitivity tests and it was evaluated

6 using these ATMES II Sysco performance metrics and the

7 question is which CALMET -- CALPUFF CALMET

8 configuration gave you the best Tracer performance?

9 CALPUFF was also run using three MMIF

10 configurations corresponding to MM5 grid resolution of

11 36, 12, and four. These ATMES Sysco performance

12 measures, there's 12 of them. They look at the spatial

13 performance, temporal performance, the global

14 performance and they conclude measures of bias and

15 error, scatter, correlation, cumulative distribution

16 and a couple of the ones down the bottom I just gave

17 four of them that I'm going to show here.

18 There are four of those spatial ones

19 that count as, like, forecast measures. Like if you

20 configured a mirror up in space that looks at the

21 overlap of the predicted cloud with the observed cloud

22 divided by the union of those two clouds. So, what

23 percent of the overlap -- things like, normalized meets

24 squared error, you know, the error, people are familiar

25 with that. Correlation coefficient, again, people are
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 250

1 familiar with that where a perfect correlation is one.

2 A fractional bias, again, it's a bias. You want to get

3 zero. And then Kolmogorov-Smirnov parameter which is

4 looking at the cumulative frequency distributions of

5 the two prediction's observations and how their maximum

6 difference.

7 We also looked at these composite cisco

8 ranking models. One is a parameter called rank that

9 Roland Draxler came up with that combines four

10 parameters of correlations, bias, the figure mirror,

11 the spatial parameter, and then the cumulative

12 distribution and he normalizes them so they each get

13 ranked one, so a perfect model will get a four.

14 One of the things we found is that the

15 fractional bias is probably not a very good metric to

16 have in this composite because you can have a model

17 that creates 100 or a minus 100 and have a zero bias,

18 but it's not performing very well. You know, it's off.

19 So it could have compensating errors and so I think

20 we're looking to revise this and maybe put an error

21 statistic in there that more -- and you don't have this

22 compensating error. We used rank in the report.

23 The other thing that we did was we

24 looked at all of the statistics and averaged across the

25 11 statistics as 12 -- it ranked as a 12 and see how
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 251

1 that changes because the models will perform

2 differently for different statistics.

3 And this is the CALPUFF model

4 performance for the statistics. I guess the same test

5 I showed before using the 12 kilometers MM5 and CALMET.

6 12 and four kilometers CALMET resolution and those A,

7 B, C, D version of RMAX1 RMAX2 and for the figure merit

8 and space, higher metrics are better and it looks like

9 the experiment four A pro forma MMIF is performing

10 best.

11 If you look at the mean squared error on

12 the right, you want to get lower numbers and experiment

13 four C is the lowest, they're all pretty close, but I'm

14 going to go to the fractional bias. There again, you

15 want zero. And they're all fairly close. And you

16 finally get to the rank metric where we want a perfect

17 -- higher score is better and one of the things we

18 noticed is that the B series is not performing as well

19 it was given to be for the -- as we thought for the met

20 model comparison.

21 So the evaluation conclusions and this

22 includes -- one of the things I found was that the

23 CALPUFF MMIF with CTEX3 was the best performance

24 configuration, outperforming all the other CALPUFF

25 CALMET configurations, but then you go to the CTEX5 and
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 252

1 it was the worst performance configuration. So, it

2 depends on your application and, but consistently

3 across the CTEX3 and CTEX5 we found out that the B

4 series the EPA FLM recommendations appear to be the

5 worst performing values for RMAX1 RMAX2 and that's in

6 contrast to what we saw in the met model. And then the

7 difference RMAX1 and RMAX2 configurations, the A

8 series, seemed to perform best, followed by C and not

9 using any ops performed worse. And then the, as I

10 mentioned, CALPUFF using CALMET with a higher than five

11 resolution performed better than generally CALPUFF.

12 Using CALMET with a four kilometers grid performed

13 better using the higher resolution.

14 So, one of the things we came up with

15 here is that you can't just go by met model evaluation

16 when you incorporate met observations into the met

17 model.

18 Also for CAPTEX3 experiment, there were

19 six long range transport models that were then compared

20 and these were evaluated using, again, the ATMES II

21 statistical metrics. There were two Lagrangian models.

22 It would be CALPUFF with MMIF, not CALMET. Two

23 Lagrangian models. CALMET and SCIPUFF. And then

24 CALMET was run with MMIF. In this case it was the best

25 performing CALPUFF configuration.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 253

1 Two Lagrangian particle models, FLEXPART

2 and HYSPLIT and then two Eulerian grid models, CAMX and

3 CALGRID. They're photochemical grid models, but they

4 run no photochemistry and no deposition.

5 These are performance statistics where

6 the best performing model has the lowest value. And

7 you look at false alarm rate and that's how many times

8 the model said you had Tracer at a monitor that was

9 none. So, how many false alarms, these are kind of

10 forecast statistics. In that case, FLEXPART was

11 performing best and maybe CAMX second best.

12 Go to fractional bias and again,

13 FLEXPART is performing best and HYSPLIT is performing

14 worse. Kolmogorov-Smirnov test, the frequency

15 distribution, well HYSPLIT is performing best and

16 FLEXPART and CALGRID are performing worst. It depends

17 on which statistic you look at and normalized mean

18 squared error, by far, HYSPLIT is performing the worst

19 and FLEXPART is performing best. So, it depends on

20 which stat you're looking at.

21 And then looking at performance

22 statistics where the best performing model -- well, you

23 want to get the highest. You want to 100 percent. For

24 figure, mirror, and space, the spatial statistics, CAMX

25 is performing best and then CALGRID is performing
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 254

1 worst. And then on the right, it looks like SCIPUFF

2 and CAMX.

3 And then on the correlation coefficient,

4 lower left, HYSPLIT actually is negative correlated

5 with the Tracer concentrations and CAMX and SCIPUFF are

6 the highest positive correlated.

7 And then for the composite rank

8 statistics, it's clear that CAMX and SCIPUFF are the

9 highest models, followed by it looks like CALPUFF and

10 FLEXPART with HYSPLIT and CALGRID being the lowest.

11 So, in conclusions, try to get us back

12 on track here -- I know it's late. The GP80 Tracer

13 field experiments, we're using this different valid or

14 reasonable CALMET configurations. The maximum CALPUFF

15 concentrations vary by a factor of three, so you can

16 get a factor of three variation just by varying your

17 inputs to CALMET. And since you have less options in

18 MMIF, meaning the pass through options, it varied by, I

19 think it's plus or minus 20 to 30 percent.

20 One of the things that we found is that

21 in order to reproduce the, quote, good performance of

22 the 1998 EPA study on the 600 kilometers ARC of the

23 GP80, we need to use the slug near-field option which

24 they used in that 1998 study. Slug is a way to

25 represent a continuous source near the plume and why it
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 255

1 was used for a 600 kilometers ARC which I guess may be

2 near the source on a global model, but is unclear to

3 me, but that's the way they ran it. We ran it without

4 the slug and we couldn't reproduce the time of the

5 Tracer on the 600 kilometers ARC, but when it was

6 turned on, it was able to. So that was kind of a head

7 scratcher.

8 As I mentioned earlier, the Savannah

9 River test. 75 Tracer. The fitted Gaussian plume

10 observations were very poor and thus we couldn't say

11 anything about modeled evaluation because the model

12 evaluation paradigm was broken for that experiment.

13 And the CAPTEX field -- brute force

14 CTEX3 and CTEX5, we found that RMAX1 equal 100, 200

15 kilometers, i.e. the FLM recommendation from the 2009

16 Clarification Memo -- it produced the best CALMET wind

17 speed wind direction performance but the worst CALPUFF

18 Tracer performance.

19 So, keeping all of the CAPTEX experiment

20 with MMIF as a driver to CALPUFF, it performed better

21 than CALPUFF CALMET for the CTEX3, but was worse in

22 CTEX5. You know, as usual, it's the atmosphere. You

23 can't say anything with certainty.

24 And then for CTEX3, you look at the

25 multi-model. You know, SCIPUFF and CAMX were the best
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 256

1 performing with CTEX3, followed by CALPUFF and FLEXPART

2 with HYSPLIT and CALGRID the worst.

3 Then CTEX5 is CAMX and HYSPLIT were the

4 best performing, followed by SCIPUFF FLEXPART and

5 CALPUFF CALGRID the worst, so there wasn't always

6 consistency.

7 The ETEX Tracer experiment which I

8 think, from looking at these three years ago at this

9 meeting, the CAMX, the HYSPLIT, and the SCIPUFF were

10 performing the best and FLEXPART and CALPUFF were

11 performing the worst. I think we'll hear more about

12 the ETEX tomorrow.

13 So, with that, I'll open up for

14 questions.

15 Or I don't. We've got questions at the

16 end of the thing. Nevermind.

17 MR. BRIDGERS: Ralph gets the gold star

18 award for getting us absolutely back on track.

19 While Bret is coming up to the podium,

20 I'll make a request for all of the presenters on

21 tomorrow's public session, at the present I have 50

22 percent of the presentations for tomorrow, so it would

23 be very helpful if at least the ones that are in the

24 morning first thing, if I could get those before

25 sometime this evening.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 257

1 Thank you.

2 MR. ANDERSON: Thank you. Bret

3 Anderson. U.S.D.A. Forest Service.

4 The outline of my talk this afternoon

5 will be another element of this project that Tyler and

6 Kirk outlined and before we begin with that, I'd like

7 to discuss the use of air quality models, you know, for

8 both NAAQS and air quality values under NEPA which is a

9 major driver in this project. Then we'll discuss the

10 design elements of the EPA FLM single source model

11 evaluation project as it relates to the consequence

12 analysis that Kirk referred to. And then to examine

13 some of the initial results of that evaluation. Also,

14 a part of this is, as we move forward into the future

15 is kind of an evaluation of the practical

16 considerations of the use of some of these your

17 learning models more routinely in the regulatory

18 framework because part of this project is also to look

19 at the comparison of the resource requirements

20 associated with that to kind of inform the public.

21 So, as I had mentioned, the NEPA

22 requirements and the FLMs, we have a very distinct

23 motivation here in this. And because prior to about

24 2005, 2006 time frame, air quality modeling for NEPA

25 projects was fairly well-defined. So, basically, you
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 258

1 had to do your standard near-field analysis and also

2 your far-field analysis so you had to deal with local

3 scale NAAQS and you had to deal with the air quality

4 related values and you had analyze, you know, each

5 option -- each resource management option relative to

6 each of those. And then, thanks to our buddies in the

7 State of Wyoming and their deployment of monitors we

8 started to find some problems. And in particular, what

9 we found was we found, you know, I'm sure you've seen

10 many articles about these, but basically we found

11 winter ozone in the Upper Green River Basin in Wyoming

12 and the Uintah Basin in Utah. And now we have to deal

13 with addressing ozone air quality modeling for

14 pollutants that occurred in times of the year and in

15 remote locations that were once considered to be urban

16 scale and summertime problems.

17 So the paradigm in NEPA modeling shifted

18 and so I speak somewhat on the behalf of the both the

19 Bureau of Land Management and the U.S. Forest Service

20 because we are unique among the land management

21 agencies that we are multi-use agencies and that we're

22 responsible for developing environmental impact

23 statements for any resource management decisions that

24 are made on Federal lands and so, typically, when you

25 see the BLM doing an EIS for a resource development,
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 259

1 you'll see a companion document coming out from the

2 Forest Service because many of our lands are adjacent

3 to one another.

4 So for air quality, this means that we

5 now have to analyze potential impacts to local -0- to

6 both local and regional air quality for each resource

7 management option that is considered. This was

8 translated into running AERMOD, CALPUFF, and CAMX. And

9 so the complexity and the cost associated with meeting

10 these needs and air quality analyses under NEPA have

11 grown considerably in the last five years.

12 In response to this, and this is what

13 Tyler highlighted in his presentation is that the

14 Department of Agriculture, the Department of Interior,

15 and EPA entered into a memorandum of understanding

16 outlining generally agreed upon principles for

17 conducting air quality related analyses under NEPA for

18 energy development projects.

19 And so the general principles are to

20 establish an agreed upon procedure for conducting air

21 quality analyses. Having a development of a formal

22 stakeholder process for input and how the modeling

23 analyses area done. And then also dispute resolution

24 procedures.

25 And one of the major themes of this is
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 260

1 to reduce the cost to both the development agencies,

2 the agencies that are responsible for the development

3 of these NEPA documents as well as the project

4 proponents through the promotion of modeling techniques

5 which allow for the leveraging of existing analyses to

6 the extent that they're practical.

7 This means that we're trying to reduce

8 the burden in the modeling requirements and this occurs

9 through two approaches.

10 The first approach is to establish what

11 we refer to as a reusable modeling framework which are

12 regional scale air quality analyses that can bracket,

13 you know, development potential in a given air shed

14 that can be leveraged to help describe, you know, the

15 potential impacts from any individual development

16 project and; two is to promote the use of a single

17 modeling platform where practical to deal with issues

18 of both ozone, PM2.5 and then the subsequent issues

19 related to air quality values which are visibility and

20 deposition for this NEPA.

21 And so you can see, we have quite a bit

22 of a resource burden here in terms of the modeling

23 requirements and so we have a very strong interest in

24 the evaluation of these various platforms.

25 So, where do the EPA and the FLMs go
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 261

1 from here? So, the NEPA requirements in the Sierra

2 Club petition necessitated that both the EPA and the

3 FLMs reassess the suitability of the existing modeling

4 paradigm in order to address these issues. In order to

5 address these needs, the EPA and the FLMs undertook

6 this project to compare the model predictions of

7 existing models and emerging models to understand both

8 the predicted impacts for resource management decisions

9 and to better understand the resource requirements and

10 challenges to implementing these.

11 So the EPA had a work order, a multi12 task work order, with Environ and under task order six

13 there was the single source LRT demonstration project.

14 And so the first element would be to apply a long range

15 transport chemical dispersion models, for example; test

16 sources as one would for both a PSD far-field class one

17 assessment which is essentially the same thing that you

18 would have to do for NEPA in describing each resource

19 management option impact. And so for this, they used a

20 2005 and 2006 annual simulation and this configuration,

21 what you see here, is that CALPUFF and CALMET and

22 CALPUFF and MMIF were examined and then also using CAMX

23 for source apportionment capabilities.

24 The other thing was to compare the air

25 quality and air quality related value metrics for class
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 262

1 one areas across all of these different ones. And so

2 this is essentially -- this is not a performance

3 analysis. This is simply a consequence analysis to

4 look at what the predictions are looking like for each

5 one of these types of things.

6 And so thank you, Ralph, for outing me

7 so I'm outing him here. This presentation just

8 documents the transport dispersion model simulations

9 that were performed and done by Environ. So, turnabout

10 is fair play. It's like in 2011.

11 So, basically, what you see is that

12 Environ used two separate modeling domains here and so

13 they used it -- for 2005 they used what we call the

14 four corners air quality task force project and that

15 had a 12, you can see it's a region 12 and a sub-region

16 4 kilometers domain that's focused over the four

17 corners area in the southwestern United States.

18 And then you'll see here, you'll see the

19 12 kilometers domain and, I don't know, is this for

20 Peyonce Basin or is this just a --Uintah

21 Ralph?

22 MR. MORRIS: Uintah.

23 MR. ANDERSON: Uintah. So anyway, this

24 is the 2006 domain. You know, this is purely just 12

25 kilometers domain here.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 263

1 So, for this evaluation, for the 2005

2 four corners air quality task force modeling database

3 that was used, they looked at five EGU sources which

4 are identified in the top figure and nine oil and gas

5 point sources -- or point and area sources that are

6 identified in the bottom figure.

7 For the 2006 study, they identified --

8 they modeled 13 EGUs and then 11 oil and gas, you know,

9 corresponding the same figures as in the previous

10 slide. And this is going to be and I'm going to talk a

11 little bit about this, but there is a lot more to

12 explain in terms of, you know, going into the report

13 and actually looking at this because there is just an

14 awful lot here and these images are going to be pretty

15 difficult in the back. But basically, what you see

16 here is -- all this is is a head-to-head consequence

17 analysis looking at the concentration predictions or

18 the deposition value or the visibility analysis

19 predictions of each modeling system compared to each

20 other modeling system.

21 So, in the left figure for top and

22 bottom which is annual NO2 at the top and sulphur

23 dioxide on the bottom is that on the left you see

24 CALPUFF and CALMET configured with CALMET according to

25 the EPA FLM recommended settings and what you --
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 264

1 compared to CAMX. So, CAMX is on the left axis and

2 CALPUFF CALMET is on the right axis and so you can see

3 that CAMX has slightly higher predictions than CAMX for

4 nitrogen dioxide for the annual standard.

5 And then when you compare in the center

6 one where you have CALPUFF MMIF on the left axis and

7 CALPUFF CALMET configuration on the right axis, you see

8 that the CALMET is slightly predicting higher relative

9 to CALPUFF MMIF and you see a similar behavior for both

10 SO2 on the bottom, you know, in this and the fact that

11 you see CAMX has slightly grading or slightly higher

12 predictions for SO2 compared to CALPUFF CALMET.

13 And when you compare CALPUFF MMIF to

14 CALPUFF CALMET in that, on the bottom here, what you

15 see is there is a slight edge towards over, you know,

16 CALMET predicting, you know, the CALPUFF CALMET

17 configuration predicting higher.

18 And then likewise for, you know, here on

19 the left when you're looking at CAMX on the left and

20 CALPUFF MMIF. CAMX has that same tendency to predict

21 higher than the CALPUFF MMIF configuration.

22 So, here, and I'm going to quit rambling

23 through these here, but I just encourage you to go look

24 at the report. Basically, what you see is, again, just

25 a consequence analysis and it's informative to look at
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 265

1 each one. How these stack up against one another here.

2 So, I'm going to slide through this and,

3 now, the one thing you see here is in the PM10, where

4 you're looking at it, because we have different issues

5 with species mapping from the secondaries that are

6 coming out of CAMX compared to what is in CALPUFF, the

7 values are going to be significantly different as a

8 result of that and so that, you know, that's the

9 important take-home message when you're examining the

10 results of the PM10 analysis.

11 And then for annual SO2 in the 2006, you

12 can see here -- you'll see some very high CALPUFF

13 CALMET and MMIF outliers here that we'll get into

14 examining that just a little bit closer. You can see,

15 for the most part, at the lower concentrations they do

16 -- there is fairly strong parity and -- but when you

17 see them in the center image here, you'll see that

18 there's a significant outlier here that was with

19 CALPUFF and CALMET.

20 And then, again, you'll see this.

21 You'll see something similar with this as far as the

22 outliers go with the CALPUFF CALMET and the CALPUFF

23 MMIF.

24 So, basically Environ did take the

25 opportunity to go back and take a look at the outlier
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 266

1 in a little bit greater detail and so basically what

2 they found was the maximum annual NO2 and SO2 by

3 CALPUFF CALMET occurs only for the smallest of the 12

4 EGUs and in that 13 EGU scenario for the 2006 study.

5 And this occurred within the Holy Cross Wilderness

6 which is a class two area. So, it's likely that they

7 were co-located with receptors.

8 The maximum CALMET, MMIF, and CAMX

9 annual NO2 concentrations were 3.1, .6, and .02

10 respectively and so the -- we understand why CALMET was

11 much higher than CAMX because CAMX was configured for

12 LRT application with a 12 kilometers grid, but why are

13 MMIF and CALMET so different?

14 And so they were looking at the wind

15 fields here and this is just a snapshot using the

16 CALDESK software to look at the MM5 winds at 12

17 kilometers overlaid with the CALMET vectors here and so

18 you can see that with CALMET, which I believe is the

19 blue, you can see that at the surface, both the surface

20 and aloft, CALMET is showing that there is much more

21 response to the terrain in the CALMET wind field as

22 compared to what the MM5 shows which you would hope to

23 expect running CALMET at a higher resolution there.

24 And then likewise, when you run it aloft

25 and you look at aloft, you'll see that CALMET still
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 267

1 shows significantly more response to the terrain than

2 the MM5 data does at that resolution. So, you can see

3 that there are, especially in this area of the domain

4 over here, you can see significant variation because

5 you can start to see more of a transition to more of a

6 zonal flow which is more indicative of synoptic scale

7 flow and so it's not -- the course of resolution and

8 prognostic data is not responding as much to the

9 terrain as what the finer scale CALMET winds would do.

10 So, looking at that outlier again.

11 CALMET was modifying and slowing the MM5 winds and it

12 occurs both at the surface and aloft. And their

13 conjecture -- they are unsure as to whether or not it's

14 the diagnostic effects or the objective analysis

15 procedure doing this. It occurs throughout the year

16 and it results in very high concentrations for the

17 CALPUFF CALMET configuration and, but as they indicate

18 here, you get much better agreement between the CAMX

19 and the CALPUFF CALMET configuration at all of these

20 other sites.

21 So, then we get into visibility which is

22 where we're very interested in here. And as you can

23 see, the visibility has a very similar thing here where

24 you see, basically, the CALPUFF and CALMET

25 configuration and CALPUFF and MMIF are yielding higher
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 268

1 results than CAMX, you know, in terms of the visibility

2 impacts.

3 Now, there is an explanation -- a

4 partial explanation for this and it's because when

5 Environ ran CALPUFF initially, they used the old IWAQM

6 default of ten parts per billion background ammonia, so

7 it's going to affect both the particulate nitrate

8 estimates you get and then also the total nitrogen

9 deposition that you get because one model will carry

10 the species around more as nitric acid which is, you

11 know, probably much more susceptible to wet deposition

12 in that as opposed to particulate nitrate. So, again,

13 but you can see that there are significant differences

14 in the visibility configuration there -- the visibility

15 predictions as a result of the various configurations.

16 And so one other thing that we had

17 Environ look at was to look at the spatial variability

18 across the receptors because one of the primary

19 concerns that the Federal Land Managers have is that,

20 you know, if you understand how visibility modeling is

21 conducted, we deploy discreet receptors within the

22 boundaries of the class one areas at a routine interval

23 of about 1 kilometer. And the reason why we do that is

24 we're interested in picking up the inhomogeneity of the

25 wind field for lack of a better term. Basically, what
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 269

1 we're looking at is we're looking to see -- we're

2 concerned about the spatial gradient of the

3 concentrations and also the spatial gradient of the

4 deposition and visibility and so one of the concerns

5 that there is is how applicable is a grid model to

6 visibility estimates or deposition estimates? Do we

7 see a high degree of variability across the predictions

8 in distant class one areas?

9 And so we asked Environ to go in and

10 look at the distribution of the visibility impacts.

11 And what you see is that four far-field receptors, so

12 when you look at each one of these class one areas and

13 I think this is the 2005 Four Corners, right? But

14 basically what you see is is that there is very little,

15 much tighter distribution across the receptors within

16 the class one areas or these further ones except for

17 the nearest class one area which is the Mesa Verde one.

18 And so this is a very important piece of

19 information that you see here. The Eulerian models in

20 close proximity to the class one areas account for much

21 less spatial variability as compared to either the

22 CALPUFF or the CALMET or the MMIF configurations for

23 that and that's what the information that we were

24 trying to drive at was is that for -- when you're doing

25 LRT assessments and you're looking at source receptor
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 270

1 distances that, you know, are not 200, 300 kilometers

2 beyond but are within 50 to 75 to 100 kilometers is

3 that is there a spatial gradient in those

4 concentrations and the answer from this is yes, there

5 is very much a spatial gradient there. And that was

6 information that we were very concerned about and has

7 some considerations as we move forward is the

8 suitability of these types of models for different

9 source receptor distances.

10 So, looking a nitrogen deposition here

11 and this is what I was talking about earlier was is

12 that and this is what you see is that CAMX has higher,

13 a factor of two higher, estimate of deposition than

14 CALPUFF. And the CALPUFF MMIF nitrogen deposition is

15 just slightly greater than that off CALPUFF CALMET.

16 And then you'll see something similar, but to a lesser

17 degree with sulphur deposition. Basically, what you

18 see is CAMX predicts slightly higher sulphur deposition

19 relative to CALPUFF and, again, MMIF has slightly

20 higher sulphur deposition predictions relative to

21 CALPUFF CALMET except for one point. And the results

22 from this are similar to what they were from the 2006.

23 And so Environ went in to look and see

24 why is CAMX estimating much higher deposition than

25 CALPUFF? And one of the first reasons is that because
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 271

1 they used different species mapping with CAMX, so

2 there's more nitrogen species and only ammonium from

3 the source and CALPUFF included ammonia assuming both

4 the full sulfate and the nitrate are neutralized.

5 So, they performed CAMX species mapping

6 according to CALPUFF rules. And, Ralph, would you care

7 to explain this a little bit further?

8 MR. MORRIS: I guess using the CALPUFF

9 species mapping where you assume nitrate and sulfate

10 would be completely neutralized by ammonium, it kind of

11 went the wrong direction.

12 I mean, it gave you more nitrogen than

13 CAMX and so maybe it will over-estimate a lot worse

14 higher nitrogen deposition in CAMX greater. And so it

15 didn't explain it. The species mappings and the extra

16 species did not, in itself, explain it.

17 MR. ANDERSON: So they basically look at

18 the fact that -- what they concluded was that CAMX was

19 carrying much more nitric acid in the model as compared

20 to CALPUFF. CALPUFF is carrying much more nitrogen in

21 the form of particulate nitrate.

22 Again, bear in mind that this analysis

23 will be redone because they initially did this

24 evaluation looking at CALPUFF configured with the old

25 IWAQM ten part per billion recommendation. So, they
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 272

1 were going to be redoing it with, I believe we said it

2 was one part per billion. It was, I think that was

3 what the FLMs recommended is one part per billion here

4 and that's much more in line with what you see from

5 monitoring studies in high terrain out in the west like

6 at Dinosaur National Monument and stuff. So, there

7 will be another revised set of CALPUFF runs with more

8 realistic background ammonia values and so this will be

9 part of it, but you'll also see additional CALPUFF runs

10 with much more realistic ammonia background.

11 So, I did want to take a little bit of

12 time to talk about some of the practical considerations

13 associated with this and these are kind of, as you can

14 tell, we are interested in looking at our Eulerian

15 models or a higher order Lagrangian chemical model such

16 as SCICHEM or if they're reactive particle models. If

17 those are -- what type of challenges would they impose

18 on the modeling community? Like if, for example, as

19 Tyler was talking about from the FLM side, we try to --

20 we're relying on the EPA to provide a guideline model

21 for us, even though applications of the guideline model

22 for air quality related values technically don't fall

23 under EPA's regulations. We try to stay in concert

24 with EPA as much as possibly can and so we're very

25 sensitive to shifts in modeling technology that would
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 273

1 be adopted by EPA because we try to adopt a more

2 uniform approach to things with EPA.

3 And so there are some practical barriers

4 to implementation when you look at the use of these

5 higher order models. And one of these things is that

6 the dispersion modeling community that is represented

7 here, most of your work is done on Windows platforms.

8 And especially for state agencies. And, unfortunately,

9 for most of the agencies, that means when you have to

10 do these large, annual simulations for photochemical

11 models, the Windows systems are typically designed in

12 such a way that you can only do serial applications of

13 runs which would me if you're doing the standard way

14 for doing an annual PM2.5 run for photochemical models

15 is typically to break it up by quarter. And run them

16 across multiple machines.

17 Well, on a Windows environment, that's

18 not possible. So, as you can see is the disconnect is

19 both meteorological and photochemical models are

20 largely Unix and Linux based platforms. And also we

21 take a look at the time that's involved with running

22 photochemical grid model simulations compared to the

23 current models that are used under the AQRV analyses.

24 So the computational considerations here

25 is is that if the community moves to these higher order
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 274

1 models is that -- is it necessary to adapt these

2 platforms to adapt to a Windows based environment which

3 may sound for the SIP modelers and the meteorological

4 modelers, that may sound like a very strange concept,

5 but bear in mind the fact that the vast majority of the

6 community that uses these models -- will be using these

7 models in the permitting arena are Windows based

8 people.

9 So that creates two problems. One is

10 that the level of fluency for Unix and Linux operating

11 systems in the dispersion modeling community is

12 typically much less and they typically aren't as fluent

13 in programming skills as they are in the Linux and the

14 Unix environment with the SIP model because we have to

15 do much more data manipulation and a lot more custom

16 programming that way.

17 And then the other issue that we run

18 into is the fact that the IT authorities within the

19 states and local permitting agencies often lack the

20 familiarity with and the resources to dedicate to

21 systems administration for Unix and Linux based

22 systems. And thus, they actively present -- in my

23 case, when I was a state modeler, they absolutely

24 refused to allow us to acquire any Linux equipment.

25 And if you were given the privilege of buying Linux
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 275

1 equipment, it was a stand-alone machine. It would not

2 sit on the network. They absolutely forbade it to be

3 on the network.

4 So, from a barrier to implementation

5 from a regulatory perspective, basically what you have

6 now is this is the clash of the worlds that George

7 talked about here. And this is where the clash really

8 begins. It's that for permit modeling, the operational

9 construct for permit modeling is a highly rigid set and

10 it's based upon a series of regulations and guidelines

11 that generally restrict operational flexibility in

12 order to promote more general consistency in the

13 application of models.

14 The operational construct for

15 meteorological and photochemical modeling is vastly

16 different. Because those are based upon a more loosely

17 binding set of EPA recommendations which typically

18 encourage the adaptation of both the science and the

19 modeling techniques to produce the most scientifically

20 feasible answer given the constraints of the state of

21 the science.

22 So, from the regulatory considerations,

23 these two differences in the operational paradigms

24 between the two communities will require both the EPA

25 and the FLMs to develop a more rigid set of operational
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 276

1 procedures similar to both the current permit modeling

2 paradigm in order to ensure that a scientifically sound

3 and consistent set of procedures prevents an anything

4 goes process which would likely develop without such

5 procedures.

6 Additionally, like what you see in the

7 NEPA context, we typically only do one year's worth of

8 evaluations, but when you see the AQRV requirements

9 under PSD or with the near-field analysis, it's three

10 and five.

11 So, if you go down this route of doing

12 this, the length of meteorological record for the

13 photochemical models will likely have to be expanded to

14 be consistent with the requirements with the guideline

15 on air quality models.

16 And then finally the -- and this is EPA

17 policy here so I won't get into this, but you know the

18 old issue of significance thresholds for single sources

19 which is a hornet's nest that I'm not going to touch

20 here.

21 And so in conclusion, basically, what we

22 have found from this study is that photochemical grid

23 models are capable of assessing single source impacts

24 for both AQRVs and ozone requirements if there would be

25 any for PSD. The source apportionment techniques that
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 277

1 Kirk talked about earlier eliminate the need for

2 multiple zero out runs or the brute force techniques.

3 However, there are, as the community thinks about this

4 and moving forward, is there are significant barriers

5 that remain to implementation of these and these come

6 in terms of both training requirements for staff,

7 computational requirements, and then also the creation

8 of a suitable regulatory framework that can accommodate

9 the requirements of permitting but respect the fact

10 that you're dealing with a much more scientifically

11 robust system.

12 MR. BRIDGERS: Jim Kelly.

13 MR. KELLY: Okay. So some of the

14 previous speakers have pointed out that a need exists

15 for a single source models that can accurately simulate

16 secondary PM2.5 and ozone formation.

17 The SCICHEM reactive plume model is one

18 that could potentially be used in single source

19 applications where consideration of secondary

20 pollutants is required. However, before the model can

21 really be applied widely in regulatory applications,

22 it's important that we can thoroughly test the model

23 under conditions relevant to those regulatory

24 applications. It's important that we can thoroughly

25 test the model under conditions relative to those
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 278

1 regulatory applications.

2 So, today I'll talk about a very

3 preliminary study which is our first implementation

4 here at EPA of the SCICHEM modeling. Basically, we'll

5 simulate the plume from the TVA Cumberland Power Plant

6 for a day in July of 1999 and then compare predictions

7 with simulations from CMAQ and some observations to

8 kind of get ourselves familiar with the performance of

9 this model.

10 So SCICHEM stands for the Second Order

11 Closure Integrated Puff Model with Chemistry. The

12 plume is represented by numerous puffs that are

13 advected and dispersed independently according to the

14 local meteorology. The name comes, in part, from its

15 use of the second order closure parameterization for

16 integrating the turbulent diffusion equation. So, the

17 dispersion rate is related to turbulent concentration

18 fluxes which are saw through that parameterization.

19 The model has some puff merging and

20 splitting capabilities which can be important under

21 inhomogeneous meteorological conditions. And what's

22 important to us is that it holds the promise of

23 potentially being able to give us comprehensive

24 simulations of chemical process as well as gas aerosol

25 as well as aqueous phases.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 279

1 So, as I mentioned, we've just started

2 to implement this model here and we started by

3 simulating the Tennessee Valley Cumberland Power Plant

4 for a day in July of 1999. Now, the reason that we

5 picked this up to begin with is that it's a well6 studied episode and so as we start to learn about the

7 behavior of this model which is new to us, we wanted to

8 benchmark our results against some simulations that had

9 come prior to us.

10 Also, this is a pretty data rich period

11 because there was a helicopter that flew transects

12 through the plume downwind of this power plant and

13 measured some suite of chemical species and so we can

14 use those measurements to help us evaluate and

15 understand the performance of the model.

16 This gives an overview of the model

17 configuration that we used in the study. And

18 basically, I'll talk about the results of two

19 simulations with SCICHEM that we've named SCICHEM-WRF

20 and SCICHEM DIAG.

21 In the SCICHEM-WRF case, the

22 meteorological used to drive the SCICHEM model is based

23 on WRF version 3.3 output that's been converted to

24 MEDOC format using the MMIF tool that Bret talked

25 about.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 280

1 For this simulation, the background

2 concentrations that we're using are time varying three

3 dimensional hourly varying concentrations that we took

4 from CMAQ photochemical grid model simulations which

5 were converted into a format that SCICHEM could read

6 using a tool developed in-house.

7 The SCICHEM DIAG simulation is based on

8 meteorology that uses observations from four met

9 stations and this met was provided to us as part of a

10 test case that we received with a pre-released version

11 of SCICHEM that EPRI was gracious to allow us to use.

12 The background concentrations for this case are set to

13 constant values and we're using, pretty much, the

14 default values associated with this test case, although

15 we adjusted ozone by a few ppb and SO2 as well to be

16 more in line with the background measurements. We're

17 using hourly emissions that were based on CEM data in

18 this simulations.

19 We also conducted some CMAQ simulations

20 for this period and so CMAQ is a three dimensional

21 photochemical Eulerian grid model. And the reason we

22 applied CMAQ is that this is something that we're very

23 comfortable and familiar with in our group. And so, as

24 we learn about the behavior of this SCICHEM model,

25 which is new to us, it helps us to understand the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 281

1 performance of that model if we can look at it through

2 this lens of CMAQ which we know pretty well.

3 So, we did some simulations with CMAQ

4 version 4.7.1. And these simulations were done at four

5 kilometers resolution shown in this inner domain down

6 here. Two points to mention is that the WRF

7 meteorology that drove the CMAQ model was the same

8 meteorology that was used in the SCICHEM-WRF case that

9 we simulated. And the emissions from the TVA power

10 plant for the SCICHEM simulation -- or for the CMAQ

11 simulation was the same as that used in the SCICHEM

12 simulations.

13 So, we'll get started by I'll show you

14 some figures comparing absolute concentration

15 predictions of CMAQ and the SCICHEM-WRF case. And

16 basically what we'll do is we'll overlay SCICHEM-WRF

17 predictions at receptor rings onto CMAQ concentration

18 fields.

19 So, this shows a plot for SO2

20 concentration. Just to orient you here, what we have

21 is this point in the center is the TVA power plant.

22 And this is basically, these three rings that you see

23 around the power plant correspond to our receptor rings

24 that we placed in the SCICHEM simulation. So, the

25 colors inside those rings correspond to SCICHEM
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 282

1 predictions and the colors outside those rings

2 correspond to concentrations from the CMAQs. We're

3 overlaying SCICHEM and these rings overtop of the CMAQ

4 field.

5 And there's three plots here which are

6 at layer one of the CMAQ model at hours of ten in the

7 morning, one p.m., and six p.m. later in the afternoon.

8 Now, if we look at this quickly and just qualitatively,

9 we see that the plume direction for the SCICHEM

10 simulation overlaps the plume for CMAQ pretty well here

11 and later into the afternoon. And so, this is kind of

12 reassuring because both models were driven by the same

13 meteorology, so it's nice to see that they put the

14 plume roughly in the same location. That wasn't

15 guaranteed necessarily.

16 Another thing we see is that possibly

17 near the source, SCICHEM is predicting high

18 concentrations of SO2 than CMAQ. That's something that

19 we'll look into in terms of dilution rates and, you

20 know, puff versus grid based averaging. We need to

21 look into some of those differences.

22 One of the things that's kind of

23 puzzling us right now, as I mentioned, is this is our

24 first implementation. Away from the plume, you see

25 these grey colors in the SCICHEM simulation. And as I
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 283

1 mentioned earlier, in this simulation, we fed the

2 SCICHEM model three D, time varying ambient background

3 concentrations from the CMAQ simulation. So, we

4 expected that, away from the plume, down here in these

5 rings, we would get back the same values that we had in

6 our CMAQ simulation, but we don't see that and so it's

7 something we'll look into.

8 We had similar behavior for NOx. These

9 are NOx concentration plots and we see kind of a

10 similar thing where the direction of the plume is the

11 same largely between SCICHEM and CMAQ and that

12 transition from the morning to afternoon happens in the

13 same way, but it looks like SCICHEM is predicting

14 higher concentration of NOx near the source at layer

15 one and, again, we'll look into this further.

16 Now, the second set of figures I'll show

17 you will attempt to just compare plume concentrations

18 for the different simulations. So, what we did was we

19 did a set of simulations where we set the emission from

20 the TVA plant to zero and then we got predictions from

21 that simulation and subtracted them from the

22 simulations where the TVA emissions were accounted for.

23 And then sort of the difference between the zero out

24 and the base case simulation should help us isolate the

25 impact of the plume. And I'll show you some figures
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 284

1 for that now.

2 So this shows the SO2 concentration

3 difference between the base case and our zero out run.

4 And up top, these are the same scenarios that we've

5 just seen. So this is comparing CMAQ predictions and

6 the SCICHEM-WRF case that we simulated. Again, the

7 plume directions are pretty similar.

8 Down on the bottom row, I'm not sure

9 everyone can see it, but this is comparing the CMAQ and

10 the SCICHEM DIAG case which, just to remind you, just

11 used the observation based meteorology. So, especially

12 in the morning hours, if we look at the direction of

13 the plume when we use the observation based

14 meteorology, it differs from the case with the WRF

15 prognostic model meteorology.

16 As the afternoon wears on, then the

17 directions kind of align a little bit better, but you

18 still see here's some red down -- pointing that there

19 is some southward trajectory of this plume in this case

20 that we're not seeing up here with the WRF case. And

21 so, not surprisingly, if you use different met fields,

22 it has some consequence.

23 These are similar results for NOx.

24 Because of the time, I won't through a lot of the

25 details, but the same differences apply here.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 285

1 And finally, this is a plot of ozone.

2 And so, we mentioned at the beginning that we're

3 interested in SCICHEM because we're interested in

4 photochemistry and secondary pollutants and so this is

5 the first example I've show you we're really starting

6 to look at that.

7 And so, just to orient you, the blue

8 colors in this figure represent cases where when you

9 add TVA emissions into the simulation, it results in

10 reductions of ozone concentrations. Those are the blue

11 colors. Whereas the red colors show when you introduce

12 the plume emissions to the simulation, it leads to a

13 net production of ozone.

14 So, if you think over here these blue

15 colors very close to the source, that represents a

16 situation where the plume is very concentrated in NO

17 and that NON will react with the ozone molecule and

18 kind of destroy ozone. But as we move further

19 downwind, some background ambient air mixes in and that

20 contains radicals and VOCs and so, under those

21 conditions, NOx is a net producer of ozone. And so we

22 see, moving from the source where we have titration of

23 ozone downwind, there's some production, we're

24 capturing these qualitative features that we'd expect.

25 Although we see for the CMAQ simulation, the reds are a
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 286

1 lot more prominent. I don't know how well you can see.

2 Within the ring of the SCICHEM simulation, the ozone

3 production downwind was a little bit less and we're

4 going to look into that.

5 This shows just a surface concentration

6 Tracer plot that compares on it -- the left, the

7 SCICHEM-WRF simulation and on the right the SCICHEM

8 DIAG simulation which used the observation based

9 meteorology. And as I've already pointed out, in the

10 morning hour, this is nine local standard time, there's

11 a pretty big difference in the direction of the plume,

12 but then later in the afternoon, the plume directions

13 are a little bit more in line.

14 Okay, so as I mentioned, one of the

15 purposes of simulating this episode is some helicopter

16 observations were available for this time period. So

17 this Google map shown here shows the TVA Cumberland

18 Power Plant and then these black transects at distances

19 downwind of the power plant is where that helicopter

20 flew through and sampled the plume coming from the

21 power plant. And the average altitude of these

22 aircraft transects was 500 meters. And so now we'll

23 compare some of our predictions with these helicopter

24 observations.

25 So this shows the SO2 measurements for
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 287

1 the plume transects. So the plot on the left is the

2 observed SO2. The plot in the middle is the CMAQ model

3 predicted SO2 concentration. And on the right is the

4 results of this SCICHEM-WRF simulation.

5 So, we see in the observations you have,

6 near the source, these bright red colors which indicate

7 that we have a fairly narrow plume of concentrated SO2.

8 And if we move downwind, the concentrations are going

9 down, presumably because of some dilution and this

10 plume broadens a bit.

11 For the CMAQ case, we have similar

12 qualitative behavior where you see the red colors for

13 the plume near the source and then the concentrations

14 just diminished a bit as we move downwind. We would

15 want to look into this a little bit more quantitatively

16 to give a full assessments, but those patterns are

17 there.

18 And we see a similar trend with the

19 SCICHEM-WRF case, but for the SCICHEM and CMAQ, it

20 appears that maybe the plume is moving a little bit to

21 the south compared to the observations in the

22 beginning.

23 This shows some similar results of NOx

24 which I don't want to dwell on because the patterns are

25 pretty similar to the case I just showed you for SO2
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 288

1 and then finally we have ozone.

2 So you see in the aircraft observations

3 that near the source there's some blues in here

4 indicating that within the plume, we're having a net

5 destruction of ozone. So ozone is falling below its

6 background value in the plume, but then downwind, now

7 we really start to see these deep reds where the NOx

8 from this power plant is leading to some net ozone

9 production.

10 Similar trends that you see here with

11 CMAQ again. There's some blues near the source and

12 then there's some transitions to these redder colors

13 down here which might indicate some ozone production.

14 But again, we want to look at this more quantitatively.

15 SCICHEM has similar behavior where you

16 have some ozone titration near the source and downwind

17 we saw less ozone production with SCICHEM, but we saw

18 some.

19 Now the final set of plots I'll show has

20 to do with comparing what I'll call centered profiles

21 for the observations to the model and I'll explain that

22 now.

23 So, what we did is we searched along

24 these receptor ARCs that we used for the SCICHEM

25 simulation and we find the maximum -- the location of
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 289

1 the maximum. And similarly, we search along the

2 aircraft transects and find the maximum. And then we

3 sort of look at the profiles and we center them all to

4 zero at the maximum.

5 So, we're saying here, okay, if there is

6 some angular displacement from the models that doesn't

7 exactly track the plume, let's try to eliminate that

8 from our evaluation by matching up the peaks and

9 centering them at zero. That's what we've done here.

10 And we have the radius of 11 kilometers, 31 kilometers,

11 and 65 downwind of the power plant and this is all an

12 elevation of 500 meters.

13 So, what we see with the black points is

14 the observations and, you know, they're pretty narrow

15 with a sharp spike near the source, but then as we move

16 downwind, this peak comes down a little bit and the

17 distribution broadens.

18 The curves that you see for the two

19 SCICHEM simulations similarly follow this trend. They

20 start out with a narrow plume with a large peak and

21 then this diminishes a bit as we go downwind and

22 there's some dilution of background air mixing in. But

23 really the striking thing about the figure is that

24 there's a pretty big difference in concentrations

25 predictions between the SCICHEM WARM case and the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 290

1 SCICHEM DIAG case. This is likely related to some

2 differences in dilution and mixing or maybe plume

3 placement and it's something that we plan to look into

4 further. Right now we don't have a full explanation.

5 Similar behavior for the NOx

6 concentration where we see similar trends. In this

7 case, actually, the SCICHEM DIAG has actually over8 predicted the observations of NOx at this far away

9 distance which is one of the cases where that happens.

10 And then finally, ozone. So, if you

11 look at ozone and for the black points and the

12 observations, again, you see this titration near the

13 source and you see this minimum in ozone. Then, as

14 you move a little bit downwind to this radius of 31

15 kilometers, you still see this minimum here, but now

16 you start to see ozone production near the edges. So,

17 what's going on is that the edge of the plume, some

18 background air is mixing in and that might have some

19 proxy radicals and VOCs that allows the NOx in the

20 plume to then become a net producer of ozone and you

21 start to get some ozone production around the edges.

22 And then when you move all the way

23 downwind, these two wings that they call them, ozone

24 wings, start to approach each other from both sides and

25 you still have a minimum here in the middle.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 291

1 Now the model simulations, they capture

2 the -- we can be critical of this, but this is just a

3 preliminary application. But we capture this trend.

4 We get ozone titration near the source and then as we

5 move downwind, the models aren't getting these ozone

6 wings, but I think there are certain configurations in

7 SCICHEM where you can try to capture these features

8 that we haven't really implemented here.

9 But then we move to the final distance.

10 We kind of flat line with our ozone in both SCICHEM

11 simulations. Part of that might be related to that we

12 didn't resolve these ozone wings here, so we moved

13 downwind, maybe we don't get that. It could also be

14 related to our background ambient concentrations.

15 Perhaps they're low in VOCs and radicals. We haven't

16 really looked into this because a lot of the work has

17 just gone into setting up the model and conducting the

18 runs.

19 So, just to summarize, we developed some

20 preliminary tools for using SCICHEM in terms of

21 processing model inputs and outputs. And so much of

22 the work that we've done today has focused really on

23 getting the model inputs set-up, running the model, and

24 extracting outputs so we can make the figures that we

25 saw today. We haven't focused too much on the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 292

1 chemistry and the details just yet.

2 So, as I mentioned, we simulated this

3 TVA Cumberland Power Plant with SCICHEM and CMAQ

4 version 4.7 and considering that we haven't spent much

5 time on the model configuration or gotten into he

6 details, we think we have some reasonable model

7 behavior in the sense that within the plume, we have

8 high SO2 and NOx concentrations. These profiles

9 broaden and go down further from the source due to

10 dilution. We see ozone titration in our plume for the

11 SCICHEM simulations which is what we'd expect. And

12 some qualitative similarities between SCICHEM and CMAQ

13 predictions we found. But clearly there is some next

14 steps.

15 The next thing we're going to look at is

16 exploring the impact of different treatments of ambient

17 background concentrations. They might play a role in

18 this ozone production issue I just mentioned. And then

19 once we get that issue addressed, we'd like to look at

20 NOx oxidation products, NOz in particular. So, there's

21 measurements of that which I haven't talked about. And

22 also we'd like to look at vertical profiles. So, some

23 of the difference between those models may have been

24 related to the different models putting the plume in

25 different vertical regions which I haven't really
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 293

1 accounted for in the comparisons. So, we'd like to

2 look into that.

3 Another thing is we'd like to consider

4 additional plume observation studies. In particular,

5 we might look at a nighttime case where we have very

6 stable meteorological conditions and see how well the

7 models perform under these stable nighttime conditions

8 compared to this daytime case here.

9 As Kirk mentioned earlier, we waiting

10 for this CMAQ advanced plume treatment model that's

11 planned to be delivered sometime in the future and when

12 we get that model, it has a sub-grid scale reactive

13 plume treatment and we're eager to see how that

14 performs for these types of simulations.

15 And then finally, we want to move away

16 once we can test and get some familiarity with the

17 models, we'd like to simulate longer time periods in

18 larger domains and start moving to more regulatory

19 relevant applications and testing the models under

20 those conditions.

21 And so we're just getting up to speed

22 here, I'd like to acknowledge the people who have

23 helped us out getting set-up and providing the SCICHEM

24 model to us.

25 Thanks.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 294

1 MR. BRIDGERS: Home stretch.

2 Ralph?

3 MR. MORRIS: I'm glad everybody is

4 sticking around for the ozone presentation. There

5 will be a test on the chemistry afterwards.

6 Anyway, this is something completely

7 different. We talked about a screening methodology for

8 single source ozone and it was developed for Sydney,

9 Australia. I'm going to present it, but the work was

10 done at Environ. It was headed up by Greg Yarwood,

11 Edward Tai, Prakash Karamchandani and sponsored by the

12 New South Wales Office of Environment and Heritage. We

13 also had our folks at Environ Australia participate and

14 since this was a screening model and you have to come

15 up with some screening thresholds as to what's

16 significant or not, the New South Wales Office of

17 Environment and Heritage was also involved in that

18 because we don't set significance thresholds.

19 I'm going to talk about the motivation.

20 Why they wanted to do this. 3D modeling of new source

21 ozone impacts. The development of the screen tool.

22 That's technical work there. The framework for

23 evaluation ozone impacts. So, that involves technical

24 and policy considerations. What is a significant ozone

25 threshold? I don't decide that. That's for EPA and
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 295

1 these folks and I'll summarize it.

2 Okay, so the city greater metropolitan

3 area region, GMR, exceeds the applicable ozone

4 standards which in Australia is one hour average of 100

5 ppb. Our old one hour was 120. And a four hour

6 average of 80. Our current eight hour is 75. So, they

7 do things a little differently down there.

8 They decide attainment, areas of

9 attainment is less than 82 percent of the standard.

10 Not 80. Not 84. 82 percent of the standard. So, if

11 your one hour ozone above 82 ppb, then that's

12 attainment. I don't know what the reasons are for it,

13 but it's to protect the most sensitive kangaroo with a

14 margin of safety.

15 Anyway, they define things differently,

16 but they need a method to quantify ozone impacts from

17 ozone sources. We talked about photochemical models

18 for ozone is preferred approach across the world. But

19 it's very resource intensive. Bret talked about that.

20 They need a technically sound screening tool to

21 evaluate which new sources require -- are likely too

22 small to require a more comprehensive photochemical

23 model application. So, that's where it started.

24 When the Australian office got the RFP

25 and sent it to us, the guys in the bottle that do this
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 296

1 stuff that they don't understand, and they talked about

2 well, we need to develop something like the Scheffe

3 Tables. I think people in this group know what the

4 Scheffe Tables are. They were developed by Dr. Scheffe

5 in 1988 using your active plume model to come up with

6 the VOC NOx screening for ozone increments. Six months

7 later, he completely disavowed ever developing them and

8 it was only years later that people came back to say,

9 you know, Rich, they're using -- can you explain these

10 Scheffe Tables. And he immediately started writing

11 letters saying, don't use it -- don't use it -- don't

12 use it. And so, they mentioned that and now we

13 actually had some of those letters in our proposal's

14 appendix saying, don't use this approach.

15 So the methodology was that we first

16 reviewed literature, identified defensible methods

17 since EPA hasn't put ozone in AERMOD yet, picked

18 photochemical modeling.

19 Roger, you're supposed to laugh.

20 We modeled several prototype new sources

21 using the photochemical grid model. In this case, we

22 used CAMX for reasons I'll explain in a second. And

23 then we used social city method which is a higher

24 order, decoupled direct method and CAMX developed a

25 parametric model of the prototypical source impacts.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 297

1 The ozone pack of the source was a function of the

2 source's NOx emissions, VOC emissions, and the source

3 location.

4 And then we implemented this parametric

5 equation in a spreadsheet, an Excel spreadsheet. It

6 can run on Windows. And that's developed a screening

7 tool. And then with the New South Wales folks, they

8 developed criteria to evaluate the impacts using a

9 tiered approach where if you can show the emissions are

10 too small to cause an impact or that this level one

11 screening uses parametric approach is less than a

12 significance threshold. So, they pick -- then you're

13 presumed not to have a significant impact and you can

14 build your new source. Whereas, if it goes above their

15 significance thresholds, then you need to go back, go

16 and do a full blown photochemical model application

17 what the ozone impacts of that source will be.

18 Okay, so this is the Sydney Greater

19 Metropolitan Area. The domain is a three kilometers

20 domain. 25 verticals up to 8,000 meters. Two episodes

21 were run. December 2003 and January 2004. That's two

22 months' episodes. That's Australia so summer is in

23 winter or however it works. The other episode is

24 December 2004 and January 2005.

25 I used CAMX version 5.3. You see the 05
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 298

1 chemistry. The meteorology comes from the Australian

2 model. Met model and air pollution model called TAPM.

3 The air pollution model is what it stands for. That

4 may be presumptuous of me.

5 The emissions are the anthropogenic

6 emissions from the Office of Environment and Heritage

7 and then we ran the MEGAN, the biometric emissions

8 model, to get the biogenic emissions. The boundary

9 conditions for that domain was from the Mozart Global

10 Chemistry Model.

11 To look at these sources, we divided up

12 the area into these five different locations and since

13 we had to pick a source location for those five

14 locations, we used the emissions weighted centroid of

15 each one of these five locations. The five locations

16 is Newcastle, I think in the North. Wollongong in the

17 south. Sydney and for Sydney we had an east, west,

18 central, and west. And those little blue dots are the

19 emissions weighted centroids of each of those areas.

20 And then we looked for the nearest industrial area to

21 that location because in some case they ended up being

22 over the water and we figured that wasn't likely.

23 For each one of these prototypical

24 sources, we had combined VOC NOx emissions of 500

25 tonnes per annum. Tonnes are metric tons and annum is
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 299

1 a year. It's metric tons per year. And we had a VOC

2 NOx emissions ratio that is 1.24 and then the stack

3 parameters we picked to essentially release the -- have

4 minimum plume rise, release these emission source near

5 the ground to get maximum ozone impact.

6 Okay. So, high order B coupled direct

7 method is a sensitivity method that's one of the

8 probing tools in CAMX and it's like the D coupled

9 direct method only it has higher order terms to

10 describe the effects of VOC and NOx on ozone. You

11 think about DDM, you think it's like Taylor's theorem

12 of linear -- you have a curve of ozone that's linear

13 approximation, but with these higher order terms we are

14 able to fit the parameters and so the bottom shows that

15 the change in ozone is a function of all of these

16 derivatives of S1 through S2. First order and second

17 order derivatives and X -- the equations are NOx

18 emissions and Y is the VOC emissions. So, you run each

19 DDM on each one of these prototypical sources and you

20 get this parametric equation to describe it.

21 And the way that's done is we look at

22 all of the days in those two month periods and pick

23 high ozone formation days that satisfy certain model

24 performance requirements and then we pick the highest

25 sensitivity coefficients from those days for each one
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 300

1 of those sources. So, you're getting kind of a worst

2 case day to get this ozone increment.

3 So, doing this we evaluated the

4 parametric equation by doing brute force runs where we

5 run the model with different sources and we use sources

6 at the same strength, 500 tonnes per annum, and then we

7 did ten times that and then 25 times that because with

8 the sensitivity tests, as you go away from what you

9 ran, your model may deviate from the brute force and so

10 one times NOx and VOC, you'll see ten times, and then

11 25 times.

12 So, we use that to evaluate it and the

13 example here is a HDDM using a parametric equation for

14 a source in one of the domains on the left. And then

15 the brute force we did zero out run for the Central

16 Sydney source and you see the location there on the

17 right. You see the pattern matches fairly well for the

18 500 tons per year source. Maximum is .23 ppb versus

19 brute force of .25. Then, even when multiplied by 25,

20 these parametric equations are developed under a 500

21 ton per year source, multiply it -- we put in 25 times

22 the emissions and we get values that are also fairly

23 close patterns are very close to using the brute force.

24 So, we're trying to look at what is the

25 range of this parametric equation? How high of
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 301

1 emissions is it still valid? We kind of say that 25

2 times it still looks valid.

3 And then here's another way of looking

4 at the valuation of the parametric equation of the

5 increment of ozone by HDDM on the left axis and then

6 the brute force ozone on the right axis. Looking at a

7 combine VOC NOx sources, this is ten times the initial

8 500 tons emissions. An only NOx source in the middle.

9 Then only VOC source. It's not surprising that these

10 parametric equations work better with VOCs than NOx

11 since NOx chemistry is much more complicated.

12 So, we had fairly good agreement and the

13 differences are within about five percent of what brute

14 force gives us in parametric equation up to about 25

15 times the original source that was done.

16 So, the fact was that -- they're

17 considering for evaluating ozone impacts is what's the

18 magnitude of the source impact? Is the source located

19 in the ozone monitoring attainment area? And then

20 sources, of course, must satisfy all of the regulatory

21 requirements which I don't know what they are in

22 Australia, but I'm sure they're -- they have other

23 criteria.

24 So, the preliminary results of the tests

25 in the New South Wales Office has developed a
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1 significant impact level where if they do this level

2 one screening and the source impact is less than .5

3 ppb, they figure it's not measurable and so you satisfy

4 the level one screening analysis and you're done. You

5 can build your source as long as you satisfy everything

6 else. And then there is a maximum allowable impact

7 level. And for non-attainment areas they picked one

8 ppb. So, if you're above .5 ppb but less than one ppb,

9 you satisfy your requirement.

10 And for attainment areas, they looked at

11 the difference between the maximum ozone is to the

12 standard and you got, you know, you've got -- you blow

13 the standard by so much and you get 25 percent of that

14 to use up by that one source. So, if the standard is

15 80 ppb and the background ozone is currently 60 ppb,

16 you've got 20 ppb to go and you get 25 percent of that

17 and so you get a 5 ppb. So, if your source's level one

18 screening is less than 5 ppb, then you can build the

19 source.

20 So, if the source impact is below the

21 maximum impact level, you can build. Level one

22 analysis satisfied and you can do it. If you go above

23 it, then you need to go to level two which involves

24 doing a site specific photochemical model application

25 of that particular source using a model like CAMX or
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 303

1 CAFM or CMAQ.

2 Okay, I'm not sure if you can see this,

3 but -- so you start up here and there is two pathways.

4 One is for attainment areas. One is for non-attainment

5 areas. The first thing you do is look at the emissions

6 of the source. If the emission is less than certain

7 thresholds, the NOx emissions plus VOC emissions is

8 less than a certain threshold, then you're off. If

9 your attainment area is less than 90 tons per year,

10 then you just need to go to the best model practice.

11 The second screening is the level one

12 screening with your less than .5 ppb deciview. And

13 then you move it. You keep on moving down and you do

14 your level one screening. Are you above the maximum

15 impact level for the attainment area? If you do, then

16 you keep moving down and then you're eventually do that

17 and you don't satisfy any of these things, you move on

18 to the level two screening which is a full blown

19 photochemical model application. But I don't think you

20 can see any of that because I can't see it.

21 The screening tool is in a spreadsheet

22 and what you do is you input your source inputs as a

23 fraction of the nominal source prototype and you tell

24 it whether it's an attainment area or non-attainment

25 area and you give it the baseline ozone and it tells
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 304

1 you what your maximum allowable increment is. This is

2 a test source that was applied for the original

3 prototype source because the emissions are the NOx and

4 VOC are the same as the test source. Then you press

5 the go button and it gives you your increment. In this

6 case it's .74 ppb which is less than the maximum

7 increment back level of 7.8, so this source would pass

8 the level one screening and therefore you would not

9 have to go to the level two and do a full blow

10 photochemical model application.

11 Now, for the -- was also ran HDDM

12 getting sensitivity coefficients out for each of the

13 individual VOC species. And the one below I showed had

14 defaults speciation which is kind of the default

15 overall sources. Here, you can put in all the species

16 -- the explicit species of your source and this is the

17 way they want you to run it. And it calculates the

18 sensitivity coefficients for VOC individual species

19 because there is a wide range of reactivity for these

20 sources and so a lot of alkanes or something is one

21 thing. If you have a lot of alkenes, it could be

22 totally different and make a lot of ozone.

23 This is an example on the bottom of the

24 different reactivity coefficients where you can see the

25 05 species. You put in all the individual species.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 305

1 You see the 05 species and you can see the actual

2 things like alkenes; ethane, olefin, isoprene are much

3 higher than other species like alkanes like paraffin

4 and some species like toluene have a negative ozone

5 reactivity. Increasing toluene actually decrease

6 ozone. These reactivities actually are tailored

7 towards each one of those five locations that we have.

8 The Newcastle, Wollongong, and the three Sydneys. So,

9 the speciation reactivity is different in those areas.

10 So, you have locational specific reactivity

11 information.

12 So, the summary is that we developed a

13 sufficient screening method for screening tools for

14 Sydney. It's very location specific. It's using a

15 location specific photochemical model application. We

16 divided up into sub-regions based on where we think the

17 chemistry would be different. We think it's

18 scientifically defensible and robust. We have, like I

19 said, location specific speciation VOC sensitivities

20 and we find the range of applicability. I think I

21 didn't mention that flowchart because I couldn't read

22 it, but if your emissions are higher than that 25 times

23 that source -- test source, then you immediately drop

24 down to level two to the explicit application of the

25 photochemical model. So, there's a range of
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 306

1 applicability of the screening. So, if the emissions

2 are really high, you've got to run the model for that

3 source.

4 It's suitable for smaller sources.

5 That's what we're focusing on. And then you use your

6 resources, your photochemical modeling tool to focus on

7 the big sources. There's no reason you can't apply

8 this general technique to other regions. Of course,

9 other jurisdictions will want to have other screening

10 and significant impact levels and maximum achievable

11 impact levels. But you have to redo the photochemical

12 model run with HDDM specific for that region and come

13 up with new coefficients.

14 So, anyway, the framework is there.

15 It's developed for local standards. It's not yet

16 finalized. I think if -- the idea of what a

17 significant ozone impact is something that they're

18 wrestling with. I think we're doing the same thing

19 here. But there is no reason why it can't just be

20 adapted for new locations.

21 So, I'm done.

22 MR. BRIDGERS: Thanks, Ralph, for that

23 presentation.

24 MR. FOX: So, I would just like to thank

25 all the speakers today and yesterday. We've had a lot
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 307

1 of information flowing. Hopefully, you all stuck

2 through the first two days. We have the public session

3 tomorrow and so I would just say that as a reminder of

4 the process and the like that I talked about this

5 morning, all I wanted to do is just reinforce that

6 given all the information flow and the like that your

7 comments are critically important in terms of providing

8 us with information about what you've seen.

9 I'd also like to emphasize that given

10 the constraints that we all have, if there's any

11 priority that you see in terms of the items that are of

12 most concern to you; either your industry, your

13 particular interests, and the like. That would be very

14 useful to get comments for us to get in this context of

15 this conference.

16 As I said, we will be summarizing those

17 public comments and either providing our internal

18 priorities or a reflection of the priorities that we

19 saw in those comments from you all and others.

20 The comment period has been extended or

21 will be extended until the end of April. So that will

22 hopefully facilitate a review of a lot of the

23 information that has been provided. We recognize that

24 there has been quite a bit and the reports are now up

25 on SCRAM and the presentations that were provided in
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 308

1 these two days as well as the reports that are still

2 due up on SCRAM by the end of the month or early April.

3 Hopefully, there is sufficient time to provide those

4 comments.

5 In the context of the PM2.5 guidance and

6 even in other situations, that doesn't slam the door,

7 hopefully, and we do expect to continue to use existing

8 avenues of communication and coordination with you all

9 so that there can be a continued evolution of the

10 digesting and information sharing and further

11 coordination on activities and the like.

12 I recognize that there may be some

13 things that you all have seen and hear here that you

14 may want to be a priority and not just a priority in

15 the context of updating Appendix W, but also maybe

16 providing updated guidance and the like. So, to the

17 extent that there are those types of items that would

18 be very important to know so that as we engage in the

19 next steps after this, we're looking at a two-pronged

20 attack in terms of looking at the feasibility and

21 suitability of taking some of these things and

22 improving upon the existing guidance as well as looking

23 down the road and what we need to do to get where we'll

24 need to be in time for the 11th Modeling Conference.

25 And I would just also reinforce the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 309

1 suggestion of a specialty conference or other types of

2 avenues for us to get together either broadly as a

3 community or in specific sectors or certain interests

4 in late 2012 or early 2013 so that we can continue to

5 have the dialogue and actually reserve time for

6 probably more detailed discussions at those times with

7 planned activities in the interim such that we can then

8 deliver results and compare things at those times.

9 So, we're looking forward to engaging

10 with you all throughout this process. We're committed

11 to undertaking both in new guidance where appropriate

12 and looking forward to modifications to Appendix W. I

13 think in a lot of the evaluations will be critical.

14 The screening tools and other types of approaches, you

15 know, just as a reminder, you know, updating Appendix W

16 to address chemistry and the like. We're going to have

17 to be pragmatic in looking at that and doing that and

18 provide techniques or tools, short of full scale

19 modeling in some cases. That may be a viable option

20 for use and so please consider those things as well.

21 I think we're going to have a question

22 and answer session so, I will turn that over.

23 MR. BRIDGERS: If we could get the

24 speakers back up here from the late afternoon session,

25 not that we need the slide, but it's there. So, Bret
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 310

1 and Tom and Kirk and Jim.

2 And again, if you could announce who you

3 are.

4 AUDIENCE MEMBER: Mark Bennett with CH2M

5 HILL.

6 This is not so much a comment, but a --

7 or not so much a question as a comment in response to

8 something that Tyler just said in terms of specialty

9 conference and to continue cooperation and coordination

10 between the modeling groups and the regulated community

11 and the EPA. So, I'm going to represent in this

12 comment as chair of AB-3, Committee of Air and Wind

13 Management Association.

14 We're having a meeting at the Courtyard

15 Marriott directly after this to talk about specialty

16 conference and coordination. Any AB-3 members are

17 welcome to attend. See me if you didn't get the email

18 and anybody who is really interested in becoming an AB19 3 member, maybe we can do something about it and make

20 you an honorary one in the meantime.

21 AUDIENCE MEMBER: Are we allowed to

22 bring beer?

23 MR. BENNETT: I have the liquid

24 refreshments covered.

25 AUDIENCE MEMBER: I am Biswanath
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 311

1 Chowdhury from Sage Management and we are developers of

2 SCICHEM, so I just wanted to comment on Jim Kelly's on

3 ozone rings.

4 So, you can get the ozone rings, but the

5 default that is a splitting criteria for the puffs, so

6 if you change the criteria then you'll get a lot more

7 puffs and then it resolves the ozone wings. I just

8 wanted to comment on that.

9 MR. KELLY: Would that configuration

10 cause problems if we wanted to do long annual time

11 simulations?

12 MR. CHOWDHURY: Yes. If you have more

13 number of puffs, then it takes longer to run.

14 MR. KELLY: But for these specialized

15 aircraft campaigns, that would be the way to go?

16 MR. CHOWDHURY: Yes. And Nouri Galani

17 from Union City, Alabama. He did a study. He did work

18 on ozone rings using SCICHEM.

19 MR. KELLY: Thanks.

20 AUDIENCE MEMBER: Bob Paine, AECOM. I

21 have a question for Ralph.

22 On the long range transport evaluation,

23 the long range transport models now are typically used

24 for class one assessments for distances 50 kilometers

25 and up and I noticed that the models were all compared
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 312

1 to much further distances and the Great Plains and

2 Savannah River Lab were not used for the model inter3 comparisons and I was wondering why those were omitted?

4 MR. MORRIS: Well, I was limited to what

5 runs were done and EPA did plenty of runs. There's no

6 shortage of runs, but those experiments were limited to

7 just two ARCs of receptors and they were focusing on

8 looking at the changes in CALPUFF performance from the

9 1998 study to current study. The other two studies

10 have a much higher density of receptors and for the

11 ETEX, we did look at the -- we were concerned about the

12 long distance because we were looking at distances of

13 500 or 1,000 kilometers downwind in addition to close

14 to the source. When they did look at model performance

15 close in and the evolution of the statistics, some, and

16 they didn't change that much. The models didn't

17 perform better closer to the source.

18 I think the main thing was just resource

19 restraints.

20 MR. ANDERSON: I can probably add since

21 I was the perpetrator of the problem.

22 There's a number of practical

23 considerations. The first one is that -- and Joe,

24 please tell me if you did get all that, I believe there

25 actually were model inter-comparisons on those other
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 313

1 experiments, at least for the Great Plains Tracer

2 experiment.

3 Ralph will tell you that in the flurry

4 to try to get report together, we didn't have time to

5 compile all the results and everything else, but I do

6 believe that there were comparisons of all of the

7 various models that were involved in the six study or

8 the six model comparison.

9 For Great Plains Tracer experiment, the

10 first problem is that it's my judgment that those two

11 experiments, there is limitations with each of those.

12 I think the Great Plains Tracer experiment, I think, is

13 a very useful one, but the Savannah River one, I really

14 question the usefulness of it. That's the first thing

15 there.

16 The second aspect of it is that while

17 there are results for the Great Plains Tracer

18 experiment, one of the things is that we have to go

19 back and do diagnostics on it because the approach that

20 EPA took in 1998 was to create a 12 hour integrated

21 average across that six, the 100 -- or in the case of

22 the 100 kilometers ARC, it was a three hour integrated

23 average and for the 600 kilometers ARC, it was a 12

24 hour integrated average. Actually, when you go back

25 and you look at the performance or you go back and look
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 314

1 through the actual observational database, what you'll

2 find is that there is an unexplained second occurrence

3 that occurs. Essentially where the Tracer cloud

4 apparently shows up at somewhere 24 to 36 hours after

5 the release, shows back up on the 600 kilometers ARC.

6 So, there is a question there about all of the models

7 were evaluated against that first transit across the

8 ARC, but none of the models were including CAMX were

9 able to pick up that second one. As a result of that,

10 we needed to go back and look at to see whether or not

11 the positioning of the 12 because when I did the MM5

12 modeling for this one, it was initialized within EPRI

13 analysis data and so those that are familiar with that

14 it's a two and a half degree by two and a half degree

15 every six hours.

16 The difficult is that you don't want to

17 take something that starts at a two and a half degree

18 and next go right down to a 36 kilometers. So,

19 standard procedure was to start with a 108, then go to

20 a 36, then go to a 12.

21 We were concerned about the fact that

22 when you're using a single nest to represent the

23 meteorological field, whether or not that 12 kilometers

24 domain was large enough to capture what apparently

25 looks like the recirculation on that and so none of the
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 315

1 models on the Great Plains Tracer experiment, when you

2 look at it from the ATMEs framework where you weren't

3 looking at that 12 hour integrated average, never

4 picked up that second wave of Tracer. It was like an

5 ordered magnitude smaller than the first wave that

6 crossed the ARC, but it raised questions as to whether

7 or not the 12 kilometers was sufficient, whether it was

8 sufficiently large to capture if there was

9 recirculation occurring. So, that was something that

10 we do need to go back and look at.

11 MR. PAINE: One other quick comment.

12 The ratings of the models were like this

13 model won and this model lost, but the Mississippi

14 Primary last night, I think the candidates were within

15 one percent of each other, so were they significantly

16 different? Were the models significantly different? I

17 have no idea.

18 MR. ANDERSON: And I think you need to

19 take a step back and look at it -- you know Kirk made a

20 very good point which is while we have presented

21 presentations or information at prior conferences, this

22 is a work in progress.

23 For example, the issue that Ralph

24 mentioned with respect to use of fractional bias as one

25 of the indicators in there, you know, where you get
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 316

1 that. What is developed -- when we did the evaluation

2 paradigm, for example. When we tried to look at an

3 evaluation paradigm, we did look at what tried to make

4 the most sense given, as Tyler was talking about, the

5 fit for purpose type of evaluation paradigm, but the

6 issue becomes is that when you take somebody else's

7 method off the shelf and you look at it and you try to

8 use it within the context of a regulatory evaluation,

9 not everything works out quite the same because the

10 things that they've placed as an emphasis on those four

11 broad categories and the metric that they used to

12 represent each of those four broad categories may not

13 be sufficient for that purpose. For a regulatory

14 purpose.

15 Like we were saying, for regulatory

16 application of a model as an example, the issue of

17 fractional bias is we are concerned in a regulatory

18 capacity, you're concerned that the model is -- that

19 there isn't a systematic bias towards under-prediction

20 and as a result of that fractional bias, when you look

21 at the way that it was used within that rank metric, it

22 takes the -- it takes the one minus the absolute value

23 of fractional bias divided by two. So, it treats over24 and under-prediction as the same. As a result of that,

25 it's not a good -- for regulatory purposes, it's not a
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 317

1 good fit.

2 We learned about this after the fact.

3 So, for example, we started going back and looking at a

4 lot more of the work that, you know, Steve Hanna and

5 Joe Chang have done with the boot statistics and

6 looking at fractional bias false-positive, fractional

7 bias false-negative, and then to start looking at

8 confidence intervals to look at the statistically

9 significance between those and so this is a work in

10 progress.

11 So, you know, I think the point was in

12 Tyler's presentation was the fact that there were a lot

13 of lessons learned in the prior evaluations and one of

14 them was that if you go back and you look at the

15 results coming out of the Rocky Mountain Acid

16 Deposition Model Project, basically what they did was

17 they took all the models that were involved in that

18 first eight model study that EPA did back in 86 and

19 then they just weighted -- you've got issues of data

20 organization. Pairing in time and space. Not pairing

21 in time and space. They looked across each of the

22 statistical categories and the data pairing and

23 weighted them the same.

24 So, in that case, and I think, you know,

25 Mesopuff and Mesopuff II were the models that were
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 318

1 involved. I think Joe probably remembers this because

2 you were heavily involved in that. Mesopuff II was the

3 best performing model for purposes of unpaired in space

4 and time and ARM3, Ralph's model, was the best

5 performing model for purposes of pairing in time and

6 space and they were ranking 21 and 20.

7 The problem became then the fit for

8 purpose which is, okay, and so that question is is that

9 because you use for long range transport assessments

10 for AQRVs in class one, you're very concerned about

11 space time pairing. And so weighting the two the same

12 in that context for regulatory applications -- when

13 you're trying to decide which one is best for

14 regulatory purposes, didn't do that.

15 So, this is a work in progress in trying

16 to establish a paradigm that makes sense where no

17 paradigm has existed and so we certainly -- I certainly

18 hope that through the comment process during this

19 period that if there are concerns about what was done,

20 that those be brought forward, but bear in mind that

21 that is a work in progress.

22 AUDIENCE MEMBER: This is Ron Lai from

23 BOEM. I have a question for Ralph.

24 I looked at the met. It is 20 percent

25 over the ocean.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 319

1 MR. MORRIS: I didn't understand the

2 question.

3 MR. LAI: I looked at the Australia

4 study. The met. 20 percent is across the ocean. Is

5 there a special approach to modify with respect to the

6 ocean or no?

7 MR. MORRIS: Well, as part of the sub8 domains do go over the ocean and you might see high

9 ozone over the ocean because of the reduced mixing.

10 So, if the plume travels over the ocean, then we do

11 account for that.

12 MR. LAI: How do you account for that?

13 MR. MORRIS: Well, it's part of the

14 modeling domain.

15 MR. LAI: The model, the meteorology

16 accounts for that?

17 MR. MORRIS: The TAPM model? The TAPM

18 model, the meteorological driver is a fairly

19 sophisticated prognostic model that incorporates

20 observations if I remember right. I don't know how

21 they ran it in this study.

22 MR. LAI: Would you just use the

23 meteorology model for the driver?

24 MR. MORRIS: Yes. We didn't run the met

25 model.
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 320

1 AUDIENCE MEMBER: My name is Rick Graw

2 and I'm with the U.S. Forest Service Air Quality

3 Program.

4 I have just a few comments to make as we

5 start thinking about emerging models and techniques

6 from the view of the U.S. Forest Service.

7 One has to do with ozone. As we start

8 thinking about the continued development of ozone

9 models and how we might apply them perhaps beyond just

10 a short term period in the urban areas, considering

11 that a lot of the ozone metrics that the Forest Service

12 uses are based upon other metrics that might be a

13 sigmoidal weighted average over time for exposure and

14 we have a number of additional metrics that aren't

15 currently being considered in the output of these ozone

16 models that I think would be really helpful for us and

17 the regional modelers to help identify areas and see

18 where the injury to vegetation is occurring as we

19 continue to develop our understanding of air pollution

20 effects on the ecosystems.

21 So, that's a request issue. I'll move

22 forward with the future development of these tools.

23 Second has to do with deposition.

24 Another request is I believe we're at a point and have

25 been for quite some time where we could at least ARC
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 321

1 graphic clouds modeled fairly accurately in these

2 models and cloud water deposition is of concern to the

3 Forest Service and again, that's not currently being

4 considered and I would like to see if or at least plant

5 the seed so-to-speak in terms of consideration of

6 including cloud water deposition into the future model

7 developments.

8 Thank you.

9 MR. BRIDGERS: Thanks, Rick.

10 Other questions? This is the last

11 chance because tomorrow it's all comment.

12 Well, I thank everybody again for

13 sticking around this afternoon. If you stay around the

14 campus too long, the gate over at Hobson Road closes at

15 six, but I don't think that's going to be much of a

16 problem right now.

17 Thank you again. I think we owe the

18 speakers another round of applause. We will start at

19 eight-thirty in the morning.

20 Have a pleasant evening.

21 (WHEREUPON, the conference was concluded.)

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25
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 324

0

0 259:5

0.1 179:15

0.2 179:15

188:6 188:7

0.4 188:6

0.75 173:3

02 266:9

05 139:25

297:25 304:25

305:1

1

1 35:23 40:9

100:24 111:5

139:24 140:1

141:5 149:4

150:10 150:11

153:8 153:9

153:10 268:23

1,000 141:20

150:6 151:5

153:17 166:6

169:20 205:21

312:13

1,800 151:2

1.2 27:15

1.24 299:2

1.28 130:2 189:13

1.282 130:5

130:15

1.5 125:5 180:18

1.6 180:3

10 50:24 105:25

110:11 124:2

135:20

10,000 50:8 143:4

170:14

10:40 93:21 93:22

100 49:25 64:24

65:19 65:25

69:21 75:5 80:14

94:25 143:23

149:2 150:8

150:25 151:20

152:4 153:21

153:22 154:14

161:21 178:19

180:14 242:11

242:13 242:24

245:20 250:17

250:17 253:23

255:14 270:2

295:4 313:21

313:22

108 314:19

10th 3:2 10:5

20:11 25:1 30:10

56:23 132:15

11 74:17 75:24

250:25 263:8

289:10

1100 138:25

11059 75:8 198:20

199:2 199:6

199:6

112 143:15

11325 75:8

11th 10:9 24:22

308:24

12 55:5 134:16

181:4 196:18

236:11 241:17

245:7 245:15

246:2 246:16

246:18 246:22

246:22 247:1

248:11 248:16

249:11 249:12

250:25 250:25

251:5 251:6

262:15 262:15

262:19 262:24

266:3 266:12

266:16 313:20

313:23 314:11

314:20 314:23

315:3 315:7

12:40 131:16

120 67:2 295:5

12060 75:7

126 132:16

12th 95:2

13 263:8 266:4

133 161:1

135 148:8

14 3:3 113:17

140 161:11 161:19

15 24:1 46:11

51:4 73:13 73:17

146:7 178:3

178:4 217:18

150 64:20 64:22

143:2 143:23

162 50:7

165 148:7

16th 24:21

27:18 28:7

17 113:17

119:18 148:11

151:23 153:21

154:6 154:8

172:11

172 139:5

18 67:8 245:15

248:12

18.3 67:4

1838 152:6

188 140:11 141:11

189 140:11 147:4
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 325

1970s 215:24

1980 242:10

1980s 155:2

1983 229:3 243:17

244:11 244:13

1986 223:3 241:14

1988 241:20 242:7

296:5

1990 107:7 206:21

1991 172:21

1994 241:24

243:19

1997 23:8 23:10

1998 242:9 242:14

254:22 254:24

312:9 313:20

1999 234:1 234:13

234:22 236:17

236:18 236:19

278:6 279:4

1-hour 5:1 6:24

7:13 40:4 53:7

94:10 94:18

94:25 95:1

95:3 96:12

97:2 97:17 98:17

103:3 104:16

107:13 110:13

111:24 112:9

118:1 119:13

125:23

1st 7:8 95:11

111:4

2

2 35:21 35:23

35:23 105:14

105:14 105:16

105:17 153:11

178:22 179:18

20 29:20 50:15

58:6 82:14 82:16

108:22 131:15

135:20 162:2

162:4 162:13

166:25 167:18

167:20 168:15

168:16 170:20

242:18 254:19

302:16 318:6

318:24 319:4

20.7 66:24

20.8 66:17

200 133:3

180:21 245:21

255:14 270:1

2000 23:21

2000s 23:21

2001 75:3 236:21

2003 219:22

297:21

2004 297:21

297:24

2005 257:24

261:20 262:13

263:1 269:13

297:24

2006 23:23

234:2 257:24

261:20 262:24

263:7 265:11

266:4 270:22

2008 24:3 28:6

2009 245:23 248:8

255:15

2010 24:23 25:7

25:7 25:16

27:8 29:8

29:25 45:9

95:2 96:16

2011 3:3 23:23

25:1 172:1

179:23 180:8

211:12 262:10

2012 12:6 12:24

22:14 309:4

2013 12:25

13:12 309:4

2017 212:13

20ish 3:23

20s 51:5 82:17

21 178:12 318:6

21st 28:3

23 178:12

178:22 300:18

230 148:7

23rd 25:15 32:1

24 27:15 178:23

314:4

24-hour 45:19

47:8 64:19

65:4 75:20

25 3:23 65:7

65:18 66:21

75:19 82:16

140:1 141:19

149:4 150:10

150:11 153:11

249:5 297:20

300:7 300:11

300:19 300:19

300:21 301:1

301:14 302:13

302:16 305:22

26 67:8 172:9

260 240:11

27 113:12 133:19

28 61:1 133:19

148:6 169:7

290 143:14

296 137:13 137:14
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 326

2nd 244:13

3

3 27:15 310:19

3.1 266:9

3.3 236:22 279:23

30 3:5 16:22

73:22 108:22

135:20 179:21

179:22 248:1

254:19

300 137:18 139:22

140:4 140:7

140:22 141:5

141:15 178:11

178:21 270:1

30th 11:15 21:15

31 72:15 245:3

246:14 289:10

290:14

32 139:16

33 144:17 215:2

215:5

34 70:24 72:7

35 23:25 65:6

66:17 148:16

154:20 240:12

36 236:9 245:6

246:2 248:17

249:11 314:4

314:18 314:20

365 45:22

375 180:5

38 75:24

381 139:3

3D 294:20

4

4 35:21 73:13

245:15 246:2

262:16

4.7 292:4

4.7.1 236:23

281:4

40 38:1 38:9

39:23 40:2 45:23

50:4 50:5 68:10

41 50:7

42 24:12

42y 192:15

193:4 194:9

194:17

43 139:17

445 144:9

45-minute 131:14

46.8 72:13

48.5 70:6

5

5 35:21 35:23

57:20 141:20

146:6 149:4

150:10 150:11

192:7 302:2

302:8 302:17

302:18 303:12

5,000 159:25

170:20

5.3 297:25

5.40 236:23

5.8 243:24

50 45:23 58:22

64:24 67:9 69:23

107:16 107:17

110:7 133:4

137:17 139:23

140:4 140:7

140:23 141:13

141:16 159:25

169:19 170:1

178:19 195:6

207:1 207:4

256:21 270:2

311:24

500 144:7

146:15 169:25

181:10 186:4

286:22 289:12

298:24 300:6

300:18 300:20

301:8 312:13

50th 169:21

169:24 170:11

170:11

57 143:15

59 71:1

6

6 25:6 133:5

133:11 148:25

149:12 156:13

266:9

60 16:21 187:18

302:15

600 242:11

243:4 254:22

255:1 255:5

313:23 314:5

60s 241:8

65 23:25 139:1

139:18 143:1

143:3 143:22

143:24 144:5

144:8 144:12

144:20 144:25

161:22 289:11

7

7.8 304:7

70 65:16 71:3

70s 149:20 241:8

74 304:6
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 327

75 67:9 95:4

97:22 255:9

270:2 295:6

780 143:10

7th 75:25

8

8 173:11

8,000 297:20

8,760 160:24

8.1 212:9

8.2 212:9

80 149:2 150:9

151:3 243:16

243:23 245:6

248:17 295:6

295:10 302:15

80s 148:21 148:23

149:19 215:24

223:13

8-1 42:21

810 141:13

82 295:9 295:10

295:11

8-2 42:21 54:22

82.6 70:2

83 50:2 50:14

84 295:10

85 50:1 68:10

86 317:18

89 161:21

8th 9:21

9

9 57:1 83:19

149:4

9/11 23:23

90 116:5 116:6

116:13 303:9

905 144:6

90s 223:13

90th 170:9 173:3

91 180:6

930 141:16

95 188:20

98 161:20

98th 45:8 45:10

45:11 45:19

66:16 66:24 69:5

69:11 70:2 78:20

81:15 81:16 82:1

82:2 82:11 82:12

82:15 83:4 83:11

94:25 103:12

111:21 112:11

177:20 177:25

99th 78:19

78:19 83:5

95:4 103:13

9th 5:6

A

AB 310:18

AB-3 220:1 310:12

310:16

ABC 10:19

ability 8:21

26:17 88:25

119:4 121:19

222:24 225:20

241:7

able 13:10

14:14 26:3 26:18

28:22 28:24

49:20 86:17 88:6

89:24 89:24

97:13 103:16

160:7 160:10

194:20 200:9

202:24 239:15

255:6 278:23

299:14 314:9

Abo 125:1

176:10 176:20

179:3 179:18

180:2 199:18

absent 204:16

absolute 33:6

33:10 55:9

281:14 316:22

absolutely

78:24 197:24

256:18 274:23

275:2

Abu 179:8

accept 19:21

60:21

acceptable 111:10

203:25 221:18

accepting 171:13

accommodate

35:2 160:7

216:21 217:1

277:8

according

263:24 271:6

278:13

accordingly 5:13

account 5:8

5:13 7:5 11:11

17:6 17:9

43:19 44:8

84:9 86:14 88:17

89:3 93:11

105:23 106:2

107:12 110:16

128:3 200:21

216:15 225:23

269:20 319:11

319:12
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 328

accounted 35:15

68:15 105:22

172:23 283:22

293:1

accounting

70:11 71:8 71:16

110:5

accounts 72:3

319:16

accuracy 119:5

221:19

accurately 277:15

321:1

achievable 306:10

achieve 68:9

122:23

acid 268:10

271:19 317:15

acknowledge 83:24

97:12 108:22

119:7 123:25

124:8 138:11

159:7 293:22

acknowledged

58:15

acknowledging

115:8

acquire 274:24

acquisition

192:19

across 3:24

14:3 14:4 14:6

28:10 29:25

46:15 46:19 53:2

59:23 94:7 98:22

113:20 149:10

185:23 204:25

208:24 218:23

224:23 250:24

252:3 262:1

268:18 269:7

269:15 273:16

295:18 313:21

314:7 317:21

319:4

action 24:25 25:6

25:12 45:10

actions 25:4

210:23

active 15:1 296:5

actively 12:21

13:2 228:11

274:22

activities 308:11

309:7

activity 106:4

actual 45:14

122:14 134:23

142:23 153:18

162:7 165:24

165:25 166:8

167:9 167:16

167:24 168:1

168:24 169:4

169:11 169:13

169:15 171:7

178:7 199:21

200:1 200:1

305:1 314:1

actually 4:4 9:22

21:4 23:7 28:2

28:17 36:15

40:15 40:16 43:1

43:7 45:21 52:20

56:21 61:4

66:2 79:21

80:1 81:5

81:14 87:13 91:2

106:2 119:21

121:13 122:3

123:17 123:18

124:16 125:7

126:11 126:19

127:7 127:8

127:10 127:14

127:16 128:6

128:8 128:21

129:8 129:13

129:17 129:24

130:6 130:11

130:23 130:25

132:20 137:7

137:16 137:17

138:18 138:20

140:19 142:2

144:13 148:17

158:13 164:24

165:23 167:10

168:23 169:13

180:24 183:24

184:1 187:2

197:25 198:2

198:7 202:16

203:14 203:17

207:12 209:14

209:14 209:17

209:19 210:11

210:13 210:25

211:8 213:1

213:5 213:23

215:17 217:4

240:12 241:6

241:21 242:4

244:12 245:6

247:15 248:1

254:4 263:13

290:7 290:7

296:13 305:5

305:6 309:5

312:25 313:24

actuals 212:22

adapt 274:1 274:2

adaptation 275:18

adapted 306:20

add 6:12 45:11

47:3 47:15 65:17

67:12 72:25 73:6

73:12 73:24
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 329

82:25 83:2 88:20

90:24 135:25

151:25 152:1

162:16 165:11

170:5 199:23

210:22 216:19

216:25 285:9

312:20

added 69:5 86:2

134:18 139:2

139:3 139:4

187:22

adding 32:13

68:14 72:6 78:17

78:19 82:11

82:16 84:16

85:14 88:9

134:12 136:15

143:9 231:1

235:17

addition 15:8

15:18 55:19 62:1

158:15 163:25

182:8 204:12

210:22 219:23

229:8 230:18

235:15 236:19

312:13

additional 8:2

17:23 55:14 65:9

96:2 96:13 97:20

98:2 100:5 104:9

112:24 132:22

134:11 135:6

157:20 183:5

191:21 226:11

235:7 272:9

293:4 320:14

Additionally

276:6

additions 211:1

211:7

address 5:12 6:22

59:25 60:3

70:7 86:10 86:24

88:1 94:17 95:14

95:18 96:3

104:20 105:1

106:23 113:1

115:14 118:21

179:9 181:1

220:11 221:12

222:10 224:22

261:4 261:5

309:16

addressed 8:3

26:7 45:9 59:8

63:24 85:17

102:9 104:4

201:13 201:14

220:14 292:19

addresses 175:12

addressing

18:20 87:3

258:13

adds 123:23

adequate 79:8

adequately 40:5

40:10 201:7

210:4 210:9

239:12

adjacent 259:2

adjusted 280:15

administration

274:21

administrative

10:22

admit 108:23

admittedly 112:1

ado 132:25 212:3

adopt 273:1

adopted 273:1

advance 20:11

advanced 88:2

293:10

advantage 14:11

17:4 17:13 76:25

81:2 108:7

108:20 112:4

114:20 116:3

advected 278:13

advection

224:15 225:21

238:21

advertised 47:25

advised 107:5

advisement 86:5

advising 93:14

AECOM 16:18 78:16

159:8 311:20

AERMET 75:8 90:12

AERMINUTE 70:18

74:4 75:8

75:18 200:23

AERMOD 6:18

7:18 9:22 9:24

32:13 33:17

34:25 38:13 39:6

40:23 41:15

48:21 52:14

54:24 75:7 87:20

87:21 88:10

88:19 90:12

92:14 93:10

93:14 96:24 98:8

99:5 99:23 99:25

100:1 100:25

103:16 103:25

115:1 115:18

115:20 116:1

116:4 116:25

118:10 119:6

119:12 119:17

120:10 120:11

120:15 121:7

121:9 121:16
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 330

121:24 122:1

122:2 122:7

122:8 123:22

124:5 125:4

126:8 126:11

126:16 126:19

127:3 128:11

129:1 129:9

130:22 131:4

132:8 151:1

159:21 162:18

163:1 163:3

163:4 163:25

164:2 164:3

164:8 164:23

165:7 165:8

165:23 165:25

167:9 167:10

167:15 168:23

169:4 169:4

169:12 169:15

171:1 171:2

175:19 179:4

179:6 186:14

186:16 188:14

188:22 189:12

190:12 198:9

215:21 230:25

259:8 296:17

AERMOD's 195:17

aerosol 278:24

AERSCREEN 210:14

AF&PA 58:11 76:14

affect 268:7

affected 189:24

affectionately

25:19 85:24

afforded 175:16

afternoon 8:10

8:14 9:6 17:25

47:20 52:19

53:14 78:15

87:24 89:20 98:3

131:24 132:3

257:4 282:7

282:11 283:12

284:16 286:12

309:24 321:13

afterwards

17:16 95:3

96:3 294:5

against 31:2

46:10 47:13

107:5 120:16

120:17 121:24

171:6 206:20

229:25 234:13

265:1 279:8

314:7

agencies 20:14

22:12 28:13

28:13 28:21

28:24 101:4

231:20 258:21

258:21 260:1

260:2 273:8

273:9 274:19

agency 3:1

10:23 12:17 19:7

23:9 28:11 29:13

30:3 36:13

40:8 52:25 176:2

184:12

agenda 3:23

3:25 4:11

14:18 132:12

132:25 191:15

aggregate 17:15

ago 75:24 91:20

108:22 124:5

214:20 256:8

agreed 57:9 121:9

128:6 259:16

259:20

agreeing 57:24

agreement 5:11

127:13 127:23

130:15 230:9

267:18 301:12

Agriculture 219:3

259:14

Ah 217:11

ahead 4:18

19:20 32:23

93:21 108:14

131:10 132:18

134:22 217:16

218:2

aid 25:4

air 3:2 23:8

31:22 41:23

77:21 103:23

137:21 139:22

141:24 148:23

153:6 156:4

187:13 218:25

229:14 229:14

257:7 257:8

257:24 258:3

258:13 259:4

259:6 259:10

259:17 259:20

260:12 260:13

260:19 261:24

261:25 262:14

263:2 272:22

276:15 285:19

289:22 290:18

298:2 298:3

310:12 320:2

320:19

aircraft 286:22

288:2 289:2

311:15

aircrafts 185:18

airplanes 233:19

airport 122:22

201:3 201:6

AIWG 91:11 96:6
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 331

132:8 133:6

134:8 138:12

145:16 146:12

156:6 157:15

158:7 158:12

Alabama 311:17

alarm 253:7

alarming 72:17

74:24

alarms 253:9

Alaska 121:25

124:3 124:8

185:20 186:1

188:9

albeit 61:21

algorithm 125:12

align 18:15

284:17

alike 163:3

alkanes 304:20

305:3

alkenes 304:21

305:2

allow 11:16 11:17

11:20 13:18

86:18 86:23

95:13 95:24

98:11 208:25

213:18 232:21

260:5 274:24

280:11

allowable 42:22

55:2 64:7

119:9 122:20

166:11 212:10

215:19 302:6

304:1

allowables

54:22 212:21

allowed 23:16

24:16 310:21

allows 112:4

290:19

alluded 63:7

71:20

ALMA 220:1

aloft 266:20

266:24 266:25

267:12

already 10:12

20:8 20:23 21:14

25:13 25:14

28:21 28:24

38:10 40:2

40:2 40:3

40:17 40:23

47:18 49:4 49:10

51:17 51:21 61:5

61:12 68:2

85:5 85:6

94:15 131:21

131:22 133:12

143:6 143:7

150:17 157:3

184:13 186:1

191:8 230:13

233:3 286:9

alter 96:18

100:16

alternative 64:10

79:7 160:21

220:19 227:25

altitude 286:21

am 4:6 60:3

74:1 131:22

206:19 212:17

310:25

ambient 7:15

41:23 77:21

97:21 101:25

119:5 137:21

139:22 141:24

153:6 156:4

171:24 172:24

173:1 173:2

173:4 173:6

173:22 174:3

174:5 174:8

174:10 174:13

175:11 176:5

176:16 184:15

185:3 190:1

207:25 210:9

283:2 285:19

291:14 292:16

American 58:10

58:20 183:20

ammonia 51:6

268:6 271:3

272:8 272:10

ammonium 51:8

271:2 271:10

Amoco 199:19

among 163:14

258:20

amount 18:6

22:7 72:7 72:8

73:18 133:12

152:16 175:4

184:20 185:5

193:19 193:19

197:11 205:20

241:1

ample 67:10

AMR 173:8

AMS 223:15

analogous 35:20

analyses 17:23

65:11 67:17

68:21 76:9

85:5 92:14 95:24

102:25 103:18

104:1 106:19

171:25 172:5

172:10 173:11
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 332

176:2 212:20

219:1 219:5

219:11 221:11

224:6 224:7

224:9 224:25

259:10 259:17

259:21 259:23

260:5 260:12

273:23

analysis 31:9

31:23 34:1

34:3 34:19 35:20

37:2 37:3 37:4

37:7 37:15 37:18

39:25 41:25

46:23 47:25

48:25 49:3 51:20

56:15 56:22

63:12 65:2 86:18

90:20 92:17

103:7 106:24

107:19 108:1

115:6 134:9

134:10 153:8

154:22 164:21

169:18 182:6

204:3 208:2

211:21 212:6

212:23 214:23

229:10 245:13

257:12 258:1

258:2 262:3

262:3 263:17

263:18 264:25

265:10 267:14

271:22 276:9

302:4 302:22

314:13

analyze 98:17

258:4 259:5

analyzer 177:2

194:22 197:12

analyzers

194:22 194:25

analyzing

101:23 133:15

and/or 62:6

Anderson 200:17

201:10 240:15

257:2 257:3

262:23 271:17

312:20 315:18

angst 103:18

angular 289:6

announce 310:2

annual 13:7 13:14

23:10 24:1 27:14

42:11 45:14

45:17 46:3

46:6 46:9

46:14 47:1 48:11

48:13 95:1 97:12

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125:24 162:20

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annum 298:25

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answer 10:17

19:10 57:15

57:19 67:15 79:1

81:23 118:9

132:3 151:16

151:18 159:11

172:19 182:12

198:13 202:19

210:19 270:4

275:20 309:22

answers 56:7

57:25 58:8 74:25

237:16

anthropogenic

236:20 298:5

anticipate

13:12 18:21

39:11 76:23

anybody 24:20

50:16 176:25

197:5 310:18

anybody's 17:21

anything 4:6 38:2

40:18 40:18

76:12 85:20

90:14 115:21

150:15 194:19

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anyway 108:14

133:7 141:12

149:16 154:22

157:24 190:2

240:8 240:13

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294:6 295:15

306:14

anywhere 125:11

APCD 92:11

API 185:16

apologies 4:18

apologize 32:19

78:12

Appalachian

243:18

apparently

314:4 314:24

appear 252:4

appears 287:20

appendix 4:14 5:3

5:12 5:13 5:24

6:22 7:3 7:10

9:23 9:24 10:2
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 333

26:21 32:15 35:2

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applause 217:15

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apples-toapples 187:21

applicability

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97:8 99:14

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applicable

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97:19 98:1

100:24 129:5

129:6 269:5

295:3

applicant 176:1

applicants

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97:13 172:3

application 36:18

71:11 98:21

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225:14 234:14

252:2 266:12

275:13 291:3

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305:24 316:16

applications

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59:15 74:9 99:22

218:19 226:2

227:12 272:21

273:12 277:19

277:21 277:24

278:1 293:19

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applied 52:12

53:2 77:2

119:8 126:20

179:5 187:19

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304:2

applies 97:21

220:19

apply 25:9 33:3

52:14 60:3

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68:6 77:10

117:12 117:13

121:16 164:6

207:3 207:6

229:24 230:3

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261:14 284:25

306:7 320:9

applying 15:25

54:12 68:13 73:1

114:5 114:19

142:1 162:22

230:25

apportionment 5:7

33:4 54:3 235:16

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261:23 276:25

appreciate

14:20 60:13

68:18 76:17

76:24 79:19

83:16 88:24

114:7 115:11

138:16 158:5

206:3 217:13

218:4

appreciated 3:7

30:4 94:4

appreciates 60:5

appreciation

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approach 7:5 7:12

7:14 7:14

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33:24 34:21 39:8

45:4 49:21 51:12

52:24 69:8

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82:9 84:24 88:11

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97:2 97:5 98:3

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111:7 111:7

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approaches

34:14 55:16

70:12 72:3

73:9 73:11 77:20

79:7 84:23 98:20

115:13 171:5

197:6 221:21

230:15 231:7

231:17 234:22

235:2 237:16

239:14 260:9

309:14

approaching

218:13

appropriate

8:16 12:8 16:3

22:25 30:14 42:3

51:20 54:19 55:6

56:10 79:6 86:18

93:16 95:24

102:4 102:7

107:12 107:14

111:16 111:23

112:5 112:18

125:15 125:20

179:10 205:14

223:18 231:17

232:7 309:11

appropriately

84:9 98:14

114:19 225:21

appropriateness

80:9 92:16

93:5 112:16

approval 99:6

99:17

approved 36:12

93:4 221:16

approving 221:19

approximation

299:13

April 4:22

11:15 11:16

11:24 13:13

18:12 21:15 22:6

95:2 234:9

307:21 308:2

APT 236:5

AQRV 11:22

224:6 273:23

276:8

AQRVs 9:2 222:8

276:24 318:10

AQS 176:5 176:7

177:4 178:25

aqueous 278:25

arbitrary 178:3

ARC 242:13 242:17

242:19 243:1

243:5 243:6

243:16 254:22

255:1 255:5

313:22 313:23

314:5 314:8

315:6 320:25

arcs 120:4

147:1 242:11

242:13 288:24

312:7

Arctic 50:24

area 8:5 28:12

43:2 55:13

73:4 84:15

93:6 97:24 102:4

105:25 107:17

120:8 126:21

127:5 135:21

147:18 147:19

147:22 148:1

148:2 155:3

185:19 190:3

206:24 206:25

207:4 207:5

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217:6 259:23

262:17 263:5

266:6 267:3

269:17 295:3

297:19 298:12

298:20 301:19

303:9 303:15

303:24 303:25

area-by-area

89:16

areas 9:14

27:15 28:21

29:22 41:22 42:1

52:9 52:9 81:1

112:25 220:3

221:20 227:10

227:18 229:15

229:19 229:20

262:1 268:22

269:8 269:12

269:16 269:20

295:8 298:19

302:7 302:10

303:4 303:5

305:9 320:10

320:17

arena 6:20 8:17

25:14 36:6 274:7

aren't 35:18

47:23 60:10

68:25 78:10

129:5 155:19

274:12 291:5

320:14

Argonne 241:15

arguably 92:1
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Arlington 148:2

ARM 150:9

171:24 172:8

172:20 173:5

174:11 175:7

175:15 179:5

180:15 183:2

183:3

ARM-2 175:8

177:17 180:18

181:6 181:22

182:15 182:17

182:21

ARM3 318:4

arrived 23:5

art 72:18

articles 12:18

258:10

articulating

60:11 79:20

artifact 177:1

artificially

179:13

Ashley 149:11

aside 57:5 105:11

108:18

as-is 144:2

asleep 19:25

ASOS 186:5

aspect 13:20 14:5

15:21 45:2 98:10

104:18 105:4

129:1 132:4

203:14 313:16

aspects 5:20

7:1 9:14 15:9

15:20 20:16 26:9

35:16 37:10

41:22 56:13 90:9

95:11 109:5

125:14

asphalt 145:16

146:2 146:4

assess 35:24

98:17 192:2

233:14

assessed 239:8

assessing 8:25

180:24 276:23

assessment

11:22 18:7 38:15

38:24 49:1

49:5 49:9 106:11

110:3 126:20

232:18 261:17

assessments 15:24

53:9 80:18

107:10 227:5

227:6 227:13

227:24 230:11

230:14 232:23

233:5 239:24

269:25 287:16

311:24 318:9

asset 196:3

assign 160:8

assigned 205:19

assimilating

147:18

assist 101:16

associated

26:17 43:20

122:16 123:5

257:20 259:9

272:13 280:14

Associates 171:22

Association 58:10

58:11 310:13

associations 12:9

assume 68:4

68:8 160:3

160:18 186:24

271:9

assumed 74:13

104:13 187:14

188:7

assuming 72:11

104:8 160:15

181:23 187:7

243:5 271:3

assumption 189:12

assumptions 179:7

179:25 180:17

181:20

assured 31:1

asterisk 188:18

Atlanta 126:21

134:7

ATMEs 242:3 249:6

249:11 252:20

315:2

atmosphere 255:22

atmospheric 181:1

233:10

attack 308:20

attain 136:5

attainment 52:9

53:18 55:11

72:19 136:7

154:8 155:4

212:4 212:8

220:16 227:7

295:8 295:9

295:12 301:19

302:10 303:4

303:9 303:15

303:24

attempt 283:17

attend 310:17

attention 26:23
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 336

attributable

126:24

attribute 222:16

attributes 222:14

222:19

auctioneer 217:10

audience 16:18

18:9 18:23

19:3 19:24

50:4 78:16 82:24

85:1 86:6 87:6

87:16 88:12

90:10 91:14 92:9

168:8 199:15

200:17 201:21

203:16 206:11

209:4 211:16

214:17 310:4

310:21 310:25

311:20 318:22

320:1

audio 211:9

augmentation

6:4 7:19

August 95:5

245:23

Australia 89:22

294:9 294:13

295:4 297:22

301:22 319:3

Australian 295:24

298:1

authorities

36:4 274:18

authority 99:16

233:25

authors 159:7

159:8

auxiliary 143:11

available 28:21

30:8 31:1

59:18 75:4

76:1 97:6 99:4

111:22 112:18

179:2 184:23

185:1 185:7

187:13 201:9

204:24 205:23

205:23 223:23

226:1 228:11

234:9 235:9

235:16 286:16

avenue 36:10

avenues 13:18

114:11 308:8

309:2

average 45:17

45:17 45:18

45:19 46:6

46:8 46:10 46:15

46:15 46:18 47:1

47:8 65:15 66:16

75:20 103:3

125:3 125:7

161:1 161:23

161:24 167:13

172:24 178:5

181:3 194:10

197:3 202:11

203:2 286:21

295:4 295:6

313:21 313:23

313:24 315:3

320:13

averaged 47:1

47:11 48:1 180:4

202:5 250:24

averages 96:17

111:21 119:13

160:2 195:2

averaging 7:6

100:15 282:20

avoidable 117:14

avoids 175:21

award 256:18

aware 14:7

14:11 66:10 90:4

95:14

away 66:13 108:10

116:6 116:6

116:14 146:15

157:22 180:21

186:4 198:1

212:21 282:24

283:4 290:8

293:15 300:8

awful 263:14

AWMA 12:25

axiom 64:19

axis 264:1

264:2 264:6

264:7 301:5

301:6

B

backed 154:10

background 8:7

11:19 23:4

25:8 25:10 26:24

27:2 34:8

34:25 35:3 35:16

37:5 37:11

41:9 43:17 43:23

44:4 45:12 45:14

46:5 46:24 47:15

47:16 48:16 50:8

50:12 51:3 51:14

59:17 64:23 65:6

67:3 69:5 69:9

70:11 77:3

77:5 78:18

81:4 81:7

82:15 84:14

98:15 102:23

106:18 107:2

110:1 111:6

111:22 112:14
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134:13 135:22

135:24 144:17

151:25 153:25

153:25 157:8

165:11 170:19

173:20 187:22

192:21 206:16

207:19 208:1

208:11 208:22

209:2 241:2

241:2 268:6

272:8 272:10

280:1 280:12

280:16 283:2

285:19 288:6

289:22 290:18

291:14 292:17

302:15

backup 160:12

Bacon 34:11

BACT 63:25

bad 50:1 171:2

Baker 53:21 226:7

226:8

balance 107:11

balancing 107:11

ballpark 130:8

banker 94:8

bar 137:25

140:9 143:17

143:20 143:20

143:21 149:13

167:7

barely 58:3

BARF 225:4

barrier 275:4

barriers 273:3

277:4

bars 140:15

140:22 229:18

BART 62:6

base 89:15

135:2 135:5

136:17 136:25

138:24 141:4

143:21 144:5

144:8 144:15

144:16 144:20

144:25 202:21

212:17 215:19

283:24 284:3

based 5:9 25:12

42:4 46:9 59:4

59:7 73:14 94:25

95:4 96:17 103:3

104:12 105:14

111:18 113:11

134:1 134:18

140:13 142:1

143:5 145:16

173:25 175:10

181:24 205:19

213:2 223:4

232:13 236:21

237:12 273:20

274:2 274:7

274:21 275:10

275:16 279:22

280:7 280:17

282:20 284:11

284:13 286:8

305:16 320:12

baseline 142:18

148:5 150:5

150:25 151:5

152:5 303:25

basic 119:14

136:6

basically 29:3

53:8 111:18

124:4 124:14

133:22 135:9

136:16 141:8

147:17 149:9

155:4 162:21

162:25 163:4

163:12 163:13

178:19 179:4

193:5 193:7

195:2 195:5

211:25 226:10

257:25 258:10

262:11 263:15

264:24 265:24

266:1 267:24

268:25 269:14

270:17 271:17

275:5 276:21

278:4 279:18

281:16 281:22

317:16

Basin 258:11

258:12 262:20

basis 14:1

14:13 28:12 48:7

52:9 89:13 89:16

93:5 98:9 99:7

99:15 110:22

112:6 214:12

230:20

bear 271:22 274:5

318:20

Bears 244:15

Beaufort 50:25

became 318:7

become 19:6 25:19

36:5 95:14

290:20

becomes 235:16

316:6

becoming 16:20

102:16 117:22

191:1 310:18

beer 310:22

begin 257:6 279:5

beginning 98:10
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285:2 287:22

begins 275:8

behalf 58:10

58:14 258:18

behavior 264:9

279:7 280:24

283:8 287:12

288:15 290:5

292:7

behind 73:20

believe 4:21

7:4 8:9 11:14

11:24 12:25 80:5

93:8 99:10 100:6

100:21 103:14

105:25 107:23

112:8 113:12

114:1 119:1

143:5 183:3

221:24 224:11

266:18 272:1

312:24 313:6

320:24

benchmark 248:1

279:8

benchmarks 246:11

beneficial 169:16

benefit 7:23

115:19 118:24

168:3 169:1

171:12 197:17

benefits 175:14

175:15

Bennett 310:4

310:23

besides 165:21

best 16:5 20:19

33:2 59:16 68:19

73:2 76:18 86:14

88:11 95:16

98:23 106:7

109:11 110:2

128:18 151:10

151:12 151:15

151:16 151:17

154:25 172:19

182:12 202:13

220:4 225:19

225:23 231:16

234:16 244:23

247:24 248:9

248:21 249:8

251:10 251:23

252:8 252:24

253:6 253:11

253:11 253:13

253:15 253:19

253:22 253:25

255:16 255:25

256:4 256:10

303:10 318:3

318:4 318:13

beta 6:16 171:4

225:2 228:10

better 15:15

63:24 63:25

72:11 72:16

79:13 90:17

110:23 113:1

117:19 118:18

118:20 122:3

124:6 125:7

125:12 125:17

125:18 126:11

126:14 127:13

128:6 129:1

129:17 130:6

131:22 141:2

171:9 172:18

180:24 183:8

185:16 188:16

190:11 190:11

226:15 226:25

229:22 230:2

230:3 232:24

234:16 237:25

246:19 248:4

248:11 248:17

251:8 251:17

252:11 252:13

255:20 261:9

267:18 268:25

284:17 301:10

312:17

beyond 61:14

61:23 104:10

270:2 320:9

bias 122:16 125:5

125:8 188:18

246:14 247:5

247:8 247:11

247:13 247:13

247:14 247:15

247:16 247:25

249:14 250:2

250:2 250:10

250:15 250:17

251:14 253:12

315:24 316:17

316:19 316:20

316:23 317:6

317:7

biased 122:7

130:15

bigger 171:11

billion 268:6

271:25 272:2

272:3

bin 177:19 177:22

177:25

binding 275:17

bins 177:18

178:18

biogenic 298:8

biogenics 236:20

biometric 298:7

Biswanath 310:25
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 339

bit 11:5 16:2

23:3 23:4 27:3

29:11 33:22 54:9

56:1 67:5

74:21 91:2 93:21

99:18 106:15

115:21 123:6

127:14 128:22

128:25 132:1

150:25 151:6

151:20 154:7

158:19 158:22

159:23 169:3

175:4 188:9

188:16 189:7

191:16 193:3

203:4 226:13

227:15 227:18

232:9 234:11

260:21 263:11

265:14 266:1

271:7 272:11

284:17 286:3

286:13 287:10

287:14 287:15

287:20 289:16

289:21 290:14

307:24

black 140:11

150:13 286:18

289:13 290:11

Blade 20:6

blend 245:22

blended 245:17

blindly 107:22

BLM 258:25

blow 302:12 304:9

blowing 189:22

blown 49:13 50:21

297:16 303:18

blue 140:7 140:21

143:20 143:20

150:7 180:14

189:8 239:1

266:19 285:7

285:10 285:14

298:18

blues 288:3

288:11

Bluett 199:19

Bob 7:4 16:18

21:11 78:16

78:23 158:23

198:2 201:22

202:17 211:21

213:21 215:12

311:20

Bob's 81:21

body 52:25

BOEM 318:23

boil 64:17

boiler 61:9

62:4 68:5

69:23 69:24

143:10 143:11

145:1

boilers 61:8

67:20 155:20

boiling 65:20

bonfire 108:16

book 116:7

boot 187:19 317:5

borderline 137:7

born 13:23

boss 4:7

bottle 295:25

bottom 42:7 72:10

84:5 155:7

190:16 229:16

234:24 237:6

237:17 238:14

246:10 247:18

247:23 248:23

249:16 263:6

263:22 263:23

264:10 264:14

284:8 299:14

304:23

bound 173:4

177:19 181:24

boundaries

135:2 268:22

boundary 298:8

box 152:5 236:12

boxes 161:16

161:18 239:6

Boylan 87:16

93:23

Boyle 57:15

BP 199:16 199:20

bracket 260:12

Brackets 20:6

break 92:8

93:20 131:10

131:15 131:19

141:11 218:1

273:15

Bret 225:4 240:15

256:19 257:2

279:24 295:19

309:25

Bret's 229:10

Brett 6:18 228:1

Brian 55:21

Brian's 85:25

Bridgers 3:4

19:15 20:2 46:17

46:22 50:6

78:2 78:23 85:16

87:12 87:22 92:7

92:18 93:19 94:2

131:7 131:20
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 340

158:10 159:1

171:17 183:14

190:20 197:23

210:21 211:10

211:14 216:6

217:9 217:22

240:3 256:17

277:12 294:1

306:22 309:23

321:9

bridging 33:11

brief 94:1 191:15

192:21 217:21

briefly 30:16

95:10 119:16

bright 110:13

287:6

bring 19:4

24:19 46:23

86:25 86:25

150:21 219:16

310:22

brings 27:17 55:7

244:16

broad 316:11

316:12

broaden 292:9

broadens 287:10

289:17

broader 9:12 15:3

broadly 309:2

Brode 26:7

32:17 46:14

46:21 78:25 79:3

79:24 81:5 81:11

81:20 82:10

82:19 82:23

83:16 83:23

116:17 116:19

119:2 132:11

151:15 184:9

198:23 200:24

201:16 202:16

202:20 207:9

208:19 209:8

209:13 211:8

211:12

broken 143:17

255:12

brought 32:23

32:24 52:1

98:4 118:13

191:17 318:20

Bruce 184:1

brute 54:2

235:5 237:7

237:12 237:19

238:11 238:18

255:13 277:2

300:4 300:9

300:15 300:19

300:23 301:6

301:13

buddies 258:6

budget 60:15

200:10

buffer 166:8

bug 23:22

build 23:4 118:20

133:16 230:12

297:14 302:5

302:18 302:21

building 43:4

135:13 182:7

186:8 187:8

187:10

built 119:8

148:21 148:22

bullet 13:21

30:20 31:9

53:4 156:12

233:8

bullets 35:13

40:15 48:14

bunch 24:18 32:22

43:2 231:16

burden 99:13

104:2 114:18

260:8 260:22

Bureau 258:19

bureaucratic

10:21

burning 21:4

burnt 19:24

busy 138:14

button 304:5

buying 274:25

bypass 160:12

162:17 164:25

215:25

C

C004 139:2

C01 139:8

C1 143:8 143:10

143:14

C2 143:8 143:10

C3 143:11

C4 140:1

CAFM 303:1

Cal 244:15

calculate 83:21

119:13 179:5

186:16

calculated

178:8 209:18

209:19

calculates 188:22

304:17

calculating 56:24

186:17
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 341

calculation

25:9 46:1 201:12

calculations

83:13 200:21

CALDESK 266:16

CALGRID 253:3

253:16 253:25

254:10 256:2

256:5

calibrated 197:9

calm 127:16

127:19

CALMET 242:7

244:19 244:23

245:3 245:5

245:8 245:12

245:13 245:14

246:7 246:8

246:15 246:21

247:15 248:5

248:10 249:1

249:3 249:5

249:7 249:7

251:5 251:6

251:25 252:10

252:12 252:22

252:23 252:24

254:14 254:17

255:16 255:21

261:21 263:24

263:24 264:2

264:7 264:8

264:12 264:14

264:16 264:16

265:13 265:19

265:22 266:3

266:8 266:10

266:13 266:17

266:18 266:20

266:21 266:23

266:25 267:9

267:11 267:17

267:19 267:24

269:22 270:15

270:21

CALPUFF 6:17

219:22 223:7

229:6 229:20

234:5 234:6

234:7 241:20

241:22 242:9

242:15 243:24

243:24 248:2

249:1 249:3

249:4 249:7

249:9 251:3

251:23 251:24

252:10 252:11

252:22 252:25

254:9 254:14

255:17 255:20

255:21 256:1

256:5 256:10

259:8 261:21

261:22 263:24

264:2 264:6

264:7 264:9

264:12 264:13

264:14 264:16

264:20 264:21

265:6 265:12

265:19 265:22

265:22 266:3

267:17 267:19

267:24 267:25

268:5 269:22

270:14 270:14

270:15 270:19

270:21 270:25

271:3 271:6

271:8 271:20

271:20 271:24

272:7 272:9

312:8

CALPUFF's 244:1

camera 94:6

campaign 235:13

campaigns

233:18 233:25

311:15

Campbell 194:8

campus 321:14

CAMx 39:1 53:17

220:20 229:5

229:17 232:13

234:5 235:5

235:15 236:23

237:6 237:18

238:15 253:2

253:11 253:24

254:2 254:5

254:8 255:25

256:3 256:9

259:8 261:22

264:1 264:1

264:3 264:3

264:11 264:19

264:20 265:6

266:8 266:11

266:11 267:18

268:1 270:12

270:18 270:24

271:1 271:5

271:13 271:14

271:18 296:22

296:24 297:25

299:8 302:25

314:8

CAMXs 238:14

Canadian 176:14

176:21

candidates 315:14

capabilities

48:22 88:5

90:4 116:3

261:23 278:20

capability

69:10 88:8

capable 276:23

capacities 203:22
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 342

capacity 88:5

88:8 160:3

203:22 316:18

capital 64:2

CAPTEX 229:3

244:3 244:10

245:1 255:13

255:19

CAPTEX3 252:18

capture 59:24

210:9 291:1

291:3 291:7

314:24 315:8

captured 210:4

capturing 285:24

CARB 31:4

carbon 238:9

238:11

cards 194:6

care 10:1 121:2

271:6

carefully 187:24

Carlo 159:17

164:21 212:23

214:18 214:22

216:13

Carolina 142:16

206:12

carried 183:24

carry 268:9

carrying 271:19

271:20

Cartesian 147:2

Carvey 113:6

case 37:19 38:4

38:10 38:16

39:17 39:24

40:17 40:18

43:14 44:18

44:25 45:1

49:2 49:6 50:9

50:19 51:10 53:6

57:15 59:3 63:16

66:10 66:19 67:1

68:7 69:25 70:24

74:22 78:10 80:5

80:12 99:21

100:25 102:25

107:21 121:13

121:25 122:5

122:6 123:1

123:18 124:17

124:23 125:6

127:14 127:18

130:6 136:25

137:2 137:20

138:24 141:4

144:5 144:20

145:3 145:5

151:5 156:20

161:18 162:12

162:15 163:7

164:16 168:3

168:13 168:14

169:6 169:8

169:11 173:23

181:6 182:16

191:3 196:7

201:14 208:19

222:25 234:25

236:17 249:4

252:24 253:10

274:23 279:21

280:10 280:12

280:14 281:8

281:15 283:24

284:3 284:6

284:10 284:14

284:19 284:20

287:11 287:19

287:25 289:25

290:1 290:7

293:5 293:8

296:21 298:21

300:2 304:6

313:21 317:24

case-by-case 7:24

8:5 15:18 36:5

36:8 39:10

93:4 98:8 99:6

99:14 110:22

118:19 214:12

cases 14:2

36:14 37:13

37:16 37:16 39:7

39:10 40:21 48:3

48:12 61:3

64:2 66:8

68:12 72:1

72:3 72:6

73:23 79:9

80:4 80:11

82:4 89:6 89:9

97:20 103:11

104:20 107:8

120:13 124:18

125:16 125:17

125:19 126:15

128:22 135:2

144:1 144:21

144:24 144:25

151:4 151:9

152:20 152:20

152:21 154:4

161:15 161:16

161:25 162:13

162:13 163:7

164:2 164:23

164:25 166:10

166:24 167:4

167:19 167:23

167:24 170:19

185:22 201:3

207:16 285:8

290:9 309:19

catching 27:3

categories 184:25

316:11 316:12

317:22
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 343

category 61:22

62:5 99:3

cause 37:8

52:17 75:19

146:18 150:19

297:10 311:10

causing 103:8

142:10 206:21

207:20 215:14

caution 108:3

123:9

cautioned 107:5

206:20

caveat 44:10

44:20 248:22

caveats 22:24

42:7 122:11

135:15 190:18

Celebrate 38:3

cells 239:6

239:11

CEM 236:18 280:17

cement 191:25

192:8 214:22

center 178:9

178:10 215:2

237:11 237:21

242:20 264:5

265:17 281:21

289:3

centered 146:3

288:20

centering 289:9

central 298:18

300:15

centroid 298:14

centroids 298:19

certain 14:1

35:16 55:21 96:3

112:25 113:20

204:23 208:10

221:6 291:6

299:23 303:6

303:8 309:3

certainly 59:13

74:18 76:25

79:13 97:17

122:23 123:19

125:8 125:16

126:5 126:14

126:23 160:16

166:10 174:7

177:8 200:14

207:3 207:18

220:25 318:17

318:17

certainty 255:23

cetera 65:24

75:23 80:10

165:16

CFR 45:23

CH2M 310:4

Chadwick 214:20

chain 213:24

chair 310:12

challenge 50:15

64:13 66:3 73:25

107:10 118:24

challenged 118:17

challenges 5:1

7:7 60:1 94:17

113:19 114:15

121:19 160:15

261:10 272:17

challenging

48:4 63:3

77:17 88:18

chamber 193:9

193:17

Championship 50:3

chance 211:5

321:11

Chang 317:5

change 59:11

75:13 77:16

130:13 140:20

140:24 141:17

142:1 142:21

144:21 163:1

166:2 189:13

199:5 212:5

212:9 212:15

226:24 299:15

311:6 312:16

changed 88:15

139:1 139:16

139:17 144:19

242:9 243:25

changes 9:25

10:11 62:16

74:13 91:19

145:4 179:20

251:1 312:8

changing 76:9

119:21 141:14

141:19 142:1

channel 177:2

characteristic

67:19 67:22

73:15

characterization

33:6 42:18 42:23

42:25 84:7 89:25

characterizations

88:21

characterizing

26:25 71:21

114:17

charge 29:24

29:24

charges 35:6

Charleston 142:16
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 344

145:5

chart 227:2

charts 138:1

140:9 143:17

158:12 167:8

chase 217:12

check 20:4

checked 78:2

chemical 53:3

55:24 61:10

66:22 67:21

69:22 93:6

221:14 223:24

224:16 233:9

261:15 272:15

278:24 279:13

chemiluminescence

191:13 192:16

192:22

chemistry 5:12

8:15 11:23 32:13

32:18 51:7 87:19

87:21 88:9 88:10

88:17 88:20

123:23 130:24

131:4 172:22

174:20 184:20

184:25 218:20

219:7 221:5

222:11 225:13

225:14 225:17

225:20 231:8

241:5 278:11

292:1 294:5

298:1 298:10

301:11 305:17

309:16

Chernobyl 242:5

chest 51:22

Chet 101:8

chew 94:23

Chicago 18:12

22:12

choice 35:23

choose 169:7

chose 166:24

166:25

Chowdhury 311:1

311:12 311:16

Chu 149:11 172:20

Chukchi 50:25

circumstances

100:3 125:14

172:17

cisco 250:7

cited 173:9

cities 28:20

city 112:7

178:9 178:10

232:14 295:2

296:23 311:17

clarification 7:9

16:16 95:8 96:11

96:13 97:23

100:20 124:12

206:15 210:24

220:13 245:24

248:8 255:16

clarified 101:22

clarify 32:9

81:12 91:23

106:15 206:19

clarifying

83:16 199:17

clash 275:6 275:7

class 27:15

85:5 85:11 224:6

227:18 229:15

229:19 261:16

261:25 266:6

268:22 269:8

269:12 269:16

269:17 269:20

311:24 318:10

clay 147:14

147:24 152:17

203:22

clean 79:2

clear 18:18 33:22

210:19 218:21

222:10 254:8

clearinghouse

25:5 25:6

25:11 32:1 45:10

114:11 210:23

clearly 121:21

128:5 130:15

161:12 292:13

client 192:1

clients 196:9

Clifty 166:13

168:5

clock 131:22

close 44:6

51:16 57:4 72:23

117:7 148:22

152:15 152:18

168:10 170:2

180:10 239:10

241:2 251:13

251:15 269:20

285:15 300:23

300:23 312:13

312:15

closely 28:14

162:6

closer 110:15

114:1 162:5

174:15 174:16

179:18 189:7

207:18 235:20

265:14 312:17
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 345

closes 321:14

closure 278:11

278:15

cloud 249:21

249:21 314:3

321:2 321:6

clouds 249:22

321:1

Club 5:10 10:7

219:13 230:8

261:2

cluster 170:24

170:24

clustering 180:10

CMAQ 39:1 53:17

220:20 235:5

235:17 236:4

236:23 237:5

237:7 238:5

238:11 238:24

278:7 280:4

280:19 280:20

280:22 281:2

281:3 281:7

281:10 281:15

281:17 282:3

282:6 282:10

282:18 283:3

283:6 283:11

284:5 284:9

285:25 287:2

287:11 287:19

288:11 292:3

292:12 293:10

303:1

CMAQs 282:2

CMAS 52:21 232:10

CO 37:23

coal 66:23

212:7 215:24

216:2

coalesced 29:21

coal-fired 142:14

code 159:20

165:20 170:12

coded 175:18

codifying 6:22

coefficient

249:25 254:3

coefficients

129:2 129:5

299:25 304:12

304:18 304:24

306:13

coffee 94:14

94:15

coherent 174:3

coke 66:23

collaborate 200:5

collaboration

12:17 184:4

collaborative

14:10 21:23 29:5

184:11

colleagues 60:7

collect 191:4

191:20 192:12

192:17 195:16

196:6 197:16

197:18 199:25

collected

122:13 153:9

192:18 196:25

197:2 199:19

199:21

collecting 121:16

156:6 191:6

191:9 191:12

194:2 195:22

200:2

collection 199:20

colliding 55:12

co-located 266:7

color 140:14

238:22

colors 112:11

141:8 141:12

238:25 281:25

282:1 282:25

285:8 285:11

285:11 285:15

287:6 287:12

288:12

column 75:9

combination 52:16

111:15 112:5

135:3 142:19

145:6 150:1

228:1

combinations

135:4 145:9

145:10 229:6

combine 45:7

93:18 107:1

165:8 170:17

187:2 187:4

189:4 301:7

combined 48:17

81:15 85:13

111:2 170:16

187:1 225:18

298:24

combines 250:9

combining 46:2

46:5 99:24

102:23 112:15

112:18

combustion

31:10 62:3

114:23

comes 21:10 59:14

71:15 149:24

162:24 278:14
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 346

289:16 298:1

comfort 230:3

comfortable 19:18

84:25 100:2

112:20 215:8

280:23

comfortably 67:5

coming 11:13 18:8

32:18 32:19

62:12 84:24

104:24 157:21

193:5 193:9

194:15 247:14

256:19 259:1

265:6 286:20

317:15

comment 5:23 6:10

10:3 11:15 11:21

16:19 20:14

21:10 33:1

76:8 76:24

106:10 129:22

203:18 205:12

206:4 206:9

220:3 307:20

310:6 310:7

310:12 311:2

311:8 315:11

318:18 321:11

comments 4:23

11:12 11:16 12:6

16:25 17:1

17:4 17:9

17:13 17:22 19:7

19:11 21:11

21:13 21:18

21:19 21:21

43:13 59:1

59:4 60:20 70:13

76:13 78:4

78:9 86:20 87:12

90:3 112:22

113:1 183:11

205:6 210:20

218:12 219:25

220:8 221:1

221:23 221:25

307:7 307:14

307:17 307:19

308:4 320:4

Commissioner

211:19

commitment 5:9

219:7

commitments 222:9

committed 10:6

309:10

committee 14:12

220:1 310:12

common 165:5

192:17 224:22

228:5

communicate 15:12

communication

308:8

communities 33:11

275:24

community 9:8

12:7 12:13

14:2 14:3 14:5

14:6 14:15

15:6 16:4

20:15 21:18

25:21 25:24

39:19 40:13

54:16 56:6 95:13

98:23 101:7

108:6 108:19

117:19 133:17

157:11 200:15

204:15 205:7

206:5 218:6

218:11 219:21

225:12 226:1

226:4 272:18

273:6 273:25

274:6 274:11

277:3 309:3

310:10

companies 60:8

companion 259:1

company 66:11

196:13

comparability

236:24

comparable

82:14 122:23

182:1 231:16

238:12 238:19

comparably 182:25

compare 46:10

47:13 68:11

81:15 128:5

166:3 182:4

229:12 233:14

243:23 246:11

261:6 261:24

264:5 264:13

278:6 283:17

286:23 309:8

compared 48:2

48:18 50:11

66:17 121:1

123:4 152:19

155:13 160:17

174:4 228:25

238:11 244:3

252:19 263:19

264:1 264:12

265:6 266:22

269:21 271:19

273:22 287:21

293:8 311:25

compares 286:6

comparing 42:3

120:10 122:7

123:9 124:15

228:7 228:21

234:21 235:13

281:14 284:5
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 347

284:9 288:20

comparison

31:24 44:24

78:13 80:20

103:2 103:20

179:1 187:21

190:7 251:20

257:19 313:8

comparisons 127:2

224:18 293:1

312:3 313:6

compelled 224:3

compensating

250:19 250:22

competing 120:17

compilation 30:7

compile 114:4

163:1 172:24

313:5

compiled 178:22

233:2

complain 60:10

complaining 60:11

79:21

complete 32:8

88:21

completed 134:5

completely 39:3

49:11 74:25

88:13 271:10

294:6 296:7

completeness

37:21

completing 114:6

complex 90:15

90:23 120:13

121:6 121:17

121:22 121:23

146:13 166:16

175:24 182:16

221:14 230:24

complexities

20:25 26:17

complexity

185:4 259:9

compliance 24:5

25:3 25:10 25:17

26:4 26:14

27:5 27:13 27:20

38:13 38:21 39:4

40:3 40:9 40:9

40:22 53:6 62:20

62:23 63:1

63:3 67:13

86:8 87:3

94:18 95:25

97:14 101:11

101:17 104:12

105:22 113:13

113:15 113:23

114:6 114:13

114:23 115:3

115:25 118:1

118:4 152:1

160:22 203:8

212:13 214:6

complicated

84:4 84:18 109:6

175:25 231:4

231:7 232:1

232:3 301:11

complication 65:9

79:11

complied 80:17

comply 106:5

complying 72:23

component 79:12

128:4 224:15

224:15

components 106:23

224:14

composed 84:14

composite 250:7

250:16 254:7

composition 44:21

comprehensive

32:13 278:23

295:22

comprehensively

109:15

compressor 147:18

148:6

compressors

145:14 145:25

147:10

comprised 29:14

133:18

compromise 130:20

computational

273:24 277:7

computer 32:18

170:14

computers 23:22

conceivable 213:9

concentrated

285:16 287:7

concentration

37:9 45:7

45:15 47:11

66:20 69:9 109:1

109:2 109:10

120:25 127:11

164:6 164:10

165:10 167:7

167:7 168:22

172:25 172:25

173:6 173:7

173:23 175:1

175:3 176:17

177:10 182:14

182:17 182:20

182:24 182:25

186:17 188:22
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 348

190:6 193:12

197:4 207:4

207:14 207:21

208:13 209:5

209:6 209:21

210:8 210:14

216:24 242:22

263:17 278:17

281:14 281:17

281:20 283:9

283:14 284:2

286:5 287:3

290:6

concentrations

26:24 27:10 34:8

35:1 37:11 43:18

44:4 45:17 45:20

46:7 46:24 47:16

47:16 48:18

50:10 50:12

51:14 54:24 65:6

75:15 77:6

100:13 103:4

104:16 106:19

109:24 110:5

111:3 111:13

111:15 111:22

123:10 125:3

126:13 126:13

126:19 127:17

127:19 143:25

152:7 162:21

163:2 163:5

163:10 163:12

163:14 173:17

173:20 174:5

177:8 177:9

179:21 180:5

180:11 181:4

181:9 181:11

181:23 181:25

182:1 188:3

190:2 193:1

208:1 209:19

254:5 254:15

265:15 266:9

267:16 269:3

270:4 280:2

280:3 280:12

282:2 282:18

283:3 283:17

285:10 287:8

287:13 289:24

291:14 292:8

292:17

concept 74:5

109:1 274:4

conceptual

49:17 174:18

concern 72:8

104:7 135:21

307:12 321:2

concerned 71:8

76:14 91:18

269:2 270:6

312:11 314:21

316:17 316:18

318:10

concerning 74:21

concerns 58:24

60:11 64:15

71:15 79:20

101:10 119:3

120:19 128:23

134:20 268:19

269:4 318:19

concert 272:23

conclude 249:14

concluded

271:18 321:21

concludes 171:16

conclusion

72:17 76:12

276:21

conclusions

159:25 171:3

181:13 181:14

182:10 191:21

195:13 198:22

248:5 251:21

254:11

concrete 109:8

110:24

concurred 106:11

concurrence

105:13 105:17

106:9

concurrent 183:7

condensable 65:11

65:17 68:14

condition

167:24 203:5

conditions

44:14 44:14

70:17 97:24

127:17 129:4

129:7 188:3

196:2 204:6

204:12 204:14

277:23 277:25

278:21 285:21

293:6 293:7

293:20 298:9

conduct 219:10

conducted

172:15 268:21

280:19

conducting

31:22 191:23

219:19 259:17

259:20 291:17

conference 3:2

5:6 9:20 9:21

10:6 10:9

10:10 12:24

20:12 21:12 22:8

30:9 30:11 56:23

75:25 105:12

105:13 132:5

132:16 132:19
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 349

200:8 206:9

216:9 219:16

219:25 222:23

307:15 308:24

309:1 310:9

310:16 321:21

conferences 12:21

13:1 20:17

315:21

confidence 7:21

8:21 126:18

188:20 222:25

317:8

confident 195:8

configuration

249:8 251:24

252:1 252:25

261:20 264:7

264:17 264:21

267:17 267:19

267:25 268:14

279:17 292:5

311:9

configurations

182:9 244:4

244:20 244:23

249:10 251:25

252:7 254:14

268:15 269:22

291:6

configured 249:20

263:24 266:11

271:24

confirmation 40:8

confound 68:21

confusion 99:24

conjecture 267:13

Connecticut

211:18 211:22

connecting 14:8

consequence

76:8 229:10

257:11 262:3

263:16 264:25

284:22

consequences 63:5

92:2

conservatism

112:3 119:8

126:5 166:23

215:9

conservative 7:14

45:13 67:13

76:15 97:9 107:9

108:1 111:7

111:10 112:1

119:7 119:11

121:21 152:19

163:13 163:16

166:7 166:10

167:25 167:25

168:2 172:4

172:8 173:16

174:10 175:8

175:9 181:22

182:23 183:1

206:22

conserving 221:9

consider 6:6 7:10

17:21 27:21

32:12 41:6 42:11

43:17 60:16 76:8

91:12 147:6

151:11 184:18

187:23 203:13

205:6 293:3

309:20

considerably

259:11

consideration 5:3

7:2 9:9 12:2

34:4 37:10 38:21

43:21 44:19 45:5

48:6 86:1 92:6

97:20 203:3

223:16 277:19

321:5

considerations

17:8 40:25

41:1 41:2

41:18 42:4 64:11

80:23 97:3

98:2 186:20

187:10 191:22

195:14 197:8

257:16 270:7

272:12 273:24

275:22 294:24

312:23

considered 8:8

41:23 42:12

44:22 48:8

79:8 99:2 103:22

105:8 186:20

187:17 203:10

207:13 258:15

259:7 320:15

321:4

considering 10:12

65:1 100:10

171:5 219:15

292:4 301:17

320:10

consistency

77:4 256:6

275:12

consistent

56:19 69:19

78:20 83:7 83:10

103:4 174:17

178:10 178:17

207:9 219:4

221:21 223:12

226:2 228:8

237:16 237:23

276:3 276:14

consistently 67:7

71:12 229:13

229:25 252:2
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 350

constant 81:8

91:24 170:18

217:1 280:13

constraints 59:15

59:19 88:25

200:11 275:20

307:10

construct 88:19

150:3 275:9

275:14

constructive

117:17

constructs 149:24

consultant's

202:4

consultation 36:3

36:10 49:13

51:18

consumed 85:6

consumption

85:3 87:10

contained 34:2

containing 187:18

contains 285:20

content 60:6

context 4:15

10:13 17:15

24:19 25:21 27:4

32:7 56:10 60:20

64:6 74:11

76:6 86:18

89:1 90:6 100:22

105:15 106:11

107:13 107:21

109:18 110:24

117:12 150:25

217:5 220:21

220:24 276:7

307:14 308:5

308:15 316:8

318:12

contexts 104:6

continental

236:10

continue 11:10

12:14 14:21

23:17 24:17

54:15 62:1 77:16

98:25 221:20

308:7 309:4

310:9 320:19

continued 134:8

181:19 308:9

320:8

continuing 33:1

60:22 137:6

157:15 186:22

238:3

continuous

34:15 80:2

80:5 80:13

81:2 81:9

81:11 82:7 104:8

104:14 142:6

160:4 191:9

196:16 204:16

254:25

continuously

160:6 160:16

contour 155:5

contract 228:15

contractors

231:19

contrast 252:6

contribute

52:17 104:15

contributes 103:9

contributing

103:8 115:6

117:5

contribution

35:14 93:12

103:5 110:17

142:4 210:10

224:24 231:13

237:9 237:11

237:14

contributions

43:19 44:1 44:16

84:9 84:12

102:24 159:8

235:11 235:12

control 61:6

63:25 68:1 68:10

72:11 72:19

72:22 77:14

135:4 137:14

140:19 142:11

147:21 203:23

controlled 61:4

61:21 104:23

137:2 140:6

143:7 143:14

144:21 145:9

149:6 154:4

154:9 245:18

controllers

192:20

controlling 64:20

102:15 102:16

167:8

controls 64:3

68:8 139:3 139:4

140:14 140:14

142:2 142:19

142:20 142:22

143:6 143:10

148:23 150:16

150:19 154:6

154:20 154:23

155:12 155:13

155:15 156:15

157:14 216:3

controversial

39:18
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 351

convective 124:17

convenient 64:17

convention 26:14

converge 169:24

convergence

169:25

converges 170:9

converging 170:2

conversation

128:12

conversations

132:17

conversion 7:14

97:10 124:19

125:9 126:6

149:2 150:8

152:4 153:21

153:23 174:6

174:22 177:12

177:14 180:14

181:1 181:8

181:24 181:25

183:9 189:8

189:10 189:15

conversions

221:14

convert 137:8

179:12

converted

188:10 192:24

193:16 193:18

279:23 280:5

converter

193:10 197:13

converting 61:16

converts 124:18

188:12 193:11

cool 226:22

cooperation 310:9

coordinate

13:21 15:8 15:11

coordinated

128:25

coordination

13:23 14:17

14:21 21:2 308:8

308:11 310:9

310:16

corners 262:14

262:17 263:2

269:13

Corporation 92:10

Corrales 85:17

correct 71:22

76:20 103:20

207:8

corrected 78:14

corrections 47:4

correctly 92:25

206:18

correlated

47:23 216:19

254:4 254:6

correlation

76:4 249:15

249:25 250:1

254:3

correlations

80:25 216:15

250:10

correspond 281:23

281:25 282:2

corresponding

249:10 263:9

corresponds

64:8 162:23

cost 155:12

193:24 195:15

197:15 259:9

260:1

count 3:24 84:9

163:11 186:9

249:19

counted 116:18

counterparts 60:8

counting 43:23

110:4

country 28:10

28:20 44:23 51:1

51:11 66:8

147:25

county 80:21

couple 25:4

25:8 26:1 26:6

34:13 37:16

40:15 56:20 60:9

66:7 70:8

74:24 75:8 79:18

92:12 136:15

138:7 138:10

149:11 152:12

158:20 174:12

181:14 182:9

184:7 187:9

188:21 194:1

197:10 211:2

239:5 249:16

coupled 299:6

299:8

course 3:25 8:1

22:11 64:25 65:9

70:11 73:20

74:11 80:12

81:24 101:20

135:12 154:6

157:7 157:18

159:11 163:25

184:5 211:18

212:2 214:13

216:22 267:7

301:20 306:8

Courtyard 310:14
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 352

cover 8:13 118:11

138:23

covered 119:22

310:24

covering 166:9

covers 112:21

Cray 170:23

Crays 170:23

create 50:14

52:11 83:19

162:9 162:19

163:5 313:20

created 83:10

83:20 135:2

158:1

creates 74:9

250:17 274:9

creation 277:7

creativity 115:11

creatures 19:16

credible 89:12

89:15 118:3

118:3 219:4

219:9 231:24

credit 160:10

Creek 166:13

168:5

creeked 34:21

criteria 35:18

54:19 186:12

221:18 297:8

301:23 311:5

311:6

criterion 109:3

207:14 210:6

critical 12:4

95:12 120:8

213:1 223:7

291:2 309:13

critically

77:10 117:23

307:7

cross 94:7 242:23

243:17 266:5

crossed 315:6

crossfertilization

14:6

crowd 133:21

240:25

CTDM 120:13

121:11 121:14

CTEX 248:5

CTEX3 244:7

244:13 245:3

245:6 251:23

252:3 255:14

255:21 255:24

256:1

CTEX4 248:19

CTEX5 244:14

244:24 248:19

251:25 252:3

255:14 255:22

256:3

cubed 140:11

149:17

culmination 30:6

culpability

134:10

Cumberland

234:1 236:19

278:5 279:3

286:17 292:3

cumulative

33:25 34:2

37:3 37:7

37:15 37:17 41:7

43:14 46:23

47:15 50:11 53:9

63:12 102:25

103:6 103:11

106:19 106:23

115:5 133:9

134:10 135:17

135:17 145:12

147:13 152:23

154:14 157:16

161:5 161:17

166:9 198:13

208:2 249:15

250:4 250:11

curious 197:5

current 4:16 5:19

6:2 6:8 7:11 8:4

17:19 18:19

41:24 62:21

67:16 67:18

68:25 69:4 72:13

72:19 75:7 76:14

88:19 100:21

115:12 129:18

130:12 170:12

170:19 172:7

173:8 173:10

173:15 175:8

179:19 181:20

183:1 191:18

192:14 194:1

199:1 220:11

224:5 243:23

273:23 276:1

295:6 312:9

currently 130:3

170:17 171:4

192:18 225:25

228:11 228:15

233:2 302:15

320:15 321:3

curve 121:7 162:6

177:16 178:1

178:1 178:24

178:25 179:6

189:8 299:12
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 353

curves 178:3

241:11 289:18

custom 274:15

cut 217:12

cutting 167:21

cycle 85:13

cycles 204:18

cylinder 197:12

D

daily 23:9

23:25 45:18 47:6

47:10 48:10 80:1

95:1 104:16

Dallas 133:5

Dallas-Fort

147:25

Dan 87:13

190:23 197:23

Dana 113:6 199:15

dangerous 205:25

dark 155:5

data 6:17 28:21

34:15 43:7 45:24

46:11 59:17

59:17 65:14 66:5

66:24 70:19

74:11 74:19 75:6

75:13 75:13 76:3

80:10 80:14 82:7

82:7 91:1

91:17 91:21

91:23 92:1

92:1 92:3

96:20 96:21

98:15 100:8

100:18 100:19

112:5 112:17

121:16 122:13

127:5 135:13

135:14 138:22

142:16 142:17

153:8 153:12

153:16 157:9

157:13 164:14

165:6 166:1

171:6 172:24

173:12 174:15

175:11 175:20

175:21 175:23

176:4 176:5

176:8 176:10

176:11 176:15

176:20 176:21

176:22 177:3

177:15 177:17

177:18 177:24

178:8 178:16

178:19 178:25

179:2 179:4

179:11 180:2

180:19 180:20

181:10 181:16

181:18 182:5

182:6 182:11

182:13 183:5

183:8 183:8

185:10 185:16

185:21 185:24

185:25 186:6

187:12 187:13

189:19 190:9

190:17 191:9

191:12 192:18

192:18 193:25

194:1 194:2

194:8 194:18

195:24 196:5

196:6 196:8

196:15 196:25

197:4 199:18

199:20 199:20

199:21 200:1

200:2 200:23

201:3 201:4

201:8 202:22

204:24 205:20

206:1 215:22

223:15 223:23

224:23 225:11

246:2 247:14

248:15 248:18

248:24 267:2

267:8 274:15

279:10 280:17

314:13 317:19

317:22

database 176:6

176:8 176:13

178:25 197:19

242:11 242:12

248:25 263:2

314:1

databases

119:18 159:21

165:23 185:12

date 95:7 172:15

dates 23:7 24:18

Dave 159:9

Dawn 138:18

day 3:25 5:4

13:15 34:16

34:18 40:20

45:23 65:20 75:7

81:8 98:10

111:23 112:13

128:3 128:4

131:8 163:11

163:11 218:3

278:6 279:4

300:2

days 16:21

16:22 30:2

65:1 116:2

163:14 241:11

299:22 299:23

299:25 307:2

308:1

day's 163:12
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 354

daytime 293:8

Dayton 244:12

244:13 244:14

DC 105:17

DDM 33:4 54:7

54:8 89:18

232:13 235:8

235:9 237:12

237:13 237:21

238:12 238:16

299:11 299:19

deal 17:15

17:18 25:2 59:15

106:16 159:15

172:10 258:2

258:3 258:12

260:17

dealing 6:25

8:2 15:18

15:19 15:22 26:9

84:1 89:9 90:5

95:16 118:12

190:16 221:13

277:10

deals 15:22 242:2

dealt 50:23 57:20

89:22

decade 77:14

176:6

December 297:21

297:24

decide 164:1

294:25 295:8

318:13

decided 4:10

23:24 103:24

156:15 165:20

169:22 192:11

193:23 194:7

decision 77:17

decisions

175:22 258:23

261:8

deciview 303:12

decoupled 296:24

decrease 305:5

dedicate 274:20

deemed 15:2

deep 288:7

default 97:22

99:4 141:21

192:7 268:6

280:14 304:14

311:5

defaults 8:4

101:25 102:2

102:5 304:14

defending 240:7

defensible

118:4 296:16

305:18

defer 8:5 78:24

deficiencies

76:20

define 37:14

109:6 109:9

109:15 129:25

130:5 209:7

209:8 224:3

295:15

defined 27:12

65:23 65:25

128:2 222:5

224:12

defining 55:23

187:21

definitely

26:21 57:25

68:18 83:22

149:17 160:6

193:24 248:17

definition

156:3 163:22

degree 25:12

55:21 187:18

269:7 270:17

314:14 314:14

314:17

degrees 221:6

248:1

delayed 20:16

deliver 132:11

309:8

delivered 30:4

293:11

delivery 216:2

delta 193:20

demonstrate

8:19 62:20 62:25

63:3 113:15

117:25 118:4

173:13

demonstrated

62:23 113:12

113:23 115:13

demonstrating

25:17 39:4 97:14

99:14 101:11

101:17 114:13

114:23 115:3

115:24 158:6

demonstration

25:3 25:10

27:5 38:13 38:21

39:5 40:22 51:23

52:13 86:8

105:22 114:7

261:13

demonstrations

26:15 27:13

27:21 104:12

156:18 220:17
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 355

227:8

dense 73:4 146:24

density 52:10

312:10

Department 176:12

211:17 219:2

219:3 259:14

259:14

depend 125:13

depending 41:20

137:8 153:24

169:23 181:16

182:11

depends 35:25

157:19 252:2

253:16 253:19

deploy 268:21

deployed 66:20

deployment 258:7

deposition 9:2

224:7 227:17

229:15 229:18

229:20 241:5

253:4 260:20

263:18 268:9

268:11 269:4

269:6 270:10

270:13 270:14

270:17 270:18

270:20 270:24

271:14 317:16

320:23 321:2

321:6

depth 227:19

DEQ 85:2

derivatives

299:16 299:17

Deroeck 87:13

describe 160:20

161:25 260:14

299:10 299:20

described 186:1

189:13

describing 183:23

261:18

description 49:17

159:16

descriptions

139:7

desert 187:14

design 45:15

45:16 45:25 47:3

56:25 66:15

66:24 67:8 67:12

96:16 100:16

110:21 111:9

112:9 136:19

140:10 141:6

141:25 142:4

144:10 144:22

145:1 145:23

147:4 160:3

163:5 164:10

167:7 168:22

186:12 227:7

257:10

designed 69:14

97:25 119:10

119:11 130:3

160:9 204:23

273:11

desk 30:4 94:8

destroy 285:18

destruction 288:5

detail 9:6 35:9

59:13 70:13

79:25 93:8 98:19

127:21 136:13

193:3 209:9

228:16 234:10

266:1

detailed 13:3

96:23 97:16

175:20 184:23

184:24 309:6

details 16:12

40:14 59:9

80:6 82:8

86:22 97:4

115:23 116:4

118:14 118:15

129:21 137:24

284:25 292:1

292:6

determine 9:14

10:23 48:16

93:16 173:7

203:4 221:18

determining

19:9 102:17

103:7 106:18

180:25

deterministic

26:11

develop 9:15

28:15 34:19

49:20 56:11

76:18 77:2

83:9 160:21

177:16 228:2

275:25 276:4

296:2 320:19

developed 31:1

55:19 55:20

89:12 151:12

165:20 172:20

178:24 236:5

280:6 291:19

294:8 296:4

296:24 297:6

297:8 300:20

301:25 305:12

306:15 316:1

developers 9:4

311:1
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 356

developing

88:16 200:12

202:3 258:22

296:7

development

9:16 30:24

32:4 96:6

219:1 258:25

259:18 259:21

260:1 260:2

260:13 260:15

294:21 320:8

320:22

developmental

119:20

developments

321:7

deviate 300:9

deviations

215:2 215:6

devil 94:11

118:14 118:15

devils 40:14

DFW 147:18

DHEC 206:12

DIAG 279:20 280:7

284:10 286:8

290:1 290:7

diagnose 118:18

diagnostic 245:11

267:14

diagnostics

313:19

dialogue 60:22

132:5 309:5

diamond 177:25

dice 162:22

Dick 214:17

diesel 143:11

144:22 186:7

difference 20:4

56:12 56:14 70:8

70:22 71:5

71:9 80:7

130:2 131:2

140:18 140:20

140:23 140:25

141:15 143:25

169:12 188:4

189:15 248:16

250:6 252:7

283:23 284:3

286:11 289:24

292:23 302:11

differences

70:9 90:13

92:4 123:8 151:8

157:7 224:25

229:23 237:24

237:25 238:1

238:19 238:20

268:13 275:23

282:21 284:25

290:2 301:13

different 13:25

14:4 14:4

28:18 29:20 34:1

34:13 37:13

37:13 41:5

48:6 54:1

55:16 65:15

66:25 67:4 67:25

67:25 69:18

69:20 70:12

71:13 71:21 81:1

83:13 83:13

84:16 84:17

84:20 84:23

90:14 97:3

112:11 120:1

133:16 138:22

140:14 140:18

141:10 145:9

147:1 149:4

162:13 162:17

163:18 163:21

163:24 165:3

166:16 166:25

170:5 170:7

170:17 171:13

171:13 172:17

180:2 188:3

188:19 190:9

193:6 195:20

196:1 197:6

203:21 203:22

204:4 204:5

204:6 204:14

204:25 208:3

208:20 216:16

216:20 223:6

225:11 225:12

227:3 228:25

229:1 229:6

229:7 229:19

229:21 231:13

233:17 233:24

234:21 235:2

238:25 239:14

245:9 245:15

245:19 251:2

254:13 262:1

265:4 265:7

266:13 270:8

271:1 275:16

283:18 284:21

292:16 292:24

292:25 294:7

298:12 300:5

304:22 304:24

305:9 305:17

315:16 315:16

differently

240:22 251:2

295:7 295:15

differs 284:14

difficult 62:20

77:18 84:5 88:19

136:5 153:14
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 357

187:20 213:12

263:15 314:16

difficulty

85:11 113:19

115:3

diffusion

224:15 225:21

278:16

digest 11:17

digesting 308:10

diligently

30:15 103:21

dilution 175:5

177:14 282:19

287:9 289:22

290:2 292:10

dim 19:25

dimension 123:24

dimensional 280:3

280:20

diminish 114:14

diminished 287:14

diminishes 289:21

Dinosaur 272:6

dioxide 263:23

264:4

direct 24:9

38:7 38:22 42:19

43:15 45:1

49:3 50:10 52:14

54:23 73:14 85:6

85:12 87:10

98:21 296:24

299:6 299:9

direction 120:5

152:15 187:14

187:18 209:23

220:2 221:24

246:8 246:13

247:16 247:16

247:25 255:17

271:11 282:9

283:10 284:12

286:11

directions

168:6 247:12

284:7 284:17

286:12

direction's 123:6

directly 31:17

41:9 101:6 101:7

310:15

dirty 48:23 150:9

disagreement

129:23

disavowed 296:7

disclaimer 22:16

disclosure 13:19

disconnect 273:18

discount 114:14

discreet 167:4

268:21

discuss 59:21

257:7 257:9

discussed 6:11

9:6 32:7 53:14

206:16 222:20

discussing

10:10 222:1

discussion 13:4

13:4 13:15

22:4 40:19

51:3 57:6

95:10 99:11

204:1 205:21

discussions 53:16

59:12 60:14

309:6

dispersed 278:13

dispersion

33:10 123:23

129:2 129:4

129:10 129:12

130:1 130:17

131:1 131:2

154:16 174:21

186:21 186:22

228:5 233:9

240:14 241:4

241:7 241:9

241:10 261:15

262:8 273:6

274:11 278:17

displacement

289:6

dispute 259:23

disseminate 14:14

distance 110:8

124:15 137:21

139:22 141:24

153:5 155:6

173:14 174:14

174:23 175:5

210:15 243:4

290:9 291:9

312:12

distances 146:5

152:12 221:15

245:21 270:1

270:9 286:18

311:24 312:1

312:12

distant 177:13

269:8

distinct 257:22

distinction 60:12

distract 68:21

distribution

68:14 84:17 95:1

95:5 104:15

120:25 121:9

128:1 160:19

161:6 161:17
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 358

162:1 162:10

162:21 166:9

166:20 167:13

178:6 201:25

202:8 202:12

203:6 204:18

205:2 205:19

215:13 215:15

216:23 249:15

250:12 253:15

269:10 269:15

289:17

distributions

159:16 171:9

203:20 203:24

204:7 204:8

250:4

diurnal 111:13

divide 162:1

162:3

divided 135:8

172:25 249:22

298:11 305:16

316:23

division 21:3

86:16 87:15

113:8 213:16

divisions 162:2

162:4 162:5

168:17

DIX 190:24

doable 154:2

155:11

docket 4:23 21:13

22:11 113:10

114:4 132:21

document 20:25

21:5 21:20 21:25

22:20 24:4

33:2 37:1

53:24 56:9 56:12

56:13 56:13

56:16 57:11

57:13 57:23

85:15 85:22

85:22 85:23

92:11 92:24

158:3 220:15

222:18 240:23

259:1

documentation

50:16

documented 124:10

166:16 240:18

243:15

documents 55:22

100:4 124:11

240:14 260:3

262:8

DOJ 56:18

domain 41:1 41:20

43:21 110:19

110:20 116:10

135:20 207:18

216:15 228:20

236:10 236:11

236:14 236:15

262:16 262:19

262:24 262:25

267:3 281:5

297:19 297:20

298:9 314:24

319:14

domains 236:9

262:12 293:18

300:14 319:8

dominates 174:19

done 9:10 15:13

23:12 25:14

27:24 32:14 35:7

35:21 36:14

36:19 38:2 38:12

38:24 39:9 48:22

48:25 49:10

51:22 51:25

52:17 52:24 74:8

90:17 99:3

100:22 108:12

113:6 113:7

124:13 125:24

128:16 129:12

135:18 136:1

148:12 149:10

152:2 158:3

158:3 187:7

200:14 200:16

202:23 213:19

218:10 221:11

228:14 228:15

232:14 240:22

246:5 259:23

262:9 273:7

281:4 289:9

291:22 294:10

299:21 301:15

302:4 306:21

312:5 317:5

318:19

door 11:4 11:7

17:2 17:20 308:6

do-over 25:25

dose 94:15

dot 236:13

dots 14:8 298:18

double 43:22 84:8

110:4 116:18

double-check

154:13

doubt 66:8

Doug 199:19

downtimes 105:2

downwash 121:25

121:25 122:5

135:13 135:14

144:1 156:2

182:7 182:8

186:25 187:7

187:10 198:10
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 359

200:21 200:24

201:12

downwashes 120:15

downwind 80:13

123:16 241:3

279:12 285:19

285:23 286:3

286:19 287:8

287:14 288:6

288:16 289:11

289:16 289:21

290:14 290:23

291:5 291:13

312:13

dozens 25:20

25:21

Dr 296:4

draft 4:16

11:20 20:10

21:17 21:20 26:5

31:17 33:1 38:18

59:4 73:7

76:23 85:22

107:7 136:13

137:24 157:25

220:23

dragged 95:21

drain 120:12

120:14 121:6

drastically 181:8

draw 232:2

Draxler 250:9

dreaded 36:6

drink 38:3

drinking 94:15

drive 153:16

155:19 269:24

279:22

driven 44:17

142:5 144:22

145:1 145:23

147:5 282:12

driver 255:20

257:9 319:18

319:23

driving 91:2 91:6

146:16

drop 151:3 168:12

168:20 169:2

305:23

dropped 149:15

dropping 178:2

drops 153:3

154:17

drove 281:7

dryers 155:20

due 75:12 120:5

124:5 128:8

142:10 190:12

214:9 238:20

292:9 308:2

during 3:18 77:11

128:5 160:14

196:14 199:20

244:11 318:18

dust 61:18 61:19

dwell 164:12

287:24

dynamic 106:3

E

eager 293:13

eagerly 76:22

earlier 49:15

54:9 54:21

55:9 55:12

84:4 96:5 101:10

151:7 184:10

200:10 222:4

255:8 270:11

277:1 283:1

293:9

early 4:5 4:22

11:24 12:25

13:13 23:20

24:24 29:8

115:16 234:9

241:8 308:2

309:4

easier 14:9

171:18 193:24

196:7

easiest 38:10

easily 175:18

east 147:20

298:17

easy 112:2 175:17

182:22 191:4

194:24 195:23

197:15

echo 200:8

economy 58:20

ecosystems 320:20

edge 152:10 186:3

264:15 290:17

edges 290:16

290:21

edits 48:19

Edward 294:11

effect 23:16 95:7

146:20 147:7

159:14 173:24

199:12 201:11

effective 15:8

16:6 95:2 95:5

95:7 170:13

193:24 195:15

197:15

effectively

28:4 49:20

51:2 104:9
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 360

108:19

effects 33:17

42:11 245:11

267:14 299:10

320:20

efficient 15:8

16:6 36:18

efficiently

197:12 232:21

effort 9:5

14:20 15:11

15:16 83:19

93:23 96:7 222:5

efforts 9:11

12:14 14:4

14:7 14:10 76:17

118:8 124:9

138:13 223:2

223:6 223:10

223:13

Egan 184:1

EGU 142:14

143:5 144:18

212:7 263:3

266:4

EGUs 145:8 211:22

212:19 263:8

266:4

eight 123:3 155:7

214:19 223:3

241:14 244:7

295:6 317:18

eighth 45:21

69:11 70:4 70:10

70:23 71:1

71:4 72:14

eight-thirty

321:19

EIS 258:25

either 4:21

7:17 10:11

12:8 12:16 89:13

97:13 101:7

101:8 104:22

113:14 113:16

118:19 123:7

175:17 177:10

191:10 201:18

206:13 244:11

269:21 307:12

307:17 309:2

Eladio 159:10

203:16 205:8

213:22

electric 166:14

elegant 88:21

element 257:5

261:14

elemental 238:9

238:11

elements 257:10

elevation 289:12

eliminate 277:1

289:7

eliminated 105:21

eliminating

208:25

else 4:6 20:19

60:16 150:16

210:24 302:6

313:5

else's 316:6

elsewhere 10:15

107:24

eluded 89:14

email 310:17

embrace 88:20

117:25

EMDIST 164:1

164:12

emergency

102:11 104:21

104:23 139:9

142:5 155:21

156:9 160:11

215:16

emerging 5:5 8:11

53:15 87:25

218:15 261:7

320:5

emission 24:9

30:25 52:10 63:8

63:9 64:7

65:13 65:14

65:22 73:15

135:4 143:9

148:25 159:14

159:16 160:3

160:8 160:13

160:16 160:19

160:22 160:24

160:25 161:9

161:12 161:14

161:20 161:23

161:24 162:3

162:23 163:24

164:5 164:17

166:22 167:2

167:13 167:13

167:15 167:21

167:21 167:24

168:17 168:20

171:7 171:8

171:13 173:14

174:4 183:8

191:9 203:6

203:24 212:7

212:10 212:12

215:10 216:23

235:9 283:19

299:4 303:6

emissions 7:5

27:1 30:22

31:7 34:5 36:1

37:5 37:24 37:24

38:7 38:9
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 361

38:20 39:22

39:23 42:18

42:22 44:11

47:23 49:24

54:21 56:3 62:14

64:22 66:4 67:14

67:25 69:21

69:23 77:22

80:21 84:11

84:13 85:7 85:13

89:10 102:10

104:5 104:12

104:22 105:3

106:20 119:9

122:14 122:15

122:21 127:22

128:2 136:17

136:18 139:3

139:14 139:18

142:6 142:9

142:21 143:4

143:13 143:21

143:22 144:8

144:15 144:16

154:10 154:12

154:17 156:1

156:10 156:25

159:18 160:5

162:3 162:7

162:10 162:12

162:15 163:8

163:19 164:1

164:3 164:4

164:14 165:9

165:24 166:1

166:5 166:8

166:11 166:15

166:20 167:9

167:11 167:17

168:1 168:2

168:12 168:17

168:24 168:24

169:4 169:5

169:13 169:16

170:18 171:9

171:11 186:11

189:17 199:21

199:23 200:1

201:25 202:8

203:20 204:7

204:8 204:17

204:18 205:18

215:25 216:16

232:13 236:18

236:20 237:8

280:17 281:9

283:22 285:9

285:12 297:2

297:2 297:9

298:5 298:6

298:7 298:8

298:14 298:19

298:24 299:2

299:18 299:18

300:22 301:1

301:8 303:5

303:7 303:7

304:3 305:22

306:1

emit 199:23 215:1

emphasis 30:24

316:10

emphasize 17:19

157:10 307:9

emphasizing 77:4

Empire 125:1

176:10 176:20

179:3 179:8

179:18 180:2

199:18

empirical 172:23

empirically

175:12

EMPOST 164:9

165:13 216:25

empty 194:16

194:24

EMVAP 159:18

164:3 164:18

164:18 165:12

165:14 165:20

166:4 166:7

166:21 167:18

168:13 168:25

169:15 169:17

169:20 169:23

201:23

encountering

86:22

encourage 24:20

102:7 264:23

275:18

encouraged 66:6

73:24

encouraging 100:6

129:13

endorsements

22:23

energy 211:17

219:1 259:18

enforce 201:25

enforceable

201:24

engage 12:7

91:7 117:13

117:19 218:11

219:20 308:18

engaged 42:15

115:15

engagement 218:7

engaging 88:16

118:8 218:17

219:15 309:9

engineering 10:19

engineers 29:16

engines 147:21

148:11 148:14

148:17 160:12

176:23 179:18
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 362

ensure 276:2

entered 259:15

entertain 216:7

entire 47:12

113:21 190:8

entitled 25:16

entrained

174:18 184:21

187:4

entrainment

124:21 174:2

174:21 177:12

177:14 180:23

entrains 186:24

envelope 161:17

202:8

EnviroMet 91:14

Environ 228:13

228:15 229:9

232:11 233:2

240:5 261:12

262:9 262:12

265:24 268:5

268:17 269:9

270:23 294:10

294:13

environment 49:19

50:15 90:8

201:24 273:17

274:2 274:14

294:12 294:17

298:6

environmental 3:1

64:14 118:6

171:22 176:12

211:17 258:22

environments

204:5

EPA 9:17 10:11

12:16 15:20 20:2

24:23 29:3

35:6 58:22 62:21

71:6 76:14 87:20

88:16 110:9

172:13 173:9

174:9 179:23

180:8 182:13

184:5 200:4

202:14 203:12

204:1 206:9

211:25 213:24

218:10 219:2

219:13 220:5

220:21 221:17

223:10 240:15

241:20 244:12

244:24 245:22

247:7 247:10

248:7 248:20

252:4 254:22

257:10 259:15

260:25 261:2

261:5 261:11

263:25 272:20

272:24 273:1

273:2 275:17

275:24 276:16

278:4 294:25

296:17 310:11

312:5 313:20

317:18

EPA's 22:22 172:1

179:16 223:3

244:21 272:23

EPD 87:17

episode 237:4

238:8 279:6

286:15 297:23

episodes 75:14

297:20 297:22

episodic 54:18

EPRI 159:9

171:4 171:14

202:3 202:3

203:16 236:5

280:11 314:12

Epsilon 183:25

equal 65:13 65:22

72:4 74:16 75:22

189:12 255:14

equally 231:17

equation 278:16

297:5 299:20

300:4 300:13

300:25 301:4

301:14

equations

299:17 300:20

301:10

equilibrium 97:24

149:5

equipment

191:19 191:20

274:24 275:1

equivalent 24:13

era 27:18

Erik 91:10

94:21 96:5

118:11 131:13

132:8 133:1

142:15 145:14

145:25 147:9

198:2 198:6

error 247:18

249:15 249:24

249:24 250:20

250:22 251:11

253:18

errors 175:24

250:19

especially 4:1

29:6 33:13 44:16

44:22 53:6

54:5 60:13 62:15

77:11 91:19

120:23 134:20
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 363

145:8 149:18

171:8 173:16

210:1 217:5

267:3 273:8

284:11

essentially 193:8

261:17 262:2

299:3 314:3

establish 7:22

259:20 260:10

318:16

established 13:22

14:16 31:25

96:24 106:21

establishing

230:21

estimate 173:4

231:2 270:13

estimates 77:3

122:15 231:1

232:12 233:14

236:25 237:6

237:6 238:13

268:8 269:6

269:6

estimating

96:25 101:1

189:18 270:24

et 65:24 75:23

80:10 165:15

ETEX 229:2

244:3 256:7

256:12 312:11

ethane 305:2

ethanol 137:13

138:17 139:10

145:15 146:1

146:3 146:9

155:20

Eulerian 53:11

53:16 220:4

220:5 221:8

253:2 269:19

272:14 280:21

Europe 229:2

European 241:24

243:19

evaluate 6:13

80:9 88:4 96:7

117:24 119:22

124:24 155:15

155:16 183:8

200:3 224:24

225:18 228:21

230:15 232:21

239:16 279:14

295:21 297:8

300:12

evaluated

119:17 128:17

138:21 159:21

165:24 234:4

234:13 244:18

244:25 245:1

249:4 249:5

252:20 300:3

314:7

evaluating

234:4 241:6

301:17

evaluation 7:20

8:9 8:14 11:22

11:23 32:8

56:1 56:2 56:4

123:22 124:13

128:16 159:21

165:19 166:2

183:24 190:8

199:22 200:20

215:22 221:17

221:21 222:23

223:10 223:11

223:14 223:22

223:24 224:10

224:11 224:13

225:7 225:17

225:17 225:18

225:24 229:5

233:9 236:7

240:9 241:20

242:10 242:16

244:22 246:8

248:24 248:25

249:2 251:21

252:15 255:11

255:12 257:11

257:13 257:15

260:24 263:1

271:24 289:8

294:23 311:22

316:1 316:3

316:5 316:8

evaluations

5:15 6:8 8:23

9:7 12:19

17:11 99:3

99:9 100:5

100:11 119:22

122:11 125:23

170:10 172:15

173:12 184:2

184:16 201:17

219:19 219:20

225:9 225:14

227:21 243:15

276:8 309:13

317:13

evening 3:13

256:25 321:20

eventually 61:9

144:14 303:16

everybody 3:5 3:8

3:12 3:14 4:1

4:10 4:12

14:24 15:6 19:12

19:16 19:17

31:18 59:5 90:21

93:22 94:3 94:23

113:10 198:3

217:13 217:25
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 364

294:3 321:12

everybody's 16:10

17:4 20:6 90:3

133:2 198:19

220:13

everyday 45:21

everyone 23:4

34:9 60:4 284:9

everyone's 39:1

everything 3:16

5:24 20:19 35:10

36:1 52:15

55:4 56:19 73:25

92:23 107:21

154:7 210:24

244:6 244:8

302:5 313:5

316:9

evidence 51:2

evidenced 84:19

evolution 308:9

312:15

evolve 79:14

evolving 17:12

226:21

exact 146:5

154:13 195:20

207:17

exactly 19:17

47:25 196:25

209:13 289:7

examine 103:9

257:12

examined 261:22

examining 265:9

265:14

example 31:3 31:9

35:13 49:22

49:23 49:23 50:1

53:13 74:6 80:20

81:24 85:13 87:8

102:10 107:15

108:4 109:9

112:8 114:15

117:23 119:10

120:6 120:12

123:15 125:21

136:25 138:17

160:11 160:23

161:18 162:11

166:24 178:11

178:21 184:10

187:25 239:4

241:13 261:15

272:18 285:5

300:13 304:23

315:23 316:2

316:16 317:3

examples 66:7

67:16 79:14

90:18 90:25 96:4

110:24 118:11

138:7 156:22

165:21 182:18

184:16 209:15

218:25

exceed 106:20

exceedance 137:20

153:2 153:18

156:10

exceedances 135:5

137:3 138:2

138:4 141:7

144:2 144:14

144:19 145:18

exceeded 115:4

145:21

exceeds 103:12

248:1 295:3

Excel 297:5

except 75:22

101:20 130:25

144:1 164:13

182:15 207:15

269:16 270:21

exception 39:15

89:8 117:21

117:22 178:15

exceptionally

202:11

exchanges 184:7

excited 226:22

exclude 48:11

52:5 208:15

exclusively 90:19

excuse 153:20

249:1

exercise 40:24

50:22 73:7

184:11 203:25

exercises 75:5

exhaust 162:16

164:17

exhaustive 96:23

exist 205:2

225:12

existed 318:17

existing 6:12

18:18 26:18 31:3

42:1 43:20

56:8 61:12

68:2 68:5

68:11 69:25 70:1

87:25 88:4

88:7 88:8

88:22 90:4

96:7 97:1 98:6

149:9 149:19

149:22 153:15

192:11 194:18

194:20 202:22

203:21 205:3

205:3 213:17

214:5 227:24
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 365

260:5 261:3

261:7 308:7

308:22

exists 93:7

277:14

exit 142:23

164:16 167:1

expand 104:18

expanded 134:17

276:13

expansion 85:4

85:8 86:12 87:7

expansive 116:11

expect 10:8 71:18

73:12 76:4

87:7 125:9

130:16 152:6

166:7 166:21

167:2 168:12

234:8 266:23

285:24 292:11

308:7

expected 56:5

71:6 168:4

168:21 169:1

169:16 283:4

expecting 16:15

85:7 88:6 121:5

expensive 200:4

experience

58:23 59:7

84:3 97:15

133:12 136:3

148:13 206:6

experiences 86:21

90:5 101:6

157:20

experiment

74:12 190:15

229:3 234:13

234:23 241:24

242:1 242:4

242:5 242:7

243:18 243:20

244:10 246:23

251:9 251:12

252:18 255:12

255:19 256:7

313:2 313:9

313:12 313:18

315:1

experiments 223:5

228:23 229:2

229:7 229:13

233:10 233:21

234:3 241:14

241:21 243:17

244:2 246:22

254:13 312:6

313:1 313:11

expert 78:24

explain 64:13

65:21 79:25 92:4

167:5 240:21

263:12 271:7

271:15 271:16

288:21 296:9

296:22

explained 57:1

explanation 268:3

268:4 290:4

explicit 35:11

117:3 304:16

305:24

explicitly 10:8

35:15 41:11

43:24 107:1

exploded 170:15

exploding 55:14

exploratory

191:24 196:21

exploring 292:16

export 194:23

exposed 80:22

exposure 126:20

320:13

express 206:8

expressed 111:9

expressing 203:18

extend 11:14

13:14 94:7

198:24

extended 21:15

92:22 100:12

307:20 307:21

extends 198:9

extensively

119:17

extent 60:1

238:20 260:6

308:17

external 196:8

extra 13:15

157:23 157:23

271:15

extracting 291:24

extrapolating

77:15

extreme 69:6 70:4

82:5

extremely 213:12

extremes 81:19

170:4

F

faces 3:10

face-to-face

184:7

facilitate 4:22

6:17 13:11 225:8

225:9 307:22

facilities 27:1
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 366

40:9 55:2 62:7

63:22 87:11

133:24 134:2

134:2 134:16

136:24 137:12

138:1 138:2

138:3 145:16

147:1 148:4

148:5 148:20

148:22 149:1

149:19 149:22

150:1 151:9

152:8 152:9

152:14 153:4

153:15 154:3

157:1 157:4

157:14 191:8

195:19 196:10

196:11 197:17

facility 37:21

37:23 38:5

38:6 38:6

43:20 44:10 68:9

134:6 134:22

136:17 137:19

140:4 141:1

144:7 145:22

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facing 95:21

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fact 12:2 13:24

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false 253:7 253:9

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fan 148:18

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favor 77:7

feasibility
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features 285:24

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February 24:22

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fellow 91:16

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field 6:12 6:12

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figure 165:22

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files 81:4 116:25

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fill 138:3 175:10

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findings 75:1

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finds 62:20

fine 17:17 151:24

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finer 239:17

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finish 4:19 113:3

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finishing 61:16
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fired 186:7

firm 86:9 113:9

firmer 87:1

firmly 112:17

first 7:13 9:20

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fits 25:23 32:6

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five 23:2 43:5

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fix 135:6

fixable 155:21

fixed 160:15

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flares 139:8

flat 90:14

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flavor 59:6

flew 279:11

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flexibilities

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flexible 95:24

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floor 171:18

183:16 198:5

flow 193:4

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flowchart 36:21

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flowing 307:1

flown 233:19

fluency 274:10

fluent 274:12

flurry 313:3
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flying 185:18

focal 92:21

focus 9:23

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110:14 222:6

306:5 312:7

fold 236:4

folks 15:17

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follow-up 91:15

96:2 200:8

footprint 58:18

58:20 153:4

footprints 155:17

forbade 275:2

force 235:5 237:7

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forecast 242:2

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foresee 86:23

foresight 228:2

Forest 58:10

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320:2 320:6

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31:11 34:4 35:15

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27:22 29:9 29:23

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49:6 54:13 56:15

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forms 27:3

formula 10:19

198:10 200:22

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formulate 68:12

formulated

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formulation 60:2

formulations

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forth 8:12 9:8

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89:15 100:21

101:20 102:20

111:20 115:7

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225:8 225:24

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foundation 26:2

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fours 246:22

fourth 39:17

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four-tiered 31:21

Fox 4:7 4:9 16:23

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fractional 188:18

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frame 13:13 60:19

60:23 213:11

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framework 37:12

224:10 225:7

257:18 260:11

277:8 294:22

306:14 315:2

frameworks 225:11

frankly 72:22

107:19

fray 52:1

frenzy 36:19

frequency 161:5

162:10 166:19

250:4 253:14

frequent 70:18

frequently 74:8

104:14

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Froning 138:18

front 35:8

61:15 94:5 197:1

198:4 239:5

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fronts 204:22

frowned 241:11

fuel 145:15 146:3

146:10 146:17

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fuels 64:10

fugitive 44:10

61:17 61:19

68:16 73:1 76:18

fulfilling 226:3

full 3:25 7:14

13:19 49:12

50:21 88:21 89:7

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150:8 167:3

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217:25 232:17

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function 167:1

173:14 174:14

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238:22 297:1

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fundamental 68:22

funding 200:13

future 9:4 21:9

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gain 15:15 79:13

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game 5:24 244:15

gap 175:10 183:1

gaps 226:12

gas 87:11

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153:10 182:16

193:8 197:12

198:14 263:4

263:8 278:24

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gathering 205:20

Gaussian 242:16

242:18 243:5

255:9

geared 233:4

gears 49:2 158:19

230:5

general 26:14

67:3 123:22
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generally 60:25

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100:24 174:16

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generate 81:6

generating

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generation 222:6

generator

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generators 102:11

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generic 114:17

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geographical

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geography 168:10

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58:9 79:23

80:4 81:9

81:17 81:22

82:13 82:21

90:16 91:22

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150:13 150:14

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10:13 11:3

11:6 14:18 58:23

68:19 76:23 86:9

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153:13 155:24

156:2 157:1

158:17 169:16

178:18 193:21

200:16 212:21

226:4 256:18

291:5 291:23

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300:1 304:12

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52:21 81:8 97:20

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101:23 102:19

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130:4 130:14

136:19 141:21

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164:10 192:24

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Glass 34:10

206:11 208:9

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glimpses 22:18

global 249:13

255:2 298:9

GMR 295:3

goal 22:13

36:20 239:21

gold 256:17

gone 10:25

20:23 22:20

36:15 39:22

65:10 119:19

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Google 286:17

gotten 18:5

83:4 112:22

153:12 292:5

GP 243:16 243:23

GP80 254:12

254:23

gracious 280:11

graciously 57:14

grade 248:2

gradient 37:9

109:1 109:2
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gram 148:25

grams 139:14

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grandfathering

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grant 5:9 10:7

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granted 50:24

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graph 149:13

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graphic 321:1

graphs 175:2

Grass 120:2

grateful 58:15

Graw 320:1

great 26:8 35:9

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313:17 315:1

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70:13 98:13

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greatly 14:19

30:4 94:3

green 136:21

140:22 144:16

180:15 236:13

239:7 258:11

Greg 52:21 294:10

grey 282:25

grid 32:22 33:5

41:18 47:9 47:12

55:3 136:21

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grins 155:1

grossly 127:3

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ground 299:5

groundwork 38:11

group 15:3

15:22 55:21

58:14 58:14

59:24 133:6

136:6 157:11

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180:15 222:16

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groups 163:8

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growing 43:4

grown 259:11

grows 154:7

growth 74:14

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guaranteed 282:15

guess 3:22 16:8

28:19 29:1 53:10

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141:21 172:1

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206:21 206:23

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220:15 220:15

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239:22 308:5

308:16 308:22

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guidances 16:20

guideline

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272:20 272:21

276:14

guidelines 275:10

gulping 94:14

guys 211:20 214:3

226:8 246:10

295:25

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habit 19:16

Hadar 146:12

half 7:1 20:10

157:22 167:14

180:6 314:14

314:14 314:17

halfway 50:12

Hamel 159:9

hand 107:22

188:11

handful 39:11

114:2

handle 121:19

217:6

handled 89:11

handling 66:23

192:19 218:13

Hanna 176:11

183:18 199:24

209:4 209:11

216:12 216:12

317:4

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130:1

happen 73:21

188:2

happened 21:7

23:20 24:13

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happens 10:16

45:20 61:13

283:12 290:9

happy 58:4

58:13 202:1

HAPs 61:14

hard 65:21

73:19 114:21

185:21 227:2

236:12 240:16

240:20 247:19

247:20 247:21

hat 23:1 202:4

haul 61:16

haven't 45:2 69:1

92:23 93:8

121:15 151:11

156:13 156:17

291:8 291:15

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292:21 292:25

having 13:7

61:6 62:8

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77:14 103:22

109:8 115:3

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175:21 179:11

216:17 228:1

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124:25

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220:16

HDDM 300:13 301:5

304:11 306:12

head 158:19

214:19 255:6

headed 294:10
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heading 74:4 82:9

heads 247:21

head-to-head

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hear 6:6 7:16 8:9

17:24 17:24 58:3

77:23 98:3 110:7

113:18 115:21

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132:24 209:5

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76:18 76:22 77:9

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hearing 3:15 14:2

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hearings 183:12

heart 61:11

222:24

heavily 61:24

61:25 62:1 65:13

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heels 25:11

Heidle 159:9

height 139:17

139:18 140:13

142:1 142:19

142:20 142:21

142:22 143:1

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144:9 144:11

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150:20 151:19

151:23 152:5

153:20 198:10

200:22 201:1

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heights 65:24

135:4 139:1

143:2 143:9

143:23 144:20

150:15 155:16

helicopter 279:11

286:15 286:19

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helicopters

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Hello 92:9 171:21

help 13:20

14:18 29:4 34:19

37:14 39:10

48:25 79:15

84:24 118:18

138:15 142:8

147:22 153:16

158:8 230:20

232:24 260:14

279:14 283:24

320:17

helped 14:17

30:18 293:23

helpful 53:24

83:25 104:1

110:12 168:13

204:11 228:9

233:7 256:23

320:16

helping 24:8

helps 24:3 106:15

155:24 280:25

Hendrick 183:25

here's 33:21 34:6

72:6 94:11 120:6

140:6 146:3

146:10 152:3

160:23 166:18

167:18 178:9

181:5 187:25

188:23 284:18

301:3

Heritage 294:12

294:17 298:6

Herman 124:8

he's 32:17 53:24

hey 113:5

Hi 211:16

high 26:11

51:16 66:20

69:11 70:4 70:10

70:10 70:22

70:23 70:23 71:1

71:2 71:3 71:4

72:14 75:15

121:15 127:16

154:1 161:8

163:13 163:19

169:8 171:11

180:10 180:12

181:2 181:3

181:4 181:23

188:25 189:2

190:6 215:15

215:25 222:12

223:19 235:24

265:12 267:16

269:7 272:5

282:17 292:8

299:6 299:23

300:25 306:2

319:8

higher 49:24

51:14 54:8 59:14

71:3 72:7

75:24 82:1

82:4 121:12

121:20 141:22

142:23 153:11
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154:15 160:13

169:3 170:3

170:3 174:2

176:24 177:7

178:11 179:14

179:21 179:22

180:18 182:1

182:24 183:6

198:17 200:22

235:8 237:21

248:15 251:8

251:17 252:10

252:13 264:3

264:8 264:11

264:17 264:21

266:11 266:23

267:25 270:12

270:13 270:18

270:20 270:24

271:14 272:15

273:5 273:25

283:14 296:23

299:9 299:13

305:3 305:22

312:10

highest 45:21

46:6 46:9

46:16 46:18

46:18 46:20 47:1

47:10 48:1

48:2 69:11

70:4 70:9

70:10 70:23 71:1

71:1 71:3 71:4

103:3 111:5

125:3 163:10

174:9 178:2

178:20 179:20

180:4 181:9

188:14 188:24

208:23 247:13

253:23 254:6

254:9 299:24

highlight 72:10

104:7

highlighted

126:10 139:15

259:13

highlights

69:16 126:5

highly 275:9

hill 120:8 166:19

310:5

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historical 150:22

223:2 243:16

history 23:7

24:21 74:10

75:17

hit 3:9 69:16

Hobson 321:14

hold 108:21

holds 100:14

278:22

Holy 266:5

Home 294:1

honorary 310:20

hook 194:20

hooked 194:9

194:16 196:5

hoops 83:12

hope 3:12 33:21

40:11 40:18 53:5

59:3 59:8 60:4

60:13 60:17

60:20 69:6 74:14

79:23 104:1

108:24 117:9

125:17 200:4

208:5 266:22

318:18

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11:19 11:25

15:14 58:6 86:25

91:9 94:8

100:9 106:15

109:19 133:2

158:25 217:18

218:6 232:23

307:1 307:22

308:3 308:7

hopes 21:4

hoping 16:21

40:20 88:6 233:3

233:20 234:15

236:3

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horizontal 55:3

238:21

hornet's 276:19

horsepower 148:25

154:12

host 15:5

hot 56:15 56:22

83:3 198:4

hour 27:16

40:10 69:22

69:23 81:9

100:25 111:6

111:23 112:12

121:3 123:19

123:19 128:3

133:15 148:25

154:12 160:15

163:9 163:10

164:6 164:7

170:25 171:25

172:3 172:8

172:11 173:8

173:11 174:11

175:11 176:5

183:2 183:4

191:2 191:7

191:24 192:2

195:12 196:15

196:18 212:11

286:10 295:4

295:5 295:5
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295:6 295:11

313:20 313:22

313:24 315:3

hourly 34:25 69:9

81:3 81:7 81:9

81:11 82:6

82:6 100:12

102:15 120:24

122:14 126:13

126:22 126:23

160:25 164:1

164:14 164:16

166:15 210:1

212:12 212:12

280:3 280:17

hours 116:2

160:24 163:8

163:11 187:17

189:22 282:6

284:12 314:4

314:15

house 36:24

housekeeping 9:25

How's 32:18

huge 18:6

143:24 154:17

155:3 169:1

219:5 248:16

Humming 183:16

hundreds 162:19

hurdle 99:13

hybrid 39:8 49:12

51:12 165:9

hydro-fracking

105:15

hyperbolic 62:24

hypothetical

182:9

HYSPLIT 229:5

253:2 253:13

253:15 253:18

254:4 254:10

256:2 256:3

256:9

I

i.e 247:14 255:15

IC 176:23 179:18

I'd 9:1 16:8

137:12 154:12

171:22 174:12

197:4 201:1

202:18 204:21

257:6 293:22

307:9

idea 55:7 127:8

128:18 210:16

226:15 233:21

243:22 306:16

315:17

ideas 14:23

18:1 21:23

125:19 202:22

identical 164:24

identification

106:24

identified

111:5 185:23

220:9 263:4

263:6 263:7

296:16

identify 3:20

76:20 109:2

185:16 244:22

320:17

identifying

12:4 58:24 109:4

109:11 185:19

ignoring 86:19

II 249:6 252:20

317:25 318:2

I'll 8:13 29:11

31:16 31:18

32:23 43:6

47:3 59:21 69:16

69:18 70:7

74:6 77:24

83:7 94:19

95:9 95:10

97:3 108:4

108:14 114:16

116:14 118:15

118:25 119:2

126:25 131:6

133:3 134:15

136:9 138:2

145:12 145:13

148:8 158:4

159:23 183:23

191:17 191:19

197:21 201:19

202:4 226:6

236:14 237:2

241:25 243:19

245:1 256:13

256:20 278:2

279:18 281:13

283:16 283:25

288:19 288:20

288:21 295:1

296:22 320:21

illustrates 108:5

175:3

illustration 91:9

108:25

I'm 4:7 19:20

20:3 22:16 30:12

30:16 30:19

36:22 42:6 51:14

51:15 57:3

58:4 58:9

58:13 58:24

60:10 60:11

60:19 62:22

62:23 68:20

68:22 69:16

71:11 71:23

71:23 74:24
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 377

78:23 79:24

92:18 92:20 94:5

94:14 94:16

111:3 117:20

119:15 120:18

122:20 122:21

123:21 129:19

133:5 136:12

138:23 142:11

147:9 157:22

158:13 159:13

159:22 160:20

161:10 161:25

164:12 167:5

168:19 171:21

176:18 191:3

191:15 191:16

193:21 202:2

208:1 208:16

226:10 226:13

226:18 227:15

240:4 240:8

240:11 244:7

246:14 249:17

251:13 258:9

262:7 263:10

264:22 265:2

276:19 284:8

294:3 294:9

294:19 301:22

303:2 306:21

310:11 320:2

image 265:17

images 263:14

imagine 10:14

13:12 25:23

50:17 78:5

167:12 167:22

171:10

immediate 86:3

86:4

immediately

296:10 305:23

impact 27:9

37:2 37:3 37:7

41:2 41:8

41:25 43:14

47:22 48:13

50:11 53:9 56:25

71:17 71:22

72:13 73:14

103:2 103:7

103:11 106:23

115:5 120:8

152:2 157:9

206:24 206:25

207:5 207:7

208:2 208:16

238:8 258:22

261:19 283:25

292:16 297:10

297:13 299:5

301:18 302:1

302:2 302:6

302:20 302:21

303:15 306:10

306:11 306:17

impacts 5:9

5:14 9:1 33:19

35:24 38:22

51:16 68:24 70:9

71:19 73:1

73:3 73:5 77:3

77:6 89:4

89:10 89:16

93:17 96:25

101:2 109:23

119:5 119:5

126:23 126:24

128:20 141:1

173:22 173:25

174:2 176:24

179:11 180:5

181:2 183:6

210:4 226:17

227:11 227:13

230:6 231:2

231:25 232:12

239:8 259:5

260:15 261:8

268:2 269:10

276:23 294:21

294:23 295:16

296:25 297:8

297:17 301:17

implement 77:14

112:2 175:17

182:22 205:13

213:2 213:5

238:4 279:2

implementation

24:6 33:8 77:8

77:12 91:19

118:10 124:4

129:18 130:12

132:8 171:5

273:4 275:4

277:5 278:3

282:24

implemented

68:2 76:16

80:1 81:3 82:6

129:9 129:24

213:23 291:8

297:4

implementing

202:25 261:10

implications

55:15

implicitly 175:12

implies 207:6

imply 109:22

importance

58:19 157:10

important 26:1

26:6 26:25

27:7 36:23

44:1 48:21

53:5 58:16 59:25

67:23 68:17

68:20 75:1

76:6 76:19

77:9 94:22 96:18
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 378

98:17 110:16

120:6 132:4

137:21 139:12

156:11 157:3

174:4 191:1

203:13 227:23

238:1 265:9

269:18 277:22

277:24 278:20

278:22 307:7

308:18

impose 104:9

272:17

imposed 104:2

121:15

impossibility

101:11

impossible

62:25 109:6

113:5

impression 91:23

improve 79:16

128:21 230:20

232:24

improved 244:1

improvement

9:16 131:3

improvements

6:3 7:19

improving 308:22

Incidentally

188:7

include 31:7 42:9

85:20 91:17

106:25 107:18

135:22 184:24

196:24 203:25

206:24 208:11

218:25 224:5

included 68:25

106:14 132:14

164:20 185:14

187:10 188:18

207:24 211:13

220:3 271:3

includes 251:22

including 37:4

65:10 68:23 77:3

92:14 96:8

103:11 106:24

208:2 240:1

314:8 321:6

inclusion 41:9

207:13

incorporate

9:24 103:22

148:16 252:16

incorporates

319:19

incorporating

43:11

increase 37:24

37:24 39:23

142:22

increased

150:20 151:19

increases

142:21 151:24

152:6 154:5

increasing 140:13

142:19 142:20

150:15 153:20

305:5

increment 27:8

84:15 85:3

85:5 85:8

85:21 86:2 86:11

86:12 87:5

87:7 224:6 300:2

301:5 304:1

304:5 304:7

increments 296:6

indeed 168:14

169:10

independent 55:20

55:22 119:22

224:13 248:24

249:2

independently

208:7 278:13

Indiana 168:8

Indianapolis

122:6

indicate 172:18

177:10 267:17

287:6 288:13

indicated 6:18

86:15 89:1

93:3 125:19

214:4

indicating 288:4

indicative

145:7 174:1

267:6

indicator

123:13 177:21

indicators

80:19 315:25

indifferent 57:23

individual 12:9

35:24 134:9

221:3 225:15

260:15 304:13

304:18 304:25

individuals

115:25

induced 148:18

industrial

51:15 57:8

61:3 61:8

62:19 73:4

134:16 298:20
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 379

industrialized

190:3

industries 205:13

industry 58:12

58:19 60:23

62:20 63:22

67:22 68:3

135:10 154:22

155:12 184:11

200:5 307:12

inert 188:23

225:18 241:1

influence 107:17

info 195:22

inform 8:23 12:15

14:12 18:2 48:25

79:15 96:5 98:13

206:2 230:20

257:20

information 8:6

9:5 11:12

11:17 11:19

12:15 14:14

14:15 15:5 15:15

28:22 30:15

33:16 36:7

43:3 47:19 53:23

69:13 80:16

84:21 94:22

98:24 99:17

99:20 100:7

101:8 102:4

103:17 105:19

109:19 110:12

110:25 112:13

114:4 117:9

117:9 118:9

118:16 123:14

132:14 132:22

155:25 156:1

156:8 156:21

157:4 157:12

160:21 161:3

184:6 190:13

190:25 191:5

191:7 191:14

191:21 192:12

192:13 195:16

197:7 197:16

197:18 197:20

204:3 204:4

204:17 205:17

205:22 205:23

213:21 217:7

220:1 225:10

226:4 230:10

233:6 238:17

269:19 269:23

270:6 305:11

307:1 307:6

307:8 307:23

308:10 315:21

informative

264:25

informed 22:4

informing 102:6

218:6

infrequently

104:25 160:13

215:20

infusion 34:23

inherently 224:19

inhomogeneity

268:24

inhomogeneous

278:21

in-house 234:12

280:6

initial 135:15

143:23 146:11

163:21 188:7

257:13 301:7

initialized

314:12

initially 14:25

29:19 104:3

133:11 136:3

137:13 141:23

149:10 222:14

268:5 271:23

injury 320:18

inner 281:5

input 5:25 6:9

8:3 12:3 18:6

54:21 81:7

116:24 128:2

158:5 164:13

248:25 259:22

303:22

inputs 41:4 74:16

74:19 75:22

76:10 91:5

122:17 137:25

142:23 167:4

223:20 224:20

236:22 254:17

291:21 291:23

303:22

inside 137:19

281:25

insofar 90:20

inspection 225:10

Insta 20:5

in-stack 8:6

98:16 101:25

145:4 149:4

156:1 157:1

157:13 175:20

178:4 179:15

179:24 181:18

188:7 192:8

195:7 197:20

install 193:24

194:8

installed 69:24

instead 179:15

190:16
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 380

Institute 183:21

integral 225:6

integrated

60:24 66:13

67:19 242:23

278:11 313:20

313:22 313:24

315:3

integrating

278:16

intended 20:9

20:13 71:6

96:7 104:10

105:1 108:2

248:3

intensive 53:20

119:23 120:2

295:19

intent 207:2

intentions 20:20

inter 312:2

interaction 83:24

109:23 128:9

interactions

13:18 13:20

222:20

inter-comparisons

8:23 224:17

312:25

interest 30:9

111:3 131:9

142:2 145:24

146:24 260:23

interested

83:14 120:23

123:14 158:8

185:17 214:18

267:22 268:24

272:14 285:3

285:3 310:18

interesting

67:1 131:3 131:5

146:10 146:19

164:19 210:12

interests

132:23 176:19

218:22 307:13

309:3

Interface 6:16

225:3

interim 27:11

106:20 309:7

Interior 219:3

259:14

intermediate

184:18 184:19

intermittent

7:8 102:10 104:5

105:9 105:20

106:6 106:13

160:11 215:18

internal 4:20

11:1 22:21

307:17

internally 21:7

213:15 228:12

interpolation

246:3

inter-pollutant

230:21 232:24

interpolluting

28:5

interpret 213:18

interpretation

207:8

interpretations

240:19

interpreted 71:24

interpreting

184:3 206:17

206:18

intersection

12:11 215:23

interval 268:22

intervals 317:8

introduce

175:24 285:11

introduced 5:6

9:19 79:11 159:5

192:23 193:17

199:3 199:5

introducing 75:18

130:9 193:10

218:3 218:9

inventories 29:17

inventory 30:22

106:25 209:1

inverse 174:14

174:23

inverted 71:2

investigate

91:7 130:18

investment 64:3

invited 14:19

59:2 132:24

158:20 158:20

involve 163:10

involved 24:18

40:20 116:1

121:4 122:1

122:12 123:23

128:16 157:18

225:15 273:21

294:17 313:7

317:17 318:1

318:2

involvement

133:20

involves 13:9

294:23 302:23

involving
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 381

105:15 220:10

Iowa 138:19

ISC 120:12 121:20

122:2 122:7

122:7 124:13

129:9 130:23

131:4

ISC-PRIME

120:14 122:1

122:3

isn't 49:23 68:17

117:21 316:19

isolate 235:12

283:24

isolated 207:24

isoprene 305:2

issuance 114:25

issue 6:21 6:21

12:16 19:4 23:22

29:5 48:10

64:1 113:25

115:23 115:24

118:21 201:11

231:3 239:3

274:17 276:18

292:18 292:19

315:23 316:6

316:16 320:21

issued 7:7

95:17 101:14

172:10 213:13

issues 5:2 5:22

6:5 18:20

32:23 32:24

42:16 43:2

44:5 54:16 55:23

59:18 59:21

59:22 59:25 60:4

63:7 63:14 63:23

68:22 79:15

86:14 86:23 87:3

91:11 95:14

95:16 96:3 100:1

101:12 101:15

108:24 112:24

113:1 114:12

118:13 127:9

133:13 134:12

134:12 146:18

175:22 210:2

212:4 214:14

218:13 218:18

218:19 219:6

220:25 221:9

222:11 235:18

260:17 260:18

261:4 265:4

317:19

items 9:25 12:5

13:4 13:15 29:20

307:11 308:17

iteration 170:6

iterations 165:16

169:23 170:1

170:5

it'll 59:11

I've 10:6 31:13

33:22 36:15

40:17 48:19 55:9

68:16 90:11

122:25 129:12

153:12 157:2

195:4 219:12

227:1 227:9

233:3 240:5

285:5 286:9

IWAQM 9:11 219:19

222:5 222:16

241:21 268:5

271:25

J

James 41:13 91:10

94:20 96:4

103:16 116:23

118:11 131:13

132:9 133:1

136:9 155:14

158:11 198:2

198:13

January 297:21

297:24

jaw 149:15

Jen 138:19

Jersey 132:16

jest 32:19

Jim 57:15 78:2

87:16 93:23

234:11 234:24

277:12 310:1

311:2

Joaquin 92:11

job 15:13 16:2

90:18 99:20

183:14

Joe 312:23

317:5 318:1

John 34:10

91:14 206:11

214:20

joined 23:9

jointly 218:10

joked 87:19

joking 108:18

journal 12:18

judgment 110:3

313:10

Julie 92:9

July 28:3 278:6

279:4

jump 75:19 155:14

jumped 74:24

June 13:13

111:5 134:6
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 382

159:5 211:12

jurisdictions

306:9

justification

7:24 28:15 50:20

52:7 79:8

97:11 99:5 99:13

99:19 102:3

justify 208:25

K

kangaroo 295:13

Karamchandani

294:11

Kelly 234:11

277:12 277:13

311:9 311:14

311:19

Kelly's 311:2

Kentucky 168:6

key 75:1 87:8

118:12 122:17

188:5 202:24

kick 94:10 131:16

kicks 51:18

kiln 192:8

196:1 214:22

214:22

kilometer 110:7

146:15 239:6

239:11 246:17

268:23

kilometers 55:5

55:6 66:12 66:21

67:2 80:14

107:16 107:17

110:11 116:5

116:6 116:13

135:20 146:6

152:12 152:13

152:14 155:6

155:7 180:3

207:1 207:5

236:10 236:11

236:14 236:15

242:11 242:12

242:13 243:4

245:6 245:7

245:7 245:15

245:20 245:21

246:3 246:18

246:22 246:23

247:1 248:10

248:12 248:13

248:18 251:5

251:6 252:12

254:22 255:1

255:5 255:15

262:16 262:19

262:25 266:12

266:17 270:1

270:2 281:5

289:10 289:10

290:15 297:19

311:24 312:13

313:22 313:23

314:5 314:18

314:23 315:7

Kincaid 166:14

169:6

Kirk 53:21

226:6 257:6

257:12 277:1

293:9 310:1

315:19

Kirk's 53:22

Kiss 34:10 85:1

knew 25:2 81:4

192:9

Knipping 203:16

205:11

knit 152:15

knowledge

133:17 136:6

205:14

knowledgeably

29:10

known 106:4

193:15 197:11

241:1

KolmogorovSmirnov 250:3

253:14

Krzak 138:19

kudos 114:7

Kumar 159:11

L

Lab 241:16 242:12

243:8 243:16

243:23 312:2

lack 44:2 98:21

189:15 268:25

274:19

Lagrangian

53:11 220:5

221:2 228:6

228:25 231:7

232:4 252:21

252:23 253:1

272:15

Lai 318:22

319:3 319:12

319:15 319:22

laid 20:19 25:13

Lake 112:7

land 5:16 9:18

187:13 187:14

222:2 222:21

248:7 258:19

258:20 268:19

lands 219:1

258:24 259:2

landscape 9:12
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 383

laptop 170:25

large 31:12 66:13

116:14 120:3

147:24 173:1

175:11 176:4

177:17 206:5

228:20 273:10

289:20 314:24

315:8

largely 273:20

283:11

larger 44:7 153:4

176:24 177:13

186:22 293:18

last 9:23 10:1

13:11 19:5 19:24

20:11 21:5 27:18

50:3 52:21 90:11

92:7 96:14 110:8

113:8 133:7

133:8 134:6

136:14 138:8

147:12 149:18

156:12 158:22

159:5 169:6

176:6 182:15

198:4 198:15

202:17 206:15

218:3 232:10

240:16 259:11

315:14 321:10

lasted 241:12

late 12:24

13:13 18:12

22:14 115:22

254:12 309:4

309:24

later 7:16 12:6

17:5 19:22 19:23

21:21 31:16

47:20 52:19

78:15 84:4

87:1 98:3 128:10

128:10 128:24

161:25 211:4

229:11 232:9

234:10 282:7

282:11 286:12

296:7 296:8

lateral 209:24

209:25 210:3

laugh 296:19

launch 131:13

190:22

layer 226:11

282:6 283:14

layouts 65:24

lead 69:7 75:14

89:4 118:9 204:2

234:15

leading 288:8

leads 285:12

lean 212:14

learn 14:3 125:18

230:1 279:6

280:24

learned 155:14

223:9 233:6

317:2 317:13

learning 227:10

234:14 257:17

least 22:10 28:20

39:5 40:21 42:11

42:19 44:11 45:1

45:3 52:8

56:24 57:14 64:4

92:22 95:12

97:22 112:19

120:21 122:14

124:1 124:2

126:4 130:8

130:20 149:15

203:7 218:16

225:4 243:7

243:22 256:23

313:1 320:25

321:4

leave 129:21

243:2 244:6

leaves 67:11

leaving 68:20

93:22

led 223:6

leftovers 131:25

legal 39:19 86:16

legally 118:3

legitimate

79:10 92:5

legitimately 92:3

Leigh 34:11

Leland 92:19

length 16:19 26:8

41:5 276:12

lens 281:2

less 16:22 23:3

37:25 38:1

38:9 45:13 66:12

72:4 111:10

131:15 144:7

146:14 154:6

154:9 154:20

166:10 168:1

168:13 172:4

173:22 175:8

177:11 178:19

179:11 187:3

201:7 248:12

254:17 269:21

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288:17 295:9

297:11 302:2

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304:6

lesser 36:17
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lessons 155:14

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let's 29:4 30:2

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49:5 74:15 93:21

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118:2 122:5

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165:19 170:23

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letter 105:13

106:9 239:8

letters 296:11

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letting 212:19

226:3

level 18:18

27:9 31:21 32:15

35:20 37:2 44:11

46:4 46:4 49:9

52:25 59:14

61:19 64:23 65:5

72:11 73:17

80:21 104:9

104:10 112:10

117:5 121:4

121:15 125:20

127:21 133:17

135:5 135:19

140:12 151:9

153:2 154:9

177:22 183:6

185:17 230:3

231:23 232:3

232:10 232:15

274:10 297:10

302:1 302:1

302:4 302:7

302:17 302:21

302:21 302:23

303:11 303:14

303:15 303:18

304:7 304:8

304:9 305:24

levels 44:15 50:8

63:9 63:25 68:10

70:10 72:21

82:14 147:23

152:2 153:11

153:25 155:4

156:10 156:25

178:2 215:19

306:10 306:11

leverage 31:2

51:24

leveraged 99:21

260:14

leveraging 260:5

light 53:6

60:13 72:24

127:17

lighter 239:1

lights 19:25

likelihood 214:25

likely 6:21 43:25

132:17 152:1

177:1 207:20

215:6 226:23

266:6 276:4

276:13 290:1

295:21 298:22

likewise 264:18

266:24

limit 148:13

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174:6 178:3

188:20 203:14

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limitations 60:14

189:16 313:11

limited 51:7 61:7

67:18 100:8

172:16 221:5

312:4 312:6

limiting 98:7

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limits 64:5 142:7

170:12 171:13

185:4 213:2

line 8:16 12:11

33:17 58:17 84:5

110:13 121:7

121:8 137:17

137:18 139:23

140:7 140:7

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141:15 146:25

150:13 155:5

173:17 180:10

215:3 221:25

227:12 227:20

232:2 235:19

242:20 272:4

280:16 286:13

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linear 299:12

299:12

linearly 192:25

lines 59:23 86:21

140:4 156:2

204:21 210:20

227:5 232:2

link 56:21 188:14

211:10 220:17

linked 56:22

165:4 210:25

224:19

links 56:20

Linux 170:24
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liquid 310:23

list 61:1 101:12

listed 29:22

100:4

listing 190:17

lists 164:20

literal 107:5

literally 117:7

literature 296:16

little 23:2

23:4 24:15 33:21

35:22 48:3 48:25

54:9 58:4

65:18 67:5 67:11

74:21 93:21

99:18 106:15

115:21 123:6

128:22 128:25

132:1 150:24

151:20 152:14

154:7 158:19

159:23 169:3

177:1 178:11

179:20 180:11

180:12 180:22

182:3 184:24

186:2 187:17

187:20 188:9

188:16 188:18

189:7 191:16

192:21 193:3

203:4 226:13

227:15 227:18

232:9 234:10

234:11 240:22

244:16 263:11

265:14 266:1

269:14 271:7

272:11 284:17

286:3 286:13

287:15 287:20

289:16 290:14

295:7 298:18

living 85:22

85:23

Liz 183:25

lo 190:2

load 167:3 167:3

local 8:5 13:8

18:13 20:14 22:2

22:12 28:13 36:4

36:13 57:7 57:18

101:4 134:7

157:19 186:5

187:11 258:2

259:5 259:6

274:19 278:14

286:10 306:15

locally 27:3

locals 21:17 22:8

located 42:9 44:6

58:21 66:12 73:4

120:8 145:22

180:21 301:18

location 44:3

109:25 162:17

188:10 189:20

231:5 282:14

288:25 297:3

298:13 298:21

300:16 305:14

305:15 305:19

locational 305:10

locations 34:18

42:2 42:2 43:5

134:3 190:9

258:15 298:12

298:14 298:15

298:15 305:7

306:20

Lochness 20:5

logarithmically

195:4

logger 193:25

194:8 195:24

196:8 197:4

logic 192:19

logically 104:13

logistics 3:9

132:1

logs 186:10

186:10 189:18

long 117:8 119:24

161:16 216:7

218:19 219:6

221:15 222:11

223:11 223:17

223:25 224:11

224:18 227:13

227:17 227:22

233:4 240:8

241:6 241:22

243:1 243:4

252:19 261:14

302:5 311:10

311:22 311:23

312:12 318:9

321:14

longer 27:2 27:25

44:14 95:22

124:22 126:25

293:17 311:13

longitudinal

209:23

long-range 5:16

8:15

long-term 123:2

looming 73:20

loop 157:20

loosely 275:16

lose 36:20
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 386

lost 315:13

lot 15:10 19:22

20:24 21:1 23:20

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31:14 36:13 42:6

42:14 44:13

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58:23 59:9 59:11

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83:8 85:7 103:15

103:18 118:15

119:16 120:20

124:10 131:14

131:21 132:13

135:2 135:10

135:23 138:14

140:18 140:23

141:18 141:20

141:22 147:14

150:6 152:17

152:19 153:4

154:4 154:24

158:21 159:6

166:23 184:13

190:25 191:11

194:14 195:19

195:21 196:11

197:16 198:8

198:14 213:3

215:7 216:8

226:17 227:24

228:14 230:18

231:12 231:14

233:5 234:14

235:19 235:19

237:1 239:15

240:17 241:10

246:20 248:6

263:11 263:14

271:13 274:15

284:24 286:1

291:16 304:20

304:21 304:22

306:25 307:22

309:13 311:6

317:4 317:12

320:11

lots 169:7 210:16

241:8 243:20

loud 83:1

Lovett 120:6

121:6 166:12

166:18

low 44:11 50:9

51:5 70:16 70:17

70:19 76:21

125:20 150:23

151:9 153:9

155:16 170:19

173:21 173:22

173:25 177:10

180:11 181:25

182:24 185:17

200:23 201:13

291:15

lower 67:5

68:11 151:6

153:10 169:3

169:4 174:16

177:7 177:9

177:12 178:16

181:23 189:2

209:2 251:12

254:4 265:15

lowers 179:13

lowest 73:22

182:14 182:17

182:20 247:10

247:13 247:16

251:13 253:6

254:10

LRT 261:13 266:12

269:25

lucky 194:22

luminescence

192:24 192:25

193:12 193:18

lunch 131:10

131:11 131:16

131:18 131:19

132:7

lurking 92:3

M

machine 275:1

machines 273:16

MACT 61:9 61:12

62:2 62:4

63:25 68:3

68:5 68:10 69:24

MACTEC 182:5

magnitude 64:13

64:18 70:9 70:24

301:18 315:5

main 29:21 139:25

140:6 155:17

207:11 312:18

mainly 133:10

240:15

main-stack 137:15

maintenance

134:23

major 24:14 37:22

38:5 38:6 40:2

60:25 61:2

61:2 61:21 257:9

259:25

majority 89:9

126:23 274:5

man 94:12 201:8

manage 203:15

management 19:9

21:3 61:19 64:16

65:22 101:9

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managers 5:16

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maneuver 215:7

manipulation

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manner 219:21

manual 107:8

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manufacturers

186:12

manufacturing

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map 148:9 286:17

mapping 265:5

271:1 271:5

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mappings 271:15

maps 136:15 138:1

138:4 158:12

March 3:3 7:8

25:15 32:1 95:11

95:17 96:8 96:14

99:9 101:14

111:4 111:8

112:22 114:25

115:17 172:1

180:8 206:14

209:20 211:2

213:14

margin 65:8 67:10

215:9 295:14

marginally 49:24

Mark 171:19

171:21 183:15

183:19 183:21

184:14 186:1

189:6 198:3

310:4

Mark's 185:11

Marriott 310:15

mass 221:9 228:19

match 123:18

248:3

matches 126:3

300:17

matching 69:12

126:12 289:8

material 3:15

147:12 241:1

materials 241:9

math 215:3

matters 116:12

116:22

max 141:13 141:16

144:13 153:17

maximum 31:24

42:22 44:15 47:2

47:12 54:22 55:2

64:6 69:4 70:1

70:15 95:1 103:5

104:16 119:9

122:20 136:19

140:9 141:6

141:25 142:4

144:10 144:22

145:1 146:9

147:4 160:3

160:5 163:12

167:11 168:2

169:11 173:21

181:11 212:10

212:11 212:21

215:1 237:4

237:9 238:8

242:19 242:20

242:21 250:5

254:14 266:2

266:8 288:25

289:1 289:2

289:4 299:5

300:18 302:6

302:11 302:21

303:14 304:1

304:6 306:10

may 3:10 5:2 5:22

6:3 6:5 6:20 7:9

8:8 12:19

13:13 17:24

17:25 18:14

21:19 21:22

24:21 27:18 28:7

28:22 28:24

37:15 37:17 39:3

39:17 39:19 44:8

44:10 44:19

48:12 48:23 52:7

53:13 56:9 79:14

82:17 84:14 96:9

99:10 105:4

105:4 105:7

109:7 112:2

113:22 123:14

125:13 125:20

127:8 128:18

129:2 142:6

142:7 142:10

145:7 145:8

151:18 152:18

152:20 153:6

153:9 154:21

157:2 157:23

160:7 171:14

172:3 172:17

173:15 173:25

174:1 174:3

174:5 174:23

189:23 190:12

196:10 198:21

203:3 204:24

205:13 208:15

210:4 210:9

214:23 215:17

215:22 217:6

223:18 232:19

242:21 242:24
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maybe 6:6 23:2

37:22 39:18

39:24 39:25 40:6

40:19 49:25 50:1

50:1 51:24 65:15

69:22 71:2

73:6 73:6

74:22 74:23

79:22 81:22

86:25 89:23 92:7

99:18 112:24

122:21 123:3

124:1 128:10

128:21 143:7

146:15 148:13

148:20 148:23

149:19 159:25

161:7 161:11

162:1 169:10

171:1 171:1

171:1 175:4

189:1 201:19

203:8 204:15

209:25 210:13

216:2 217:7

244:2 250:20

253:11 271:13

287:20 290:2

291:13 308:15

310:19

McCarthy 5:10

28:4 32:7 52:4

89:2 211:19

MCHISRS 210:23

mean 16:24

17:20 38:25 50:6

92:5 99:15 117:6

123:13 125:13

134:19 135:17

137:4 150:13

152:20 153:9

153:13 154:3

188:16 188:18

196:14 201:5

202:20 205:9

209:13 210:5

251:11 253:17

271:12

meaning 39:1

64:24 254:18

means 17:20 22:19

58:22 71:10

109:10 131:16

210:3 245:11

259:4 260:7

273:9

meant 59:24

meantime 310:20

measurable 302:3

measure 241:3

measured 173:21

173:25 174:8

174:9 174:13

176:25 193:19

279:13

measurements

233:10 233:15

234:1 234:1

235:14 279:14

280:16 286:25

292:21

measures 223:22

249:12 249:14

249:19

measuring 186:6

191:14 193:22

241:4

meat 38:17

mechanically 81:6

mechanism

109:25 174:18

mechanisms 223:24

MEDOC 279:24

meet 9:16 14:1

68:4 68:5 88:5

218:14 222:7

meeting 4:23

14:13 222:13

256:9 259:9

310:14

meetings 13:24

184:7 218:18

meets 249:23

Meg 83:1

MEGAN 298:7

megawatt 143:10

member 16:18 18:9

18:23 19:3

50:4 60:8

66:11 78:16

82:24 85:1

86:6 87:6

87:16 88:12

90:10 91:14 92:9

168:8 199:15

200:17 201:21

203:16 206:11

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214:17 310:4

310:19 310:21

310:25 311:20

318:22 320:1

members 133:19

133:23 159:8

159:9 310:16

memo 25:15

25:20 25:23

25:24 26:1

28:4 28:17

32:1 32:2 32:7

35:4 35:9

38:11 47:18 52:4
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memorandum 259:15

Memorandums

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memos 25:21

95:8 101:7

106:21 107:3

124:12

mention 15:21

35:8 49:15 55:12

57:5 113:3 281:6

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mentioned 10:6

12:23 13:6 13:22

15:9 21:14 21:22

22:5 55:9

55:17 56:8

59:1 74:7

86:12 91:16 98:9

101:10 132:7

166:13 200:10

219:12 222:1

222:4 224:2

235:2 239:2

241:15 252:10

255:8 257:21

279:1 282:23

283:1 285:2

286:14 292:2

292:18 293:9

296:12 315:24

merging 278:19

merit 251:7

Mesa 269:17

Mesopuff 317:25

317:25 318:2

mesoscale 6:16

223:5 225:2

246:12

mess 79:2 194:19

message 116:10

265:9

met 56:2 74:19

91:17 91:23

100:18 121:14

122:12 127:5

135:13 142:16

142:17 186:5

194:15 236:22

244:17 244:18

247:1 251:19

252:6 252:15

252:16 252:16

280:8 280:9

284:21 298:2

318:24 319:4

319:24

metal 214:21

meteorological

41:3 59:17

75:6 75:12 75:13

91:1 138:22

186:6 223:20

223:21 224:14

224:20 224:23

225:5 228:4

239:9 273:19

274:3 275:15

276:12 278:21

279:22 293:6

314:23 319:18

meteorology

43:6 54:18 75:23

145:6 225:1

228:9 278:14

280:8 281:7

281:8 282:13

284:11 284:14

284:15 286:9

298:1 319:15

319:23

meter 137:17

137:18 139:22

139:23 140:4

140:4 140:10

140:22 140:23

141:5 141:13

141:15 143:1

143:22 144:6

144:8 144:25

149:17

meters 139:1

139:17 139:18

140:7 140:7

141:16 143:2

144:7 144:20

148:11 148:16

151:23 153:21

154:8 154:20

180:21 181:10

186:4 286:22

289:12 297:20

method 7:16

54:2 97:21

98:7 98:7 100:10

171:24 172:17

173:5 173:15

175:7 181:22

182:12 182:21

183:3 184:15

185:3 191:1

192:7 195:9

205:8 295:16

296:23 296:24

299:7 299:7

299:9 305:13

316:7

methodologies

172:14

methodology

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223:14 294:7

296:15
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methods 35:24

97:16 98:9 98:12

120:9 172:7

172:12 175:10

181:1 182:2

182:4 182:19

183:3 218:8

296:16

metric 208:21

209:2 250:15

251:16 298:25

299:1 316:11

316:21

metrics 223:15

249:6 251:8

252:21 261:25

320:11 320:12

320:14

metropolitan

295:2 297:19

Mexico 125:1

126:7 128:13

176:12 176:21

199:22

Meyers 172:21

mic 78:10

Michigan 94:12

micro 42:12

microgram 72:4

72:5 140:11

144:6

micrograms

50:15 64:24 65:7

65:25 70:25 72:7

73:18 136:20

137:8 140:10

141:17 144:9

144:12 144:17

149:16 150:6

151:2

microphone 217:23

microphones 3:20

middle 42:13 53:4

120:14 190:1

287:2 290:25

301:8

midnight 19:22

19:23 19:23 47:4

midwest 51:15

178:14

mid-yellows 153:1

Mike 34:10 85:1

87:12 200:17

milestone 72:21

mill 66:13

66:22 69:17

69:20 136:25

Milligan 142:15

mills 60:24 67:20

69:18 90:14

mimic 84:23

mimicked 31:16

mind 62:15

79:21 208:6

226:19 271:22

274:5 318:20

mindful 3:17

4:2 42:17 80:6

minimum 61:18

64:4 167:3

187:23 290:13

290:15 290:25

299:4

Minnesota

145:20 146:22

minor 63:19

74:8 246:3

minus 250:17

254:19 316:22

minute 74:2 148:9

194:10 195:2

197:3

minutes 4:5

23:2 58:4 58:6

131:15 131:23

170:25 216:7

217:17 217:17

mirror 249:20

250:10 253:24

misinterpretation

123:12

misleading 243:9

miss 123:7

missing 45:24

127:20

missions 186:15

mission's 29:16

Mississippi

315:13

Missouri 138:18

142:17

mistaken 92:18

Mitchell 92:10

93:13

mix 59:21

mixed 185:5

mixes 285:19

mixing 172:22

180:23 289:22

290:2 290:18

319:9

MM4 245:6 248:18

MM5 244:25

245:5 245:7

245:7 245:8

245:14 246:2

246:16 246:18

247:2 247:3

247:14 247:25

248:4 248:15

249:10 251:5
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MMIF 43:9 225:2

228:2 246:1

246:15 247:1

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251:9 251:23

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254:18 255:20

261:22 264:6

264:9 264:13

264:20 264:21

265:13 265:23

266:8 266:13

267:25 269:22

270:14 270:19

279:24

MMIFSTAT 225:8

mobile 126:17

126:19 126:24

127:22

mode 146:11 193:7

193:15 193:15

model 6:4 6:16

6:20 7:17 7:19

8:23 9:3 25:5

25:9 31:25 33:12

33:13 33:20

34:23 35:11 39:1

39:2 39:13 40:24

41:11 45:9 45:11

47:8 51:4

54:12 54:17 55:1

55:24 64:25 65:8

66:2 67:13

69:5 69:10 69:10

70:1 70:15 72:12

74:5 74:10 74:16

74:16 74:18 75:2

75:4 75:6

75:15 75:20

75:22 75:22

75:23 76:9 76:21

77:16 78:18 81:3

81:7 82:3

82:16 88:16 89:5

89:5 89:8

90:22 92:4 92:12

96:25 98:14

101:1 102:6

103:10 109:4

109:13 119:4

119:8 119:10

119:14 119:19

119:21 120:13

120:22 120:23

120:24 121:11

121:22 122:17

123:13 126:17

126:18 127:11

127:18 128:2

128:6 130:3

130:14 131:2

143:3 143:25

144:2 144:8

149:6 154:24

155:9 160:2

160:17 161:8

161:10 162:11

162:18 166:3

175:25 186:25

187:6 190:8

200:19 216:14

220:19 222:7

223:3 225:3

225:5 225:16

225:19 225:20

227:7 228:4

231:12 232:6

232:12 232:17

233:14 234:16

234:18 235:21

236:25 237:15

241:14 241:23

242:24 244:3

244:18 244:22

245:5 246:12

247:25 248:6

248:9 248:14

250:13 250:16

251:3 251:20

252:6 252:15

252:17 253:6

253:8 253:22

255:2 255:11

257:10 261:6

262:8 268:9

269:5 271:19

272:15 272:20

272:21 273:22

274:14 277:17

277:20 277:22

277:25 278:9

278:11 278:19

279:2 279:7

279:15 279:16

279:22 280:4

280:21 280:24

281:1 281:7

282:6 283:2

284:15 287:2

288:21 291:1

291:17 291:21

291:23 291:23

292:5 292:6

293:10 293:12

293:24 294:14

295:23 296:5

296:21 296:25

297:16 298:2

298:2 298:2

298:3 298:8

298:10 299:23

300:5 300:9

302:24 302:25

303:10 303:19

304:10 305:15

305:25 306:2

306:12 312:2

312:14 312:25

313:8 315:13

315:13 316:16

316:18 317:16

317:18 318:3
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 392

318:4 318:5

319:15 319:17

319:18 319:19

319:23 319:25

321:6

modeled 31:24

41:21 45:7

46:2 46:6 46:7

48:17 50:11

73:14 77:22

82:12 111:2

115:4 117:4

135:5 136:17

137:18 139:21

139:22 139:23

142:6 142:16

142:17 142:18

143:1 143:4

143:13 143:15

143:15 143:23

146:12 146:13

149:1 153:5

162:15 169:9

173:5 179:20

195:10 202:9

221:6 221:6

228:22 244:12

255:11 263:8

296:20 321:1

modeler 91:16

274:23

modelers 13:8

18:13 22:2 29:15

137:16 138:19

149:11 274:3

274:4 320:17

modeling 9:20

9:21 10:5

10:10 18:4 18:16

19:1 20:10 20:11

20:15 21:18

23:12 25:16

28:23 29:6 30:10

31:8 31:14 31:22

32:10 33:7

33:8 33:10 33:10

35:19 36:9 36:11

40:16 40:20 41:1

41:20 42:8 42:20

43:24 46:8 47:25

49:13 49:15

49:17 49:20

50:21 51:23

51:25 52:13 53:1

53:4 55:18

56:4 56:23

63:7 63:14 63:18

63:23 64:1

64:6 64:15 64:17

65:12 65:22

67:15 67:17

67:24 74:8 75:25

76:19 77:8 77:11

85:11 85:21 86:1

90:2 94:18

98:6 102:24

106:25 107:1

107:18 109:16

110:22 116:4

116:6 116:8

117:2 119:9

121:19 122:20

123:10 128:15

132:15 133:6

133:13 133:17

133:25 134:6

134:14 134:17

134:21 134:22

134:23 135:11

135:15 135:16

135:19 146:23

153:16 153:18

157:6 159:15

160:14 165:16

166:22 168:1

170:21 179:16

181:21 183:4

191:8 191:24

192:5 195:17

196:21 196:23

202:6 203:12

211:24 212:1

212:15 212:22

214:9 219:25

220:15 220:22

224:18 224:23

227:3 227:4

227:4 227:19

227:25 228:7

228:19 228:22

229:1 230:9

231:9 232:1

232:14 233:1

234:22 235:1

236:6 236:9

238:2 239:11

239:22 240:15

257:24 258:13

258:17 259:22

260:4 260:8

260:11 260:17

260:22 261:3

262:12 263:2

263:19 263:20

268:20 272:18

272:25 273:6

274:11 275:8

275:9 275:15

275:19 276:1

278:4 294:20

296:18 306:6

308:24 309:19

310:10 314:12

319:14

models 5:5 5:8

5:17 5:19 6:3

6:14 8:11 8:15

8:16 8:19 8:24

9:7 9:13 9:15

12:19 15:25

26:18 32:8 32:22

33:3 53:11 53:12

53:15 53:17

54:17 60:2

72:2 74:12

76:5 76:10 78:19

80:10 87:25 88:1
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 393

88:5 88:7 88:8

88:22 88:24

89:13 89:18 90:5

90:9 91:3 120:11

120:16 121:24

184:23 200:3

219:14 219:17

220:4 220:4

220:5 220:8

220:14 220:20

220:21 221:2

221:8 221:10

221:17 221:19

221:19 221:22

222:15 222:16

223:1 223:12

223:17 223:20

223:25 224:5

224:5 224:12

224:18 225:14

225:25 228:6

229:14 229:24

231:8 232:4

232:4 233:9

233:13 233:22

234:4 235:3

235:4 238:22

245:1 246:5

250:8 251:1

252:19 252:21

252:23 253:1

253:2 253:3

254:9 257:7

257:17 261:7

261:7 261:15

269:19 270:8

272:15 272:16

273:5 273:11

273:14 273:19

273:23 274:1

274:6 274:7

275:13 276:13

276:15 276:23

277:15 282:12

289:6 291:5

292:23 292:24

293:7 293:17

293:19 295:17

311:23 311:25

312:16 313:7

314:6 314:8

315:1 315:12

315:16 317:17

317:25 320:5

320:9 320:16

321:2

model's 241:6

MODELS 3:2

modern 27:18

modes 193:6

modification

213:10

modifications

63:20 88:7

309:12

modified 101:24

102:1 103:16

134:2

modify 17:6 34:24

35:2 98:4 319:5

modifying 6:10

210:13 213:7

267:11

modules 163:25

175:25

molar 98:7

molecule 285:17

moment 205:24

money 246:20

monitor 25:8

41:10 43:23

45:24 45:24

66:12 66:21 67:3

67:7 78:20

80:2 80:3

80:22 84:7

112:14 116:15

116:16 116:17

116:19 123:1

123:4 123:7

123:11 123:11

123:16 124:25

126:7 126:8

127:2 127:2

127:12 127:24

128:17 128:17

128:18 128:20

128:21 129:15

129:16 129:17

129:19 130:7

160:17 166:4

167:9 187:19

189:23 190:17

192:16 192:17

193:1 193:4

193:22 194:4

197:9 203:15

208:21 208:22

210:4 210:9

239:7 239:9

239:9 253:8

monitored 43:16

45:7 46:3

81:16 81:16

82:11 102:24

107:2 109:24

110:1 111:2

111:6 111:9

111:15 127:11

180:4 181:2

188:3

monitoring

23:11 26:24 27:9

34:20 42:2

42:5 43:18

66:5 76:3

80:18 96:16

121:14 135:23

135:25 151:25

172:24 173:12

173:16 173:19

175:11 176:14
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 394

177:4 180:3

180:21 186:4

186:5 187:11

189:20 191:10

200:2 204:17

208:24 272:5

301:19

monitors 78:18

80:5 80:7

80:13 80:19 81:3

120:7 123:3

125:2 127:4

128:13 130:4

166:19 168:6

168:22 169:7

173:2 176:22

179:14 185:13

191:11 192:11

258:7

monkey 26:13

monologue 58:3

Monster 20:5

Monte 159:17

164:21 212:23

214:18 214:22

216:13

month 4:22 9:21

11:13 83:19

103:15 127:12

157:21 157:21

195:25 240:10

299:22 308:2

months 23:14

101:15 211:2

212:1 296:6

297:22

Monument 272:6

moonshine 139:13

Moore 149:11

morning 3:4

3:11 21:14 93:25

131:25 172:9

184:15 186:19

200:20 256:24

282:7 283:12

284:12 286:10

307:5 321:19

Morris 52:19

240:4 240:5

262:22 271:8

294:3 312:4

319:1 319:7

319:13 319:17

319:24

mostly 125:6

247:1

motivating 225:1

motivation 257:23

294:19

MOU 219:4

mountain 178:14

178:16 178:18

241:19 317:15

mouthful 42:6

move 8:8 16:5

17:3 19:9 20:9

60:18 63:6 72:10

79:16 93:20

100:8 108:10

109:7 111:4

118:5 157:11

157:17 164:25

202:24 206:2

222:22 222:24

236:3 237:24

257:14 270:7

285:18 287:8

287:14 289:15

290:14 290:22

291:5 291:9

293:15 303:13

303:17 320:21

moved 7:13

26:12 27:10

43:14 291:12

moves 273:25

moving 23:25 28:3

51:21 85:21

105:24 106:2

106:3 114:8

171:17 220:2

221:24 223:22

224:11 231:6

249:3 277:4

285:22 287:20

293:18 303:13

303:16

Mozart 298:9

multi 261:11

multi-model

255:25

multiple 187:1

216:14 218:23

234:18 244:3

244:19 244:22

245:1 273:16

277:2

multiplied 300:19

multiply 173:6

186:18 300:21

multi-source

217:6

multi-tier 230:22

multitude 20:23

multi-use 258:21

multi-year

103:3 111:21

203:2 203:9

myriad 92:22

myself 142:15

240:7

N

NAAQS 5:2 6:25

23:10 24:12

25:17 31:24
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 395

33:24 42:1

42:4 44:25 45:13

46:4 47:5

47:14 51:6 51:17

56:25 62:21 67:9

78:12 78:13 83:8

83:10 83:12

83:21 86:8

87:3 94:19 96:15

98:18 100:14

100:16 100:24

101:23 102:13

102:14 102:16

102:22 103:12

103:21 108:12

113:4 114:24

117:4 121:2

126:21 136:23

140:12 144:2

144:19 145:17

145:21 150:13

150:14 156:9

171:25 172:11

180:6 183:6

191:2 191:24

192:3 192:5

195:12 224:6

227:8 257:8

258:3

NACAA 29:3 29:8

29:15 30:8

35:5 43:1 52:3

53:8 54:14 57:16

71:20 73:11 78:4

87:17 89:14 93:2

93:10 230:13

230:22

Naresh 159:10

narrow 59:23

124:20 187:16

287:7 289:14

289:20

narrowing 9:12

national 50:3

58:11 96:21

100:18 102:1

241:16 272:6

nationwide 58:21

natural 87:11

145:14 145:25

147:10 153:10

naturally 59:20

nature 35:10

35:25 49:18

49:19 97:21

102:19 102:20

108:11 110:23

112:3 204:17

NCAA 244:16

nearby 102:23

106:24 109:4

109:12 109:22

110:2 110:10

117:3 120:8

123:11 149:24

177:11 201:6

207:12

nearest 122:21

269:17 298:20

near-field

33:18 40:24

101:2 172:22

173:17 174:20

175:13 254:23

258:1 276:9

necessarily 38:24

71:18 89:4 121:2

122:19 122:23

123:12 129:6

136:1 142:12

156:24 282:15

necessary 7:21

9:14 32:9 64:1

84:11 96:10

101:16 107:20

157:5 172:4

219:19 222:25

225:6 274:1

necessitated

261:2

necessity 76:7

negative 254:4

305:4

NEI 236:21

NEPA 56:18 218:25

219:5 227:23

257:8 257:21

257:24 258:17

259:10 259:17

260:3 260:20

261:1 261:18

276:7

nest 276:19

314:22

nesting 236:10

239:18

nests 235:24

net 37:23 39:23

285:13 285:21

288:4 288:8

290:20

Netherlands

185:14

network 23:11

80:17 146:24

176:14 176:22

275:2 275:3

neutralized 271:4

271:10

Nevermind 256:16

Newcastle

298:16 305:8

newer 234:6

nice 30:5

144:16 282:13

niche 224:13

night 19:24
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 396

315:14

nighttime 293:5

293:7

nine 28:19 139:19

263:4 286:10

Nipping 159:10

nitrate 51:8

268:7 268:12

271:4 271:9

271:21

nitrates 85:10

nitric 268:10

271:19

nitrogen 229:18

264:4 268:8

270:10 270:14

271:2 271:12

271:14 271:20

NO2 5:1 6:24 7:13

35:21 40:1

40:4 40:10

53:7 78:18

91:9 93:14 93:17

94:11 94:19

94:25 95:8 96:15

96:22 96:25

100:13 101:1

101:19 101:21

102:13 103:13

107:4 110:23

111:12 113:13

113:24 123:21

123:23 124:18

124:24 126:12

126:13 126:21

127:11 128:11

128:14 128:18

133:6 133:15

134:14 136:12

136:24 137:5

137:6 138:17

140:15 140:21

141:9 141:21

145:13 145:19

145:22 156:24

157:6 157:13

163:9 172:3

172:11 172:25

173:7 173:21

174:19 179:12

179:21 180:6

181:3 182:14

182:20 183:6

184:22 186:16

188:1 188:10

188:12 188:14

189:5 190:1

191:2 191:5

191:6 191:14

191:24 192:2

192:5 192:7

192:13 192:16

193:11 193:16

193:18 193:21

193:22 193:25

194:5 194:11

194:21 195:3

195:5 195:8

195:12 195:17

196:14 197:11

197:13 197:20

211:3 211:12

234:24 263:22

266:2 266:9

nobody 20:7

134:21

nobody's 149:15

no-brainer 37:20

nominal 110:7

303:23

non 52:8 155:3

165:1 212:3

285:17

non-attainment

28:12 147:19

147:22 153:17

155:4 212:2

302:7 303:4

303:24

non-complex

120:12

non-controlled

150:22

non-cumulative

134:6

non-downwash

120:12

none 188:4

253:9 314:8

314:25

nonetheless

23:9 27:23

40:7 50:7

50:17 51:1 55:22

132:21

non-linear 221:14

non-modeling

64:14

non-regulatory

99:4

normal 40:25

135:12 164:8

normalize 75:5

normalized 249:23

253:17

normalizes 250:12

normally 216:24

north 128:14

128:17 128:18

128:20 179:3

179:8 179:10

179:14 298:16

Northeast 178:14

note 68:16

70:21 73:10

83:24 96:18 99:8

149:15 158:15
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 397

174:16 179:7

184:3

noted 88:23

91:1 99:22 100:5

105:7 172:9

172:21

notes 96:11

nothing 41:23

60:15 117:5

117:6 117:7

notice 10:2

10:4 76:7 176:23

178:1 180:18

188:5

noticed 251:18

311:25

Notwithstanding

229:12

Nouri 311:16

novel 115:7

nowhere 190:1

NOx 38:9 39:22

50:14 128:11

128:14 147:19

147:21 153:25

155:11 172:22

172:25 173:5

173:19 173:20

174:25 174:25

175:3 176:5

176:6 176:16

177:2 177:2

177:7 177:9

177:10 178:2

178:12 178:21

179:5 180:6

180:14 181:2

186:16 186:17

188:1 188:9

188:22 189:1

190:1 191:5

191:6 191:7

191:9 191:12

192:7 192:11

192:13 192:16

192:17 192:18

193:15 193:19

193:20 193:22

194:2 194:11

194:21 195:3

195:5 195:8

196:11 197:9

232:13 236:18

237:4 283:8

283:9 283:14

284:23 285:21

287:23 288:7

290:5 290:8

290:19 292:8

292:20 296:6

297:2 298:24

299:2 299:10

299:17 300:10

301:7 301:8

301:10 301:11

303:7 304:3

NOXCEMS 195:20

196:10 196:19

NOz 292:20

NSPS 62:2

NSR 24:6 36:22

55:11 107:8

230:7 230:15

nuances 42:7

42:14 42:16

86:11

nudge 20:1

nugget 24:3 28:2

nuggets 27:6

numerous 176:22

278:12

O

O3 192:23 193:10

Oakley 234:2

OAQPS 29:19

119:12 133:20

184:5 204:10

212:24

objective 29:12

223:21 242:8

244:21 245:13

267:14

objectively 50:19

objectives 224:4

obligation 15:4

114:18

observation 248:4

284:11 284:13

286:8 293:4

observational

314:1

observations

34:24 76:13

126:3 242:17

242:18 243:11

245:13 245:16

245:25 250:5

252:16 255:10

278:7 280:8

286:16 286:24

287:5 287:21

288:2 288:21

289:14 290:8

290:12 319:20

obviously 10:19

15:10 18:13

20:17 29:14

41:14 43:4 56:20

59:5 59:8 63:4

64:16 68:18 69:1

70:3 73:7

74:14 74:22

75:11 76:11

76:22 77:6

80:6 81:17

83:6 85:21

97:9 98:16
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 398

107:25 110:13

131:25 141:19

160:4 162:4

164:22 166:22

169:9 170:3

179:23 189:9

221:13 221:23

occur 104:14

104:24 163:8

163:20 243:11

occurred 128:8

258:14 266:5

occurrence 314:2

occurring

173:24 174:1

174:22 175:6

175:13 215:1

315:9 320:18

occurs 260:8

266:3 267:12

267:15 314:3

ocean 50:25

318:25 319:4

319:6 319:8

319:9 319:10

OCS 50:24

October 20:12

20:21 27:7

232:11 244:13

odd 202:11

offered 28:19

57:14

offering 60:19

office 28:15

29:17 36:4 36:13

57:7 57:18

99:6 99:16 105:6

106:7 108:8

110:21 115:15

236:8 294:12

294:16 295:24

298:6 301:25

offices 101:5

113:9 117:14

133:20

officially 10:5

offset 32:4

52:5 52:11 52:14

72:2 73:11 89:17

93:2 93:11 93:15

231:1 231:4

offsetting 175:24

OFM 244:24

OGC 21:3 213:16

oh 116:18

116:21 198:25

214:19

Ohio 168:7 168:10

244:12 244:13

244:14

oil 19:22 19:23

19:23 21:4

47:4 147:14

176:14 176:21

198:14 263:4

263:8

okay 10:15

18:25 35:6

49:1 50:14

60:4 66:7

81:12 82:23 86:6

94:2 131:20

141:9 145:21

147:11 148:19

149:21 150:25

152:3 159:4

165:13 168:11

169:22 183:18

198:25 199:1

199:4 199:8

199:14 201:10

202:2 202:15

216:5 217:9

245:3 277:13

286:14 289:5

295:2 297:18

299:6 303:2

318:8

Oklahoma 142:15

143:15 143:18

145:3 223:4

242:10

old 18:14 23:1

54:2 64:19

108:21 165:11

170:23 206:21

211:18 216:4

268:5 271:24

276:18 295:5

older 145:8

243:24

olefin 305:2

OLM 7:17 99:1

99:22 113:16

113:17 124:4

125:6 125:8

125:12 125:16

126:6 127:5

127:6 137:1

137:2 137:4

149:3 149:8

150:11 151:5

151:8 172:6

176:10 179:2

179:6 180:9

180:11 180:15

180:17 181:6

181:15 182:23

183:24 184:14

185:2 186:15

186:18 188:11

188:16 188:17

189:5 189:8

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212:18 256:13

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160:13 161:19

163:18 195:19

203:23 205:10

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63:20 64:8 64:10

74:9 119:24

120:7 186:10

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204:12 204:18

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61:12 66:14

66:23 90:7 160:4

161:4 196:16

216:1

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275:11 275:14

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58:13 61:11

61:15 61:17

62:14 64:25 65:8

73:16 90:6 104:8

105:7 160:14

206:7

opinion 151:12

206:8

opinions 197:5

opportunities

74:15 76:8 77:19

77:19

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22:3 76:24

79:7 265:25

opposed 268:12

ops 242:19 252:9

option 89:24

131:4 254:23

258:5 258:5

259:7 261:19

309:19

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options 99:5

101:19 102:1

124:1 124:5

124:24 128:11

128:11 157:8

183:9 195:18

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64:18 100:8

103:19 121:14

122:15 167:16

187:16 200:6

213:8 232:13

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261:12 261:12

272:15 273:5

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276:2 278:10

278:15 296:24

299:6 299:9

299:13 299:16

299:17

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12:25 13:25

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59:13 120:4

original 9:11

128:15 143:2

144:12 175:15

179:16 180:7

182:13 189:12

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132:10 147:16

172:20 179:22

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41:17 56:15 83:1
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 400

83:25

OTAQ's 83:17

others 6:5 18:7

19:9 29:9

35:18 96:5 106:7

115:16 116:24

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Otherwise 158:16

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ourselves 65:12

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265:25 267:10

outliers 265:13

265:22

outline 140:3

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outlined 47:18

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outlining 259:16

outperforming

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output 164:6

164:8 164:10

165:14 210:14

228:4 279:23

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outputs 151:1

291:21 291:24

outreach 118:8

outside 5:23

100:1 140:25

147:22 282:1

outstanding 11:21

183:14

overall 19:8

69:11 82:2 111:5

120:15 124:16

127:25 134:19

141:17 304:15

overcome 59:19

over-estimate

129:3 271:13

overlaid 266:17

overlap 249:21

249:23

overlapping

218:22

overlaps 282:10

overlay 281:16

overlaying 282:3

overlays 5:14

overly 76:15

107:9 107:25

119:6 173:15

206:22

overnight 3:13

over-predict

125:5 126:9

169:10 190:6

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168:23 188:12

188:15

over-predicting

127:3 189:9

190:5

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128:7 169:3

over-ranging

177:2

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overstate 161:12

overtop 282:3

overview 134:15

226:11 226:11

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oxidation 292:20

oxidizer 139:11

oxidizing 174:19

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18:18 19:1 52:22

55:18 88:10 89:3

89:22 98:6 98:15

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128:17 128:20

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P

p.m 282:7 282:7

pace 59:11

paces 226:1

pack 297:1

package 152:15

packed 4:11 216:8

Page 25:20

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25:24 32:2

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pages 240:11

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Paging 217:23

pain 95:16

Paine 16:18 78:16

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paired 34:14 69:8

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82:5 103:10

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pairing 85:11

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Palau 124:25

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panel 57:6 93:24

panelists 58:5

paper 58:10 60:24

64:5 67:19

201:23

paradigm 8:14

8:18 224:3

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paradigms 275:23

paraffin 305:3

parameter 250:3

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parameterization

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parameters

42:25 134:3

143:8 144:5

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162:16 182:8

186:21 186:23

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parametric 296:25

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paramount 36:9

49:14 49:16

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pardon 23:2

pardons 19:21

parity 265:16

partial 268:4

participants 15:2

participate 12:21

211:6 294:13

participated 13:2

participation

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particle 231:8

253:1 272:16

particular 4:15

22:23 35:18 85:6

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127:12 185:19

192:1 193:21

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205:16 229:19

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particularly

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particulars 108:9

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particulate 268:7

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partly 245:18

partners 219:8

219:23 222:2

pass 154:4

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passed 136:22

144:3

passes 137:2

pass-fail 248:2

past 23:21 43:6

54:6 74:17 92:20

100:1 117:24

124:1 162:12

218:16 241:13

Pat 129:10 130:1

path 54:10 209:24

pathways 303:3

patient 217:13

pattern 300:17

patterns 52:10

111:13 287:16

287:24 300:23

Patulski 83:1

83:18

pay 26:23

paying 246:20

peak 120:25 130:9

160:15 160:25

161:24 163:7

166:1 166:5

166:11 167:14

167:14 168:17

168:24 169:5

169:8 171:9

289:16 289:20

peaked 187:8

peaking 215:15

peaks 289:8

peaky 171:8

peer 12:18

peers 60:7

penalty 221:4

people 5:23 10:14

14:7 15:1

17:19 17:23

18:21 19:13

19:19 23:1 30:17

31:13 36:19 57:9

60:9 74:23 78:11

87:14 112:20

114:5 114:6

119:12 120:19

123:25 131:14

133:21 135:10

138:12 156:7

158:5 184:2

185:21 204:4

208:5 209:14

229:4 230:11

239:22 242:1

244:6 247:20

249:24 249:25

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139:17 139:19

139:19 140:10

143:14 143:15

143:16 148:6

148:7 148:7

148:8 148:25

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161:1 161:11

161:19 161:21

161:22 170:19

197:9 212:11

268:6 271:25

272:2 272:3

298:25 299:1

300:6 300:18

300:21 303:9

percent 65:16

65:18 65:19 67:9

68:11 71:1

71:3 73:13 73:17

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136:20 149:2

149:2 150:8

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152:4 153:21

153:22 154:15

161:7 161:20

161:21 179:22

180:14 188:20

192:10 195:7

195:9 195:10

249:23 253:23

254:19 256:22

295:9 295:10

301:13 302:13

302:16 315:15

318:24 319:4

percentage 162:23

169:8

percentile 45:8

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45:19 66:16

66:24 69:5 69:11

70:2 81:15 81:16

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95:1 95:4 103:12

103:13 111:22

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16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 403

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percentiles

82:1 169:22

perfect 124:16

126:2 127:14

130:8 250:1

250:13 251:16

perform 164:22

172:10 233:13

251:1 252:8

293:7 312:17

performance 6:8

55:25 56:2

56:4 61:7

70:15 76:21

120:10 122:11

123:13 130:20

172:18 179:1

180:1 180:8

181:15 181:19

189:19 200:19

223:21 224:3

224:19 225:6

244:1 246:12

246:19 247:3

248:6 248:9

248:11 248:21

249:6 249:8

249:11 249:13

249:13 249:14

251:4 251:23

252:1 253:5

253:21 254:21

255:17 255:18

262:2 278:8

279:15 281:1

299:24 312:8

312:14 313:25

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165:17 252:9

252:11 252:12

255:20 262:9

271:5

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168:4 171:25

180:19 181:7

225:19 233:22

244:23 247:24

250:18 251:9

251:18 252:5

252:25 253:6

253:11 253:13

253:13 253:15

253:16 253:18

253:19 253:22

253:25 253:25

256:1 256:4

256:10 256:11

318:3 318:5

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182:1 182:25

293:14

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6:10 7:15 7:16

9:25 12:24 16:22

73:22 91:12

98:10 108:15

109:20 110:25

115:18 116:2

188:15 291:15

320:9

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195:25 196:18

202:6 202:7

203:9 206:9

279:10 280:20

286:16 307:20

318:19 320:10

periodic 14:1

14:13

periodically 13:1

periods 75:14

201:9 293:17

299:22

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20:15 23:17

29:15 63:20

63:20 64:5

74:8 74:9 90:7

102:18 113:23

123:10 142:7

142:7 155:21

161:9 172:3

181:21 194:3

201:24 203:5

203:14 213:2

213:23 227:12

275:8 275:9

276:1

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24:17 65:3 101:5

106:1 108:17

113:12 113:13

118:5 133:14

156:8 172:9

238:2

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32:6 63:9 157:19

201:23 205:1

214:8 220:24

232:16 274:7

274:19 277:9

perpetrator

312:21

Perry 214:17

perspective

29:7 42:12

44:5 44:12 59:10

60:5 60:23

121:10 130:21

156:12 207:25

275:5

pertinent 147:14

petition 5:10

10:7 89:2 219:13

230:8 261:2

Petroleum 183:20

Peyonce 262:20
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 404

phase 9:4 9:11

9:11 119:20

222:5

phases 119:20

278:25

philosophical

72:20

Phong 149:11

photochemical 5:8

32:22 33:12

33:13 33:20

38:25 39:13

50:21 53:1 54:12

54:17 54:25 56:4

72:2 85:25 89:13

89:18 220:14

225:16 228:6

229:1 231:9

231:12 232:4

232:6 232:17

234:21 235:1

235:3 235:4

235:21 236:9

236:25 237:15

253:3 273:10

273:14 273:19

273:22 275:15

276:13 276:22

280:4 280:21

295:17 295:22

296:18 296:21

297:16 302:24

303:19 304:10

305:15 305:25

306:6 306:11

photochemicals

56:9

photochemistry

253:4 285:4

pick 46:19 47:2

47:12 83:4

131:11 297:12

298:13 299:22

299:24 314:9

picked 242:21

279:5 296:17

299:3 302:7

315:4

picking 75:14

268:24

picks 124:17

picture 127:24

150:24 186:2

186:8 194:14

pictures 194:12

piece 190:13

269:18

pieces 35:5 56:16

208:3 208:6

pipeline 62:12

pitfalls 108:6

placed 41:22

281:24 316:10

placement 41:19

290:3

places 71:24

117:10 189:6

232:5

Plains 229:3

242:10 312:1

313:1 313:9

313:12 313:17

315:1

plan 68:5 76:25

85:13 86:4 290:3

planned 105:2

105:4 106:4

293:11 309:7

planning 55:11

184:2 192:3

plans 20:19

21:9 33:9

80:18 86:3 87:20

203:8

plant 66:22 66:22

120:6 120:7

123:16 124:14

137:13 138:18

139:10 145:15

145:16 146:1

146:2 146:3

146:4 146:9

155:20 185:13

185:18 186:3

202:1 202:21

234:2 278:5

279:3 279:12

281:10 281:21

281:23 283:20

286:18 286:19

286:21 288:8

289:11 292:3

321:4

plants 119:24

204:5 205:17

212:7

plastered 185:23

plate 16:2

platform 228:5

260:17

platforms

260:24 273:7

273:20 274:2

plausible 136:7

play 244:15

262:10 292:17

played 201:18

play-in 244:15

plays 28:3 34:22

PLC 194:5

plea 29:3

pleasant 3:13

321:20

please 16:16
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 405

19:20 22:21

45:22 183:10

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pleased 184:4

plenty 63:17

312:5

plot 120:20 152:9

153:24 154:7

174:24 177:4

181:5 181:12

189:5 229:16

234:23 238:6

238:25 281:19

285:1 286:6

287:1 287:2

plots 120:19

126:1 141:3

144:4 152:3

174:12 177:6

177:16 180:2

188:21 210:12

211:13 282:5

283:9 288:19

plotted 174:24

174:25 176:15

180:1

plug 156:16

plume 11:23

32:8 33:5 98:7

124:20 129:3

129:11 129:25

130:5 130:10

172:22 174:20

184:21 184:23

185:13 185:18

186:24 188:10

209:24 233:10

233:19 235:25

236:1 236:4

238:24 239:17

242:16 242:18

242:20 242:22

243:5 254:25

255:9 277:17

278:5 278:12

279:12 282:9

282:10 282:14

282:24 283:4

283:10 283:17

283:25 284:7

284:13 284:19

285:12 285:16

286:11 286:12

286:20 287:1

287:7 287:10

287:13 287:20

288:4 288:6

289:7 289:20

290:2 290:17

290:20 292:7

292:10 292:24

293:4 293:10

293:13 296:5

299:4 319:10

plumes 99:24

124:14 174:3

187:1 187:2

187:4 238:20

plume's 210:3

plus 70:1

120:13 121:12

121:14 151:2

205:21 212:18

254:19 303:7

PM 18:14 19:1

20:9 24:10 43:15

50:15 61:6 61:14

61:21 61:23

64:22 65:2 65:11

65:17 65:19

68:14 85:13

87:18 89:23

118:2 220:9

220:16 227:25

PM10 23:15

23:18 24:17

24:25 64:19 65:1

65:9 65:16 265:3

265:10

PM2.5 4:16 5:14

9:1 11:20

16:20 17:17

20:10 21:11

23:10 23:19 24:6

25:2 25:10 25:15

25:17 26:4 26:19

27:8 27:14 27:20

27:21 27:23 29:6

30:25 31:22

34:17 34:25

36:15 37:25 38:2

38:7 38:19 38:22

39:21 40:11

40:22 42:2

42:3 42:4

42:17 42:19

44:21 45:3 45:17

46:3 47:10 48:25

49:3 49:6 49:7

49:18 50:10

52:14 52:24

52:25 54:13

55:18 55:24

57:10 65:4 65:14

66:12 66:20

67:17 68:13 72:4

73:14 73:15

73:21 78:21

79:12 84:14 85:6

86:1 88:9 89:4

91:13 92:12

93:18 219:14

220:22 221:6

222:8 224:9

227:11 227:17

230:7 230:14

230:21 231:1

238:16 239:24

260:18 273:14

277:16 308:5

PM2.5's 24:11

podium 132:25

190:23 256:19
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 406

Podrez 171:20

171:21 183:19

point 18:11 18:15

26:21 27:19

36:23 48:2 75:21

79:3 79:5

86:10 87:23 88:3

90:16 90:21

92:19 92:21 93:7

108:15 112:19

117:16 122:10

125:11 131:5

137:12 139:2

152:24 155:13

156:4 172:13

174:2 201:10

207:11 207:22

213:7 215:5

216:18 216:20

218:5 221:5

263:5 263:5

270:21 281:21

315:20 317:11

320:24

pointed 115:16

222:22 277:14

286:9

pointing 27:14

28:8 284:18

points 30:20

31:10 94:10

143:9 165:3

174:15 174:16

177:18 177:25

178:17 178:19

183:5 209:20

221:12 221:12

281:6 289:13

290:11

polar 147:2

policy 21:3 23:15

24:18 24:25

27:19 27:25 52:8

59:22 59:25 60:3

65:10 86:16

86:16 87:15

113:8 202:4

213:16 276:17

294:24

political 117:16

polled 113:8

polling 113:11

pollutant 35:18

pollutants

24:14 33:25 38:1

40:1 220:9

258:14 277:20

285:4

pollution 298:2

298:3 320:19

poor 221:20 243:8

255:10

pop 75:16

popular 111:1

populate 158:2

population 42:10

portion 129:11

Portland 123:15

ports 194:9

194:20 194:23

position 28:10

positioning

314:11

positive 254:6

possibilities

39:14

possibility 88:14

231:10

possible 16:6

79:14 122:17

123:12 163:14

202:13 214:2

228:8 229:25

232:10 241:2

273:18

possibly 43:11

272:24 282:16

post 103:23

132:18

posted 30:9

137:10 210:25

211:11

post-process

103:17 115:19

163:2

post-processing

48:24 93:15

103:19 103:25

post-processor

103:23 162:25

163:3 163:23

164:9 175:19

potential 17:25

35:14 109:22

110:17 175:23

199:23 215:1

224:8 224:24

259:5 260:13

260:15

potentially

98:4 152:18

195:22 197:17

218:14 219:16

232:20 277:18

278:23

pound 212:11

pounds 69:21

69:23

power 66:22 67:20

120:6 120:7

124:14 185:13

185:18 186:3

204:5 205:16

278:5 279:3

279:12 281:9
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 407

281:21 281:23

286:18 286:19

286:21 288:8

289:11 292:3

ppb 94:25 95:4

136:20 137:8

178:11 178:21

179:21 280:15

295:5 295:11

300:18 302:3

302:8 302:8

302:8 302:15

302:15 302:16

302:17 302:18

303:12 304:6

practical 77:7

122:19 257:15

260:6 260:17

272:12 273:3

312:22

practically 76:16

practice 73:2

77:10 109:11

303:10

practices 68:20

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pragmatic 309:17

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reduced 139:18

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reducing 62:13

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reference 27:16

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referred 25:22

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refine 109:19

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refined 7:17 7:22

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100:10 111:14

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refineries 137:7

refinery 66:22

reflect 60:1

reflected 32:4

65:3 77:21 167:4

reflection 307:18

reflects 59:14

refreshments

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refused 274:24

regard 91:15

100:11 107:23

regarding

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regards 25:7 29:6

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regenerative

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regime 93:6

region 25:6 50:24

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regions 58:22

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RegText 35:22

regularly 13:1

regulate 61:13

regulated 61:24

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regulations

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regulatory 5:19

6:2 6:9 6:14

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reinforce 307:5

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reinforced 32:1

reiterated 89:2

relate 206:6

related 31:14

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229:14 258:4

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relates 257:11

relationship

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relationships

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relative 33:6

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relatively

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release 4:17 6:16

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57:10 103:15

115:1 225:2

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released 25:15

244:11

releases 243:18

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relevance 244:16

relevant 7:2

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reliable 30:25

relied 201:4

relies 83:2

rely 28:1 28:24

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relying 24:17

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remain 277:5

remainder 22:18

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remained 139:6

remarks 77:24

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remedied 215:17

remember 45:4

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remembers 318:1

remind 3:14

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reminder 307:3

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remote 258:15

remotely 27:4

remove 28:10

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removed 28:9 51:6

51:19 52:4

removing 120:4

renewals 63:21

74:9

repeal 24:23

repeat 118:15

replace 120:11

replacing
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120:17 121:11

replicated 163:4

report 30:3

30:8 66:15

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reported 180:7

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reporting 219:20

reports 11:22

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represent 178:4

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representation

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representative

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representativenes

s 98:15

representatives

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represented

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273:6 278:12

representing 93:6

represents 98:5

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reproduce

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request 19:24

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requested 32:3

requests 87:15

require 28:13

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required 8:20

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requirement 96:19

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requirements

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53:7 61:19 63:18

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103:19 197:10

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requires 99:5

110:2 224:13

re-running 116:1

reruns 142:3

research 9:15

148:12 157:5

reserve 309:5

residual 62:10

resolution 55:3

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resolutions

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resolve 63:14

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resolved 63:7

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resolves 311:7

resource 53:20

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88:25 221:4

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resources 16:5

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16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 418

respect 5:1 23:17

26:3 27:15 27:21

29:14 31:8 31:20

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35:23 36:1 37:25

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57:21 85:3 85:20

205:12 219:9

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respectful 4:2

respectfully

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respectively

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respects 16:4

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responding 221:25

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response 81:21

259:12 266:21

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responsibilities

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responsibility

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responsible

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rest 22:15 138:3

restful 3:13

restraints 312:19

restrict 275:11

restrictions

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result 45:12

63:24 66:18 67:4

67:13 69:5

70:1 70:2 70:5

75:4 75:20 75:23

82:3 82:16

104:11 107:9

123:8 160:16

166:5 166:7

184:22 265:8

268:15 314:9

316:20 316:24

resulted 126:22

179:20 219:22

results 67:24

68:1 68:11

69:4 69:19 70:15

71:12 74:10

74:16 75:9 75:16

75:18 77:16

81:14 81:16

83:14 83:22

90:13 91:3 91:11

92:5 94:21 100:6

101:23 107:2

113:25 122:24

123:10 128:21

129:14 136:12

138:23 141:24

145:7 146:8

159:22 159:23

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167:6 169:17

171:7 171:8

174:7 179:22

181:17 195:1

199:13 200:20

206:22 229:17

236:15 236:16

238:14 239:16

240:18 240:23

257:13 265:10

267:16 268:1

270:21 279:8

279:18 284:23

285:9 287:4

287:23 301:24

309:8 313:5

313:17 317:15

retained 24:1

retrospective

242:3

reusable 260:11

reveals 63:23

reversing 128:19

review 4:20

5:18 10:22 10:24

11:1 12:19

18:5 20:14

21:7 22:7

22:21 41:14

62:10 95:20

95:22 96:2

105:18 126:22

134:19 182:23

205:7 211:6

307:22

reviewed 92:15

92:23 93:8

186:19 296:16

reviewers 241:16

reviewing 92:2

176:2 241:18

revise 23:24

250:20

revised 23:24

73:20 96:16

183:4 243:14

272:7

revisit 77:20

revisited 128:12

RFP 295:24

rich 74:10

159:9 279:10
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 419

296:9

Rick 320:1 321:9

rigid 275:9

275:25

rigorous 232:22

rigs 106:1

ring 286:2

rings 281:17

281:22 281:23

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282:3 283:5

311:3 311:4

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ripe 5:3 5:21

rise 299:4

risk 62:10 126:20

River 223:4

242:12 243:8

243:16 243:23

255:9 258:11

312:2 313:13

RMAX 245:18

245:19

RMAX1 247:6

248:20 251:7

252:5 252:7

255:14

RMAX2 247:7

248:20 251:7

252:5 252:7

road 43:12

91:13 107:25

308:23 321:14

roads 61:16

roadway 174:4

roadways 127:5

173:19

Robinson 57:17

93:24

robust 99:3

121:13 122:12

125:3 196:2

277:11 305:18

Rocky 241:19

317:15

Roger 26:7

32:17 32:20

46:12 46:13

48:22 78:25 79:1

87:19 99:10

100:5 103:16

104:17 107:23

116:23 119:1

132:10 132:17

151:7 179:8

184:8 184:15

186:19 189:5

189:13 190:24

200:18 200:19

206:14 210:21

215:22 296:19

Roger's 46:13

84:19 185:11

190:10 226:12

Roland 250:9

role 26:25

35:16 54:4

201:19 292:17

rolling 162:21

166:17

Ron 318:22

roof 187:9

room 3:11 10:15

19:17 19:19 20:5

23:1 29:9

30:19 33:23

34:10 41:17

53:22 67:12

72:25 73:5 85:18

87:14 159:10

185:20 215:7

217:24

rooted 111:20

112:17

rose 101:12

rough 158:1

roughly 67:9

180:19 282:14

round 217:15

321:18

route 276:11

routine 105:7

268:22

routinely 99:23

257:17

routines 103:25

row 237:5 237:6

284:8

rows 239:5

RPM 184:24

RSL 157:21

RTP 171:21

rule 24:21

24:23 28:6 39:16

43:5 85:8 87:8

89:8 117:21

117:22

rulemaking 6:23

10:3 10:11 10:24

214:3 214:4

rulemakings 54:5

rules 15:23

24:6 62:6 95:6

147:19 147:21

271:6

run 39:1 39:14

47:19 54:24

72:14 75:10

75:10 75:21 76:5

93:10 93:14

115:18 121:14

130:3 142:25
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157:2 162:18

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170:21 171:1

196:1 198:1

214:24 216:23

216:23 237:20

245:14 246:16

248:13 249:9

252:24 253:4

266:24 273:14

273:15 274:17

284:3 297:6

297:21 299:18

300:5 300:15

304:17 306:2

306:12 311:13

319:24

running 39:13

55:5 72:12 164:2

165:8 166:5

169:12 214:20

214:21 216:6

221:4 259:8

266:23 273:21

291:23

runs 135:17

142:14 142:15

143:18 143:19

147:13 151:23

164:8 184:9

187:6 189:25

198:14 198:20

240:19 246:9

248:6 272:7

272:9 273:13

277:2 291:18

300:4 312:5

312:5 312:6

rural 177:4

177:24 178:8

Ryan 57:21

57:24 58:1

58:9 85:17 90:11

90:11 93:24 96:5

S

S1 299:16

S2 299:16

safe 161:11

safety 295:14

Sage 311:1

sales 136:18

Salt 112:7

Sam 211:16

Sampieri 211:17

214:7

sample 34:17

193:5 193:8

sampled 286:20

sampler 45:21

samples 34:16

45:22

sampling 236:1

San 92:11

Sands 176:14

176:21

Sasquatch 20:5

sat 19:17 19:18

satisfied 302:22

satisfy 23:18

299:23 301:20

302:3 302:5

302:9 303:17

Savannah 223:4

242:12 243:8

243:16 243:22

255:8 312:2

313:13

save 216:24

saw 59:5 59:6

71:24 73:12 85:2

97:1 116:12

252:6 278:18

288:17 288:17

291:25 307:19

SC2 150:13

SC5 149:18 150:22

scale 42:13

67:3 89:8 90:1

149:15 150:21

152:25 227:12

227:20 230:6

232:17 233:7

233:12 258:3

258:16 260:12

267:6 267:9

293:12 309:18

scaled 195:4

scales 77:5 227:3

227:5

scatter 249:15

scavenging 175:23

179:9

scenario 64:10

72:11 72:18

137:2 139:2

139:4 140:10

145:20 147:17

148:5 148:10

148:15 148:19

149:14 149:14

149:18 150:4

150:5 150:20

151:10 152:5

153:19 154:19

266:4

scenarios 37:13

67:25 69:20

71:13 102:15
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 421

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134:25 135:3

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schedule 4:2 18:9

19:5 57:5 58:7

78:9 94:20 131:7

131:12 131:23

132:10 158:18

158:18 217:18

scheduled 20:12

schedules 138:14

Scheffe 296:2

296:4 296:4

296:10

schematic 193:4

scheme 238:23

schemes 223:17

238:21

Schewe 90:10

SCICHEM 6:18

53:13 184:24

234:5 234:12

234:14 234:25

272:16 277:17

278:4 278:10

279:19 279:20

279:22 280:5

280:7 280:11

280:24 281:10

281:11 281:24

281:25 282:3

282:9 282:17

282:25 283:2

283:11 283:13

284:10 285:3

286:2 286:7

287:19 288:15

288:17 288:24

289:19 289:25

290:1 290:7

291:7 291:10

291:20 292:3

292:11 292:12

293:23 311:2

311:18

SCICHEM-WRF

279:19 279:21

281:8 281:15

281:16 284:6

286:7 287:4

287:19

science 88:3

184:2 275:18

275:21

scientific 90:8

186:20 192:15

194:8

scientifically

275:19 276:2

277:10 305:18

SCIPUFF 229:5

252:23 254:1

254:5 254:8

255:25 256:4

256:9

scope 4:13 5:22

16:9 16:14 59:23

score 251:17

SCRAM 30:10

124:11 132:15

132:19 136:14

137:10 158:14

210:22 210:25

211:7 228:11

240:10 307:25

308:2

scratch 132:11

scratcher 255:7

screen 3:16

185:24 207:1

236:7 294:21

screening 7:12

35:10 35:20 37:2

52:23 71:7

90:1 97:1 97:5

97:16 175:16

206:16 207:4

231:23 232:3

232:10 232:12

232:15 232:21

294:7 294:14

294:15 295:20

296:6 297:6

297:11 302:2

302:4 302:18

303:11 303:12

303:14 303:18

303:21 304:8

305:13 305:13

306:1 306:9

309:14

Sea 50:25

SEAR-1000 194:8

194:10 194:13

194:16

search 289:1

searched 288:23

season 111:23

128:3

seasonal 32:5

44:9 44:19

48:4 48:7

48:16 81:24

81:25 82:1

111:13 112:6

seats 3:6 3:8

94:3 131:21

198:4

second 21:20

26:11 31:18

32:11 34:9 47:17
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 422

48:8 48:15

72:9 72:11 97:14

97:19 112:1

139:15 139:17

139:19 139:20

142:24 143:14

143:15 143:16

145:19 148:10

161:1 161:11

161:20 161:21

161:22 205:18

229:8 253:11

278:10 278:15

283:16 296:22

299:16 303:11

313:16 314:2

314:9 315:4

320:23

secondaries 265:5

secondarily 26:18

27:22 33:19 49:5

54:13

secondary 5:14

9:1 27:3 31:8

31:11 33:15 34:4

35:15 37:6 38:15

38:23 39:5 40:11

43:19 44:1

44:1 44:5

44:16 45:2 54:20

68:24 71:8 71:16

71:16 71:22 72:1

72:3 73:5 77:4

77:6 79:12 84:12

85:9 85:12 87:10

87:18 88:1

89:4 89:10 89:23

92:13 93:11

93:17 100:24

219:14 222:8

226:16 227:11

231:2 231:8

231:25 277:16

277:19 285:4

seconds 3:6

193:14

Section 35:21

35:23 42:21 57:1

83:19

sections 167:22

sector 61:24

61:25 62:1 91:20

187:18

sectoring 91:18

sectors 62:19

309:3

seed 321:5

seeing 17:12

70:14 84:22 85:4

123:14 127:2

130:9 169:11

169:14 178:6

226:23 237:10

284:20

seem 106:4 113:25

217:25 246:17

seemed 91:2

91:5 145:4 188:4

252:8

seems 75:12

82:5 88:10 154:4

156:20

seen 20:7 33:23

36:14 41:4

59:4 98:12 108:5

119:16 123:1

152:22 153:10

153:11 166:13

190:10 258:9

284:5 307:8

308:13

select 164:5

selected 177:20

selecting 189:22

selection 163:6

selections 170:6

semiqualitative 73:9

send 197:11

senior 19:8 21:2

sense 33:17 50:11

54:23 55:1

61:1 145:2 208:4

208:8 210:7

292:7 316:4

318:16

sensitive

141:24 145:4

157:6 159:24

162:8 179:24

181:18 272:25

295:13

sensitivities

89:17 91:8

140:15 237:13

305:19

sensitivity 51:25

70:14 91:1

129:15 137:4

169:18 182:4

182:6 189:25

223:19 235:8

235:9 245:4

246:1 249:4

249:5 299:7

299:25 300:8

304:12 304:18

sent 32:25

116:9 193:16

295:25

sentiment 200:8

separate 103:23

162:18 220:14

262:12

separately 106:17

sequence 163:19
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 423

sequences 165:5

SER 24:10 49:7

49:25

serial 273:12

series 127:11

234:6 247:6

247:9 247:10

247:17 247:24

251:18 252:4

252:8 275:10

serious 83:23

114:14

seriousness 85:19

SERs 24:8 24:11

24:13 36:25

37:22 39:22 40:2

serve 200:14

served 7:12

Service 96:21

100:18 257:3

258:19 259:2

320:2 320:6

320:11 321:3

session 6:7 13:11

53:15 78:8

131:24 132:1

132:3 216:7

216:9 217:14

218:3 256:21

307:2 309:22

309:24

sessions 3:19

132:4

sets 75:13

153:8 161:17

165:6 166:1

175:23 176:8

176:15 176:20

179:2 179:4

180:2 182:5

182:6 182:13

183:5 190:9

190:17 215:22

223:23

setting 90:18

291:17

settings 80:20

166:16 245:23

248:7 248:20

263:25

set-up 148:3

150:4 162:8

164:2 191:18

192:14 192:17

193:25 194:5

194:14 194:23

194:24 196:24

236:6 291:23

293:23

seven 133:20

Seventeen 113:15

several 36:14

83:19 100:4

124:5 164:14

184:6 184:25

187:2 229:1

240:6 296:20

shaded 247:6

shale 147:14

147:24 152:17

shallow 168:11

shape 102:6 124:6

share 58:25

shared 60:6 92:19

123:15

sharing 59:4

308:10

sharp 289:15

shear 243:7

shed 260:13

shelf 316:7

she's 5:11

shift 158:19

shifted 258:17

shifts 272:25

shop 187:9

short 90:1

117:8 119:23

149:8 154:2

160:2 221:22

309:18 320:10

shortage 312:6

shorter 209:18

shortly 89:15

95:3 214:10

short-term 173:19

showed 26:8 81:14

81:19 82:14 91:3

100:6 128:4

162:10 184:15

189:6 189:6

189:9 215:14

240:2 251:5

287:25 304:13

showing 24:5

124:14 130:2

137:21 149:17

151:24 166:18

227:3 236:14

246:14 246:16

266:20

shown 172:16

209:16 281:5

286:17

shows 52:16 125:8

126:1 137:4

142:9 152:2

191:4 219:7

239:4 266:22

267:1 281:19

284:2 286:5

286:17 286:25
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 424

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314:4 314:5

shrub 187:14

shut-down 215:16

shuts 17:20

sides 290:24

Sierra 5:10

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sight 36:20

sigma 130:13

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sigmas 129:25

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sign 219:3

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SIL 27:14 33:25

37:15 37:17 46:4

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SILs 27:11

similar 18:3

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similarities

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similarly 218:9

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simple 90:20

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simpler 36:16

36:16 163:15

simplified 175:16

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simply 173:5

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simulate 69:9

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simulated

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simulating

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simulation 231:12

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simulations 84:20

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simulator 164:4
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SIP 13:10 18:4

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SIPS 134:24

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sit 22:3 275:2

site 43:7 44:6

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sited 123:17

sites 34:20

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sitting 30:18

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situation 36:9

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situations

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six 62:13 62:17

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sixth 238:5

size 55:24

58:18 68:13

187:3 236:12

skill 121:4

skills 274:13

skip 126:25

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skipped 248:6

slam 308:6

slamming 17:2

slew 158:20

slices 166:25

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slicing 151:2

slide 32:10

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slides 22:17

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slight 264:15

slightly 167:10

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slipped 131:8

slots 194:16

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slowing 267:11

slug 215:24

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smaller 16:20

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smallest 266:3

smart 247:20

SMCs 27:11

snapshot 195:2

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Snyder 91:10

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SO2 5:1 6:24

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SO6 242:12

social 296:23

software 163:4

187:19 266:16

Sold 217:12

sole 109:3

solely 12:17

solenoid 193:6

193:13

solicit 33:1

soliciting

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Solid 140:11

solidified 14:18

solution 53:13

118:19 169:24

solvable 156:9

solve 150:2

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solved 68:24

155:10

somebody 316:6

somehow 25:19

33:18

someone 71:22

sometime 21:21

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somewhat 32:6

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somewhere 54:7

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82:21 146:9

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sooner 17:5 17:14

sophisticated

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sophistication

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sorry 47:14 168:9

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sort 22:22

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sorted 177:18

sorts 64:10

so-to-speak 321:5

sound 36:17

77:2 232:20

239:22 274:3

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sounds 139:12

source 5:7 5:8

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sources 7:8

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source's 205:2

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south 128:14

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142:16 179:14

206:11 287:21

294:12 294:16

297:7 298:17

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southeast 44:17

51:15 178:14

southward 284:19

Southwest 178:15

southwestern

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space 85:12

87:9 103:11

249:20 251:8

253:24 317:20

317:21 318:3

318:6 318:11

spacing 152:13

span 198:2

spark 18:1

sparsity 100:7

spatial 44:2 77:5

106:3 138:5

140:5 141:3

141:17 144:4

152:3 152:9

154:6 238:20

249:12 249:18

250:11 253:24

268:17 269:2

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spawned 242:4

speak 29:10 58:13

115:10 134:1

202:3 205:15

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speakers 14:19

57:12 132:24

217:14 277:14

306:25 309:24

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speaking 4:7 58:9

94:11

speaks 74:4 76:7

spearheading

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special 26:23

66:20 319:5

specialized

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specialty 12:24

309:1 310:8

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speciation

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species 238:16

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specific 8:5

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specifically

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92:23 97:25 99:1

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specificity 7:9

specifics 104:19

speculating

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speed 70:17 70:19

200:23 201:13

246:8 246:13

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255:17 293:21

speeds 70:16
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spell 20:4

spend 36:22 41:12

56:1 155:24

spent 292:4

spike 105:3

289:15

spirit 60:20

106:5

splitting 244:1

278:20 311:5

spoke 151:7

spoken 3:16

sponsored 124:4

183:20 294:11

spot 32:20

56:15 56:22 83:3

spread 163:13

177:8

spreadsheet

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spring 44:18

112:12

Springfield

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145:3

squared 249:24

251:11 253:18

stability 74:5

77:11 91:4

stable 44:13

44:14 124:18

129:4 129:7

188:2 293:6

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stack 65:24 65:24

122:6 134:2

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stacks 142:10

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staff 29:16 64:14

64:16 159:8

165:22 277:6

stage 23:3

90:18 133:22

153:7 153:19

197:2

stake 58:17 62:18

62:18

stakeholder 58:16

101:7 259:22

stand 75:9

stand-alone 275:1

standard 7:6 7:13

8:2 18:14

18:15 18:18

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standing 94:5

standpoint 12:5

25:3 86:17

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stands 159:18

278:10 298:3

star 141:6 256:17

starch 155:19

start 36:11

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started 3:5

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134:16 156:5

258:8 279:1

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295:23 296:10

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starting 9:12

53:18 75:3

79:5 138:25

165:3 216:18

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231:14 235:4

285:5

starts 10:5 170:7

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start-up 215:16

stat 246:7 253:20

state 13:8

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51:22 57:7 61:18

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134:7 156:19

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stated 10:8

35:9 125:11

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statement 8:13

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172:2 199:18

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statements 40:1

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state-of-theart 72:21

states 18:6

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22:4 22:7 29:4

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55:5 58:22 63:17

74:7 80:17 80:25

133:19 135:9

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state's 66:14

States 262:17

static 74:13

station 166:12

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stations 147:18

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280:9

statistic

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statistical 197:6

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statistically

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statistics 164:15

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status 5:20

7:17 23:17 63:21

171:3 192:2

stay 132:2 272:23
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stayed 187:5

staying 4:4

steady 47:24

steam 67:20

steel 136:25

steep 166:20

step 37:14

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46:25 68:8 100:9

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steps 28:18

34:3 230:10

292:14 308:19

Steve 25:20

176:11 183:17

190:21 198:3

199:24 209:4

216:12 317:4

sticking 294:4

321:13

Stinger 214:17

stop 70:7 77:24

108:1 131:6

stopped 141:11

storage 187:9

story 51:9

117:8 126:25

straight 152:4

189:1 192:6

straightforward

33:9 45:4 49:4

107:15 182:22

strange 274:4

strategies 223:15

strategy 49:20

137:14

strength 300:6

stress 11:10

49:13 95:15

114:3 186:15

stressed 14:25

107:3

stressing 11:9

stretch 294:1

striking 289:23

stringency 104:10

172:2

stringent 40:4

40:5 65:5

strong 210:3

260:23 265:16

structure 78:9

215:9

structures 182:8

stuck 307:1

studied 279:6

studies 6:12 6:12

6:13 119:24

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124:23 173:9

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192:9 201:15

221:17 272:5

293:4 312:9

stuff 90:12

111:18 119:16

136:5 146:12

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156:8 157:17

158:13 226:23

240:21 243:7

272:6 296:1

sub 30:22

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319:7

subcommittees

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sub-grid 33:6

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55:7 235:25

235:25 236:4

239:17 293:12

subject 61:5 61:9

61:12 61:18 62:4

123:11

submit 70:13

106:10 113:10

132:20 212:1

212:2

submitted 212:16

submitting 12:18

sub-region 262:15

sub-regions

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subsequent

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subsequently 24:4

174:19

subset 178:8

178:9

subsets 14:1

176:7 178:14

substance 188:23

substantial

70:8 72:7 73:18

substantially

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substantive 16:13

subtle 60:12

subtracted 283:21

suburban 177:24
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 432

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sub-workgroup

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successful 156:18

successfully

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114:9

Sudbury 244:12

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suffice 63:2

sufficiency

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sufficient 7:20

11:25 18:19 55:6

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308:3 315:7

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sufficiently

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suggest 92:1

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116:11

suggested 111:8

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suggesting

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suggestion 309:1

suggestions

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suit 222:17

suitability 6:8

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225:19 261:3

270:8 308:21

suitable 88:2

221:3 277:8

306:4

suite 6:13 279:13

sulfate 44:17

271:4 271:9

sulfates 85:9

sulfur 215:25

216:4

sulphur 263:22

270:17 270:18

270:20

sum 34:14 38:23

summaries 80:24

137:5 137:6

summarize 12:5

22:10 189:16

291:19 295:1

summarized 75:2

summarizes 125:2

summarizing

307:16

summary 66:15

75:21 76:13

136:11 136:13

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146:14 182:21

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summer 44:15

44:18 112:12

115:22 147:12

198:15 211:4

297:22

summertime 258:16

sums 69:8 70:5

72:14 79:25 82:5

support 103:14

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156:21 173:10

220:2

supporting 228:12

supposed 296:19

sure 5:21 8:20

12:10 14:7 14:10

15:6 16:8

16:11 42:24

43:22 56:11

56:19 68:15

71:23 110:4

110:4 110:14

132:2 156:2

157:22 158:14

199:1 202:7

206:17 208:3

208:7 224:21

240:11 258:9

284:8 301:22

303:2

surface 187:13

245:20 266:19

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286:5

surge 160:23

surprise 167:14

surprising 155:18

301:9

surprisingly

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surrogacy 65:1

65:10

surrogate 23:15

24:17 24:25

27:18 61:14

175:4

surrounding 27:1

susceptible

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suspect 70:12

70:14 70:20

77:18

switch 49:2

switches 193:13

switching 230:5

Sydney 89:22
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Sydneys 305:8

synoptic 267:6

Sysco 249:6

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system 41:15

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systematic

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systems 191:10

224:23 227:25

228:7 228:19

229:1 230:2

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T

table 27:13 29:21

33:21 42:21

54:22 78:25 90:3

90:12 91:16

126:10 136:12

137:23 160:1

212:9

Tables 296:3

296:4 296:10

tackle 29:5 55:10

158:8

Tai 294:11

tail 10:20 11:3

12:23 177:1

177:1 178:6

238:23

tailored 305:6

take-home 265:9

taking 3:8

93:15 94:3

114:19 162:22

178:20 180:23

192:22 217:25

225:22 230:25

308:21

talk 7:4 14:22

20:3 26:8

34:23 40:14

42:15 44:24 47:6

47:21 49:5 52:18

55:22 57:3 57:24

59:20 60:16

67:17 92:13 94:7

123:21 132:8

132:9 142:13

145:12 145:13

145:25 147:10

159:13 159:23

171:16 176:11

183:19 183:20

184:13 190:22

191:16 191:17

191:19 196:13

197:23 214:7

226:13 226:18

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talked 6:15

25:4 28:7

35:10 36:2

43:9 49:2

49:10 54:8

102:22 136:4

141:21 184:14

198:3 210:13

222:14 225:7

228:2 228:16

230:7 234:9

244:18 275:7

277:1 279:24

292:21 294:7

295:17 295:19

296:1 307:4

talking 4:13 4:25

5:4 18:12 19:1

21:1 29:15 34:21

49:3 50:18 54:23

83:1 106:5 179:8

212:20 241:25

270:11 272:19

316:4

talks 41:4 128:10

128:10 158:20

208:10 219:13

226:12 226:19

tall 122:6

taller 209:17

tandem 163:20

170:8 183:22

tanks 187:9

TAPM 298:2 319:17

319:17

targeted 62:5

152:7

task 136:2 213:12

261:12 261:12

262:14 263:2

Taylor's 299:11

technical 13:22

14:16 28:15

29:13 50:20

59:21 60:2 86:17

90:8 184:8 213:4
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 434

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technically 59:13

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technique 34:19

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techniques 5:5

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technologies

203:23

technology

62:10 69:25

272:25

television 94:6

temperatures

186:11

templates 175:18

temporal 44:3

77:5 249:13

temporally 47:23

temporary 193:25

ten 24:10 37:25

38:8 58:4

58:22 65:7

66:1 73:19

113:13 121:20

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214:19 268:6

271:25 282:6

300:7 300:10

301:7

tend 129:3 203:7

tended 248:10

tendency 126:9

264:20

ten-minute 69:14

Tennessee

146:12 233:25

279:3

term 109:16

119:23 119:24

124:22 160:2

268:25 320:10

termed 69:17

terms 4:14 5:3

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277:6 282:19

291:20 299:9

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terrain 41:21

90:15 90:19

90:22 121:17

121:22 121:23

145:6 145:10

146:13 146:16

146:17 146:19

166:16 169:7

182:16 266:21
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 435

267:1 267:9

272:5

test 63:10

97:10 153:14

172:15 189:24

240:9 242:9

243:14 251:4

253:14 255:9

261:15 277:22

277:25 280:10

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tested 170:14

184:9 186:14

testing 105:7

171:4 171:15

176:9 179:1

182:4 196:12

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tests 75:2 129:12

173:19 181:19

189:14 240:24

241:9 245:4

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300:8 301:24

Texas 147:20

147:20 152:22

TEXAX 234:2

thank 19:16 82:23

86:6 87:11 87:13

93:23 94:4

116:21 131:18

138:13 168:9

171:20 171:22

183:13 183:18

190:19 190:21

190:24 206:3

209:3 214:15

214:16 217:20

257:1 257:2

262:6 306:24

321:8 321:12

321:17

thankful 124:2

Thankfully 97:5

thanks 41:12

77:25 93:18

93:19 226:8

226:8 240:4

258:6 293:25

306:22 311:19

321:9

That'll 11:16

themes 259:25

themselves

24:14 76:10

theorem 299:11

thereabouts 133:4

therefore 36:1

105:21 106:14

162:6 177:3

177:12 304:8

there'll 89:21

there's 6:7

11:1 15:10 16:10

16:15 20:24 21:1

24:18 26:6

33:3 33:4 35:9

36:2 39:7

40:19 41:23 42:6

42:7 42:14 42:16

47:18 52:18

52:20 53:12 54:1

54:7 55:17 56:17

63:16 64:11 66:8

66:10 66:11

66:19 69:13 76:3

78:3 78:8 79:6

80:12 84:15

85:10 86:3 86:11

109:5 109:25

118:7 120:20

122:6 122:10

124:19 124:20

124:25 125:1

125:5 126:25

127:14 129:23

133:18 135:24

141:3 144:1

146:20 147:14

155:8 156:21

157:7 168:18

170:12 172:14

179:11 184:13

184:16 185:6

185:13 186:2

186:4 186:7

187:3 187:8

187:11 188:8

188:24 189:10

189:20 193:5

194:14 195:21

196:4 197:10

201:6 209:6

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210:16 210:16

210:18 211:1

213:17 221:8

223:12 229:8

235:2 237:1

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246:14 249:12

265:18 271:2

282:5 285:23

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289:22 289:24

292:20 305:25

306:7 307:10

312:5 312:22

thermal 139:11

Thermo 192:15

They'd 242:17

they'll 7:1

159:11
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 436

they're 8:17 8:19

17:11 17:14

22:18 22:19

34:10 38:8

40:1 40:4 40:5

53:19 55:22

61:12 63:11

68:17 94:6

105:24 116:12

117:16 120:1

131:21 154:3

163:20 168:19

178:10 181:7

187:1 191:12

209:7 210:25

216:19 229:21

233:4 237:23

238:1 238:12

239:10 251:13

251:15 253:3

260:6 272:16

289:14 291:15

296:9 301:16

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they've 53:17

57:20 209:16

218:5 316:10

thinkings 196:22

third 13:21

34:6 38:16

97:6 97:15 112:3

139:8 148:15

161:2 161:24

233:8

Thirdly 102:9

thoroughly 57:1

92:2 185:6

277:22 277:24

thoughts 14:23

17:21 90:3 217:8

218:12

thousands 162:20

three-hour 100:23

threes 151:22

three-tiered 7:11

97:1

three-year

45:16 45:18

100:15

threshold 70:19

294:25 297:12

303:8

thresholds 187:22

276:18 294:15

294:18 297:15

303:7

threw 36:21

throughout

11:10 11:10

14:15 17:7 22:17

43:21 47:24

86:24 91:24

222:23 223:6

267:15 309:10

throw 50:2 130:22

throws 26:13

Thurman 91:10

132:9 133:1

136:11 198:19

199:2 199:5

199:9 199:12

thus 222:23

255:10 274:22

tie 205:1

tied 21:12 156:10

156:24

tier 32:9 32:14

32:21 47:17

47:21 47:24 48:8

48:15 53:9

53:9 53:10 53:11

69:8 78:21 78:22

83:14 83:21

83:21 92:13 97:6

97:9 97:13 97:14

97:16 97:19 98:5

98:20 101:24

102:1 111:6

111:8 111:11

112:1 112:1

112:4 113:14

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124:1 149:2

149:3 150:8

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153:23 154:1

157:8 172:4

172:6 172:12

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181:17 182:2

183:2 184:16

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192:6

tiered 97:5

101:21 111:7

297:9

tiers 32:10

34:1 34:2

92:12 97:4

97:9 111:25

tight 156:19

tighter 110:19

269:15

tile 237:3 238:5

tiles 237:2

238:10

till 93:21

timeline 22:5

timely 20:13 84:1

118:22

tinkering 194:4

Tino 183:25

tired 22:17

tissue 66:13

66:21
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 437

title 61:2

titration

285:22 288:16

290:12 291:4

292:10

today 4:11 4:15

4:15 7:16

11:10 15:14

16:12 20:3 20:21

22:10 23:6

26:3 60:7

76:12 138:9

138:24 171:23

179:9 184:10

190:25 226:13

236:15 278:2

291:22 291:25

306:25

tolerances 134:4

toluene 305:4

305:5

Tom 310:1

tome 240:23

tomorrow 6:7

11:11 22:10

216:11 256:12

256:22 307:3

321:11

tomorrow's 256:21

ton 69:13 300:21

tonight 244:15

tonnage 154:17

tonnes 298:25

298:25 300:6

tons 24:10

24:12 37:25 38:1

38:8 39:23

40:3 49:25 50:14

138:25 139:4

143:4 148:6

148:7 148:7

148:8 298:25

299:1 300:18

301:8 303:9

tool 43:9 52:23

53:1 83:20

90:1 202:3

205:13 205:16

225:3 225:8

228:2 228:10

231:23 232:3

232:10 232:21

232:23 279:24

280:6 294:21

295:20 297:7

303:21 306:6

tools 23:13 26:19

27:12 51:21 75:4

75:25 83:9 99:10

114:8 115:20

117:13 225:9

226:15 232:3

291:20 299:8

305:13 309:14

309:18 320:22

top 31:21

101:12 195:3

233:3 233:8

234:24 237:5

237:7 237:11

238:3 238:10

247:4 263:4

263:21 263:22

284:4

topic 20:3

topics 111:1

total 69:22

119:18 129:10

129:12 130:1

130:17 130:17

130:25 131:1

172:25 173:5

174:25 178:11

178:21 179:5

180:13 187:3

229:18 229:20

268:8

totally 56:10

117:14 304:22

touch 276:19

touched 228:18

233:3 233:11

toward 82:9

167:23

towards 100:9

109:8 189:23

233:4 234:15

264:15 305:7

316:19

tower 194:15

town 186:2 186:3

Tracer 120:3

225:18 228:22

229:2 229:3

229:13 240:9

240:24 241:13

241:24 242:9

243:1 243:5

243:14 243:18

243:19 249:8

253:8 254:5

254:12 255:5

255:9 255:18

256:7 286:6

313:1 313:9

313:12 313:17

314:3 315:1

315:4

Tracers 242:1

track 48:22 203:6

231:13 235:9

237:14 254:12

256:18 289:7

trade 12:9

13:25 58:11

trade-off 95:22
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 438

trading 28:5

230:21 232:25

traditional 77:20

165:8

traditionally

225:16

traffic 52:10

train 158:17

training 30:5

83:21 277:6

trajectory 284:19

trans-chemistry

220:10

transects

279:11 286:18

286:22 287:1

289:2

transformation

71:9 223:24

224:16

transit 314:7

transition

19:15 267:5

283:12

transitions

288:12

translate

122:19 205:9

228:4

translated 259:8

transparency

13:19

transport 5:16

8:15 27:2 53:4

85:8 87:8 218:19

219:7 220:10

222:11 223:12

223:17 223:25

224:12 224:18

227:13 227:17

227:22 228:19

233:5 240:9

241:4 241:6

241:7 241:22

252:19 261:15

262:8 311:22

311:23 318:9

travels 319:10

TRC 200:17

treat 106:8

225:21 241:7

treated 105:20

106:13

treating 102:13

102:21

treatment 7:8

33:14 54:9 54:10

55:8 99:25

101:19 102:9

104:4 215:18

235:25 236:4

239:17 293:10

293:13

treatments 292:16

treats 316:23

tremendous

14:18 22:7 168:3

trend 169:14

177:7 287:18

289:19 291:3

trends 288:10

290:6

tribal 22:12

101:4

tried 33:22

54:2 91:11

165:23 177:19

207:22 242:2

316:2 316:3

trigger 37:22

63:8

triggered 38:7

38:19 40:3 49:7

triggering

39:20 39:21

Trinity 90:11

true 54:2 243:6

truly 200:2

try 4:2 15:11

16:3 29:11

74:6 79:1 81:1

84:19 94:17

94:19 94:20

95:10 98:23

99:13 103:22

106:2 106:22

114:16 118:8

119:2 119:15

131:13 133:3

135:1 157:4

160:9 177:15

205:8 210:12

218:11 219:8

230:3 231:15

231:20 233:14

233:21 244:5

254:11 272:19

272:23 273:1

289:7 291:7

313:4 316:7

trying 13:17

17:18 18:15 21:5

40:7 51:18 54:24

72:5 84:18

87:9 104:20

110:11 133:16

133:16 158:8

165:21 185:9

185:15 190:7

214:1 221:12

225:23 227:10

228:21 229:22

260:7 269:24

300:24 318:13

318:15
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 439

TSD 132:16 132:22

turbine 146:2

146:4 146:10

146:17 146:20

182:16

turbulent

278:16 278:17

turn 16:7 77:25

118:25 132:25

136:9 171:18

183:16 197:21

226:6 309:22

turnabout 262:9

turned 255:6

turns 170:10

TVA 234:13 234:23

234:25 236:17

278:5 281:9

281:21 283:20

283:22 285:9

286:17 292:3

tweaking 12:20

twenty-four

100:23

twice 167:16

two-pronged

308:19

Tyler 4:7 21:8

21:21 22:5

29:2 94:9 171:23

172:9 203:11

203:17 204:10

206:14 212:24

215:12 217:3

217:22 217:23

226:9 228:18

230:7 241:15

257:5 259:13

272:19 310:8

316:4

Tyler's 20:22

226:11 317:12

type 8:6 8:18

9:25 13:3

13:18 17:23

26:11 33:5 33:10

40:23 44:18 47:7

51:2 51:20

52:6 54:18 61:20

82:7 87:11

93:1 93:6

94:21 110:25

118:23 148:3

148:25 149:19

149:25 154:22

159:17 161:9

162:8 193:22

195:20 196:16

196:21 197:18

203:18 204:3

204:16 204:23

212:6 213:8

215:17 220:18

222:5 222:18

224:2 227:23

232:23 272:17

316:5

types 12:9

15:23 18:17

18:20 31:7 52:10

64:21 80:23

86:13 86:13

88:24 89:25

90:25 91:8 91:11

93:3 103:24

104:8 104:21

105:1 105:3

105:24 106:8

107:9 112:25

115:12 115:14

117:15 117:16

118:6 118:12

157:13 171:8

203:24 219:10

219:17 225:8

226:15 227:4

227:6 227:13

227:20 227:25

228:7 230:2

230:4 230:11

231:18 232:22

236:2 237:17

239:19 262:5

270:8 293:14

308:17 309:1

309:14

typical 33:24

36:22 38:5 38:12

65:6 68:13

134:16 161:4

170:21 179:19

typically 73:12

122:12 122:14

123:3 246:11

258:24 273:11

273:15 274:12

274:12 275:17

276:7 311:23

typing 32:17

U

U.S 3:1 20:2

176:7 236:10

258:19 320:2

320:6

U.S.D.A 257:3

Uintah 258:12

262:20 262:22

262:23

ultimate 36:20

ultimately 109:20

117:10 176:1

223:6

ultra 235:23

unambiguous 90:21

unbiased 77:2

119:11 122:9

uncertainties
16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 440

127:22

uncertainty 120:5

122:16 123:5

unchanged 139:6

unclear 255:2

uncontrolled

136:17 143:4

143:13 143:21

144:5 144:15

144:24 144:25

uncorrelated

165:4

uncritical 107:6

under-predicted

127:18 169:13

underpredicting

167:10 189:3

190:12

underprediction

127:15 130:10

247:9 316:19

316:24

understand 5:22

23:5 27:4

42:25 45:25

49:18 51:19

53:20 55:15

78:17 88:24

95:15 97:12

108:9 110:23

113:17 113:18

116:2 118:19

119:13 204:4

204:19 205:8

215:14 221:11

229:23 231:15

237:25 261:7

261:9 266:10

268:20 279:15

280:25 296:1

319:1

understanding

15:16 16:10

16:13 57:10

79:13 96:9 98:22

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40:17 40:18
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117:11 117:18

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165:20 183:21

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16766-2 AIR QUALITY MODELS, 10TH CONFERENCE OF 03/14/2012 PAGE 447

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