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Kay McGovern & Associates

Suite 117, 314 West Millbrook Road C Raleigh, NC 27609-4380

(919) 870-1600 C FAX 870-1603 C (800) 255-7886

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

11th CONFERENCE ON AIR QUALITY MODELING

THURSDAY, AUGUST 13, 2015

ENVIRONMENTAL PROTECTION AGENCY

RESEARCH TRIANGLE PARK, NORTH CAROLINA

8:30 a.m.
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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A G E N D A

Opening of the Public George Bridgers, OAQPS 251

Hearing (Day 2)

Public Presentations and Comments

Virtual Facilities Beth Barfield, ERM for 253

NAAQS Study NAAQS Implementation

Coalition

Outstanding Issues for David Heinold, AECOM for 259

Modeling Low-Level AF&PA and American Wood

Emission Sources Council

Impacts of Measurement Zachery Emerson, NCASI 267

Uncertainty and Emissions for AF&PA

Variability on Modeled

Impacts

Use of EMVAP for Bob Paine, AECOM 277

Determining Permitted Carlos Szembek, ERM 281

Emission Rates for

Highly Variable Sources

Related to Appendix W 8.2

Building Downwash: Ron Petersen, CPP, Inc. 287

Problems, Solutions,

and Next Generation

Background Concentrations Sergio Guerra, CPP, Inc. 296

and the Need for a New

System to Update AERMOD

The Use of CAMD Data in David Long, AEP 309

Air Quality Modeling

Initial Feedback on Chris Rabideau, Chevron 317

Proposed Revisions to for API

Appendix W

Procedural Concerns Cindy Langworthy 326

Related to EPA's Hunton & Williams for

Regulations and Guidance UARG

on Air Quality Modeling
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A G E N D A

 (continued) 

Is AERSCREEN Always George Schewe 335

Greater than AERMOD Trinity Consultants

Preliminary Appendix W Jeff Bennett 343

Comments - Ozone Barr Engineering

Oral Comments

Bridget Lee, Sierra Club 352

Peter Guo, Apex TITAN 356

George Schewe, Trinity Consultants 358
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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1 F U R T H E R P R O C E E D I N G S 8:33 a.m.

2 Mr. Bridgers: So while everybody is taking

3 their seats, I'll go ahead and call the public hearing and

4 the conference back to order. I hope that everybody had a

5 pleasant evening. The weather last night was phenomenal. I

6 forget to mention before everybody broke for evening that

7 there was a meteor shower last night. I guess it is August. 

8 I did get home late, but I didn't look up. 

9 Nonetheless, thank you for coming back this

10 morning. I know we lost a few people but we have ten more

11 public presentations this morning, and I already know that we

12 have at least a couple of slide decks that have been

13 requested during the open forum session that will follow

14 right before lunch and then right after lunch. 

15 So I did want to go through--this is just some

16 real quick logistics. Most of you that were here yesterday,

17 this is old hat, but I just wanted to reiterate that this

18 conference is a public hearing. It's a public hearing both

19 in the context of Section 320 of the Clean Air Act, which

20 requires us to have triennial modeling conferences but it's

21 also a public hearing with respect to the proposed rulemaking

22 for the guideline--for the revisions to the Guideline on Air

23 Quality Model. 

24 So again, everyone that speaks today needs to

25 identify themselves and their affiliation. As such, I'm
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KAY McGOVERN & ASSOCIATES (919) 870-1600

Suite 117, 314 West Millbrook Road FAX 870-1603

Raleigh, North Carolina 27609-4306 (800) 255-7886

1 George Bridgers with the Air Quality Modeling Group here at

2 the USEPA. Everything that is said is going to be

3 transcribed, and that will become part of the record and

4 submitted to the docket. 

5 We will not allow any question and answering. And

6 anyone that has not requested a spot to speak, which will be

7 the first ten presentations, out the front door here Nan has

8 a sign-up sheet that you can put your name on, but I'll go

9 ahead and say that later in the after lunch period we'll have

10 a session that--not assigning myself to any religious

11 affiliation, but the Quakers, where if the spirit moves you,

12 you can come up and speak at the mic. All of our

13 presentations during the open forum, we will respect them the

14 same as we have our other speakers, and they will have up to

15 15 minutes, if they would like, to use that time. 

16 After the conference, if you would like to 

17 submit--I'm sure everybody that probably spoke will be

18 submitting final comments in writing to the docket. You

19 have--I think it's 74 days before we get to October 27. 

20 We have a brisk schedule. It's not ridiculous,

21 but we're going to do like we did yesterday. Each speaker

22 again has 15 minutes and maybe a few seconds here or there,

23 but we need to keep people on track, and so I just ask for

24 everybody to be respectful. 

25 And so we that, we'll actually be ahead of
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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1 schedule, and we'll go ahead and call Beth--is Beth here, she

2 is--to the podium. And while Beth is coming up, again, if

3 there are any questions or issues, find me and I will try to

4 resolve them. So without further ado, Beth, the forum is

5 yours. The podium is yours.

6 Ms. Barfield: My name is Beth Barfield. I'm

7 with Environmental Resources Management. We call it ERM. 

8 Today I am speaking on behalf of the National Ambient Air

9 Quality Standards Implementation Coalition. Last afternoon

10 the last speaker was Rob Kaufmann, and he gave you the

11 comments that were more policy oriented than the ones that

12 I'll be presenting today are the more technical oriented, the

13 ones that we're talking about, the specific comments to the

14 proposed revisions. 

15 Yesterday also Rob mentioned a song of Mighty

16 Mouse. And I was kind of surprised because we have further

17 at ERM shortened that acronym from NAAQS Implementation

18 Coalition to NIC. So I thought he was going to do Mickey

19 Mouse, you know, because we're N-I-C, and we're saying why. 

20 We like to see the revisions, and that is really the theme of

21 my presentation. Before I went into my singing, I should

22 have asked how to do this. Do you do this? Okay. There we

23 go.

24 Okay. The study objectives that we had when we

25 were preparing even--not even comments, but recommendations
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KAY McGOVERN & ASSOCIATES (919) 870-1600

Suite 117, 314 West Millbrook Road FAX 870-1603

Raleigh, North Carolina 27609-4306 (800) 255-7886

1 for what we would like to see for revisions for the Appendix

2 W, we looked at the challenges that the members of the NIC

3 were having when they were doing their air permitting and

4 their compliance demonstrations that were dependent on

5 dispersion modeling, and we wanted to identify the key

6 technical changes that we would like to see in the Appendix W

7 revisions. 

8 The--NIC, see there, that's what we're calling

9 it--is comprised of various trade organizations and

10 individual member companies who were having these challenges

11 in demonstrating their compliance when they were doing their

12 permitting and even just regular ambient compliance modeling

13 that would be required by the states. 

14 What we came up with, the three that I'm going to

15 focus on today, are the recommendations on how the background

16 concentration should be handled, also the suggestion that the

17 low wind beta options be changed to default instead of non18 default beta, and similarly, that the two refinements should

19 become default as opposed to non-default beta options.

20 What I'll do today is just present to you the

21 general approach that we took in preparing our study and then

22 how we develop the virtual sources and then the results that

23 we have that support the recommendations that we made. 

24 This is really again not so much a list of things

25 that I would hope that you could read during this
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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Raleigh, North Carolina 27609-4306 (800) 255-7886

1 presentation, but just to show you that there is a lot of

2 different guidance that has to be followed, and we would like

3 to see these things compiled in one Appendix W. It looks

4 like it's going to be. 

5 We began with three hypothetical types of

6 facilities. One was a gas fired electrical generating unit--

7 these are combined cycle and simple cycle units--also then a

8 gas refinery and some industrial manufacturing, just to look

9 at three different types of facilities. 

10 These were again virtual facilities. Nobody is

11 planning to build any of these. They were a compilation of

12 different units that are in different facilities throughout

13 the U.S. by the member companies. There was quite of a

14 negotiation process about what our facilities should look

15 like. 

16 So here with the electric generating unit, as I

17 mentioned, we have both combined and simple cycle. And then

18 the refinery we have all the process heaters and flares. We

19 tried to make all of the facilities look like they were

20 very--generally well controlled, not brand new but existing

21 units; and then a manufacturing facility. 

22 Similarly, we picked three different types of

23 location, ones with flat terrain, ones with complex terrain,

24 and one with rolling terrain. The reason that we took

25 specific locations is because we wanted to have actual
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Raleigh, North Carolina 27609-4306 (800) 255-7886

1 meteorology and ambient monitoring data that we could use in

2 our analysis. 

3 And again, none of those sites are really being

4 considered. They were empty sites. We chose them as empty

5 because we didn't want the background concentrations to

6 include something that was already there that we were going

7 to be modeling on top of. 

8 So there's our site in Louisiana. And you can see

9 the relative location of the site with the refinery, the

10 power plant, and the manufacturing location. And then here

11 is--the Montana site was our complex one. And in North

12 Carolina King's Mountain is out there.

13 The first comment that we wanted to make on the

14 changes that the EPA is proposing is from the 8.3.2 c.iii

15 that "For short-term standards, the diurnal or seasonal

16 patterns of the air quality monitoring data may differ

17 significantly from the patterns associated with the modeled

18 concentrations." 

19 And we found out that this does occur frequently. 

20 If you look at the table here, we have the averaging period

21 for PM2.5 and O2 and SO2. We presented the design value--and

22 this is for the North Carolina site--the design value if we

23 take just the annual numbers, and then also how when we

24 paired it with the modeling results, you can see that PM2.5

25 didn't go down much, but NO2 and SO2 really made a
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Raleigh, North Carolina 27609-4306 (800) 255-7886

1 significant difference. And in our cases, it was the

2 difference between demonstrating attainment and not.

3 The next one is the modification to the AERMOD

4 system. This has been discussed earlier, yesterday, and that

5 was on changing the low wind from a default--to a default

6 from the non-default beta options. And in our cases, we

7 found that the North Carolina--or all the results changed

8 more significantly in the fugitive emissions than in the

9 cumulative impact, so I've put both of those here. You can

10 see the difference. And with the annual average, similarly

11 we see more of the results are significantly impacted with

12 the fugitive emissions, not so much with the whole

13 cumulative. 

14 And I think, now, Bob talked too about the stacks

15 yesterday. And we found a similar difference between those. 

16 And this is again using the low wind option 2. We haven't

17 done this with 3.

18 The third is how the treatment of nitrogen dioxide

19 would be in. In the preamble it was mentioned that the ARM2

20 would be replaced. And we had discussions yesterday about

21 this in more detail. Rich Hamel went through that. The

22 difference--now, what we had used was the .3 instead of the

23 .5. We haven't redone it for those numbers. And what we

24 would like to see is this to be changed also to default so

25 that we don't need to spend the time and the money coming up
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KAY McGOVERN & ASSOCIATES (919) 870-1600

Suite 117, 314 West Millbrook Road FAX 870-1603

Raleigh, North Carolina 27609-4306 (800) 255-7886

1 with a site specific demonstration. 

2 This is the differences that we found. You can

3 see I listed them all for North Carolina, Montana, and

4 Louisiana with the Tier 1, 2, and 3. You can see with the--

5 even with the Tier 2 ARM2 that there was a significant

6 reduction in the proposed--in, sorry, the calculated

7 concentrations. 

8 So in summary, we would like to support the EPA's

9 revised changes on how background concentrations should be

10 determined. And by that we mean using the diurnal--seasonal

11 diurnal type of background calculations instead of a one

12 number design value. 

13 And that's because, as we showed, that there is a

14 low likelihood between the time and the place that the--or

15 the time, I should say, that the monitored value would be

16 peaking and the model value would also be peaking. 

17 We also support the designation of the low wind

18 speed options to default in both AERMET and in AERMOD. And

19 finally, we support also the change of the non-default beta

20 options for the NOx refinements to default options. And I

21 will take questions offline at another time. Thank you for

22 your attention.

23 Mr. Bridgers: Thank you, Beth. We're zooming

24 right along. If David--is David--oh, David is here, yes. 

25 All right, David.
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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1 Mr. Heinold: Good morning, everyone. My

2 name is David Heinold. I'm an air quality meteorologist with

3 AECOM. And I'm speaking on the behalf of the American Forest

4 & Paper Association and the American Wood Council. 

5 This morning what I'd like to talk about are some

6 issues that are out there that have been out there kind of

7 forever for low level emission sources. When we mean low

8 level emission sources we mean things like fugitive dust,

9 emissions that are essentially non-buoyant from low rise

10 buildings that are very common to industry throughout the

11 U.S.

12 So one of the characteristics of these types of

13 sources that make them particularly problematical in terms of

14 modeling, well, location of these sources relative to what

15 are--have been considered historically off-site receptors is

16 often very close. 

17 In other words, some of these are smaller

18 facilities that are--that are nearby fence lines so that if

19 you have emissions close to the ground, you could easily have

20 model concentrations that are disproportionately high in

21 reference to the annualized emissions from those sources. So

22 because of that modeling those sources and characterizing

23 them are extremely important. They are also many times

24 emitted in--among the flow obstacles like buildings and tanks

25 and piles and whatnot. Again it makes them characterizing
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KAY McGOVERN & ASSOCIATES (919) 870-1600

Suite 117, 314 West Millbrook Road FAX 870-1603

Raleigh, North Carolina 27609-4306 (800) 255-7886

1 very important.

2 And more recently, because we now have tighter

3 NAAQS--that is the background concentrations are getting

4 perilously close to the ambient air quality standards,

5 especially for example for annual PM2.5--if we model them with

6 excessive conservatism, they're often likely to demonstrate a

7 model that's not in compliance when there really isn't an

8 issue, especially if there isn't an issue really where

9 legitimate off-site receptors should be located. 

10 So based on that, there are essentially two issues

11 that I'm going to talk about today among all of these. The

12 first is the identification of what has been characteristi13 cally identified as fence line receptors as a standard way of

14 establishing what receptors should need to be modeled. 

15 And the second is taking a look at issues that

16 have been brought up at previous modeling conferences

17 regarding PM10 and PM2.5, and the rate that they are

18 transported off site and the physical dynamics that affect

19 those concentrations as they go off site. 

20 So placement of near-field receptors; so the idea

21 that we're discussing here is in 2014 EPA came up with risk

22 assessment guidance that has been essentially long-standing

23 but clarified, and it talked about how to characterize 

24 off-site risk, and in that they stated that one should be

25 evaluating exposure where exposure can actually occur,
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1 essentially put in a nutshell. 

2 And so if we take that concept and address it in

3 terms of the NAAQS, we should consider for the particular

4 NAAQS, say it's an annual average, can exposure occur for an

5 appreciable portion of that. Is it possible? And if it's

6 not, is that a legitimate receptor? 

7 And for the short-term NAAQS, say the 24 hour PM2.5

8 or the NO2 or the SO2, one hour, could a person be at that

9 location 98 percent or more than 2 percent of the days for

10 PM2.5? Could they be in that location more than 1 percent of

11 the days in terms of SO2? If it's impossible for someone to

12 be in those locations, those should not be legitimate

13 receptors.

14 So the examples that we'll be talking about will

15 be things like railroad right-of-ways, controlled access

16 highways, public roadways, inaccessible terrain, perhaps

17 rivers with rapids where it would be difficult for someone to

18 be there located. And we're suggesting that this be

19 evaluated on a case by case basis. 

20 So what we're now seeing is a hypothetical example

21 of a facility along a river that is--also has a restricted

22 access highway, say an interstate, to its north and to its

23 east it has a railroad property, a railroad right-of-way. 

24 And what we display here with the yellow stars is what would

25 typically be the set of receptors that would be used for
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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1 modeling. And each of those red dots essentially indicate a

2 low level source of emissions so that one would expect

3 concentrations to decrease rapidly with distance.

4 So what we're showing here is a different way of

5 looking at the receptors depending on the particular NAAQS. 

6 If we look on the river side, we have an annual PM2.5 NAAQS of

7 12 micrograms per cubic meter. And at the fenceline for this

8 hypothetical example, we had a concentration of 14 micrograms

9 per cubic meter, which is above the NAAQS and across the

10 river it's low, similar relief on the northbound side. 

11 And the legitimacy of putting that on the other

12 side of the river is that is someone--could someone possibly

13 be on that river for an annual period. Now, if there was

14 a--if there--if boats were docked there along that river,

15 then clearly that would be a possible receptor, but for

16 instances where it's not a river that's really accessible or

17 used for that purpose, then it's not legitimate really to put

18 receptors along the river. 

19 The limited access highway, we know there's laws

20 in place that don't allow people to loiter on the highway for

21 nearly a large part of 24 hours, so those receptors perhaps

22 should be on the north side of that. And even for the

23 railroad property, we know we're not supposed to be there at

24 all because you're going to be hit by trains and for walking

25 the tracks, there are other laws regarding--you know,
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1 independent of the Clean Air Act or any other kind of

2 environmental act that prevents people from being there. 

3 And we're not necessarily talking about relative

4 risk but just the fact that they're breaking the law being

5 there. And should that--is there any legitimacy to putting

6 receptors on the tracks, and we maintain that there is not

7 legitimacy.

8 So now I'd like to go to a different subject

9 matter, the second topic that we want to discuss, is that

10 fugitive dust studies have shown over the years there are

11 mechanisms when you have high concentrations of emissions

12 from a fugitive dust source, say like from a haul road, that

13 would affect the transport off site. 

14 And what we're discussing here, not to go into

15 details, but this was previously discussed by Dr. Chatten

16 Cowherd at the EPA's 10th Modeling Conference, and

17 essentially there are three aspects of this that we want to

18 reiterate. 

19 Number one, roadway dust from industrial sources

20 is different than standard highways in that you would have

21 individual sources that are moving along a line rather than a

22 whole string of vehicles that would actually represent a line

23 source, which is the way we model it in AERMOD. 

24 And so there are refinements that could be applied

25 that account for that transient nature. And the indications
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KAY McGOVERN & ASSOCIATES (919) 870-1600

Suite 117, 314 West Millbrook Road FAX 870-1603

Raleigh, North Carolina 27609-4306 (800) 255-7886

1 of previous studies have indicated that within the 100 meters

2 you could have a sizeable decrease in the modeled concen3 tration due to the lateral dispersion and the long wind

4 dispersion of a moving source. 

5 And the two effects that affect off site transport

6 are the agglomeration of the particles when they're in very

7 high concentration so that the PM2.5 will essentially stick

8 together and become PM10 and some of the PM10 size or the

9 coarse particles would agglomerate to some extent and get out

10 of the PM10 range. And that's been shown to have an effect

11 for very high concentration sources which are associated with

12 fugitive dust.

13 And if the sources--if there's buildings and

14 vegetation near the sources, it's also been shown that the

15 vegetation especially has a large effect on scavenging the

16 dust as it begins to move off site, and that can be a very

17 large effect. And the rate of scavenging is much greater

18 than it would be say estimated by just using the standard

19 deposition algorithms in ISC, which dates me--in AERMOD,

20 which uses the same methodology as ISC used, by the way. 

21 Okay, so just some pictures of what we're looking

22 at in terms of intermittency. So on the left we see a

23 representation of a roadway segment and how it would be

24 modeled in AERMOD as a series of line sources. And here we

25 see that would represent something like the roadway we see
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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1 that's below it where you have, you know, a string of cars or

2 vehicles in a row so that a line source fairly represents it. 

3 And on the right side we see something more like a

4 haul road in this case. It looks like some kind of a dirt

5 road in a rural area where you have very intermittent

6 traffic, and so it's really a traveling point source, so more

7 of the representations say that FAA uses in modeling aircraft

8 where it looks at individual moving sources might be more

9 appropriate than using a line source in AERMOD.

10 For the second effect, this is an illustration

11 of--we can see vehicles had traveled along these roadways and

12 we can see the residual dust and we can see the transport as

13 it approaches the vegetation. But we also can note that the

14 vegetation--there's the ability to scavenge out much of those

15 emissions. And if one has a roadway that is--where there's

16 quite a bit of vegetation surrounding it on one side or the

17 other, we should be able to account for that in the model so

18 we're not indicating off-site transport of the PM2.5 and the

19 PM10 and account for that degradation.

20 So where are we now? Well, since the 10th

21 Modeling Conference, AISI and Dr. Cowherd have presented the

22 concepts to OAQPS and there have been further discussions at

23 annual workshops. And EPA has indicated at that time they

24 aren't really interested or think it's appropriate to

25 actually change the AERMOD because that's--in other words, if
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1 we want to evaluate these effects we should look externally

2 to AERMOD to figure out how to perhaps characterize the

3 emissions in a more exact way that would account for these

4 types of effects. So an industry group is currently--

5 presently investigating the feasibility of incorporating

6 these concepts into refined modeling procedures for fugitive

7 dust sources.

8 So what's going on right now is we have current

9 activities to review the literature and the basis for these

10 theoretical studies and see what types of degradation factors

11 are appropriate. We're looking at the characterization of

12 the fugitive dust emission reduction factors, that is what

13 parameters are important to take into account in calculating

14 those factors, and then also developing a conceptual design

15 of how they could be implemented in AERMOD into either pre or

16 postprocesses that wouldn't affect the dispersion calculated

17 in AERMOD, but it would essentially accomplish a correction

18 to the emission rates that are being simulated. 

19 So possible future activities, then, would be to

20 design and implement the algorithms, identify existing field

21 studies that might further support the use of the algorithms,

22 and if deemed necessary perhaps develop more field data.

23 So what are we talking about in terms of the

24 requests based on these types of effects? Well, essentially

25 we suggest that the Appendix W discussion regarding receptors
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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1 gives some--a lot of the users and regulatory authorities to

2 consider the potential for public presence either by physical

3 barriers or by other regulatory or laws that prevent exposure

4 in critical cases where the receptors should not legitimately

5 be placed, and perhaps at a minimum at least Appendix W

6 should provide some indication that local regulators in

7 regions should have the ability to kind of be free thinkers,

8 not be lockstepped.

9 As was noticed previously in the placement of

10 receptors by EPA when they said we don't want to use the

11 puzzle book anymore as the way to do things necessarily, we

12 would suggest the same thought process should go on in the

13 placement of near-field receptors. 

14 We'd also suggest in the discussion in Appendix W

15 regarding fugitive dust emissions that, again, flexibility be

16 given to the characterization of the sources to account for

17 factors that are not addressed directly in the model such as

18 discussed here. And that is the end of my remarks. Thank

19 you.

20 Mr. Bridgers: Thank you, David. So next up

21 we have Zach from NCASI, and I'll let you announce all your

22 coauthors.

23 Mr. Emerson: Hi. I am Zach Emerson from the

24 National Council for Air and Stream Improvement for the pulp

25 and paper industry. We call ourselves NCASI. Coauthors on
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KAY McGOVERN & ASSOCIATES (919) 870-1600

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1 this talk are Tim Hunt with AF&PA and Dave Heinold, who just

2 spoke. 

3 I'm going to talk a bit today about what are some

4 of the impacts of data handling on modeled impacts. I'm not

5 a modeler. I work in emissions data and emissions measure6 ments. But what we see is that a lot of times as modelers

7 pick up an inventory or pick up emissions data, they don't

8 consider some of the underlying uncertainty that went into

9 that data when evaluating what the impacts are. So hopefully

10 what I'll talk about today can be useful going forward.

11 I'll talk about some measurement uncertainty and

12 what it is and what it means, and then a specific example of

13 measurement uncertainty due to measurements that are really

14 close to zero, really close to the detection limit. The

15 example I'll use will be PM2.5 emissions from paper machines,

16 and then we'll look at some of the modeled impacts of

17 different decisions on how that data is handled. I'll talk

18 for a second about emission variability at the end, but I

19 think the next speaker will hit that more.

20 So with measurement uncertainty, measurement--

21 obviously when you take a measurement, you've got some

22 uncertainty in what the actual number is. So if you look at

23 measurement at some arbitrary level here--we'll say that's at

24 the practical quantitation limit, PQL--it will have an

25 uncertainty that's that dotted line. 
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1 It's a Gaussian line really, and really the error

2 in that measurement is such that you've got 99 percent

3 certainty. If you've got a number at the PQL or a number--a

4 result at that number, you've got a 99 percent confidence

5 that it's under that curve. Each method will have its own

6 curve really, and really every test event will have its own

7 curve based on how confident you are in the result. 

8 You can see as you move this curve closer and

9 closer to the blank level--in this case, we'll call the blank

10 level zero--then you'll reach a point where this curve will

11 overlap with the zero result. The point at which that curve

12 exactly hits zero, that's actually what the method detection

13 limit is. That's the point at which you have a 99 percent

14 confidence that your number is statistically different from

15 zero. 

16 And this is really the minimum number at which you

17 even know that a pollutant is there. If you're doing a

18 measurement for PM and you're below the method detection

19 limit, then you have no confidence that PM is actually even

20 there.

21 The PQL is another statistically defined term. 

22 That's the limit below which you have any confidence in that

23 value as a number. Values between the MDL and the PQL, you

24 know it's there or you have 99 percent confidence that the

25 material is there, but you don't have 99 percent confidence
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1 in the actual number itself. You just know it's there. And

2 these are all kind of classically statistically defined. 

3 So this matters when you've got low pollutant

4 level concentrations. You could see going back that there's

5 a spread in that curve, and if you're closer to the lower

6 level, then that spread--the spread is usually a constant

7 number, then it's a high percentage of your actual response. 

8 So if the level is close to the detection limit, your

9 uncertainty may be 50 percent or higher of your actual

10 measurement. 

11 So as I said, data below the MDL, you can't even

12 say that the pollutant is there. Now, when you're dealing

13 with PM, if you measure long enough, you'll probably measure

14 something. You can always get a number if you're measuring a

15 filter or measuring the goo at the bottom of the beaker.

16 So the example that we'll talk about are paper

17 machines. And these are processes that are usually located

18 inside of a building with a lot of different vents. The

19 vents have high flow rates with very, very low concen20 trations. They're also usually relatively low-- they're low

21 relative to boiler stacks, obviously. And they have a high

22 flow rate, low concentration. They're usually relatively

23 cold. So they can have a pretty high impact on the model

24 results. 

25 Although this kind of analysis really has
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1 applications for a lot of different kind of sources, a tank

2 farm or some other stuff, anything where the emissions levels

3 are thought to be low, and the actual emission source is low

4 or close to the fence line, then it'll--this will matter.

5 Here's some example test data. I hope that you

6 can tell from the colors. So the orange test data, these are

7 all measurements on different vents on a paper machine. In

8 this case, there's eight vents although the machine itself

9 probably had more, and they ran for a number of hours, and

10 this is the collected mass at the end of that run. 

11 So the orange results are all cases where the

12 analyzed mass at the end of the run--this is PM2.5. I didn't

13 say that. All the measured mass at the end of the run was

14 less than the method detection limit as defined by EPA. And

15 the other results are all cases where the mass was greater

16 than the detection limit, but still less than the PQL. 

17 So you can see that for these examples there's one

18 that there's not a lot of mass collected at all and most of

19 it is less than the detection limit. This is also our--kind

20 of our response because a lot of times when we bring these

21 issues to a modeler, they say, well, get better data. Well,

22 these are cases where we went 8 hours, 4 hours, 16 hours on a

23 paper machine. And this is--it's pretty good data. We

24 just--there's nothing there. 

25 So then when you roll all these results up--these
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1 are all vents on a--say on a single paper machine. When you

2 roll all these results up, you'll get an emission factor for

3 the machine, which then you figure up what the emission rate

4 is. In this case, we'll look at tons per year. Let's see if

5 I can move that. Okay.

6 So in this case, we took the results from--this

7 says a small paper machine and a large paper machine. Really

8 that's a small--that's a machine at a small facility and

9 machines at a large facility. The large facility--this is

10 probably four machines here with an annual production or a

11 daily production rate of about 1800 tons per day of either

12 tissue or linerboard. The small facility would be about 400,

13 I think is the number. 

14 So look at, in this case, this tissue and

15 linerboard. So I'll go ahead and direct you for the tissue

16 machine. If you--this is the impact of different data

17 handling procedures. If you look at the previous data on the

18 previous slide and everywhere that those result less than the

19 MDL and use the zero, the annual production rate--or annual

20 emission rate is PM2.5 or 1.4 tons per year. 

21 For--if you use at the measurement detection

22 level, which a lot of regulators will tell you would be what

23 you should do, you would get about 30 tons per year. If you

24 use the detected mass--that is, you use the actual number

25 that's here and that's probably your tester, that's probably
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1 what they'll do if they give you the result--you'll get about

2 18 tons per year. 

3 You can see there's a big spread in the difference

4 here for tissue. It's a lot more dramatic for tissue than

5 for linerboard, although it's still--this is a pretty big

6 difference. If you look between 2.4 and 3.6, that's still a

7 50 percent difference relative to the data handled with MDL

8 equal to zero.

9 Now, for the large machine, because really all

10 we've done is scaled up the emission--or scaled up the

11 emission rate based on the production, the impact is about

12 the same. You go from about 6 tons per year to 140 tons per

13 year for the machines at a large facility for a tissue mill. 

14 So why does this matter? Okay. So for--a

15 facility, a paper mill, has a lot of different processes. 

16 It's got the power to run the unit. It's got the processes

17 to make pulp, to reclaim the chemicals in the process, and

18 then to actually make the paper. 

19 The actual emissions associated with--this is a--

20 these are for the tissue mill. To actually make the fiber or

21 to--I mean, excuse me, to make the tissue, it's about 8

22 percent of the total emissions for the facility with the

23 balance being 92. That comes from power, recovery, and other

24 processes. 

25 When you put these into a model facility, and this
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1 was done by Dave Heinold, because of the--one, because of

2 where the machines are and because they're low, then they can

3 have a pretty big impact on what the actual model results

4 are. 

5 So the design value concentration--this is the

6 annual 2.5 impact--I've lost my cursor--due to the machines

7 is about 60 percent. So it's a big difference between 8 and

8 60. And one, this is due to the fact that these are usually

9 close to the fence line and they're pretty low.

10 So why does this matter? So we've got--these are

11 our model design concentrations for a variety of facilities,

12 large and small tissue and linerboard. The purple bars in

13 this graph are the rest of the facility. This is the fence

14 line impact for annual 2.5 due to the rest of the facility,

15 and then the bars--the blue, red, and green bars are the--due

16 to the paper machine--or due to tissue or paper machines

17 under different handling procedures. 

18 So the blue bars, if you just took the MDL, used

19 it as zero, or took all the data that's less than the MDL and

20 used it as zero, you can see that there's a big difference

21 between the blue and the green bar. I'll point out that this

22 is--you go from about 1 to 20. That's bigger than the actual

23 standard. So your uncertainty in the impact is bigger than

24 the standard. 

25 So even for a small tissue mill, the impact is
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1 between less than 1 to about 21⁄2. If you've only got 2 to

2 begin with between the background and the modeled impact,

3 then that will be important. And the linerboard, as I said,

4 is--the impact is smaller, although still significant.

5 Now, EPA released guidance in 2011--this was a

6 memo written by Westlin and Merrill--taking data from the

7 Boiler MACT exercise to figure out what the detection limits

8 are, and in that process outlined that you shouldn't use data

9 that's less than the MDL for anything, really for any

10 regulatory action, so we think it's appropriate to kind of

11 exclude it on this basis.

12 I'll talk just a second about emissions

13 variability. I think the next speaker will hit it more. I

14 just want to point out what emissions variability actually is

15 when you look at it. When you model at maximum concen16 tration--this is CEMS data. This is hourly SO2 CEMS data for

17 recovery boilers. It's actually two recovery boilers ducted

18 to the same stack, so really the variability should smooth

19 out a little bit. 

20 The permitted value for this stack is 800 pounds

21 per hour. So the max here that they ever saw was about 800

22 pounds per hour, but the mean was 80. The median was 7. So

23 you can see it's a big impact if you take that permitted

24 value. Even if you take the 99th percentile value, that's

25 still one and a half times--or the max is still one and a
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1 half times the 99th percentile value. The 99th percentile

2 value is one and a half times the 95th percentile value. 

3 So we think it's still very conservative and very

4 protective to use the 99th percentile or 95th percentile

5 emission rate instead of modeling permitted values. And

6 that's not modeling actual. That's still modeling a very

7 high rate relative to where they actually run, but it's just

8 a little more reasonable when you put that into the model.

9 So some of our conclusions, you can't ignore the

10 uncertainty and measurements. You can't take that number as

11 gospel and pass it forward when you're doing these modeling

12 exercises. As Bob said, the models themselves have an

13 uncertainty or assumed uncertainty of 10 to 40 percent, and

14 the measurements probably have at least that. And if you're

15 up against the standard, if you're one over the standard, you

16 really have no confidence that you're actually over or under

17 the standard. 

18 So our recommendations are that the update to

19 Appendix W should include some guidance on how to handle data

20 or the AERMOD implementation guidance should include that,

21 and the guidance should provide some flexibility to

22 regulators and the modelers in how they handle these data,

23 especially for measurements that are really low. We think

24 that those measurements should be excluded under previous EPA

25 guidance; and also some guidance on how to handle sources
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1 that are either intermittent or highly variable. And that's

2 all I've got. 

3 Mr. Bridgers: Thanks, Zach. To that end,

4 yesterday, I was always wondering why we were running ahead

5 of schedule. A few of you heard me make the joke that it was

6 because we weren't having Q and A. That was the odd thing. 

7 We always have that. 

8 But the other thing that was oddly different

9 yesterday is there was no clapping. So that was also just

10 unusual; I guess formal public hearing, or at least all the

11 public hearings I go to are pretty drab. No offense to any

12 of the speakers, especially the EPA speakers from yesterday

13 morning.

14 Okay. Bob, are you really on the schedule again? 

15 Is Carlos--are you presenting or is Carlos presenting?

16 Mr. Paine: We're both presenting.

17 Mr. Bridgers: A tag team; okay, awesome. 

18 Mr. Paine: Tag team.

19 Mr. Bridgers: So AECOM is going to offer some

20 comments.

21 Mr. Paine: And ERM.

22 Mr. Bridgers: And ERM, excuse me.

23 Mr. Paine: Okay. I'm back, Robert Paine

24 from AECOM, but I'm going to have Carlos do most of the talk. 

25 I'm going to--we're going to talk about EMVAP, the Emissions
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1 Variability Processor, and an application, sort of a hypo2 thetical application of that tool, basically what this slide

3 says. 

4 Especially with very short term ambient standards,

5 the issue of very short term emission rates comes into play,

6 and that's what sort of made us design this tool because we

7 foresaw this issue back even before the 2010 ambient

8 standards were promulgated. 

9 So intermittent sources do present modeling

10 challenges, and obviously if you assume a peak emission rate

11 all the time, we know that you will overpredict the concen12 trations of a model. Now, the Technical Assistance Document

13 for modeling does provide that for existing sources actual

14 emission rates should be modeled. 

15 And that reminds me that in the proposal for

16 modeling off-site sources, there are three factors in the

17 proposal, and only one of them has been changed from

18 allowable to actual. They all should be made actual. There

19 should not be the word "allowable" in that table for

20 background sources. So that should be fixed in the final

21 proposal, or in the final rulemaking.

22 But now let's go back to EMVAP. We're going to

23 show how an actual distribution of emission rates can be

24 turned into a way to model the variability of emissions in a

25 model. Here's an example of a time series or 8760 hours of
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1 an emission source. And you can see that the emissions can

2 go up to over 130 grams per second on this graph. 

3 We turn that into a cumulative frequency

4 distribution in the next graph, and this is what we use as a

5 tool to provide input to our model because we basically put

6 on several boxes, each with a discrete amount of frequency. 

7 What you can do then is you can model each of those boxes

8 separately in a model and put them on a shelf, put them on--

9 and then access those concentrations in a random number

10 generator processor called EMVAP. 

11 So the basic--the basic approach is to

12 characterize this emission variability based on actual data

13 or data from a similar facility and then use that as a tool

14 to select the concentration in your processor. And notice

15 these boxes are such that there's room on top of these

16 individual bars so there's some conservatism in the EMVAP

17 processor, also the fact that we--if we randomly select the

18 high emissions. 

19 Usually high emissions are clustered, and with the

20 form of the standard being, you know, the highest hour of a

21 day, we tend to spread out those high days--those high hours

22 over several days in the EMVAP processor. So the

23 conservatism was both in the characterization of the empty

24 space above those emissions in each box and also the

25 clustering.
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1 So EMVAP is--you may have heard from other

2 presentations is a Monte Carlo model that randomly takes

3 those boxes after you run the model for each of those

4 categories and puts the other simulated annualizations of the

5 emission--of the concentrations at all the receptors you'd

6 want to model and then applies the appropriate summary

7 statistics for the one hour SO2 or NO2 standards. You can

8 also take sources that do not vary as separate runs of AERMOD

9 and add those together in EMVAP. 

10 You can also--let's go back to this slide on

11 the--if you model the actual emissions and then you compare

12 the results to modeling EMVAP, you can determine what is the

13 percentile of all of your results that would be conservative. 

14 And it turns out that the 50th percentile is often satisfying

15 that requirement. 

16 So for example if you ran 1,000 simulations,

17 annual simulations, you might get a maximum daily one hour

18 result that varies according to this simulation up to 1,000. 

19 If you then do a cumulative distribution, we often find that

20 the 50th percentile is conservative relative to modeling

21 actual hourly emissions, and that would be the result you

22 would pick from the EMVAP simulation to determine the design

23 concentration.

24 The available information on EMVAP is freely

25 available from the EPRI web site. We've seen this link in
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1 several applications. You can also get SCICHEM for example

2 from this or the distance debug AERMOD model. We presented

3 this last year in much more detail at the modeling workshop,

4 and there are peer reviewed journal articles available in the

5 December 2014 issues of both the Environmental Manager and

6 the Journal. I'm going to then provide Carlos for the rest

7 of this presentation.

8 Mr. Szembek: Hello. I am Carlos Szembek. 

9 I'm with ERM in the Boston office. And so I'm going to sort

10 of talk about a different, an alternative, way of using EMVAP

11 from this article that was discussed in the Journal for using

12 the--for using permitted emission rates to achieve a permit.

13 So just a little history. In the past, EPA

14 guidance has been to have--if you have an averaging time for

15 an emission rate, it should be the same as the averaging time

16 of the applicable NAAQS that you're applying it to. Hence

17 the initial approach with the one hour standard was to--for

18 an emission rate that would be also one hour. 

19 It was put forward by EPA to have this idea of a

20 critical value, and this critical value would be an hourly

21 emission rate that would be--that would show attainment with

22 the NAAQS. But there is a complication in all of this. We

23 have--how do you actually use a one hour emission rate that

24 would actually handle emission--highly variable emission

25 rates? 
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1 So there were some caveats placed. And EPA

2 expected it's possible for the states to develop control

3 strategies that would account for the variability in one hour

4 emission rates. And what was put forth was that this could

5 be accomplished by actually having averaging times that are

6 longer than one hour but still demonstrate the attainment of

7 the one hour NAAQS.

8 Okay. So I'll read some of these. EPA expects

9 that any emission limits with an averaging time longer than

10 one hour might need to be less than the critical value to

11 compensate for the loss of stringency inherent in applying

12 the longer term averaging limit. 

13 In terms of variability becoming zero, so constant

14 emissions, the long term average would exceed--would equal

15 the critical value. 

16 If the frequent emissions above the critical value

17 are sufficiently sporadic, they will have little effect on

18 the longer term compliant emission rate. 

19 But there's still a challenge, so how to demon20 strate that with an hourly emission variability the longer

21 term emission rate is still protective of the NAAQS. 

22 EPA has this example for dealing with that. So it

23 seems the variable patterns after the control measures would

24 still be the same. You would first actually calculate what

25 that critical value was, so in this example, 600 pounds per
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1 hour. 

2 You would then analyze your emission distribution

3 and find the 99th percentile of your one hour emissions, in

4 this case 800 pounds per hour. You would then obtain the

5 percentage of the--it should be 30 day rolling average

6 emission rates, and it would be 720 pounds per hour. 

7 You would then find the ratio of those two

8 numbers, in this case .9, and multiply--use that to scale the

9 critical value to 540 pounds per hour, and that would be a 30

10 day rolling average limit. 

11 So EMVAP--the EMVAP approach is an alternative to

12 this EPA approach that actually provides additional

13 confidence in protecting the NAAQS because it actually is a

14 modeling demonstration and it encompasses by design the

15 emission variability.

16 So here I'm going to discuss quickly the stepped

17 process of how to apply EMVAP. Some of these I'll actually

18 go through rather quickly and you can kind of review the

19 nuances in the slides later. So--but it's a four step

20 process. 

21 So first you would actually find the critical

22 value. Then you would--you would analyze your facility's

23 emission distribution that will be used in EMVAP and then run

24 AERMOD to generate inputs into EMVAP, and then finally you're

25 ready to run EMVAP to find out what your compliant emission
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1 rate would be. 

2 Okay. So I'm not going to run through all the

3 details here, but this is one way of obtaining the critical

4 value, so this would be a straightforward AERMOD run with a

5 constant emission rate that would show compliance with the

6 NAAQS. 

7 Then you would look at your emission variability. 

8 So in this case, you know, you can see it's a highly variable

9 emission rate. And, you know, here we've marked the long

10 term average of 97.7 grams per second. And it's worth noting

11 that here actually the peak emission rate is more than twice

12 the long term average. 

13 You would then--as Bob showed, you would organize

14 those in a cumulative distribution. And here we actually did

15 more gate points to establish the bins that would be run on

16 these boxed emissions. We've also marked where the critical

17 value is in relation to these emission rates.

18 Then step 3, and you would actually now take each

19 of those bins that were generated here and have a separate

20 AERMOD run that you would then run at just 1 gram per second

21 and would later be scaled up in EMVAP. 

22 And then run--here you would be running EMVAP in a

23 default mode--there was a critical value mode, but in this

24 case you'd be running it in default mode, and using that

25 distribution of the boxed bins. And as long as you can--the
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1 main issue here is you're confirming that the results at the

2 50th percentile are in compliance with the NAAQS. You can

3 then proceed. 

4 And you'll note here that EMVAP adjusts the

5 emissions, that if it didn't work, you would actually have to

6 adjust your emissions downward until you showed compliance,

7 so it sometimes can be an iterative process. For this

8 example, we're assuming that the run actually did show

9 compliance at the 50th percentile. 

10 So what we're showing here is that the long term

11 average is just sort of noted here to show that it's actually

12 below the critical value that was found. And one thing to

13 note is that what we're--what we're, you know, highlighting

14 here that there are only three bins that are at or above the

15 critical value. These will actually be--you'll go forward

16 and using those for the permit language. 

17 So to kind of generate this coupled short term

18 limit for a hypothetical source, and I'll kind of just read

19 through this, with a critical value emission rate of 120

20 grams per second, suppose the bins at or above 120 grams per

21 second as shown were 200 grams per second with a 5 percent

22 bin width--and so you can see that. That's the tall, slender

23 one. Then we have--the next bin is 150 grams per second at

24 10 percent bin width. And then the third is at 120 grams per

25 second, which is our critical value, and that's also at 10
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1 percent bin width. 

2 So the remaining lower emission bins have emission

3 rates below the critical values. These actually tend not to

4 drive the main exceedances of the NAAQS. And as you see, as

5 Bob had noted also, you know, again there's conservatism, so

6 there's a lot of area above the curve captured by these bins. 

7 So for this case the permit language would be as

8 follows. The hourly emission rate will never exceed 200

9 grams per second. The hourly emission rate will be at or

10 above--or at or below 150 grams per second for at least 95

11 percent of the hours in the long term period. The hourly

12 emission rate will be at or below 120 grams per second for at

13 least 85 percent of the hours in the long term period.

14 All right. So the overall conclusions, the two--

15 the second and third are main take-aways--SO2 and NO2 one

16 hour NAAQS are probabilistic, so EMVAP is consistent with

17 that approach. It's a probabilistic model. EMVAP can be

18 used with an emission distribution with a rigorous modeling

19 approach to demonstrate that a coupled short term limit would

20 be protective of a one hour NAAQS standard. And again,

21 here's the source for EMVAP, and our contact information--

22 four, three, two, one. Okay. 

23 Mr. Bridgers: Well played. So we have two

24 more presentations before our first break in the morning. 

25 Both of these are by CPP, and first up is Ron Petersen here. 
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1 And I hope this works, Ron.

2 Dr. Petersen: Good morning; Ron Petersen,

3 CPP. I'm going to talk about building downwash, some

4 problems, solutions, and what I perceive as maybe the next

5 generation downwash model. 

6 Basically, why is this important? Well, let's

7 look at--we've got, you know, the needs of society, the

8 environment, industry, and really to have all these needs,

9 stakeholders, reach the best decisions, you need an accurate

10 model. It's someplace in the middle. You want it to be the

11 most accurate so you can make the best decisions.

12 And kind of an overview of some of the problems

13 with building downwash right now, the theory is based on

14 research done before 2000, basically. The original research

15 used a limited number of solid building shapes. 

16 Schulman and Petersen at the 10th Modeling

17 Conference presented some information illustrating some of

18 the problems with certain types of buildings, long and wide

19 buildings. The theory is not suitable for porous,

20 streamlined, wide or elongated structures. 

21 CPP's evaluation recently has identified some

22 problems in the theory. And there's also some recent and

23 past model comparisons with observations that show problems

24 in the downwash arena.

25 So some examples here of these problems from the
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1 10th Modeling Conference, and Lloyd Schulman presented a very

2 interesting talk at that conference, and you can't really see

3 the charts from that, but in summary what he found that for

4 wide buildings he got factors of 3 to 14 increases due to

5 building downwash when the width of the building was four

6 times the height. For long buildings, he had concentration

7 increases of 4 to 10 when the length was four times the

8 height, and that was at GEP stack. So you were getting

9 significant concentration increases. He also mentioned an

10 ALCOA field study where the model was overpredicting by about

11 a factor of 10 for a very long, big building.

12 More recently Keith Baugues at IDEM showed some

13 results where AERMOD was overpredicting by a factor of 2, and

14 also when paired in space and time very poor correlation with

15 field observations. And again, these were relatively tall

16 stacks compared to the building, so could we have a building

17 downwash problem going on here?

18 A while back AECOM had a field study where they

19 measured concentrations on a nearby high-rise, and the model

20 was overpredicting by about a factor of 10. And when

21 equivalent building dimensions were used in place of BPIP

22 inputs, the concentrations still were higher by a factor of

23 4. When you actually ran with no building inputs, the model

24 still overpredicted by a factor of 2, which suggests really

25 in this case the plume was probably escaping the building
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1 downwash zone entirely, so due to the plume rise.

2 So, you know, I kind of was thinking what's

3 causing some of these problems. So I got to digging into the

4 theory that's in the model formulation document written by

5 Schulman and Scire, and it's based on a technical paper, so 

6 there really is no formulation document. It's a technical

7 paper. 

8 So why is the model overpredicting? We've

9 got--one of the problems, we create these artificial

10 buildings. You can see the blue shape there. That's the

11 building in plan view. BPIP creates that gray building

12 that's much longer than the real building. 

13 And so what happens, the wake starts growing much

14 further upwind. And the point that we're going to have the

15 maximum turbulence occurs much further downwind than the real

16 situation. So you're really having the wake being

17 characterized incorrectly in the model. 

18 Some of the turbulence calculations in the wake

19 are flawed. And I'm not going to--I just have the equations

20 there for reference and documentation, but they're in the

21 paper. Basically what the model is assuming is that the

22 turbulence enhancement, or the building downwash increase, is

23 constant all the way up to the top of the wake. And that's

24 really an unrealistic assumption. And it's making an

25 assumption of uniform approach flow, constant wind field with
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1 height. 

2 This is kind of what we found here. The top slide

3 shows what's going on in PRIME. And you've got the wake

4 growing. You can see that white line. That's the calculated

5 wake height. And the enhanced turbulence doesn't start until

6 the lee end of the building where the red zone is. So the

7 maximum turbulence starts occurring right at the downwind

8 edge of the building, and it goes all the way up to the top

9 of the wake, which is the white line. 

10 What we found in reality is that the enhanced

11 turbulence zone really doesn't go much above the top of the

12 building, and it decreases quickly back to ambient levels. 

13 So this can explain why some of these taller stacks are

14 getting higher numbers. They shouldn't be really impacted

15 much by the high turbulence in the wake zone. 

16 Another problem that we found in this area is that

17 under stable stratification the turbulence in the wake is

18 enhanced by a factor of 10, while under neutral conditions,

19 it's only enhanced by a factor of about 5.7, unstable by

20 about a factor of 3. And so that really is not--there's no

21 evidence supporting this relationship is a function of

22 stability. Everything that was done was done under neutral

23 conditions to develop the theory. 

24 We did a little limited research on our own at

25 CPP, put in a building, a 1:1:2 building, which means it's
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1 two times as long as its height and its width is the same as

2 its height. We measured the turbulence enhancement in this

3 plot. What you see here on the horizontal axis is the

4 turbulence increase is a function of distance downwind of the

5 building. You can see that black object there. 

6 And the maximum turbulence enhancement you can see

7 is below the top of the building where that value of 1 is. 

8 Above the top of the building you have very little turbulence

9 enhancement. It goes back to ambient very quickly, so kind

10 of supporting that little chart I showed you based on wind

11 tunnel measurements. And the decay of turbulence enhancement

12 is much quicker for this particular building than the theory.

13 We also did some large eddy simulation runs using

14 CFD. And--well, that should have been running, but--let's go

15 back to that. Yeah, you can see the blue there is the

16 velocity deficit zone in the wake of that building, and you

17 can see it kind of confirms the wind tunnel measurements. 

18 It's the same building basically that we simulated in the

19 wind tunnel. And it shows most of the turbulence enhancement

20 or velocity deficit is occurring below the top of the

21 building. 

22 So what are the problems we found in the downwash

23 model? The streamline calculation comparison that's in the

24 model formulation document--when we went through the

25 calculations the example showed the streamlines going
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1 downward. When we actually followed the procedure in the

2 manual, we calculated they should all be going horizontal, so

3 there seems to be some problem in what's written in the

4 document, so I'm not really sure what the model is actually

5 doing for that case, but the example doesn't match what's in

6 the paper.

7 The second one is there's a discontinuity in the

8 streamlines right at the leading edge of the building. Right

9 there in that figure where you see the zero on the building

10 there's a discontinuity. Right at zero the slope is a factor

11 of 4, and immediately upwind of that is a factor of 2. So

12 there's a factor of 2 change in the trajectory of the plume

13 right at that point. 

14 Another weakness is the streamlines for all

15 structures are calculated assuming the buildings are solid. 

16 So like a lattice type structure where the flow goes through

17 it--you know, oil platforms, things like that--the flow will

18 more likely be horizontal. A real quick fix to that would be

19 to change the streamline calculation in the model to be

20 horizontal, and that would improve model performance

21 dramatically for that situation. 

22 The top picture is a lattice structure. The

23 middle one, no structure, and you can see is very similar to

24 the lattice structure. And the bottom one is what it looks

25 like in AERMOD. It assumes a solid building.
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1 So the solutions really to these problems--the

2 short term fix really is--one that's being used currently is

3 equivalent building dimensions where you do a wind tunnel

4 study to find the building dimension to input to get the

5 correct dispersion. 

6 I should mention that even that is not fixing the

7 problem completely because, as you notice, there are some

8 theoretical problems, so even with the equivalent building

9 dimension you're going to tend to overestimate the

10 concentrations for the reasons I just went through. 

11 So really the next generation is to develop an

12 improved AERMOD model, which actually--or which actually this

13 would be the PRIME algorithm, which also is in SCICHEM, and

14 you would have to do some modifications to BPIP too to make

15 everything work correctly. 

16 To do this, you'd really need a collaboration

17 between EPA and industry to get this done in a quick manner, 

18 and it's something that I really think should be done outside

19 of the current regulatory framework. It's just something

20 that should be done to improve the model. Current science--

21 as was mentioned earlier, we need to advance the state of the

22 science. That should be the goal of all us here. 

23 The short term fix, as I mentioned, is equivalent

24 building dimensions. Those are fairly expensive, time

25 consuming, but that is a tool that can be used in the interim
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1 to give better predictions. 

2 If you find you have a problem--lattice

3 structures, long buildings--really the only fix right now is

4 equivalent building dimensions, because right now for these

5 types of structures--hyperbolic cooling towers let's say--the

6 model is overpredicting by a factor of 2 to 4, 4 to 8 for

7 short buildings with a large footprint, 2 to 3.5 for lattice

8 structures, and 2 to 5 for very long, narrow structures. So

9 currently the model has some significant overestimates going

10 that the only way to correct is through a wind tunnel

11 modeling study at the current time. 

12 So of course why does--equivalent buildings don't

13 solve the problem, they do help a little bit, but why do they

14 help? You can see this picture here on the bottom is a very

15 long structure. You can see the high turbulence zone right

16 at the lee edge of the building, so a plume could hit and go

17 off that building, immediately hit that zone, be mixed with

18 the ground. That's kind of that yellow color in the picture. 

19 The equivalent buildings we usually find are much

20 shorter, and so the wake height is much shorter, which means

21 when you put that into the model, even though you're still

22 intersecting the wake zone incorrectly, the concentrations

23 hit further downwind or lower. So it is an improvement, but

24 even if the model were fixed, you'd even get lower concen25 trations than using EBD.
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1 Kind of just another picture, this is the BREEZE

2 downwash analyzer that we can kind of visualize what's going

3 on with the building. It kind of shows the building and then

4 the BPIP input, which is in blue, and you can see how much

5 bigger the building is. 

6 Well, we ran that case in AERMOD just to see what

7 it looked like with this building and then with an equivalent

8 building, and here's kind of what the plumes look like close

9 in. You can see the real building, that blue line there is

10 the wake, top of the wake, and you can see the plume, 1

11 sigma-z, how much wider it is than with the equivalent

12 building, the top picture, because on the top picture the

13 plume is just slightly above the top of the wake, so you're

14 getting very little enhancement in the plume, further

15 illustrating what I was saying here how the model is

16 overestimating the enhanced dispersion downwind of the

17 building. 

18 Of course, there are cases where the model is

19 underpredicting, so you really need to--you know, for a

20 sustainable type of situation, you want to cover everything. 

21 You've got to make good decisions, bad, or--you know,

22 overpredictions, underpredictions, whatever. You've got to

23 go both ways.

24 The corner vortex is an issue where the model is

25 probably underestimating by a factor of 2. Upwind terrain
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1 effects are not in the model either. And the model is

2 probably underestimating by a factor of 2 to 6 for that

3 situation. So these are all things that could be developed. 

4 These latter two are probably more complex or are going to

5 take a little more research, but the ones that we talked

6 about first are pretty easy fixes.

7 So what do we do? Correct the bugs, fix the known

8 problems, incorporate the current state of the science. We

9 need to advance the state of the science, expand the types of

10 structures that can be accurately handled. 

11 We really need a well documented and verified

12 model formulation document and code for PRIME, and a section

13 needs to go into Appendix W that we can update things quickly

14 and create collaboration with industry and EPA to kind of

15 move on this path forward to develop a more accurate building

16 downwash model. Thank you.

17 Mr. Bridgers: Thank you, Ron. And for our

18 last presentation before the break, we have Sergio.

19 Dr. Guerra: Thank you very much; Sergio

20 Guerra with CPP. In the last two presentations, we've seen

21 new understanding of the science, and it's very important for

22 us to figure out how we can incorporate that into the model

23 as soon as possible so that we can continue advancing the

24 science.

25 I'm going to be covering background concentrations
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1 and the need for a new system to update AERMOD, so we're

2 going to go through what is in the guidance right now related

3 to background concentrations, an alternative approach, and

4 then going to Appendix W, what have we learned and what would

5 be a new method to go forward.

6 So going to the draft guidance, Section 8.3, there

7 are a few options that you can use to get your background

8 concentrations. You can--if you have a source that is

9 impacting the monitor, you can exclude the 90 degree downwind

10 sector from the source in question, and that was--that's in

11 the current guidance and it's also in the proposed.

12 The second one is a new one. You can modify the

13 ambient data record with monitor--when the monitor is

14 impacted by unusual events. I call them exceptional events,

15 but these are things like the Canadian forest fires, like

16 construction, things that are not reasonably controlled that

17 can be excluded for other purposes in designing concentration

18 calculations. You can do that by removing hourly or daily

19 data or you can do it by scaling or adjusting, basically

20 multiplying by a factor or adding or subtracting.

21 The guidance also talks about pairing monitoring

22 and modeled data on a temporal basis. You can do it by

23 season, by hour of day, and on rare occasions on like the

24 paired sums approach, on an hour by hour basis. And then the

25 fourth way is you can use a regional scale photochemical grid
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1 model to give you the background that you can use.

2 The first one, the 90 degree downwind sector, this

3 is some peer reviewed research done by Doug Murray and

4 Michael Newman, and they did exactly that for the Kincaid

5 database for SO2 and SF6. And what you would expect is that

6 when your downwind--your receptors are downwind from the

7 source, you would exclude all those receptors and just take

8 the receptors around it.

9 In the case of SF6 there is nothing else that

10 would contaminate those samples. However, we found--or what

11 was found in this research is that the 99th percentile for

12 example for SF6 is 247, and then outside of the sector you

13 still have 188. So it's a good idea, but unfortunately it

14 does not work. Winds are not stable enough, and you do have

15 some impacts outside of that 90 degree sector downwind of

16 your source.

17 Excluding unusual events--I have to commend EPA. 

18 I've been talking about this for a while, you know, that we

19 shouldn't be including exceptional events on the data set

20 that is used to calculate the background. And EPA kind of

21 responded to that by the draft guidance.

22 This is a satellite image from I think June 29 of

23 this year when all these forest fires were basically coming

24 down into the U.S. through Minnesota. In fact Minnesota had

25 a lot of--along with other states had a lot of health type of
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1 events due to these forest fires. I was not in Minnesota at

2 the time, but even in Fort Collins we got the effects from

3 this plume. They say that you can run but you can't hide, so

4 that's what happens in some of these exceptional events.

5 But why do we care about these exceptional events?

6 We care about these exceptional events because they basically

7 make a normal distribution be skewed, be stretched to the

8 right like you can see here. And when you stretch that

9 distribution you basically pull with it all your percentiles.

10 So whereas your 98th percentile in normal

11 distribution might be 5 micrograms, when you have an excep12 tional event, that pulls the whole distribution to the right

13 and it may be 10 micrograms, 50 micrograms.

14 This is from a peer reviewed journal article that

15 I wrote for PM2.5 showing that same trend. And as you can see

16 here, anything above maybe 20 is statistically an outlier. 

17 For NO2 I did the same thing and we see the same pattern. 

18 And for SO2 I guess that's the most dramatic where you have

19 very few observations that are really, really high, that are

20 really pulling that distribution to the right and making the

21 98th percentile, or the 99th in the case of SO2, be much

22 more--much higher than what it would be normally.

23 Another thing that you have to consider here is

24 that you need met data in order to address some of the--in

25 order to subtract those days that are impacted by exceptional
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1 events or unusual events. But in many cases we do not have

2 that. We have a monitor, but we do not have met data or a

3 met station that is close by or representative. So an

4 alternative method is necessary when there's no met data

5 available.

6 Now, let's talk about temporal pairing. This is

7 the current Section 9.1, Model Accuracy, and it basically

8 says that models are more accurate for the annual standard

9 than for the one hour standard and that we are reasonable in

10 knowing the magnitude of the highest concentration, but we do

11 not know the exact time or the exact location of it.

12 This is absent from the guidance as far as I could

13 tell, and I think that if there's evidence to the contrary

14 that this is not true anymore, that evidence should be

15 released to the public. Otherwise, it should be included so

16 that we know basically what are the limitations of the model

17 and we can use the model correctly.

18 This is what a perfect model would do on an hour

19 by hour basis. You have one monitoring observation the first

20 hour, and if you have a receptor at that hour you compare the

21 two and if they give you the same number, it would create

22 this 45 degree angle, this 1 to 1. However, this is what we

23 find in real life. This is again the Gibson generating

24 station, a peer reviewed journal, Kali Frost.

25 And it's showing two things: first, that that is
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1 not following that pattern, so basically there is no

2 relationship between the monitored concentrations and the

3 predicted concentrations. And the second thing is that--a

4 little bit more troublesome--is that when the monitor is

5 showing really high concentrations, the model is showing very

6 low and vice versa. So when we do pairings in time and

7 space, you have to keep this in mind, are you comparing the

8 right values or not, and according to this it's not the case. 

9 This is the same article that I showed by Doug

10 Murray and Mike Newman, and it shows the same pattern for a

11 different station for the Kincaid database. And then they

12 took it a step further and said, well, let's just pair them

13 in time, basically any receptor. Let's try to see if any of

14 the receptors match with the monitors, and it just had like a

15 slight improvement, but obviously that pattern is not correct

16 like it should be.

17 So how do we evaluate AERMOD? Most of us are used

18 to seeing something like this. These are Q-Q plots. You

19 rank them from highest to lowest and you compare the highest

20 on the monitor with the highest with the model and then the

21 second highest with the second highest and something like

22 that. 

23 So basically you compare them. You uncouple them

24 from time and space. And that's how we evaluate the model

25 because it's probabilistic also in that sense. We are
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1 looking at the probability of having an exceedance somewhere

2 at some hour throughout your evaluation study and evaluation

3 time. So basically temporal matching is not justifiable

4 based on the information that I've seen, so it should be

5 avoided. 

6 And just a word about the photochemical grid

7 model, it's something that--it's a complete different

8 scaling, complete different parameterization, different

9 assumptions, chemistry. 

10 So we're not comparing apples to apples; it's like

11 apples to oranges, watermelons. They are very different, so

12 I think we should reconsider that, and even some of EPA

13 guidance says that we should take photochemical grid models

14 in a relative fashion, and you know, sort of remember we

15 looked at this at some point--well, let's use these data for

16 background. And we couldn't use it because it was way, way

17 higher than the standard itself. So I think this might not

18 be ready for prime time yet.

19 An alternative to pairing background and predicted

20 concentrations--if we look at the probability of these two

21 events happening, the 98th worst concentration from the model

22 happening at the same time as the 98th worst concentration

23 from your monitor, the probability is equal to the product of

24 both probabilities, in this case .02 times .02, as long as

25 both distributions are independent from each other.
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1 And as I showed earlier, if we had something like

2 a 45 degree angle pattern to the data, we could say that

3 there is, but there really isn't. So in this case we had

4 .0004 probability or a 99.96 percentile combined probability

5 of those two events happening at the same time. This is

6 obviously more conservative than the model if you do it for

7 the 99th percentile, and it's also one exceedance every 27

8 years. 

9 So I've been advocating for the 50th percentile. 

10 I'm glad to hear that for EMVAP it's working as well. But

11 the 50th percentile is a good measure because it's not

12 heavily influenced by these exceptional events. It's more

13 toward the middle of the concentration and it's still

14 conservative because it's being pulled just like the other

15 percentiles. So when you look at that, the combined

16 distribution and the combined probability is even more

17 conservative than the form of the standard.

18 But now let's talk about the Guideline on Air

19 Quality Models. It was published in 1978 and its purpose was

20 to streamline and to create consistency across the country

21 and across industry, basically one model for everyone to use. 

22 The critics back then said that the rigidity of the rules

23 would inhibit innovation and would render the Guidance

24 obsolete as technology and science advanced.

25 It was a very heated argument, from what I
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1 understand. It was very controversial at the time. I was

2 not there. I was only 2, so I did not know enough about

3 dispersion modeling, but nonetheless there is a record of it.

4 So what I'm proposing is in line with what the APM

5 Committee has proposed, the establishment of a Technical

6 Review Advisory Committee, TRAC, with the ability to

7 evaluate, approve, and incorporate new methods without the

8 need to undergo a long and infrequent rulemaking process.

9 TRAC would be composed of the leading experts from

10 EPA, industry, and academia with one purpose, to evaluate new

11 dispersion modeling techniques and incorporate scientifically

12 valid methods to the regulatory model in an expedient manner. 

13 The APM Committee from the Air and Waste Management

14 Association can provide a good framework for TRAC.

15 And why do we need to do this? Well, first of all

16 because of timing. Updates to the guidance require a long

17 and complicated rulemaking process. I mean it's taken us ten

18 years to come here again. Current system results in a

19 lengthy time gap between the proposal of new and advanced

20 techniques and their implementation for widespread use. And

21 again, the current mechanism does not allow for an expedient

22 update of the model to incorporate fixes that we are finding

23 like Dr. Petersen mentioned about downwash or new techniques.

24 Now, let's talk about the rulemaking process. To

25 keep up with the new methods and science, EPA was supposed to
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1 update the guidance through the rulemaking process, basically

2 through the formal public comment. Instead it gave us--and

3 again, it's really hard because EPA has their plate full and

4 they have a lot of responsibilities on top of that, so this

5 is really stretching them to the limit.

6 So instead what EPA has done is issued nonbinding

7 guidance or technical assistance--technical assistance

8 documents without formal evaluation or public involvement. 

9 However, as the courts have said, if you have a nonbinding

10 document that dictates what you have to do in order to get a

11 permit, for all practical purposes that document is binding. 

12 So that's from the Appalachian Power v. EPA, D.C. Circuit,

13 2000.

14 But in reality--the court is another thing, you

15 know, but what we have really done is we have circumvented

16 the evaluation process that is supposed to go into the model,

17 and many times we make changes to the model with good

18 intentions that end up having very unfortunate type of

19 situations or results.

20 The other thing is we need to take action because

21 it might take ten years to update the guidance again, so we

22 need to do this right now. The science is constantly

23 evolving and we need to make sure that we promote the science

24 so that we can incorporate it and we can improve it every

25 time more and more. 
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1 And we need to recognize also that the current

2 system is not working. It's not working for EPA. It's not

3 working for industry. It's not working to protect the

4 environment. And all the stakeholders need to take charge of

5 this and be involved and collaborate because only by

6 collaboration can we do something. We cannot wait another

7 ten years to see what happens.

8 And then one of the main things is create

9 consistency. For example there are some beta options in

10 AERMOD, but at the same time there are many updates that have

11 been made to the model. AERMOD was updated 11 times,

12 AERSCREEN five times, AERMET six times, AERMAP three times,

13 AERMINUTE three times. AERSURFACE even got its update. 

14 BPIP, like Dr. Petersen mentioned, hasn't been updated, but

15 we're working on that.

16 And then another thing is there have been updates

17 that have been enhancements like when we did not have a form

18 of the standard that the model could give you, that was a

19 very welcome enhancement. But there have been also bug fixes

20 that are necessary, but there have been miscellaneous changes

21 such as the change on calculating downwash above GEP stack

22 height, that whereas it is true that there is downwash at 40

23 percent, what Dr. Petersen showed is that the downwash is

24 being overstated, so there was not a proper evaluation to

25 identify this problem and now we've created another problem
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1 by trying to fix a problem.

2 So basically it's not clear what changes can be

3 made by EPA and what changes need to wait until the rule4 making process. That's why we need something like the TRAC

5 that I'm promoting.

6 And basically the Technical Review Advisory

7 Committee would promote collaboration, share responsibility

8 among all the stakeholders, result in a more efficient

9 process and improve the timing of implementation of the

10 science into the model. And this will create consistency,

11 which is the goal of the model--of the Appendix W, excuse me.

12 So again, the APM Committee from the Air & Waste

13 would be the ideal framework because the major players are

14 part of it already, and as technology and science advance we

15 need to evolve with it and we need to make sure that we can

16 lead again, you know, because in this case we've seen models

17 like ADM like used in Europe that are basically having better

18 science than the one that we have here. So let's prove the

19 critics of 1978 wrong and let's update Appendix W so it can

20 work efficiently, as it was intended. 

21 And in 30 seconds I have a summary of my comments.

22 I'll just say about the 90 degree downwind sector, that's not

23 solving the problem, unfortunately. Unusual events need to

24 be excluded from the monitoring data and we need to make sure

25 that we provide enough tools so that we can do that in an
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1 efficient way.

2 The use of the 50th percentile that I've been

3 advocating for is one of those options that should be

4 considered. And then the statement about model accuracy for

5 long and short term averages should remain in the updated

6 guidance, and otherwise evidence should be provided that it's

7 no longer valid.

8 And the temporal matching is not justifiable

9 because AERMOD's accuracy is suspect on a temporal basis. 

10 And background values from photochemical grid modeling should

11 be reconsidered until we know for sure that it's something

12 that is technically feasible.

13 And again, the main thing right now is we need to

14 form a Technical Review Advisory Committee with the ability

15 to evaluate and approve any changes to the model because

16 that's an urgent need at the time. Thank you very much.

17 Mr. Bridgers: Thank you, Sergio. So we have

18 reached the point of our first break. What I do want to 

19 say--I'm not going to talk for five minutes to fill the time,

20 but what I am going to say, there's going to be some 

21 changes--slight changes to the agenda right before lunch. 

22 We have received two requests for additional

23 presentations that would be given, quote, during the oral

24 comments. And so I'm going to have those two presentations

25 right before lunch, and then we'll have the open forum
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1 immediately following lunch, and so that's just shifting that

2 just a little bit. And that way for folks that happen to

3 need to leave at lunch for an airplane flight or whatnot,

4 they'll see all of the PowerPoint presentations that are

5 going to be given.

6 And so with that, it is 10:00. I'm going to

7 adjust the time, so we need to be back at 10:20. Thank you.

8 (A recess was taken from 10:02 a.m. to 10:20 a.m.)

9 Mr. Bridgers: So thank you for everybody

10 coming back after the break. We're going to jump right into

11 the presentations. Oh, just a real quick note. I know that

12 there are some in this room that are looking for rides to the

13 airport. And so there's a white drawing board over here that

14 the ride list is starting to form, so if there are extra

15 spots in cars that are going over to the airport, you know,

16 see if you might help some of your colleagues.

17 Next up we have David Long. And David, I'll let

18 you present your topic.

19 Mr. Long: Good morning. My name is David

20 Long. I am an engineer with the American Electric Power

21 Service Corporation, which is the technical services

22 organization for American Electric Power. And this morning

23 I'm going to talk about the use of data collected by the

24 Clean Air Markets Division, or CAMD for short, in its

25 application to air quality modeling. Under the one hour SO2
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1 standard, we're seeing a lot of this data source trying to be

2 used, and there are issues with this data simply because of

3 its design. 

4 When the 1990 Clean Air Act amendments were

5 passed, EPA was told to develop a program for monitoring S02

6 and NOx from the utility industry as part of what was known

7 as the--what's known as the Acid Rain Program. This data was

8 intended when it was put together as an accounting program

9 for the use and consumption of allowances that were assigned

10 to the various electric generating units. The data

11 management system is therefore set up to overstate emissions

12 if there's any errors in measurements. 

13 And the regulations implementing this monitoring

14 program are found in 40 CFR Part 75. And they do go into a

15 great deal of detail on how the monitoring systems are to be

16 operated and what data is to be collected and reported. 

17 Most of the data in CAMD is an hourly database,

18 which for an hourly program people look at it and say,

19 "Wonderful, we have hourly data." And the typical data

20 that's reported includes gross load on the generating unit,

21 typically in megawatts, but not all sources reported

22 megawatts; SO2 emitted in both pounds per hour and ppm; NOx

23 emitted in pounds per hour, pounds per million Btu and ppm;

24 stack flow rate in standard cubic feet per hour, and that's a

25 critical issue that it's standard cubic feet; a diluent gas,
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1 CO2 or O2, so you're not always going to have CO2 data, but

2 with some of the changes coming based on the Clean Power

3 Program, CO2 probably will be added to the mandatory list;

4 and then a calculation of heat input in million Btu per hour. 

5 Obviously with all the data that's been collected

6 over the past 20 plus years and reported, there's a massive

7 database out there, and it isn't overall terrible data. For

8 Part 75 purposes in fact it's very good data.

9 However, when we've tried to apply this to one

10 hour SO2 where trying to get an accurate picture of the

11 emissions impacts on an hourly basis, the data have--we start

12 to find the shortcomings in the accounting program reporting. 

13 One of the other problems we have is for modeling

14 to get the plume height correct, we need hourly temperatures

15 to temporally match up with the flow rates and to back those

16 standard cubic feet per hour values into actual cubic feet so

17 the velocities are correct. 

18 Guess what we don't have to report? Temperature.

19 Even though the data is captured because we have to use it to

20 take the actual cubic feet that is typically measured by the

21 CEMS systems and converted into standard cubic feet, CAMD

22 doesn't require us to report temperatures at this time. In

23 order to get the modeling accurate, you need accurate

24 temperature data, so that's another shortcoming. 

25 Another shortcoming, as I mentioned earlier, this
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1 was set up as an accounting program. There are extensive

2 missing data substitution--invalid data substitution

3 protocols in Part 75. Basically you get a value every hour

4 regardless of whether the CEMS are working correctly or not,

5 and it may be substituted. 

6 There are flags when data is substituted or even

7 when it isn't to tell you that this is actually what the CEMS

8 system read. When you go to CAMD and request an hourly data

9 set from them, you don't get the flags typically unless you

10 specifically ask for them. 

11 So you're looking at this massive list of hourly

12 flows and hourly emissions, and how much of it's actually

13 what was measured and how much of it was actually substituted

14 and may be substituted using any number of different

15 protocols you can't tell just looking at the basic data set

16 CAMD issues. And to find some of that, you either have to

17 get into the flags themselves or go down to the sensor level

18 data that is actually reported.

19 Now, one of the things that also is included in

20 the CAMD data--and again, for accounting purposes, it's

21 what's called a bias adjustment factor. Bias adjustment

22 factors by their definition in Part 75 cannot go below 1.0.

23 And these are based on RATA tests on the CEMS systems that

24 are done annually or semiannually depending on the accuracy

25 determined during the most recent RATA. 
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1 If the monitor is reading below the actual value

2 and you flunk the calculation for bias, you then put in a

3 bias adjustment factor on the monitor data greater than 1. 

4 If it goes the other way where the monitor is reading higher

5 than the test method, you don't go below 1. It stays at 1 so

6 you're again overstating emissions and consuming a few extra

7 allowances under the accounting rules that were set up. It

8 doesn't help us, though, a lot if we're trying to get

9 accurate representation of emissions from the unit.

10 Another issue that I've hit on several times

11 already, temperature data. We don't have that in the CAMD

12 data. One of the interesting parts is for units that use dry

13 control technologies, the temperatures vary much more wildly

14 than you set on wet scrubbed units, although you do see

15 temperature variations there too, and those can--and that can

16 affect the plume performance in the model. 

17 Temperature for the modeling studies, you can

18 obtain it from several sources. One would be a state

19 permitting or an inventory database. However, the

20 temperature you get there is typically a single value and

21 it's based on full load operating conditions that don't

22 necessarily represent the hourly variation and unit

23 operation. So you have no guidance on how to adjust the flow

24 rates back from standard conditions to actual conditions

25 based on that data source.
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1 You can also go back--and one of the things that

2 we've been doing with the data we've been putting together

3 for the various state agencies we've been working with is we

4 pull the sensor level data, where we do get the hourly values

5 that were used to adjust the actual measured flows back to

6 standard, and then we can bring them back up to the actual

7 flow rates and get a more accurate representation of the exit

8 velocities for the model. You know, obviously how you

9 recognize the temperature data can impact the exit velocity

10 and lead to improper dispersion if you don't get it right.

11 Flow calculations: now, here we see how the bias

12 adjustment factor comes into play. You know, the bias

13 adjustment factor is applied to the flow sensor data, not to

14 the temperature data, the flow sensor. So if the flow sensor

15 was determined to be reading low, it gets raised, and in this

16 case it's probably an appropriate increase. But if the

17 sensor is reading slightly high, it doesn't go down and that

18 can introduce some error into the calculations.

19 Emissions, and I do have a slight error in this

20 equation. I'm just showing a single application of the bias

21 adjustment factor, and it's actually applied to both the

22 value of the SO2 concentration and to the flow individually,

23 not as a single factor on the emission. 

24 So you can get into a situation where if you have

25 an error with--or I shouldn't say an error, but a bias
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1 adjustment on both the flow and the SO2, you can

2 significantly increase the emissions possibly in ways that

3 aren't completely realistic. And again, if there's

4 substituted data, it occurs at the sensor level, not at the

5 emissions level. And substitutions can be very interesting. 

6 If you have just a limited amount of missing data,

7 typically the technique that is used is called hour

8 before/hour after, which if the unit is sitting there

9 operating in a stable mode, it's probably going to be a

10 pretty accurate estimator of what happened during the hour or

11 two of missing data. If the unit isn't stable because the

12 load is being shifted, it's hard to say how that would impact

13 you. But the impacts of an hour before/hour after are still

14 probably going to be relatively minor.

15 The other extreme of data substitution is what's

16 called the maximum potential concentration. And if you get

17 that value thrown in, it can send you all over the place. 

18 And typically if you're just doing a real basic QA of the

19 data, and just looking at it, you can see these because

20 you'll get a--you'll be going along and all of a sudden you

21 get a very crazy number showing up in the data set.

22 Some of the good examples I've seen of this are

23 where you're going along with a unit sitting at a stable

24 condition with an FGD system running, no evidence of any

25 equipment problems, and the emission rate suddenly jumps from
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1 .2 or .3 of a pound per million to 2 or 3 pounds per million

2 or higher. So, you know, that can just totally drive an air

3 quality model nuts when you throw that kind of a change into

4 it. And exit velocities can jump to two or three times the

5 design value of the stack and potentially go well into the

6 very high velocities that you just don't design a smokestack

7 for in most cases. 

8 Hourly temperature data, you get into needing to

9 select the proper sensors to obtain reasonable temperatures. 

10 If there's a problem with it, if you have too low of a

11 temperature, it will again affect the dispersion of the plume

12 and give unreasonable and incorrect results.

13 One other special case of data substitution, and

14 this is where you see--where we've seen a lot of the MPC data

15 show up, is in a start-up of a new stack when a wet scrubber

16 is started up on an existing facility. Yes, some of the data

17 is substituted using stack testing, which is a very good

18 source of data when you have it. 

19 But you don't have that from the minute the units

20 are fired up in this case. It's some hours later, and you

21 may not see it continuously for the several weeks it takes to

22 get the CEMS systems up and running. So you end up with

23 substitution based on the old stacks, which are unscrubbed,

24 and it just is very crazy and you have to manually correct

25 all of that data.
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1 You know, the recommendations we would offer at

2 this point is that the modeling group here at OAQPS should be

3 involved with CAMD as we move into the fall as they're

4 planning on revising the emissions data reporting program

5 that's used for the utilities to report data and to work with

6 them to try to include parameters that are needed to help

7 improve our ability to model using their data set, because

8 again, it's an hourly data set, and there are a lot of good

9 possibilities of what you can do with that data. Sometimes

10 it just takes a lot of work and quality assurance to make

11 sure that the data is something suitable for use in air

12 quality modeling.

13 And, you know, again, remember, this was set up as

14 an accounting program, not as a program to collect data for

15 air quality modeling. We're just using it for that. And we

16 have to keep that in mind as we work with the data and QA it

17 prior to it being included in an air quality model. Thank

18 you.

19 Mr. Bridgers: Thank you, David. Up next we

20 have Chris presenting for API. Chris?

21 Mr. Rabideau: Thanks, George. My name is

22 Chris Rabideau. I am with Chevron, but today I am speaking

23 as the chair of the American Petroleum Institute's Air

24 Modeling Group. 

25 First of all, I just want to--we appreciate the
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1 efforts done by EPA and the IWAQM3 group in developing the

2 proposal package. We know there's been a lot of work going

3 into that package. We also appreciate EPA's review and the

4 consideration of all the API sponsored research that have 

5 gone into some of the improvements that are in the proposal

6 package as well. 

7 I also want to acknowledge EPA for opening up the

8 annual modelers meetings to the stakeholders for at least,

9 you know, one day for the last couple of years. That's also,

10 I think, been very beneficial to some of the progress of the

11 projects that we've been working on.

12 Also, since we had to do these presentations a

13 couple of days ago to get them into George, obviously this is

14 based on our initial review and understanding, which, you

15 know, some has changed based on the presentations yesterday

16 and interpretations of what we were seeing. So obviously you

17 know we'll be following up with written comments. They'll be

18 a lot more detailed. 

19 So today's comments are basically what we had--

20 what we saw at the beginning and at least what our

21 recommendations are. Obviously based on yesterday some of

22 these things are in there. We just want to make sure that--I

23 just want to clarify that at the beginning.

24 As we undertake the process to revise Appendix W--

25 and again, as many have said before, you know, this is ten
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1 years in the making--you know, we need to consider a

2 structure that's more timely for the incorporation of the

3 model updates. We should not be delayed for several years on

4 model updates. 

5 We know in the next year there's going to be some

6 new, more robust NO2 evaluation databases. You know,

7 there's--two NO2 studies being done right now that API has

8 been supporting will be WRAP NO2 drill rigging and the PRCI

9 project that's going to be starting here very soon. 

10 You know, these data sets are going to allow more

11 rigorous evaluation of these models. And, you know, these

12 data sets could also suggest that model refinements need to

13 be done. And we need to have the ability to timely

14 incorporate those technical advances. 

15 And it has been suggested--you know, there's a

16 tier structure which I'll get into, and also a subject that

17 we had brought up at our last--API's comments at the last

18 modeling conference of a technical advisory panel.

19 Again, some of the lack of clarity has been

20 addressed with some of the presentations yesterday, but

21 basically--first of all, I also want to make sure we

22 understand that the low wind project that Bob Paine had

23 mentioned and that was mentioned in some of the EPA comments

24 was not just an API project. It was also supported by UARG,

25 so it was a joint low wind project as well. 
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1 But we support the adoption of the U* option. 

2 Again, we understand the beta option,but the plan is to go to

3 a default option with the promulgation of Appendix W. We

4 still haven't evaluated LowWind3,but we plan to do so for our

5 written comments.

6 With respect to the NO2 improvements, again, we

7 support the adoption of ARM2. Again, we appreciate API--or

8 EPA working with API in getting that--getting that into the

9 system. However, it's unclear as to what steps would be

10 required to allow the use of a lower in-stack ratio. 

11 As we saw yesterday by Rich Hamel of ERM, you

12 know, only 4.5 percent of the in-stack ratios in the database

13 are greater than .5. So if an applicant has site specific

14 in-stack ratio that is less than .5, we're going to suggest

15 that adjustments should be allowed, you know, without the

16 need for additional approval or going through the modeling

17 clearinghouse. There should be--there should be a way to set

18 that up since, you know, 95 percent of what we think is going

19 to happen is going to be less than .5. 

20 We support the classification of ARM2, OLM, and

21 PVMRM2. Again, I think the understanding there has been

22 clarified, but we haven't had time to evaluate PVMRM2 so we

23 plan to do so for our written comments.

24 Again, and I think these issues have been

25 addressed in a number of the presentations before mine, but,
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1 you know, there are still some outstanding needs with AERMOD. 

2 There's obviously the building downwash issue that's been

3 touched both on the long and narrow as well as the GEP height

4 issue, also on the background. And again I think some of

5 these things have been addressed and some of them have not

6 been. 

7 Again, with modeling nearby we want to make sure

8 that the actuals are being used, not allowable. Also, we

9 think there should be some more flexibility in the use of

10 monitoring data to not just characterize the contribution

11 from nearby sources but also potentially in the place of

12 modeling. And then also we want to make sure the background

13 excludes anything that's impacted by exceptional events.

14 With respect to CALPUFF and long range transport,

15 again, I think we agree with some of the statements made that

16 I think EPA is underestimating the times when a long range

17 transport is going to be required. I think there still

18 is--there's still a need for a long range transport model. 

19 We have a concern that there is no consensus on acceptable--

20 on an acceptable model, which we feel there probably will be

21 significant delays in permitting. 

22 I think we've got a typo here. In the absence of

23 a preferred long--we're suggesting CALPUFF version 6.42 that

24 has the advanced chemistry, you know, should be allowed as a

25 refined screen model, again in some of the work that we have
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1 done in the past on it, putting--improving the chemistry in

2 CALPUFF. So we'd like to see that potentially be used.

3 Again, also, if there is--I know there's been some

4 prior concerns with CALMET, so obviously WRF/MMIF can also be

5 used to be--to input into CALPUFF. So hopefully the use of

6 CALPUFF should not be an issue there.

7 With respect to the ozone and secondary PM2.5, a

8 lot of unknowns. We know that there's going to be, you know,

9 further rulemaking on this. So with MERPs, you know, in

10 principle we think that's a reasonable approach. However, we

11 really can't comment on how it's--what the numbers are

12 because we haven't seen anything yet, so obviously there's a

13 lot more to come on that, so it's hard for us to access the

14 appropriateness of whatever, you know, this approach is going

15 to be, but in principle the idea of a MERP does sound like a

16 good idea.

17 With respect to the first tier, again, there's

18 questions about where is this--where does the data exist,

19 because obviously there's still some more guidance that needs

20 to come out with the proposal. There's still not a--I think

21 as somebody said yesterday, it's still--the story is still n

22 not complete yet. It's still out. So we still have a lot

23 more details that need to be--need to be addressed on this.

24 With respect to the second tier, again, what's

25 classified as only, you know, saying--being used in special
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1 situations, which is an application of more sophisticated

2 chemical transport model, I think--again, I think this is

3 going to be used more frequently than EPA is presuming in the

4 proposal. 

5 And I think obviously developing photochemical

6 grid model databases from scratch is costly and time

7 consuming. And again, that's--for the ones that are going to

8 need to do the permitting that's obviously an issue for us. 

9 I think this was also mentioned yesterday. We

10 need consistencies in the regulatory modeling. For far-field

11 modeling, development of the common databases that have been

12 preevaluated and tested and could alleviate some of those

13 issues.

14 And I know that with this last bullet there's some

15 concern there that we're saying there may be--now, there's a

16 role for Lagrangian photochemical models using a range of

17 realistic background concentrations to identify potential

18 worst case ozone and secondary PM2.5 impacts. You know, some

19 could say to get a realistic background you need to run the

20 photochemical grid model, so why don't you just do the

21 photochemical grid model. 

22 So maybe--what we're proposing is maybe like a

23 tier 1.5, somewhere in the middle, where you could use

24 Lagrangian and, you know, put in some worst case background

25 so that you don't have to do the full-blown photochemical
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1 grid model. So maybe there's some flexibility there. 

2 Obviously we'll follow that up some more in our written

3 comments, but that's just a suggestion.

4 Obviously this--George presented this yesterday on

5 Model Clearinghouse. It seemed like reading through the

6 proposal that there's a lot of places in there for going

7 through the Model Clearinghouse for approval. And, you know,

8 George showed the letter yesterday of a month. 

9 Our concern is all the stuff that goes up before

10 that. There's all the work with the state and with the

11 regions and trying to get them to get to the Clearinghouse. 

12 So again, if everything has got to go through the

13 Clearinghouse, obviously there's concern with the process,

14 also just with the issues of being responsive because of the

15 staffing resources that EPA has to deal with. So we just

16 feel that there's potentially a lot of things that could go

17 to the Clearinghouse. I'm not sure if that's the--you know,

18 the right answer for everything.

19 You know, this is--I think Sergio talked about

20 this, and I think Air & Waste also brought this up yesterday,

21 and I just want to recognize Steve Hanna, who--basically this

22 was his brainstorm when we presented our comments at the 10th

23 Modeling Conference of the need for a scientific advisory

24 panel. 

25 I think an external panel could strengthen and
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1 expedite some of these model improvements and could be used

2 as part of this tiered structure that was suggested yesterday

3 by Air & Waste. Again, the panel can review the proposed

4 methodologies, can review any specific model revisions, you

5 know, model--the panel can review the beta options before the

6 release to help, you know, with the bug fixes so that there's

7 not numbers of bug fixes after each other. Again, it's just

8 a--and also the panel could provide input on any guidance

9 that's issued prior, you know, to that. 

10 So again, I think, you know, we suggested this, or

11 Steve suggested this, at the 10th Modeling Conference and I

12 think it's still a premonition. I think it sounds like

13 there's other people that are also behind this issue, so I

14 think, you know, this is one of our suggestions.

15 And just to kind of also wrap up here with the

16 tiered approach that Air & Waste brought up yesterday, I

17 think it's a good idea. We also support that. I think with

18 the one--one added suggestion is that I think for the one

19 year testing and debugging period, knowing how long it took

20 to get ARM2 and some of the low wind projects through the

21 system, I think our suggestion would be that the testing and

22 debugging be concurrent with the EPA assessment.

23 So when a project comes to EPA and it's ready to

24 go, instead of--while they're also reviewing it, put it out

25 there for testing and debugging so that the modeling
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1 community can also look at it at the same time and try to

2 shorten up their review process, because you're still going

3 to have--as I suggested, you're still going to have a 90 day

4 comment period to take in any comments on any of the--any

5 improvements. But at least maybe some of that testing and

6 debugging could be done concurrently so that we can kind of

7 shorten up the time period on that. And that wraps it up.

8 Mr. Bridgers: Thank you, Chris. So we'll

9 transition from Chris to Cindy Langworthy. And Cindy is

10 going to offer some comments from UARG.

11 Ms. Langworthy: Thank you, George. I'm Cindy

12 Langworthy of Hunton & Williams, and I am pleased to have the

13 opportunity to speak with you on behalf of the Utility Air

14 Regulatory Group, UARG. UARG is a voluntary group of

15 electric generating companies and national trade

16 associations. The vast majority of electric energy in the

17 United States is generated by individual members of UARG or

18 other members of UARG's trade association members. UARG

19 participates on behalf of its members in proceedings under

20 the federal Clean Air Act that affect the interests of

21 electric generators.

22 Air quality modeling has an impact on many of the

23 activities of UARG's members. For example, such modeling

24 influences the siting and design of new power plants, and it

25 affects the operation of existing power plants. To ensure
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1 that business decisions concerning these facilities are based

2 on accurate information, it is vital that EPA's recommended

3 models and modeling tools realistically estimate, not greatly

4 overestimate, the impact of power plant emissions on air

5 quality. EPA's proposed rule on revision of the modeling

6 guideline explicitly recognizes this. It says that the use

7 of modeling practices that are overly conservative may

8 unnecessarily complicate permitting.

9 But overly conservative modeling practices do more

10 than just complicate permitting. Overly conservative

11 practices can lead to predictions that air quality problems

12 exist when in fact they do not. And that in turn can force

13 expensive and unnecessary facility redesign or emission

14 reduction measures. It can even lead to cancellation of

15 planned facilities or shutdown of existing ones in order to

16 address problems that do not exist in the real world. And

17 the more stringent EPA makes its air quality standards, the

18 greater the problems caused by overly conservative models. 

19 There is simply less room for error.

20 UARG plans to submit detailed written comments on

21 a broad range of issues raised by the proposed changes to the

22 modeling guideline. My comments today, though, are going to

23 focus on one overarching concern: EPA's continued reserva24 tions about accepting model improvements developed with

25 support from industry groups in order to make timely improve-
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1 ments to EPA's preferred models.

2 And I will say that I've heard some stuff here. 

3 I'll try and take it into account as I'm talking. I'm sort

4 of in the same position as Chris--that is, this was written

5 and approved, you know, before we heard the discussions here. 

6 Industry groups have repeatedly shown that they

7 are willing at their own expense, and sometimes considerable

8 expense, to retain recognized air quality modeling experts to

9 undertake rigorous, well-vetted projects to develop tools

10 that address identified inadequacies in current models and

11 modeling techniques. They coordinate with EPA on such

12 projects, seeking and responding to feedback received from

13 the agency. 

14 But when industries then provide EPA with improved

15 modeling tools, EPA does not necessarily, and I would say as

16 a matter of course, act promptly to incorporate the improved

17 tools in the modeling guideline or in guidance. Instead it

18 has been our experience that approval of new techniques

19 developed by anyone outside of the federal government are

20 delayed. 

21 Often the best that happens is that after months

22 or years EPA will label those techniques as non-default beta

23 options, which means they cannot be employed unless the user

24 is willing to undertake burdensome, time-consuming, case

25 specific demonstrations of the worthiness of the new
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1 techniques. 

2 Thus, in addition to the cost of retaining

3 recognized modeling experts to carefully develop and test

4 model improvements, the regulated community faces the cost of

5 regulatory delay and uncertainty when seeking a permit to

6 build or to modify a source. And a climate of delay and

7 uncertainty discourages businesses from building new sources

8 or upgrading existing sources. Not only does this hurt the

9 economy, but it can also mean that existing sources with

10 higher emissions are not replaced or updated.

11 This is what has happened with industry developed

12 techniques to improve the prediction of short-term pollutant

13 concentrations during low wind speed conditions. EPA took a

14 step in the right direction in 2012 when it incorporated

15 three improved techniques, LowWind1, LowWind2, and U*, into

16 AERMOD and AERMET. Since then, however, these options have

17 been treated as non-default beta options, and running AERMOD

18 and AERMET with non-default beta options turns those

19 preferred models into nonpreferred or alternative models.

20 In other words, in order to use an improved

21 technique to model short term concentrations during low wind

22 speed conditions, users find themselves turning the preferred

23 AERMOD and AERMET into nonpreferred versions of the model. 

24 And that burden--they then have the additional burden of

25 having to demonstrate to permitting authorities the
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1 appropriateness of such nonpreferred alternative models from

2 both a theoretical and a performance perspective. And I'm

3 concerned that this already cumbersome process is about to

4 get worse because EPA has proposed that written approval will

5 be required from the Model Clearinghouse each time an

6 alternative model is going to be used.

7 UARG members had hoped that the proposed revision

8 of the modeling guideline would signal a major change in

9 EPA's attitude towards model improvements developed by those

10 outside of EPA. In particular, we hoped, indeed we expected,

11 that EPA would announce that it would no longer treat the

12 techniques for addressing AERMOD model overprediction under

13 stable, low wind speed conditions as non-default beta

14 approaches but would instead consider them acceptable by

15 default. As you have heard repeatedly at this conference,

16 these techniques have been shown to improve AERMOD's

17 performance significantly. 

18 Language from the preamble to the proposed rule

19 encouraged us to think that EPA was in fact proposing such

20 action. The preamble states that EPA is proposing to--

21 proposing updates to the AERMOD modeling system to address a

22 number of technical concerns expressed by stakeholders and

23 goes on to explain that among the updates are proposed

24 options to address AERMOD model overprediction under stable,

25 low wind speed conditions. Great. 
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1 But the user's guides for AERMET and AERMOD still

2 refer to them as non-default and/or beta options, which means

3 their use would still require approval of an alternative

4 model. And we were encouraged yesterday by comments from EPA

5 that this may not be the case when the current rulemaking is

6 complete, and we certainly hope that's true.

7 And it appears that other industry-sponsored model

8 development work has not been incorporated into the default

9 models, although it may be. Although a modified version of

10 the ARM2 screening technique developed with industry support

11 has been incorporated into AERMOD, again, the AERMOD's user

12 guide indicates that this technique is a non-default data

13 option. 

14 And an industry developed improved chemistry

15 algorithm for CALPUFF has not even been considered, as EPA

16 proposes to downgrade CALPUFF to the status of a screening

17 model. We were told that that would happen, that

18 consideration would take place, during this modeling

19 conference. 

20 Furthermore, EPA does not seem--seems to have

21 determined some limitations on how Lagrangian models can be

22 used, which means that the SCICHEM model, a sophisticated

23 Lagrangian model with photochemistry that we heard about

24 yesterday, that industry developed at considerable expense

25 with significant feedback from EPA will not necessarily have
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1 a role in the modeling guideline. We certainly hope it does. 

2 But the preamble to the proposed rule indicates

3 that a Lagrangian model may be the type of model to be used

4 on a case by case basis for second level screening assess5 ments for Class I significance and cumulative increment

6 analyses. And again, on a case by case basis means it's an

7 alternative model.

8 In these times of limited budgets, EPA should take

9 advantage of the fact that industries are willing to

10 undertake at their own expense model improvement projects to

11 address identified inadequacies in the current suite of

12 tools. EPA should be able to review and approve the use of

13 such techniques promptly and should not let the improvements

14 linger as beta options for years.

15 The above described options to address AERMOD

16 model overprediction under stable, low wind speed conditions

17 have been beta options for three years. Three years as a

18 beta option is more than long enough. The modeling guideline

19 should be revised now to give appropriate stature to reviewed

20 and validated tools that recognized modeling experts have

21 developed with financial support from industry. Such

22 improvements should no longer be relegated to alternative

23 model status.

24 Moreover, it should not be necessary for the

25 regulated community to have to wait more than ten years in
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1 order to see improvements in the modeling tools incorporated

2 in the modeling guideline. Nothing in the Clean Air Act

3 requires that a proposal to revise the modeling guideline be

4 tied to one of the triennial conferences, which, by the way,

5 are triennial, not every ten years. Nothing says that

6 changes to the guideline must be made all at one time instead

7 of as each new tool or option becomes available.

8 In summary, UARG continues to urge EPA to adopt a

9 more agile approach to updating the modeling guideline to

10 ensure that it keeps pace with the needs of all stakeholders

11 and with the efforts of stakeholders to provide the agency

12 with well-conceived, well-vetted improvements to existing

13 modeling techniques. UARG encourages EPA take full advantage

14 of model development work being performed by recognized

15 experts and funded by the regulatory community--the regulated

16 community. 

17 And if I could just add a little aside here, I

18 understand that the modeling guideline is a rule and that

19 revising it requires notice and comment, rulemaking. I'm a

20 lawyer. I believe in this. What is not clear to me,

21 however, is what constitutes a bug fix that does not require

22 rulemaking, and what is a change that does, and I suggest

23 that EPA clarify the distinction.

24 In any event, the agency should now revise it's

25 proposed--the modeling guideline by classifying the helpful
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1 new techniques, including those for low wind speed

2 conditions, as acceptable by default, not as non-default or

3 beta options. And Model Clearinghouse approval for the use

4 of such new techniques should not be required. It may well

5 be that EPA can take this action at the end of the current

6 rulemaking, and we would welcome that. 

7 Even if taking such action would require the

8 current proposed Modeling Guideline revision to be re9 proposed, however, that should not stop the agency from

10 taking that correct step. UARG believes that any delay

11 caused by a re-proposal to include the fact that these will

12 become default options would be offset by the time that would

13 be saved by the ability to conduct modeling analyses without

14 the need to go through approval for an alternative model. 

15 Furthermore, UARG recommends that EPA revise its

16 modeling guideline much more often than once every ten years. 

17 EPA should put in place a mechanism that ensures the agency

18 will revise its modeling guideline whenever new modeling

19 techniques have been shown to improve model performance. And

20 the agency should consider whether revisions to the Guideline

21 are needed each time it revises a NAAQS, and if they are, it

22 should proceed to make those revisions promptly. Thank you.

23 Mr. Bridgers: Thank you, Cindy, and UARG. So

24 now we're going to go off of the agenda that you have in

25 front of you and we're going to go with two more
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1 presentations, and if there's time we also have already

2 requested one oral comment that we'll squeeze in before

3 lunch. So pardon me just a minute to pull this over. 

4 (Pause.)

5 There we go. So now I'll yield 15 minutes to

6 George Schewe--where's George--oh, there's George, with

7 Trinity Consultants.

8 Mr. Schewe: Good morning. My name is

9 George Schewe. I'm a meteorologist with Trinity Consultants

10 in Covington, Kentucky. And I'm going to keep this very

11 simple. A lot of you know I teach some classes for Trinity

12 and a few other places and I can speak for about an hour from

13 one slide, so I've got 18 slides, so we're going to cut it a

14 little shorter today. 

15 One way to keep it simple is I'm kind of speaking

16 here on behalf of all the little guys that aren't here, the

17 local agencies, some of the state agencies, Chris Beekman

18 from--he's a meteorologist up in Ohio EPA. He called and

19 said, "Can you kind of be paying attention to what's going on

20 for me? We can't afford to get down here." 

21 The city of Cincinnati has air toxics problems,

22 and one of the things that they still do--they don't have all

23 the CAMx capabilities--are some of them still run AERSCREEN. 

24 Some of them are still running SCREEN3. And so we've been

25 trying to wean them away from running SCREEN3 and just
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1 running AERSCREEN. 

2 So what I did was I just looked at AERSCREEN and I

3 said is it really conservative, okay? So that is the whole

4 thing I'm going to talk about today. Here's the quote from

5 the existing Appendix W. I won't read to you all of it, but

6 the purpose of screening is to eliminate the need of more

7 detailed modeling so it will not cause--so it's supposed to

8 give you a conservative answer. 

9 I couldn't find that in the new one, but what I

10 found that was close was in Section 4.2.1(b), and it says,

11 "As discussed in paragraph 2.2(a), screening models or

12 techniques are designed to provide a conservative estimate of

13 concentrations." 

14 So if I've got AERSCREEN, and I've got an air

15 toxics program in my state and I'm not going to run CAMx for

16 an air toxics of 1 ton of formaldehyde from somebody in town,

17 I want to know if it's going to be conservative enough to

18 protect the standard so that I don't have to tell that little

19 mom and pop operation that they've got to run AERMOD and it's

20 going to cost them more to run AERMOD than it is to produce

21 whatever they're producing and put down a little bit of

22 formaldehyde. So that's what I tried to do.

23 So what I did was, unfortunately--I did this

24 couple or three months ago--I used some older versions of the

25 model. So one of the things that I need to do is rerun this
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1 with the newer versions of the models to see if it makes any

2 difference, so AERSCREEN 14147, the current version of AERMOD

3 which is not current anymore, 14134. I used the current

4 versions of BPIP and AERSURFACE and AERMAP and basically set

5 it up using some of the default and suggested sources that

6 have been in some of the examples for AERMOD, ISC, and all of

7 the other models over the years, okay? 

8 That one little bullet there at the end, which you

9 in the back can't see because it's way down on the bottom, is

10 that I've actually compared SCREEN3 to AERSCREEN, and if you

11 use all the defaults and do the best job you can do on

12 defining the Bowen ratio and the surface roughness and the

13 albedo that you get fairly similar results between SCREEN3

14 and AERSCREEN, at least for the current--the previous

15 version.

16 So what was my methodology, seven different source

17 types, and basically four different locations: Orlando,

18 Florida surface data, LaGuardia, Bowling Green, and Dalhart,

19 Texas. Those are really different types of meteorological

20 stations. These are little windroses for each of those

21 stations on an eastern U.S. map just to kind of show the

22 general patterns of wind that are going on there. And you

23 can see LaGuardia, New York has a very diverse pattern, a big

24 component from the northeast and from the northwest, which is

25 not unusual. 
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1 So what are the differences between some of these

2 stations? I took the two that were the most diverse from

3 each other and just put their little graphical Google Earth

4 image on here. LaGuardia in the top is situated near water,

5 but it's also situated in a very highly urbanized,

6 industrialized and commercialized area, as compared to

7 Dalhart, Texas, which is in the middle of a lot of circular

8 watering systems, watering plants for agricultural, so very

9 different in terms of that.

10 So what did that mean in terms of assigning

11 albedo? So I downloaded the AERSURFACE, the 1992, which is

12 now 23 years old, data and ran the AERSURFACE model on that

13 to get the albedo, the Bowen ratio, and surface roughness,

14 which I then put into AERMET to run the AERMET meteorology

15 data for using in the AERMOD part of the analysis but then

16 also used these inputs for the AERSCREEN part of the analysis

17 because you have to put those in. 

18 The biggest difference there is, well, first,

19 Bowen ratio and the surface roughness. Those are quite

20 different from LaGuardia. So I just want to point that out

21 because when I show you some results in a few minutes, you'll

22 see how that affected the results possibly.

23 These are the sources I selected. Again, these

24 are derived from some example problems that are available for

25 AERMOD and have been kind of historically used over the
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1 years, so four different stacks, from a little short stack up

2 to a GEP stack, and then an area source that was square, an

3 area source that was circular, and then a volume source that

4 was kind of a nominal dimensions, about the same size as the

5 area source because a lot of us use those for roadways and

6 things like that, so just put each of those into the models.

7 For receptors, the last bullet, no terrain; I

8 didn't want to have terrain affecting the analysis here, so

9 everything is flat. We're in eastern or western Illinois. 

10 But then I've got two different receptor grids. For the

11 AERMOD analysis I just had a circular grid, which we don't

12 use much anymore, but I used a circular grid to kind of

13 simulate all the different 10 degree sectors of wind for

14 AERMOD. And then for AERSCREEN I just kind of took every15 thing to the east of each of these facilities because that's

16 the way the wind blows most of the time in AERSCREEN, from

17 the west. 

18 Okay. The rest of the methodology, no downwash,

19 so no buildings or anything that's going to affect any of our

20 analysis. I ran AERMOD with each source for each receptor

21 for each meteorological location. And then I did one hour,

22 24 hour, and annual block averages--24 hour and annual block

23 averages, or I guess 24 hour is block average annuals,

24 though--everything and then all the one hour values. And

25 then I ran AERSCREEN/MAKEMET with each source for each
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1 receptor for each of those and then used all the same inputs

2 for the AERSURFACE data that you need to run MAKEMET.

3 So what was my expectation? My expectation, based

4 on reading the two Appendix W, were that I would always get

5 higher concentrations with AERSCREEN for each situation. And

6 that would be something that I could use as a screening tool. 

7 I had a second expectation on this slide and I

8 took it off because I still use AERSCREEN occasionally just

9 to model some sources to find out where's the hot spots. And

10 as my friend Ryan Gesser--Ryan, are you still here--said to

11 me years ago, "Why should I run AERSCREEN? Why don't I just

12 throw everything in AERMOD and run AERMOD because then we can

13 put in multiple sources." Oh, yeah, you can only do one

14 source at a time. I didn't run all seven of these at once,

15 okay? So, but I took that expectation out because I was

16 limited to 15 minutes.

17 So here are the answers. These are for one hour. 

18 I know you guys in the back can't see these. But AERSCREEN

19 is in the third column, the concentration you got for one

20 hour from each airport. And then AERMOD is in the fourth

21 column. And then I just hit a simple yes or no, was it

22 conservative or not, okay? I'll highlight a few of these for

23 you. 

24 So if we look at Dalhart, it looks pretty good

25 except for the square area source. If you look down here at
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1 Orlando, it looks pretty good for all the sources. It was

2 pretty conservative for all the sources, and it was generally

3 above AERMOD. Then you look at Bowling Green, and for

4 Bowling Green neither one of the area sources passed. All

5 the point sources still passed, so that was good. AERSCREEN

6 was still conservative for the point sources. But then you

7 look finally at LaGuardia, and it's all nos except for the 35

8 meter stack. So for LaGuardia we did not get AERSCREEN to be

9 conservative, so I was a little concerned about that.

10 The last three columns over there were my little

11 hot spot analysis. I was trying to again figure out if it

12 really picked where the hot spots were, and again the same

13 kind of mixed results, did a pretty good job, but--so I

14 decided, okay, I'll plot these and just kind of see what they

15 look like. 

16 So the northwest corner for all the meteorologists

17 there, that graphic for Orlando, AERSCREEN was always above

18 the AERMOD models, so it was conservative. But then if you

19 start looking at some of the other sources or locations,

20 Bowling Green, Kentucky, there's our area source there. It

21 was underestimated. And if you look again at Dalhart, it was

22 underestimated. If you look at LaGuardia, just about

23 everything again was underestimated. So it really wasn't

24 screening--a good screening tool for doing sources in the

25 LaGuardia type area.
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1 The same kind of deal in the 24 hour concen2 trations, and if you look at all the locations there, all of

3 them on a 24 hour basis--and I didn't give you a tabular view

4 of the 24 hour. All of them you did get higher numbers. 

5 Even at LaGuardia it did pretty good. You can see down at

6 the bottom corner here there are a few that were still below

7 at some of the source types, and I didn't pick out which

8 kinds those were. 

9 On an annual basis, I think in this case all of

10 the answers were above the screening level--or the screening

11 level was above what you would get in AERMOD. So it did good

12 job on the longer term averages. As somebody earlier this

13 morning pointed out in one of their slides, the model does

14 pretty good for longer averages somewhere in space and time,

15 but when you get down to the shorter, one hour periods, maybe

16 it doesn't do as well.

17 Okay, so what are our observations? AERSCREEN is

18 not always higher than AERMOD for one hour concentrations,

19 especially--they're always higher for stacks in rural

20 situations, though. That was pretty good, I guess. But

21 they're not always lower for--and they're lower for most

22 source types in an urban situation. And again, I only based

23 that on one urban situation, that being LaGuardia, so--and

24 AERSCREEN is generally higher on a 24 hour and annual basis.

25 So my conclusions were AERSCREEN provides a good,
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1 basic tool for consideration of sources very quickly. It's

2 not always conservative. It does better in rural situations,

3 meaning it's conservative. It does well for rural and area

4 sources. And it doesn't do as well--it underpredicts for

5 area sources--I'm sorry, in most locations. And it does

6 pretty well for longer averaging periods.

7 So as--Bob and I talked about this just a few

8 minutes before--or this morning, and, you know, one

9 consideration is rerun it with 15181. Another question,

10 though, I perhaps have for our panel of modelers here for

11 OAQPS is can you in MAKEMET, and maybe James--I think you

12 were the AERSCREEN guy yesterday--I can ask questions from

13 the podium, right? 

14 Does MAKEMET have an option to include U* or the

15 low end options? And if we make it default in AERMOD, will

16 it also become default in AERSCREEN? So that was kind of one

17 last question. So I thank you very much. Have a good day.

18 Mr. Bridgers: Thank you, George. It's nice

19 to have another George up on the podium. So we have one last

20 presentation that has prepared slides, Mr. Jeff Bennett. And

21 Jeff, I am going to turn the podium to you.

22 Mr. Bennett: Good morning. As George told

23 you, my name is Jeff Bennett. I'm an air quality engineer

24 for Barr Engineering out of our Jefferson City, Missouri

25 office. I'm going to be giving a few comments today on the
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1 ozone--single source ozone modeling component to the 

2 Appendix W proposed rulemaking. 

3 And what I--I mean George asked for people to say

4 who they're representing, and I would tell--I've been a

5 regulator for a long time. I was a regulator at the state of

6 Missouri for about 18 years, and so I've never really been

7 able to say--I'm representing myself today as a concerned

8 citizen, so there you go. That's who I'm representing--a

9 concerned air quality modeler may be better.

10 So just a quick, very quick, summary of what EPA

11 is--what appears, what my read of the initial--my initial

12 review of the Appendix W rulemaking and what they're trying

13 to do, they want to develop some MERPs. They're above what

14 the existing SERs are on a national basis, which, you know, I

15 think Chris from API said people would generally support. I

16 think that's true. 

17 They want to utilize photochemical modeling of

18 areas around the project as a means to arrive at a calculated

19 ratio of precursors to downwind ozone impact, so basically

20 existing air quality data, photochemical modeling data to use

21 to develop these calculated ratios of ozone to precursors.

22 The third and the fourth ones are sort of combined

23 I think a little bit in the rulemaking, but a new photo24 chemical modeling analysis, and a lot of yesterday was

25 talking about the Lagrangian piece, to determine specific
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1 projects will either have impacts above or below the SIL to

2 avoid a NAAQS modeling analysis, a cumulative NAAQS modeling

3 analysis. 

4 And the last one of course is the one that's sort

5 of exactly the opposite of what George was just talking

6 about. It's not air screening. It's the most complicated

7 thing you can probably do in terms of modeling analysis,

8 which is full photochemical modeling.

9 All right, so regulatory context. As I said, I'm

10 an old regulator from the state of Missouri. I was working

11 there for about 18 years. And we went through a rulemaking

12 in about 2004, 2005, and it was called Missouri 10 CSR 

13 10-6.345, and it was affectionately called the Upwind NOx

14 rule. So we've already gone through--"we," this is the old

15 "we." 

16 When I was working for the state of Missouri, we

17 went through a--went through a process as part of our NOx SIP

18 component of our ozone--our ozone SIP, I'm sorry. As part of

19 our ozone SIP we went through a process and developed an

20 upwind NOx rule for five counties upwind of the St. Louis

21 area. It was difficult. I don't begrudge EPA trying to go

22 down this road. I think it's going to be a long and winding

23 pathway. 

24 But the idea was we wanted to--we wanted to sort

25 of alleviate some of the control requirements on existing
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1 sources within an nonattainment area by trying to limit the

2 amount of impact that was coming in from upwind areas. So it

3 was a tradeoff situation. And so the concept was we were

4 going to develop this de minimis threshold, right? 

5 And so there's a lengthy rule. It's been--

6 subsequently the rule has been revoked because it was never

7 used, quote-unquote, used, and the St. Louis area got

8 redesignated so there was--there was not a need for it at the

9 time. They've been redesignated back into nonattainment now,

10 but I guess the air standards are getting more stringent. 

11 You can move back and forth; right? 

12 So the idea is that you can go check out the rule

13 and see what it says, but the question you always--that comes

14 immediately to everybody's mind is what was the number, what

15 did you come up with, what's this upwind NOx rule, what's the

16 MERP or the--what I call the local MERP. 

17 It was 900 tons of NOx per ozone season. May

18 through September was the ozone season. It still is. And

19 that is equivalent to about 2100 tons a day, a lot better

20 than the 40 tons a day for the SER, and so we had--we had

21 developed that process. We went through the evaluation to

22 generate that number. 

23 What I'm not doing today is suggesting that that's

24 the right number. That's not what we're talking about. It's

25 not necessarily 2150 tons. It's not necessarily 900 tons for
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1 ozone season, but it does--the process we went through raises

2 a lot of sort of more detailed technical issues that the

3 rulemaking and the subsequent guidance don't really get into,

4 and so that's what I'm going to focus on a little bit today.

5 So area specific considerations, and you can read

6 that stuff. I mean the photochemical models are great

7 because you include all the sources in there. It has back8 ground in it. You don't have to worry about adding in other

9 stuff or combining models. It's a single model to predict

10 concentrations to the extent it has the capability of doing

11 that, which you have to go through an evaluation exercise to

12 get a SIP approved, and so there was that benefit. 

13 But in St. Louis we had gone through that exercise

14 and so we had the capability of going down the pathway of

15 using this existing data set to go through the analysis that

16 EPA's talking--that EPA is considering doing as part of the

17 Appendix W proposed rulemaking.

18 The fact is that that's a difficulty, though. 

19 When you attempt to use a photochemical grid model, which

20 takes a long time to run--everybody gets that--for single

21 source analysis, it's impacted by all the sources that are

22 around it, the background, the ratio of VOC to NOx. 

23 I mean there's a whole pile of existing informa24 tion that has to be accounted for every time you run the

25 model and every modeling step. And so it changes every time
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1 that the model kicks over to the next meteorological period. 

2 And so the idea is that it causes--and not only is it a

3 benefit, it also causes some potential problems.

4 And so what did we find out? From a VOC/NOx ratio

5 perspective in St. Louis, the reason why it's not called the

6 upwind VOC rule was because the St. Louis area is extra7 ordinarily NOx limited. It's got a whole--Chet probably

8 remembers this from back in the day, but it's the biogenic

9 volcano in the Ozarks, which is south of St. Louis. And so

10 there's a whole pile of additional biogenic VOC that's

11 hanging around, and so the VOC controls doesn't make any

12 difference. 

13 And so there wasn't an upwind VOC rule because you

14 could have changed a 10,000 ton a year VOC source or 100,000

15 ton a year VOC source and it wouldn't have made any

16 difference. And so that's why it's not called the upwind VOC

17 rule. It's called the upwind NOx rule. 

18 And so the idea is that that's going to be the

19 case in a lot of areas. You're going to have this same

20 problem in every--in every--in every county, every state,

21 every area, every rural or urban area, and you're going to

22 have these discussions. And so this is a specific issue that

23 EPA is going to have to address and deal with, I think.

24 So the next issue--so it's the next level. So

25 you've got levels of this stuff. So the first level on the
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1 decision making process is which model am I going to use?

2 What episodes am I going to evaluate. Am I going to use

3 source apportionment or am I just going to use a brute force

4 method? 

5 I mean those are all sort of first level

6 questions. What's the form of the model concentration that

7 we're going to compare to the SIL? There's all these sort of

8 specific things that are sort of rudimentary in terms of

9 moving past the initial hurdles that EPA is talking about

10 here. 

11 And so the next levels--there's several levels of

12 conversation. It's about stack heights and VOC to NOx ratio

13 and specific ozone speciation, and that's what this slide

14 talks about. So the reaction chemistry for all ozone models,

15 all photochemical models, is set up on profiles. 

16 And so there's a grouping there. You don't model

17 every VOC species. There are groupings of species, right? 

18 And so each one of those have got a different reactivity. If

19 it didn't have a different reactivity, why would you have a

20 speciation profile? You're going to have to--you have to

21 have it--you have to have it show up as different species in

22 order for the model to predict appropriately. And so that's

23 the idea. You need to speciate stuff.

24 And so what does that mean for downwind ozone

25 impacts? Eladio yesterday in his presentation went through
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1 and was looking at highly reactive species versus somewhat

2 unreactive species, I think. And so his results would be

3 consistent with what I would have expected, which is you're

4 going to have higher--more reactive VOC species show up with

5 additional impacts and less reactive species, and so VOC

6 tonnages, one ton to the next, are not the same, and so

7 you're going to have to address that issue as well.

8 But generally, my overall comment--and we're

9 trying to get out of here on time for lunch, George, so there

10 you go. I'm doing what I can for you and everybody else. 

11 The idea is that you guys have done a bunch of good work. I

12 think that you've evaluated this process and went through a

13 constructive and difficult arena to get down to the path of

14 trying to propose a MERP. Ultimately that's really what

15 we're talking about, I think, for a lot of sources that are

16 going to be out there. 

17 The problem is that the level of detail that's in

18 the rule right now and the guidance isn't sufficient to talk

19 about a lot of these issues that are--that I didn't even

20 bring up today. There's dozens of issues like this you're

21 going to have to get through. 

22 And so to provide substantive comments, and I

23 think a lot of folks have said this already, you're going to

24 have to come up with additional detail. The concept is

25 solid, we believe, or I believe in general, but you're going
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1 to have to--you're going to have to come up with additional

2 things to provide the ability to provide comments from a

3 more--on a more detailed basis. That's what it amounts to. 

4 And that's all I've got, with five minutes to go. I think I

5 set the record.

6 Mr. Bridgers: So since we have got into the

7 slight bit of clapping, I think now that we have finished all

8 of the presentations, at least with prepared slides, I think

9 that--for the EPA presenters yesterday and then all the

10 public presenters from the stakeholder community, I think we

11 should all give a round of applause.

12 (Applause.)

13 Mr. Bridgers: And EPA graciously appreciates

14 the effort from all the stakeholder community to provide

15 those comments to us in slide form, and we look forward to

16 your written comments by the 27th.

17 Considering it's 11:30, and we still have a little

18 bit of time before we break for lunch, we have had one

19 request for oral comments. I think it would be appropriate

20 to put that in before the lunch break, and depending on how

21 time goes, if there are some others that feel that they want

22 to say something, I think we can run up to say 11:45 or

23 11:50. We'll just see how it goes, and then we'll break for

24 lunch.

25 And immediately following lunch, I will open the
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1 forum back up for anyone else that may have a desire to

2 speak. I'm not going to put forth any expectations. My

3 anticipation is it's probably going to be smaller, but all

4 the comments will be transcribed, so any that have to leave

5 for the airport at lunch, you can catch that as soon as we

6 post it to the web site in about a month. 

7 So Bridget, are you--so next we have Bridget Lee. 

8 Let me find a good slide to put up, Bridget, maybe this one. 

9 Yeah, forget the times but the slide looks pretty good. So

10 Bridget Lee is with the Sierra Club, and 15 minutes.

11 Ms. Lee: I wish I had had this slide on

12 Wednesday morning when I was trying to find my parking spot. 

13 Good morning, everyone. I'm Bridget Lee. I'm an attorney

14 with the Sierra Club. And I expect that we will be

15 submitting detailed written comments in October, but since

16 I'm here, I'll just share a few very brief thoughts.

17 First, the Sierra Club is very concerned with the

18 proposal to incorporate the LowWind3 and U* beta options as

19 regulatory defaults. In 2013 we submitted to EPA an

20 evaluation of the performance of the beta options, which

21 demonstrated that they decreased model performance and

22 increased the variability of impacts from tall stack sources

23 as compared to the accepted regulatory defaults. 

24 The possibility that the adoption of these options

25 as defaults could lead to underprediction of air quality
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1 impacts causes great concern within the environmental and

2 public health community. We question the reliance on the

3 Idaho Falls and Oak Ridge studies, which were excluded by EPA

4 during the initial evaluation of the model. Moreover, these

5 four year old studies are not likely to be representative of

6 air quality impacts from tall stack sources, and we don't see

7 any compelling reason that the agency should rely on the

8 studies now.

9 Second, with respect to the tier demonstration

10 approach for addressing single source ozone and PM2.5 impacts,

11 we're still in the process of assessing this approach but

12 again have concerns about whether it would fulfill EPA's

13 obligations under Section 165. Thank you for your time and

14 attention.

15 Mr. Bridgers: Well, thank you, Bridget. So

16 we now have a few more minutes before we break for lunch. 

17 And we--typically we have a podium microphone set up in the

18 foyer, or excuse me, the aisleway there, but it's going to be

19 easier for transcription if you come to the podium. So if

20 there are any people in the audience that would like to offer

21 any oral comments at this time, you have 15 minutes of time

22 if you want it. 

23 (Pause.)

24 Mr. Bridgers: We needed to pay for some

25 Jeopardy music or something. We'd have to pay copyright,
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1 though. Merv Griffin gets his. Well, seeing no immediate

2 interest in providing additional oral comments to the record,

3 I will reiterate for those that will be leaving for the

4 airport over the lunch break that the comment period now

5 extends for--I think it's 74 days to October 27th. 

6 I will during the lunch break update our web site. 

7 I wanted to take the registration link off because that

8 becomes sort of invalid, but also to make sure all the

9 presentations are clearly posted. I know that there is one

10 or two that we had little slight corrections that are not

11 changing the record, but there's just some plots that need to

12 move around. But they'll be available. 

13 And as said, it will be three or four weeks at the

14 minimum before the transcripts are ready, but as soon as the

15 transcripts are provided to us and we review them, we will

16 put those on the web site and put those in the docket, so

17 there should be at least--I'm figuring at least 45 days left

18 in the comment period for those that need to review the

19 transcripts and prepare their final comments.

20 And so with that, I will suspend the conference

21 and public hearing until 1 o'clock, and we will reconvene

22 exactly at 1 o'clock for those that come back, if you would

23 like to provide oral comments. Otherwise I wish those that

24 are traveling safe travels, and see you later.
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1 F U R T H E R P R O C E E D I N G S 1:00 p.m.

2 Mr. Bridgers: Okay. I am going to in just a

3 moment call the public hearing and conference--oh, everybody

4 got quiet, so I'm going to go ahead and do it. I'm going to

5 call the conference and public hearing back to order. I hope

6 everybody enjoyed a pleasant lunch.

7 So really quickly, just because it's a formality,

8 I just want to reiterate things that everybody in this room

9 probably already knows, but since this is the oral session, I

10 just want to make sure that everybody knows that this is a

11 public hearing. 

12 Everything that was said is going to be tran13 scribed and put in the docket. We're going to require that

14 everybody identify themselves and their affiliation--George

15 Bridgers, USEPA, here, Air Quality Modeling Group in RTP. 

16 We're going to have a session for oral comments. 

17 At such time that we do not have anybody else requesting a

18 speaking spot, then I will go ahead and close the public

19 hearing and conference. But that being said, written

20 comments for the next 74-ish days can be submitted to the

21 docket by October 27th. And on the information web site for

22 the conference and public hearing, there's a docket link. If

23 not, you can search for it--or it's even in this presentation

24 here.

25 The other thing--this is just a side note. Over
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1 the lunch period I updated the 11th Modeling page, the web

2 site, so all the presentations are now more clearly posted. 

3 They're not embedded in links--I mean they're still embedded

4 in links in the agenda, but there's a page now that has all

5 of them listed, and so you can just go to the 11th Modeling

6 Conference page and see that.

7 If there are any nonsignificant--let me make sure

8 I say this right--changes that need to be made by any of the

9 speakers--I know there was a couple of plots that didn't

10 print right with the .pdf--we can accommodate those, but if

11 there's any other substantive changes in the presentations,

12 they were given in a public record and I can't change them

13 now, including ours.

14 Let's see; what else? Other than that, I think

15 that we'll start--we do have one requested oral comments

16 here. And I will go back to the presentation I closed just

17 to have a nice pretty background on the screen. So Peter,

18 I'll give the floor to you. And just like everybody else,

19 you have up to 15 minutes if you need it.

20 Mr. Guo: Hello, everyone. My name is

21 Peter Guo. I work with Apex TITAN. We are a consulting firm

22 in Texas. I'll just take a few minutes, you know. We are

23 still working on the detailed comments about the proposed

24 regulation, so right now I'll just make quick brief comments.

25 We do have some concern for the additional
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1 requirement of the PM2.5 secondary modeling requirement and

2 replacement of SCREEN3 with AERSCREEN. I think that

3 everyone--I think many people already talked about the

4 secondary--PM2.5 secondary formation modeling, so I won't

5 repeat our comments. I will just discuss the use of SCREEN3

6 here real quick.

7 As I mentioned, we do the permit modeling for the

8 oil and gas industry in Texas, and we have been using SCREEN3

9 for more than a decade, so SCREEN3 have demonstrated a very

10 useful tool for--you know, for the permit modeling in Texas. 

11 So as proposed, SCREEN3 will be replaced with AERSCREEN at

12 this time, so we just suggest that SCREEN3 should be still

13 kept on the list of the preferred models, you know, give the

14 industry the option to use simple and, you know, quick tools

15 to do the permit modeling, you know.

16 We know, you know, AERSCREEN will give us a more

17 accurate result, but actually, you know, AERSCREEN will

18 require additional, you know, information such as, you know,

19 detailed meteorological data. You know, you have to collect,

20 Bowen ratio, you know, rough surface, or other, you know,

21 terrain, even the terrain information or building downwash

22 information, so--and in the meantime AERSCREEN will just give

23 a similar, you know, result compared with the SCREEN3 result,

24 you know, for the oil and gas, you know, permitting project.

25 And so overall, so I would suggest, you know,
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1 still, you know, the SCREEN3 should be kept on the list of

2 preferred models, and I think that's all I have today. Thank

3 you very much.

4 Mr. Bridgers: Thank you, Peter. And so at

5 this point having no one that has requested time through Nan

6 up front, I will, like I did before lunch, open the floor up. 

7 And probably what I'll do is leave the floor open for two or

8 three minutes. I might not stand up here in front of you

9 because we don't have the Merv Griffin music. But the

10 microphone is open, and if anybody feels that they would like

11 to offer some oral comments to the record, the floor would be

12 yours.

13 (Pause.)

14 Mr. Schewe: Good afternoon. My name is

15 George Schewe. I'm not going to speak for 15 minutes. I

16 just have a few things to say. I'm with Trinity Consultants. 

17 That's our advertisement part.

18 The data requirements rule, the SO2 consent

19 decree--we're in the middle of doing a lot of stuff for a lot

20 of clients, and the Appendix W changes coming right in the

21 middle of this is not helping us, as of course no time would

22 Appendix W changing help any of the analyses we have to do

23 for permitting or for air toxics or anything else.

24 So just having said that, I thought I'd leave you

25 with that thought as you're going down the road of trying to
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1 get some of these studies completed. Thank you.

2 Mr. Bridgers: Thank you, George. We still

3 have plenty of time till 5 o'clock today. I think George

4 just wanted to get the last word in.

5 (Pause.)

6 Mr. Bridgers: It's as awkward for me as it is

7 for you guys. At least in the other meetings I manage, I

8 just wait for someone to make a motion that we close the

9 meeting, all in favor.

10 As the hearing officer, at least I'm not seeing

11 any motion toward the microphone, so I will make one last

12 call. Raise your hand if you're thinking; if not--going

13 once, going twice. Okay.

14 With that case, I will call a close to the 11th

15 Conference on Air Quality Modeling and for the public hearing

16 related to the proposed rulemaking for the Guideline on Air

17 Quality Models. We appreciate all of your participation, and

18 as I've said many times, safe travels and all that.

19 But I will also say that feedback, good, bad and

20 otherwise--I've said that to a few people--we welcome through

21 the comments formally on the proposed rulemaking, but also

22 with respect to the conference. We're not going to do a

23 formal suggestion box or a survey, but if there are things

24 that for future modeling conferences that you would think

25 that we should improve upon or change--coffee; yeah, can't do
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1 it, unless we do it somewhere where you offer free coffee.

2 But nonetheless, I appreciate feedback, good, bad

3 and otherwise. So if there are things that you would like to

4 see changed in future modeling conferences other than

5 question and answer sessions--can't do it unless we're not

6 doing a rulemaking--but please send them along. You should

7 be able to find my e-mail address without any problem.

8 The other thing, and Tyler was good to remind me,

9 I know we've said offline to a bunch of people we are

10 planning or are in the preliminary planning stages for next

11 year's Regional, State, and Local Modelers Workshop. 

12 I will not say that we are completely set upon a

13 destination because we have to get a bunch of things

14 approved, but it would be a city that in 2005 did have an

15 encounter with Katrina, if all goes to plan. And we also did

16 have a Regional, State, and Local Modelers Workshop there

17 that same year prior to Katrina.

18 But nonetheless, we're looking right now at

19 planning a weeklong meeting, so the first half would be

20 dispersion and the second half would be photochemical

21 modeling with an overlap day. And not that we've completely

22 set on it, but the thoughts would be as possibly the middle

23 day where we have an overlap between both curricula or both

24 disciplines is that we would also have invited stakeholders

25 come in. But it will happen in September. It will happen
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1 after that we finalize the rule package, and so I think that

2 would be a forum that we could have a lot more interaction

3 than over the last couple of days.

4 So again, we appreciate everybody's participation

5 and just have a great rest of your day if you're staying here

6 enjoying the weather, or if you're traveling, I hope your

7 travels are safe.
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STATE OF NORTH CAROLINA

COUNTY OF WAKE

C E R T I F I C A T E

I, Kay K. McGovern, do hereby certify that the

foregoing pages 251 through 361 represent a true and accurate

transcript of the proceedings held at the United States

Environmental Protection Agency in Research Triangle Park,

North Carolina, on Thursday, August 13, 2015.

I do further certify that I am not counsel for or

employed by any party to this action, nor am I interested in

the results of this action.

In witness whereof, I have hereunto set my hand

this 10th day of September, 2015.

_______________________________

Kay K. McGovern, CVR-CM

Court Reporter

 /s/ Kay K. McGovern